Develop statistic models using SAS
Eswar Vempati
PHD_THESIS_FINAL.pdf
ABSTRACT
Purpose – In line with the on-going global and domestic reforms in agriculture and allied
sectors, the Indian Government is reducing its direct market intervention and encouraging
private participation based on market forces. This has led to increased exposure of
agricultural produce to price and other market risks, which consequently emphasize the
importance of futures markets for price discovery and price risk management. The purpose of
this paper is to analyse the causal relationship between agricultural commodities among
Indian states for selected agricultural commodities and forecasting commodity price for
Odisha.
Design/methodology/approach – Market integration and price forecasting model for 4
agricultural commodities are built and the forecasting has been done for the year 2019 by
using Johansen’s Cointegration analysis, Granger causality tests and ARIMA modelling. Unit
root test procedures such as Augmented Dickey-Fuller and non-parametric Phillips-Perron
were initially applied to examine whether the price data series are stationary or not. The
hypothesis has been tested using econometric software packages like R and Eviews.
Findings – Results show that Cointegration exists significantly among Indian agricultural
commodity market prices (for selected agricultural commodities). There exists causal
relationship among markets i.e. price of other states affect forecast price of Odisha.
Forecasting models for Cotton, Turmeric, Greengram and groundnut are selected with the
help of AIC, SC, AICc, BIC criterion. Price forecasting result is found for the year 2019 and
validated with MAPE and RMSE criterion and found significant.
Practical implications – The results of this study are useful for various stakeholders active
in Agricultural commodities markets such as producers, traders, commission agents,
commodity Exchange participants, regulators and policy makers.
Originality/value – There are very few studies that have explored the efficiency of the
commodity futures market in India in a detailed manner, especially at individual commodity
level with price forecasting.
Keywords: Market integration and Causality, Price forecasting, Seasonality, Validation of
model.
TABLE OF CONTENTS
1. INTRODUCTION ................................................................................................................. 1
1.1. Background of the Study ................................................................................................ 1
1.2 Conceptual model and formulation of Hypothesis: ......................................................... 2
1.3: Past studies with empirical evidence: ............................................................................. 3
1.4: Research Gap: ................................................................................................................. 6
1.5: Research Problem: .......................................................................................................... 6
1.6: Objective and Hypothesis: .............................................................................................. 6
1.7: Research Questions: ....................................................................................................... 6
1.8: Research Objectives: ...................................................................................................... 7
1.9: Hypothesis: ..................................................................................................................... 7
1.10. The relevance of the Study: .......................................................................................... 8
1.11. Scope and Limitations: ................................................................................................. 8
2. BACKGROUND STUDY .................................................................................................. 13
2.1: Brief Agricultural Profile of Odisha State: ................................................................... 13
2.2: Status of Agricultural Marketing Reforms ................................................................... 16
2.3. Commodity selection .................................................................................................... 19
3. LITERATURE REVIEW .................................................................................................... 30
3.1: Price Forecasting .......................................................................................................... 30
3.2: Seasonal Index: ............................................................................................................. 36
3.3: Marker Integration ........................................................................................................ 37
4. THE NARRATION OF THE RESEARCH DESIGN ........................................................ 44
4.1. Chapter Objective ......................................................................................................... 44
4.2. Research Objectives ...................................................................................................... 44
4.3 Research Hypotheses: .................................................................................................... 44
4.4 Study Methodology: ...................................................................................................... 45
5. RESULT AND DISCUSSION ............................................................................................ 57
5.1 Results for Descriptive statistics and correlation coefficient: ....................................... 57
5.2 Unit Root Test ................................................................................................................ 68
5.3 Market Integration ......................................................................................................... 70
5.4: SEASONAL INDEX .................................................................................................... 80
5.5: Forecasting: .................................................................................................................. 82
5.6: Validation of Models: ................................................................................................... 89
6. SUMMARY, CONCLUSION AND POLICY IMPLICATIONS ...................................... 93
6.1 Summary: ....................................................................................................................... 93
6.2 Specific Objectives: ....................................................................................................... 93
6.3: Findings of the Study .................................................................................................... 94
6.4: Policy Implications ....................................................................................................... 97
LIST OF TABLES
Table 2.3.1.1: Area, Production and Productivity of Cotton in India ...................................... 20
Table 2.3.1.2: Area, Production and Productivity of Cotton in India ...................................... 21
Table 2.3.2.1: Major Country-Wise Export of Turmeric from India ....................................... 23
Table 2.3.2. 2: Major state-wise area and production of Turmeric in India ............................. 24
Table 2.3.2. 3: Details of catchment areas of market of turmeric in Odisha ............................ 24
Table 2.3.3.1 Major Green gram Producing States of India ..................................................... 25
Table 5.1.1: Descriptive Statistics of price data for major Cotton producing states ................ 57
Table 5.1.2: Descriptive Statistics of price data for major Turmeric producing states ............ 60
Table 5.1.3 Descriptive Statistics of price data for major Green gram producing states ......... 62
Table 5.1.4 Descriptive statistics of price data for major Groundnut producing states ........... 65
Table 5.2.1 Unit root result for price data for major Cotton producing states ......................... 68
Table 5.2.2 Unit root result for price data for major Turmeric producing states ..................... 69
Table 5.2.3 Unit root result for price data for major Green gram producing states. ................ 69
Table 5.2.4 Unit root result for price data for major groundnut producing states .................... 70
Table 5.3.1.1 Estimates of the correlation coefficient for the price of Cotton between pairs of selected states in India .............................................................................................................. 71
Table 5.3.1.2 Estimates of the correlation coefficient for the price of Turmeric between ....... 71
Table 5.3.1.3 Estimates of the correlation coefficient for the price of Green gram between pairs of selected states in India ................................................................................................. 72
Table 5.3.1.4 Estimates of the correlation coefficient for the price of Groundnut between pairs of selected states in India ................................................................................................. 72
Table 5.3.2.1: Johansen Cointegration Results for Cotton ....................................................... 73
Table 5.3.2.2: Johansen Cointegration Results for Turmeric ................................................... 74
Table 5.3.2.3: Johansen Cointegration Results for Green gram ............................................... 75
Table 5.3.2.4: Johansen Cointegration Results for Groundnut ................................................ 76
Table 5.3.3.1: Granger Causality Test for Cotton .................................................................... 77
Table 5.3.3.2: Granger Causality Test for Turmeric ................................................................ 78
Table 5.3.3.3: Granger Causality Test for Green gram ............................................................ 78
Table 5.3.3.4: Granger Causality Test for Groundnut .............................................................. 79
Table 5. 4.1: Seasonal Index for Cotton ................................................................................... 80
Table 5.4.2: Seasonal Index of Turmeric ................................................................................. 81
Table 5.4.3: Seasonal Index of Green gram ............................................................................. 81
Table 5.4.4: Seasonal Index of Groundnut ............................................................................... 81
Table 5.5.1: ARIMA (p, d, q) model for Cotton, Turmeric, Green gram and Groundnut ....... 83
Table 5.4.2: ARIMA (p, d, q) (P, D, Q) model for Cotton, Turmeric, Green gram and Groundnut. ................................................................................................................................ 84
Table 5.4.3: Forecasting for Odisha from January to December of the year 2019 for Cotton, Turmeric, Green gram and Groundnut. .................................................................................... 84
Table 5.4.4: AIC, AICc, BIC for selected crops ...................................................................... 88
Table 5.4.5: Error estimates ..................................................................................................... 89
LIST OF FIGURES
Figure 1 Figure 2.1.1: Odisha State Agricultural Map .......................................................................... 14
Figure 2 Figure 2.1.2: Agro-climatic Zones of Odisha ......................................................................... 15
Figure 3 Figure 2.1.2 Map of Odisha Showing Agro-climatic Zones .................................................. 16
Figure 4 Figure 2.3.1.1: World Cotton Production Scenario .................................................................. 22
Figure 5 Figure 2.3.1.2: Map of Major Cotton Producing States of India .............................................. 22
Figure 6 Figure 2.3.2.1: Map of Major Turmeric Producing States of India .......................................... 25
Figure 7 Figure 2.3.3.1: Map of Major Green gram Producing States of India ...................................... 26
Figure 8 Figure 2.3.4.1: Major Countries share in Groundnut Production ............................................. 27
Figure 9 Figure 2.3.4.2: Major Groundnut production Area in India ...................................................... 28
Figure 10 Figure 5.1.1: Descriptive Statistics of price data for major Cotton producing states ............. 58
Figure 11 Figure 5.1.2: Graphical presentation of price data for major Cotton producing states ........... 59
Figure 12 Figure 5.1.3: Descriptive Statistics of price data for major Turmeric producing states ......... 61
Figure 13 Figure 5.1.4: Graphical Presentation of price data for major Turmeric producing states ...... 61
Figure 14 Figure 5.1.5: Descriptive Statistics of price data for major Green gram producing states ..... 63
Figure 15 Figure 5.1.6: Graphical Presentation of Price Data for Major Green gram Producing States 64
Figure 16 Figure 5.1.7: Descriptive statistics of price data for major Groundnut producing states ....... 66
Figure 17 Figure 5.1.8: Graphical Presentation of price data for major Groundnut producing states .... 67
Figure 18 Figure 5.4.1: Before After Differentiation of Price Series for Odisha .................................... 86
Figure 19 Figure 5.4.2: Forecasting for each crop .................................................................................. 87
LIST OF ANNEXURE
Annexure I: Price of Cotton in selected State markets of India Annexure II: Price of Turmeric in selected State markets of India Annexure III: Price of Green gram in selected State markets of India Annexure IV: Price of Groundnut in selected State markets of India
LIST OF ABBREVIATIONS
ARIMA Auto regressive integrated moving averages
SARIMA Seasonal auto regressive integrated moving averages
GARCH Generalised auto regressive conditional Heteroskedasticity
MAPE Mean Absolute Percentage Error
RMSE Root Mean Square Error
AIC Akaike Information Criterion
BIC Bayesian information criterion
ACF Autocorrelation function
PACF Partial autocorrelation function
ADF Augmented Dickey-Fuller
PP Phillips-Perron
APMC Agricultural Produce Market Committee RMC Regulated Market Committees
OAPM Odisha Agriculture Product Market
FYP Five Year Plan
OSAMB Odisha State Agricultural Marketing Board
NAC National Advisory Council
GP Gram Panchayat
PACS Primary Agricultural Credit Society
MSP Minimum Support Price
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1. INTRODUCTION
1.1. Background of the Study
Market integration is a simple thing i.e value transmission among two each vertically
/spatially related marketplaces. The operative meaning of marketplace coordination is
indistinguishable items sell at a uniform cost crosswise over various markets which in other
words called as the “law of one price (LOP)” and applicable for homogeneous commodities
(Monke and Petzel, 1984). Maximization of Profit and transportation without price, supply
and selling are the assumptions required for the LOP to hold. Domestic market integration
exists in the domestic economy if LOP holds (Bradford and Lawrence, 2004)segmentation
which occurs due to a lack of integration. A market is said to be geologically divided if the
zone of the purchaser and dealer impacts the terms of exchange generously and a market is
said to be perfectly competitive if it is fully integrated (Knetter and Goldberey, 1996). The
LOP should control spatial value relations in a frictionless undistorted world where the
maximum communication and market reconciliation compares the standard challenge model
(Conforti, 2004). It was concluded that market prices are affected by significant transaction
costs. (Meyer 2004). Markets are said to be spatially integrated on the off chance that the
distinction in costs between the two locales is simply because of transport costs (Ravallion,
1986). “Co-developments of costs and all the more for the most part, to the smooth
transmission of value sign and data crosswise over spatially isolated markets, are generally
called spatial market integrations” (Goletti, Ahmed and Farid, 1995). These meanings of
value separation and marketing mix need significant results that are required for estimation
and translation.
Estimation of the bivariate connection coefficient between value changes in various choice
markets is the most well-known system utilized in the earlierstudies for analysing market
incorporation. The examinations dependent on bivariate relationship were found to have
included methodological drawbacks, the most genuine one appears to have happened because
of their inability to perceive the probability of misleading incorporation within the sight of
normal exogenous pattern (for example general inflation), heteroscedastic residuals in the
relapse with non-stationary value information and normal periodicity (seasonality in
agriculture sector) or auto correlated. Secondly, the value of 'r' will be reduced if transfer
costs are high or there are seasonal reversals in trade flows (Acharya and Agarwal, 1994).
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1.2 Conceptual model and formulation of Hypothesis:
When price forecasting of the Agricultural commodities market comes into the picture; direct
gain to farmers can be assumed. But there exist fluctuations in the prediction due to the
impact of the following attributes:
1. Impact of other state market price. 2. Impact of substitute commodity price in own state and other states. 3. Impact of complementary commodity price in own state and other states. 4. Natural calamity impact on commodity price.
Due to limited resources and time for this study, the only factor that can be studied is other
state market prices. The picture of the considered model is given under:
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The primary question arises from the background study to know where to sell the produce.
The question can be solved by doing market integration. Another question of when to sell can
be solved by price forecasting. According to this study, a good forecast can be found by
knowing the impact of other states market prices on Odisha state market price. Before
conducting the study some empirical evidence reviewed.
1.3: Past studies with empirical evidence:
Past empirical evidence helps the research work in formulating and specifying the objectives,
preparing a suitable schedule, selecting a representative sample from the population,
undertaking the survey, analysing the data, interpreting the market, comparing, making
deductions, alternations or suggestions whenever necessary. Hence, a review of available
research studies on the following broad heading has been observed with and presented in this
chapter.
1.3.1: Price Forecasting
The assortment of perceptions made successively after some time is a time series. In statistics
approaches of analysing the time series data and constitute an important area (Chatfield, C.,
2005). Elucidation, depiction, controller, and prediction are classifications of a time series
data analysis to fulfil several objectives.
Pani et al. (2019) compared weekly green gram price forecasting for the state of Odisha in
India with the help of MAPE criteria. The study revealed that the GARCH model has
outperformed the SARIMA model hence forecasting was done using GARCH model for
three months. Mohapatra et al. (2018) forecasted Odisha groundnut monthly price using
ARIMA (1, 1, 1) (1, 0, 1) model and indicated that the fitted model was suitable for
forecasting price of groundnut in Jajpur district. Mitra, D. (2017) established a time series
model for forecasting oilseed and pulses price in India. Darekar et al. (2017) forecasted a
price of common paddy for India by using ARIMA technique. Applying ARIMA model
Darekar and Reddy (2017) forecasted Kharif paddy price of India from September to
November 2017-18; and performance was measured testing MAPE, AIC and BIC and found
that the most illustrative model for price forecasting of paddy at all India level was ARIMA
model. Darekar and Reddy (2017) using Box-Jenkins ARIMA modelling method, forecasted
pigeon pea price in India for the period November to January 2017-18 and tested the
reliability of model using the goodness of fit methods like MAPE, AIC and BIC and an
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upward trend was seen in months coming ahead. Darekar and Reddy (2017) forecasted
soybean price using ARIMA approach in India and performance of model got tested using
AIC, SBC and MAPE approach and suggested that the ARIMA model was the most
representative model for forecasting soybean prices at all India level as well as the in the
soybean producing states which would help farmers, processors and trade INR Rs. Darekar
and Reddy (2017) forecasted cotton price in India using ARIMA modelling method and
performance got measured using AIC, SBC and MAPE approach and predicted increase in
prices as well as area allocation to cotton in major cotton-producing states of India. Darekar
and Reddy (2017) using ARIMA model forecasted the maize price in India and proposed
farmers to increase acreage under maize wherever suitable as the forecasted maize price
during harvesting season in the Indian market from September to December 2017-18 seem to
be increasing. Panasa et al. (2017) forecasted maize monthly modal prices in Telangana,
India using ARIMA model with the help of SAS 9.3 software and advocated that there would
be an increasing trend in maize price in Badepalli market in future. Jadhav et al. (2017)
forecasted prices of maize, ragi and paddy of Karnataka state of India for the period
September 2016 to August 2020 and found that predicted prices of selected commodities
were somewhat accurate which validated the findings. Evaluation of forecast was done using
the criteria of MSE, MAPE and Theils U coefficient criteria. Ramanujam and Viswanathan
(2018) examined various methods for forecasting prices of black pepper price in India and
established that the ARIMA model is the best fit with unbiased estimates of forecasts.
Darekar and Reddy (2018) forecasted mustard prices in major mustard producing states of
India and found that “ARIMA (0, 1, 0) (0, 1, 1)” is the most appropriate model to predict the
mustard price. Darekar and Reddy (2018) forecasted wheat price in India and ascertained that
“ARIMA (0, 1, 1) (0, 1, 1)” model is most appropriate for forecasting future prices wheat
with 95 per cent accuracy level.
1.3.2: Seasonal Index
Keith et al. (1997) studied Delhi and Kolkata potato price and arrival and examined seasonal
potato price indices for the same. Mipramavar and Gummagolmath (1998) studied a seasonal
index of arrivals and prices in potato regulated markets of northern Karnataka and found
seasonal fluctuations throughout the year and interpreted the reason behind it. Chahalet al.
(2004) studied green peas arrivals and prices in selected markets of Punjab to find out
seasonal indices of market. Navadkaret al. (2005) observed a seasonal index of tomato price
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and arrival in Pune and found there exist huge fluctuations in the seasonal index during
harvest time and before harvest time.
1.3.3: Market Integration and Causal relationship between markets
The degree of price integration in agricultural commodity marketplaces is a basic determinant
of price program for agriculture in creating nations, especially enormous ones. If agrarian
markets are not incorporated, at that point any nearby nourishment shortage will in general
endure, as inaccessible markets (with no shortage) won't have the option to react to the value
sign of such detached marketplaces (Dreze and Sen, 1995). Without market integration
nourishment shortage, even starvations may occur frequently (Currey and Hugo, 1985).
Challenging for such integration is, along these lines, integral to deciding the level
(geological) at which rural value arrangement ought to be focused, at any rate in the short-
run. If every single agrarian market were not coordinated at the general level, at that point a
general farming value approach won’t be appropriate. It may increasingly suitable to focus on
a typical cost arrangement to a lot of coordinated markets. In the more drawn out run, it is
basic to upgrade advertise incorporation no matter how you look at it to procure the upsides
of a huge market.
(Capelle-Blancard, G., & Coulibaly, D.,2011) studied agricultural commodity and livestock
of futures markets and established a causal relationship with the help of panel causality test
by Granger and Wald test. (Ali, J., & Bardhan Gupta, K.,2011) recommended that there
exists no long-run relationship among future and spot rates of commodities by studying
selected agricultural commodities in futures and spot prices.
Reviewing literature it is found that usage of Cointegration checks for learning the efficacy of
futures marketplaces is huge “Chowdhury, 1991; Lai and Lai, 1991; Crowder and Hamed,
1993; Beck, 1994; Kellard et al., 1999; Yang et al., 2001; McKenzie et al., 2002; McKenzie
and Holt, 2002; Kellard, 2002; Liu, 2004; Wang and Ke, 2005”. (Wang et al, 2005) realised
the need of establishing the adeptness to have a predictive signal on price convergence in the
futures marketplace with the advantage of Co-integration.
The Cointegration among the spot cost and the prospects cost is a fundamental circumstance
for showcase productivity. It guarantees the long run harmony connection among the given
two arrangements. The nonattendance of Cointegration infers that fates costs give little data
about development in real money cost, demonstrating that prospects advertise aren’t
proficient. A similar methodology has been utilized in the present examination. In the wake
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of investigating the presence of Cointegration among prospects and spot costs, it is a basic to
check causality to survey the bearing of the connection “Malliaris and Urrutia, 1998;
Silvapulle and Moosa, 1999; Bryant et al., 2006”.
1.4: Research Gap:
Limited research evidence is found in which extended investigations were made
concerning the comparison of forecasted outcomes with actual outcomes.
Limited evidence is there, showing the seasonal index for crop price.
Little research evidence is there, showing Market Integration of major respective
crop-producing states in India.
Limited research is there showing a causal relationship between agricultural
commodity price data.
1.5: Research Problem:
In the above context of research gap analysis, the current study identified the following
research problem statement. The research problem of the present study is:
To study the appropriate Techniques for forecasting agricultural commodity price data with
accuracy study, and comparing different price series to get suggest proper market integration
to members in the agricultural supply chain; which leads to increased efficiency of the
agricultural industry.
1.6: Objective and Hypothesis:
The present study is having the broad objective to find out whether the Odisha agricultural
commodity prices are integrated with other major producing states of India or not. Another
objective is to find out the causal relationship among agricultural commodities market. Then
the study is trying to find out Seasonal Indices for commodities as these commodities are
seasonal. Finally, the study ends with Price Forecasting and validation of each commodity for
the state of Odisha. It is assumed that Odisha crop market is not integrated with other state
markets.
1.7: Research Questions:
What are the flaws of existing price forecasting method?
What are the negative impacts of price forecasting outcomes, if not done in an
appropriate time?
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Do forecasted figures match with real-life situations?
What are the major bottlenecks in the agricultural supply chain due to the lack of
market information?
Does right information in the exact time for the exact people lead to well efficiency?
What are the major policy implications towards enhancement of efficiency
concerning every member of the agricultural supply chain?
What are the factors affecting direct gain to farmers from price forecasting?
1.8: Research Objectives:
To study the Market Integration of major respective crop-producing states in India.
To find out the causal relationship between the prices series of selected crop-
producing states in India.
To determine the seasonal index of crop price in Odisha market.
To shapeasuitablepredicting model and to predict the agricultural commodity price for
Odisha Market.
1.9: Hypothesis:
Hypothesis for the present study like a null (H0) and alternative (H1) has been formulated as
follows:
1. H0: A normal distribution is followed by the series and with Skewness as zero and
kurtosis as three.
H1: A not-normal distribution is followed and with Skewness different from zero and
kurtosis different from three.
2. H0: The series under consideration is not stationary.
H1: The series under consideration is stationary.
3. H0: There is no co-integration between the commodity price of Odisha and other
major crop-producing states.
H1: There is co-integration between the commodity price of Odisha and other major
crop-producing states.
4. H0: There is no causality between the commodity price of Odisha and other major
crop-producing states.
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H1: There is causality between commodity price of Odisha and other major crop-
producing states.
5. H0: The variation among the real and predicted price is maximum.
H1: The variation among the real and predicted price is minimum
1.10. The relevance of the Study:
Forecasting will enable the stakeholders to make judicious decisions through price
forecasting. Farmers get market information about the future prices of different commodities
and can make a decision: where to sell and when to sell. Where to sell question can be solved
by knowing market integration and when to sell question can be solved by price forecasting.
Price forecasting has an impact on sowing and harvesting of crop and storage. Farmers can
adjust to sow, harvest and store. They can sell when the forecasted price is high and store
when it is low.
1.11. Scope and Limitations:
The study area is limited to India. Further study can be conducted to integrate agricultural
commodity market internationally. Another scope is that the study can be conducted within
different markets of Odisha. Price forecasting is limited to the state of Odisha. Price
forecasting can be done for other states by seeing the causal relationship among states to
minimize errors in future forecasting.
Chapter Plan:
The presentation of the study is organized under the following six chapters.
I. The first chapter (continuing chapter) deals with the introduction, rationale and
objectives of the study.
II. The second chapter deals with the background of the study.
III. The third chapter is a collection of the literature.
IV. The methodology adopted in the study and the background information regarding the
study area is presented under chapter 4.
V. The fifth chapter is a synthesis of the results made over outcomes of the study in line
with the objectives and discussion.
VI. The sixth chapter provides gleaning of the results i.e. summary, conclusion and policy
implication.
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References:
1. A Dash, et al. (2017) .Forecasting of food grain production in Odisha by fitting ARIMA model. Journal of Pharmacognosy and Phytochemistry. 6(6): 1126-1132. http://www.phytojournal.com/archives/2017/vol6issue6/PartP/6-5-541-759.pdf.
2. Acharya, S. S., &Agarwal, N. L. (1994). Agricultural prices-analysis and policy. Agricultural prices-analysis and policy appropriate?”, Journal of Agricultural Economics, Vol. 53, pp. 513-29.
3. Ashwini Darekar and A Amarender Reddy. (2017). Price forecasting of pulses: case of pigeon pea. Journal of Food Legumes 30(3): 42-46.
4. Ashwini Darekar, A. Amarender Reddy. (2017). Price forecasting of maize in major states. MaizeJournal.
5. Ashwini Darekar, et al. (2017). Forecasting of Common Paddy Prices in India. Journal of Rice Research 2017, Vol 10 No. 1. http://www.icar-iirr.org/journal% 202017-10-13.pdf.
6. Beck, S.E. (1994), “Cointegration and market efficiency in commodity futures markets”, Applied Economics, Vol. 26, pp. 249-57.
7. Bradford, Scott and Robert Z. Lawrence, 2004. Has Globalization Gone Far Enough? The Costs of Fragmented Markets. Institute for International Economics, Washington, DC. February 2004.
8. Chowdhury, A.R. (1991), “Futures market efficiency: evidence from Cointegration tests”, The Journal of Futures Markets, Vol. 11, pp. 577-89.
9. Commodity futures markets: a new look”, The Journal of Futures Markets, Vol. 21.
10. Crowder, W.J. and Hamed, A. (1993), “A Cointegration test for oil futures market efficiency”, The Journal of Futures Markets, Vol. 13, pp. 933-41.
11. Darekar A and AA Reddy. (2018). Forecasting wheat prices in India. Forecasting wheat prices in India. Wheat and Barley Research 10(1).
12. Darekar A and AA Reddy. (2018). Oilseeds Price Forecasting: Case of Mustard In India. Agricultural Situation in India.
13. Darekar, A. and Reddy, A. A. (2017). Cotton Price Forecasting in Major Producing States. Economic Affairs, 62(3):1-6.Print ISSN: 0424-2513, Online ISSN: 0976-4666, 2017.
14. Darekar, A. and Reddy, A. A. (2017). Forecasting of Common Paddy Prices in India. Journal of Rice Research, 10(1): 71-75.
P a g e | 10
15. Darekar, A. and Reddy, A. A. (2017). Predicting market price of soybean in major India studies through ARIMA model. Journal of Food Legumes 30(2): 73-76.
16. Departmet of Agriculture Cooperations and Farmers Welfare. Ministry of agriculture & farmers Welfare. http://agricoop.nic.in/recentinitiatives/minimum-support-prices- msp-and-bonus-kharif-crops-2017-18-season.
17. DipankarMitra. (2017). Hierarchical Time-series Models for Forecasting Oilseeds and Pulses Production in India. Economic Affairs, Vol. 62, No. 1, pp. 103-111, March 2017 DOI: 10.5958/2230-7311.2017.00045.9.
18. Directorate of Marketing & Inspection (DMI), Ministry of Agriculture and Farmers Welfare, Government of India. Available from: http://agmarknet.gov.in/.
19. Dr V Ramanujam and Dr T Viswanathan. (2018). An Empirical Analysis of Forecasting Volatility of Pepper Price in the Spot Market in India. Research Gate.
20. Dreze, J., Sen, A., &Hussain, A. (1995). The political economy of hunger: selected essays. Oxford University Press.
21. Goletti, Francesco, Raisuddin Ahmed, and NaserFarid, 1995. ‘Structural Determinants of Market Integration: The Case of Rice Markets in Bangladesh’. Developing Economies. June 1995; 33(2): 185–202.
22. Gooijer, J.G.D. and Hyndman, R.J. (2006). 25 years of time-series forecasting. Int. J. Forecasting. 22(3): 443-73.
23. Hemavathi et al. (2017). Forecasting Food grains Area and Production in India using ARIMA Model. Statistical Approaches on Multidisciplinary Research, Volume I, DOI: Surragh Publishers, India, ISSN: 2349 – 4891.
24. K. Ghosh, et al. (2014). Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES-rice model. METEOROLOGICAL APPLICATIONS Meteorol. Appl. 22: 525–533 (2015) Published online 1 December 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/met.1483. https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.1483.
25. Kellard, N. (2002). Evaluating commodity market efficiency: are cointegration tests appropriate?. Journal of Agricultural Economics, 53(3), 513-529.
26. Kellard, N., Newbold, P., Rayner, T. and Ennew, C. (1999), “The relative efficiency of commodity futures markets”, The Journal of Futures Markets, Vol. 19, pp. 413-32.
27. Knetler, Michael M and Penelope K. Goldberey. 1996. ‘Goods Prices and Exchange Rates: What have We Learned?’, NBER Working Paper 5862. Cambridge, MA: National Bureau of Economic Research.
P a g e | 11
28. Lai, K.S. and Lai, M. (1991), “A Cointegration test for market efficiency”, The Journal of Futures Markets, Vol. 11, pp. 567-75.
29. Maps of India. (2017). https://www.mapsofindia.com/top-ten/india-crops/cotton.html.
30. McKenzie, A.M. and Holt, M.T. (2002), “Market efficiency in agricultural futures markets”,Applied Economics, Vol. 34, pp. 1519-32.
31. McKenzie, A.M., Jiang, B., Djunaidi, H., Hoffman, L. and Wailes, E.J. (2002), “Unbiasedness and market efficiency tests of the US rice futures market”, Review of Agricultural Economics, Vol. 24 No. 2, pp. 474-93.
32. Meyer, Jochen. 2004. ‘Measuring Market Integration in the Presence of Transaction Costs—A Threshold Vector Error Correction Approach’. Agricultural Economics. Special Issue December 2004; 31(2–3): 327–34.
33. Monke, Eric and Todd Petzel, 1984. ‘Market Integration: An Application to International Trade in Cotton’. American Journal of Agricultural Economics. 66(4), November: 481–7. pp. 279-300.
34. Pushpa Savadatti. (2017). Forecasting of Area, Production, and Productivity of Food Grains in India : Application of ARIMA Model. Indian journal of economics and research.DOI: 10.17010/aijer/2017/v6i6/120114. http://indianjournalofeconomicsandresearch.com/index.php/aijer/article/view/120114.
35. Rahul Tripathy, et al. (2014). Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models. Advances in Agriculture Article ID 621313, 9 pages http://dx.doi.org/10.1155/2014/621313. https://www.hindawi.com/journals/aag/2014/621313/
36. Ravallion, Martin, 1986. ‘Testing Market Integration’. American Journal of Agricultural Economics. February 1986; 68(1): 102–09.
37. Shruti Mohapatra, et al. (2018). Price forecasting of groundnut in Odisha. The Pharma Innovation Journal 2018; 7(3): 111-114. Available from: <http://www.thepharmajournal.com/archives/2018/vol7issue3/PartB/7-2-33- 193.pdf>.
USDA (2018). https://www.usda.gov/media/agency-reports
38. V. Jadhav, et al. (2018). Application of ARIMA Model for Forecasting Agricultural Prices. J. Agr. Sci.Tech.Vol.19:981992.
39. Venkatesh Panasa, R. VijayaKumari, G. Ramakrishna and Kaviraju, S. (2017). Maize Price Forecasting Using Auto-Regressive Integrated Moving Average (ARIMA) Model. Int.J.Curr. Microbiol. App.Sci.6 (8):2887-2895.
P a g e | 12
40. Wang, H.H. and Ke, B. (2005), “Efficiency tests of agricultural commodity futures markets in China”, The Australian Journal of Agricultural and Resource Economics, Vol. 49, pp. 125-41.
41. Wisner, B., & Luce, H. R. (1993). Disaster vulnerability: scale, power and daily life. GeoJournal, 30(2), 127-140.
42. Yang, J., Bessler, D.A. and Leatham, D.J. (2001), “Asset storability and price discovery in
43. Zotteri, G., Kalchmidt, M. and Caniato, F.(2005). The impact of aggregation level on forecasting performance. Int. J. Prod. Eco., 93-94: 479-91.
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2. BACKGROUND STUDY
2.1: Brief Agricultural Profile of Odisha State:
Odisha being an agrarian economy state contributes nearly 15.4 per cent to the Net State
Domestic Product (NSDP). In agriculture sector of Odisha about 73% of the population are
engaged. Among these agricultural workers 44.3% are cultivators and 28.7%agrarian
labourers (1991 census). Out of the total state population 87% people are staying in rural
areas. Though the most part of people are working for agricultural production the
contribution of agricultural produce to NSDP is slightest. Agriculture, animal husbandry,
fisheries and forestry are sub sectors of Agriculture. The GSDP of agriculture sector in the
State’s during the past decade has been declining. As growth rate in the industries and
services sector are high. GSDP in Odisha is 15.4 per cent according to 2104-15 advance
estimates and it is 18.4 percent in the year of 2018-19 (Odisha Economic Survey).
Agriculture sector is vital for this state in spite of this low contribution as it affords
employment as well as sustenance of over 60 percent of the population. Growth trend of
Odisha is affected by natural shocks like droughts, cyclones and flash floods. The sector is
growing robustly in real term after 2004-05 to 2012-13. Mainly after 2013-14 the state facing
cyclonic storms in each year this caused a negative growth rate. The cyclonic storms are
Phailin (2013-14), Hudhud (2014-15), Titli (2017-18) and Fani (2019-20) and flash floods
destroyed Odisha agricultural growth. As per advance estimates Odisha agriculture and allied
sector expects nearly 2 percent growth in each year after 2103-14; but this is practically
unachievable due to calamity of nature. The food grains production of the state generally
fluctuates from year to year due to susceptibility of the State to natural calamities. Comparing
food grain production of 2012-13 with 2013-14 a huge difference found. In 2012-13 the food
grain production was 102.10 lakh tones and in 2011-12,63.16 lakh tones and in 2013-14,
83.60 lakh tones. After 2013-14 the production growth realised till 2019-20 year very slow.
Among total food grains paddy contributes 90 percent of production despite of shift from
paddy to cash crops. Due to the acceptance towards HYV of paddy has increased till date the
contribution towards food grain production remain same irrespective of lowering area of
cultivation. The Odisha state agricultural map is presented under figure 2.1.1.
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Figure 1 Figure 2.1.1: Odisha State Agricultural Map
Odisha is having of about 61.80 lakh hectares cultivated land from 155.71 lakh hectares of
geographical area. The cultivated area constitutes 39.69% of the total geographic area. The
farming community of the state is having 90% small and marginal farmers. According to the
physiographic point of view the – “Northern Plateau, the Eastern Ghat Zone, the Central
Table lands and the Coastal zone” are four zones of the state. On the basis of climate, soil,
rainfall, topography and cropping patterns the State has been delineated into 10 Agro-
Climatic Zones (Figure 2.1.2). The State natural resource offers immense scope for
agricultural growth. The state is extremely appropriate for a wide variety of food grains, cash
crops, and horticultural crops. For the development of agriculture and allied sectors the State
has taken several steps. So as to give unique accentuation development and allotment of
assets to these parts, a different Agriculture spending plan is being displayed from 2013-14
consequently upgrading the spending expense of horticulture and unified divisions from '
7162 crores in 2013-14 to '14930 crores during 2017-18 and it has additionally been
proposed to improve to '16765 crores during 2018-19. The state Odisha is having 30 districts.
The districts coming under these agro-climatic zones are presented under Figure 2.1.3.
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Figure 2 Figure 2.1.2: Agro-climatic Zones of Odisha
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Figure 3 Figure 2.1.2 Map of Odisha Showing Agro-climatic Zones
2.2: Status of Agricultural Marketing Reforms
(i) Acceptance of provision allied to Private marketplaces:
The provisions for private markets or yards managed by persons other than APMCs are
suggested by the Model Act. 17 states and UTs out of 37 are having provisions for private
market yards. Few states have provisions through executive permission and few have
provisions for direct purchase and the states are Tamilnadu and Madhya Pradesh
respectively. A rule by Andhra Pradesh has formulated under which Rs. 50,000 is a license
fee and Rs 10 crores is the minimum cost for private market’s setting. For paddy/rice private
markets, Odisha is not permitted. A minimum distance of these markets from the APMC
markets is prescribed for some states. Maharashtra, Karnataka, Gujarat and Tamil Nadu are
the states who have applied licence for private market. Only state that has private market so
far is Maharashtra.
(ii) Endowment on behalf of direct marketing:
For procurement of agricultural harvest directly from farmers the processors, exporters,
graders, packers, etc. are provided for grant under model act. Only 17 states and UTs have so
far made this provision. For such procurement centre in Andhra Pradesh Rs. 50,000 license
P a g e | 17
fee is prescribed. For direct purchases of certain commodities by selected/identified
processors there is an exemption of market fee in Punjab and Chandigarh. Only three states
having common license for direct procurement from farmers are Maharashtra, Gujarat and
Karnataka.
(iii) Contract Farming Provisions:
Contract farming is registered under model act. The contract is established between farmers
and APMCs. The model act under contract framing allows APMCs to purchase contracted
produces directly from producers. Exemption of market fees are there for such kind of
contracted produce. 19 states and UTs including Odisha incorporated contract farming
provisions except fees exemption. Market fees are exempted for only 11 states in India under
contract agreement. Only 30% of market fees are exempted for the state of Karnataka. In
Andhra Pradesh buyers need a bank guarantee for purchase of contracted produce under
APMCs Act. Arbitration process is not time bound in contract farming; according to APMCs
act.
(iv) Single Point levy of Market Fee:
Single point levy of market fee provisions are provided by only 13states. The market rates
mainly vary within 0.5 to 2% range. Market fees are recovered by APMCs at the check gate
as well as outside the physical APMCs yard. The dual fee collection at APMCs hampers the
smooth flow of goods and services. Service taxes on several items are relaxed in some states.
Agricultural Marketing System in Odisha:
The Odisha state is having particularly diverse agricultural marketing system than other
states. The marketing system of the state is fully functional and regulated. It is seen that the
possession and working of the business sectors aren't uniform with the physical markets
being claimed by various offices, for example, RMCs, Municipalities, Panchayats and
furthermore simply private people. After the enactment of OAPM Act 1956 and the Rules
made there under in 1958 the market Regulation Scheme in Odisha State came into force.
Now Odisha is having 66 Marketing Committees which was 15 during 2nd FYP. It happened
as per Govt Notification No. 598dated 06.02.1957 and with the performing of Odisha
Agricultural Produce Markets Act, 1956.A lot of rules to execute the fore said Act likewise
encircled as “Odisha Agricultural Produce Markets” Rules, 1958 vide Notification –“No
18221 dated 24.05.1958”. Farming Marketing organisations in Odisha are –“OSAMB,
P a g e | 18
Directorate of Agricultural Marketing and RMCs”. Reinforce of these bodies for
guaranteeing proficient administration of the segment is required here.
Operation and management of markets:
In terms of ownership and management of marketplaces a peculiar condition overcomes in
the state of Odisha. Market places like -“RMCs, Local Bodies like Municipalities and NAC,
Gram Panchayats, and private persons/ associations” are different modes of ownership of the
markets. Municipality manages GP and Municipal markets or private persons take lease of
these markets. Farmers get space for selling their produce by the lesees. Farmers don’t
chance for fetching maximum price as they face constraints of selling their produce and with
no option they sell their thing to local traders at lowest prices. The loss due to market gap
producers loses their zeal of producing things. The low production causes consumers to pay
more.
Market infrastructure:
Buyers and sellers are not bound to assemble at the earmarked market yards of RMCs. Due to
this reason these market yards are not suitable for regular use. Markets under –“Gram
Panchayats, Municipal and other local body markets, and private markets” are having very
poor infrastructure.
Price discovery and system of sale in markets:
For cotton open auction method is practiced from long a back. At RMCs procurement bodies
and mills procure cotton through uncluttered auction. Procurement agencies purchase Paddy
at MSP from –“RMCs, PACS. Local bodies (Municipality, NAC), Gram Panchayat and
private markets”, they never follow open auction selling. Due to lack of staffs and
infrastructure, open auction is non-existent in several market places. Hence, to run the open
auction process smoothly proper infrastructure and trained staffs are required.
Market functionaries:
Market functionaries like-“Traders, trader-cum commission agents, warehouseman, brokers”
for livestock markets, etc. are having licences issued by different RMCs. Weigh men are
having licences issued only by 4-5 RMCs. In the marketing movement of agricultural
produces many unlicensed village traders and commission agents are involved. At village and
P a g e | 19
Gram Panchayat level markets these kinds of traders operate where RMCs can’t control
them.
Market charges:
For paddy and cotton procurement in the RMC markets the prescribed market charges are
duly enforced. But there is hell and heaven difference of charges at other markets. At other
markets lesees illegally take farmers produce for sell by paying them very less price. As the
farmers are unaware of market prices they sell their produce to the traders, village
aggregators.
Spot payment:
In different districts and markets farmers are putting complain regarding late payment against
their produce. It happens only at private markets as these are not in the control of RMCs.
Illegal traders take advantage of farmers by giving them greed of more profit. Innocent
farmers choose these cunning people without taking instant payment which encourages these
traders to pay late. For proper weighing of farming produce carried for sale in RMCs
weighing equipment is provided. But license for weighment is provided in few markets. Free
weighing facility with trained staffs at market gate may ensure farmers regarding proper
weight of their produce and the objective of regulated market may get fulfilled.
2.3. Commodity selection
2.3.1: Cotton:
In the year 2018-19 cotton is cultivated with an area of 158 thousand hectares, producing 4.5
lakh bales and productivity is 4.84 bales/ha (GoI, 2019). Emphasis was set on increase of
area, quality/ hybrid seeds use, training of farmers and IPM practices strengthening etc.
through 2018-19. Under Table 2.3.1.1 area, production and productivity of major cotton
producing states of India is given.
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Table 2.3.1.1: Area, Production and Productivity of Cotton in India
Odisha Andhra Pradesh
Gujarat Haryana Karnataka Maharashtra Madhya Pradesh
Punjab Rajasthan Telengana Tamilnadu
2009-
10
Area 0.54 14.75 26.3 5.07 4.55 35 6.11 5.11 4.44 - 1.04
Prod 1 54.5 98 15.3 12.25 65.8 15.3 13 12 - 5
Yield 315 628 635 511 458 319 424 432 459 - 817
2010-
11
Area 0.74 17.84 26.3 4.92 5.45 39.3 6.5 5.3 3.35 - 1.22
Prod 2.05 59.5 106 17 11.1 87.8 17.7 18.5 10.1 - 7.2
Yield 471 538 686 587 346 379 463 593 513 - 1003
2011-
12
Area 1.02 18.79 29.6 6.41 5.54 41.3 7.06 5.6 4.7 - 1.33
Prod 3.5 60 122 26 15 76 18 20 18 - 6.5
Yield 583 543 700 690 460 313 433 607 651 - 831
2012-
13
Area 1.19 24 25 6.14 4.85 41.5 6.08 4.8 4.5 - 1.28
Prod 4 84 93 26 17 81 19 21 17 - 6
Yield 571 595 633 720 596 332 531 744 642 - 797
2013-
14
Area 1.24 23.89 25.2 5.36 6.62 41.9 5.14 4.46 3.93 - 1.52
Prod 4 78 124 24 23 84 19 21 14 - 5
Yield 548 555 837 761 591 341 628 800 606 - 559
2014-
15
Area 1.27 8.21 27.7 6.48 8.75 41.9 5.74 4.2 4.87 17.13 1.87
Prod 3 26.5 112 23 34 80 19 13 17 50.5 6
Yield 402 549 687 603 661 325 563 526 593 501 545
2015-
16
Area 1.25 6.66 27.2 6.15 6.42 42.1 5.63 3.39 4.48 17.73 1.42
Prod 3 23.75 90 14.5 19.5 76 18 6.25 15 58 6
Yield 408 606 562 401 516 307 544 313 569 556 718
2016-
17
Area 1.36 4.72 23.8 5.7 5.1 38 5.99 2.85 4.71 14.09 1.42
Prod 3 19 95 20.5 18 88.5 20.5 9 16.5 48 5
Yield 375 684 678 611 600 396 582 537 596 579 599
2017-
18
Area 1.45 6.44 26.2 6.69 5.46 42.1 6.03 2.91 5.84 18.97 1.85
Prod 3.5 20.5 104 22.5 18 85 20.5 11.5 22 55 5.5
Yield 410 541 674 572 560 343 578 672 640 493 505
2018-
19
Area 1.58 5.51 27.1 6.65 5.75 41.2 6.97 2.84 4.96 17.94 1.4
Prod 4.5 20 92 27 18 81 24 11.5 22 53 6
Yield 484 617 577 690 532 334 585 688 754 502 729
Area in Thousand Hectares, Production in Lakh Bales, Yield in Bales/Ha
Cotton is produced in India from decades. Under table 2.3.1.2 the area, production,
productivity of cotton from 1947 to 2019 in India is presented.
P a g e | 21
From 1947 to 2019 cotton area for cultivation has been increased by three times with hundred
times more production. This is because of green revolution. Addition of effective inputs
increasing production in small area. India’s cotton production contributes 36% towards
worlds total cotton production. India is the second largest producer of Cotton after China.
The shares of cotton production of different countries are China (36%), US (13%), Pakistan
(11%), Brazil(6%), Uzbekistan(4%), Turkey(2%) and Australia (2%). This information is
presented under figure 2.3.1.1.
P a g e | 22
Figure 4 Figure 2.3.1.1: World Cotton Production Scenario
The major cotton producing states of India map is presented under figure 2.3.1.2.
Figure 5 Figure 2.3.1.2: Map of Major Cotton Producing States of India
Under the Technology Mission on cotton and one of the major commercial crops the
“Intensive Cotton Development Programme (ICDP)” is now employed as a Small Mission -
II. In the KBK districts one of the major Commercial Crops are predominately grown in the
Kharif season including Cotton. Bolangir, Kalahandi, Ganjam and Rayagada districts are
P a g e | 23
having increased cotton production within a decade. Area, production and productivity of
Odisha show an increasing trend (Table 2.3.1.1).
2.3.2: Turmeric
Turmeric (Curcuma longa L) is an important commercial spice crop grown in India is known
as ‘Indian Saffron’. Turmeric is used as curry powder in Indian culinary due to its in varied
forms as a condiment, flavouring and colouring agent. Drug industry and cosmetic industry
are widely using it due to its anti-cancer and anti-viral activities. In religious and ceremonial
occasion’s turmeric and its by-product Kum-Kum is used. Turmeric is becoming as ideal
produce as a food colorant due to the cumulative demand for naturally found products as
food flavours. It is native of South Asia predominantly in India. India is the major producer
of the rhizome; turmeric contributes 78% of world’s turmeric production. China, Myanmar,
Nigeria, Bangladesh and others contribute 8%, 4%, 3%,3% and 4% respectively. India
exports turmeric to countries like U.S.A, IRAN, U.A.E, and Malaysia etc. The export
quantity and value for it is presented under table 2.3.2.1.
Table 2.3.2.1: Major Country-Wise Export of Turmeric from India
Andhra Pradesh, Karnataka, Odisha, Tamilnadu, West Bengal, Maharashtra, Kerala, Assam,
Bihar, Meghalaya, Tripura, Uttar Pradesh and Arunachal Pradesh are turmeric producing
states of India (Spices Board, India). Among them Andhra Pradesh, Karnataka, Maharashtra,
P a g e | 24
Telengana and Tamilnadu are market competitors of Odisha in context of Turmeric. The area
and production quantity for these major producing states are presented under table 1.2.
Table 2.3.2. 2: Major state-wise area and production of Turmeric in India
Area and production of Odisha for turmeric crop increasing from last three years due to
increasing demand for the quality of turmeric in domestic as well as international market.
Turmeric is produced in tribal belt of Odisha namely Kalahandi, Koraput and Mayurbhanj.
The details of blocks under these districts producing turmeric are presented under table
2.3.2.3.
Major turmeric producing states are presented under figure 2.3.2.1.
Figure 6Figure 2.3.2.1: Map of Major Turme
2.3.3: Green gram
Odisha contributes 5.42 % towards India’s total
producing Green gramare Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Rajasthan,
Tamilnadu and Telengana. The
Table 2.3.3.1 Major
Figure 2.3.2.1: Map of Major Turmeric Producing States of India
Odisha contributes 5.42 % towards India’s total Green gram production. Other states that are
are Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Rajasthan,
Tamilnadu and Telengana. The contributions of these states are presented under table 2.3.3.1.
Table 2.3.3.1 Major Green gram Producing States of India
P a g e | 25
ric Producing States of India
production. Other states that are
are Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Rajasthan,
contributions of these states are presented under table 2.3.3.1.
Producing States of India
Green gram can be five times in a year. The seasons for green gram cultivation are: Kharif,
Rabi, Pre-rabi, Rice fallow and Summer.
well as some states of India. Green gram usually cooked as lentil in Odisha recipe. In some
other states it is cooked in different ways. Among them Kachori, Moong dal Halva, Chilla
etc. are famous. Green gram is the third largest producing crop of Odisha after Paddy and
Black gram (Biri). Every Odisha farmer cultivates Moong just after Paddy cultivation as their
traditional practice. In figure 2.3.3.1 major green gram producing states are presented. In the
figure dark colour shows 20-
20 lakh tonnes production and light colour shows less than 10 lakh tonnes production of
Green gram.
Figure 7 Figure 2.3.3.1: Map of Major
Green gram can be five times in a year. The seasons for green gram cultivation are: Kharif,
rabi, Rice fallow and Summer. Green gram is locally called Moong in Odisha as
well as some states of India. Green gram usually cooked as lentil in Odisha recipe. In some
other states it is cooked in different ways. Among them Kachori, Moong dal Halva, Chilla
m is the third largest producing crop of Odisha after Paddy and
Black gram (Biri). Every Odisha farmer cultivates Moong just after Paddy cultivation as their
traditional practice. In figure 2.3.3.1 major green gram producing states are presented. In the
-35 lakh tonnes producing states. Medium light colour shows 10
20 lakh tonnes production and light colour shows less than 10 lakh tonnes production of
.
Figure 2.3.3.1: Map of Major Green gram Producing States of India
P a g e | 26
Green gram can be five times in a year. The seasons for green gram cultivation are: Kharif,
Green gram is locally called Moong in Odisha as
well as some states of India. Green gram usually cooked as lentil in Odisha recipe. In some
other states it is cooked in different ways. Among them Kachori, Moong dal Halva, Chilla
m is the third largest producing crop of Odisha after Paddy and
Black gram (Biri). Every Odisha farmer cultivates Moong just after Paddy cultivation as their
traditional practice. In figure 2.3.3.1 major green gram producing states are presented. In the
35 lakh tonnes producing states. Medium light colour shows 10-
20 lakh tonnes production and light colour shows less than 10 lakh tonnes production of
Green gram Producing States of India
P a g e | 27
2.3.4: Groundnut:
India contributes 14.5 percent to world groundnut production, which is the second largest
producer after China. China contributes 42.4 percent of groundnut to world production. Other
countries like Sudan (2.2%), Argentina (2.6%), Indonesia (3.1%), Burma (3.7%), US(4.4%)
and Nigeria (7.8%) contributes towards world total groundnut production. The graph of
world groundnut production is presented under figure 2.3.4.1.
Figure 8 Figure 2.3.4.1: Major Countries share in Groundnut Production
Odisha, Andhra Pradesh, Gujarat, Haryana, Karnataka, Maharashtra, Madhya Pradesh,
Punjab and Haryana states are considered for analysis in this study due to availability of data.
In figure 2.3.4.2 major groundnut producing states are presented.
P a g e | 28
Figure 9 Figure 2.3.4.2: Major Groundnut production Area in India
Groundnut constituted 34 percent of the total oilseed acreage in the state of Odisha
contributing more than 68 percent of the total oilseeds produced in the state during the
triennium ending 2017-18. Groundnut as a cash crop has been losing its shine in Odisha
mainly because of lack of timely availability of quality seeds and high price volatility. Area
allocation to groundnut in the state during the last two and half decades declined from 318
thousand hectares during triennium ending 1995-96 to 210 thousand hectares in 2017-18
registering negative growth of 0.93 percent per annum. Production of groundnut declined
from 4.66 to 3.74 lakh tons during the same period. However, productivity of the crop has
increased from 1465 kg/ha to 1783 kg/ha (different issues of Odisha Agricultural Statistics).
Groundnut is grown in the state during kharif (the autumn crop sown at the beginning of the
summer rains) as well as Rabi (the grain crop sown in September and reaped in the spring)
season. Area under Rabi groundnut comprises of 67 percent of the total groundnut area and is
mostly rain fed. Major decline in area was observed in kharif season where area decreased
from 164 thousand hectares in 1993-94 to 65 thousand hectares in 2017-18. Rabi area under
the crop declined from 166 thousand hectares to 133 thousand hectares during the period.
Among the districts, Bargarh registered largest decline in area from about 50 thousand
hectares to 20.6 thousand hectares during the period. In fact all the major groundnut
P a g e | 29
producing districts viz., Bolangir, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Dhenkanal,
Angul, Ganjam and Bargarh experienced major shift in area allocation against groundnut
during the period (figure 1). Some of the non-traditional districts like Balasore, Subarnapur,
Kalahandi, Kandhmal, Boudh, Keonjhar, Koraput, Nawarangpur, Deogarh and Mayurbhanj
registered minute area escalation under the crop. Decline in area has been mainly attributed
to unstable market and high price fluctuations.
References
1. Agricultural Market Intelligence Centre, PJTSAU. (2018). https://pjtsau.edu.in/files/AgriMkt/2018/TurmericOLDec2018.pdf
2. Cotton Advisory Board(CAB)P-Provisional
3. Spices Board of India. http://www.indianspices.com/marketing/price/domestic/daily- price
4. DES, Ministry of Agri. & FW, Govt. of India.
5. https://www.usda.gov
6. Cotton Advisory Board of India (CAB)
7. (GoI, 2019)
8. https://duckduckgo.com/?q=cotton+production+scenario+&kh=1&kn=1&kac=1&iar =images&iax=images&ia=images&iai=https%3A%2F%2Fimage.slidesharecdn.com %2Fmandeepsummertrainingreport-100915150000-phpapp01%2F95%2Fmandeep- summer-training-report-35-728.jpg%3Fcb%3D1284562881
9. (http://www.indianspices.com)
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3. LITERATURE REVIEW
Review of literature is helpful in finding gap from past studies and drawing blue print for
further research. It is helpful in formulating objectives, suitable schedule preparation,
selection of representative sample from the population, data analysis, result interpretation,
preparing recommendations etc. Hence a review of literatures related to market integration
and price forecasting is presented under this chapter. A literature review is an inquiry and
assessment of the accessible writing in our given subject by choosing an area.
3.1: Price Forecasting
1. Rangsan
Nochai, Titida
Nochai. (2006)
The author conducted a study in Thailand for the price forecasting of
oil palm with an objective to find out appropriate ARIMA model
with a consideration of minimum MAPE. The forecasting price and
actual price showed minimum deviation through validation. Oil
prices of three types for five years i.e 2000-2004 is taken for study.
Which we can see that the MAPE for each model very small. The
result depicts that ARIMA(210), ARIMA(101), ARIMA(300) model
are developed for forecasting these are farm price of oil palm,
wholesale price of oil palm, pure oil price of oil palm respectively.
2. Liew Khim Sen,
et al b. (2006).
The author fitted a model “ARMA (1, 0)” to forecast the pepper
price of Sarawak state of Malaysia. The author abled to find out this
model after examining 25 fitted models with low AICc criteria. The
ARMA (1,0) model which is found from this study is helpful for
pepper farmers and traders of Sarawak district. According to the
author updating and continuous monitoring of this model is very
useful as the model is parsimonious.
3. Reza
Moghaddasi,
Bita Rahimi
Badr. (2008)
The author found best fitted model for forecasting wheat price of
Iran. This article specified and estimated econometric models like
“structural and time series” models by evaluating and comparing
common criteria like “RMSE(root mean square error), MAE(mean
absolute error), MAPE(mean absolute percentage error) and Theil
inequality coefficient”. Findings of the study tell that the ARIMA
(325) model fits best for wheat price prediction.
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4. Dr Alioune
DIENG. (2008)
The author investigated the performance of parametric models for
forecasting selected vegetable prices by using parametric
econometric models. The models are “naive model, the exponential
smoothing and the Box and Jenkins autoregressive integrated
moving average (ARIMA) model, non-parametric model of spectral
analysis”. It is found from the study that Box and Jenkins ARIMA
model fits best for prediction of vegetable price.
5. Fabian Torben
Bosler. (2010)
The researchers compared Johansen method, ARIMA model and
ANN model to forecast oil price. It is found from the study that the
nonlinear model fits best to predict oil price. Hence, the authors used
the non-linear model to forecast oil price.
6. Gan-qiong Li, et
al. (2010)
They studied a short-term forecasting of tomato price is done using
ANN model and compared with time series model ARIMA. Results
revealed that ANN model is best fitting than time series model. A
good correlation was observed between the real prices and the
modelled price, with a relative error less than 5.0%.
7. Assis, K. et al.
(2010)
Authors took an attempt to forecast cocoa bean prices by comparing
the performance of different forecasting techniques. Cocoa bean
prices from January 1992 - December 2006 was used for analysis.
With the help of criteria like “MAPE, RMSE, Theil’s inequality
coefficient the exponential smoothing, autoregressive integrated
moving average (ARIMA), generalized autoregressive conditional
Heteroskedasticity (GARCH) and the mixed ARIMA/GARCH
models” were compared. The study reveals that the mixed
ARIMA/GARCH model outperformed other models like ARIMA
and GARCH.
8. Zahid Asghar, et
al. (2012)
Authors captured the exogenous breaks and outliers using automatic
modelling to forecast wheat and rice prices for Pakistan. In this
process outliers were identified as observations with huge residuals.
With the help of criterions like –“Root Mean Square Error (RMSE)
and Mean Absolute Percentage Error (MAPE)” the proposed model
is matched with ARIMA model. Result reveals that Structural
Breaks, Automatic Model outperforms traditional model.
P a g e | 32
9. Purna Chandra
Padhan. (2012)
To forecast annual productivity of selected agricultural product the
author applied ARIMA model considering annual data from 1950 to
2010 of 34 different products. The forecasting is done for more five
years. Model validity is verified with criteria like Adjusted R2,
Minimum AIC and lowest MAPE value. Results revealed that
cardamom provides lowest AIC values.
10. Dr. Sandra
Cristina de
Oliveira, et al.
(2012)
The authors considered cash crops like groundnut, sugarcane,
banana and orange, received by Brazilian producers to study
performance of time series model. The data was taken during August
1994 and December 2009. The study reveals that the models which
are suitable for forecasting commodity price are the exponential
smoothing models and Box & Jenkins models.
11. V. Sivapatha
sundaram, et al.
(2012)
The authors collected data for paddy production for Sri Lanka for
the period of 1952 to 2010 from the Census and Statistics
Department of Sri Lanka. They have developed a time series model
for prediction of production trend of Paddy crop for three years like
2011 to 2013.
The best model for forecasting which is found from the study is
ARIMA (2,1,0). MAPE for this study was 10.5 which were good for
model validation.
12. Sayedul Anam,
et al. (2012)
Authors studied the Agricultural contribution to GDP from the year
1972 to 2010 for Bangladesh. Both ARIMA and GARCH model are
found as best fitted model for this study. The proposed model for
forecasting are ARIMA (1, 2, 1) and GARCH (1,1). The study
outcome is expected to help in identifying influence of Agricultural
sector on GDP.
13. Petram Akbari
Parg, et al.
(2013).
The authors forecasted monthly price of Privileged Sadri rice for the
year 2012-4 to 2013-3 in Guilan province. They have taken the
study as it is one of the major rice producing provinces in Iran. TES
and SARMA models were used in this context and the efficacy of
model was compared with the help of MAPE criterion. Results
revealed that SARIMA model outstripped TES model.
14. S. P. Bhardwaj, The authors forecasted spot prices of Gram for Delhi market with
P a g e | 33
et al. (2014) the help of ARIMA-Box Jenkins and GARCH models. Literature
review revealed that volatility cannot be captured by ARIMA model.
Hence GARCH model is used. The model which is found suitable
with the help of AIC, SIC, RMSE, MAPE and MAE criterion is
GARCH (1, 1). The conclusion drawn from the study is GARCH
model is better than ARIMA model to forecast daily price.
15. Edward Kamau
Gathondu.
(2014)
Authors felt that price forecasting of vegetables is very sensitive due
to their high perishable nature and seasonality. It is also felt that
better information regarding price is required to meet price risk. The
author studied for markets in Nairobi, Mombasa, Kisumu, Eldoret
and Nakuru wholesale markets for the vegetable crops like tomato,
potato, cabbages, kales and onions. To get better price forecasting
four different models has been used which are Autoregressive
Moving Average (ARMA), Vector Autoregressive (VAR),
Generalized Autoregressive Condition Heterostadicity (GARCH)
and the mixed model of ARMA and GARCH. The best models
which are found as suitable for forecasting are ARIMA are; Potato
“ARIMA (1,1,0)”, Cabbages “ARIMA (2,1,2)”, tomato “ARIMA
(3,0,1)”, onions “ARIMA (1,0,0)”, Kales “ARIMA (1,1,0)” .
Further, the mixed model of ARMA (1, 1) and GARCH (1, 1).
16. R. Sharma, et al.
(2014)
They found that the crop cumin is very sensitive to rain during
harvesting time. Rain causes much kind of fungal disease and crop
loss which leads to price fluctuation. This reason motivated the
author to do study on cumin price and to forecast the price for
Rajasthan market during the period Dec 2014 to Feb 2015. Using
ARIMA model it is found that the cumin price will remain stable
during the forecast period.
17. Niranjan
Jayaramu.
(2015)
They took the objective to forecast the price of corn, soybean and
wheat for United States using regression model. The forecasts
showed that corn prices for 2014 range from $4.74 to $5.013, while
soybean prices for the same time period range from $11.085 to
$11.803 and wheat prices range from $6.14 to $6.58.
18. J. SHRUTHI. The authors found that the real income is directly influenced by
P a g e | 34
(2015) prices of agricultural commodities which are direct access to food.
Onion and Tomato are selected are these are main source of
consumption in daily basis. The monthly arrivals as well as prices of
Bengaluru and Kolar APMC market for of onion and tomato are
collected for ten years to time series analysis. ARIMA, SARIMA,
GARCH models are being used out of which ARIMAX, ARIMA
and GARCH models are found suitable for forecasting tomato price.
SARMA model is found suitable for onion price. The models for
both the crops got validation with MAPE criterion.
19. Gurudeo Anand
Tularam, Tareq
Saeed. (2016)
Authors forecasted West Texas Intermediate (WTI) oil prices.
Comparing consecutive models the authors found that the ARIMA
(2, 1, 2) model is efficient than ES or HW models. Hence the same
model is used for forecasting WTI oil price.
20. Wang Xin and
Wang Can.
(2016)
The author selected cucumber of Shandong Shouguang wholesale
market for price forecasting and found ARIMA (3, 1, 2) model is
best fit for forecasting.
21. Dr V
Ramanujam and
Dr T
Viswanathan
(2018)
Authors found from literature review that for pepper farmers and
traders price forecasting plays a dominant role in decision making.
According to the authors a single bias in forecasting may lead to a
great loss. Hence accuracy in forecasting is much more important
which the end product of appropriate model selection is. In this
study the author tested similar models and used one for price
forecasting pepper price of Indian spot markets.
22. Naveena K., et
al., (2017)
The author studied the Arabica plantation coffee price to forecast the
price using ARIMA-ANN model. As the variety has created its own
identity in the international market; the price forecasting may attract
the future entrepreneurs to cultivate the same variety and fetch
maximum benefit. Root Mean Square Error, Mean Absolute
Percentage Error are used for measuring forecasting performance.
The hybrid ARIMA-ANN model is found superior than other model.
23. A. Darekar, and
A. Reddy,
(2017)
The authors predicted common paddy price during kharif harvesting
season, 2017-18 in India. ARIMA model is used for price
forecasting in this study. Reliability of the model is tested by MAPE,
P a g e | 35
AIC and BIC criterion. Author used R programming software for
this study. The study outcome says that the price of paddy is in
increasing trend for the year 2017-18.
24. ASHWINI
DAREKAR and
A
AMARENDER
REDDY (2017)
The authors conducted a study on pigeon pea price forecasting
during the year 2017-18 for the states like Maharashtra, Rajasthan,
Uttar Pradesh, Madhya Pradesh and Gujarat. In this study ARIMA
model is used for predicting price. Reliability of the model is tested
by –“MAPE, AIC and BIC criterion”. The model validation is done
for the year 2016-17. Author used R programming software for this
study. The study outcome says that the price of pigeon pea will be
rule in the range of –“Rs. 4,300 - 7,600 per quintal during November
to January 2017-18”.
25. A. Darekar, and
A. Reddy,
(2017)
The authors predicted 2017-18 soybean price of major soybean
producing states of India using ARIMA Box-Jenkins’s model. The
data for selected crops is collected for the period of 11 years from
AGMARKNET website. The market price for soybean for each state
is the outcome of this study; which is examined with the help of
SBC, AIC, and MAPE.
26. A. Darekar, and
A. Reddy,
(2017)
The authors conducted a study on Cotton in India with the help of
AGMARKNET data. They forecasted 2017-18 kharif cotton prices
during harvest. ARIMA model is used for price forecasting.
Criterions like AIC, SBC and MAPE are used for measuring
performance of the selected model.
27. A. Darekar, and
A. Reddy,
(2017)
In this study price forecasting of wheat is done for major maize
producing states of India. ARIMA (011) (011) model is found more
suitable for forecasting and the same model is validated using MAE,
MAPE and RMSE criterion.
28. Panasa
Venkatesh, et. al
(2017)
The authors predicted monthly maize modal prices for Telengana.
The “ARIMA (2, 1, 1) model” is found the best model by comparing
consecutive models with the help of criterions like AIC, BIC and
MAPE. The study suggested increasing in Badepalli maize market
price during October to February.
29. V. Jadhav, et al. The authors validated Karnataka state major crop Paddy, Ragi and
P a g e | 36
(2017)
Maize prices for the year 2016. Univariate ARIMA techniques are
used to forecast prices for the year 2020. MSE, MAPE and Theil’s U
coefficient criterions are used for validation.
30. Dr. Ramanujam
V and Dr. T
Viswanathan
(2018)
According to the authors a single bias in forecasting may lead to a
great loss. Hence accuracy in forecasting is much more important
which the end product of appropriate model selection is. In this
study the author tested similar models and used one for price
forecasting pepper price of Indian spot markets.
31. Darekar A and
AA Reddy.
(2018)
In this study major mustard producing state prices of India is
studied. ARIMA (010)(011) model is found more suitable for price
forecasting. Criterions like MAPE, MAE and RMSE are used for
validating the model. Price predicting for mustard for major
producing states are done for the year 2017-18.
32. Darekar A and
AA Reddy.
(2018)
In this study price forecasting of wheat is done for major wheat
producing states of India. ARIMA (011) (011) model is found more
suitable for forecasting and the same model is validated using MAE,
MAPE and RMSE criterion.
33. Pani, Rojalin et
al. (2019)
The author forecasted green gram price compared weekly green
gram price forecasting for the state of Odisha in India with the help
of GARCH as well as ARIMA model. The results of two models are
compared by the criterion MAPE and found the best model as
GARCH. Further forecasting for the year of 2018 is done using
GARCH model.
34. Pani, Rojalin et
al. (2019)
The authors forecasted Groundnut price for Odisha market for the
year of 2019. The authors used ARIMA model to forecast groundnut
price. Model is got validated with MAPE and RMSE.
3.2: Seasonal Index:
Agricultural costs regularly pursue a seasonal example since generation is seasonal and
storage capacity is expensive. Sometimes, seasonal demand, (for example, occasion
utilization) may likewise add to regularity in agrarian costs. Prices for all perishable and semi
perishable commodity are seasonal. Storage is also expensive which causes lowest price
during harvest and high price before harvest.
P a g e | 37
1. A. A., Jambhale, et
al. (2012)
The authors carried out a study during 2010-11 in Baramati
district Pune for selected agricultural commodities. It is found
that there is no seasonal relationship between the per quintal
price of wheat, gram, soybean, onion but there is seasonal
fluctuation in groundnut.
2. Sabur, S. A.,
Hossain, M., &
Palash, M. S.
(2006)
The researchers studied Onion price of Rajshahi and Dhaka of
Bangladesh. It is found that seasonal price variation is highest in
Rajshahi and lowest in Bangladesh. Another finding of the study
is that whole Bangladesh price regarding seasonal index is
decreasing in the near past.
3. Bera, B. (2017)
The study reveals the seasonality in potato price in Bishnupur
and Jhargram markets of West Bengal. The study concluded by
saying there presents negative relationship between current
price and market arrival. It is found from the study that highly
perishable commodities are mostly seasonal driven.
4. Sharma, H. (2016) The author studied wheat price in Sriganganagar district of
Rajasthan. Wheat price is found highest during off season and
lowest during harvest season. The author calculated seasonal
index and compared month wise.
5. Patel, S. A., &
Patel, J. M. (2013)
The researchers studied Cumin price of Unjha and found that
seasonal price variation is lowest during harvest season and
highest during off season.
3.3: Marker Integration
1. Capelle-Blancard, G.,
& Coulibaly, D. (2011)
Causality between prices and index based trading activity of
commodities is examined by the authors of this study. The
commodities that are involved in this analysis are “twelve
grain, livestock, and other soft commodity”. Panel Granger
causality is used for analysis. It is found from the study that
there is no relationship among the prices and index based
trading activity of commodities.
P a g e | 38
2. Ali, J., & Bardhan
Gupta, K. (2011)
The authors conducted a study to find out relationship
among futures and spot prices for all the Selected
agricultural commodities. To test the required objective
authors used Johansen’s Cointegration analysis and Granger
causality tests. Study reveals no relation among future and
spot prices except wheat.
3. Atanu
Ghoshray, Madhusudan
Ghosh. (2011)
The author used maximum likelihood method of
Cointegration to test inter-state and intra-state longitudinal
integration of wheat markets in India. It is found from the
study that there exists long run spatial relationship among
markets.
4. Wilkinson, J., &
Ghoshray, A. (2013)
Relationship between oil and agricultural commodity prices
are studied in this paper. Sample commodities which are
considered for this study are crude oil, corn, soybean and
sugar. Study period is from 1980 to 2012. It is found from
the study that Oil price is affecting sugar and corn prices.
Other crop prices are found having long run relationship
among themselves.
5. Kumar Soni, T. (2014)
The working paper tested relationship between future and
spot prices of selected agricultural commodities is tested in
this working paper. The results show that there exists long
run relationship in three commodities out of four
commodities under study.
6. Omar, M. I., Dewan,
M. F., & Hoq, M. S.
(2014)
The study is conducted in different hilly regions of
Bangladesh to have an idea about marketing system of
Banana. It is found from the study that the Banana market
except hilly region is well integrated. The market in hilly
region is not well integrated due to lack of transportation
facility.
7. A. Kapusuzoglu, M.
Karacaer Ulusoy.
(2015)
Relationship among agricultural commodity price and oil
price is established in this study. The commodities under
consideration are Wheat, Corn and Soybean. The findings of
P a g e | 39
the study say that long run relationship is not working
among agricultural commodities as well as between oil
prices.
8. M. H. Wani, et al.
(2015)
The authors of the current study examined Apple market
price in India. The authors compared Apple price of
different varieties among major producing states of India
and established causal relationship among them. 39 and 18
bi-directional causations respectively under different market
situations are revealed from the study.
9. Pardhi, R. M. (2016)
The study is conducted in the state of Uttar Pradesh for
Mango. The market price of Mango in Lucknow and
Varanasi markets are compared variety wise. Also the
Mango price is compared with other fruit market value. The
study reveals that the price of Mango in different selected
markets are affected by other sweet, nutritive and medically
preferred fruits like Apple, Orange and Pomegranate.
10. Musunuru, N. M.
(2017)
The study proposed to establish relationship among
agricultural commodity price and meat prices in U.S. It is
found from the study that commodity as well as meat prices
are affected by own past prices only.
References:
1. Ali, J., & Bardhan Gupta, K. (2011). Efficiency in agricultural commodity futures markets in India: Evidence from Cointegration and causality tests. Agricultural Finance Review, 71(2), 162-178.
2. ASHWINI DAREKAR and A AMARENDER REDDY. (2017). Price forecasting of pulses: case of pigeon pea. Journal of Food Legumes 30(3): 42-46.
3. Ashwini Darekar, A. Amarender Reddy. (2017). Price forecasting of maize in major states. Maize Journal.
4. Atanu Ghoshray, Madhusudan Ghosh. (2011) How Integrated is the Indian Wheat Market?. Journal of Development Studies 47:10, pages 1574-1594.
P a g e | 40
5. Bera, B. (2017). A Study on the Variability in Market Arrivals and Prices of Potato in Some Selected Markets of West Bengal. International Journal of Agriculture Sciences, ISSN, 0975-3710.
6. Capelle-Blancard, G., & Coulibaly, D. (2011). Index trading and agricultural commodity prices: A panel Granger causality analysis. International Economics, 126, 51-71. [Elsevier]
7. Darekar A and AA Reddy. (2018). Forecasting wheat prices in India. Forecasting wheat prices in India. Wheat and Barley Research 10(1). Homepage: http://epubs.icar.org.in/ejournal/index.php/JWR.
8. Darekar A and AA Reddy. (2018). Oilseeds Price Forecasting: Case Of Mustard In India. Agricultural Situation in India.
9. Darekar, A. and Reddy, A. A. (2017). Cotton Price Forecasting in Major Producing States. Economic Affairs, 62(3):1-6.Print ISSN : 0424-2513, Online ISSN : 0976- 4666.
10. Darekar, A. and Reddy, A. A. (2017). Predicting market price of soybean in major India studies through ARIMA model. Journal of Food Legumes 30(2): 73-76, 2017.
11. Darekar, A. and Reddy, A. A. 2017. Forecasting of Common Paddy Prices in India. Journal of Rice Research, 10(1): 71-75.
12. Darekar, AS., Pokharkar, VG and Datarkar, SB. 2016. Onion Price Forecasting In Kolhapur Market of Western Maharashtra Using ARIMA Technique. International Journal of Information Research and Review 03(12): 3364-3368.
13. Dr Alioune DIENG. (2008).
14. Dr V Ramanujam and Dr T Viswanathan. (2018). An Empirical Analysis of Forecasting Volatility of Pepper Price in the Spot Market in India. Research Gate.
15. Dra. Sandra Cristina de Oliveira, et al. (2012). A study about the performance of time series models for the analysis of agricultural prices. GEPROS. Gestão da Produção, Operações e Sistemas, Ano 7, nº 3, p. 11-27.
16. Edward Kamau Gathondu. (2014). Modeling of Wholesale Prices for Selected Vegetables Using Time Series Models in Kenya. Student thesis. College of Biological and Physical Sciences School of Mathematics, University of Nairobi.
17. Fabian Torben Bosler. (2010). Models for Oil Price Prediction and Forecasting. A Thesis Presented to the Faculty of San Diego State University.
18. Gan-qiong Li, et. al. (2010). Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks. Agriculture and Agricultural Science Procedia 1 278–287
P a g e | 41
19. J. SHRUTHI. (2015). An Analysis of Price Forecasting Techniques for Onion and Tomato Crops. Student Thesis. Department of agricultural marketing, co-operation and business management university of agricultural sciences Bengaluru- 560 065.
20. Jambhale, A. A., Bhoite, D. P., & Shinde, A. V. (2012). Seasonal indices and price behaviour in agriculture produce market committee, Baramati Dist. Pune (MS). Agriculture Update, 7(1/2), 42-46.
21. K. Assis, et al. (2010). Forecasting Cocoa Bean Prices Using Univariate Time Series Models. Journal of Arts Science & Commerce ISSN 2229-4686.
22. Kapusuzoglu A., Karacaer Ulusoy M. (2015): The interactions between agricultural commodity and oil prices: an empirical analysis. Agric. Econ. – Czech, 61: 410-421.
23. Kumar Soni, T. (2014). Cointegration, linear and nonlinear causality: Analysis using Indian agriculture futures contracts. Journal of Agribusiness in Developing and Emerging Economies, 4(2), 157-171.
24. Liew Khim Sen, MahendranShitan, HuzaimiHussain. (2006). Time series modelling and forecasting of Sarawak black pepper price. MPRA Paper No. 791, posted 13. November.
25. Musunuru, N. M. (2017). Causal relationships between grain, meat prices and exchange rates. International Journal of Food and Agricultural Economics (IJFAEC), 5(1128-2018-044), 1-10.
26. Naveena K., et al., (2017) Hybrid Time Series Modelling for Forecasting the Price of Washed Coffee (Arabica Plantation Coffee) in India.. International Journal of Agriculture Sciences, ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 9, Issue 10, pp.-4004-4007
27. Niranjan Jayaramu. (2015). Impact of Seasonality on Agricultural Commodity Price Behaviour. A thesis presented to the department of agricultural sciences in candidacy for the degree of Master of Science. Northwest Missouri state university Maryville Missouri
28. Omar, M. I., Dewan, M. F., & Hoq, M. S. (2014). Analysis of price forecasting and spatial co-integration of banana in Bangladesh. Eur. J. Business Manage, 6(7), 244- 255.
29. Pardhi, R. M. (2016). MARKET INTEGRATION AND PRICE FORECASTING OF MANGO: AN EMPIRICAL ANALYSIS (Doctoral dissertation, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi).
30. Patel, S. A., & Patel, J. M. (2013). A comparative study of arrivals and prices of agricultural commodities at APMC using Time Series Analysis.
P a g e | 42
31. Petram Akbari Parg, et al. (2013). AmiApplication of Seasonal Models in Modelling and Forecasting the Monthly Price of Privileged Sadri Rice in Guilan Province. ARPN Journal of Agricultural and Biological Science, ISSN 1990-6145.
32. Purna Chandra Padhan. (2012). Application of ARIMA Model for Forecasting Agricultural Productivity in India. JOURNAL OF AGRICULTURE & SOCIAL SCIENCES ISSN Print: 1813–2235; ISSN Online: 1814–960X 11– 017/AWB/2012/8–2–50–56 http://www.fspublishers.org.
33. R. Sharma, et al. (2014). Price of cumin in Rajasthan using ARIMA approach. International J. Seed Spices 5(1), January:76-78.
34. Rangsan Nochai, Titida Nochai. (2006). ARIMA Model for Forecasting Oil Palm Price. Proceedings of the Second IMT-GT Regional Conference on Mathematics, Statistics and Applications UniversitySains Malaysia, Penang, June 13-15.
35. Reza Moghaddasi, Bita Rahimi Badr. (2008). An Econometric Model for Wheat Price Forecasting in Iran. International Conference on Applied Economics – ICOAE.
36. S. P. Bhardwaj, et al. (2014). An Empirical Investigation of Arimaand Garch Models in Agricultural Price Forecasting. Economic Affairs 2014, 59(3) : 415-428.
37. Sabur, S. A., Hossain, M., & Palash, M. S. (2006). Marketing System, Seasonality In Prices And Integration Of Onion Markets In Bangladesh. Bangladesh Journal of Agricultural Economics, 29(454-2016-36546), 93-105.
38. Sayedul Anam, et al. (2012).Time Series Modelling of the Contribution of Agriculture to GDP of Bangladesh. European Journal of Business and Management www.iiste.org ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) Vol 4, No.5.
39. Sharma, H. (2016). Trend and seasonal analysis of wheat in selected market of Sri Ganganagar district. Economic Affairs, 61(1), 127.
40. Tularam, G.A. and Saeed, T. (2016) Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, 6, 226- 235. http://dx.doi.org/10.4236/ajor.2016.63023
41. V. Jadhav, et al. (2017). Application of ARIMA Model for Forecasting Agricultural Prices. J. Agr. Sci. Tech. Vol. 19: 981-992.
42. V. Sivapathasundaram, et al. (2012). Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model. Tropical Agricultural Research Vol. 24 (1): 21 – 30.
43. Venkatesh Panasa, R. VijayaKumari, G. Ramakrishna and Kaviraju, S. (2017). Maize Price Forecasting Using Auto Regressive Integrated Moving Average (ARIMA) Model. Int.J.Curr. Microbiol. App.Sci.6 (8):2887-2895.doi: https://doi.org/ 10.20546/ijcmas.2017.608.345.
P a g e | 43
44. Wang Xin and Wang Can. (2016). Empirical Study on Agricultural Products Price Forecasting based on Internet-based Timely Price Information. International Journal of Advanced Science and Technology Vol.87 (2016), pp.31-36 http://dx.doi.org/10.14257/ijast.2016.87.04
45. Wani, M. H., Paul, R. K., Bazaz, N. H., & Manzoor, M. (2015). Market integration and price forecasting of apple in India. Indian Journal of Agricultural Economics, 70(902-2016-68375), 169-181.
46. Wilkinson, J., & Ghoshray, A. (2013). A Cointegration analysis of oil and agricultural prices. Review of Market Integration, 5(3), 249-270.
47. Zahid Asghar, et al. (2012). Structural Breaks, Automatic Model Selection and Forecasting Wheat and Rice Prices for Pakistan. Pak.j.stat.oper.res. Vol.VIIINo.1 pp1-20.
48. PANI, R., BISWAL, S. K., & MISHRA, U. S. (2019). Green gram weekly price forecasting using time series model. Revista ESPACIOS, 40(07).
49. PANI, R., et al. (2019). Groundnut price forecasting using time series model
Revista ESPACIOS, 40(25).Groundnut price forecasting using time series model
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4. THE NARRATION OF THE RESEARCH DESIGN
Introduction
This section summarises the research methodology of this study. This study uses t-statistics,
JB-statistics, ADF test, Cointegration test, Granger causality test, Seasonal Index, ARIMA
model to test the hypothesis.
4.1. Chapter Objective
Objectives of this chapter are as follows:
1. To identify and clearly define the objectives of the study.
2. To identify and give a clear understanding of the most suitable econometric tools to
be used in the study for judiciously achieving the research objectives.
4.2. Research Objectives
The present study objective is to build a suitable model for forecasting and to predict the
agricultural commodity price for Odisha and to see the seasonal index of the commodity.
Another broad objective of the study is to find out whether the commodity price of Odisha is
integrated or at least in the process of getting integrated with any of the respective major
crop-producing states. Cointegration and Causality techniques are detected from the
extensive literature review as the best techniques to see market integration. Hence objectives
are specified as follows:
1. To study the Market Integration of major respective crop-producing states in India.
2. To find out the causal relationship between the price series of selected crop-producing states in India.
3. To determine the seasonal index of crop price in Odisha market.
4. To build a suitable forecasting model and to forecast the agricultural commodity price for Odisha Market.
4.3 Research Hypotheses:
Hypotheses for the present study like a null (H0) and alternative (H1) have been formulated as
follows:
6. H0: The series follows a normal distribution and with Skewness as zero and kurtosis
as three.
P a g e | 45
H1:The series follows a normal distribution and with Skewness different from zero
and kurtosis different from three.
7. H0: The series under consideration is not stationary.
H1:The series under consideration is stationary.
8. H0: There is no co-integration between the commodity price of Odisha and other
major crop-producing states.
H1: There is co-integration between the commodity price of Odisha and other major
crop-producing states.
9. H0: There is no causality between the commodity price of Odisha and other major
crop-producing states.
H1: There is causality between commodity price of Odisha and other major crop-
producing states.
10. H0: The variation between the actual and forecasted price is maximum.
H1: The variation between the actual and forecasted price is minimum
4.4 Study Methodology:
The methodology selected for the present study described under the following heads:
Sample Commodity
Data on the commodity and period of the study.
Techniques of data analysis.
4.4.1 Sample Commodity
Indian markets are not sufficiently integrated; is the main argument against agricultural trade
liberalization of India. The current study makes an organized attempt to measure the degree
of integration amongst selected agricultural commodity markets in India. Now, when talk of
price forecasting, market integration and causality of agricultural commodity price
forecasting arise; weekly price for each commodity seems suitable. Hence, it has been
decided in the current study to collect commodity price for each state weekly basis. The
longitudinal price series data of agricultural commodity for the current study is collected
from secondary sources i.e. AGMARKNET website. The analysis carries 708 data points.
The collection period of historical data points is from January 2004 to December 2018. The
data points have been assembled as per the availability. The agricultural commodities that
have been selected for this study are Cotton, Green gram, Turmeric and Groundnut.
P a g e | 46
4.4.2 Data on the commodity and period of the study
Crops States Period of Study No. of
Observations
Cotton
Andhra Pradesh 01/01/2004 To 31/12/2018 720
Gujarat 01/01/2004 To 31/12/2018 720
Haryana 01/01/2004 To 31/12/2018 720
Karnataka 01/01/2004 To 31/12/2018 720
Madhya Pradesh 01/01/2004 To 31/12/2018 720
Maharashtra 01/01/2004 To 31/12/2018 720
Odisha 01/01/2004 To 31/12/2018 720
Punjab 01/01/2004 To 31/12/2018 720
Rajasthan 01/01/2004 To 31/12/2018 720
Telengana 01/01/2004 To 31/12/2018 720
Tamilnadu 01/01/2004 To 31/12/2018 720
Turmeric
Andhra Pradesh 01/01/2004 To 31/12/2018 720
Karnataka 01/01/2004 To 31/12/2018 720
Maharashtra 01/01/2004 To 31/12/2018 720
Odisha 01/01/2004 To 31/12/2018 720
Tamil Nadu 01/01/2004 To 31/12/2018 720
Telengana 01/01/2004 To 31/12/2018 720
Green gram
Assam 01/01/2004 To 31/12/2018 720
Gujarat 01/01/2004 To 31/12/2018 720
Karnataka 01/01/2004 To 31/12/2018 720
Kerala 01/01/2004 To 31/12/2018 720
Madhya Pradesh 01/01/2004 To 31/12/2018 720
Maharashtra 01/01/2004 To 31/12/2018 720
Odisha 01/01/2004 To 31/12/2018 720
Rajasthan 01/01/2004 To 31/12/2018 720
Tamil Nadu 01/01/2004 To 31/12/2018 720
Telengana 01/01/2004 To 31/12/2018 720
Groundnut
Andhra Pradesh 01/01/2004 To 31/12/2018 720
Gujarat 01/01/2004 To 31/12/2018 720
Karnataka 01/01/2004 To 31/12/2018 720
Madhya Pradesh 01/01/2004 To 31/12/2018 720
Maharashtra 01/01/2004 To 31/12/2018 720
Odisha 01/01/2004 To 31/12/2018 720
Rajasthan 01/01/2004 To 31/12/2018 720
Tamil Nadu 01/01/2004 To 31/12/2018 720
Uttar Pradesh 01/01/2004 To 31/12/2018 720
P a g e | 47
4.4.3 Techniques of Data Analysis:
The current study applies various suitable techniques for the design of the following:
1. Descriptive Statistics.
2. Normality Test.
3. Stationarity test.
4. Cointegration Test.
5. Test of Causality.
6. Test for seasonal Index.
7. Test for ARIMA modelling.
8. Price Forecasting.
9. Validation of model.
4.4.3.1 Conversion of Price series to Return Series
Return series is the natural log form of the price series, which is used to get symmetry in the
data series. The formula which is used for determining return is given below:
1 ln[ / ]*100t t tY C C
Where,
Yt= Return series at time t in percentage,
Ln = Natural log with base e,
Ct = Price at time t,
Ct-1= Price at time t-1.
4.4.3.2 Calculation of Descriptive Statistics.
Descriptive statistics for all commodities computed for the total period of study. The
descriptive statistics which are calculated are "mean, median, maximum, minimum, standard
deviation, Skewness, kurtosis”.
4.4.3.3 Normality Test for all-time series data.
P a g e | 48
Normality test is conducted to know whether the data sets are distributed normally or not. To
test the normality of a data series descriptive statistics, as well as JB statistics, are required to
be studied.
4.4.3.4 Stationarity test for all-time series data.
ADF Test:Dickey and Fuller study three differential-structure autoregressive conditions to
identify the nearness of a unit pull for a given and observed time series :
The existence of –“α (a drift term) and βt (a linear time trend)”; the deterministic
elements are realised after taking the difference between the three equations.
The key intention of testing is to know regardless of whether the coefficient γ equivalents to
zero, which resources the process will be proved of having a unit root; thus, the
null hypotheses of y=0 is verified again for substitute hypothesis y<0. The null and
alternative hypotheses to the above concern are followed:
P a g e | 49
The ADF test guarantees that the null hypothesis is acknowledged except if there is
solid proof against it to dismiss for the other Stationarity hypotheses.
4.4.3.5 Test for ARIMA modelling.
Time series analysis
Any time series can contain a few or the majority of the accompanying segments:
Every series which follow longitudinal data form may have all or some of the following
components:
1. T-Trend
2. C-Cyclical
3. S-Seasonal
4. I- Irregular
From different ways of combining these components addition and multiplication of the
components are used to decompose the data. The additive and multiplicative models are
given below:
Trend Component:
In time series trend is the long term pattern. A trend can be positive or negative; depending
on increasing or decreasing pattern of that time-series data. If the series never show any
fluctuation the series will be called stationary series.
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Cyclical component
A cyclical pattern will be found by seeing an up and down movement around a given trend of
any pattern. The type of industry or business being analysed influences the cycle duration.
Seasonal component
At the point when the time series shows customary variances during that month consistently
or through a similar quarter of each year; seasonality occurs. For example during the
December retail sales peak.
Irregular component
The unpredictable components are called irregular component and it is called a random
variable. In forecasting all the components are modelled according to the objective is except
the irregular component which remains unexplained.
Autocorrelation function
Autocorrelation is the linear dependence of a variable with itself at two points in time.
For stationary processes, autocorrelation between any two observations only depends on the
time lag pbetween them.
Autocorrelation refers to the way the observations in a Time series are related to each
other and is measured by the simple correlation between current observation (Yt) and
observation from p periods before the current one (Yt-p). That is for a given series Yt,
autocorrelation at lag p is the correlation between the pair (Yt, Yt-p) and is given by
Partial autocorrelation function
Partial autocorrelations are used to measure the degree of association between Yt and Yt-p
when the Y effects at other time lag 1,2,3,.....,p-1 are removed.In general, a partial correlation
is a conditional correlation. It is the correlation between two variables under the assumption
P a g e | 51
that we know and takes into account the values of some other set of variables. For instance,
consider a regression context in which y = response variable and x1, x2, and x3 are predictor
variables. The partial correlation between y and x3 is the correlation between the variables
determined to take into account how both y and x3 are related to x1 and x2.In regression, this
partial correlation could be found by correlating the residuals from two different regressions:
(1) Regression in which we predict y from x1 and x2, (2) Regression in which we
predict x3 from x1 and x2. We correlate the "parts" of y andx3 that are not predicted
by x1 and x2.
Note that this is also how the parameters of a regression model are interpreted. Think about
the difference between interpreting the regression models:
P a g e | 52
Exponential Smoothing
One of the methods of forecasting the next value Xn+1, of a time series Xt, t= 1,2,……n is to
use a weighted average of past observations:
𝐗n(1)= λ0. Xn + λ1.Xn-1 +…...
The popular method of exponential smoothing assigns geometrically decreasing weights:
λi = α ( 1- α) i ; 0< α<1
Such that 𝐗n(1)= α . Xn + α (1- α ). Xn-1 + α(1- α) 2. Xn-2 + …….
In its basic form, exponential smoothing applies to time series with no systematic trend
and/or seasonal components. It has been generalized to the " Holt-Winters"- a procedure to
deal with the time series containing trend and seasonal variation. In this case, three
smoothing parameters are required, namely α (for the level), β (for the trend) and γ (for the
seasonal variation).
Model building
(i) The stage of Identification
Auto-Regressive (AR), Integrated (I) and Moving Average (MA) are three parts of ARIMA.
AR: Observed values of AR mainly depend upon "some linear combination of previously observed values" up to defined maximum lag lets p.
MA: The observed values are random error terms with “some linear combination of previous error term” till lag q.
The mixed ARMA model can be written as:
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The above equation is identified as non-seasonal ARMA (p,q) model.
Where, Фp (B) = AR operator (non-seasonal)
Ɵq (B) = MA operator (non-seasonal)
As the time series are non-stationary. To attain Stationarity it has to be differenced. The
integration part (I) otherwise known as differencing (d). After performing integration the
series gets free from trend or seasonality component and only contains the irregular
component which is otherwise known as noise component.
Where, Δd = difference operator (non-seasonal)
d= Required order of differencing (to get stationary series).
The above equation is called the general, or natural, non-seasonal ARIMA (p, d, q) model.
SARIMA: The explained model “ARIMA (p, d, q) (P, D, Q)”
Where,
Фp B s = AR operator (seasonal)
= ‘I’ operator (seasonal)
‘S’ is the seasonal frequency and ƟQ (B s) is the seasonal MA operator.
Estimation Stage: SARIMA model got fitted and using diagnostics statistics the model
accuracy was tested.
Diagnostic: The best model was selected by diagnosing as below.
(i) AIC (Akaike Information Criteria) with low value: Estimation of AIC is done by AIC =
(−2log 𝐿 + 2𝑚), where, 𝐿is the likelihood function and 𝑚 = 𝑝+ 𝑞.
s d sp p t qD QS tФ B Ф B Y B B ò
D S
(ii) Low BIC (Bayesian Information Criteria): Sometimes, BIC is being estimated by BIC =
log 𝜎2 + (𝑚log 𝑛)/𝑛.
(iii) The MAPE (Mean Absolute
model.
Forecasting Stage: Using the data green gram prices were forecasted for the months from
April 2018 to June 2018. These three months forecasted data were compared with actual
prices of April 2018 to June 2018 green gram price of Odisha. Again the weekly price data of
green gram for 14.6 years (from January 2004 to June 2018, 696 data points) for Odisha, has
been used for forecasting the prices of months from July 2018 to September 2018.
Box and Jenkins (1976), was the first who popularized the ARIMA model and hence this
model is often referred to as Box
Forecasting are four stages of ARIMA analysis.
Mean absolute percentage error
To construct fitted time
used. MAPE is otherwise known as
formula for MAPE is as follows:
Where, At - actual value & Ft
Absolute values for this calculation has been summed for every forecasted point and got
divided with n, the number of fitted points
equal to 10% is well accepted and in some cases the range 20% to 30% is accept
4.4.3.6 Test for seasonal Index.
Seasonal movements are the periodic and regular movements in time series with a period of
less than one year. Hence, daily, weekly, monthly, quarterly and half
movements can be studied from time
present study, only monthly seasonal indices are constructed to know the intra
movements.
(ii) Low BIC (Bayesian Information Criteria): Sometimes, BIC is being estimated by BIC =
(iii) The MAPE (Mean Absolute Percent Error) is used as a measure of the accuracy of the
Using the data green gram prices were forecasted for the months from
April 2018 to June 2018. These three months forecasted data were compared with actual
2018 to June 2018 green gram price of Odisha. Again the weekly price data of
green gram for 14.6 years (from January 2004 to June 2018, 696 data points) for Odisha, has
been used for forecasting the prices of months from July 2018 to September 2018.
d Jenkins (1976), was the first who popularized the ARIMA model and hence this
model is often referred to as Box-Jenkins models. Identification, Estimation, Diagnostic,
Forecasting are four stages of ARIMA analysis.
Mean absolute percentage error (MAPE)
time series values in statistics with accuracy, the MAPE method is
used. MAPE is otherwise known as mean absolute percentage deviation
formula for MAPE is as follows:
t - forecast value.
Absolute values for this calculation has been summed for every forecasted point and got
divided with n, the number of fitted points and then multiplied with 100.
equal to 10% is well accepted and in some cases the range 20% to 30% is accept
4.4.3.6 Test for seasonal Index.
the periodic and regular movements in time series with a period of
less than one year. Hence, daily, weekly, monthly, quarterly and half
movements can be studied from time-series data by isolating seasonal effect. But in the
present study, only monthly seasonal indices are constructed to know the intra
P a g e | 54
(ii) Low BIC (Bayesian Information Criteria): Sometimes, BIC is being estimated by BIC =
Percent Error) is used as a measure of the accuracy of the
Using the data green gram prices were forecasted for the months from
April 2018 to June 2018. These three months forecasted data were compared with actual
2018 to June 2018 green gram price of Odisha. Again the weekly price data of
green gram for 14.6 years (from January 2004 to June 2018, 696 data points) for Odisha, has
been used for forecasting the prices of months from July 2018 to September 2018.
d Jenkins (1976), was the first who popularized the ARIMA model and hence this
Jenkins models. Identification, Estimation, Diagnostic,
the MAPE method is
mean absolute percentage deviation (MAPD). The
Absolute values for this calculation has been summed for every forecasted point and got
and then multiplied with 100. MAPE of less or
equal to 10% is well accepted and in some cases the range 20% to 30% is accepted.
the periodic and regular movements in time series with a period of
less than one year. Hence, daily, weekly, monthly, quarterly and half-yearly periodic
data by isolating seasonal effect. But in the
present study, only monthly seasonal indices are constructed to know the intra-year
P a g e | 55
For monthly data, a twelve-month moving average is expected to eliminate the seasonal
movements if they are of constant pattern and intensity. Hence, for finding out the seasonal
indices, the percentage of 12 months moving average is found out. As per the norms of the
multiplicative model, each observation in a time series is the product of T, C, S and I(Trend,
Cyclical component, Seasonal component and irregular component).
A predicting tool was used to decide demand for cotton, turmeric, Green gram, groundnut in
an Odisha w.r.t India. Such an index was based on data from previous years that highlighted
seasonal differences in production.
4.4.3.7 Test for Cointegration.
Cointegration Test
Engle-Granger test (Engle-Granger, 1987) is the most utilized Cointegration test.
Serious methodological defects are common for this test. Engle-Granger test contains most
the most undesirable feature i.e the selected variables normalization may be very sensitive to
the test results. The presence of more than two variables in the regression creates this kind of
problem.
Co-integration test is very helpful in finding a meaningful relationship between two
non-stationary data series. If we take two data series which are non-stationary at their level
form and converted to stationary after an equal number of differencing then their linear
combination must become stationary. More specifically we can take Index 1 and Index 2 and
we can write the above relationship like:
Index 1t = β1 + β2 Index2t + ut
or, ut= Index1t – β1 – βIndex2t
or, Index1t – β1 – βIndex2t = I (0) or stationary,
or, Index 1 and Index 2 are cointegrated i.e both the series are in the same wavelength. To
conduct the Cointegration test among data sets there are two necessary conditions:
1. The given time series data set should be non-stationary at level form.
2. The given time series data sets must become stationary after the same order
differencing.
P a g e | 56
There are two types of Cointegration tests are being accepted globally which are Engle-
Granger Single test of Cointegration test and Johansen and Jeseleius joint test of
Cointegration.
Johansen and Jeseleius joint test of Cointegration:
This test is a procedure for testing co-integration of some I(1) time series; named next to
Soren Johansen. This test is applicable for analysing more than one co-integrating
relationship among data sets hence it is very effectively applicable than “Engle-Granger test”.
This test can be done in two different ways i.e. one with trace and another with Eigen-value.
4.4.3.8 Test for Causality.
Granger Causality test
Granger causality test is used to know the impact of one-time series over other. Other
implication for this test is to know can one-time series be forecasted by using other time
series. In the year 1969 Clive Granger (Robert F. Engle et. al, 1987) proposed the model
which was named after his name and called Granger Causality test. Two principles were the
focus of study by Granger. The two principles are:
a. “Effect comes after its cause happen”.
b. “Unique information about the future values of an effect is intact with its cause”.
The hypothesis that has been used to test cause and effect of X on Y.
P[Y(t+1) ϵ A/I(t)] ≠ P[Y(t+1) ϵ A/I-X(t)]
Where; P-Probability,
A-Arbitrary non-empty set,
References
Robert F. Engle and C. W. J. Granger, Econometric, Vol. 55, No. 2 (Mar., 1987), pp. 251-
276
P a g e | 57
5. RESULT AND DISCUSSION
In this chapter, the results found are presented per the objectives of the study. The
results obtained were categorized into six sections.
The first segment of this chapter deals with the descriptive statistics and Normality
test of the state price data for all major producing states for respective agricultural
commodities. The second section tells regarding the unit root test of the longitudinal data
points. In the third section market integration of selected commodities and causal
relationship among markets is presented. The fourth section consists of analysis regarding
market-wise Seasonal Index of commodities. The fifth section of this chapter presents price
forecasting of commodities for the state of Odisha during the year 2019.
5.1 Results for Descriptive statistics and correlation coefficient:
Descriptive statistics like "Mean, Median, Maximum, Minimum, Standard Deviation,
Skewness, Kurtosis, and Jarque-bera" are included under table 5.1. The computed value of
Jarque Bera (JB) and its respective p-values for each of level data series at a 5% level of
significance has been depicted in the table (Table 5.1.1).
Table 5.1.1: Descriptive Statistics of price data for major Cotton producing states.
States Mean Median Std. Dev. Skewness Kurtosis Jarque-
Bera P Value
Odisha 3351.15 3592.37 1100.04 -0.14228 1.8 45.63 0.00*
Andhra Pradesh
3553.68 3832.01 1248.58 0.052961 1.9537 33.18 0.00*
Gujarat 3684.31 3928.81 1220.22 0.04606 1.7992 43.51 0.00*
Haryana 3743.92 4049.05 1357.41 -0.00280 1.7879 44.08 0.00*
Karnataka 3697.83 3950.39 1265.47 0.070112 1.7756 45.57 0.00*
Maharashtra 3479.38 3557.1 1234.48 0.02056 1.7551 46.54 0.00*
Madhya Pradesh
3500.06 3607.33 1181.64 0.037433 1.8532 39.62 0.00*
Punjab 3739.11 4079.14 1361.73 0.134709 2.0105 31.55 0.00*
Rajasthan 3687.42 4037.58 1383.25 -0.04895 1.7509 47.1 0.00*
Telengana 3509.3 3731.3 1129.02 0.018974 1.879 37.74 0.00*
Tamilnadu 3699.37 3898.64 1293.55 0.125548 1.9805 33.07 0.00*
Data for Cotton analysis is given Under Annexure I
P a g e | 58
The price data for cotton for different states for the study period shows a standard deviation
from the range 1100-1383, whereas the mean for different states ranges from 3351-3743.
Except for Odisha, Haryana, Rajasthan all data series are positively skewed. Adding to this
Skewness statistics whether positive or negative but are less than 1; which implies that the
distribution of the selected price series is almost symmetric. All eleven data series are
platykurtic as kurtosis for all data sets are less than three. It implies both the things i.e the
series are short tails about the mean indicating data series are not normally distributed and
return was positive under the sample period. After performing JB statistics it has been
confirmed that all the data series for cotton are not normally distributed. Descriptive statistics
for price data for cotton-producing states of India is presented under figure 5.1.1.
Figure 10 Figure 5.1.1: Descriptive Statistics of price data for major Cotton producing states
0
20
40
60
80
100
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Series: ODISHA Sample 1 720 Observations 720
Mean 3348.947 Median 3592.370 Maximum 5698.940 Minimum 0.000000 Std. Dev. 1105.160 Skewness -0.167464 Kurtosis 1.874205
Jarque-Bera 41.38776 Probability 0.000000
0
20
40
60
80
100
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500
Series: ANDHRAPRADESH Sample 1 720 Observations 720
Mean 3553.683 Median 3832.010 Maximum 6529.950 Minimum 1443.000 Std. Dev. 1248.576 Skewness 0.052961 Kurtosis 1.953700
Jarque-Bera 33.17888 Probability 0.000000
0
10
20
30
40
50
60
70
80
1000 2000 3000 4000 5000 6000
Series: GUJRAT Sample 1 720 Observations 720
Mean 3674.583 Median 3920.155 Maximum 6596.650 Minimum 440.9500 Std. Dev. 1228.685 Skewness 0.021692 Kurtosis 1.854271
Jarque-Bera 39.43729 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000
Series: HARYANA Sample 1 720 Observations 720
Mean 3743.923 Median 4049.050 Maximum 6790.230 Minimum 1396.000 Std. Dev. 1357.408 Skewness -0.002803 Kurtosis 1.787914
Jarque-Bera 44.07555 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000
Series: KARNATAKA Sample 1 720 Observations 720
Mean 3697.825 Median 3950.385 Maximum 7080.710 Minimum 1575.660 Std. Dev. 1265.474 Skewness 0.070112 Kurtosis 1.775555
Jarque-Bera 45.56789 Probability 0.000000
0
10
20
30
40
50
60
70
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500
Series: MADHYAPRADESH Sample 1 720 Observations 720
Mean 3498.668 Median 3607.330 Maximum 6575.390 Minimum 930.0600 Std. Dev. 1184.069 Skewness 0.029737 Kurtosis 1.864791
Jarque-Bera 38.76710 Probability 0.000000
0
10
20
30
40
50
60
70
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
Series: MAHARASHTRA Sample 1 720 Observations 720
Mean 3479.382 Median 3557.098 Maximum 6147.890 Minimum 1146.650 Std. Dev. 1234.479 Skewness 0.020560 Kurtosis 1.755119
Jarque-Bera 46.54261 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000
Series: PUNJAB Sample 1 720 Observations 720
Mean 3739.106 Median 4079.135 Maximum 6841.410 Minimum 1522.340 Std. Dev. 1361.734 Skewness 0.134709 Kurtosis 2.010486
Jarque-Bera 31.55170 Probability 0.000000
P a g e | 59
Graphical representation of state-wise cotton price data is given under figure 5.1.2.
Figure 11 Figure 5.1.2: Graphical presentation of price data for major Cotton producing states
0
10
20
30
40
50
60
70
2000 3000 4000 5000 6000 7000 8000
Series: RAJASTHAN Sample 1 720 Observations 720
Mean 3687.421 Median 4037.575 Maximum 8100.000 Minimum 1440.000 Std. Dev. 1383.250 Skewness -0.048952 Kurtosis 1.750864
Jarque-Bera 47.09781 Probability 0.000000
0
10
20
30
40
50
60
70
1000 2000 3000 4000 5000 6000 7000 8000
Series: TAMILNADU Sample 1 720 Observations 720
Mean 3699.370 Median 3898.635 Maximum 7850.500 Minimum 1218.040 Std. Dev. 1293.552 Skewness 0.125548 Kurtosis 1.980535
Jarque-Bera 33.07073 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
Series: TELENGANA Sample 1 720 Observations 720
Mean 3509.297 Median 3731.295 Maximum 6020.980 Minimum 1628.740 Std. Dev. 1129.018 Skewness 0.018974 Kurtosis 1.879012
Jarque-Bera 37.74162 Probability 0.000000
0
1,000
2,000
3,000
4,000
5,000
6,000
100 200 300 400 500 600 700
ODISHA
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
ANDHRAPRADESH
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
GUJRAT
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
HARYANA
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
KARNATAKA
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
MADHYAPRADESH
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
MAHARASHTRA
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
PUNJAB
P a g e | 60
Table 5.1.2: Descriptive Statistics of price data for major Turmeric producing states.
States Mean Median Std. Dev.
Skewness Kurtosis Jarque-Bera P Value
Odisha 4976.744 4839.965 1851.797 0.925078 4.280725 151.9 0.00*
Andhra Pradesh
5110.339 4806.575 2874.364 1.201377 4.59773 249.779 0.00*
Karnataka 5737.307 5482.37 3026.492 1.180891 4.81791 266.4844 0.00*
Maharashtra 6505.953 6066.83 3331.243 0.791095 3.22185 76.57631 0.00*
Telengana 5208.046 5037.83 2921.365 1.137101 4.419805 215.6351 0.00*
Tamilnadu 5957.728 5992.195 3168.087 1.015735 3.99914 153.7546 0.00*
Data for Turmeric analysis is given under Annexure II
The price data for turmeric for different states for the study period shows a standard deviation
from the range 1851-3331, whereas the mean for different states ranges from 4976-6505. All
data series are positively skewed. Except for Odisha and Maharashtra skewness for all data,
series are more than one; which implies that the distribution of these is almost symmetric. All
six data series are leptokurtic as kurtosis for all data sets are more than three. Kurtosis more
than normal distribution implies both the things i.e the series are more concentrated at the
mean with a high peak curve and negative return is high than positive distribution during the
sample period. After performing JB statistics it has been confirmed that all the data series for
turmeric are not normally distributed. Descriptive statistics for price data for turmeric
producing states of India is presented under figure 5.1.3.
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
100 200 300 400 500 600 700
RAJASTHAN
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
TAMILNADU
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
TELENGANA
P a g e | 61
Figure 12 Figure 5.1.3: Descriptive Statistics of price data for major Turmeric producing states
Graphical representation of state-wise turmeric price data is given under figure 5.1.4.
Figure 13 Figure 5.1.4: Graphical Presentation of price data for major Turmeric producing states
0
20
40
60
80
100
120
140
160
2000 4000 6000 8000 10000
Series: ODISHA Sample 1 720 Observations 720
Mean 4976.744 Median 4839.965 Maximum 11166.67 Minimum 1232.930 Std. Dev. 1851.797 Skewness 0.925078 Kurtosis 4.280725
Jarque-Bera 151.9000 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
0 2000 4000 6000 8000 10000 12000 14000
Series: ANDHRAPRADESH Sample 1 720 Observations 720
Mean 5110.339 Median 4806.575 Maximum 15300.75 Minimum 102.7200 Std. Dev. 2874.364 Skewness 1.201377 Kurtosis 4.597730
Jarque-Bera 249.7790 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
2000 4000 6000 8000 10000 12000 14000 16000 18000
Series: KARNATAK Sample 1 720 Observations 720
Mean 5737.307 Median 5482.370 Maximum 17571.43 Minimum 1560.000 Std. Dev. 3026.492 Skewness 1.180891 Kurtosis 4.817910
Jarque-Bera 266.4844 Probability 0.000000
0
10
20
30
40
50
60
70
0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Series: MAHARASHTRA Sample 1 720 Observations 720
Mean 6505.953 Median 6066.830 Maximum 18201.27 Minimum 227.8700 Std. Dev. 3331.243 Skewness 0.791095 Kurtosis 3.221850
Jarque-Bera 76.57631 Probability 0.000000
0
20
40
60
80
100
2000 4000 6000 8000 10000 12000 14000 16000
Series: TAMILNADU Sample 1 720 Observations 720
Mean 5957.728 Median 5992.195 Maximum 16862.76 Minimum 1561.970 Std. Dev. 3168.087 Skewness 1.015735 Kurtosis 3.999140
Jarque-Bera 153.7546 Probability 0.000000
0
10
20
30
40
50
60
70
80
2000 4000 6000 8000 10000 12000 14000 16000
Series: TELENGANA Sample 1 720 Observations 720
Mean 5208.046 Median 5037.830 Maximum 16016.06 Minimum 1361.000 Std. Dev. 2921.365 Skewness 1.137101 Kurtosis 4.419805
Jarque-Bera 215.6351 Probability 0.000000
0
2,000
4,000
6,000
8,000
10,000
12,000
100 200 300 400 500 600 700
ODISHA
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
100 200 300 400 500 600 700
ANDHRAPRADESH
0
4,000
8,000
12,000
16,000
20,000
100 200 300 400 500 600 700
KARNATAK
0
4,000
8,000
12,000
16,000
20,000
100 200 300 400 500 600 700
MAHARASHTRA
P a g e | 62
Table 5.1.3 Descriptive Statistics of price data for major Green gram producing states.
States Mean Median Std. Dev. Skewness Kurtosis Jarque-
Bera P
Value
Odisha 4349.569 4798.095 1664.942 -0.26296 1.828536 49.46758 0.00*
Gujarat 4193.124 4161.66 1723.513 0.390489 2.609021 22.88371 0.00*
Karnataka 4583.758 4630.295 1783.279 0.243354 2.185622 27.00291 0.00*
Kerala 5960.815 6440.33 2212.461 -0.07952 1.801424 43.8563 0.00*
Maharashtra 4361.785 4411.155 1768.894 0.209336 2.1468 27.09709 0.00*
Madhya Pradesh
3432.401 3087.46 1618.605 0.49546 2.134902 51.90957 0.00*
Rajasthan 3952.826 3868.1 1606.495 0.395703 2.453271 27.75708 0.00*
Tamilnadu 3751.41 3749.875 1531.077 0.141939 2.096939 26.88315 0.00*
Telengana 3838.881 3736.01 1541.793 0.347891 2.369662 26.44319 0.00*
Uttar Pradesh 4278.75 4294.26 1536.219 0.140682 2.331175 15.7948 0.00*
Data for Green gram analysis is given in Annexure III
The price data for the green gram for different states for the study period shows a standard
deviation from the range 1534-2212, whereas the mean for different states ranges from 3432-
5960. Except for Odisha and Kerala, all data series are positively skewed. The distribution of
the selected price series are almost symmetric as Skewness statistics whether positive or
negative but are less than 1. All ten data series are platykurtic as kurtosis for all data sets are
less than three. It implies both the things i.e the series are short tails about the mean
indicating data series are not normally distributed and return was positive under the sample
period. After performing JB statistics it has been confirmed that all the data series for green
gram are not normally distributed. Descriptive statistics for price data for green gram
producing states of India is presented under figure 5.1.5.
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
100 200 300 400 500 600 700
TAMILNADU
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
100 200 300 400 500 600 700
TELENGANA
P a g e | 63
Figure 14 Figure 5.1.5: Descriptive Statistics of price data for major Green gram producing states
Graphical representation of state-wise green gram price data is given under figure 5.1.6.
0
10
20
30
40
50
60
70
1000 2000 3000 4000 5000 6000 7000 8000
Series: ODISHA Sample 1 720 Observations 720
Mean 4349.569 Median 4798.095 Maximum 7836.510 Minimum 1200.000 Std. Dev. 1664.942 Skewness -0.262960 Kurtosis 1.828536
Jarque-Bera 49.46758 Probability 0.000000
0
10
20
30
40
50
2000 3000 4000 5000 6000 7000 8000 9000
Series: GUJRAT Sample 1 720 Observations 720
Mean 4193.124 Median 4161.660 Maximum 9298.170 Minimum 1255.060 Std. Dev. 1723.513 Skewness 0.390489 Kurtosis 2.609021
Jarque-Bera 22.88371 Probability 0.000011
0
10
20
30
40
50
60
1000 2000 3000 4000 5000 6000 7000 8000 9000
Series: KARNATAKA Sample 1 720 Observations 720
Mean 4583.758 Median 4630.295 Maximum 9324.340 Minimum 1175.000 Std. Dev. 1783.279 Skewness 0.243354 Kurtosis 2.185622
Jarque-Bera 27.00291 Probability 0.000001
0
10
20
30
40
50
60
2000 3000 4000 5000 6000 7000 8000 9000 10000 11000
Series: KERALA Sample 1 720 Observations 720
Mean 5960.815 Median 6440.330 Maximum 11395.36 Minimum 1640.950 Std. Dev. 2212.461 Skewness -0.079516 Kurtosis 1.801424
Jarque-Bera 43.85630 Probability 0.000000
0
10
20
30
40
50
60
2000 3000 4000 5000 6000 7000 8000
Series: MAHARASHTRA Sample 1 720 Observations 720
Mean 4361.785 Median 4411.155 Maximum 8584.060 Minimum 1465.050 Std. Dev. 1768.894 Skewness 0.209336 Kurtosis 2.146800
Jarque-Bera 27.09709 Probability 0.000001
0
20
40
60
80
100
1000 2000 3000 4000 5000 6000 7000
Series: MP Sample 1 720 Observations 720
Mean 3432.401 Median 3087.460 Maximum 7457.940 Minimum 890.1400 Std. Dev. 1618.605 Skewness 0.495460 Kurtosis 2.134902
Jarque-Bera 51.90957 Probability 0.000000
0
10
20
30
40
50
60
1000 2000 3000 4000 5000 6000 7000 8000
Series: RAJASTHAN Sample 1 720 Observations 720
Mean 3952.826 Median 3868.100 Maximum 8028.890 Minimum 1179.480 Std. Dev. 1606.495 Skewness 0.395703 Kurtosis 2.453271
Jarque-Bera 27.75708 Probability 0.000001
0
10
20
30
40
50
60
1000 2000 3000 4000 5000 6000 7000 8000
Series: TAMILNADU Sample 1 720 Observations 720
Mean 3751.410 Median 3749.875 Maximum 7860.380 Minimum 859.0000 Std. Dev. 1531.077 Skewness 0.141939 Kurtosis 2.096939
Jarque-Bera 26.88315 Probability 0.000001
0
10
20
30
40
50
1000 2000 3000 4000 5000 6000 7000 8000
Series: TELENGANA Sample 1 720 Observations 720
Mean 3838.881 Median 3736.010 Maximum 7936.340 Minimum 1136.110 Std. Dev. 1541.793 Skewness 0.347891 Kurtosis 2.369662
Jarque-Bera 26.44319 Probability 0.000002
P a g e | 64
Figure 15 Figure 5.1.6: Graphical Presentation of Price Data for Major Green gram Producing States
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
ODISHA
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
100 200 300 400 500 600 700
GUJRAT
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
100 200 300 400 500 600 700
KARNATAKA
0
2,000
4,000
6,000
8,000
10,000
12,000
100 200 300 400 500 600 700
KERALA
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
100 200 300 400 500 600 700
MAHARASHTRA
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
MP
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
100 200 300 400 500 600 700
RAJASTHAN
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
TAMILNADU
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
100 200 300 400 500 600 700
TELENGANA
P a g e | 65
Table 5.1.4 Descriptive statistics of price data for major Groundnut producing states.
States Mean Median Std. Dev. Skewness Kurtosis Jarque- Bera
P Value
Odisha 3094.596 2969.65 1266.534 0.313252 1.902768 47.89276 0.00*
Andhra Pradesh
3117.737 3222.48 1092.524 0.04372 1.764823 45.99926 0.00*
Gujarat 3257.103 3183.5 1099.761 0.38722 2.360419 30.26465 0.00*
Karnataka 3109.705 3252.5 1137.994 0.097059 1.786456 45.3111 0.00*
Madhya Pradesh
2874.465 2750.585 1029.563 0.288463 1.961464 42.34203 0.00*
Maharashtra 5361.221 5016.9 2018.512 0.285116 1.876403 47.62902 0.00*
Rajasthan 2978.083 2953.425 995.5171 0.150632 1.975675 34.20008 0.00*
Tamilnadu 4188.21 4213.315 1537.222 0.245737 1.907436 43.05728 0.00*
Uttar Pradesh 3226.496 2980.175 943.8316 0.463096 2.246258 42.77879 0.00*
Data for Groundnut analysis is given in Annexure IV
The price data for groundnut for different states for the study period shows a standard
deviation from the range 943-2018, whereas the mean for different states ranges from 2978-
5361. All data series are positively skewed. The distribution of the selected price series are
almost symmetric as Skewness statistics whether positive or negative but are less than 1. All
ten data series are platykurtic as kurtosis for all data sets are less than three. It implies both
the things i.e the series are short tails about the mean indicating data series are not normally
distributed and return was positive under the sample period. After performing JB statistics it
has been confirmed that all the data series for groundnut are not normally distributed.
Descriptive statistics for price data for groundnut producing states of India is presented under
figure 5.1.7.
P a g e | 66
Figure 16 Figure 5.1.7: Descriptive statistics of price data for major Groundnut producing states
Graphical representation of state-wise groundnut price data is given under figure 5.1.8.
0
20
40
60
80
100
120
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000
Series: ODISHA Sample 1 720 Observations 720
Mean 3094.596 Median 2969.650 Maximum 6796.360 Minimum 1204.880 Std. Dev. 1266.534 Skewness 0.313252 Kurtosis 1.902768
Jarque-Bera 47.89276 Probability 0.000000
0
20
40
60
80
100
120
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Series: ANDHRAPRADESH Sample 1 720 Observations 720
Mean 3117.737 Median 3222.480 Maximum 5672.130 Minimum 1010.110 Std. Dev. 1092.524 Skewness 0.043720 Kurtosis 1.764823
Jarque-Bera 45.99926 Probability 0.000000
0
10
20
30
40
50
60
70
80
1000 2000 3000 4000 5000 6000
Series: GUJRAT Sample 1 720 Observations 720
Mean 3257.103 Median 3183.500 Maximum 6514.910 Minimum 638.9400 Std. Dev. 1099.761 Skewness 0.387220 Kurtosis 2.360419
Jarque-Bera 30.26465 Probability 0.000000
0
20
40
60
80
100
120
1500 2000 2500 3000 3500 4000 4500 5000 5500
Series: KARNATAKA Sample 1 720 Observations 720
Mean 3109.705 Median 3252.500 Maximum 5531.280 Minimum 1283.400 Std. Dev. 1137.994 Skewness 0.097059 Kurtosis 1.786456
Jarque-Bera 45.31110 Probability 0.000000
0
10
20
30
40
50
60
70
80
90
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
Series: MADHYAPRADESH Sample 1 720 Observations 720
Mean 2874.465 Median 2750.585 Maximum 6069.320 Minimum 894.9000 Std. Dev. 1029.563 Skewness 0.288463 Kurtosis 1.961464
Jarque-Bera 42.34203 Probability 0.000000
0
10
20
30
40
50
60
70
1000 2000 3000 4000 5000 6000 7000 8000 9000
Series: MAHARASHTRA Sample 1 720 Observations 720
Mean 5361.221 Median 5016.900 Maximum 9553.850 Minimum 316.1600 Std. Dev. 2018.512 Skewness 0.285116 Kurtosis 1.876403
Jarque-Bera 47.62902 Probability 0.000000
0
20
40
60
80
100
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Series: RAJASTHAN Sample 1 720 Observations 720
Mean 2978.083 Median 2953.425 Maximum 5319.910 Minimum 1126.420 Std. Dev. 995.5171 Skewness 0.150632 Kurtosis 1.975675
Jarque-Bera 34.20008 Probability 0.000000
0
10
20
30
40
50
60
70
2000 3000 4000 5000 6000 7000 8000
Series: TAMILNADU Sample 1 720 Observations 720
Mean 4188.210 Median 4213.315 Maximum 7964.980 Minimum 1663.490 Std. Dev. 1537.222 Skewness 0.245737 Kurtosis 1.907436
Jarque-Bera 43.05728 Probability 0.000000
0
20
40
60
80
100
120
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
Series: UTTARPRADESH Sample 1 720 Observations 720
Mean 3226.496 Median 2980.175 Maximum 5966.010 Minimum 1692.180 Std. Dev. 943.8316 Skewness 0.463096 Kurtosis 2.246258
Jarque-Bera 42.77879 Probability 0.000000
P a g e | 67
Figure 17 Figure 5.1.8: Graphical Presentation of price data for major Groundnut producing states
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
ODISHA
0
1,000
2,000
3,000
4,000
5,000
6,000
100 200 300 400 500 600 700
ANDHRAPRADESH
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
GUJRAT
1,000
2,000
3,000
4,000
5,000
6,000
100 200 300 400 500 600 700
KARNATAKA
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
MADHYAPRADESH
0
2,000
4,000
6,000
8,000
10,000
100 200 300 400 500 600 700
MAHARASHTRA
1,000
2,000
3,000
4,000
5,000
6,000
100 200 300 400 500 600 700
RAJASTHAN
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
100 200 300 400 500 600 700
TAMILNADU
1,000
2,000
3,000
4,000
5,000
6,000
7,000
100 200 300 400 500 600 700
UTTARPRADESH
P a g e | 68
5.2 Unit Root Test
It is found that the data series not normally distributed i.e they follow a trend. Now test for
Stationarity is to be done. By applying the ADF test and PP test it is found that the data
points are non-stationary at their level form. The probability values are given in the
respective table which is followed by ADF and PP test results at 1st difference. The null
hypothesis is data series are non-stationary.
Table 5.2.1 Unit root result for price data for major Cotton producing states
States
ADF test p-values at level
data
PP test p-values at level
data
ADF Test at 1st difference PP Test at 1st difference
Computed Value
Critical Value @ 1% Level
P Value
Computed Value
Critical Value @ 1% Level
P Value
Odisha 0.6998 0.0152 -10.92 -3.44 0.00* -62.48 -3.44 0.00*
Andhra Pradesh
0.3078 0.3320 -28.61 -3.44 0.00* -28.59 -3.44 0.00*
Gujarat 0.2406 0.0558 -26.42 -3.44 0.00* -47.64 -3.44 0.00*
Haryana 0.4922 0.4984 -26.41 -3.44 0.00* -26.41 -3.44 0.00*
Karnataka 0.3906 0.2514 -39.32 -3.44 0.00* -39.78 -3.44 0.00*
Maharashtra 0.1780 0.1439 -36.25 -3.44 0.00* -37.40 -3.44 0.00*
Madhya Pradesh
0.1127 0.0122 -26.99 -3.44 0.00* -51.87 -3.44 0.00*
Punjab 0.3745 0.2180 -25.08 -3.44 0.00* -43.38 -3.44 0.00*
Rajasthan 0.3677 0.1403 -22.71 -3.44 0.00* -50.47 -3.44 0.00*
Telengana 0.3413 0.3583 -28.63 -3.44 0.00* -28.6506 -3.44 0.00*
Tamilnadu 0.1330 0.0178 -26.05 -3.44 0.00* -57.24 -3.44 0.00*
Note: Null Hypothesis: There is unit root. Alternative Hypothesis: There is no unit root. ‘*’=Null hypothesis rejected
Since the price series for all states of cotton are found stationary after same order difference
(Table 5.2.1), we can call them integrated of order '1' or I(1) and further the test can move for
Cointegration. It is because the prerequisite for the test of co-integration is all the data sets to
be going through Cointegration test must be at the same order differencing.
P a g e | 69
Table 5.2.2 Unit root result for price data for major Turmeric producing states.
States
ADF test p-values at level
data
PP test p-values at level
data
ADF Test PP Test
Computed Value
Critical Value @ 1% Level
P Value
Computed Value
Critical Value @ 1% Level
P Value
Odisha 0.0309 0.0000 -3.44 -11.98 0.00* -3.44 -60.66 0.00*
Andhra Pradesh
0.4381 0.3270 -3.44 -35.74 0.00* -3.44 -35.13 0.00*
Karnataka 0.1719 0.0207 -3.44 -28.22 0.00* -3.44 -50.20 0.00*
Maharashtra 0.4759 0.2247 -3.44 -22.49 0.00* -3.44 -32.58 0.00*
Telengana 0.3969 0.2842 -3.44 -25.45 0.00* -3.44 -40.73 0.00*
Tamilnadu 0.4724 0.3214 -3.44 -23.83 0.00* -3.44 -31.85 0.00*
Note: Null Hypothesis: There is unit root. Alternative Hypothesis: There is no unit root. ‘*’=Null hypothesis rejected
Also, the price data for turmeric (Table 5.2.2) is found stationary after 1st order differencing.
As all series are found non-stationary after 1st order differencing are eligible for
Cointegration test.
Table 5.2.3 Unit root result for price data for major Green gram producing states.
States
ADF test p-values at level
data
PP test p-values at level
data
ADF Test PP Test
Computed Value
Critical Value @ 1% Level
P-Value Computed
Value
Critical Value @
1% Level
P-Value
Odisha 0.7754 0.0004 -16.8155 -3.44 0.00* -159.95 -3.44 0.00*
Gujarat 0.1805 0.0786 -20.6923 -3.44 0.00* -54.783 -3.44 0.00*
Karnataka 0.3187 0.1732 -19.7343 -3.44 0.00* -38.1996 -3.44 0.00*
Kerala 0.5393 0.5338 -37.4334 -3.44 0.00* -38.4876 -3.44 0.00*
Maharashtra 0.1957 0.1447 -39.7328 -3.44 0.00* -41.3464 -3.44 0.00*
Madhya Pradesh
0.1682 0.0325 -23.7535 -3.44 0.00* -60.5893 -3.44 0.00*
Rajasthan 0.1342 0.1555 -33.9873 -3.44 0.00* -36.4781 -3.44 0.00*
Tamilnadu 0.0890 0.0109 -25.835 -3.44 0.00* -45.7479 -3.44 0.00*
Telengana 0.2083 0.1033 -17.5593 -3.44 0.00* -53.8819 -3.44 0.00*
Note: Null Hypothesis: There is a unit root. Alternative Hypothesis: There is no unit root. '*'=Null hypothesis rejected
P a g e | 70
Since the price series for all states of green gram are found stationary after same order
difference(Table 5.2.3), we can call them integrated of order '1' or I(1) and further the test can
move for Cointegration. It is because the prerequisite for the test of co-integration is all the
data sets to be going through Cointegration test must be at the same order differencing.
Table 5.2.4 Unit root result for price data for major groundnut producing states
States
ADF test p- values at level
data
PP test p-
values at level
data
ADF Test PP Test
Computed Value
Critical Value @
1% Level
P Value
Computed Value
Critical Value @ 1% Level
P Value
Odisha 0.8307 0.0006 -14.7829 -3.44 0.00* -157.363 -3.44 0.00*
Andhra Pradesh
0.4034 0.3505 -25.3568 -3.44 0.00* -39.9429 -3.44 0.00*
Gujarat 0.3605 0.3173 -32.2561 -3.44 0.00* -32.0998 -3.44 0.00*
Karnataka 0.3801 0.3017 -25.834 -3.44 0.00* -42.1742 -3.44 0.00*
Maharashtra 0.1034 0.0046 -19.4252 -3.44 0.00* -54.1066 -3.44 0.00*
Madhya Pradesh
0.2866 0.0296 -17.9577 -3.44 0.00* -65.5735 -3.44 0.00*
Rajasthan 0.2354 0.2253 -23.6004 -3.44 0.00* -42.6712 -3.44 0.00*
Tamilnadu 0.2248 0.2386 -38.4949 -3.44 0.00* -42.7163 -3.44 0.00*
Uttar Pradesh
0.0134 0.0062 -23.525 -3.44 0.00* -40.8033 -3.44 0.00*
Note: Null Hypothesis: There is unit root. Alternative Hypothesis: There is no unit root. ‘*’=Null hypothesis rejected
As the price series for all states of groundnut is found stationary after same order difference
(Table 5.2.4), we can call them integrated of order '1' or I (1) and further the test can move
for Cointegration. It is because the prerequisite for the test of co-integration is all the data
sets to be going through Cointegration test must be at the same order differencing.
5.3 Market Integration
Tests like –“Co-integration test, Granger causality test, ARIMA tests” can be conducted as it
is found that all the data points for all the crops are found not normally distributed and
stationary.
Before going for other tests correlation test is to be conducted to get the correlation
coefficient as to get the strength of a linear fit among two variables both geometrically and
statistically.
P a g e | 71
5.3.1 Correlation Coefficient:
The bivariate correlation coefficient among the price series of the selected market pairs in the
state was reported under Table 5.3.1.
Table 5.3.1.1 Estimates of the correlation coefficient for the price of Cotton between pairs of selected states in India
States
O d
is ha
A n
d h
ra
P ra
d es
h
G u
ja ra
t
H ar
ya na
K ar
na ta
ka
M ah
ar as
ht ra
M ad
hy a
P ra
d es
h
P un
ja b
R aj
as th
a n
T el
en ga
n a
T am
iln a
du
Odisha 1 0.884 0.879 0.895 0.893 0.887 0.856 0.878 0.893 0.884 0.876
Andhra Pradesh
1.000 0.954 0.937 0.958 0.915 0.906 0.892 0.928 0.972 0.937
Gujarat 1.000 0.926 0.946 0.907 0.904 0.869 0.923 0.955 0.926
Haryana 1.000 0.930 0.909 0.899 0.960 0.957 0.931 0.929
Karnataka 1.000 0.935 0.915 0.881 0.923 0.958 0.928
Maharashtra 1.000 0.914 0.865 0.908 0.926 0.897
Madhya Pradesh
1.000 0.857 0.909 0.913 0.889
Punjab 1.000 0.925 0.872 0.890
Rajasthan 1.000 0.925 0.917
Telengana 1.000 0.934
Tamilnadu 1.000
Table 5.3.1.2 Estimates of the correlation coefficient for the price of Turmeric between pairs of selected states in India
States Odisha Andhra Pradesh
Karnataka Maharashtra Telengana Tamilnadu
Odisha 1 0.7575241 0.7795803 0.7421987 0.7657257 0.7600149
Andhra Pradesh
1.0000000 0.9421633 0.9484813 0.9789891 0.9796924
Karnataka 1.0000000 0.9129828 0.9328896 0.9443296
Maharashtra 1.0000000 0.9384981 0.9559820
Telengana 1.0000000 0.9692240
Tamilnadu 1.0000000
P a g e | 72
Table 5.3.1.3 Estimates of the correlation coefficient for the price of Green gram between pairs of selected states in India
S T
A T
E S
O D
IS H
A
G U
JR A
T
K A
R N
A T
A K
A
K E
R A
L A
M A
H A
R A
S H
T R
A
M P
R A
JA S
T H
A N
T A
M IL
N A
D U
T E
L E
N G
A N
A
ODISHA 1 0.8065 0.8053 0.8872 0.8077 0.7593 0.7909 0.7946 0.7649
GUJRAT
1.0000 0.9311 0.9264 0.9387 0.8469 0.9243 0.8923 0.9272
KARNATAKA
1.0000 0.9420 0.9605 0.8210 0.9207 0.9195 0.9481
KERALA
1.0000 0.9393 0.8609 0.9084 0.9074 0.9151
MAHARASHTRA
1.0000 0.8098 0.9210 0.9243 0.9587
MP
1.0000 0.7855 0.7591 0.7880
RAJASTHAN
1.0000 0.8904 0.9235
TAMILNADU
1.0000 0.9274
TELENGANA
1.0000
Table 5.3.1.4 Estimates of the correlation coefficient for the price of Groundnut between pairs of selected states in India
STATES ODISHA ANDHRA PRADESH
GUJRAT KARNATA
KA MADHYA PRADESH
MAHARA SHTRA
RAJASTHA N
TAMILNA DU
UTTARPRAD ESH
ODISHA 1 0.83 0.78 0.82 0.75 0.73 0.81 0.84 0.74
ANDHRAPRADESH 1.00 0.92 0.96 0.89 0.86 0.93 0.93 0.86
GUJRAT 1.00 0.93 0.91 0.87 0.94 0.91 0.87
KARNATAKA 1.00 0.90 0.89 0.92 0.93 0.84
MADHYAPRADESH 1.00 0.80 0.91 0.85 0.83
MAHARASHTRA 1.00 0.84 0.88 0.77
RAJASTHAN 1.00 0.89 0.86
TAMILNADU 1.00 0.84
UTTARPRADESH 1.00
From table –"5.3.1.1, table 5.3.1.2, table 5.3.1.3, table 5.3.1.4" it is found that all the data
series are having very good correlation as well as a positive correlation. Hence the study is
going in the right direction in context for finding market integration.
P a g e | 73
5.3.2 Johansen Co-integration Test
The test for co-integration can be done with two different techniques like Engle and Granger
Test of Cointegration and Johansen's test of Cointegration. It is found from section 5.1 and
5.2 that the data series is not normally distributed and series are made stationary after the
same order differencing. In this study, Johansen's test of Cointegration has been used and the
test results for different crops have been given under table 5.3.2.
Table 5.3.2.1: Johansen Cointegration Results for Cotton
Trace Test Hypothesized
Number of Cointegration
Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability Significance at 5% level
Odisha 0.209379 932.4733 285.1425 0 yes
Andhra Pradesh 0.19165 764.024 239.2354 0 yes
Gujarat 0.171219 611.4746 197.3709 0.0001 yes
Haryana 0.14196 476.8222 159.5297 0 yes
Karnataka 0.118822 367.0464 125.6154 0 yes
Maharashtra 0.112164 276.3487 95.75366 0 yes
Madhya Pradesh 0.094784 191.0488 69.81889 0 yes
Punjab 0.066543 119.6484 47.85613 0 yes
Rajasthan 0.054303 70.27513 29.79707 0 yes
Telengana 0.038845 30.2425 15.49471 0.0002 yes
Tamilnadu 0.002556 1.835211 3.841466 0.1755 No
Max-Eigenvalue Test Hypothesized
Number of Cointegration
Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability Significance at 5% level
Odisha 0.209379 168.4493 70.53513 0 yes
Andhra Pradesh 0.19165 152.5494 64.50472 0 yes
Gujarat 0.171219 134.6523 58.43354 0 yes
Haryana 0.14196 109.7758 52.36261 0 yes
Karnataka 0.118822 90.69776 46.23142 0 yes
Maharashtra 0.112164 85.29992 40.07757 0 yes
Madhya Pradesh 0.094784 71.40038 33.87687 0 yes
Punjab 0.066543 49.37326 27.58434 0 yes
Rajasthan 0.054303 40.03263 21.13162 0 yes
Telengana 0.038845 28.40729 14.2646 0.0002 yes
Tamilnadu 0.002556 1.835211 3.841466 0.1755 No
P a g e | 74
Table 5.3.2.2: Johansen Cointegration Results for Turmeric
Johansen Cointegration test
Trace Test
Hypothesized Number of
Cointegration Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.168195 418.7372 95.75366 0.0001 yes
Andhra Pradesh
0.124356 286.6961 69.81889 0.0001 yes
Karnataka 0.114147 191.4819 47.85613 0 yes
Maharashtra 0.077757 104.5788 29.79707 0 yes
Telengana 0.058288 46.54012 15.49471 0 yes
Tamilnadu 0.004842 3.480183 3.841466 0.0621 No
Max-Eigenvalue Test
Hypothesized Number of
Cointegration Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.168195 132.0411 40.07757 0 yes
Andhra Pradesh
0.124356 95.21419 33.87687 0 yes
Karnataka 0.114147 86.90308 27.58434 0 yes
Maharashtra 0.077757 58.03873 21.13162 0 yes
Telengana 0.058288 43.05993 14.2646 0 yes
Tamilnadu 0.004842 3.480183 3.841466 0.0621 No
P a g e | 75
Table 5.3.2.3: Johansen Cointegration Results for Green gram
Johansen Cointegration test
Trace Test
Hypothesized Number of
Cointegration Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.176629 525.0546 239.2354 0 yes
Gujarat 0.12299 386.0958 197.3709 0 yes
Karnataka 0.097018 292.2618 159.5297 0 yes
Kerala 0.085504 219.2939 125.6154 0 yes
Maharashtra 0.066334 155.3857 95.75366 0 yes
Madhya Pradesh 0.047447 106.3108 69.81889 0 yes
Rajasthan 0.043229 71.55523 47.85613 0.0001 yes
Tamilnadu 0.028675 39.95883 29.79707 0.0024 yes
Telengana 0.023246 19.15629 15.49471 0.0134 No
Uttar Pradesh 0.003266 2.339352 3.841466 0.1261 No
Max-Eigenvalue Test
Hypothesized Number of
Cointegration Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.176629 138.9588 64.50472 0 yes
Gujarat 0.12299 93.83403 58.43354 0 yes
Karnataka 0.097018 72.96783 52.36261 0.0001 yes
Kerala 0.085504 63.9082 46.23142 0.0003 yes
Maharashtra 0.066334 49.0749 40.07757 0.0038 yes
Madhya Pradesh 0.047447 34.75561 33.87687 0.0392 No
Rajasthan 0.043229 31.5964 27.58434 0.0144 No
Tamilnadu 0.028675 20.80254 21.13162 0.0555 No
Telengana 0.023246 16.81694 14.2646 0.0193 No
Uttar Pradesh 0.003266 2.339352 3.841466 0.1261 No
P a g e | 76
Table 5.3.2.4: Johansen Cointegration Results for Groundnut
Johansen Cointegration test
Trace Test Hypothesized
Number of Cointegration
Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.165116 546.744 197.3709 0.0001 yes
Andhra Pradesh 0.139825 417.3527 159.5297 0 yes
Gujarat 0.106651 309.3582 125.6154 0 yes
Karnataka 0.098294 228.4962 95.75366 0 yes
Madhya Pradesh 0.077853 154.3108 69.81889 0 yes
Maharashtra 0.059746 96.19758 47.85613 0 yes
Rajasthan 0.039376 52.02681 29.79707 0 yes
Tamilnadu 0.028993 23.22335 15.49471 0.0028 yes
Uttar Pradesh 0.002964 2.128053 3.841466 0.1446 No
Max-Eigenvalue Test
Hypothesized Number of
Cointegration Equations
Eigenvalue Trace
Statistic 0.05 Critical
Value Probability
Significance at 5% level
Odisha 0.165116 129.3913 58.43354 0 yes
Andhra Pradesh 0.139825 107.9945 52.36261 0 yes
Gujarat 0.106651 80.86203 46.23142 0 yes
Karnataka 0.098294 74.18537 40.07757 0 yes
Madhya Pradesh 0.077853 58.11323 33.87687 0 yes
Maharashtra 0.059746 44.17077 27.58434 0.0002 yes
Rajasthan 0.039376 28.80346 21.13162 0.0034 yes
Tamilnadu 0.028993 21.09529 14.2646 0.0036 yes
Uttar Pradesh 0.002964 2.128053 3.841466 0.1446 No
From –“table 5.3.2.1, table 5.3.2.2, table 5.3.2.3, table 5.3.2.4” it is found that the trace
statistics and max eign values are more than the critical value at 0.05 so it can be confirmed
as the data series are co-integrated. The study rejects the null hypothesis as the probability
value is less than 0.05. The analysis confirmed the existence of co-integration among selected
agricultural commodity markets.
The directional movement from a dependent variable to independent variables is required to
be predicted. Hence, the cause and effect relationship is measured through Granger causality
to predict the future price movement.
P a g e | 77
5.3.3: GRANGER CAUSALITY TEST:
To determine whether the price of one market is useful for forecasting another, Granger
causality test is used. Data series are made stationary before running –"Granger Causality
test". To test the null hypothesis F-statistics is appointed. The null hypothesis, in this case, is
taken as one time series does not granger causes another. The p-value is less than 0.05 then
the null hypothesis is rejected i.e the alternative hypothesis is accepted and if the p-value is
more than 0.05 then accept the null hypothesis i.e reject the alternative hypothesis. The
interpretation for each analysis for causality is given after each commodity result table. Here
our objective is to forecast Odisha market price, hence causality of Odisha market with other
markets are given under table 5.3.4.
Table 5.3.3.1: Granger Causality Test for Cotton
Granger Causality Test
Null Hypothesis: F-Statistic Prob.
COTAP does not Granger Cause COTOD 34.4132 0.00000
COTOD does not Granger Cause COTAP 4.11119 0.01680
COTGUJ does not Granger Cause COTOD 28.6563 0.00000
COTOD does not Granger Cause COTGUJ 13.1252 0.00000
COTHAR does not Granger Cause COTOD 35.5572 0.00000
COTOD does not Granger Cause COTHAR 11.5697 0.00001
COTKAR does not Granger Cause COTOD 44.1563 0.00000
COTOD does not Granger Cause COTKAR 6.80129 0.00120
COTMAH does not Granger Cause COTOD 44.7062 0.00000
COTOD does not Granger Cause COTMAH 12.5363 0.00000
COTMP does not Granger Cause COTOD 32.3059 0.00000
COTOD does not Granger Cause COTMP 18.2208 0.00000
COTPN does not Granger Cause COTOD 29.4008 0.00000
COTOD does not Granger Cause COTPN 11.2605 0.00002
COTRAJ does not Granger Cause COTOD 36.2529 0.00000
COTOD does not Granger Cause COTRAJ 23.8659 0.00000
COTTEL does not Granger Cause COTOD 29.1822 0.00000
COTOD does not Granger Cause COTTEL 6.86159 0.00110
COTTN does not Granger Cause COTOD 30.1673 0.00000
COTOD does not Granger Cause COTTN 15.7259 0.00000
From table 5.3.3.1 it is clear that cotton price of Odisha is affected by –"Andhra Pradesh,
Gujarat, Haryana, Karnataka, Maharashtra, Madhya Pradesh, Punjab, Rajasthan, Telengana,
and Tamilnadu". In other hands, Odisha market affects –"Gujarat, Haryana, Karnataka,
P a g e | 78
Maharashtra, Madhya Pradesh, Punjab, Rajasthan, Telengana, and Tamilnadu" market cotton
price.
Table 5.3.3.2: Granger Causality Test for Turmeric
Granger Causality Test Null Hypothesis: F-Statistic Prob.
TURAP does not Granger Cause TUROD 19.7295 0.00000
TUROD does not Granger Cause TURAP 1.21174 0.29830
TURKAR does not Granger Cause TUROD 22.2877 0.00000
TUROD does not Granger Cause TURKAR 12.983 0.00000
TURMAH does not Granger Cause TUROD 20.7795 0.00000
TUROD does not Granger Cause TURMAH 1.76908 0.17120
TURTEL does not Granger Cause TUROD 24.1226 0.00000
TUROD does not Granger Cause TURTEL 1.37251 0.25410
TURTN does not Granger Cause TUROD 22.2029 0.00000
TUROD does not Granger Cause TURTN 1.39534 0.24840
From table 5.3.3.2 it is clear that turmeric price of Odisha is affected by –“Andhra Pradesh,
Karnataka, Maharashtra, Telengana, and Tamilnadu”. Odisha market only affects Karnataka,
market turmeric price.
Table 5.3.3.3: Granger Causality Test for Green gram
Granger Causality Test
Null Hypothesis: F-Statistic Prob.
GUJRAT does not Granger Cause ODISHA 14.2493 0.0000
ODISHA does not Granger Cause GUJRAT 4.70791 0.0093
KARNATAKA does not Granger Cause ODISHA 11.8599 0.0000
ODISHA does not Granger Cause KARNATAKA 5.58461 0.0039
KERALA does not Granger Cause ODISHA 28.2297 0.0000
ODISHA does not Granger Cause KERALA 1.06513 0.3452
MAHARASHTRA does not Granger Cause ODISHA 13.4621 0.0000
ODISHA does not Granger Cause MAHARASHTRA 3.62551 0.0271
MP does not Granger Cause ODISHA 9.16463 0.0001
ODISHA does not Granger Cause MP 6.08463 0.0024
RAJASTHAN does not Granger Cause ODISHA 13.0931 0.0000
ODISHA does not Granger Cause RAJASTHAN 3.23111 0.0401
TAMILNADU does not Granger Cause ODISHA 9.92752 0.0001
ODISHA does not Granger Cause TAMILNADU 14.18 0.0000
TELENGANA does not Granger Cause ODISHA 11.602 0.0000
ODISHA does not Granger Cause TELENGANA 2.59358 0.0755
UP does not Granger Cause ODISHA 13.2883 0.0000
ODISHA does not Granger Cause UP 10.7422 0.0000
P a g e | 79
From table 5.3.3.3 it is clear that green gram price of Odisha is affected by –“Gujarat,
Karnataka, Kerala, Maharashtra, Madhya Pradesh, Rajasthan, Telengana, Tamilnadu, and
Uttar Pradesh". Odisha market green gram price affects –"Gujarat, Karnataka, Madhya
Pradesh, Rajasthan, Tamilnadu, and Uttar Pradesh" market Green gram price.
Table 5.3.3.4: Granger Causality Test for Groundnut
Granger Causality Test
Null Hypothesis: F-Statistic Prob.
ANDHRAPRADESH does not Granger Cause ODISHA 23.0432 0.0000
ODISHA does not Granger Cause ANDHRAPRADESH 6.70515 0.0013
GUJRAT does not Granger Cause ODISHA 16.4306 0.0000
ODISHA does not Granger Cause GUJRAT 1.20856 0.2992
KARNATAKA does not Granger Cause ODISHA 21.9745 0.0000
ODISHA does not Granger Cause KARNATAKA 5.78357 0.0032
MADHYAPRADESH does not Granger Cause ODISHA 14.8645 0.0000
ODISHA does not Granger Cause MADHYAPRADESH 12.9736 0.0000
MAHARASHTRA does not Granger Cause ODISHA 13.1033 0.0000
ODISHA does not Granger Cause MAHARASHTRA 2.69852 0.0680
RAJASTHAN does not Granger Cause ODISHA 20.3705 0.0000
ODISHA does not Granger Cause RAJASTHAN 5.86921 0.0030
TAMILNADU does not Granger Cause ODISHA 26.0325 0.0000
ODISHA does not Granger Cause TAMILNADU 5.05793 0.0066
UTTARPRADESH does not Granger Cause ODISHA 13.4254 0.0000
ODISHA does not Granger Cause UTTARPRADESH 6.06584 0.0024
From table 5.3.3.4 it is clear that groundnut price of Odisha is affected by –“Andhra Pradesh,
Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Rajasthan, Tamilnadu, and Uttar
Pradesh”. Odisha market Green gram price affects –“Andhra Pradesh, Karnataka, Madhya
Pradesh, Rajasthan, Tamilnadu, and Uttar Pradesh” market groundnut price.
From the causality test, it has been found that Odisha agricultural commodity price is
affected by other states respective commodity price. Hence, other states have a mediating or
moderating effect on Odisha market agricultural commodity price depends upon the direction
of causality. Hence, the forecasted price for Odisha may be affected by other states respective
commodity price.
P a g e | 80
5.4: SEASONAL INDEX
Seasonal movements are the periodic and regular movements in time series with a period less
than one year. Hence, daily, weekly, monthly, quarterly and half-yearly periodic movements
can be studied from time-series data by isolating seasonal effect. But in the present study,
only monthly seasonal indices are constructed to know the intra-year movements.
For monthly data, a twelve-month moving average is expected to eliminate the seasonal
movements if they are of constant pattern and intensity. Hence, for finding out the seasonal
indices, the percentage of 12 months moving average is found out. As per the norms of the
multiplicative model, each observation in a time series is the product of T, C, S and I(Trend,
Cyclical component, Seasonal component and irregular component).
A forecasting tool was used to determine the demand for cotton, turmeric, Green gram,
groundnut in a given market place for a typical year. Such an index was based on data from
previous years that highlighted seasonal differences in production.
Seasonal index for selected commodities is presented under table 5.4.1 to 5.4.4
Table 5. 4.1: Seasonal Index for Cotton
Andhra Pradesh
Gujarat Haryana Karnataka Madhya Pradesh
Maharashtra Odisha Punjab Rajasthan Telengana Tamilnadu
January 96.39 97.36 97.61 101.84 105.88 103.45 102.54 92.31 99.42 98.43 100.53
February 97.93 101.57 100.00 100.97 105.97 103.61 101.94 95.56 100.88 99.15 102.38
March 98.71 100.41 101.76 96.10 103.40 103.30 100.86 96.22 101.31 99.01 102.39
April 97.58 98.62 101.75 94.90 101.39 101.90 101.17 95.31 101.70 98.93 104.13
May 95.09 95.56 103.64 93.55 93.38 95.64 92.53 97.51 100.58 96.25 99.22
June 97.77 98.99 101.24 95.66 95.56 96.04 98.15 97.18 99.88 98.85 97.66
July 104.11 104.35 101.24 101.90 92.13 93.81 101.85 97.18 100.26 103.89 103.54
August 107.23 102.94 100.48 102.99 99.50 96.84 94.93 91.82 99.83 106.16 101.89
September 105.85 100.54 95.77 102.77 95.12 99.48 100.42 87.22 94.89 103.41 99.49
October 102.47 98.83 98.72 99.83 99.28 97.95 101.42 88.78 99.43 97.11 98.68
November 98.30 100.03 99.61 103.51 103.40 103.45 100.25 90.66 100.81 98.86 93.68
December 98.55 100.81 98.18 105.97 105.00 104.53 103.94 91.59 101.00 99.95 96.41
P a g e | 81
Table 5.4.2: Seasonal Index of Turmeric
Andhra Pradesh
Karnataka Maharashtra Odisha Tamilnadu Telengana
January 103.20 100.66 99.35 97.35 102.06 92.42
February 95.04 92.89 110.42 91.90 97.70 87.86
March 94.06 97.90 111.90 92.11 99.09 87.84
April 96.33 99.86 106.73 92.93 98.80 90.15
May 98.89 100.02 100.83 102.48 98.45 94.03
June 96.93 99.18 92.49 104.61 95.76 94.54
July 100.48 101.92 94.69 100.30 100.63 97.99
August 102.98 101.87 93.90 100.83 101.29 100.97
September 97.92 99.25 91.52 107.03 97.78 100.25
October 100.61 100.11 94.86 103.03 99.56 100.59
November 105.68 102.82 99.60 102.51 104.79 105.29
December 107.87 103.53 103.71 104.93 104.09 106.56
Table 5.4.3: Seasonal Index of Green gram
Gujarat Karnataka Kerala Madhya Pradesh
Maharashtra Odisha Rajasthan Tamilnadu Telengana Uttar
Pradesh
January 97.91 98.60 98.06 94.96 96.53 101.85 103.88 102.57 96.31 99.68
February 93.68 103.68 99.39 94.27 98.21 96.01 100.69 108.20 99.37 101.26
March 101.16 104.16 98.55 96.99 100.72 101.43 98.99 106.82 99.30 101.96
April 102.33 108.39 99.84 104.84 107.13 97.37 93.82 107.75 108.32 105.17
May 102.85 106.03 101.45 103.16 102.83 93.28 97.41 101.95 103.46 102.37
June 98.21 104.34 100.58 101.91 96.62 104.35 97.04 89.20 96.88 100.10
July 101.72 98.99 100.17 100.84 103.14 97.07 96.61 94.39 96.60 98.42
August 99.24 94.80 99.79 99.99 100.73 94.98 94.26 94.25 96.63 97.89
September 96.59 88.90 98.94 102.94 93.90 105.23 97.42 95.79 96.32 94.62
October 102.11 93.56 98.31 101.64 98.55 100.21 105.79 104.57 100.97 97.80
November 102.57 98.79 102.23 101.36 100.24 98.01 106.70 101.95 104.70 101.05
December 101.63 99.76 102.67 97.11 101.41 110.22 107.38 92.56 101.15 99.67
Table 5.4.4: Seasonal Index of Groundnut
Andhra Pradesh
Gujarat Karnataka Madhya Pradesh
Maharashtra Odisha Rajasthan Tamilnadu Uttar
Pradesh
January 94.22 99.30 94.15 101.15 99.32 97.96 98.76 92.94 98.23
February 99.16 97.93 101.22 99.69 94.71 92.55 96.57 95.71 98.48
March 102.55 99.66 104.11 101.64 94.03 91.76 98.29 93.57 98.37
April 103.36 105.01 101.59 105.53 96.63 99.82 101.85 97.84 104.53
May 103.37 106.21 100.75 104.86 97.27 95.89 100.45 101.99 109.74
June 103.00 107.21 102.35 100.96 99.96 98.93 96.18 104.26 101.86
July 97.81 109.92 103.62 97.25 102.71 95.51 96.38 107.09 94.99
August 99.40 108.13 99.42 99.63 104.64 96.29 96.50 110.61 97.38
September 100.93 101.42 95.17 81.42 105.58 99.06 103.54 104.11 99.52
October 99.84 99.15 99.80 100.59 103.32 107.75 106.13 99.25 98.18
November 98.67 98.90 100.50 103.22 101.21 100.09 101.99 95.93 98.24
December 97.71 102.07 97.31 104.04 100.60 101.91 103.35 96.69 100.48
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5.5: Forecasting:
ARIMA has three parts like Auto-Regressive (AR), Integrated (I) and Moving Average
(MA).
AR: Observed values of AR mainly depend upon some linear combination of previously
observed values up to defined maximum lag lets p.
MA: The observed values are random error terms with some linear combination of previous
error term till lag q.
The mixed ARMA model can be written as:
The above equation is known as non-seasonal ARMA (p,q) model.
Where, Фp (B) = AR operator (non-seasonal)
Ɵq (B) = MA operator (non-seasonal)
The time series has to be differenced to achieve Stationarity as these are non-stationary. The
integration part (I) otherwise known as differencing (d). After performing integration the
series gets free from trend or seasonality component and only contains the noise or the
irregular component to be modelled
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Where, Δd = difference operator (non-seasonal)
d= Required order of differencing (to get stationary series).
The above equation is known as the general, or pure, non-seasonal ARIMA (p,d,q) model.
SARIMA: The explained model ARIMA (p, d, q) (P,D,Q)
Where,
Фp B s = AR operator (seasonal)
= I operator (seasonal)
ƟQ (B s) is the seasonal MA operator and ‘S’ is the seasonal frequency.
Before identifying seasonal ARIMA model non-seasonal (p, d, q) part is identified and the
results are depicted in Table 5.5.1.
Table 5.5.1: ARIMA (p, d, q) model for Cotton, Turmeric, Green gram and Groundnut
AR MA AIC SC
Cotton 1 3 14.5539 14.5666
Turmeric 1 1 16.3479 16.3606
Green gram 1 1 15.1462 15.1589
Groundnut 1 1 15.1462 15.1589
Out of the 24 ARIMA models for each crop tested for non-seasonal component some are
found suitable as they are having lowest AIC and SC which are shown in table 5.4.1. From
the unit root test for these price series all are found stationary after first difference. From
these two results, ARIMA (p, d, q) for cotton, Turmeric, Green gram and Groundnut are –
“ARIMA (1, 1, 3), ARIMA (1, 1, 1) ,ARIMA (1, 1, 1), and ARIMA (1, 1, 1)”. Afterwards
seasonality test is performed and results are presented in Table 5.4.2.
s d sp p t qD QS tФ B Ф B Y B B ò
D S
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Table 5.4.2: ARIMA (p, d, q) (P, D, Q) model for Cotton, Turmeric, Green gram and Groundnut.
Crops (p,d,q)(P,D,Q) AIC AICc BIC
Cotton
(1,1,3)(1,1,1) 12.5385 11.57667 12.54151 (1,1,3)(1,1,3) 12.5077 11.5586 12.51085 (1,1,3)(2,1,1) 12.5215 11.56598 12.52452 (1,1,3)(3,1,1) 12.464 11.5149 12.4672
Turmeric
(1,1,1)(1,1,1) 14.5172 14.52013 13.54268 (1,1,1)(2,1,1) 14.509 14.51191 13.54077 (1,1,1)(2,1,2) 14.4908 14.4938 13.529 (1,1,1)(1,1,2) 14.5046 14.50758 13.53644
Green gram
(1,1,1)(1,1,1) 13.57 13.57291 12.59546 (1,1,1)(2,1,1) 13.5692 13.57218 12.60104 (1,1,1)(2,1,2) 13.5493 13.5523 12.5874 (1,1,1)(1,1,2) 13.5647 13.56764 12.5965
Groundnut
(1,1,1)(1,1,1) 13.19796 13.20085 12.2234 (1,1,1)(2,1,1) 13.14299 13.14594 12.17479 (1,1,1)(2,1,2) 13.13247 13.1355 12.1706 (1,1,1)(1,1,2) 13.19913 13.20207 12.23093
From table 5.4.2 it is found that cotton, turmeric, Green gram and groundnut have suitable
ARIMA(p,d,q) (P,D,Q) model are ARIMA(1,1,3) (3,1,1), ARIMA(1,1,1) (2,1,2),
ARIMA(1,1,1) (2,1,2) and ARIMA(1,1,1) (1,1,2) respectively. These models are selected
comparing consecutive models and considering lowest AIC, AICc, BIC values.
Table 5.4.3: Forecasting for Odisha from January to December of the year 2019 for Cotton, Turmeric, Green gram and Groundnut.
Months Weeks Price forecasting for the year 2019
Cotton Price Rs/Qtl
Turmeric Price Rs/Qtl
Green gram Price Rs/Qtl
Groundnut Price Rs/Qtl
January
1st week 4551.34 5838.83 6773.21 4492.92
2nd week 4591.88 5882.62 6789.40 4618.10
3rd week 4524.94 5952.49 6919.50 4987.85
4th week 4549.04 5923.65 6934.60 4614.38
February
1st week 4563.12 5850.48 6810.61 4392.12
2nd week 4608.05 5893.78 6759.19 4405.17
3rd week 4538.11 5964.74 6828.39 4491.12
4th week 4562.62 5937.59 6975.63 4462.93
March
1st week 4572.65 5875.05 6939.64 4314.33
2nd week 4607.84 5911.57 6900.99 4561.85
3rd week 4550.66 5970.93 6984.52 4663.32
4th week 4570.77 5947.25 6999.46 4614.15
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April
1st week 4586.59 5880.03 6950.90 4364.69
2nd week 4631.08 5920.81 6920.06 4468.49
3rd week 4557.48 5987.15 7008.99 4657.83
4th week 4583.42 5961.06 7071.19 4503.50
May
1st week 4598.85 5895.07 7010.06 4307.01
2nd week 4645.53 5934.28 6971.78 4440.68
3rd week 4569.77 5998.13 7054.32 4555.82
4th week 4596.30 5972.98 7118.88 4491.14
June
1st week 4610.04 5906.97 7067.92 4288.73
2nd week 4653.20 5946.48 7032.21 4449.64
3rd week 4582.53 6010.77 7117.26 4591.89
4th week 4607.29 5985.42 7169.76 4494.10
July
1st week 4622.55 5919.23 7115.71 4277.67
2nd week 4667.83 5958.80 7080.18 4406.75
3rd week 4593.23 6023.21 7165.21 4553.92
4th week 4619.40 5997.84 7224.21 4450.47
August
1st week 4634.75 5931.76 7169.66 4247.09
2nd week 4680.98 5971.25 7133.45 4391.48
3rd week 4605.26 6035.51 7217.92 4525.25
4th week 4631.78 6010.19 7275.86 4437.89
September
1st week 4646.53 5944.07 7222.30 4229.24
2nd week 4691.62 5983.60 7186.44 4372.86
3rd week 4617.64 6047.93 7271.25 4515.38
4th week 4643.55 6022.58 7328.33 4417.43
October
1st week 4658.68 5956.47 7274.31 4208.30
2nd week 4704.21 5995.99 7238.40 4347.13
3rd week 4629.35 6060.30 7323.14 4487.53
4th week 4655.58 6034.96 7380.96 4392.20
November
1st week 4670.80 5968.85 7326.98 4185.29
2nd week 4716.69 6008.37 7291.02 4328.15
3rd week 4641.36 6072.68 7375.73 4467.12
4th week 4667.74 6047.34 7433.31 4373.47
December
1st week 4682.77 5981.22 7379.41 4164.99
2nd week 4728.32 6020.75 7343.49 4306.39
3rd week 4653.53 6085.06 7428.23 4447.22
4th week 4679.72 6059.72 7485.79 4351.33
From table 5.4.3 found that cotton price is in a slightly increasing trend. Turmeric price
remains stable throughout the year. Green gram price is showing a higher trend towards the
end of the year. Groundnut price is at the higher decreasing trend towards the end of the year.
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All the pictorial representations are depicted in figure 5.4.2. Cotton price for this year will
dwell within Rs. 4500-4600/- Qtl which will increase around by Rs. 100/- Qtl from June
onwards. Turmeric price will remain Rs. 5800-5900/- Qtl from January to April and from the
preceding months the price will increase by Rs. 100-200/- Qtl. The result shows more
fluctuation in the forecasting price for both Green gram and groundnut. While Green gram
price is in the increasing trend on the other hand groundnut price is slightly decreasing trend.
Consecutively the Green gram price fluctuates within Rs. 6700-6900/- Qtl from January to
April followed by an increasing trend in price till December with price Rs. 7400/- Qtl.
Groundnut price fluctuates within Rs. 4100-4600/- Qtl. This forecasting is for farmers and
other stakeholders to take on-time decision to fetch maximum benefit.
Figure 18 Figure 5.4.1: Before After Differentiation of Price Series for Odisha
a. Cotton Price Data at Level Form b. Cotton Price Data at 1st difference
c. Turmeric Price Data at Level Form d. Turmeric Price Data at 1st difference
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e. Green gram Price Data at Level Form
f. Green gram Price Data at 1st difference
g. Groundnut Price Data at Level Form h. Groundnut Price Data at 1st difference
Figure 19 Figure 5.4.2: Forecasting for each crop
(a) Forecast for Cotton (b) Forecast for Turmeric
(c) Forecast for Green gram (d) Forecast for Groundnut
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Figure 5.4.3: Residual for each crop
(a) Residual for Cotton (b) Residual for Turmeric
(c) Residual for Green gram (d) Residual for Groundnut
It is clarified from the figure 5.4.3 that these are the –“standardized Residuals, ACF of
Residuals, Normal Q-Q Plot of Standardized Residuals and p values for Ljung- Box statistic
of ARIMA model”. Majority of the correlation function coefficients are within the limit
which signifies them as good models. Quantile plots of standardised residuals are at good fit.
Residuals for cotton, turmeric, green gram and Groundnut are presented in sub pictures a, b, c
and d respectively.
Table 5.4.4: AIC, AICc, BIC for selected crops
Cotton Turmeric Green gram Groundnut AIC 12.46404 13.13247 13.54926 13.13247
AICc 12.46717 13.13547 13.55225 13.13547
BIC 11.51492 12.17063 12.58742 12.17063
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AIC, AICc, BIC values for Cotton, Turmeric, Green gram and groundnut are presented in the
table 5.4.4.
Table 5.4.5: Error estimates
Crops ar1 ma1 ma2 ma3 sar1 sar2
Cotton
Estimate 0.4076 -0.714 -0.714 -0.0936 -0.0025 -0.1128
SE 0.2029 0.2072 0.2072 0.084 0.0424 0.0384
t.value 2.0085 -3.4459 -3.4459 -1.1139 0.0424 -2.9348
p.value 0.045 0.0006 0.0006 0.2657 0.9532 0.0034
Turmeric
Estimate 0.0367 -0.6 -0.6161 -0.1564 -0.3975 -0.6023 SE 0.0683 0.0558 0.1137 0.0395 0.113 0.1114
t.value 0.5383 -10.749 -5.4188 -3.962 -3.5162 -5.4081 p.value 0.5906 0 0 0.0001 0.0005 0
Green gram
Estimate 0.2785 -0.8211 -0.5831 -0.2261 -0.4459 -0.4525
SE 0.0729 0.0502 0.0894 0.0531 -0.0854 0.0889
t.value 3.82 -16.3528 -6.5241 -4.2546 -5.2195 -5.0881
p.value 0.00 0.00 0.00 0.00 0.00 0.00
Groundnut
Estimate 0.312 -0.92 -0.7823 -0.3521 -0.2982 -0.497
SE 0.0549 0.0307 0.0738 0.0461 0.0815 0.0905
t.value 5.6828 -29.9823 -10.5986 -7.6329 3.659 -5.4939
p.value 0.00 0.00 0.00 0.00 0.00 0.00
Parameters and their estimates and standard errors of the selected agricultural commodities
are presented in table 5.4.5.
5.6: Validation of Models:
Validation is required to test the model accuracy and validity. The validations for agricultural
commodities are presented under table 5.6.
5.6.1: Validation for the model Cotton
-2,000
0
2,000
4,000
6,000
8,000
10,000
630 640 650 660 670 680 690 700 710 720
COTODF ± 2 S.E.
Forecast: COTODF Actual: COTOD Forecast sample: 621 720 Included observations: 100 Root Mean Squared Error 580.3353 Mean Absolute Error 509.1147 Mean Abs. Percent Error 10.84849 Theil Inequality Coefficient 0.066498 Bias Proportion 0.769615 Variance Proportion 0.009438 Covariance Proportion 0.220948
P a g e | 90
Mean absolute percentage error of the Odisha market cotton price is observed as 10.85 and
RMSE value is 580.34 (Figure 5.6.1). It implies that the forecasted values are close to the
actual values, and help improve the accuracy of forecasting.
5.6.2: Validation for the model Turmeric
Our considered criteria for validation i.e. MAPE and RMSE for the model turmeric are 3.34
and 261.55 (figure 5.6.2). It implies that the forecasted values are very much close to the
actual values, and are very much helpful in improving the accuracy of forecasting.The results
indicate this model for turmeric is an excellent model.
5.6.3: Validation for the model Green gram
Validation results against our considered criteria MAPE and RMSE are 11.19 and 715.66.
These values indicate that this model is good (figure 5.6.3). It implies that the forecasted
values are close to the actual values, and help improve the accuracy of forecasting.
-2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
630 640 650 660 670 680 690 700 710 720
TURODF ± 2 S.E.
Forecast: TURODF Actual: TUROD Forecast sample: 621 720 Included observations: 100 Root Mean Squared Error 261.5505 Mean Absolute Error 198.0651 Mean Abs. Percent Error 3.340820 Theil Inequality Coefficient 0.021532 Bias Proportion 0.000018 Variance Proportion 0.084033 Covariance Proportion 0.915949
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
630 640 650 660 670 680 690 700 710 720
ODISHAF ± 2 S.E.
Forecast: ODISHAF Actual: ODISHA Forecast sample: 621 720 Included observations: 100 Root Mean Squared Error 715.6657 Mean Absolute Error 634.3819 Mean Abs. Percent Error 11.19284 Theil Inequality Coefficient 0.058196 Bias Proportion 0.361535 Variance Proportion 0.597748 Covariance Proportion 0.040717
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5.6.4: Validation for the model Groundnut
The MAPE for the model of groundnut is 11.82; which is very good for forecasting and
RMSE value 545.55 also indicates the same thing (Figure 5.6.4). It implies that the forecasted
values are close to the actual values, and help improve the accuracy of forecasting.
3,000
3,500
4,000
4,500
5,000
5,500
6,000
6,500
7,000
7,500
630 640 650 660 670 680 690 700 710 720
ODISHAF ± 2 S.E.
Forecast: ODISHAF Actual: ODISHA Forecast sample: 621 720 Included observations: 100 Root Mean Squared Error 684.8784 Mean Absolute Error 545.5537 Mean Abs. Percent Error 11.82667 Theil Inequality Coefficient 0.068960 Bias Proportion 0.121344 Variance Proportion 0.458741 Covariance Proportion 0.419914
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Summary:
O bjectives Remarks
Calculation of Descriptive Statistics. Calculated for all t ime series Go for normality test
Normality Test for all-time series data. All data series are found Not normally distributed
Go for unitroot test
Stationarity test for all-time series data. All data series are found
stat ionary after 1st difference
Go for Cointegration test , Causality test , Forecasting
1 Test for Cointegration. All the markets are
cointegrated Go for Causality test
2 Test for Causality. Causal relat ion of Odisha market with other states
calculated
Impact of causal relat ionship decide impact
on forecasting
3 Test for seasonal Index. Seasonal Index for all
markets calculated Go for ARIMA model
identification
Test for ARIMA modelling.
Seasonal as well as non- seasonal components for
all commodities for Odisha calculated
Go for Forecasting
Price Forecasting. Forecasing is done for the
year 2019 Go for validation
Validation of model.
4
D at
a R
ef in
in g
Techniques of Data Analysis
All the four models are validated with MAPE and RMSE criterion
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6. SUMMARY, CONCLUSION AND POLICY IMPLICATIONS
6.1 Summary:
This concluding chapter of the thesis concludes the analysis and discussion, policy
implications and recommendations that have been given may help marketing and price
analysis of selected agricultural commodities in the state as well as in the country at large.
Due to changing consumption pattern of the population Indian agriculture diversified into the
production of high value crops. High value product decides trend of consumption; which is a
major cause of studying high value crop prices. Price fluctuation/volatility has a catastrophic
effect on the stake holders involved in the production to end use. This fluctuation is
responsible for risk of farming community.
Cotton, Turmeric, green gram, Groundnut is the important high value crops. These are
seasonal in nature. Cotton is sown in the month of June and harvested in the month of
December, where turmeric is sown in the month of May and harvested in the month of
January. Green gram is cultivated in five seasons Kharif (sowing June-july and harvesting
September-October), Pre rabi (sowing September-October and harvesting December-
January), Rabi(sowing November-December and harvesting February-March), Rice fallow
(sowing December-January and harvesting February-March), Summer (sowing June-july and
harvesting September-October). Groundnut is sown twice a year in Odisha i.e Kharif and
Rabi. Kharif and Rabi groundnut are sowing-harvesting periods as June-October and
October-February respectively.
6.2 Specific Objectives:
To study the Market Integration of major respective crop-producing states in India.
To find out the causal relationship between the prices series of selected crop-
producing states in India.
To determine the seasonal index of crop price in Odisha market.
To shape a suitable predicting model and to predict the agricultural commodity price
for Odisha Market.
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6.3: Findings of the Study
6.3.1: Findings for the Crop Cotton
All the data series of selected states for cotton are found non normal and made
stationary after 1st order difference.
Correlation test is conducted to get correlation coefficient as to know the strength of a
linear fit among two variables both geometrically and statistically. The test approved
further test i.e. co-integration test.
It is found from the co-integration test that the trace statistics and max eign values are
more than critical value at 0.05 so it can be confirmed as the data series are co-
integrated. The analysis confirmed the existence of co-integration among selected
state agricultural commodity markets of cotton.
The directional movement from dependent variable to independent variables is
required to be predicted. Hence, the cause and effect relationship is measured through
Granger causality to predict the future price movement.
Causality result shows that cotton price of Odisha is affected by –“Andhra Pradesh,
Gujarat, Haryana, Karnataka, Maharashtra, Madhya Pradesh, Punjab, Rajasthan,
Telengana, and Tamilnadu”. In other hand Odisha market affects –“Gujarat, Haryana,
Karnataka, Maharashtra, Madhya Pradesh, Punjab, Rajasthan, Telengana, and
Tamilnadu” market cotton price.
Odisha cotton is having lowest seasonal index 92.53 in the month of May and highest
seasonal index 103.94 in the month of December. It is because the sowing period for
cotton is May and harvesting time is December. The interpretation is coming from the
result that during sowing time price is highest and during harvest it is lowest.
To forecast cotton price ARIMA (Box Jenkins Model) is employed. As cotton is
seasonal in nature Seasonal ARIMA model is employed. The employed model for
cotton price forecasting is –“ARIMA (1,1,3)(3,1,1)”. The AIC, AICc and BIC values
are 12.464, 11.5149 and 12.4672 respectively.
The cotton price for the year 2019 will dwell between Rs. 4538.11-4691.62/- Qtl.
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6.3.2: Findings for the Crop Turmeric:
All the data series of selected states for Turmeric are found non normal and made
stationary after 1st order difference.
Correlation test is conducted to get correlation coefficient as to know the strength of a
linear fit among two variables both geometrically and statistically. The test approved
further test i.e. co-integration test.
It is found from the co-integration test that the trace statistics and max eign values are
more than critical value at 0.05 so it can be confirmed as the data series are co-
integrated. The analysis confirmed the existence of co-integration among selected
state agricultural commodity markets of Turmeric.
The directional movement from dependent variable to independent variables is
required to be predicted. Hence, the cause and effect relationship is measured through
Granger causality to predict the future price movement.
Causality result shows that turmeric price of Odisha is affected by –“Andhra Pradesh,
Karnataka, Maharashtra, Telengana, and Tamilnadu”. Odisha market only affects
Karnataka market turmeric price.
Odisha Turmeric price data is having lowest seasonal index 91.40 in the month of
February and highest seasonal index 107.03 in the month of September. It happens
due to moisture in weather so as rhizomes moisture content affect the price.
To forecast turmeric price ARIMA (Box Jenkins Model) is employed. As turmeric is
seasonal in nature Seasonal ARIMA model is employed. The employed model for
turmeric price forecasting is –“ARIMA (1,1,1)(2,1,2)”. The AIC, AICc and BIC
values are 14.4908, 14.4938 and 13.5290 respectively.
The turmeric price for the year 2019 will dwell between Rs. 5838.83-6047.93/- Qtl.
6.3.3: Findings for the Crop Green gram:
All the data series of selected states for green gram are found non normal and made
stationary after 1st order difference.
Correlation test is conducted to get correlation coefficient as to know the strength of a
linear fit among two variables both geometrically and statistically. The test approved
further test i.e. co-integration test.
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It is found from the co-integration test that the trace statistics and max eign values are
more than critical value at 0.05 so it can be confirmed as the data series are co-
integrated. The analysis confirmed the existence of co-integration among selected
state agricultural commodity markets of green gram.
The directional movement from dependent variable to independent variables is
required to be predicted. Hence, the cause and effect relationship is measured through
Granger causality to predict the future price movement.
Green gram price of Odisha is affected by –“Gujarat, Karnataka, Kerala, Maharashtra,
Madhya Pradesh, Rajasthan, Telengana, Tamilnadu, and Uttar Pradesh”. Odisha
market Green gram price affects –“Gujarat, Karnataka, Madhya Pradesh, Rajasthan,
Tamilnadu, and Uttar Pradesh” market Green gram price.
Odisha green gram price data is having lowest seasonal index 93.28 in the month of
May and highest seasonal index 110.22 in the month of December.
To forecast Green gram price ARIMA (Box Jenkins Model) is employed. As green
gram is seasonal in nature Seasonal ARIMA model is employed. The employed
model for green gram price forecasting is –“ARIMA (1,1,1)(2,1,2)”. The AIC, AICc
and BIC values are 13.5493, 13.5523 and 12.5874 respectively.
The green gram price for the year 2019 will dwell between Rs. 6759.19-7485.79/- Qtl.
6.3.4: Findings for the Crop Groundnut:
All the data series of selected states for groundnut are found non normal and made
stationary after 1st order difference.
Correlation test is conducted to get correlation coefficient as to know the strength of a
linear fit among two variables both geometrically and statistically. The test approved
further test i.e. co-integration test.
It is found from the co-integration test that the trace statistics and max eign values are
more than critical value at 0.05 so it can be confirmed as the data series are co-
integrated. The analysis confirmed the existence of co-integration among selected
state agricultural commodity markets of groundnut.
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The directional movement from dependent variable to independent variables is
required to be predicted. Hence, the cause and effect relationship is measured through
Granger causality to predict the future price movement.
Groundnut price of Odisha is affected by –“Andhra Pradesh, Gujarat, Karnataka,
Maharashtra, Madhya Pradesh, Rajasthan, Tamilnadu, and Uttar Pradesh”. Odisha
market Green gram price affects –“Andhra Pradesh, Karnataka, Madhya Pradesh,
Rajasthan, Tamilnadu, and Uttar Pradesh” market groundnut price.
Odisha groundnut price data is having lowest seasonal index 91.76 in the month of
March and highest seasonal index 101.91 in the month of December.
To forecast Groundnut price ARIMA (Box Jenkins Model) is employed. As
groundnut is seasonal in nature Seasonal ARIMA model is employed. The employed
model for groundnut price forecasting is –“ARIMA (1,1,1)(2,1,2)”. The AIC, AICc
and BIC values are 13.1324, 13.1355 and 12.1706 respectively.
The groundnut price for the year 2019 will dwell between Rs.4229.24-4987.85/- Qtl.
6.4: Policy Implications
On the basis of findings of the research, following recommendations are made. These
policies may help stakeholders of agriculture sector to improve efficiency in marketing of
agricultural commodity and farmer’s income.
1. Regular price forecasting of major crop producing states at different points of time.
2. Establishment of specialized market infrastructure.
3. Establishment of warehouses to reduce post-harvest losses.
4. Use of IT for dissemination of market information like commodity forecasted price,
volatility in price, major market prices etc.
5. On time marketing decisions like selling, storing etc should be taken carefully to fetch
maximum benefit.
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BIBLIOGRAPHY
1. A Dash, et al. (2017) .Forecasting of food grain production in Odisha by fitting ARIMA
model. Journal of Pharmacognosy and Phytochemistry. 6(6): 1126-1132.
http://www.phytojournal.com/archives/2017/vol6issue6/PartP/6-5-541-759.pdf.
2. Acharya, S. S., &Agarwal, N. L. (1994). Agricultural prices-analysis and
policy. Agricultural prices-analysis and policy.
3. Ali, J., & Bardhan Gupta, K. (2011). Efficiency in agricultural commodity futures
markets in India: Evidence from Cointegration and causality tests. Agricultural Finance
Review, 71(2), 162-178.
a. appropriate?”, Journal of Agricultural Economics, Vol. 53, pp. 513-29.
4. Ashwini Darekar and A Amarender Reddy. (2017). Price forecasting of pulses: case of
pigeon pea. Journal of Food Legumes 30(3): 42-46.
5. ASHWINI DAREKAR and A AMARENDER REDDY. (2017). Price forecasting of
pulses: case of pigeon pea. Journal of Food Legumes 30(3): 42-46.
6. Ashwini Darekar, A. Amarender Reddy. (2017). Price forecasting of maize in major
states. MaizeJournal.
7. Ashwini Darekar, A. Amarender Reddy. (2017). Price forecasting of maize in major
states. Maize Journal.
8. Ashwini Darekar, et al. (2017). Forecasting of Common Paddy Prices in India. Journal
of Rice Research 2017, Vol 10 No. 1. http://www.icar-iirr.org/journal%202017-10-
13.pdf.
9. Atanu Ghoshray, Madhusudan Ghosh. (2011) How Integrated is the Indian Wheat
Market?. Journal of Development Studies 47:10, pages 1574-1594.
10. Beck, S.E. (1994), “Cointegration and market efficiency in commodity futures
markets”,Applied Economics, Vol. 26, pp. 249-57.
P a g e | 99
11. Bera, B. (2017). A Study on the Variability in Market Arrivals and Prices of Potato in
Some Selected Markets of West Bengal. International Journal of Agriculture Sciences,
ISSN, 0975-3710.
12. Bradford, Scott and Robert Z. Lawrence, 2004. Has Globalization Gone Far Enough?
The Costs of Fragmented Markets. Institute for International Economics, Washington,
DC. February 2004.
13. Capelle-Blancard, G., & Coulibaly, D. (2011). Index trading and agricultural
commodity prices: A panel Granger causality analysis. International Economics, 126,
51-71. [Elsevier]
14. Chowdhury, A.R. (1991), “Futures market efficiency: evidence from
cointegrationtests”,The Journal of Futures Markets, Vol. 11, pp. 577-89.
15. commodity futures markets: a new look”, The Journal of Futures Markets, Vol. 21,
16. Crowder, W.J. and Hamed, A. (1993), “A cointegration test for oil futures market
efficiency”,The Journal of Futures Markets, Vol. 13, pp. 933-41.
17. Darekar A and AA Reddy. (2018). Forecasting wheat prices in India. Forecasting wheat
prices in India. Wheat and Barley Research 10(1).
18. Darekar A and AA Reddy. (2018). Forecasting wheat prices in India. Forecasting wheat
prices in India. Wheat and Barley Research 10(1). Homepage:
http://epubs.icar.org.in/ejournal/index.php/JWR.
19. Darekar A and AA Reddy. (2018). Oilseeds Price Forecasting: Case Of Mustard In
India. Agricultural Situation in India.
20. Darekar A and AA Reddy. (2018). Oilseeds Price Forecasting: Case Of Mustard In
India. Agricultural Situation in India.
21. Darekar, A. and Reddy, A. A. (2017). Cotton Price Forecasting in Major Producing
States. Economic Affairs, 62(3):1-6.Print ISSN : 0424-2513, Online ISSN : 0976-4666,
2017.
22. Darekar, A. and Reddy, A. A. (2017). Cotton Price Forecasting in Major Producing
States. Economic Affairs, 62(3):1-6.Print ISSN : 0424-2513, Online ISSN : 0976-4666.
P a g e | 100
23. Darekar, A. and Reddy, A. A. (2017). Forecasting of Common Paddy Prices in India.
Journal of Rice Research, 10(1): 71-75.
24. Darekar, A. and Reddy, A. A. (2017). Predicting market price of soybean in major India
studies through ARIMA model. Journal of Food Legumes 30(2): 73-76.
25. Darekar, A. and Reddy, A. A. (2017). Predicting market price of soybean in major India
studies through ARIMA model. Journal of Food Legumes 30(2): 73-76, 2017.
26. Darekar, A. and Reddy, A. A. 2017. Forecasting of Common Paddy Prices in India.
Journal of Rice Research, 10(1): 71-75.
27. Darekar, AS., Pokharkar, VG and Datarkar, SB. 2016. Onion Price Forecasting In
Kolhapur Market of Western Maharashtra Using ARIMA Technique. International
Journal of Information Research and Review 03(12): 3364-3368.
28. Departmet of Agriculture Cooperations and Farmers Welfare. Ministry of agriculture &
farmers Welfare. http://agricoop.nic.in/recentinitiatives/minimum-support-prices-msp-
and-bonus-kharif-crops-2017-18-season.
29. Dhakre D S and Bhattacharya D.2014. Statist ical investigation of price
behaviour of potato in Agra, Indian Research Journal of Extension
Education, 14 (2):12-15.
30. DipankarMitra. (2017). Hierarchical Time-series Models for Forecasting Oilseeds and
Pulses Production in India. Economic Affairs, Vol. 62, No. 1, pp. 103-111, March 2017
DOI: 10.5958/2230-7311.2017.00045.9
31. Directorate of Marketing & Inspection (DMI), Ministry of Agriculture and Farmers
Welfare, Government of India. Available from: http://agmarknet.gov.in/.
32. Dr Alioune DIENG. (2008).
33. Dr V Ramanujam and Dr T Viswanathan. (2018). An Empirical Analysis of Forecasting
Volatility of Pepper Price in the Spot Market in India. Research Gate.
34. Dr V Ramanujam and Dr T Viswanathan. (2018). An Empirical Analysis of Forecasting
Volatility of Pepper Price in the Spot Market in India. Research Gate.
P a g e | 101
35. Dra. Sandra Cristina de Oliveira, et al. (2012). A study about the performance of time
series models for the analysis of agricultural prices. GEPROS. Gestão da Produção,
Operações e Sistemas, Ano 7, nº 3, p. 11-27.
36. Dreze, J., Sen, A., &Hussain, A. (1995). The political economy of hunger: selected
essays. Oxford University Press.
37. Edward Kamau Gathondu. (2014). Modeling of Wholesale Prices for Selected
Vegetables Using Time Series Models in Kenya. Student thesis. College of Biological
and Physical Sciences School of Mathematics, University of Nairobi.
38. Fabian Torben Bosler. (2010). Models for Oil Price Prediction and Forecasting. A
Thesis Presented to the Faculty of San Diego State University.
39. Gan-qiong Li, et. al. (2010). Short-Term Price Forecasting For Agro-products Using
Artificial Neural Networks. Agriculture and Agricultural Science Procedia 1 278–287
40. Goletti, Francesco, Raisuddin Ahmed, and NaserFarid, 1995. ‘Structural Determinants
of Market Integration: The Case of Rice Markets in Bangladesh’. Developing
Economies. June 1995; 33(2): 185–202.
41. Gooijer, J.G.D. and Hyndman, R.J. (2006). 25 years of time-series forecasting. Int. J.
Forecasting. 22(3): 443-73.
42. Hemavathi et al. (2017). Forecasting Food grains Area and Production in India using
ARIMA Model. Statistical Approaches on Multidisciplinary Research, Volume I, DOI:
Surragh Publishers, India, ISSN: 2349 – 4891.
43. J. SHRUTHI. (2015). An Analysis of Price Forecasting Techniques for Onion and
Tomato Crops. Student Thesis. Department of agricultural marketing, co-operation and
business management university of agricultural sciences Bengaluru- 560 065.
44. Jambhale, A. A., Bhoite, D. P., & Shinde, A. V. (2012). Seasonal indices and price
behaviour in agriculture produce market committee, Baramati Dist. Pune
(MS). Agriculture Update, 7(1/2), 42-46.
45. K. Assis, et al. (2010). Forecasting Cocoa Bean Prices Using Univariate Time Series
Models. Journal of Arts Science & Commerce ISSN 2229-4686.
P a g e | 102
46. K. Ghosh, et al. (2014). Development of a rice yield prediction system over
Bhubaneswar, India: combination of extended range forecast and CERES-rice model.
METEOROLOGICAL APPLICATIONS Meteorol. Appl. 22: 525–533 (2015)
Published online 1 December 2014 in Wiley Online Library (wileyonlinelibrary.com)
DOI: 10.1002/met.1483.
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.1483.
47. Kapusuzoglu A., Karacaer Ulusoy M. (2015): The interactions between agricultural
commodity and oil prices: an empirical analysis. Agric. Econ. – Czech, 61: 410-421.
48. Kellard, N. (2002). Evaluating commodity market efficiency: are cointegration tests
appropriate?. Journal of Agricultural Economics, 53(3), 513-529.
49. Kellard, N., Newbold, P., Rayner, T. and Ennew, C. (1999), “The relative efficiency of
commodity futures markets”, The Journal of Futures Markets, Vol. 19, pp. 413-32.
50. Knetler, Michael M and Penelope K. Goldberey. 1996. ‘Goods Prices and Exchange
Rates: What have We Learned?’, NBER Working Paper 5862. Cambridge, MA:
National Bureau of Economic Research.
51. Kumar Soni, T. (2014). Cointegration, linear and nonlinear causality: Analysis using
Indian agriculture futures contracts. Journal of Agribusiness in Developing and
Emerging Economies, 4(2), 157-171.
52. Lai, K.S. and Lai, M. (1991), “A Cointegration test for market efficiency”, The Journal
of Futures Markets, Vol. 11, pp. 567-75.
53. Liew Khim Sen, MahendranShitan, HuzaimiHussain. (2006). Time series modelling and
forecasting of Sarawak black pepper price. MPRA Paper No. 791, posted 13.
November.
54. Maps of India. (2017). https://www.mapsofindia.com/top-ten/india-crops/cotton.html.
55. McKenzie, A.M. and Holt, M.T. (2002), “Market efficiency in agricultural futures
markets”,Applied Economics, Vol. 34, pp. 1519-32.
56. McKenzie, A.M., Jiang, B., Djunaidi, H., Hoffman, L. and Wailes, E.J. (2002),
“Unbiasedness and market efficiency tests of the US rice futures market”, Review of
Agricultural Economics, Vol. 24 No. 2, pp. 474-93.
P a g e | 103
57. Meyer, Jochen. 2004. ‘Measuring Market Integration in the Presence of Transaction
Costs—A Threshold Vector Error Correction Approach’. Agricultural Economics.
Special Issue December 2004; 31(2–3): 327–34.
58. Monke, Eric and Todd Petzel, 1984. ‘Market Integration: An Application to
International Trade in Cotton’. American Journal of Agricultural Economics. 66(4),
November: 481–7.
59. Musunuru, N. M. (2017). Causal relationships between grain, meat prices and exchange
rates. International Journal of Food and Agricultural Economics (IJFAEC), 5(1128-
2018-044), 1-10.
60. Naveena K., et al., (2017) Hybrid Time Series Modelling for Forecasting the Price of
Washed Coffee (Arabica Plantation Coffee) in India.. International Journal of
Agriculture Sciences, ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 9, Issue 10,
pp.-4004-4007
61. Niranjan Jayaramu. (2015). Impact of Seasonality on Agricultural Commodity Price
Behaviour. A thesis presented to the department of agricultural sciences in candidacy
for the degree of Master of Science. Northwest Missouri state university Maryville
Missouri
62. Omar, M. I., Dewan, M. F., & Hoq, M. S. (2014). Analysis of price forecasting and
spatial co-integration of banana in Bangladesh. Eur. J. Business Manage, 6(7), 244-255.
63. PANI, R., BISWAL, S. K., & MISHRA, U. S. (2019). Green gram weekly price
forecasting using time series model. Revista ESPACIOS, 40(07).
64. PANI, R., et al. (2019).Groundnut price forecasting using time series model
65. Pardhi, R. M. (2016). MARKET INTEGRATION AND PRICE FORECASTING OF
MANGO: AN EMPIRICAL ANALYSIS (Doctoral dissertation, Institute of
Agricultural Sciences, Banaras Hindu University, Varanasi).
66. Patel, S. A., & Patel, J. M. (2013). A comparative study of arrivals and prices of
agricultural commodities at APMC using Time Series Analysis.
P a g e | 104
67. Petram Akbari Parg, et al. (2013). Ami Application of Seasonal Models in Modelling
and Forecasting the Monthly Price of Privileged Sadri Rice in Guilan Province. ARPN
Journal of Agricultural and Biological Science, ISSN 1990-6145.
68. Purna Chandra Padhan. (2012). Application of ARIMA Model for Forecasting
Agricultural Productivity in India. JOURNAL OF AGRICULTURE & SOCIAL
SCIENCES ISSN Print: 1813–2235; ISSN Online: 1814–960X 11–017/AWB/2012/8–
2–50–56 http://www.fspublishers.org.
69. PushpaSavadatti. (2017). Forecasting of Area, Production, and Productivity of Food
Grains in India : Application of ARIMA Model. Indian journal of economics and
research.DOI: 10.17010/aijer/2017/v6i6/120114.
http://indianjournalofeconomicsandresearch.com/index.php/aijer/article/view/120114.
70. R. Sharma, et al. (2014). Price of cumin in Rajasthan using ARIMA approach.
International J. Seed Spices 5(1), January:76-78.
71. Rahul Tripathy, et al. (2014). Forecasting Rice Productivity and Production of Odisha,
India, Using Autoregressive Integrated Moving Average Models. Advances in
Agriculture Article ID 621313, 9 pages http://dx.doi.org/10.1155/2014/621313.
https://www.hindawi.com/journals/aag/2014/621313/
72. Rangsan Nochai, Titida Nochai. (2006). ARIMA Model for Forecasting Oil Palm Price.
Proceedings of the Second IMT-GT Regional Conference on Mathematics, Statistics
and Applications UniversitiSains Malaysia, Penang, June 13-15.
73. Ravallion, Martin, 1986. ‘Testing Market Integration’. American Journal of Agricultural
Economics. February 1986; 68(1): 102–09.
74. Revista ESPACIOS, 40(25).Groundnut price forecasting using time series model
75. Reza Moghaddasi, Bita Rahimi Badr. (2008). An Econometric Model for Wheat Price
Forecasting in Iran. International Conference on Applied Economics – ICOAE.
76. S. P. Bhardwaj, et al. (2014). An Empirical Investigation of Arimaand Garch Models in
Agricultural Price Forecasting. Economic Affairs 2014, 59(3) : 415-428.
P a g e | 105
77. Sabur, S. A., Hossain, M., & Palash, M. S. (2006). Marketing System, Seasonality In
Prices And Integration Of Onion Markets In Bangladesh. Bangladesh Journal of
Agricultural Economics, 29(454-2016-36546), 93-105.
78. Sayedul Anam, et al. (2012).Time Series Modelling of the Contribution of Agriculture
to GDP of Bangladesh. European Journal of Business and Management
www.iiste.org ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) Vol 4, No.5.
79. Sharma, H. (2016). Trend and seasonal analysis of wheat in selected market of Sri
ganganagar district. Economic Affairs, 61(1), 127.
80. Shruti Mohapatra, et al. (2018). Price forecasting of groundnut in Odisha. The Pharma
Innovation Journal 2018; 7(3): 111-114. Available from:
<http://www.thepharmajournal.com/archives/2018/vol7issue3/PartB/7-2-33-193.pdf>.
81. Tularam, G.A. and Saeed, T. (2016) Oil-Price Forecasting Based on Various Univariate
Time-Series Models. American Journal of Operations Research, 6, 226-235.
http://dx.doi.org/10.4236/ajor.2016.63023
a. USDA (2018). https://www.usda.gov/media/agency-reports
82. V. Jadhav, et al. (2017). Application of ARIMA Model for Forecasting Agricultural
Prices. J. Agr. Sci. Tech. Vol. 19: 981-992.
83. V. Jadhav, et al. (2018). Application of ARIMA Model for Forecasting Agricultural
Prices. J. Agr. Sci.Tech.Vol.19:981992.
84. V. Sivapathasundaram, et al. (2012). Forecasting of Paddy Production in Sri Lanka: A
Time Series Analysis using ARIMA Model. Tropical Agricultural Research Vol. 24 (1):
21 – 30.
85. Venkatesh Panasa, R. VijayaKumari, G. Ramakrishna and Kaviraju, S. (2017). Maize
Price Forecasting Using Auto-Regressive Integrated Moving Average (ARIMA) Model.
Int.J.Curr. Microbiol. App.Sci.6(8):2887-2895.
86. Venkatesh Panasa, R. VijayaKumari, G. Ramakrishna and Kaviraju, S. (2017). Maize
Price Forecasting Using Auto Regressive Integrated Moving Average (ARIMA) Model.
Int.J.Curr. Microbiol. App.Sci.6(8):2887-2895.doi: https://doi.org/10.20546/
ijcmas.2017.608.345.
P a g e | 106
87. Wang Xin and Wang Can. (2016). Empirical Study on Agricultural Products Price
Forecasting based on Internet-based Timely Price Information. International Journal of
Advanced Science and Technology Vol.87 (2016), pp.31-36 http://dx.doi.org/
10.14257/ijast.2016.87.04
88. Wang, H.H. and Ke, B. (2005), “Efficiency tests of agricultural commodity futures
markets in China”, The Australian Journal of Agricultural and Resource Economics,
Vol. 49, pp. 125-41.
89. Wani, M. H., Paul, R. K., Bazaz, N. H., & Manzoor, M. (2015). Market integration and
price forecasting of apple in India. Indian Journal of Agricultural Economics, 70(902-
2016-68375), 169-181.
90. Wilkinson, J., & Ghoshray, A. (2013). A cointegration analysis of oil and agricultural
prices. Review of Market Integration, 5(3), 249-270.
91. Wisner, B., & Luce, H. R. (1993). Disaster vulnerability: scale, power and daily
life. GeoJournal, 30(2), 127-140.
92. Yang, J., Bessler, D.A. and Leatham, D.J. (2001), “Asset storability and price discovery
in
93. Zahid Asghar, et al. (2012). Structural Breaks, Automatic Model Selection and
Forecasting Wheat and Rice Prices for Pakistan. Pak.j.stat.oper.res. Vol.VIIINo.1 pp1-
20.
94. Zotteri, G., Kalchmidt, M. and Caniato, F.(2005). The impact of aggregation level on
forecasting performance. Int. J. Prod. Eco., 93-94: 479-91.
P a g e | 107
Websites: 1. (GoI, 2019)
2. (http://www.indianspices.com)
3. Agricultural Market Intelligence Centre, PJTSAU. (2018).
https://pjtsau.edu.in/files/AgriMkt/2018/TurmericOLDec2018.pdf
4. Cotton Advisory Board of India (CAB)
5. Cotton Advisory Board(CAB)P-Provisional
6. DES, Ministry of Agri. & FW, Govt. of India.
7. https://duckduckgo.com/?q=cotton+production+scenario+&kh=1&kn=1&kac=1&iar=im
ages&iax=images&ia=images&iai=https%3A%2F%2Fimage.slidesharecdn.com%2Fman
deepsummertrainingreport-100915150000-phpapp01%2F95%2Fmandeep-summer-
training-report-35-728.jpg%3Fcb%3D1284562881
8. https://www.usda.gov
9. Spices Board of India.
http://www.indianspices.com/marketing/price/domestic/daily-price
P a g e | 108
PUBLICATIONS
1. PANI, R., BISWAL, S. K., & MISHRA, U. S. (2019). Green gram weekly price
forecasting using time series model. Revista ESPACIOS, 40 (07).
2. PANI, R., et al. (2019). Groundnut price forecasting using time series model.
Revista ESPACIOS, 40 (25).Groundnut price forecasting using time series model
3. PANI, R., BISWAL, S. K., & MISHRA, U. S. (2019). , Journal of Advanced
Research in Dynamical and Control System, 11, 236-243.
Annexure I: Price of Cotton in Selected State markets of India Price of Cotton in Odisha market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2462.43 1950.00 2331.65 2235.02 2919.52 2919.52 2931.21 3879.12 3797.68 3910.45 4296.58 4100.15 4110.77 4800.00 4965.95 2561.58 1950.00 2245.38 2368.21 2904.55 2904.55 2878.77 4394.47 4025.82 3908.89 4453.69 4345.79 4236.38 5200.00 4882.55 2492.56 1680.00 1723.05 1946.27 2312.58 2789.26 2896.21 5226.46 3698.39 3981.55 4610.79 4028.77 4173.57 5010.31 4882.55 2314.87 2119.82 1527.60 1730.88 2278.56 2520.93 2803.95 5138.36 2266.31 4075.68 4756.69 4194.69 4222.63 5267.80 4836.37
Feb
2494.90 1905.64 1465.93 1893.72 2398.78 2908.61 2800.00 5251.76 2633.70 4200.00 4643.82 3969.43 4218.68 5313.75 4698.03 2494.90 1905.64 1465.93 1893.72 2398.78 2908.61 2800.00 4646.56 3942.08 4100.00 4729.88 4043.28 4371.75 5337.93 4660.95 2494.90 1905.64 2200.67 1893.72 2398.78 2908.61 2872.36 2934.82 3942.08 4000.00 4785.55 4104.04 4193.27 5354.25 4432.03 2494.90 1905.64 2200.67 1893.72 2398.78 2908.61 2872.00 5251.76 3400.00 4052.00 4742.69 4125.79 4257.44 5354.25 4394.9
Mar
2368.17 1830.04 1654.19 1907.24 2306.30 2761.08 2894.97 5414.38 3357.69 4104.00 4704.23 4085.50 4166.22 5401.64 4388.96 2942.01 1266.71 1900.00 1907.24 2306.30 2761.08 3264.74 5414.38 3628.85 4115.64 4629.28 4045.20 3902.50 5478.22 4533.48 1859.76 1754.43 1909.00 1750.00 2892.63 2817.86 3084.20 3652.53 3723.93 4112.40 4708.77 4020.01 4034.36 5478.22 4533.48 1794.33 2371.26 1953.61 2028.58 3009.21 2917.35 3634.51 3579.28 3900.00 4127.28 4500.00 3827.27 4138.46 4752.24 4533.48
Apr
2198.37 2000.00 1950.00 2310.81 2899.33 2899.33 2903.65 4044.09 3819.02 4109.16 4835.25 3871.15 4250.00 5115.23 4349.41 2637.13 1936.15 2219.69 2383.06 2901.97 2901.97 2860.00 5687.26 3904.54 3911.83 4563.88 3852.24 4300.00 4933.74 4134.29 2372.90 1664.98 1641.40 1912.24 2322.04 2800.00 3009.48 4897.05 3576.81 4180.76 4292.50 3833.33 4266.00 5024.48 4100.00 2397.41 1400.00 1468.74 1736.97 2220.00 1761.41 2942.27 5698.94 3661.65 4170.93 4169.81 3815.14 4525.00 4979.11 4100.00
May
2000.00 2500.00 1552.88 1934.85 1666.19 2280.71 2917.65 3325.56 3200.00 4190.29 4038.46 3800.00 4496.00 5001.80 4225.00 2000.00 2500.00 1488.49 2033.79 1100.00 2540.35 2800.00 3039.41 2838.19 4190.29 4490.91 4050.00 4352.00 4400.00 4350.00 2000.00 2500.00 1100.00 2083.26 1383.10 2605.41 2806.42 3182.49 3019.10 4190.29 4200.00 4150.00 4326.00 4400.00 4350.00 2000.00 2500.00 1488.49 2108.00 1241.55 2810.23 2806.42 3110.95 2928.64 4190.29 4572.73 3900.00 4235.00 4400.00 4350.00
Jun
1826.23 2500.00 1600.00 2132.73 1100.00 2800.00 2806.42 3683.20 3901.78 4179.72 4436.37 4250.00 4263.00 4400.00 4398.00 1826.23 2500.00 1600.00 2132.73 1100.00 2800.00 2806.42 3683.20 3869.48 4179.72 4300.00 4225.00 4352.00 4400.00 4448.00 1826.23 1766.52 2207.55 2132.73 2905.48 2670.47 2800.00 3738.48 3900.00 4179.72 4400.00 4050.00 4336.00 4400.00 4448.00 1826.23 2686.52 1913.82 2132.73 3003.84 3015.04 3391.95 3521.69 3903.55 4179.72 4500.00 4125.00 4228.00 4400.00 4465.00
Jul
2202.33 1986.13 2279.98 2279.17 2950.02 2950.02 2929.61 4281.96 3835.40 3895.40 4450.00 3900.00 4563.00 4400.00 4473.00 2630.44 2010.61 2148.22 2440.99 2918.40 2918.40 2868.13 5453.40 3945.49 3907.37 4475.00 4350.00 4500.00 4500.00 4498.00 2371.12 1674.32 1596.06 1872.67 2301.84 2745.88 2912.79 5474.62 3274.87 4150.59 4462.50 4300.00 4426.00 4500.00 4482.00 2300.00 1400.00 1322.77 1500.00 2010.00 2800.00 2935.06 5400.70 3715.52 4142.04 4468.75 4250.00 4263.00 4500.00 4490.00
Aug
2050.00 2500.00 1561.39 1786.75 1666.19 2601.00 2725.00 3325.56 3400.00 4000.00 4465.63 4200.00 4236.00 4500.00 4492.00 1943.97 2500.00 1680.69 2018.32 1100.00 2601.00 2800.00 3502.55 3400.00 4000.00 4250.00 4360.00 4330.00 4500.00 4513.00 1871.99 2500.00 1718.38 1979.22 1383.10 2601.00 3150.00 3414.06 3400.00 4000.00 4360.00 4280.00 4423.00 4500.00 4496.00 1854.96 2500.00 1807.54 2093.05 1241.55 2601.00 3239.49 3458.30 3400.00 4000.00 4425.00 3950.00 4400.00 4500.00 4502.00
Sep
1800.00 1915.66 1800.00 2073.50 2124.18 2740.37 3418.76 3669.33 3800.00 4000.00 4200.00 4125.00 4456.00 4500.00 4504.00 1837.94 2217.87 1800.00 2249.90 2558.82 2960.87 3500.00 4213.53 3800.00 4000.00 4625.00 4350.00 4526.00 4500.00 4527.00 1800.00 1915.66 1756.06 1940.12 3006.81 2505.96 3328.98 3734.14 3733.14 4000.00 4428.00 4220.00 4536.00 4500.00 4505.00 1837.94 2217.87 1896.70 2206.87 2993.47 2974.78 3598.03 3604.51 3916.56 4234.69 4370.00 3950.00 4600.00 4500.00 4513.00
Oct
2336.58 1970.41 2257.97 2292.92 2946.96 2946.96 2882.82 4822.54 4108.26 3891.97 4600.00 4360.00 4550.00 4500.00 4516.00 2135.13 1883.98 1950.02 2419.54 2925.00 2925.00 2896.69 5379.34 3613.98 3962.20 4530.00 4120.00 4560.00 4500.00 4539.00 2362.75 1420.00 1640.83 1850.14 2269.53 2600.00 2863.21 5556.34 2684.50 4171.10 4220.00 4280.00 4356.00 4500.00 4517.00 2386.99 1590.62 1376.50 1868.75 2603.22 1400.00 2877.65 5600.00 3586.71 4000.00 4680.00 4325.00 4365.00 4321.78 4525.00
Nov
2140.99 1991.10 1849.59 1955.14 2988.06 2643.43 2800.00 3900.00 3871.30 4300.00 4330.00 4200.00 4200.00 4320.00 4527.00 1970.50 1991.10 1849.59 1955.14 2988.06 2600.00 3559.03 3200.00 3886.76 4135.00 4490.00 4155.56 4200.00 4321.78 4550.00 1970.00 1991.10 1849.59 1955.14 2988.06 2601.00 3179.52 3703.06 3893.93 4135.00 4535.00 4200.00 4595.54 4190.31 4528.00 2140.00 1991.10 1849.59 1955.14 2988.06 2601.00 3369.27 4000.00 3896.96 4135.00 4380.00 4101.09 4638.77 4303.91 5229.64.
Dec
1894.99 2522.95 1935.01 2182.12 2998.78 2971.95 3274.39 3648.60 3902.21 4172.90 4050.00 4100.00 4743.13 4190.31 5446.84 1800.00 2522.95 1912.06 2182.12 2603.22 3000.00 3608.00 3664.68 3901.10 4076.92 4050.00 4078.68 4676.60 4564.66 5326.34 1800.00 2040.56 1889.10 1983.22 3008.63 2587.00 3660.51 3680.76 3900.00 4135.00 4040.94 4088.87 4790.08 4962.00 5250.64 1989.98 2437.20 1941.67 2214.29 2956.73 2964.55 3551.86 3743.51 3906.50 4256.97 4200.79 4132.66 4725.66 4962.00 5401.81
Price of Cotton in Andhra Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2057.48 1739.69 1880.88 1974.45 2134.79 2756.91 3272.32 4314.64 3917.86 3868.58 4620.22 4024.74 4264.13 5127.62 4823.41 2057.48 1941.40 1881.09 1994.37 2291.26 2688.21 3146.23 4343.96 3946.42 3882.29 4506.92 4013.84 4249.66 5116.62 4823.41 2057.48 1830.71 1913.04 2008.32 2698.03 2619.50 3073.32 4923.91 4084.81 3941.27 4686.77 3970.50 4260.41 5325.01 4724.45 2057.48 1924.00 1857.56 2068.34 2727.81 2450.05 2885.32 5274.15 3896.26 3812.44 4468.71 3920.43 4174.72 5373.01 4850.77
Feb
2057.48 1730.17 1882.89 2016.59 2291.22 2474.89 2968.72 5473.60 3828.84 3932.53 4642.18 3879.49 4089.74 5145.73 4542.61 2057.48 1757.72 1835.00 2147.01 2699.13 2518.91 2948.52 6421.76 3861.33 3937.14 4789.84 3866.08 4131.92 5192.67 4555.54 2505.20 1669.36 1867.63 2247.37 2722.78 2431.00 3051.80 6277.15 3796.24 4074.99 4692.03 3880.31 4168.94 5129.58 4506.19 2000.00 1822.65 1817.29 2104.88 2758.42 2515.00 2968.52 5869.50 3847.49 4257.66 4481.90 3917.88 4181.30 5234.40 4341.27
Mar
2200.00 1551.23 1788.88 2174.11 2780.22 2534.00 2961.86 6343.89 3579.12 4275.38 4509.07 3825.36 4187.18 5294.51 4324.00 2100.00 1798.00 1757.11 2255.80 2777.43 2653.49 3027.64 6131.59 3586.75 4659.19 4745.28 3734.93 4207.82 5262.00 4235.99 2150.00 1575.00 1847.20 2277.20 2752.75 2539.14 3026.17 6015.45 3771.71 4437.37 4724.39 3772.62 4137.07 5317.49 4326.49 1900.00 1547.79 1859.21 2297.42 2778.24 2517.71 3081.59 6222.72 3852.94 4299.61 4538.75 3888.63 4132.34 5306.88 4323.87
Apr
2025.00 1551.34 1738.32 2380.67 2366.00 2562.46 3184.60 6314.89 3839.63 3900.00 4550.79 4027.55 4201.04 5526.21 4325.18 1850.00 1564.83 1968.48 2370.71 2322.17 2588.96 3165.72 6073.39 3769.95 3900.00 4583.57 4143.98 4570.57 5501.21 4324.53 1937.50 1543.61 1976.12 2280.35 2331.11 2586.08 3229.09 4898.41 3742.97 3900.00 4775.53 4105.12 4640.08 5407.61 4324.85 1893.75 1599.68 1838.23 2192.40 2345.00 2549.23 3052.34 4934.29 3787.00 3778.57 4680.91 4187.89 4660.86 5371.15 4324.69
May
2000.00 1453.90 1835.02 2134.03 2371.67 2664.61 3159.31 4228.18 3813.82 3787.50 4575.20 4233.25 4708.61 5303.60 4324.77 1900.00 1443.00 1834.29 2197.37 2448.57 2835.59 3134.40 3606.04 3818.52 3845.83 4816.40 4206.29 4860.75 5286.00 4324.73 1950.00 1657.87 1821.86 2197.86 2599.54 2858.44 3060.15 3860.53 3774.15 3785.71 4841.17 4154.32 4760.14 5082.94 4324.75 2000.00 1657.87 1944.35 2245.49 2642.10 2803.61 3259.78 3964.00 3777.54 3800.00 4586.88 4086.00 4854.09 4930.89 4324.74
Jun
2000.00 1554.47 1807.50 2221.81 2542.29 2756.50 3321.31 4077.70 3779.24 3756.82 4513.47 4296.99 5116.16 5313.71 4324.75 2000.00 1489.72 1846.83 2290.85 2592.20 2712.36 3327.06 3790.57 3835.18 4651.17 4550.92 4356.03 5649.83 5191.64 4324.74 2000.00 1457.38 1811.95 2209.11 2980.25 2818.82 3348.74 3261.65 3884.72 4422.87 4690.67 4257.72 5743.24 5179.23 4324.74 2000.00 1517.09 1833.77 2228.91 3117.50 2827.13 3284.86 3195.26 3975.79 4035.09 4891.96 4088.00 5605.58 5155.78 4324.74
Jul
2159.57 1455.02 1819.10 2266.10 3289.00 2878.16 3453.40 3423.25 4106.02 4483.35 5295.53 4443.63 5756.32 5182.75 4324.74 2156.75 1455.02 1910.25 2255.81 3225.33 2845.50 3490.60 3984.84 4405.73 4892.94 5177.91 4290.07 6078.30 5197.40 4324.74 2340.56 1720.23 1991.97 2330.36 3256.67 2855.46 3459.81 3374.22 4558.64 5089.48 4998.25 4387.88 6529.95 5141.34 4324.74 2000.00 1466.00 2161.23 2377.28 3256.25 2912.64 3445.67 3268.25 4634.50 5369.74 4794.63 4312.85 6125.89 5146.51 4324.74
Aug
2172.27 1478.50 2206.26 2441.41 3285.50 2895.98 3478.96 3562.54 4482.45 4914.68 4698.27 4139.79 5415.25 5081.36 4324.74 2180.56 1546.26 2241.16 2388.32 3201.71 2834.65 3652.59 3589.94 4692.75 5171.91 4663.47 3991.07 6201.76 5177.14 5841.00 2295.62 1635.83 2383.43 2376.04 3268.91 2863.22 3625.72 3765.47 4780.36 5373.31 4896.94 4115.98 5936.50 5260.80 6162.01 2338.83 1762.51 2417.34 2218.40 3382.35 2837.41 3727.17 4065.41 4619.09 5376.13 4820.08 4246.69 5708.62 4911.12 5515.00
Sep
2420.45 1737.93 2322.76 2156.69 3325.63 2750.00 3923.37 3869.22 4463.03 5503.84 4861.70 4313.96 5329.60 4726.95 5838.51 2317.50 1735.16 2159.85 2012.51 3353.99 2653.79 4251.62 3866.13 4292.29 5143.98 4711.60 4412.73 5660.67 4549.10 5676.75 2272.83 1658.25 2195.12 2185.38 3200.00 2669.88 4001.81 4247.45 4083.49 5284.93 4368.84 4387.73 5971.89 4177.33 5757.63 2077.52 1572.20 2125.81 2403.23 3334.63 2657.95 4133.70 4080.64 4034.53 5496.70 4198.42 4287.05 4701.81 4071.54 5717.19
Oct
1945.65 1580.64 2324.93 2205.31 3155.00 2813.86 4168.46 4146.88 4003.66 5543.97 4195.78 4097.86 5320.66 4258.95 5737.41 1844.32 1666.23 2091.09 1958.01 3200.00 2714.17 4231.91 4416.30 4146.81 5398.23 4159.44 4042.33 5166.71 4334.03 5702.53 1818.66 1812.93 2011.00 2125.00 2830.02 2660.09 4159.78 4303.93 4238.15 4821.76 4140.80 3961.73 5327.78 4288.94 5939.00 1832.87 1739.24 2115.64 1943.42 2700.00 2784.45 4184.64 4333.56 4144.33 4517.99 4222.04 4003.73 4831.71 4282.66 5849.92
Nov
1923.00 1715.32 2115.38 1743.18 2800.00 2842.50 4126.61 4285.18 4130.84 4410.01 4041.49 4081.95 4940.42 4171.63 5758.00 2138.00 1669.15 2052.26 1814.44 2970.00 2855.03 4179.07 4144.39 3874.81 4402.93 3898.94 4054.99 4799.66 4149.10 5805.37 1900.57 1720.01 1988.27 1754.81 2880.00 2914.86 4096.43 4004.13 3793.66 4237.45 4033.37 4057.63 4829.65 4087.34 5634.79 1866.05 1922.31 2005.91 1916.15 2590.63 3089.55 4017.41 3820.20 3792.25 4071.19 4013.41 4095.23 4823.62 4190.95 5552.67
Dec
1801.46 2070.79 1989.85 1925.01 2970.00 3155.19 3962.61 3790.98 3766.12 4008.30 4020.60 4085.69 4936.52 4396.30 5533.35 1817.21 1909.23 1999.81 1947.95 2698.00 3227.39 3781.96 3858.04 3809.09 4156.44 4023.73 4130.41 4887.71 4675.35 5584.74 1918.31 1914.41 1922.92 1979.95 2561.75 3183.46 4170.85 3826.93 3870.76 4130.64 4002.40 4094.10 4706.33 4866.30 5555.94 1665.60 1896.46 1956.41 2040.52 2477.74 3105.34 4195.93 3835.77 3859.22 4214.84 3983.36 4109.47 4857.64 4846.55 5470.00
Price of Cotton in Gujarat market 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2601.26 1918.99 2179.27 2136.74 2645.38 2757.28 3298.40 4438.44 4275.14 4144.50 4888.24 4070.30 4645.99 5257.78 5083.48 2656.74 1932.28 2196.34 2141.59 2692.05 2740.05 3287.81 4572.36 4370.58 4128.75 5032.40 4007.25 4576.01 5412.78 5046.55 2657.74 1883.85 2146.69 2157.05 2761.11 2719.89 3272.45 4894.06 4372.69 4132.80 5278.39 3894.64 4640.29 440.95 5025.20 2510.70 1873.90 2078.54 2161.02 2745.83 2690.21 3198.77 5419.97 4310.95 4114.48 5247.58 3906.75 4551.40 5565.96 4866.58
Feb
2722.07 1882.03 2059.02 2239.10 2738.21 2649.74 3164.09 5844.84 4324.52 4103.55 5066.85 3925.82 4543.73 5510.85 4814.68 2639.60 1870.73 2055.69 2336.16 2716.97 2680.53 3271.18 6596.65 4279.35 4136.05 5076.73 3908.21 4409.91 5494.18 4840.11 2716.15 1853.17 2052.34 2404.28 2713.72 2722.99 3298.37 6283.14 4206.24 4387.95 5115.63 3953.08 4401.14 5429.77 4783.36 2480.21 1886.12 2014.90 2365.66 2843.65 2699.04 3279.25 5966.46 4055.40 4691.30 4850.55 3899.52 4400.72 5505.92 4778.79
Mar
2618.91 1852.26 1965.61 2391.48 2910.50 2656.93 3281.07 6273.06 4005.61 4747.91 4843.76 3748.22 4352.82 5397.06 4754.81 2507.53 1849.79 1928.15 2409.03 2838.09 2617.44 3298.64 6080.46 3754.31 4789.30 4753.71 3899.59 4371.15 5416.11 4850.33 2385.91 1795.42 1957.03 2394.43 2732.60 2672.79 3312.07 6227.36 3913.39 4796.91 4698.53 3947.96 4327.84 5305.98 4656.49 2077.34 1773.23 1978.37 2364.68 2684.10 2659.83 3216.28 6241.63 4069.02 4672.94 4959.61 4008.52 4188.72 5502.24 4683.76
Apr
2179.93 1754.21 2008.74 2456.92 2717.51 2806.67 3342.46 6226.63 4096.69 4747.20 4418.94 4297.27 4504.44 5441.37 4460.44 2221.66 1741.60 1959.12 2464.53 2736.90 2859.34 3255.79 5810.85 3984.18 4809.63 4447.67 4351.61 4628.97 5333.29 4491.80 2197.77 1808.75 1976.78 2412.86 2718.01 2848.85 3244.81 4938.90 3885.48 4664.43 4450.91 4349.08 4624.70 5236.58 4394.82 2153.89 1836.11 1992.55 2264.73 2677.09 2798.36 3115.77 4673.35 3802.89 4606.60 4577.48 4334.00 4658.56 5172.14 4454.24
May
2151.38 1811.19 1995.15 2290.69 2657.50 2771.13 3019.96 3881.23 3948.79 4442.70 4935.74 4508.26 4630.18 5041.65 4666.79 2359.53 1753.42 2011.49 2338.53 2677.88 2870.55 3001.89 3553.88 3877.74 4395.96 4816.49 4594.86 4619.97 5014.97 4545.27 2255.46 1760.93 2015.44 2376.17 2754.91 2908.95 3246.23 3455.46 3744.77 4600.89 4597.14 4550.79 4700.30 5021.80 4526.31 2000.00 1871.28 1967.66 2364.05 2824.95 2817.08 3334.71 4104.40 3634.78 4611.99 4393.29 4378.41 4888.93 5018.45 4776.99
Jun
2000.00 1830.63 1922.67 2357.75 2802.11 2820.07 3298.63 3799.35 3618.84 4745.50 4434.62 4540.84 5164.29 4839.14 5078.51 2392.77 1916.55 2072.27 2387.63 2963.45 2857.51 3245.34 3837.72 3780.98 4883.87 4391.76 4490.77 5290.93 4914.04 5523.96 2513.33 2045.40 2111.87 2342.19 2937.58 2820.44 3315.86 3645.61 3689.35 5057.93 4429.50 4416.39 5495.13 5000.45 5493.12 2539.17 3346.57 2026.11 2366.42 3120.43 2881.53 3414.10 798.55 3951.67 5265.94 4684.54 4462.35 5459.26 4915.04 5490.38
Jul
2527.60 1983.92 2068.16 2266.10 2967.23 3016.74 3307.48 3737.54 4384.29 5313.36 4558.72 4537.86 5736.58 4407.64 5482.50 2050.28 3663.77 2079.96 2255.81 3102.52 3083.54 3416.17 3239.62 4332.06 5472.59 4537.45 4567.19 6154.74 4661.34 5624.72 2644.68 2030.13 2123.25 2330.36 2958.10 2917.87 3492.61 3237.97 4680.56 5188.48 4562.43 4473.26 6539.12 4985.51 5592.33 2716.97 2004.83 2207.18 2637.87 3194.36 2949.06 3541.64 3213.97 4691.28 5107.95 4513.89 4448.36 5916.79 4958.96 5676.91
Aug
2212.50 1978.48 2282.17 2618.37 2788.29 2803.41 3146.29 3624.45 4406.59 5134.67 4469.44 4451.34 5702.82 4981.50 5659.00 2059.63 1751.71 2207.70 2494.73 1813.85 2810.39 3397.26 3869.53 4345.78 5520.72 4572.48 4398.35 6325.10 4971.00 5815.93 1915.19 1750.14 2129.13 2488.33 2773.95 2753.86 2728.07 4021.36 4755.66 5541.88 4763.68 4576.85 6214.39 4969.90 5893.81 2042.77 1828.16 2171.99 2409.48 2361.07 2639.10 3799.27 4032.11 4520.59 5633.72 4578.48 4615.93 5835.39 4965.29 5630.02
Sep
2242.48 1968.57 1999.29 2235.31 3115.15 2500.53 3694.12 4005.01 4046.43 5782.53 4455.98 4691.19 5653.02 4927.93 5598.21 2359.76 1960.76 2019.78 2351.61 2907.52 2348.30 3931.80 4193.66 3633.85 5605.55 4424.36 4662.78 5394.18 4838.46 5620.75 2243.83 1840.23 1927.45 2360.14 2899.88 2503.60 3723.28 4215.99 3705.88 5396.21 4242.81 4181.76 5663.81 4588.04 5409.21 2218.64 1917.52 1883.58 2419.41 2869.59 2649.70 3645.74 4035.24 3712.81 5279.40 3982.02 3999.82 5288.96 4206.92 5486.70
Oct
2041.00 1813.70 2109.74 2340.80 2543.93 2710.79 3728.74 4254.48 3785.55 4944.54 3914.49 4030.63 5204.64 4495.20 5476.49 2012.60 1898.00 2112.83 2356.26 2604.69 2663.97 4197.23 4340.46 4007.76 4489.91 3955.77 4319.46 4959.26 4705.08 5644.23 2048.64 2070.47 2114.66 2438.73 2730.26 2901.27 4239.31 4351.25 4169.86 4659.06 3953.88 4303.70 4924.53 4426.98 5731.63 2149.78 2022.85 2226.28 2495.22 2700.00 2918.18 4404.28 4336.90 4288.34 4623.75 3859.16 4260.21 4718.65 4501.81 5668.20
Nov
2183.10 1968.35 2225.77 2452.58 2800.00 2985.60 4196.81 4533.53 4207.27 4704.25 3976.09 4197.04 4792.24 4446.07 5637.50 2184.42 1953.40 2196.04 2437.81 2970.00 3009.07 4461.26 4329.46 4251.05 4692.99 3938.59 4083.42 4863.01 4457.48 5517.61 2147.56 1934.31 2196.86 2440.81 2721.83 3113.61 4301.44 4298.38 4200.16 4645.41 3966.49 4194.89 4980.74 4506.81 5457.21 2048.89 1950.11 2164.59 2419.12 2666.45 3316.69 4286.56 4094.01 4130.97 4627.19 3960.06 4232.41 5015.18 4497.67 5362.27
Dec
1932.47 1966.23 2155.35 2397.55 2748.08 3347.67 4307.95 4092.37 4112.00 4622.16 3961.89 4291.88 4997.79 4596.61 5279.74 1949.94 1962.08 2144.53 2425.55 2734.68 3312.34 4233.94 4139.28 4179.80 4663.70 4069.73 4349.20 4972.34 4776.28 5367.42 1946.07 1963.40 2138.83 2464.83 2736.62 3314.56 4383.67 4149.55 4214.86 4731.81 4096.35 4571.02 4971.26 4976.65 5295.99 1945.34 2008.41 2154.24 2511.38 2746.79 3315.44 4389.00 4164.34 4154.71 4781.93 4117.32 4668.21 5042.34 5063.62 5149.42
Price of Cotton in Haryana market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2248.74 1675.00 1857.85 1868.52 2525.04 2712.15 3250.20 4639.71 4402.09 4206.59 5305.09 4213.22 4739.08 5184.22 5337.48 2270.77 1693.34 1862.10 1885.42 2582.76 2800.03 3259.76 4786.26 4590.24 4199.39 5382.56 4103.90 4563.10 5331.30 5312.47 2540.58 1657.89 1877.83 1921.18 2598.86 2860.96 3212.68 4960.75 4628.33 4145.22 5414.97 3999.30 4689.25 5430.45 5408.69 2452.27 1628.08 1855.34 1932.34 2571.01 2882.65 3160.43 5219.26 4544.61 4115.70 5342.94 3992.63 4613.17 5563.26 5258.07
Feb
2385.70 1682.47 1716.24 2036.06 2650.55 2824.22 3156.45 5540.90 4408.63 4142.12 5414.90 3966.93 4565.99 5589.07 5160.28 2673.49 1754.87 1852.51 2071.84 2508.95 2729.92 3144.62 6311.06 4381.33 4237.92 5402.86 3872.47 4493.52 5685.42 5129.92 2594.14 1708.92 1857.30 2169.49 2555.28 2685.85 3131.13 6489.26 4234.89 4419.24 5377.79 3997.09 4596.31 5628.13 5210.77 2481.56 1752.51 1750.85 2197.64 2655.96 2719.94 3204.47 6175.91 4012.24 4685.79 5361.23 4021.51 4577.47 5835.03 5057.63
Mar
2544.45 1724.45 1833.38 2262.20 2926.73 2748.67 3121.42 6526.78 4086.93 4796.51 5351.44 3995.41 4551.59 5831.44 5016.70 2538.89 1764.56 1757.38 2317.97 2681.54 2893.49 3070.54 6494.92 4139.22 4777.15 5300.59 4027.78 4595.81 5764.04 4990.23 2541.67 1663.33 1789.03 2235.54 2767.13 2900.52 3164.10 6441.70 3986.36 4735.10 5270.86 4117.93 4518.10 5815.55 4915.18 2540.28 1682.57 1811.61 2306.08 2664.01 3086.43 3191.30 6662.85 3940.14 4675.85 5283.76 4312.64 4553.72 5724.49 4869.73
Apr
2540.98 1712.01 1949.62 2309.34 2695.16 2750.32 3193.36 6790.23 4015.15 4660.83 5358.92 4347.77 4609.92 5704.03 4931.55 2540.63 1735.14 1956.15 2278.58 2725.73 2724.39 3105.37 6726.54 3913.60 4642.46 5355.46 4496.03 4885.17 5773.81 4790.96 2540.80 1676.00 1942.80 2227.82 2798.81 2637.26 3149.37 6758.39 3808.18 4513.78 5419.69 4389.45 4885.17 5428.21 4881.47 2540.71 1700.00 2012.36 2133.45 2792.32 2680.83 3127.37 6742.46 2705.16 4492.64 5305.59 4521.06 4923.89 5796.64 4811.21
May
2540.76 1750.00 1952.78 2109.94 2795.57 2659.04 3138.37 6750.42 4197.09 4659.12 5565.55 4878.96 4981.59 5758.70 5051.45 2540.74 1725.00 1968.68 2062.63 2793.94 2669.93 3132.87 6746.44 4203.62 4643.72 5746.61 4621.78 4882.07 5728.25 5002.38 2540.75 1733.52 1865.13 2100.00 2794.75 2664.49 3135.62 6748.43 4093.88 4647.66 5523.51 4595.92 4837.97 5728.42 5022.45 2540.74 1590.00 1758.42 1901.00 2794.35 2667.21 3134.24 6747.44 3968.67 4667.87 5553.06 4558.34 4896.42 5605.64 5131.98
Jun
2540.74 1661.76 1811.78 2000.50 2794.55 2665.85 3134.93 6747.94 3877.69 4743.27 5471.67 4514.94 5129.11 5375.00 5398.22 2540.74 1625.88 1785.10 1950.75 2794.45 2666.53 3134.59 5552.43 3946.27 4958.28 5503.53 4346.90 5012.77 5446.94 5700.84 2540.74 1643.82 1798.44 1975.63 2794.50 2666.19 3134.76 4750.00 3907.06 4983.71 5434.01 4361.65 5070.94 5467.68 5627.03 2540.74 1634.85 1791.77 1963.19 2794.47 2666.36 3134.67 4671.00 4024.40 5121.52 5738.61 4320.00 5041.85 5269.80 5712.19
Jul
2540.74 1639.34 1795.10 1969.41 2794.49 2666.27 3134.71 4710.50 4083.63 5184.57 5699.67 4411.00 5056.39 5368.74 5641.89 2540.74 1637.09 1793.43 1966.30 2794.48 2666.32 3134.69 4690.75 4239.04 5260.42 5530.51 4365.50 5049.12 5319.27 5756.29 2540.74 1638.21 1794.27 1967.85 2794.48 2666.30 3134.70 4700.63 4575.82 5251.51 5615.09 4388.25 5052.76 5344.01 5703.13 2540.74 1637.65 1793.85 1967.07 2794.48 2666.31 3134.70 4695.69 4586.92 4974.78 5572.80 4376.88 5050.94 5331.64 5683.82
Aug
2540.74 1637.93 1794.06 1967.46 2794.48 2666.30 3134.70 4698.16 4474.00 5100.00 5593.95 4382.56 5051.85 5337.82 5680.65 2540.74 1637.79 1793.96 1967.27 2794.48 2666.30 3134.70 4696.92 4514.84 5037.39 5583.37 4379.72 5051.40 5334.73 5527.78 2540.74 1637.86 1794.01 1967.37 2794.48 2666.30 3134.70 4697.54 4612.62 5068.70 5588.66 4381.14 5051.62 5336.28 5356.27 2540.74 1676.00 2076.13 2106.59 2903.49 2666.30 3134.70 4697.23 4721.77 5300.00 4500.00 4380.43 5051.51 4350.16 5463.04
Sep
2540.74 1551.00 2007.89 2187.82 2932.72 2666.30 3841.08 4101.00 4395.73 4956.36 4495.36 4169.89 4797.33 4245.74 5498.55 2540.74 1791.10 1978.15 2125.13 2917.54 2599.69 3682.31 3428.70 4125.94 4953.87 4583.83 4221.11 4842.49 4131.60 5406.91 2540.74 1806.19 2004.90 2128.61 2992.80 2651.84 3712.38 3603.37 4019.07 5034.30 4288.33 4302.45 5203.18 4289.60 5265.79 2540.74 1758.67 1970.11 2064.54 2943.82 2749.30 3682.31 3839.36 3766.24 5143.91 4129.53 4337.49 5007.59 4383.39 5264.87
Oct
2540.74 1396.00 1970.67 2001.64 2913.93 2808.02 3820.27 4308.66 3987.17 5082.88 4493.19 4374.50 4961.37 4466.35 5265.33 2540.74 1794.63 1935.84 2054.38 2891.46 2831.18 4100.29 4476.37 4239.56 5114.27 4522.37 4360.82 4728.77 4721.77 5265.10 2540.74 1791.33 1944.55 2151.71 2884.62 2896.80 4088.80 4409.96 4554.98 5083.87 4313.87 4537.15 4674.43 4756.12 5265.22 2540.74 1787.92 1946.29 2201.35 2865.27 3025.96 4064.38 4406.00 4491.57 4977.57 4233.17 4365.04 4847.49 4870.22 5265.16
Nov
2540.74 1813.78 1954.63 2260.47 2987.04 3079.15 3893.74 4316.41 4443.23 5207.48 4155.01 4439.60 4776.97 4912.20 5265.19 2540.74 1870.11 1931.74 2250.05 2940.67 3084.35 4291.29 4149.54 4474.55 5237.36 4033.72 4321.55 4853.36 4767.54 5265.17 2540.74 1868.90 1986.70 2312.54 2879.61 3160.54 4549.95 4290.66 4433.87 5145.84 4026.37 4237.35 4994.48 4778.34 5265.18 1715.03 1909.56 1979.53 2388.52 2785.81 3271.72 4426.02 4254.20 4251.55 5023.47 4013.11 4287.12 4834.25 4824.98 5265.18
Dec
1655.99 1892.17 1989.28 2351.99 2775.34 3274.83 4516.23 4135.66 4275.03 4961.93 3925.57 4290.81 4810.44 4826.81 5265.18 1676.75 1907.54 1934.71 2310.66 2713.03 3251.15 4376.46 3982.35 4288.11 4985.15 3915.37 4296.84 4709.28 4888.65 5265.18 1664.33 1879.37 1896.43 2373.20 2720.13 3198.40 4603.73 4032.53 4230.73 4933.91 3984.83 4441.82 4721.56 5061.89 5265.18 1673.28 1853.87 1897.01 2415.40 2704.30 3254.60 4619.09 4195.33 4193.14 4900.14 4122.75 4689.83 4968.42 5348.88 5265.18
Price of Cotton in Karnataka market 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2731.63 1987.59 3036.65 2180.61 2509.73 2536.81 3400.47 4720.29 4164.01 4146.75 5278.93 4297.79 4916.81 5414.54 5365.47 2754.02 2045.25 2709.95 2102.69 2581.48 2607.91 3273.62 4798.81 4217.62 4003.34 5455.12 4251.00 5022.25 5483.59 5301.19 2668.17 1957.77 2478.37 2233.67 2595.41 2644.95 3389.08 5008.72 4294.07 4124.64 5560.08 4049.74 4762.31 5575.06 5283.27 2630.69 1898.79 2237.74 2301.20 2534.12 2643.91 3316.44 5438.41 4205.45 4081.85 5415.05 3961.89 4832.73 5538.74 4992.11
Feb
2476.56 1808.40 2474.56 2083.37 2521.01 2588.47 3197.27 5708.76 4069.27 4060.85 5335.20 3969.70 4906.74 5707.49 4930.52 2501.15 1697.24 1977.79 2331.19 2470.06 2569.91 3205.32 6173.72 4071.44 4046.96 5324.36 4133.18 4609.16 5710.12 4906.82 2486.84 1670.73 1783.05 2317.81 2416.78 2521.21 3137.63 6391.92 3952.34 4147.33 5262.19 4039.48 4386.50 5623.92 4737.95 2339.59 2981.00 1744.74 2200.72 2478.94 2453.61 2926.12 5968.86 3825.16 4340.93 5243.33 3951.66 7080.71 5551.92 4493.41
Mar
2359.61 1664.86 1695.45 2219.60 2585.48 2570.33 3224.91 6322.87 3597.49 4335.55 5056.68 3992.68 4400.48 5606.87 4336.64 2275.17 1713.01 1659.72 2272.00 2477.59 2521.21 3117.47 6093.87 3649.23 4514.15 4979.61 3905.61 4356.84 5501.05 4354.88 2286.39 1680.66 1673.09 2274.17 2589.45 2453.61 3107.02 5867.82 3729.34 4598.59 4940.94 3896.64 4250.31 5577.10 3998.11 2325.65 1670.43 1673.88 2289.54 2333.03 2570.33 3202.97 5857.63 3697.91 4462.98 4884.49 3917.15 4311.72 5533.92 4200.73
Apr
2338.44 1654.54 1700.33 2266.97 2462.41 2469.45 3269.06 6005.72 3996.07 4599.32 4759.19 3981.42 4226.01 5440.07 4029.47 2279.89 1638.44 1739.63 2286.26 2432.87 2518.84 3189.42 5993.69 3778.06 4514.29 4643.60 4199.48 4512.71 5541.73 4048.31 2214.76 1635.65 1752.98 2287.77 2438.27 2512.28 3060.28 5113.22 3635.06 4344.51 4794.25 4211.59 4636.15 5354.92 4409.42 2160.47 1649.68 1820.47 2111.17 2321.32 2836.07 2921.94 4436.66 3475.15 4284.56 4947.45 4668.06 4593.63 5195.37 4216.70
May
2117.31 1651.33 1734.74 2141.02 2393.57 2871.49 2994.17 4106.33 3690.52 4128.58 4822.00 4291.68 4880.63 5189.50 4001.61 2138.04 1649.62 1757.02 2126.94 2375.98 2970.38 3046.98 3463.99 3622.62 4310.96 4955.44 4343.14 4552.78 4771.76 4315.30 2170.58 1623.15 1841.39 2130.76 2480.82 2667.65 3214.59 4685.47 3658.04 4328.79 4930.58 4216.42 4899.75 5077.38 4403.36 2292.79 3399.29 1704.90 2161.19 2537.64 2654.28 3125.02 3662.88 3695.78 4517.84 4840.96 4358.34 4884.56 5243.30 4735.91
Jun
2267.24 1636.00 1683.99 2203.74 2612.61 2602.78 3270.82 3398.75 3726.22 4595.58 4969.20 4413.67 5054.62 5143.94 4698.69 2207.52 1575.66 1753.32 2237.22 2626.22 2669.47 3136.55 3116.48 3868.95 4678.48 4971.73 4535.31 5186.40 5772.00 4857.33 2266.81 1625.25 1668.62 2079.26 2743.84 2695.03 3375.80 3055.52 3928.55 4596.89 4893.90 4364.61 5304.22 5415.59 4928.64 2377.93 1764.55 1863.21 2264.97 2847.42 2412.38 3098.21 3488.27 4030.83 5055.21 5014.89 4450.43 4825.21 5128.74 5215.87
Jul
2357.51 1702.62 1739.17 2213.20 2900.25 2665.13 3419.69 3301.83 4284.57 4812.47 5362.56 4363.08 5878.12 5377.60 5338.77 2455.81 1757.85 1786.38 2318.87 2496.00 2649.27 3438.59 3387.96 4506.91 4834.62 5345.75 4198.54 5699.18 5164.48 5410.73 2443.51 1874.24 2001.12 2398.11 2973.81 2738.62 3466.60 3465.02 4668.25 4679.81 5415.90 4456.94 6213.09 5543.75 5277.63 2478.23 2106.98 2117.55 2290.23 3035.94 2836.07 3418.81 3409.45 4534.09 4473.62 5210.14 4456.96 5999.09 5315.16 5627.55
Aug
2417.78 1855.89 2183.42 2533.42 2936.91 2871.49 3537.81 3569.47 4725.35 4720.92 5116.05 4457.34 5896.20 5290.53 5633.21 2425.66 1807.20 2178.91 2414.83 2880.50 2970.38 3252.54 3861.79 4601.38 4546.81 4504.19 4342.92 5890.60 5247.30 5724.65 2294.43 2074.96 2290.55 2376.31 2766.27 2751.40 3401.78 3859.99 4644.88 4858.03 4929.40 4349.41 5866.76 5294.75 5180.58 2264.30 1848.09 2487.35 2370.00 2757.36 2877.38 3231.51 3874.63 4523.01 4969.05 4722.08 5176.24 5394.29 5235.47 5443.71
Sep
2317.95 1957.68 2309.76 2436.44 2799.87 2409.27 3566.69 4027.30 4759.90 5041.95 4807.10 4445.90 5594.26 5179.81 5703.89 2338.05 1892.97 2381.54 2312.90 2714.53 2346.49 3416.16 4107.63 4392.31 5377.50 4708.76 4555.97 5693.21 5196.27 5851.89 2503.95 1885.36 3785.00 2364.45 2421.58 2074.08 3858.07 4176.16 4063.44 4866.07 4318.90 4583.57 5680.24 5079.76 5662.29 2330.62 2024.93 2575.48 2235.06 2746.79 2476.76 3710.44 3993.81 4035.67 4862.57 3999.69 4727.96 5677.30 4995.33 5665.34
Oct
2223.21 2081.00 2236.82 2187.33 2642.79 2471.14 3773.51 4038.99 3930.56 4888.30 4030.77 4625.79 5533.43 4620.74 5812.55 2183.84 1888.18 2336.49 2222.49 2663.47 2792.17 3952.13 4037.76 4061.43 5076.17 4166.09 4449.08 5752.48 4654.10 5714.56 1944.79 2139.20 2433.11 2203.78 2646.31 2621.66 4088.14 3992.51 3993.84 4777.00 4246.38 4437.79 5420.91 4310.68 5852.62 2050.81 2482.84 2330.51 2278.31 2534.92 2702.26 4313.50 3978.55 3993.76 4544.60 4126.36 4445.89 5260.60 4466.46 5832.01
Nov
2358.95 2311.02 2234.16 2728.19 2742.78 2976.05 4106.32 4121.36 4132.23 4428.98 4211.86 4508.44 5304.03 4246.68 5769.54 2497.01 2984.55 2205.10 2379.86 2767.81 3045.20 4337.89 4080.20 4164.03 4674.19 4121.07 4367.22 5279.21 4508.82 5798.61 2504.39 2853.11 2406.47 2307.08 2734.19 3253.01 4347.53 4049.28 4091.14 4631.61 4115.10 4522.01 5621.46 4770.42 5773.38 2242.43 3040.61 2357.74 2387.82 2733.31 3394.61 4146.81 4011.22 4062.24 5115.50 4227.45 4585.48 5350.57 4771.90 5849.71
Dec
2218.42 2983.45 2515.73 2392.11 2720.57 3488.03 4198.74 3942.72 4240.29 5029.39 4302.45 4614.90 5300.23 5008.61 5831.01 2113.43 3123.06 2213.20 2380.06 2799.64 3448.22 4162.61 3998.52 4202.33 5042.30 4276.12 4642.94 5433.29 5250.19 5913.12 2055.45 3657.43 2199.88 2401.59 2608.63 3404.52 4257.44 3949.11 4137.70 5186.36 4284.80 4543.33 5363.93 5292.05 5827.07 2056.00 3390.25 2081.32 2429.82 2704.14 3298.67 4380.56 3972.36 4070.75 4310.50 4221.11 4917.73 5345.34 5277.09 5693.90
Price of Cotton in Madhya Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
3438.35 1762.37 2008.80 1986.27 2449.82 2674.12 3317.81 4483.71 4089.74 4025.29 4755.37 4150.66 4887.28 5329.19 5070.96 5943.31 1724.29 2072.71 1996.17 2493.81 2678.16 3282.30 4719.66 4228.55 4037.28 4821.41 4130.81 4588.68 5407.75 5008.45 2624.02 1668.18 1960.43 2028.35 2548.89 2697.40 3260.19 5111.01 4306.73 4031.85 4837.66 3993.16 4577.99 5465.23 5058.42 2524.89 3986.04 1879.21 2090.25 2552.32 2685.93 3104.65 5498.96 4254.02 4041.40 4892.40 3943.45 4616.00 5500.36 4971.00
Feb
2528.93 2888.90 1834.57 2269.59 2605.09 2676.47 3021.51 5862.29 4331.36 4042.45 4951.96 3924.36 4554.12 5553.94 4942.21 2568.27 1711.60 1885.45 2272.51 2561.12 2657.74 3119.42 6575.39 4165.81 4053.18 4926.76 3968.39 4491.83 5545.65 4967.65 2408.40 2936.26 1815.51 2327.95 2584.28 2682.35 3164.52 6312.89 4082.94 4198.56 4971.03 3944.11 4451.92 5555.25 4913.31 2240.14 2245.92 1723.74 2315.36 2646.30 2615.59 3169.47 5742.52 3802.72 4477.16 4949.60 3898.89 4457.35 5569.92 4794.63
Mar
2204.95 2264.73 1779.80 2343.50 2740.29 2560.38 3165.36 5987.68 3829.87 4547.98 4968.46 3851.61 4451.23 5580.71 4763.49 2048.55 1729.68 1765.73 2352.96 2731.34 2682.35 3178.97 5845.29 3447.04 4564.68 4950.99 3807.11 4441.15 5506.70 4756.34 2021.67 2108.12 1810.65 2339.78 2678.09 2615.59 3162.66 5894.14 3602.44 4655.98 4911.01 3850.18 4435.01 5516.69 4625.17 2058.89 1791.42 1799.11 2359.88 2690.58 2560.38 3164.46 6048.87 3691.26 4604.72 4947.27 3949.98 4292.80 5402.16 4611.69
Apr
2099.54 1743.28 1877.41 2292.16 2620.71 2691.91 3224.91 5978.00 3708.31 4730.58 4886.10 4114.67 4403.91 5497.41 4683.58 2150.51 1759.93 1894.08 2278.26 2616.69 2705.26 3238.58 5641.45 3547.11 4686.08 4788.24 4177.29 4653.67 5379.05 4587.71 2058.58 1707.59 1917.86 2245.18 2609.82 2740.18 3140.57 4713.79 3576.78 4524.70 4650.49 4117.49 4695.34 5136.47 4591.79 2065.49 1670.98 1946.08 2132.33 2571.22 2722.72 3001.26 4271.31 3698.33 4489.07 4574.84 4094.78 4757.74 5033.69 4723.76
May
1950.02 1635.44 1943.30 2072.65 2571.53 2731.45 2911.04 3646.76 3761.59 4357.50 4618.59 4125.20 4638.52 4969.37 4709.37 2024.23 1736.33 1894.16 2127.28 2645.29 2200.00 2960.85 3200.04 3778.95 4254.48 4553.54 4094.76 4637.78 930.06 4718.90 1898.23 1706.99 1930.54 2102.74 2615.07 2540.00 2934.97 3284.28 3782.67 4199.68 4582.60 3963.81 4517.98 4982.01 4632.35 1968.74 1764.54 1946.92 2110.73 2585.16 2370.00 3116.29 3528.47 3606.04 4358.57 4568.64 4007.51 4531.72 4794.78 4687.68
Jun
1946.21 1643.54 1895.33 2286.35 2600.12 2455.00 3133.38 3328.18 3632.26 4067.60 4479.05 4118.02 4635.13 4728.72 4843.32 1877.15 1786.01 1927.00 2276.36 2592.64 2412.50 3116.48 3217.44 3656.80 4006.91 4493.49 4074.23 5084.63 4922.12 4715.97 2104.98 1300.00 1911.17 2362.75 2825.00 2433.75 3028.81 3102.65 3767.72 4213.17 4463.35 3944.12 4389.21 5030.65 4838.44 2223.22 1700.00 1919.08 2319.56 2825.00 2423.13 3000.00 2944.37 3749.70 3881.07 4402.43 3891.75 5636.38 5019.25 4991.76
Jul
2050.00 1500.00 1915.12 2341.15 2440.00 2428.44 3000.00 3278.87 3918.68 3963.64 4263.38 3407.34 4000.00 5017.15 4915.10 2136.61 1600.00 1917.10 2330.35 2632.50 2425.78 3000.00 2818.38 3875.71 3915.79 4332.91 4029.41 4818.19 5151.97 4953.43 2093.31 1550.00 1916.11 2335.75 2693.60 1225.00 3000.00 3032.99 4284.61 4000.00 4737.19 4020.00 1706.67 4667.21 4934.27 2114.96 1575.00 1916.61 3128.91 2690.00 1825.39 3770.00 3363.73 3719.89 3802.96 4535.05 3091.22 3262.43 5082.88 4943.85
Aug
2104.13 1200.00 3292.37 3286.90 2974.68 1525.20 3000.00 3250.31 3500.00 3901.48 4636.12 3555.61 6000.00 4690.69 4939.06 2109.54 1387.50 2604.49 3300.00 3140.99 2200.00 3075.00 3366.48 3609.95 3852.22 4585.58 3323.42 4631.22 4888.00 4941.45 2106.84 1293.75 2948.43 2748.30 3325.95 2766.58 3882.12 3308.40 4545.00 3876.85 4610.85 3439.51 5315.61 4620.00 4940.25 2108.19 1340.63 2776.46 2785.56 3301.73 2560.47 1829.20 3758.82 4942.80 3864.54 5056.00 3381.46 4973.41 4649.33 4940.85
Sep
2179.88 2526.90 2115.89 2703.62 3253.25 2336.53 3121.72 3549.93 1851.48 3608.62 3981.24 3559.72 5159.82 3796.61 4940.55 2152.50 1991.96 2446.17 2280.00 3277.49 2172.70 3161.29 4459.97 4043.47 5347.98 3462.31 3878.08 5024.35 3644.33 4940.70 2189.29 1742.93 1813.82 2199.59 3240.00 2409.36 3411.23 3623.85 3401.41 5169.81 3355.35 3839.69 4836.62 3059.56 4940.63 2117.06 1476.42 1850.93 2208.52 3258.75 2600.90 3533.99 3909.77 3354.89 4771.63 3424.84 3709.02 4851.85 3470.13 4940.67
Oct
1919.92 1676.21 1916.16 1938.42 2776.25 2747.35 3705.22 3965.13 3461.77 4623.64 3753.35 3936.09 4687.19 4013.16 4746.58 1839.14 1712.42 1975.69 1998.96 2403.01 2718.75 4140.04 4356.62 3748.26 4665.65 4051.49 4068.88 4266.77 4277.46 5269.77 1853.97 2000.67 1987.50 2230.82 2620.27 2790.09 4311.52 4450.99 4014.46 4702.18 3977.05 4164.93 4472.39 4211.38 5413.71 1956.96 1896.28 2087.73 2327.20 2663.56 2887.92 4358.44 4414.06 4241.93 4682.35 3995.36 4104.52 4494.54 4330.58 5396.14
Nov
1989.39 1893.39 2093.44 2291.39 2790.87 2986.66 4236.11 4497.15 4160.51 4680.82 3969.08 4064.80 4656.75 4228.31 5420.42 2017.20 2193.39 2050.93 2289.48 2761.77 2946.75 4366.05 4361.08 4103.27 4701.28 3954.39 4033.84 4726.83 4298.43 5490.77 2027.26 2048.88 2043.87 2297.38 2678.07 3039.13 4272.48 4308.93 4095.16 4638.29 3982.24 4042.62 4941.31 4362.87 5517.50 1842.32 2224.86 2020.93 2248.37 2646.19 3155.30 4123.93 4542.60 4007.45 4685.27 3972.51 4138.73 4962.45 4433.13 5493.72
Dec
1794.52 2440.85 2018.19 2285.40 2693.00 3202.06 4192.84 4066.69 4028.44 4683.40 3956.30 4141.37 5004.53 4474.54 5525.61 1832.00 2809.90 2013.97 2265.20 2703.00 3257.38 4058.76 4021.30 4047.12 4592.53 4062.38 4279.96 5024.55 4634.40 5503.68 1752.51 2091.69 2011.94 2327.61 2647.40 3248.18 4243.71 3992.10 4078.90 4641.15 4115.73 4471.40 5038.97 4909.70 5460.99 1763.51 1974.91 2008.73 2388.54 2637.74 3347.44 4350.09 3977.61 4062.34 4655.08 4125.04 4838.15 5137.21 5091.76 5411.73
Price of Cotton in Maharashtra market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2533.78 2060.92 2024.21 1954.98 2443.59 2782.62 3154.74 4417.13 3890.19 3933.2 4804.61 4070.27 4579.99 5313.45 4938.14 2546.11 2056.5 2017.1 1954.31 2542.78 2752.24 3062.85 4740.93 4002.85 3933.32 4976.1 4067.21 4485.83 5316.41 5043.77 2682.9 2076.35 1971.02 1980.52 2573.49 2755.55 3086.97 5180.22 4057.46 3947.65 5021.45 4050 4532.63 5443.16 5029.68
2687.11 2016.37 1899.51 2006.53 2558.71 2744.62 3018.25 6147.89 3984.48 3950.92 5046.89 4135.94 4467.28 5611.44 4899.1
Feb
2656.74 1925.66 1842.7 2032.78 2553.43 2754.63 2984.44 5480.88 3976.18 3940.7 5029.15 4050.32 4429.88 5589.7 4808.24 2655.5 1871.12 1796.41 2057.42 2503.76 2750.15 3138.82 5861.64 3933.85 3987.8 5014.02 4050 4365.76 5608.65 4803.91
2556.43 1767.18 1744.19 2111.06 2513.09 2671.34 3101.71 6147.89 3853.2 4132.1 4976.48 4050.16 4432.93 5511.97 4703.73 2376.85 1662.2 1726.56 2149.05 2611.19 2675.29 3082.74 5480.88 3695.42 4461.32 4972.74 4050.08 4401.16 5614.71 4606.73
Mar
2259.95 1667.63 1718.56 2146.37 2728.5 2695.95 3088.41 5861.64 3637.12 4494.08 4876.95 4300 4424.86 5571.47 4600.57 2114.77 1671.45 1706.02 2173.26 2722.42 2671.34 3081.86 5759 3428.58 4609.25 4992.16 4467.39 4425.54 5513.41 4609.99 2206.71 1531.57 1732.92 2246.36 2664.35 2675.29 3160.96 5726.83 3658.29 4676.42 4942.69 4300 4356.26 5487.02 4544.63 2226.73 1534.9 1692.72 2243.42 2594.21 2695.95 3131.46 5941.56 3631.22 4540.13 4908.06 4145.91 4401.43 5502.82 4524.09
Apr
2205.05 1533.98 1827.43 2250.96 2634.17 2620.27 3160.14 6006.43 3615.42 4722.95 4907.61 4275.75 4535.01 5471.19 4544.65 2295.43 1732 1841.34 2262.88 2484.65 2713.59 3170.95 5593.84 3574.78 4670.17 4779.58 4344.76 4706.56 5369.62 4591.89 2124.66 1632.99 1903.35 2288.34 2524.1 2727.68 3023.83 4496.46 3553.43 4413.47 4794.82 4340.78 4677.61 5351.91 4580.06 2229.06 1875 1897.58 2119.64 2493.35 2674.95 2984.19 4238.35 3582.62 4233.09 4870.49 4288.17 4631.61 5225.14 4511.82
May
2223.28 2200 1880.77 1955.6 2562.43 2660.45 2723.82 3494.29 3788.64 4241.39 4861.34 4373.01 4762.86 5104.15 4483.11 2176.37 1196.91 1821.29 2171.01 2605.13 2635.41 2749.56 3172.09 3757.26 4205.83 4805.57 4344.86 4717.68 4956.74 4545.93 2177.43 1698.455 1531.18 2000 2213.12 2647.93 2711.12 2869.81 3578.03 4257.67 4720.91 4290.05 4779.96 4997.63 4541.99 2200.38 1146.65 1783.66 2025.44 2409.125 2451 2719.49 3261.19 3490.54 4343.38 4634.33 4352.11 4920.54 5030.26 4690.63
Jun
2268.11 1422.553 1232.16 1450 2311.123 2350 2930.64 2999.06 3487.29 4439.61 4615.31 4350 5220.08 5176.16 4847.54 2220.84 1241.38 2300 1737.72 2360.124 2400.5 2968.9 3034.79 3547.84 4831.45 4662.55 4351.055 5352.54 5226.05 5210.76 2159.65 1331.966 1766.08 1593.86 2335.623 2375.25 3100.06 2986.44 3631.42 4441.67 4604.4 4350.528 4939.73 5165.45 5178.96 2220.62 1286.673 2348.45 1665.79 2347.873 2320 3034.48 2987.42 3714.93 4267.89 4644.44 4350.791 4502.5 5146.59 5157.98
Jul
2328.03 1309.32 2200 1629.825 2341.748 2350 3431.82 2906.22 3959.46 3942.63 4868.38 4350.659 4460.64 5130.78 5168.47 2141.67 1297.996 2274.225 1647.808 1150 2700 3356.41 2873.81 3611.46 2589.42 4767.17 4100 4481.57 5154.11 5163.225 1912.9 1303.658 2237.113 2513.61 1745.874 2750 3393.29 2516.05 3510.07 3266.03 4715.8 4225.33 4471.11 5158.54 5165.848
1904.76 1300.827 2800 1630 1447.937 2674.95 3377.01 4150 3560.765 2750 4741.485 4162.665 4476.338 5131.34 5164.536
Aug
2447.83 1302.243 2518.556 2071.805 1596.906 2660.45 3205.77 3434.88 5400 3008.01 4728.643 3407.375 4700 4988.6 5165.192 2620.59 1301.535 2659.278 1850.903 2100 2635.41 3442.08 4047.45 5400 2879.01 4735.064 3785.02 4588.17 4942.86 5164.864 2111.29 1301.889 2588.917 1961.354 2455.6 2721.72 2880.09 3998.77 4979.03 2943.51 4731.853 3596.197 4644.084 4950 5165.028
1800 1301.712 2624.098 1906.128 2277.8 2631.74 3526.9 3748 2471.09 2911.26 4733.458 3690.609 4616.13 4946.43 5164.946
Sep
1955.645 2200 2887 1511 2873.86 2491.76 3308.04 2801.9 5232.14 2927.38 4732.656 3643.403 4630.11 4550 5164.987 1877.823 2200 2755.549 1708.564 2932.61 2295.95 3375.37 4230.6 4950 2919.32 4733.057 3667.006 4623.116 5500 5164.967 2318.35 2200 3050 1912 2383.33 2123.42 3442.28 4956.43 3698.68 2923.35 4732.856 3655.204 4626.61 4800 5164.977 1941.65 1795 2055.71 1810.282 2769.97 2650.27 3446.01 4731.74 4250 2921.34 4732.957 4500 4624.86 4457.29 5164.972
Oct
1238 1726.92 1991.21 1986.47 2707.47 2653.16 3561.67 3550.19 3071.95 2922.34 4200 4077.602 5000 4278.56 4968.7 1640.53 1753.54 1945.34 2102.4 2335.55 2705.37 4116.96 4274.31 3503.46 4550 3650.96 4206.43 4620.75 4136.41 5391.8 2241.39 1894.98 2000.6 2186.19 2652.74 2661.29 4259.82 4258.56 3699.36 4415.54 3668.04 4274.14 4632.39 4021.89 5690.85 1802.38 1820.29 2014.1 2277.31 2713.73 2869.91 4104.74 4185.53 4102.46 4396.08 3850 4275.62 4647.87 4208.63 5798.14
Nov
2079.24 1924.25 2060 2279.41 2834.72 2957.4 4107.94 4335.97 3996.93 4421.56 3948.06 4177.7 4623.49 4289.23 5705.29 2062.12 1737.18 2017.52 2294.03 2815.07 2976.3 4295.27 4232.8 4058.97 4541.62 4061.81 4155.38 4820.19 4361.63 5770.31 2063.37 2012.49 1990.13 2283.06 2826.69 2983.38 4209.22 4181.44 4016.25 4564.96 4054.4 4148.71 4966.26 4441.29 5735.05 2012.86 2042.49 1971.23 2273.08 2817.91 3061.22 4084.91 3933.53 3936.65 4536.28 4051.78 4147.45 4593.82 4476.6 5597.63
Dec
2055.03 2082.22 1979.94 2256.12 2822.54 3105.6 4146.15 3950.65 3937.07 4497.86 4050.69 4158.52 4968.19 4551.2 5551.92 2163.91 2072.82 1979.63 2233.38 2812.66 3133.75 4010.45 3876.9 3964.6 4527.3 4075.92 4195 4948.39 4672.9 5584.01 2151.64 2030.34 1967.56 2248.67 2790.23 3105.32 4159.96 3760.36 3967.39 4535.39 4205.77 4360.38 4979.62 4974.1 5535.03 2121.15 2001.89 1958.4 2355.17 2778.33 3147.88 4247.92 3760.6 3939.83 4584.44 4053.11 4546.17 5077 5097.16 5416.39
Price of Cotton in Punjab market 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2332.34 1698.15 1910.51 2092.49 2525.90 2719.20 2957.00 4900.68 4385.54 4109.78 5178.09 4209.25 4719.66 5399.26 5421.57 2403.42 1688.68 1909.14 2100.87 2591.72 2737.14 3272.66 4917.94 4588.02 4123.96 5251.21 4211.65 4656.35 5582.60 5479.46 2462.00 1669.26 1948.24 2079.32 2628.64 2750.50 3184.74 5077.13 4588.48 4137.21 5378.43 4063.88 4741.19 5631.47 5431.99 2492.38 1635.45 2010.61 1958.83 2633.03 2770.98 3124.09 5289.75 4476.41 4153.74 5305.74 4016.81 4661.48 5897.49 5339.22
Feb
2467.70 1619.64 2070.31 2190.84 2634.99 2759.24 3129.13 5883.14 4454.04 4178.63 5296.16 4071.75 4622.66 5839.61 5342.00 2527.29 1640.69 2070.91 2210.00 2631.90 2738.38 3192.06 6658.07 4404.39 4310.25 5425.82 4027.23 4585.76 5932.12 5368.18 2477.90 1644.61 2093.31 2286.44 2665.96 2715.76 3137.80 6670.18 4222.07 4551.34 5390.72 4079.33 4640.22 5789.17 5299.16 2463.03 1652.05 1906.65 2344.19 2721.75 2747.39 3161.25 6350.35 4089.37 4791.16 5318.20 4100.00 4646.18 5970.07 5222.01
Mar
2399.28 1694.47 1935.37 2464.11 2758.33 2820.19 3210.45 6626.71 4193.51 4823.99 5305.93 4078.94 4599.69 5816.33 5195.57 2334.18 1713.44 1943.23 2507.38 2722.42 2715.76 3251.31 6602.92 4232.63 4839.25 5298.20 4087.94 4585.77 5707.95 5193.82 2367.28 1689.58 1969.86 2452.11 2664.35 2747.39 3334.00 6627.55 4153.36 4781.40 5280.57 4209.88 4523.85 5652.85 5138.38 2446.17 1672.73 1971.94 2436.60 2594.21 2820.19 3331.73 6841.41 4146.72 4705.00 5283.37 4345.43 4529.28 5836.99 4793.41
Apr
2546.63 1624.50 2060.21 2440.63 2695.48 2788.28 3375.68 6686.83 4102.99 4751.39 5310.08 4460.06 4687.90 5991.62 4875.53 2596.39 1640.27 1662.50 2387.14 2693.20 2804.24 3353.71 6669.17 3945.48 4678.09 5358.95 4515.73 4788.27 5914.31 4704.17 2590.00 1767.20 1861.36 2413.89 2729.28 2796.26 3364.69 6678.00 3944.93 4714.74 5385.38 4487.90 4750.00 5952.96 4827.71 2593.20 1771.25 1761.93 2400.51 2711.24 2800.25 3359.20 6673.59 4070.29 4553.26 1522.34 4501.81 4975.00 5933.63 4830.68
May
2591.60 1838.60 1811.64 2407.20 2720.26 2798.25 3361.95 6675.79 4360.20 4680.00 5628.87 4494.85 4862.50 5943.30 4829.20 2592.40 1804.93 1786.78 2403.86 2715.75 2799.25 3360.57 6674.69 4231.53 4616.63 5685.81 4498.33 4918.75 5938.47 4270.00 2592.00 1821.76 1799.21 2405.53 2718.01 2798.75 3361.26 6675.24 4165.15 4648.32 5688.04 4496.59 4890.63 5940.88 4549.60 2592.20 1813.34 1793.00 2404.69 2716.88 2799.00 3360.92 6674.96 4083.62 4632.47 5630.32 4497.46 4904.69 5939.67 4409.80
Jun
2592.10 1817.55 1796.11 2405.11 2717.44 2798.88 3361.09 6675.10 4124.39 4640.39 5571.07 4497.03 4897.66 5940.28 4479.70 2592.15 1815.45 1794.55 2404.90 2717.16 2798.94 3361.00 6675.03 4104.00 4636.43 5651.94 4497.25 4901.17 5939.98 4444.75 2592.12 1816.50 1795.33 2405.00 2717.30 2798.91 3361.04 6675.07 4114.19 4638.41 5610.00 4497.14 4899.41 5940.13 4462.22 2592.13 1815.97 1794.94 2404.95 2717.23 2798.92 3361.02 6675.05 4109.10 4637.42 5630.97 4497.19 4900.29 5940.05 4453.49
Jul
2592.13 1816.24 1795.13 2404.98 2717.27 2798.91 3361.03 6675.05 4111.65 4637.92 5620.49 4497.16 4899.85 5940.09 4457.85 2592.13 1816.11 1795.04 2404.97 2717.25 2798.92 3361.03 6675.05 4110.37 4637.67 5625.73 4497.18 4900.07 5940.07 4455.67 2592.13 1816.17 1795.09 2404.97 2717.26 2798.92 3361.03 6675.05 4111.01 4637.79 5623.11 4497.17 4899.96 5940.08 4456.76 2592.13 1816.14 1795.06 2404.97 2717.25 2798.92 3361.03 6675.05 4110.69 4637.73 5624.42 4497.17 4900.02 5940.08 4456.22
Aug
2592.13 1816.16 1795.07 1750.00 2717.25 2687.50 3361.03 6675.05 4110.85 4637.76 5623.76 4497.17 4899.99 5940.08 4456.49 2592.13 1816.15 1840.00 2077.48 2717.25 2672.81 3361.03 6675.05 4110.77 4637.75 5624.09 4497.17 4900.00 5940.08 4456.35 2118.90 1851.00 1857.54 2119.52 2717.25 2720.99 3361.03 3445.00 4110.81 4637.76 4761.00 4497.17 4900.00 4411.00 4456.42 2160.43 1725.40 1950.61 2093.58 2900.00 2633.67 3112.07 5060.03 4235.17 4637.75 4638.87 4025.00 5000.00 4377.74 4456.39
Sep
2201.20 1696.80 1972.06 2256.29 2769.91 2526.19 3634.00 3896.02 3927.95 4851.03 4693.23 4237.75 4755.26 4535.36 4456.40 2251.01 1781.93 1930.32 2200.41 2791.30 2534.49 3743.51 3400.70 3756.93 4879.16 4546.96 4264.22 5418.49 4466.37 4456.40 2107.60 1779.18 1940.41 2175.42 2815.52 2521.51 3689.66 3816.49 3711.03 5003.98 4268.89 4517.04 5461.19 4334.59 4456.40 2007.66 1683.98 1943.29 2106.72 2748.14 2730.38 3703.80 3953.92 3577.02 5068.10 4098.90 4463.82 5171.37 4262.85 4456.40
Oct
1934.42 1693.93 1889.14 1982.05 2775.59 2729.51 3778.19 4239.47 3946.47 5087.21 4181.92 4489.16 4955.10 4612.46 4456.40 1910.38 1709.52 1871.68 2001.84 2803.35 2692.34 4020.67 4461.83 4273.54 4981.52 4152.89 4448.15 4618.64 4863.31 4456.40 1883.88 1752.34 1901.15 2107.05 2765.26 2805.57 4007.43 4265.11 4441.56 4057.23 4044.39 4593.95 4783.16 4735.07 4456.40 1870.70 1733.57 1920.62 2202.06 2813.68 2868.16 4153.35 4375.04 4330.91 5199.06 4213.94 4582.54 4682.88 5121.80 4456.40
Nov
1836.01 1763.55 1936.88 2256.22 2776.08 2980.45 4412.70 4289.83 4329.22 4008.91 4160.90 4449.39 5016.11 4662.07 4456.40 1837.99 1827.13 1948.95 2323.19 2850.88 3010.94 4608.18 4202.40 4359.68 4994.13 4064.50 4295.02 5062.13 4597.26 4456.40 1831.07 1932.96 2051.73 2376.79 2798.19 3072.12 4621.83 4216.54 4205.40 5092.25 4028.23 4282.92 5129.07 4896.98 4456.40 1779.65 1958.42 2081.14 2425.50 2776.83 3299.26 4570.64 4163.30 4144.58 5015.66 3960.41 4322.72 5055.56 4885.14 4456.40
Dec
1718.31 2013.27 2104.31 2404.76 2754.18 3317.78 4628.47 3954.23 4166.21 4973.57 3945.74 4343.42 4937.85 5012.36 4456.40 1717.71 1977.32 2107.45 2377.65 2729.10 3280.01 4556.94 4006.03 4216.44 4775.14 3958.60 4370.76 4894.84 5107.52 4456.40 1670.63 1934.45 2106.47 2416.07 2714.23 3235.11 4724.43 4014.87 4174.23 5007.21 3975.45 4499.48 4833.56 5263.76 4456.40 1674.64 1932.05 1999.13 2467.54 2706.41 3245.69 4812.54 4026.28 4177.98 5019.88 4139.53 4638.11 4864.08 5452.68 4456.40
Price of Cotton in Rajasthan market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2227.44 1652.06 1776.32 1856.24 2435.34 2855.15 3128.88 4714.99 4432.07 4277.73 5351.98 4370.53 4815.44 5015.99 5342.36 2221.28 1661.06 1758.74 1900.28 2411.98 2892.55 3128.88 4892.36 4661.88 4310.39 5481.01 4324.75 4715.93 5314.51 5441.80 2304.52 1650.65 1730.65 1880.91 2472.84 2914.02 3127.03 5087.34 4747.12 4262.91 5512.67 4208.12 4697.20 5406.76 5440.24 2355.17 1576.38 1729.77 1910.96 2456.11 2871.07 3116.70 5283.24 4586.76 4270.76 5417.52 4038.52 4678.62 5546.18 5311.66
Feb
2334.36 1628.53 1723.91 1998.80 2477.70 2814.45 3046.00 5777.25 4428.05 4320.96 5455.72 4141.23 4599.04 5512.09 5243.80 2338.51 1652.78 1732.94 2049.39 2456.74 2735.57 3042.37 6412.86 4359.61 4470.63 5377.92 4195.90 4547.34 5510.43 5170.38 2318.81 1630.27 1750.41 2093.57 2520.69 2649.12 3033.40 6420.00 4212.82 4665.27 5336.66 4260.93 4637.88 5556.29 5069.68 2292.86 1600.06 1709.02 2124.65 2547.84 2614.48 2990.16 6008.55 4020.48 4696.26 5322.09 4211.38 4595.08 5795.89 4962.45
Mar
2362.77 1604.25 1684.25 2162.42 2551.25 2747.03 2983.14 6654.54 4063.39 4807.84 5318.10 4264.08 4625.60 5861.95 5045.10 2033.82 1593.33 1645.22 2211.89 2563.28 2649.12 3036.81 6618.08 4101.32 4777.12 5327.46 4084.79 4633.61 5851.22 5057.49 1787.79 1617.75 1651.46 2182.13 2566.64 2614.48 3018.03 6486.50 4032.91 4699.91 5261.50 4306.69 4584.14 5762.43 4866.12 2150.00 1572.09 1679.07 2249.71 2576.90 2747.03 3068.02 6465.38 3972.78 4747.76 5257.52 4458.65 4272.74 5743.45 4828.00
Apr
1968.90 1620.18 1746.16 2184.31 2476.39 2905.22 3197.35 4914.96 3888.25 4678.69 5382.55 4476.83 4675.41 5703.41 4916.54 2059.45 1556.06 1944.72 2313.94 2650.76 2910.08 3069.03 6617.10 3644.46 4659.16 5399.13 4519.90 4733.84 5765.65 4851.08 2014.17 1638.50 1785.47 2205.45 2616.19 2992.13 3275.84 5700.00 3633.85 4636.82 5492.98 4427.08 4830.02 5885.08 4860.88 2036.81 1832.66 1784.44 2215.00 3033.24 2951.11 3400.13 5858.33 3682.07 4626.32 5723.67 4402.06 5278.12 5894.80 4871.55
May
2025.49 1735.58 1714.42 2225.00 2824.72 2971.62 3133.33 5779.17 4130.14 4167.11 5479.36 4267.23 5200.00 5877.67 4862.13 2031.15 1784.12 1450.00 2195.00 2928.98 2945.05 3009.44 5818.75 4160.02 4655.24 5488.81 4197.22 5146.15 5652.49 5034.33 2028.32 1759.85 1582.21 2210.00 2876.85 3001.83 2924.00 4400.00 4067.99 4675.40 5235.58 4177.34 5173.08 5426.25 5075.44 2029.73 1688.11 1676.94 2202.50 2902.91 2850.00 2966.72 5109.37 3954.33 4762.20 5064.52 4105.80 5159.61 5426.25 5118.61
Jun
2029.03 1723.98 1629.58 2206.25 2889.88 2935.00 2945.36 4754.69 3887.21 4724.61 5118.46 4030.56 5166.34 5426.25 5295.19 2029.38 1706.05 1500.00 2204.38 2896.40 2892.50 2956.04 4932.03 3790.33 4727.14 5074.54 4802.69 5162.98 5426.25 5618.20 2029.20 1715.01 1564.79 2205.31 2893.14 2913.75 2950.70 4843.36 3824.62 4740.00 5071.43 4056.00 5164.66 5426.25 5704.61 2029.29 1710.53 1532.39 2204.84 2620.00 2903.13 2953.37 4887.69 4112.54 5000.00 5156.00 4025.00 5163.82 5426.25 5660.20
Jul
2029.25 1712.77 1548.59 2490.00 2756.57 2908.44 2952.04 4865.53 4179.85 4997.37 5122.31 4040.50 5164.24 5426.25 5682.41 2029.27 1711.65 1540.49 1440.00 2688.28 2905.78 2952.70 4876.61 4327.91 5052.38 4965.00 4032.75 5164.03 5426.25 5671.30 2029.26 1712.21 1544.54 1965.00 2722.43 2907.11 2952.37 4871.07 4679.99 4925.58 5040.00 4036.63 5164.14 5426.25 5676.85 2025.00 1711.93 1542.52 1702.50 2705.36 2906.45 2952.54 4873.84 4721.90 5070.78 5002.50 4034.69 5164.08 5426.25 5674.08
Aug
1904.33 1712.07 1543.53 8100.00 2713.89 2789.00 2952.45 4872.45 4866.23 5235.00 4250.00 4035.66 5164.11 5426.25 5675.47 1904.33 1712.00 1543.02 4901.25 2650.00 2741.57 2952.49 4873.15 4871.88 5152.89 4626.25 4035.17 5164.10 5426.25 5674.77 2045.22 1712.04 1710.20 2116.00 2553.00 2750.88 2952.47 3500.00 4600.00 3231.00 3100.00 4035.41 5164.10 3980.17 5675.12 2002.11 1553.17 1875.12 1925.58 2839.56 2640.50 2952.48 3625.00 4781.20 4688.62 4500.00 3800.00 5164.10 4241.38 5674.95
Sep
2020.34 1759.31 1902.42 2024.37 3063.38 2635.40 3040.69 4524.17 4538.33 4746.16 4321.93 3779.78 4501.67 4354.65 5675.03 2004.49 1719.40 1850.27 2012.96 2912.71 2678.35 3396.27 4086.67 2861.24 4740.96 3975.51 4261.67 4747.12 4433.67 5674.99 2029.55 1702.15 1919.62 2060.89 2963.19 2727.46 3458.35 3828.57 2602.01 5126.93 4067.55 4493.63 5255.69 4352.07 5675.01 1991.42 1661.79 1919.94 2140.09 2982.03 2834.07 3564.65 3999.84 3334.25 5047.53 4391.31 4401.33 5115.13 4382.80 5675.00
Oct
1882.80 1648.22 1950.79 2057.59 2898.34 2892.30 3848.92 4423.60 3919.13 5061.82 4203.20 4421.68 4979.37 4496.84 5298.33 1853.89 1678.08 1922.28 2037.88 2892.92 2944.27 3948.37 4577.72 4175.93 5142.70 4549.57 4469.12 4687.37 4690.76 5396.18 1819.40 1706.37 1873.43 2103.17 3016.27 2999.22 3747.00 4453.19 4612.32 5117.17 4396.47 4555.86 4706.33 4763.96 5461.02 1832.69 1684.04 1917.38 2133.04 2948.59 3049.68 3943.35 4503.10 4639.70 5126.00 4410.69 4531.29 4605.95 4873.19 5510.51
Nov
1806.04 1721.43 1907.42 2117.25 3036.94 3124.41 3964.47 4426.14 4565.85 5138.76 4358.61 4462.82 4682.66 4825.76 5516.01 1758.25 1752.33 1874.88 2195.51 3015.97 3087.76 4332.01 4248.75 4620.17 5121.17 4201.71 4348.95 4742.83 4770.28 5499.11 1788.13 1731.41 1922.65 2312.27 2946.75 3162.97 4375.19 4330.52 4716.92 5070.39 4224.55 4299.36 4657.31 4860.90 5601.46 1753.18 1799.61 1905.78 2394.94 2967.14 3302.50 4228.28 4228.23 4518.93 5083.29 4268.82 4292.80 4676.18 4883.79 5516.76
Dec
1689.94 1846.10 1936.08 2328.78 2961.90 3341.53 4353.27 4212.36 4430.64 5011.53 4159.07 4322.24 4846.70 5016.93 5450.80 1694.65 1832.66 1917.18 2333.07 2955.03 3262.14 4292.45 4141.01 4383.51 5128.11 4107.20 4324.28 4657.31 5013.24 5507.62 1648.57 1786.07 1907.16 2372.56 2891.14 3140.72 4516.05 4135.93 4358.97 4940.72 4125.99 4506.23 4676.18 5203.23 5478.89 1639.02 1764.37 1868.33 2370.25 2783.94 3181.78 4573.37 4195.14 4202.82 5041.48 4270.45 4731.61 4846.70 5350.00 5489.78
Price of Cotton in Telengana market 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2493.17 1830.68 2035.06 1942.90 2238.98 2892.39 3093.45 4375.78 3841.75 3846.57 4478.84 4009.11 4263.56 5056.19 4797.98 2513.67 1670.34 1960.10 1942.38 2347.36 2779.67 3074.02 4538.17 3946.53 3864.31 4613.24 4005.05 4241.73 5110.70 4776.09 2735.02 1684.70 1952.97 1959.55 2390.64 2884.47 3010.39 4949.45 3957.04 3865.48 4636.01 4019.04 4287.57 5151.06 4788.65 2690.35 1765.41 1897.91 2001.65 2365.30 2910.46 2985.22 5199.92 3893.14 3860.98 4498.82 4010.50 4262.37 5336.28 4711.60
Feb
2502.68 1703.08 1865.68 2021.47 2359.68 2861.66 2969.90 5412.11 3856.19 3859.43 4592.51 3988.29 4230.06 5289.89 4586.43 2618.88 1792.14 1838.09 2046.80 2378.93 2891.80 2957.97 6003.89 3816.63 3849.03 4611.45 3952.78 4189.21 5238.36 4554.62 2539.66 1637.10 1719.23 2117.43 2371.95 2754.11 3020.47 5970.94 3739.36 3887.78 4516.62 3946.72 4157.59 5227.50 4491.68 2579.66 1665.78 1719.23 2114.82 2519.43 2690.36 3033.28 5520.91 3590.49 4128.43 4461.38 3920.93 4173.24 5238.10 4478.66
Mar
2498.45 1628.74 1801.77 2069.77 2621.27 2615.76 3005.79 5841.58 3473.58 4238.13 4298.22 3879.76 4158.45 5266.07 4473.51 2395.42 1659.72 1794.56 2139.46 2606.27 2754.11 3004.65 5788.81 3501.45 4398.69 4344.03 3911.62 4165.65 5232.99 4447.72 2358.33 1753.66 1776.48 2117.51 2592.97 2690.36 3072.86 5810.12 3678.79 4488.16 4386.80 3905.11 4147.69 5237.31 4412.78 2511.28 1738.47 1816.51 2109.61 2517.18 2615.76 3085.19 5889.09 3717.63 3950.00 4467.51 3888.80 4131.98 5226.77 4369.43
Apr
2426.92 1829.20 1907.24 2186.52 2602.50 2764.59 3188.12 5892.23 3676.47 4202.44 4464.07 3945.28 4189.10 5241.36 4397.28 2223.96 1843.66 1891.04 2231.84 2596.05 2647.42 3117.80 5846.00 3542.29 3875.00 4486.17 4067.53 4445.94 5149.16 4502.83 2467.80 1849.25 1889.30 2240.87 2600.87 2716.48 3149.04 5243.92 3539.07 3875.00 4416.41 4054.14 4411.18 5105.83 4558.45 2195.40 1793.47 1793.37 2198.57 2611.67 2712.39 3072.47 4558.07 3588.23 3875.00 4415.40 4052.32 4360.89 5114.45 4468.71
May
2254.10 1835.38 2003.14 2172.46 2576.54 2755.76 2988.83 3695.35 3739.04 3875.00 4493.70 4104.11 4434.26 5047.02 4516.72 2365.78 1758.65 1976.72 2154.48 2690.01 2838.61 3077.77 3347.88 3794.89 4034.87 4392.15 4173.75 4474.93 5025.71 4485.29 2327.19 1816.07 1982.60 2182.96 2798.46 2962.92 3028.17 3198.21 3698.93 4000.00 4466.30 4116.82 4494.20 5062.09 4615.38 2188.38 1785.95 1992.75 2229.32 2921.47 2915.19 3280.22 3275.13 3547.46 4017.44 4377.79 4034.97 4491.78 5015.17 4754.92
Jun
2256.12 1774.07 1962.37 2221.71 2998.90 2831.63 3195.68 2981.97 3567.50 4291.50 4466.54 4091.38 4695.86 4908.62 4961.17 2315.49 1726.18 1945.23 2423.96 2986.73 2820.66 3194.32 3002.16 3598.15 4288.49 4377.01 4111.30 5023.51 4889.04 5286.69 2317.95 1809.77 1986.07 2248.56 3071.71 2810.36 3219.05 3012.36 3702.02 4325.65 4216.39 4092.34 5137.71 4904.18 5069.43 2423.86 1798.56 2023.46 2254.33 3386.07 2815.87 3284.30 2984.93 3964.99 4345.12 4715.31 3988.77 4939.11 4842.83 5257.89
Jul
2574.73 1850.00 2044.46 2321.84 3505.16 2861.09 3275.39 2954.82 4010.03 4573.72 4606.21 4038.63 4827.01 4935.68 5163.66 2564.96 1830.59 2057.03 2359.66 3426.18 2880.75 3349.81 2919.22 4160.82 4584.45 4531.74 4106.39 5394.78 4919.61 5210.78 2624.81 1880.04 2064.49 2389.38 3544.23 2881.85 3428.86 2815.14 4416.37 4567.55 4552.57 4101.78 6020.98 4864.69 5187.22 2649.14 1910.44 2092.04 2412.79 3630.00 2830.42 3504.89 2940.74 4473.92 4656.80 4547.85 4099.97 5799.31 4816.28 5199.00
Aug
2706.90 1938.75 2123.50 2398.09 3567.12 2839.92 3385.21 3281.49 4403.98 4647.11 4534.38 4004.78 5653.04 4869.84 5193.11 2711.34 1891.35 2156.33 2434.53 3540.80 2938.62 3521.31 3291.88 4442.59 4768.75 4595.90 4041.47 5720.56 4601.52 5196.05 2699.86 1824.30 2327.66 2411.77 3526.58 2923.09 3682.13 3459.27 4377.69 5054.48 4578.22 4013.92 5067.30 5007.83 5194.58 2626.37 1828.66 2344.87 2392.57 3433.33 2837.83 3827.11 3727.22 4323.19 5261.80 4614.44 4054.76 4581.15 4937.32 5195.32
Sep
2719.88 1826.33 2251.64 2391.29 3391.30 2741.50 3961.73 3602.19 4254.54 5278.95 4475.40 4044.83 5105.57 4978.30 5194.95 2604.32 1850.24 2233.94 2373.58 3412.32 2620.71 4459.82 3888.79 4243.82 5135.19 4442.41 4124.40 5050.51 4938.36 5195.13 2322.09 1835.83 2178.25 2395.87 3350.00 2606.51 4392.13 3724.39 3937.35 5069.92 4176.47 4245.88 4272.82 4754.12 5195.04 2073.57 1836.83 2005.94 2349.60 3149.12 2638.35 4199.63 1685.72 3758.59 4085.78 3915.05 4054.81 4625.09 4914.35 5195.09
Oct
1932.88 1843.22 2120.81 2156.14 2900.48 2723.41 3764.95 3558.13 3622.66 4368.21 3665.65 3821.06 5170.65 4606.73 5169.73 1816.25 1796.96 2022.96 1943.13 2647.15 2694.11 3735.75 3763.91 3626.88 4176.98 3851.11 3732.94 4694.94 4409.08 5348.59 1919.34 1829.30 1990.54 2031.13 2810.41 2789.71 3911.98 3896.95 3587.25 4143.33 3799.03 4016.65 4826.27 4178.68 5534.81 1921.08 1791.46 2018.26 2101.54 2728.78 2929.38 3970.28 3992.90 3810.56 4070.59 3925.29 4021.28 4549.75 4074.96 5611.15
Nov
1933.65 1794.10 1992.87 2026.20 2769.60 2940.90 3843.31 4067.67 3891.15 4105.81 3955.02 3989.87 4582.93 4156.99 5572.98 1920.89 1867.21 1975.80 2066.80 2913.85 2961.75 4013.34 3954.98 3881.68 4108.20 3961.61 3990.34 4605.50 4293.10 5592.07 1976.54 1888.55 1969.00 2058.50 2907.83 2963.08 3875.49 3946.89 3889.50 4100.63 4012.11 4005.71 4818.06 4347.11 5582.52 1896.31 1958.64 1979.40 2063.22 2905.67 3009.63 3825.37 3743.14 3871.80 4147.01 4019.01 4006.55 4711.28 4357.31 5587.29
Dec
1863.91 2069.14 1960.51 2041.95 2929.46 3039.74 3873.74 3694.70 3858.59 4133.90 4010.05 4009.51 4894.85 4434.26 5584.91 1879.80 2033.97 1946.54 2053.78 2889.95 3096.34 3824.51 3722.64 3861.61 4159.47 4012.38 3999.95 4825.74 4574.07 5388.56 1883.36 2038.49 1950.84 2068.52 2816.53 3055.78 3981.58 3754.26 3831.13 4154.72 4017.42 4038.20 4877.73 4818.79 5384.46 1856.92 1968.69 1921.83 2109.30 2904.89 3062.04 4131.13 3729.65 3851.45 4216.21 4023.98 4205.72 4875.02 4924.65 5330.44
Price of Cotton in Tamil Nadu market 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2658.14 2268.54 2244.43 2369.92 2387.54 2619.57 3287.23 4488.39 1218.04 4176.21 3920.53 3529.82 4610.02 4841.69 4603.96 2667.60 2394.66 2220.48 2392.27 2390.84 2795.67 3340.05 4729.82 4351.28 4175.04 5950.53 3825.86 4833.59 6717.29 4871.51 2680.72 1802.66 2128.13 2215.40 2465.20 2841.01 3276.61 5618.93 4337.82 4558.84 5347.02 3855.50 5049.99 6200.92 5085.18 2806.86 2595.56 2088.47 2309.58 2460.29 2872.85 3344.54 5691.65 4254.49 4109.81 5943.40 3725.44 5029.01 6480.10 5073.14
Feb
2731.03 2122.59 2114.43 2230.47 2580.97 2800.00 3179.26 5916.68 4061.61 4239.90 5557.84 3760.27 4609.64 6368.35 4929.29 2730.58 1877.34 2110.22 2254.31 2477.09 2553.77 3534.07 6458.94 3942.27 4027.79 5126.82 3880.60 4662.57 6184.76 4951.34 2676.59 1775.69 2116.58 2450.01 2399.63 2749.47 3395.03 7850.50 3893.28 3971.30 5267.38 3939.14 4594.14 5771.44 4997.44 2626.26 1784.00 2141.77 1750.44 2470.76 2793.14 3314.79 6401.74 3834.53 4050.68 4971.91 4176.84 4569.23 5950.73 4579.45
Mar
2687.87 1914.27 2094.81 2362.92 2510.24 2764.60 3200.40 6288.67 3539.50 4373.91 4717.74 4234.87 4620.43 5835.21 4641.37 2646.97 1803.26 2132.52 2108.29 2329.55 2749.47 3258.01 6587.84 3742.85 4768.35 4909.69 4015.74 4565.38 5984.07 5099.80 2635.02 1882.86 2100.46 2426.61 2456.02 2793.14 3715.41 6459.06 3816.25 4813.69 4824.50 3959.17 4608.22 5895.39 4826.34 2756.00 1979.12 2062.52 2544.72 2565.56 2764.60 3394.53 6333.29 4135.51 4606.47 4776.34 4149.18 4780.97 6069.82 4655.30
Apr
2739.42 2124.18 2054.18 2496.14 2363.97 2819.56 3422.18 6311.85 4216.22 4795.46 4826.64 4226.23 4927.93 5865.02 5202.23 2834.89 2024.76 2011.80 2545.30 2539.00 2832.07 3383.05 6700.00 4258.93 4694.72 5102.99 4261.80 4689.39 5988.83 4941.73 2750.93 1942.35 2143.11 2573.30 2870.19 2795.97 3271.30 6015.22 3921.88 4668.56 5020.25 4072.39 4897.56 6082.50 5234.47 2601.64 1951.55 2089.79 2186.74 2772.63 2725.29 3119.16 4947.23 3867.80 4459.58 5232.23 4015.02 4848.29 5846.09 5016.80
May
2648.89 1998.66 2108.30 2317.66 2938.00 2764.49 3142.51 4612.32 3824.24 4249.36 5173.65 4130.42 5341.32 5487.53 5049.63 2665.86 1927.31 2092.11 2198.40 3000.00 2734.55 2983.40 4252.24 3981.60 4545.46 5374.27 4135.88 5046.54 5359.88 5309.81 2694.81 1879.09 2081.44 2250.14 2893.44 2757.83 3088.29 4372.85 3935.54 4715.78 4929.49 4155.87 5134.60 4330.48 5080.84 2547.75 1872.36 2049.74 2425.67 2687.67 2793.53 2842.74 4294.25 3878.92 4866.35 4845.70 4084.90 4942.17 5219.52 5183.60
Jun
2521.46 1880.73 1966.41 2302.10 3050.00 2777.69 2925.27 4558.21 3903.99 4740.66 4761.42 4099.16 5273.95 5531.51 5096.16 2516.55 1699.20 1963.42 2461.00 2564.24 2668.51 3104.52 4252.56 4222.58 4750.39 4372.49 4125.39 5389.50 5163.07 5049.73 2337.92 1677.63 1843.38 2378.81 2634.78 2680.24 2968.04 4075.98 4042.69 4704.42 4758.27 3925.62 5439.30 4933.32 5150.77 2347.07 1644.94 1781.47 2341.94 2854.55 2736.46 3182.32 3934.91 3451.99 4869.64 5000.83 3950.36 5459.68 4864.55 5095.21
Jul
2364.75 1565.36 1767.69 2481.18 3419.72 2746.66 3564.90 3985.45 4688.98 4931.60 5046.43 4067.40 5683.59 4761.87 5122.99 2300.33 1777.84 1801.92 2536.12 3400.00 2796.25 3360.08 4088.50 5175.46 5212.98 4831.31 4032.34 5964.82 4994.98 5109.10 2327.04 1819.07 1929.35 2626.48 3409.86 2959.80 3343.75 3832.60 5516.35 5176.70 4964.31 4158.80 6301.40 4995.55 5116.05 2394.81 1819.75 1957.80 2611.95 2975.00 2916.80 3588.00 3665.96 5283.04 5266.88 4948.76 4250.16 5913.81 5058.10 5112.57
Aug
2439.16 1766.41 1922.21 2477.18 1701.01 2966.59 3508.62 3470.53 4854.08 5386.86 4808.44 4140.38 5993.82 5127.02 5114.31 2439.87 1825.29 2203.41 2451.94 3287.50 2819.36 3333.88 2390.61 5332.84 5534.26 4883.08 4168.50 5872.58 5015.28 5113.44 2484.97 1694.02 2107.02 2385.59 1600.00 2842.78 4155.74 3215.48 5170.75 5606.65 4614.94 4140.10 5992.47 4980.16 5113.87 2486.18 1725.59 2270.77 2141.09 2443.75 2686.88 3843.48 4244.65 4968.59 5397.61 4726.71 4293.44 5945.52 5414.87 5113.66
Sep
2494.23 1564.13 2152.07 1875.76 1600.00 2599.24 4077.67 4032.88 4304.59 5336.73 4540.03 4409.80 5597.38 4798.12 5113.77 2390.12 1531.50 2104.37 2073.34 2800.00 2628.41 4295.73 4408.59 4070.88 5192.31 4357.86 4565.31 5258.75 4831.57 5113.71 2374.65 1524.27 2445.94 1865.61 2200.00 2284.67 4172.31 3534.97 3645.03 4655.98 4452.20 4590.71 6271.01 5228.07 5113.74 2298.14 2454.01 2147.04 2382.09 2500.00 2550.62 4426.12 4550.99 3453.57 5114.11 4005.99 4498.07 6319.12 4540.86 5113.73
Oct
2096.08 1571.85 2109.92 2323.13 3350.00 2620.70 3977.07 4395.03 3233.06 4351.10 3678.03 4631.19 6155.57 4864.31 5298.16 2227.46 1582.71 1976.98 2294.70 3000.00 2439.41 4757.60 4365.86 4235.49 4692.11 4158.13 4475.36 5468.40 5414.72 5606.54 1952.18 1635.01 1662.64 1791.88 3175.00 2517.23 4706.83 4501.12 4530.43 5298.51 4210.77 4106.94 4524.05 4352.38 5179.42 2184.13 1604.17 1537.88 2242.57 3087.50 2526.94 4014.36 4457.92 4562.04 4338.02 4191.69 3978.66 5012.35 4465.55 5336.91
Nov
1468.65 1708.91 1796.31 1973.40 3131.25 2698.92 3658.14 4392.13 5242.58 5149.57 3922.82 4222.24 4695.17 4337.40 4773.65 1984.84 1835.83 2973.58 1444.86 2913.85 2530.45 3500.00 4036.06 4512.55 4098.42 3601.09 4006.67 4691.56 2963.79 4839.21 1529.90 1943.70 2102.26 3482.49 2700.77 2505.29 4000.00 4306.38 4115.93 5033.85 3760.52 4020.97 4808.11 3786.12 5130.76 2029.82 2113.08 2085.55 2578.93 2671.91 2774.68 3172.00 4338.13 4838.02 4632.89 3210.58 3919.29 5211.11 3810.77 4219.96
Dec
1516.67 2198.40 2070.69 2467.28 2585.06 3037.27 3586.00 3398.89 4333.42 4634.69 3920.81 3757.96 4970.00 3347.07 4801.84 2058.49 2416.22 2057.58 2618.73 2474.25 2029.69 5442.61 3914.31 4086.12 4356.89 4047.98 3502.14 4512.28 4178.08 4850.71 2148.76 2299.91 2506.50 2115.00 2519.11 3288.67 4375.61 4326.15 4485.49 5005.18 3904.10 4365.17 4782.20 4449.57 4963.81 2216.91 2183.91 2474.80 2228.44 2612.02 3260.46 4616.80 4092.52 4460.72 4690.35 3698.66 4477.43 4780.82 4624.00 4866.69
Annexure II: Price of Turmeric in Selected State markets of India Price of Turmeric in Odisha market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
4101.21 3290.91 4000.00 2029.61 3000.00 2886.17 4886.96 5200.00 4151.40 4633.33 7031.25 5551.72 7200.00 6100.00 6100.00 2500.00 4000.00 4000.00 2750.00 3000.00 4900.00 5200.00 5200.00 4186.46 4600.00 7500.00 5242.86 7014.06 6100.00 6100.00 4101.21 4000.00 4000.00 1917.55 3742.00 3500.00 5246.67 6000.00 4100.00 4600.00 8500.00 5439.10 7200.00 6100.00 6100.00 4101.21 4000.00 4216.72 3092.00 2434.90 3500.00 5200.00 6000.00 3900.00 6389.47 6250.00 5179.49 7200.00 6100.00 6112.20
Feb
3500.00 4000.00 3825.14 3122.49 3270.65 3500.00 5200.00 6000.00 5520.00 4400.00 4000.00 5000.00 7109.09 6100.00 5900.00 4000.00 4000.00 4000.00 1988.23 2717.91 3547.83 5600.00 5394.27 4378.89 4589.47 3856.18 5000.00 7703.23 6100.00 5700.00 4000.00 4000.00 4000.00 2111.76 2914.62 3675.00 5246.67 4966.67 4331.14 4400.00 3712.37 5000.00 6600.00 6100.00 5700.00 2782.40 4000.00 3676.47 3151.96 3044.39 4983.15 5200.00 6000.00 3586.71 4500.00 4126.32 5041.67 6712.50 6120.00 5700.00
Mar
3263.01 4000.00 3529.41 2000.00 2993.06 6602.94 5881.82 5717.90 3400.00 6296.52 3766.67 5020.84 4800.00 6200.00 5787.11 4000.00 4000.00 3875.00 1934.91 2842.42 3800.00 6100.00 5394.27 3125.66 5036.07 4200.00 5031.25 4800.00 6200.00 5787.11 4000.00 4000.00 3957.01 1700.00 2842.05 4000.00 5200.00 6197.13 5179.06 4783.90 4200.00 5026.04 4800.00 6450.00 6100.00 4000.00 4000.00 3452.81 1934.91 2911.80 5200.00 6000.00 6598.56 5827.92 4738.89 4185.77 5028.65 4800.00 6450.00 6100.00
Apr
4101.21 3301.18 4000.00 1771.15 3000.00 2800.00 4515.79 6799.28 3516.54 5567.31 4024.24 5027.35 5000.00 6450.00 6113.33 2500.00 2724.31 4000.00 2750.00 3393.27 4125.00 8884.27 6258.25 3200.00 5235.92 4003.00 5028.00 5200.00 6450.00 6113.33 4071.43 4157.89 4000.00 3000.00 2750.00 3500.00 6277.78 6850.00 3221.32 6246.84 4053.08 5327.67 5260.00 6450.00 6100.00 4000.00 4000.00 3183.33 2535.79 3300.00 3652.02 6000.00 6950.00 3200.00 6215.53 4101.75 5507.83 5180.00 6450.00 6100.00
May
3678.57 4000.00 3619.15 2012.28 2351.40 3500.00 6100.00 7000.00 8175.00 5889.95 4970.31 5425.00 5800.00 6450.00 6100.00 3911.76 4000.00 4000.00 1900.00 2946.49 3500.00 9327.27 7000.00 7625.00 5391.65 4959.38 5350.00 5760.00 6450.00 6100.00 4000.00 4000.00 4000.00 2813.10 2744.46 4000.00 10000.00 7000.00 4870.42 5475.68 4981.25 5620.00 5790.00 6417.19 6100.00 2020.96 4000.00 1622.00 2813.10 3044.06 5474.47 10117.65 7000.00 3188.03 7377.54 4937.50 5027.83 5800.00 6384.38 6100.00
Jun
4000.00 4000.00 3242.86 2880.58 2921.82 5457.14 10090.91 6895.35 5248.10 6491.08 5025.00 5201.98 5685.00 6318.75 6100.00 4000.00 4000.00 4000.00 2750.00 2800.00 4000.00 10000.00 6790.70 3815.21 6320.41 4850.00 5376.12 5850.00 6187.50 6100.00 4000.00 4000.00 3498.83 1700.00 2800.00 5200.00 10800.00 8733.33 4429.41 4034.04 5200.00 5490.00 5500.00 5925.00 6066.67 4000.00 4000.00 2349.39 2333.33 2800.00 5200.00 10250.00 9432.69 5234.16 5255.56 4500.00 5262.25 6580.95 5400.00 6000.00
Jul
4101.21 4134.80 4000.00 1540.61 3000.00 2500.00 10214.00 8700.00 5000.35 4085.00 4850.00 5034.49 7000.00 5400.00 5900.00 2782.40 4067.40 4000.00 3250.00 2750.00 3524.39 10000.00 7028.57 3612.83 4670.28 4675.00 5333.33 7050.00 5400.00 5900.00 3200.00 4000.00 4000.00 2000.00 2941.69 3500.00 11000.00 7700.00 3546.94 4045.45 4762.50 5655.00 5500.00 6100.00 5900.00 4000.00 4000.00 4009.98 2443.10 3984.38 3995.98 10500.00 4300.00 3976.44 6246.84 4718.75 5382.35 6580.95 6100.00 5900.00
Aug
3980.45 4000.00 1881.81 1946.88 2328.12 3500.00 10200.00 4000.00 5113.28 5475.68 4740.63 5589.55 7100.00 6100.00 5900.00 4000.00 4000.00 4000.00 1908.99 2934.73 3508.57 10214.29 4356.94 6360.87 5263.45 4729.69 5538.24 7150.00 6100.00 5900.00 4000.00 4000.00 4000.00 2188.52 3149.65 3500.00 10431.58 4600.00 11063.01 4045.45 4735.16 5508.45 7160.00 6100.00 5900.00 4000.00 4000.00 2063.49 2750.00 3000.00 3575.51 10285.71 4279.64 3310.71 6000.00 4732.42 5588.60 7300.00 6100.00 5900.00
Sep
3258.04 4000.00 3583.33 3141.97 3713.19 3600.00 10400.00 4889.82 8687.50 4070.35 4733.79 5958.70 7200.00 6100.00 5900.00 2676.47 4000.00 3200.77 2945.99 2800.00 3760.00 10200.00 5194.91 6135.97 7500.00 6442.11 5890.62 7156.00 6100.00 5900.00 4000.00 4000.00 2220.96 2750.00 2815.83 5200.00 11166.67 5347.45 6763.31 5296.53 5221.00 6150.59 7098.00 6100.00 5900.00 3326.15 4000.00 1520.72 2339.13 2950.00 5200.00 10000.00 5423.75 8650.00 5974.38 5831.56 6111.11 7100.00 6100.00 5900.00
Oct
2500.00 4000.00 4000.00 1928.26 3000.00 2800.00 11000.00 5500.00 4900.00 6389.47 5526.28 6172.90 6600.00 6100.00 5900.00 2782.40 4000.00 4000.00 4955.88 2797.15 2957.69 10000.00 4966.67 5827.92 4500.00 4200.00 6183.70 6650.00 6100.00 5900.00 4128.57 4000.00 4000.00 2750.00 2998.16 3390.62 10800.00 5483.33 4900.00 4738.89 4000.00 6208.44 6580.00 6100.00 5900.00 3500.00 4000.00 4728.57 3194.97 2750.00 5000.00 10400.00 5438.80 4900.00 6215.53 4610.50 6196.07 6600.00 6100.00 5900.00
Nov
2198.45 4000.00 2359.51 2636.80 3419.02 3500.00 10117.65 5719.40 3188.03 7377.54 4305.25 6202.26 6700.00 6100.00 5900.00 4000.00 4000.00 4000.00 2114.20 2736.75 3590.91 10250.00 5859.70 5234.16 5255.56 4457.88 6500.00 6590.00 6100.00 5900.00 4000.00 4000.00 4000.00 2750.00 2839.71 4000.00 10500.00 5929.85 7857.14 4829.93 4381.56 7000.00 6700.00 6100.00 5900.00 4000.00 3185.71 1990.14 2750.00 2832.10 3726.53 10437.50 6000.00 4300.00 6000.00 4419.72 6750.00 6600.00 6100.00 5900.00
Dec
3623.81 4000.00 3750.00 2340.16 2832.10 4000.00 11000.00 5394.27 4466.66 7500.00 4400.64 6875.00 6650.00 6100.00 5900.00 4000.00 4000.00 2956.28 2750.00 2780.82 4000.00 11000.00 5717.90 4900.00 6750.00 4410.18 6812.50 6500.00 6100.00 5900.00 4000.00 4000.00 3000.00 2750.00 3000.00 5200.00 9750.00 4279.64 4300.00 7125.00 4405.41 6843.75 6700.00 6100.00 5900.00 3038.02 4000.00 1232.93 2750.00 2914.50 5200.00 9000.00 9638.98 4000.00 6937.50 4407.79 6828.13 6600.00 6100.00 5900.00
Price of Turmeric in Andhra Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2629.60 2466.89 2342.62 1711.64 2438.46 3461.33 9588.95 14881.33 4046.60 4610.04 4852.04 6916.91 7963.57 6458.28 6732.36 2657.25 2219.17 2376.23 1707.78 2513.73 3408.45 9386.62 14668.61 4230.99 4541.08 4817.17 6673.76 8394.97 5949.07 6579.91 2643.42 2155.74 2035.40 1651.01 2529.70 3597.25 9227.29 13265.04 4204.98 4550.95 4803.61 6899.50 8207.00 6076.58 6910.36 2650.34 1950.00 1972.41 1590.81 2414.35 3666.86 8645.62 13821.23 3942.51 4185.63 5069.54 6419.18 7808.32 6065.12 6256.56
Feb
2646.88 2083.35 1668.43 1702.52 2474.62 3649.96 8660.52 9888.04 3974.36 4557.88 5201.66 6594.83 7997.66 5925.82 6178.09 2792.14 2186.51 1689.20 1674.42 2642.44 3572.07 8451.47 9476.32 4039.68 4407.11 5100.55 6507.93 7778.60 5899.83 5769.22 2719.51 2172.09 1678.82 1714.86 2698.50 3590.96 9024.10 9248.91 4059.30 4562.68 5105.02 6956.53 8059.45 5761.94 5968.62 2785.00 2143.37 1684.01 1833.39 2748.08 3698.17 8400.53 9439.00 4244.01 4566.05 5001.43 6922.85 7745.91 5637.99 6039.34
Mar
2636.67 2088.28 1912.21 1879.14 2926.50 3806.87 8550.03 9416.54 3838.15 4501.60 102.72 6816.27 7625.18 5874.81 5861.72 2710.84 2081.57 2117.62 1943.95 2834.14 3851.09 8909.81 9512.44 3305.12 4948.05 5191.26 6511.36 7749.29 5592.69 6021.25 2673.75 2103.00 2014.92 1893.36 2694.29 4044.85 10433.78 8909.11 3364.99 5117.63 4934.41 6767.61 7750.23 5424.53 6201.70 2692.29 1989.65 2066.27 1954.44 2809.25 4246.36 9887.93 8765.00 3223.03 5032.84 4874.99 6641.54 7538.98 5137.00 6111.48
Apr
2683.02 1804.11 2040.59 1923.90 2842.21 4580.98 10489.00 8303.84 3176.73 5075.24 4796.93 6862.33 7553.71 5090.57 6156.59 2687.66 1900.40 1932.34 1962.52 2826.60 4542.55 10929.15 8745.03 3002.53 5054.04 4883.25 6765.93 7563.41 4865.78 6134.03 2685.34 1948.67 1966.61 1897.61 2785.35 4517.15 11596.35 8144.11 2689.64 5064.64 5095.60 6648.94 7524.09 4974.85 6145.31 2686.50 1899.27 1970.33 1830.80 2794.96 4506.14 12288.07 7881.66 2712.03 5059.34 5238.18 6600.76 7563.73 5346.17 6139.67
May
2685.92 1928.91 1919.11 1807.25 2836.06 4757.74 13576.16 7617.89 2866.05 5061.99 5209.56 6470.22 7409.81 5679.28 6142.49 2686.21 1888.69 1992.41 1862.84 2907.21 4819.13 12925.21 7439.12 3219.50 5060.66 5335.26 6562.65 7312.41 5992.70 6141.08 2686.07 1877.31 2225.17 1839.35 3015.00 4886.75 13486.65 6858.72 3256.16 5061.32 5148.91 6734.46 7389.23 5924.55 6141.78 2686.14 1868.68 2120.83 1826.44 3317.84 4807.07 13141.71 6078.14 2999.75 5060.99 4802.47 6624.17 7142.49 5959.21 6141.43
Jun
2686.10 1895.83 2175.90 1813.21 3414.34 4806.08 12986.27 5701.59 2865.56 6000.00 4791.92 6704.52 7164.21 5579.53 6141.61 2686.12 1865.65 2083.96 1794.81 3447.92 4839.44 12893.71 5550.59 2859.68 4437.01 4808.62 6618.16 7201.89 5992.63 6141.52 2686.11 1892.52 2018.68 2453.08 3494.82 4904.09 13127.24 5218.56 2920.64 4574.81 4732.19 6567.80 7285.80 6153.85 6141.56 2686.11 1932.75 2060.92 2229.78 3595.68 4995.45 13023.49 5702.14 3011.01 4647.99 4930.86 6418.80 7448.21 6269.86 6141.54
Jul
2483.49 1851.01 2136.32 1853.02 3679.69 5006.22 13345.71 5767.88 3131.44 4783.53 4718.07 6486.28 7062.74 6868.55 6141.55 2574.88 1910.39 1997.45 1865.35 3660.56 5187.59 13555.05 6066.74 3664.95 4469.45 4722.90 6517.37 7064.24 6823.02 6141.55 2527.74 1843.68 1977.43 1875.08 3907.30 5503.10 13741.86 5758.65 4008.93 4473.64 4869.23 6496.49 7319.72 6518.87 6448.89 2591.69 1845.38 2039.73 1928.94 3933.03 6033.43 13886.62 6162.46 4599.35 4404.36 4939.85 6373.20 7544.69 6658.50 6333.68
Aug
2587.22 1842.08 2225.77 1888.75 3840.25 6967.16 13384.01 5666.22 4808.92 5344.32 5035.29 6453.04 7294.61 6997.32 6313.06 2590.15 1971.92 2218.88 1981.53 3499.20 7327.18 13383.48 5422.69 4712.20 5390.00 5133.88 6573.97 7065.33 6448.03 6054.99 2628.37 1981.45 2237.37 1937.10 3540.50 7049.04 13270.93 5361.21 4947.65 4241.47 5153.28 6512.24 6694.32 6873.71 5798.43 2647.92 2123.76 1967.18 1936.46 3386.50 7121.90 12675.53 4792.90 4944.96 4254.08 5189.34 6804.93 6585.97 6799.04 5880.45
Sep
2748.61 2154.68 2166.93 1904.95 3453.00 6863.36 11943.00 5201.91 4735.82 4306.42 5026.02 7011.27 6448.03 6873.71 5604.67 2830.25 2199.55 2218.67 1913.22 3419.75 6863.36 12224.27 4504.75 4620.81 4114.01 5003.64 7062.84 6242.02 6505.47 5777.36 2917.44 2167.25 2263.95 1889.55 3579.50 7242.53 12582.86 3897.87 4681.30 4042.67 4738.61 6946.42 6243.30 6773.75 5997.13 2926.45 2235.71 2046.50 1828.57 256.16 7188.76 13026.74 3764.33 4486.67 4218.75 4739.64 6338.14 6508.39 6912.38 5844.63
Oct
2957.38 2341.10 2085.00 1855.49 3101.92 6620.79 12957.97 4262.44 4274.13 4061.76 5005.92 7116.59 6542.48 6164.14 5821.87 2858.37 2419.95 2063.42 1835.40 3415.84 8065.47 12760.37 4846.04 4282.81 4078.57 4933.02 7024.95 6586.38 6386.35 5554.98 2795.30 2310.91 2032.30 1822.46 3391.41 8565.56 12864.00 4819.05 4204.86 4024.79 4947.41 7141.75 6672.33 6708.84 5451.97 2651.99 2341.10 1961.40 1880.59 3249.79 9293.10 12666.62 4495.81 4173.73 4061.93 5007.54 7623.41 6572.15 6657.81 5827.37
Nov
2603.77 2419.95 1806.85 1962.50 3331.97 10377.76 12766.44 4501.40 4487.59 4114.92 5061.90 7954.83 6551.18 6664.62 6047.00 2688.45 2310.91 1838.48 1907.62 2899.64 10768.46 13314.07 4084.70 4333.68 4240.30 5059.63 7761.12 6417.96 5909.76 5509.63 2462.50 2545.09 1862.59 1956.47 3115.81 10238.50 13906.19 4032.30 4387.13 4346.74 5187.92 8466.13 6407.84 7091.20 5880.28 2489.33 2565.89 1853.19 2092.19 3394.77 9287.99 14644.83 4087.51 4358.68 4544.45 5198.38 8444.11 6765.84 6820.47 5910.96
Dec
2539.19 2570.84 1781.63 2232.39 3342.45 10034.27 15079.05 3947.46 4355.33 5290.15 5109.01 8046.24 6479.93 6759.04 5898.09 2323.21 2498.42 1706.94 2251.42 3401.22 9026.05 15300.75 4095.76 4426.98 4427.46 5294.19 8662.88 6697.42 6285.52 5495.19 2373.42 2387.31 1725.17 2287.17 3403.89 9614.37 14741.06 3771.54 4530.80 4574.65 5724.48 8444.73 6669.47 6732.36 5861.78 2313.18 2309.37 1732.58 2337.16 3485.75 8732.65 15137.44 4134.44 4465.53 4518.94 6625.91 8236.30 6532.35 6579.91 5416.55
Price of Turmeric in Karnataka market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
3000.00 2507.42 3000.00 2142.86 3065.00 5207.98 10166.67 17571.43 3859.30 4887.59 5406.21 6919.52 7890.30 8140.00 7118.18 3000.00 2453.71 3000.00 2750.00 3700.00 5207.98 7800.00 17571.43 4113.55 5184.71 5433.33 7716.19 8064.71 8261.11 7761.62 3000.00 2480.57 3000.00 2750.00 2406.25 3875.00 9916.67 17500.00 3977.18 5001.67 5479.63 8058.10 8223.42 7879.80 6052.21 3000.00 2400.00 2388.89 1800.00 2738.89 3950.00 10240.74 11563.64 3936.09 5111.49 5877.43 7909.98 7094.20 3049.01 2933.45
Feb
3000.00 2538.46 2359.05 2275.00 2757.14 3900.00 9000.00 9766.10 4133.58 6248.00 5937.54 7187.47 7187.88 8151.26 7101.49 3980.00 2626.47 2384.62 2009.09 2202.44 3600.00 8303.70 10941.11 4578.20 5441.86 5975.59 7453.87 8799.30 8600.49 4024.22 3980.00 2464.76 1980.25 2168.42 2877.31 3029.03 6028.76 8070.71 5545.06 5728.64 6209.69 7387.44 9432.72 8987.70 4648.15 3986.11 2545.62 1560.00 1960.00 2100.00 3068.27 7166.23 7622.64 4076.55 6188.47 5645.00 7273.18 8764.70 8187.50 8613.56
Mar
3521.74 2450.00 2272.44 2066.67 2594.00 3172.73 8535.39 12052.38 4354.04 5851.62 6326.77 7695.19 9020.26 8118.95 6803.76 4576.19 2615.38 2426.92 2013.02 2995.83 3800.00 6818.48 9243.40 4462.89 6747.43 5751.55 7979.72 9799.14 6928.14 6068.76 2827.42 2491.29 1929.20 2325.00 2388.23 2819.84 9940.00 10940.00 3867.78 6472.92 6321.17 7856.48 8463.02 8525.04 7087.15 3461.82 2280.77 2432.00 2176.47 3569.23 3892.31 10413.79 9897.44 3223.25 7125.45 6301.47 7623.19 8759.10 7711.01 6820.16
Apr
3516.22 3000.00 2168.33 2744.44 3700.00 3635.00 9851.56 10007.14 3318.93 7031.17 6361.44 7557.26 9300.63 7652.63 7111.77 3611.76 2000.00 2405.26 1956.68 3700.00 3800.00 10863.64 9105.77 3403.37 6930.46 6450.45 7407.98 8808.72 7037.50 6945.11 3000.00 2200.00 2676.32 2733.33 2971.43 4626.47 11380.95 8856.41 3438.15 6622.40 6314.04 7865.51 9029.90 7963.33 6938.04 2666.67 2100.00 2866.15 3320.69 3335.72 3340.00 13004.90 9235.25 3675.39 6520.58 6502.35 7529.98 8582.99 6573.31 6507.98
May
2833.34 2493.75 2620.00 2400.00 3153.57 3114.29 11000.00 9868.29 3640.54 6216.95 5587.91 7356.56 9943.09 7419.08 6720.43 2750.00 2100.00 2288.46 3128.57 3700.00 3227.15 12526.32 9252.27 3578.22 6817.95 6320.12 7625.87 8324.83 6605.31 6780.75 3600.00 3000.00 2722.50 2764.29 3418.75 4100.00 14481.73 8588.00 3557.86 6570.28 6554.47 7629.81 8976.27 6433.88 7025.36 3000.00 2550.00 2610.00 2007.83 3140.00 4679.17 14000.00 7832.41 3178.99 6479.39 6647.88 7285.06 8563.10 6376.97 7129.39
Jun
3200.00 3000.00 2732.14 3200.00 3700.00 4750.00 14000.00 6900.61 3956.23 6748.87 6299.42 7416.58 8200.68 5967.20 7523.61 3100.00 3000.00 2715.38 3200.00 3700.00 4515.00 16362.54 6812.69 3232.05 6593.43 6047.73 7768.29 9155.70 6230.51 6080.30 3000.00 3000.00 2200.00 2450.00 2216.00 3954.55 14224.65 6699.43 2970.03 6505.82 5618.85 7503.92 8429.15 6714.61 7787.26 3050.00 3000.00 2166.67 2241.67 3758.33 4675.00 12565.71 7311.71 3289.61 5651.77 6265.87 7021.52 8055.84 5589.91 7399.32
Jul
2840.00 3000.00 2555.00 3200.00 3413.33 4666.67 16000.00 7902.65 3659.21 5869.14 6201.10 8394.30 8076.27 6466.40 6669.27 2945.00 3000.00 2360.84 1900.00 3115.25 4750.00 11149.17 7135.91 4153.57 5789.26 6695.29 7089.90 8049.66 8502.44 7138.13 2892.50 1900.00 2535.71 2838.89 3840.00 4994.44 16000.00 7399.42 4559.28 6195.93 5485.11 7244.68 8399.72 6552.82 7433.83 2918.75 2450.00 2460.00 3200.00 3500.00 4872.22 16153.68 7649.11 4953.72 6032.67 6500.65 6619.24 7985.93 6877.59 7706.67
Aug
3000.00 2175.00 2497.86 1900.00 4500.00 4121.88 17000.00 7518.81 5111.10 6234.29 5733.73 6322.83 8680.56 7524.79 8243.75 3000.00 2400.00 2370.00 3200.00 4100.00 4750.00 15314.44 7092.13 3949.19 4666.67 6529.36 7189.89 7350.25 7403.72 7960.53 3000.00 2287.50 2400.00 2200.00 3620.00 4750.00 14000.00 5711.70 6876.49 6172.73 6077.98 6644.68 7993.62 7755.66 7638.93 3000.00 2343.75 1877.60 3200.00 3528.57 4750.00 14000.00 5908.90 6029.06 5200.00 6350.30 6736.88 7753.81 7136.00 7883.94
Sep
3000.00 3000.00 2495.83 3200.00 3600.00 4750.00 12666.67 5655.74 6210.00 5104.17 6781.62 6559.93 8148.87 7876.92 7830.70 3000.00 2671.88 2850.00 3200.00 3800.00 4750.00 13333.34 5359.76 6300.11 6300.00 5819.44 6714.58 7848.55 7920.37 6586.92 3000.00 2835.94 2760.53 2766.67 3700.00 4750.00 11968.75 5128.63 5916.27 6929.03 6930.86 6972.29 7854.75 7105.75 7659.35 3000.00 3000.00 2000.00 2266.67 4033.33 4750.00 13318.75 4689.80 4977.56 5645.11 5962.50 6203.25 7859.85 7101.50 7242.94
Oct
3000.00 3000.00 2690.91 3200.00 3500.00 7560.00 11666.67 4572.31 6090.99 6164.52 6032.00 6099.69 7800.97 8679.31 7084.29 3000.00 3000.00 2322.22 3700.00 3500.00 5975.00 12166.67 1902.30 5642.76 5571.05 6273.97 6731.82 7612.68 7812.69 8336.79 3000.00 3000.00 2325.00 2242.86 2906.00 8150.33 14832.58 5673.43 5132.87 6925.61 5238.83 7149.53 7818.37 7666.20 7779.58 3000.00 3000.00 2325.00 2680.00 2760.00 8000.00 12686.67 5344.41 5031.28 4753.33 4627.40 7900.86 7144.72 7835.11 6997.97
Nov
3000.00 3000.00 2850.00 3700.00 2085.71 8000.00 13068.18 5021.70 4734.19 5025.00 6372.41 7564.93 7905.49 8040.18 10300.00 3000.00 3000.00 2141.67 3700.00 3673.08 8450.00 14216.67 4506.75 5096.20 4700.00 5331.67 7248.94 7905.17 6126.39 6581.16 3000.00 3000.00 2850.00 2500.00 3800.00 8100.00 15000.00 4660.10 4444.01 4314.29 4899.06 7973.08 10082.81 6714.55 6637.23 3000.00 3000.00 2546.43 3700.00 3575.00 8428.57 15014.67 4371.76 5313.52 4335.71 5360.38 8730.77 8746.65 7328.33 6173.00
Dec
3000.00 3000.00 2750.00 3528.57 3875.00 8418.18 14981.80 4244.43 4902.46 5173.33 5727.32 8613.47 6713.40 7728.33 7140.00 3000.00 3000.00 2648.22 2491.73 3500.00 8800.00 15500.00 4151.46 4958.04 4688.89 5970.95 8863.48 7714.95 6954.55 6635.91 3000.00 2500.00 2500.00 2045.20 2500.00 8733.33 15368.42 3957.64 4350.33 4675.00 5021.40 9122.61 8673.05 7118.18 6779.29 3000.00 2500.00 2750.00 2268.47 3000.00 10772.73 16375.00 3869.80 4801.15 4321.43 6599.50 7615.79 8405.46 7761.62 7318.50
Price of Turmeric in Maharashtra market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
4601.18 3537.21 2833.83 1991.10 3406.31 4021.55 9221.72 11934.98 5267.54 5859.13 6225.76 9022.24 9993.88 7747.02 8753.81 4748.96 4290.7 3344.50 2034.79 2849.51 4093.22 9218.69 8219.85 4975.16 5724.79 6093.01 8762.47 9669.03 7698.44 9325.51 4485.64 4405.97 227.87 2570.23 2764.59 4594.96 9906.23 14070.53 4614.21 5664.79 6345.90 8648.13 9656.91 7748.11 10815.87 4109.85 3690.62 2964.83 2424.50 2956.33 4555.24 11082.46 16358.54 5880.10 6068.26 6388.52 9196.90 10645.95 8519.60 10987.55
Feb
3981.35 3451.69 3093.13 2545.00 3486.95 5399.44 11243.84 15208.86 5896.83 6367.26 6615.55 9362.09 11166.40 8520.86 10491.18 4347.82 3319.84 3227.34 2833.88 3683.76 4850.01 11840.84 13639.50 5978.31 6839.23 7978.56 9885.87 10489.72 9082.67 9829.13 4465.52 3261.3 3262.00 2802.52 3535.59 4889.89 11321.64 13808.17 5955.43 6916.12 7513.53 11264.85 10798.35 9099.81 10596.34 4135.7 3067.26 3136.24 2936.65 3773.45 5154.06 11407.74 13959.51 6060.20 5897.53 7600.46 10680.30 10689.36 9588.48 8804.66
Mar
4059.75 3169.85 3113.29 3241.58 2646.95 5105.40 11238.21 13941.78 5911.96 5601.92 8294.96 10193.38 10737.82 8679.94 10222.47 4327.24 3036.1 3292.23 3043.88 3752.19 5303.76 11664.88 14592.05 5844.51 10372.15 8121.77 10472.23 10396.86 9140.13 8746.95 3032.93 3034.62 3160.30 2932.23 3781.50 5670.03 12958.82 13150.42 5114.13 9964.76 8737.94 9833.04 10482.72 9093.64 9160.44 3249.46 3215.92 3188.63 2826.60 3866.83 5902.75 12345.50 13208.60 3993.86 10013.23 8249.12 10638.74 10177.32 8814.02 8732.00
Apr
3315.64 3238.27 3078.67 2883.47 3740.21 6060.33 12897.20 12996.87 4305.55 6805.49 6801.99 10142.76 9988.11 8824.39 8542.48 3229.04 4315.64 2785.86 2769.17 3794.97 5894.17 12482.54 12006.31 4566.70 8457.63 6815.19 10066.13 10073.43 8975.50 8895.48 3239.49 4178.05 2787.07 2724.07 3658.72 5603.78 12812.53 11455.85 4631.86 8888.84 7067.17 9985.11 10193.97 8600.20 8203.30 3053.29 4392.55 2881.94 2695.70 3621.77 5591.33 13956.83 11327.57 5293.32 8248.45 7476.15 9707.22 9680.17 7895.42 8014.43
May
2981.25 2954.51 2223.40 2533.64 3811.55 5669.62 15431.95 10746.21 4279.25 7629.92 7259.37 9623.42 9352.41 7320.77 8047.17 3112.25 2758.05 2957.24 2537.80 3684.01 5679.83 14826.99 10118.05 4786.31 7136.89 7323.23 9607.91 10070.99 7622.01 7950.37 3222.25 2929.74 2920.49 2512.01 3798.28 5611.52 14714.14 9633.27 4826.95 6142.33 7132.81 9092.08 9240.18 7713.10 7681.63 2961.5 2582.46 2855.94 2395.11 4065.86 5481.57 14635.91 9625.81 4587.84 7316.03 7019.74 8968.57 8947.58 7309.19 7643.21
Jun
3228.42 2419.35 2602.77 2155.48 4197.51 5513.69 15282.53 8120.24 4056.52 7037.94 6744.10 8581.37 8765.00 6482.06 7102.97 2940.47 2675.74 2394.58 2078.01 4147.68 4807.72 14562.48 8709.40 3508.01 6898.67 6131.37 8459.64 8767.28 5618.81 7314.34 3009.44 2687.08 2404.85 2131.14 4207.13 5340.57 14202.02 8209.83 3985.10 6520.02 5966.91 7882.79 8415.04 6564.53 7643.54 3001.84 2763.73 2597.81 2101.75 4237.93 5456.12 14963.73 7146.48 4086.88 6953.80 6492.63 7805.02 8592.40 6651.60 7701.71
Jul
3191 2567.13 2405.01 2069.52 4274.69 5508.55 15246.00 8623.32 3621.58 6539.23 6780.37 7928.26 8622.09 7366.85 7277.26 3195.14 2371.34 2535.83 2098.49 4343.04 5744.00 14468.99 8168.27 4382.97 6368.32 6639.31 8124.87 7963.62 7731.23 7205.11 3273.66 2317.97 2445.67 2086.08 4138.57 6061.90 15440.44 7261.07 5691.60 6319.77 6260.57 8185.63 7896.84 7785.59 7140.76 3727.97 2198.97 2682.98 2220.68 4146.80 6406.70 15060.55 7754.28 6391.90 6003.20 6436.41 7950.00 8258.13 7516.76 7205.05
Aug
3372.46 2214.53 2211.72 2117.04 4312.40 7255.65 15159.55 6863.17 5775.51 5699.76 7046.03 7864.37 8371.36 8131.05 7198.64 3893.44 2702.53 2410.94 1990.57 4383.52 7539.96 14726.73 6034.91 5633.44 5219.51 6519.18 7902.56 8357.22 7980.84 7075.79 3650.51 2521.84 2492.76 2165.28 3769.57 7383.15 13665.72 6150.35 6006.07 5249.10 6744.88 8495.85 7966.14 8182.83 6987.27 3494.22 2658.39 2466.61 2196.21 3924.49 6858.25 12722.49 6243.71 5671.81 5111.67 6844.75 8492.17 7492.96 7852.08 7108.76
Sep
3436.39 2592.51 2393.03 2167.74 4069.31 7724.97 14113.08 5550.01 5482.20 5328.06 6488.04 9169.57 7442.74 7941.22 6833.04 3471.38 2531.78 2442.93 1892.20 4367.66 7724.90 14207.60 5349.17 5475.19 5252.49 5982.44 9012.12 7430.82 7927.38 6885.83 3564.71 2584.29 2339.25 2176.43 3935.34 7938.60 13248.04 4689.99 5434.73 5197.85 6065.40 8561.01 7368.64 7790.34 6707.57 3296.87 2781.11 2391.79 2066.60 3972.98 7869.62 15454.31 5121.02 5116.67 4640.11 6406.95 8140.12 7291.76 7749.75 6686.16
Oct
3671.43 2523.85 2252.34 2294.08 3922.29 7835.09 15048.99 5367.70 4841.00 4958.37 6536.17 8503.33 7522.83 8144.90 6800.62 3063.68 3328.19 2283.24 2174.22 3653.34 8459.91 14332.17 6214.41 4800.44 4906.84 7305.28 8639.57 7474.21 7983.79 6867.99 4487.24 2973.9 2344.79 2276.99 4346.11 9633.33 14149.39 5712.04 4517.45 4914.44 6932.20 9300.74 7330.21 8022.84 6884.85 3486.32 3846.83 2139.95 2451.76 4815.18 9562.59 14282.83 5176.57 4581.78 5404.00 6856.45 9619.00 7857.47 7497.39 7190.44
Nov
3465.75 3819.51 2094.79 2259.10 4012.82 9677.86 14072.93 5264.59 4619.26 5493.82 6889.64 9982.48 7779.20 7639.25 7411.69 2978.85 3747.8 1949.97 2086.98 4013.56 11699.08 14799.70 5354.60 4982.98 5335.25 6881.26 9646.91 7835.67 7673.35 7280.59 3760.65 3639.33 1490.98 2613.55 4170.10 12279.49 15143.38 5499.93 4894.34 5749.19 7126.03 10206.65 7675.70 7510.36 7216.09 2957.93 3450.08 1963.48 3115.38 4322.67 13221.22 14349.43 5400.24 4810.01 5552.93 7420.18 10051.12 7878.49 7660.46 6922.62
Dec
3546.42 3433.69 2762.77 2483.29 4467.83 11610.20 15990.59 4858.64 4620.32 5821.51 7588.46 9838.77 7875.22 7878.46 6848.88 3799.38 3208.33 2116.25 2902.12 4620.61 12008.93 14537.11 5208.70 5241.73 5752.68 7250.32 9802.01 7946.14 7738.02 7212.14 3442.93 2891.13 2395.35 3057.63 4503.95 11756.38 18201.27 5367.45 5495.49 5905.41 7526.79 10194.33 7879.86 8014.43 7060.70 3775.83 3113.52 2349.33 2379.53 4886.46 11934.57 16728.04 5251.90 5229.11 5839.53 7762.20 9759.13 7816.70 8047.17 7310.38
Price of Turmeric in Tamilnadu market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
3455.95 2846.77 3551.17 2130.70 2898.15 3991.44 11300.00 16114.36 4150.06 6211.19 5882.64 7112.07 9838.00 7898.95 6959.55 3455.95 2853.71 2583.97 2163.64 2899.73 4049.17 10750.00 15604.13 4163.05 6255.45 5988.15 7687.05 9377.30 7744.87 7067.77 3479.94 2746.39 2557.47 2126.30 2800.00 4106.89 10492.31 14299.62 4001.42 5418.26 5330.60 7536.05 8985.23 7794.38 7590.90 3313.55 2614.22 2401.24 2186.27 2862.39 4284.37 10847.44 13425.34 4199.14 5826.20 6363.92 7615.76 8920.92 7842.67 6767.72
Feb
3418.10 2660.76 2319.45 2147.13 2886.50 4328.87 10696.58 13862.48 4516.51 5904.63 6443.20 7495.96 8994.85 7939.51 6886.88 3401.72 2637.49 2473.67 2120.84 2973.84 4335.77 10678.78 10930.42 4796.55 5976.32 6397.16 7632.44 9046.25 7956.15 7394.02 3436.55 2533.54 2394.35 2108.34 3167.49 4253.75 10740.93 11394.08 4397.03 6049.00 6525.29 7844.08 8932.14 7772.95 6704.76 3313.43 2483.12 2363.28 2141.38 3222.22 4308.81 10705.43 7644.12 4080.53 6045.83 6306.90 7967.20 8426.37 7696.90 6338.57
Mar
3293.71 2463.02 2381.40 2156.68 3402.91 4331.11 10708.38 11905.21 3898.01 6129.95 5686.58 7858.02 8790.16 7866.60 7494.47 3288.69 2413.91 2426.42 2143.62 3466.58 4428.07 10718.25 10754.93 3561.59 7026.38 6661.98 7564.27 8859.01 7538.42 6753.48 3118.06 2285.88 2429.84 2161.33 3401.88 4693.95 11488.64 10603.49 3548.69 7341.79 6551.58 7587.89 9121.04 7468.45 6751.60 3181.19 2349.90 2408.74 2191.38 3404.37 4922.35 12450.00 10481.66 3735.95 7062.17 6475.16 7403.87 9478.69 7757.49 6824.15
Apr
3110.50 2087.47 2225.00 2010.97 3265.16 5321.55 12350.00 10120.65 3450.00 6988.25 6501.28 7577.72 9110.25 7249.49 6808.89 3106.20 2225.50 2262.70 2184.69 3241.81 5432.23 12500.00 8611.90 3346.75 7745.45 6503.43 8102.15 9168.74 7089.36 6801.63 2976.42 2356.17 2319.77 2035.73 3341.27 5313.26 12433.33 9212.28 3430.04 7729.11 6999.69 7703.06 8916.43 6849.57 7083.40 2875.25 2341.81 2247.63 2056.96 3310.43 5326.96 12427.78 9227.94 3310.03 7285.75 7175.62 7610.04 8945.26 6578.24 6941.68
May
3017.38 2362.96 2325.29 2036.98 3379.34 5564.20 15395.39 8721.04 3429.06 6829.61 6872.06 7569.85 8663.72 6167.70 7262.62 2946.32 2352.39 2506.50 2073.41 3735.43 5275.42 13418.83 8566.44 3637.55 6756.50 6788.86 7328.02 8557.04 6002.39 7191.96 3059.45 2220.12 2537.99 1999.61 3724.24 5455.56 14459.20 7769.37 3712.82 6666.69 6747.69 7425.84 9059.89 6401.36 7497.80 3140.87 2236.27 2504.15 1915.32 3731.40 5264.83 15308.44 7151.99 3450.36 6614.04 6183.87 7042.44 8321.65 6372.79 7026.14
Jun
3100.16 2231.32 2456.76 2003.82 3853.34 5236.28 14784.00 7089.36 3389.18 6435.17 6179.42 7097.11 8222.96 6288.19 7072.50 3083.82 2280.15 2390.24 1992.80 3792.37 5375.80 14663.20 7223.89 3505.59 6033.99 5996.24 6909.63 8275.60 6325.79 7176.32 3040.69 2274.97 2400.04 2034.96 3591.76 5338.82 15034.58 7073.68 3597.44 6000.10 6233.20 6545.06 8178.68 6397.22 7118.59 3014.32 2365.76 2418.64 2058.13 3850.29 5364.65 15027.26 7485.19 3843.06 6229.23 6150.24 6624.49 8243.83 6934.72 7601.39
Jul
3027.51 2371.01 2468.56 2135.23 4322.22 5491.87 14826.24 7397.64 4146.10 6585.22 6114.63 6828.27 8338.04 6876.83 7079.20 3085.98 2397.67 2514.31 2094.10 4350.00 5683.52 14948.20 7198.67 5206.30 6096.69 6227.25 7149.38 8256.35 6883.64 7209.39 3047.96 2472.94 2517.98 2162.18 4388.89 6151.56 15057.54 6593.37 5535.72 6104.96 6318.24 6844.90 8495.25 7269.25 7241.28 3131.22 2477.17 2526.72 2154.49 4375.00 6734.51 14837.10 6803.22 6595.37 6099.48 6347.97 7156.77 8463.79 7155.48 7018.11
Aug
3069.00 2466.00 2503.59 2159.27 4400.00 7982.48 14947.61 6197.32 6536.19 5975.07 6306.41 7157.06 8523.51 7312.48 7153.47 3008.17 2608.10 2447.53 2155.42 4300.00 8857.15 14079.05 5727.22 6014.91 5932.76 6282.15 6847.79 8507.19 7229.50 6958.65 2966.52 2521.84 2489.57 2109.10 4350.00 8024.91 14163.66 5815.42 6158.55 5571.50 6089.22 6986.82 8002.79 7198.75 6956.61 3015.69 2658.39 2440.62 2054.75 4325.00 8125.00 13729.24 5338.19 6380.53 5690.26 6086.44 6845.43 7840.74 7134.31 6730.24
Sep
3065.04 2592.51 2448.17 1561.97 4337.50 8434.52 13196.19 5143.42 6288.19 5225.83 6049.11 7471.43 7928.62 7383.61 6536.92 3158.63 2923.72 2504.68 2100.62 4331.25 8434.52 13877.42 4601.79 5884.79 5470.04 5947.23 7398.88 7706.03 7337.52 6311.67 3339.57 2859.67 2467.63 2124.04 4334.38 8130.21 14201.86 4375.51 5613.23 5394.84 5868.03 7235.17 7900.06 7129.31 6342.68 3290.16 2867.88 2409.64 2100.18 4000.00 8199.19 14292.58 4727.94 5419.51 4939.61 5861.93 7307.25 8035.51 7003.29 6705.63
Oct
3347.94 2770.81 2331.01 2114.92 4167.19 8210.98 14244.33 4978.70 5380.18 5007.82 5791.04 7616.25 8102.74 6974.85 7065.76 3249.73 2749.99 2314.66 2133.66 4083.59 8747.19 14010.34 5694.64 5228.20 5398.46 6129.64 7769.17 7832.87 7385.80 6624.48 3225.50 2734.09 2323.06 2182.68 3296.30 9799.16 13868.56 5302.50 5218.32 5355.57 6069.46 7730.07 7989.25 7263.54 6450.41 3146.00 2811.67 2251.15 2244.96 3689.95 11100.00 13958.62 4870.30 4577.43 5340.33 6167.80 8238.44 7930.34 7236.13 6541.52
Nov
3096.59 2916.81 2170.85 2256.18 3937.04 11900.00 14155.52 5023.52 5191.58 5225.77 6093.50 8251.89 7934.22 7568.48 6989.84 3123.50 2912.61 2226.62 2245.28 3900.00 11500.00 13818.47 4642.01 5152.44 5202.37 6293.36 8696.95 7783.55 7338.77 6552.63 3111.50 2834.24 2240.86 2372.96 4114.15 13106.76 14917.87 4507.98 5002.65 5570.69 6161.11 9107.19 7962.99 7132.86 6588.53 3091.60 2851.54 2212.87 2601.21 3878.39 12303.38 15455.08 4385.75 4912.69 5729.90 6211.12 9456.24 8188.14 7179.71 6594.67
Dec
2854.03 2833.55 2077.79 2780.66 4066.96 3249.77 15893.80 4161.68 5012.19 5665.50 6169.69 9061.19 8171.61 6891.63 6420.11 2789.65 2804.44 2013.06 2746.89 4028.34 10800.00 16095.52 4204.76 5166.91 5621.39 6415.36 9834.08 8283.50 7280.47 6531.59 2841.96 2726.67 2027.36 2872.44 3979.64 10500.00 16059.72 3931.00 5556.42 5608.75 7005.24 9754.93 8142.20 7106.23 6418.14 2772.91 2693.17 2110.04 2988.45 3898.45 10650.00 16862.76 4285.34 5078.20 5263.91 8198.79 9521.93 8002.39 7162.94 6341.90
Price of Turmeric in Telengana market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2616.25 2261.83 2372.15 1447.48 2420.00 3200.00 9499.44 12158.26 4244.06 5366.34 4875.28 6318.93 6139.14 6863.02 7409.61 2600.53 2382.50 2460.28 1861.67 2420.00 3200.00 7875.00 12086.59 3652.13 5457.82 5081.68 5376.26 6891.15 6102.68 7027.80 2608.39 2322.17 2100.21 1438.37 2415.00 3468.02 7689.00 12916.08 3886.74 4904.80 4843.98 5931.36 6769.19 6466.46 6324.21 2604.46 2284.88 2169.49 1761.86 2327.17 3733.49 9141.62 10181.62 3852.43 4487.64 5470.03 5791.20 7075.08 6554.68 5950.67
Feb
2584.81 2303.52 2333.07 1899.41 2318.17 3664.79 9026.09 9217.62 3984.42 4473.02 5600.16 5872.23 7011.33 6569.04 5612.25 2594.64 2284.88 2138.07 1875.02 2250.99 3234.83 8624.48 8834.70 3297.79 4438.41 5773.84 5936.40 7350.01 6461.23 5509.62 2525.50 2345.00 2102.21 1870.21 2538.73 2822.05 7990.95 8905.38 3838.21 4512.50 5531.93 6425.17 7520.06 5901.20 5525.64 2560.07 2085.50 2120.14 1768.31 2619.43 3026.77 7496.45 9989.69 3782.84 4579.41 5529.36 6972.44 7447.35 5530.56 5454.40
Mar
2542.78 2066.53 1851.78 1846.49 2839.04 3192.37 7749.15 10014.08 3726.11 3098.94 5630.66 6719.18 7370.17 5192.91 5623.59 2551.43 2076.02 1784.32 1711.82 2525.66 3048.28 9005.49 10155.25 3343.40 4004.99 5550.00 6826.34 7304.09 5100.75 5581.26 2547.10 1888.61 1917.15 1732.44 2475.80 3275.69 10865.50 9290.92 3199.44 5846.09 4768.40 6468.00 7620.20 5252.15 5660.99 2549.27 2050.00 1876.76 1657.25 1881.38 3588.11 10655.38 8266.46 3451.19 4925.54 5558.57 6532.39 7464.63 5430.18 5607.61
Apr
2548.19 1894.73 1958.15 1801.49 2900.00 3766.79 10748.79 7971.31 3261.26 5385.82 5664.13 6102.31 7601.51 5032.37 5515.46 2548.73 1995.63 1888.09 1743.48 2706.96 3921.39 10821.40 8842.82 2920.28 6000.00 5514.14 6189.18 7764.40 5377.82 5199.55 2548.46 1890.42 1807.31 1725.04 2599.47 3697.21 11621.95 8027.55 2827.56 3000.00 4904.85 6808.74 7840.17 5300.30 5854.84 2548.59 2341.81 1986.40 1758.01 2397.47 4242.36 12700.01 7626.16 2854.26 4500.00 5121.65 7015.42 8023.09 5365.88 7262.77
May
2548.52 2362.96 1609.62 1732.63 2521.89 4438.14 14382.96 7269.86 2941.18 3750.00 4785.88 6253.31 7912.06 4624.26 7376.84 2548.56 2352.39 1837.05 1817.22 2874.63 4765.17 13889.99 7179.53 3427.38 4125.00 5517.99 6793.58 7773.22 4863.00 7409.61 2548.54 2357.67 2045.50 1786.23 3066.91 5258.33 13398.48 7274.31 3624.90 3937.50 5294.53 6924.53 8024.35 4927.89 6841.63 2548.55 2355.03 1924.31 1774.89 3360.00 4803.27 13204.02 6515.66 3558.64 4031.25 5263.90 6964.16 7830.95 4647.04 6591.87
Jun
2548.54 1858.00 1920.62 1796.01 3529.80 4724.47 14215.25 5962.99 3274.83 4922.68 5000.45 7028.87 7731.82 4752.44 6527.02 2548.55 1832.78 1952.75 1807.20 3372.64 5082.52 13700.58 5962.86 3293.08 4850.84 5039.75 6887.95 7748.85 5101.66 6559.45 2548.54 1845.39 1962.49 1809.07 3503.00 4950.64 13601.23 5976.11 3336.00 5120.04 4972.31 6603.71 7886.96 5418.36 6703.84 2548.55 1839.09 1952.75 1791.38 3515.00 5038.28 13852.04 6573.71 3496.61 5245.77 5181.58 6566.00 7967.56 5774.70 6880.96
Jul
2506.72 1965.55 1962.66 1883.38 3568.95 5160.84 13726.64 6274.91 3593.93 5290.10 5201.40 6472.91 7801.59 5526.07 6561.45 2491.59 1936.69 1996.44 1843.70 3849.41 5215.37 14580.43 6424.31 4302.29 4960.98 4732.00 6659.19 7773.74 6365.23 6526.80 2602.24 1942.98 2067.12 1823.92 3931.36 5403.61 14084.47 6349.61 4683.97 5118.29 5339.91 6725.44 8006.46 6918.16 6466.57 2552.85 1960.26 2127.00 1906.88 3568.95 6283.21 11224.01 6386.96 4623.07 4872.34 5404.22 6560.83 7959.55 6728.16 6402.19
Aug
2434.14 1955.24 2072.62 1897.18 3992.00 7144.22 13228.50 6368.29 5333.77 4686.30 5413.96 6679.35 7959.69 7112.35 5878.72 2444.66 2048.85 2198.70 1936.98 4054.29 7905.85 14127.18 6377.62 5358.13 4614.99 5494.86 6941.28 7763.44 7002.14 5905.63 2544.07 2041.13 2152.21 1922.68 4023.15 7480.00 13640.86 6372.95 5083.30 4483.96 5348.60 6582.38 7172.79 6887.63 5815.49 2470.20 2177.93 2202.43 1920.59 3478.40 7121.01 11528.00 6375.29 5076.14 4605.49 5602.64 6925.16 6834.72 6893.99 5736.55
Sep
2654.68 2247.34 2220.05 1976.61 3115.00 7377.77 12514.31 6374.12 5348.28 4558.98 5549.68 7567.97 6823.52 7139.15 5815.90 2725.18 2147.72 2326.46 1895.68 3296.70 7377.77 12978.99 6374.70 5300.51 4491.67 5301.93 7543.19 6711.77 7155.64 5637.31 2986.12 2197.53 2361.83 1853.86 3597.00 7428.88 13024.15 6374.41 5441.55 4021.10 5066.43 7123.02 7047.36 6908.46 5776.56 2831.05 2430.04 2161.82 1950.18 3000.00 7381.41 13840.72 6374.56 4885.00 4233.22 4951.66 7355.86 7018.48 6327.89 5978.56
Oct
3093.06 2356.60 2250.72 1919.00 3298.50 7451.64 13661.16 4906.90 4508.05 4266.33 4915.87 6182.73 7214.93 6913.40 6083.62 2898.05 1985.00 2129.43 1851.13 3149.25 8216.22 13349.58 5700.00 4267.56 4449.22 5368.16 7592.82 6923.72 6524.98 6183.85 2667.74 2334.72 2139.29 1957.13 3223.88 9048.29 14634.43 5356.43 4495.64 4325.81 5611.64 7509.02 7062.49 6258.36 6009.66 2879.83 2322.95 2016.04 1917.49 3186.56 11000.00 12882.86 4883.85 4855.87 4304.67 5284.28 8196.48 6670.79 6742.94 6191.08
Nov
2773.79 2640.36 1933.15 1794.51 3205.22 10024.15 13123.75 5305.48 4617.13 4161.60 5226.09 8159.88 7150.84 6865.77 6100.37 2826.81 2481.66 2022.90 2021.00 3195.89 10512.07 12986.97 5107.83 3966.46 4467.98 5454.25 8465.78 7018.64 6775.99 6145.73 2803.00 2561.01 2003.70 1938.71 3200.55 11741.03 13958.43 5240.64 4704.52 5070.99 5522.96 9306.21 7014.18 7159.32 6123.05 2100.00 2521.33 2091.83 1361.00 3198.22 9400.00 14977.99 4849.23 4717.75 5142.61 5535.06 9080.97 7575.16 7236.14 6134.39
Dec
2683.71 2545.33 2010.07 2100.00 3199.39 10570.52 15477.27 4889.75 4426.77 4889.31 4753.00 8944.99 6956.95 7172.67 6128.72 2183.96 1957.97 1837.61 2148.62 3198.81 9736.54 14000.00 5062.11 4358.47 4950.46 5505.16 8340.67 7222.39 7161.28 6131.55 2433.84 2180.23 1844.84 2300.00 3199.10 9166.67 14738.64 4686.71 4892.84 5216.96 6379.69 9108.71 7149.78 7262.77 6130.13 2510.63 2261.54 1900.25 2224.31 3198.95 9541.67 16016.06 4797.39 5234.63 5037.38 7175.34 7244.04 6886.57 7376.84 6264.04
Annexure III: Price of Green gram in Selected State markets of India Price of Green gram in Odisha market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2250.00 2250.00 2975.00 2913.09 3004.07 3693.76 5274.18 5551.38 5263.52 5374.39 6672.13 5279.43 7247.24 5191.6 6449.36 1702.47 2000.00 2441.29 2222.61 3068.48 3877.10 4231.48 4865.36 4204.32 6139.22 6731.03 5070.03 7836.51 5491.19 6449.36 1750.00 1750.00 2694.87 2045.46 2930.28 3546.65 4346.80 4148.38 3858.62 4961.24 6532.30 5472.14 7541.88 5491.19 6449.36 1759.55 1759.55 3127.69 2584.32 3219.43 3390.51 5455.85 4933.05 3711.36 5249.12 6080.50 5004.89 7803.53 5491.19 6990.08
Feb
1521.81 1521.81 2422.29 2447.68 2235.43 3313.19 5538.77 4395.48 3554.14 4941.22 6570.03 5125.38 5044.84 5409.03 5815.21 1300.00 1300.00 2092.92 2560.00 2814.69 3225.42 5580.26 3319.61 3915.64 4604.37 6640.81 6594.41 5070.52 5864.82 5815.21 1500.00 1500.00 3235.51 2439.65 2812.05 3287.50 5734.38 4323.34 4218.11 5298.45 6477.90 5920.07 5966.42 5838.75 6371.58 1250.00 1250.00 2281.17 2418.40 2490.71 3693.73 7006.25 5177.21 4912.64 4772.59 6400.20 6165.07 5504.34 5388.17 6371.58
Mar
1389.16 1389.16 2440.93 2870.41 3526.73 3739.35 4000.00 5344.99 4736.47 4998.74 5701.78 5329.50 5919.50 5767.8 5622.37 1596.18 1596.18 2443.18 2900.66 3505.40 3731.89 3200.00 5377.63 5404.51 5895.11 6516.29 5040.56 6359.10 5388.17 5610.06 2248.28 2248.28 2043.51 3042.63 3918.32 5728.62 6086.42 3878.21 5315.35 6199.56 6567.80 5066.98 7001.00 5412.42 5610.06 1872.30 1872.30 2080.40 3028.23 3493.20 3954.84 5217.86 4893.61 5564.57 6775.01 5377.68 5695.58 6164.10 5412.42 5593.72
Apr
1833.79 2250.00 2958.50 3234.04 3179.34 3640.35 4832.80 5091.45 4996.50 5642.75 4957.99 5851.06 6584.10 5337.47 5668.59 1702.47 2000.00 2930.49 2429.98 3200.87 3843.99 4414.17 5034.06 4849.91 5162.05 5539.99 5792.47 6157.29 5336.71 5668.59 1750.00 1750.00 2898.57 2353.12 3179.38 3753.52 4513.13 4307.89 3664.07 4907.67 5368.57 5796.48 5942.38 5336.71 5668.59 1750.00 1750.00 2857.05 2748.38 2827.77 3250.10 5406.28 5089.98 3812.70 5059.90 5399.52 5589.74 6217.97 5208.31 5810.53
May
1514.97 1514.97 2389.09 2681.00 2365.90 3351.71 5650.00 4291.54 3529.86 4717.76 5197.92 5649.11 5774.52 5155.7 5810.53 1775.03 1276.88 2490.58 2331.91 3029.71 3194.81 6000.00 3989.81 3659.93 4993.76 5280.63 5663.18 6203.08 4977.3 5810.53 1385.00 1385.00 2504.49 2825.48 2512.44 4282.70 5500.00 3985.51 4800.12 4630.60 5309.51 5711.02 5616.73 5170.31 5783.15 1400.00 1400.00 2066.26 2972.74 2394.55 2434.47 6369.15 5029.12 4532.53 4920.63 5519.23 5653.09 5901.31 5368.66 5783.15
Jun
1498.84 1498.84 2682.17 3505.71 3510.06 3799.76 4000.00 5485.00 4994.16 5976.08 5064.03 5879.04 6259.26 5227.02 5784.01 1907.79 1907.79 3034.73 3072.95 3424.73 3735.16 6200.00 4860.41 4722.91 5808.55 5233.59 5655.05 5636.99 5227.02 5624.41 1901.08 1901.08 3685.00 3239.01 3767.11 6198.97 6200.00 4961.09 5965.88 6255.67 5692.52 5588.41 6324.62 5331.9 5596.49 1883.33 1883.33 3065.49 2945.09 3423.52 4855.36 5522.18 4506.46 5853.47 6219.73 5644.06 5691.39 6069.84 5331.9 5598.14
Jul
2041.90 2250.00 2843.89 2632.98 3026.27 3635.59 4465.49 4995.45 4878.06 4937.87 5746.95 5623.11 5974.48 5331.9 5587.22 1702.47 2000.00 2991.61 2501.23 3137.47 3961.89 3832.00 4511.66 4465.71 4837.02 5418.64 5714.83 6298.92 5366.88 5633.56 1750.00 1750.00 2811.19 2635.34 3125.08 3659.79 4395.53 4351.97 3711.72 4885.24 5840.38 5784.33 6450.87 5366.88 5610.26 1500.00 1500.00 2744.83 2586.84 3086.17 3322.74 5487.82 4702.77 5790.68 5008.96 5564.84 6009.16 6536.88 5366.67 5647.7
Aug
1454.42 1454.42 2466.64 2556.65 2409.31 3385.25 4800.00 4144.07 3632.25 4793.02 5476.06 5010.05 6410.00 5366.67 5598.52 1200.00 1200.00 2622.54 2442.51 2389.92 3033.31 5554.77 5259.52 4520.08 4826.56 5553.92 5610.77 6450.00 5366.67 5596.2 1553.85 1553.85 2453.53 2612.40 2617.15 3848.20 4661.58 3740.51 4884.26 4796.19 5826.00 5551.74 6398.00 6606.14 5599.19 1400.00 1400.00 2746.97 2480.57 2441.62 3313.79 4794.78 5462.03 5153.12 4946.98 5956.81 5745.99 6410.00 6606.14 5709.49
Sep
1927.85 1490.28 2575.21 2404.40 3465.15 3889.21 4000.00 5145.47 5646.04 5893.01 5913.87 5925.52 6405.00 6606.14 5805.08 1654.07 1654.07 2371.43 2282.70 2884.80 3708.71 3635.90 4644.21 5753.12 6040.11 6190.71 6085.26 6351.00 6395.65 5635.71 2086.36 2086.36 1805.71 3152.19 3538.90 5113.76 6200.00 5138.85 5422.02 6236.50 6092.55 5953.13 6385.00 6154.27 5691.12 2016.67 2016.67 3220.47 2917.93 3707.62 4574.22 5551.24 4201.57 6054.68 6227.50 6080.83 5940.24 6384.00 6154.27 6120.17
Oct
1937.84 2250.00 1922.82 2682.38 2963.28 4010.24 4505.27 4981.91 4727.86 6190.05 4600.00 5995.10 6398.00 6323.01 6978.43 1702.47 2000.00 2818.88 1970.32 3079.75 3508.05 5048.42 4276.71 4328.32 4716.84 5706.11 6481.39 6447.00 6250.38 6976.67 1750.00 1750.00 2980.85 2803.90 3098.14 3612.12 5199.37 4506.02 3783.94 4838.95 5551.62 6362.75 6437.00 6323.01 6961.04 1441.67 1441.67 2558.09 2608.79 2826.44 3458.73 4084.10 4573.36 3613.91 5158.02 5461.64 6970.86 6450.00 6152.44 6968.77
Nov
1344.26 1344.26 2431.70 2850.77 2530.23 3104.96 3100.00 3698.44 3735.19 4628.83 5487.15 6561.49 6795.68 6152.44 6917.13 1300.00 1300.00 2483.37 2594.76 2223.67 3285.29 5309.16 3944.64 4318.57 5041.41 5704.94 6246.21 5549.11 6152.44 6721.96 1284.38 1284.38 2340.54 2549.28 2488.26 3725.00 3618.18 4712.91 4585.92 4991.21 5893.17 6742.19 5772.34 6676.15 6547.76 1448.39 1448.39 2426.28 2518.17 3140.35 3872.00 7152.17 5733.97 4583.67 5275.83 5313.38 6768.94 6861.37 6264.52 6898.28
Dec
2233.33 2233.33 3185.57 2813.13 3731.57 3741.94 4000.00 5529.76 5516.45 5664.54 5418.04 6652.04 5866.67 6469.3 6984.82 1806.15 1806.15 2195.82 3262.56 4012.63 5216.56 6200.00 4451.60 5756.25 6176.27 5928.51 7086.14 5352.51 6472.37 6908.95 1824.33 1824.33 2663.20 2905.48 3799.98 5649.04 6200.00 5215.62 6096.30 6373.07 5530.29 7273.35 5392.85 7000 6952.57 1467.48 1467.48 2750.00 3285.51 3616.82 4810.05 5546.89 4403.42 4747.58 6359.61 5164.84 7221.13 5691.02 7000 6725.33
Price of Green gram in Gujarat market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1429.56 1766.31 2606.78 2947.69 2009.56 2954.63 5660.97 3500.44 3602.08 5194.95 5610.84 7330.32 6942.65 4580.93 4650.75 1437.94 1714.05 2605.59 3070.32 2041.89 3022.96 5752.93 3668.66 3558.42 5841.98 5465.65 7467.08 6738.55 4345.67 4634.54 1417.27 1708.66 2610.97 3110.17 2031.64 3130.71 5726.92 3995.66 3491.94 5531.96 6112.94 7420.07 6833.52 4519.55 4700.34 1396.90 1725.86 2547.45 3199.28 1994.51 3291.52 4870.44 3876.72 3518.22 5411.93 5343.16 8357.23 7137.71 4473.25 4676.63
Feb
1360.03 1546.11 2586.27 3117.55 1997.27 3106.01 5237.53 3709.51 3619.77 5431.64 5951.80 7749.95 6392.12 4369.32 4663.05 1341.13 1864.08 2634.78 3066.15 2050.92 3200.53 4379.90 3592.33 3360.48 5404.08 6468.52 5499.97 6654.77 5461.02 4496.45 1343.01 1481.90 2840.77 2936.74 2167.84 3056.05 3648.52 3618.39 4087.25 5044.08 5623.86 6107.44 6629.28 4377.87 4361.64 1281.96 1972.35 2882.33 2904.56 2295.15 3262.68 3699.79 3517.81 3442.05 4917.06 5266.03 6570.69 6504.80 4966.51 4569.09
Mar
1255.06 2027.93 2844.04 2789.06 2289.75 2972.73 2904.62 3347.54 3873.43 4388.48 6766.95 6485.20 6800.93 4698.63 4535.12 1307.00 1994.40 2951.74 2835.36 2207.98 3480.60 3558.60 3326.82 4008.37 5290.42 7955.99 6111.20 7666.53 4614.72 4488.88 1307.55 1887.97 3447.74 2841.96 2155.55 3402.27 4037.37 3538.57 3722.11 5447.26 7150.69 6860.00 6829.02 5354.78 4584.80 1327.45 1354.70 3592.69 2578.80 2094.68 3795.85 6487.79 3362.95 4545.77 6115.80 8667.01 8686.84 7622.23 6755.89 5177.36
Apr
1354.32 1960.07 3413.66 2798.94 1401.80 3904.75 4730.32 3600.63 4054.84 5301.16 4524.83 8343.51 6775.64 5295.93 5064.62 1360.18 1578.33 3341.93 2903.30 2112.09 3889.89 4311.51 3823.17 4505.40 5829.60 5667.46 9298.17 7551.64 4823.04 5130.51 1327.50 2228.80 3153.85 2807.05 2086.01 3958.89 5324.71 3419.45 4178.56 6435.21 5557.85 6881.32 7146.99 4910.51 4805.48 1339.39 2176.58 3239.97 2873.61 2110.05 3848.22 5654.86 3462.92 4106.14 6091.23 5939.76 7643.43 6102.75 4644.33 5332.94
May
1463.99 2221.18 3288.31 2726.89 2105.66 3864.89 6275.28 3898.77 4399.31 5182.64 5164.37 7449.37 6605.00 5300.11 5201.71 1545.80 2324.89 3390.60 2575.24 2319.79 3827.15 6467.60 4251.38 4241.94 5725.41 5927.78 7708.45 6415.34 5016.24 5360.53 1524.15 2326.51 3340.17 2435.48 2251.18 3928.90 6637.30 4280.29 4018.39 5089.31 5244.47 7453.72 6028.85 4678.33 5211.07 1470.14 2304.86 3217.89 2500.76 2373.58 3689.69 5327.81 4064.25 3991.11 5228.71 4901.04 7947.03 5749.93 4371.40 4936.29
Jun
1470.55 2355.23 3088.42 2456.72 2334.20 3666.92 5852.69 4147.79 3972.86 5159.40 4702.05 7504.37 5755.76 4318.47 4949.26 1462.02 2388.80 3091.28 2498.50 2412.69 3774.00 5689.88 4018.01 3967.19 5187.77 4879.24 7117.45 5905.50 4438.32 4438.32 1516.90 2446.38 3118.36 2620.61 2495.81 3630.05 5689.76 4002.77 4053.64 5101.18 5142.15 8082.25 5765.01 4408.71 4408.71 1530.68 2283.38 2794.58 2674.97 2419.99 3700.01 5214.41 4017.11 4414.13 4805.83 5406.48 7972.41 5713.55 4325.12 4325.12
Jul
1519.56 2335.87 2684.48 2464.13 2362.53 3963.66 4935.08 4105.41 4297.39 4479.67 5804.96 8174.47 5820.48 5526.90 4878.70 1608.35 2460.11 2689.42 2468.89 2505.69 4312.62 5949.19 3730.11 4336.74 4760.00 6258.01 8609.61 5907.70 5001.69 5287.31 1637.97 2458.81 2739.28 2458.67 2927.70 4155.23 4142.49 3630.99 4955.84 5578.38 5995.35 8136.33 5627.27 4456.00 5207.10 1663.03 2389.94 2712.56 2359.30 2854.18 3840.20 4206.08 3380.53 4882.51 5556.95 6178.68 7930.36 5432.53 4375.32 4810.39
Aug
1485.45 2443.07 2401.46 2309.86 2853.27 4262.96 4219.29 3649.31 4881.02 5361.31 5828.08 8321.09 5171.34 4638.37 4711.15 1522.68 2409.56 1709.85 2350.46 2524.84 5201.11 5213.27 3674.36 5663.66 5743.46 5877.98 8504.25 5198.93 4684.47 4411.13 1587.03 2042.67 2251.88 2189.71 2469.54 4363.13 3973.11 3638.84 5354.70 4543.23 6143.81 7033.49 5097.56 5850.26 4640.85 1597.49 1958.30 2208.01 2147.47 2582.21 4043.15 3747.84 3871.36 5309.81 5511.74 6068.96 8022.87 4558.65 4957.97 4685.02
Sep
1557.77 2125.31 2343.66 2023.56 2799.82 3791.08 3473.31 3815.71 4507.57 4168.09 5976.12 8402.17 5348.77 4487.43 4589.88 1676.74 2294.31 2741.88 1959.67 2817.34 3761.67 3953.71 3826.46 4821.59 4569.20 6283.86 7838.27 4679.89 4629.21 4608.28 1722.47 2465.38 2846.88 1958.93 2795.76 3685.62 3701.25 3874.79 4912.04 4522.80 6420.38 8205.03 5178.92 4622.91 5070.98 1828.59 2681.70 2907.70 1536.40 2624.43 3965.90 3815.35 3743.07 4471.02 4606.77 5814.75 8686.84 5213.73 4339.70 4915.76
Oct
1809.47 2836.41 3039.37 2191.76 2516.02 4475.01 4005.23 4218.79 4660.52 4701.20 5962.66 8343.51 5271.59 4356.01 5149.47 1797.67 2255.59 3404.56 2346.60 2610.18 4837.65 3768.79 3856.43 5131.74 4866.68 6427.37 9298.17 4912.44 4426.55 5356.30 1816.57 2689.67 3249.76 2384.15 2790.65 5241.49 3784.92 3798.55 5019.58 4774.28 6386.40 7314.19 4759.58 4875.12 5214.53 1855.46 2171.27 3166.11 2272.83 2994.29 5186.23 3529.86 3934.75 4888.62 4817.66 6512.30 7609.73 5020.17 4630.62 5375.67
Nov
1811.74 2196.98 3239.61 2181.33 2820.84 5659.37 3479.19 3576.23 5078.09 4546.21 7084.09 7653.65 5063.77 4635.05 5527.30 1779.85 2272.71 3109.88 2206.50 2792.17 5243.62 3539.05 3659.48 5431.41 5156.76 6932.50 7375.97 6460.83 4780.20 5683.57 1544.47 2320.04 3098.52 2142.34 2792.50 3884.78 3468.67 3630.70 5284.56 5502.24 6696.83 7310.61 6118.71 4905.55 5385.15 1739.56 2297.47 3026.18 2147.85 2820.05 5153.53 3524.75 3530.56 5360.09 5595.91 5983.83 7146.97 4291.68 4723.97 5635.00
Dec
1762.72 2289.80 2953.77 2111.76 2793.01 4864.72 3671.58 3501.08 5196.65 5697.64 7012.07 6893.66 4812.97 4791.56 5523.48 1760.38 2326.44 2961.97 2061.00 2940.05 5194.81 3559.74 3942.76 5286.20 5391.62 7100.19 7016.25 4396.86 4920.87 5039.57 1715.80 2445.48 2915.08 2090.84 2805.62 5885.58 3436.97 3646.82 5263.67 5346.29 7389.93 6953.06 4410.00 4763.10 5399.13 1726.64 2553.87 2887.37 2042.64 3398.79 4849.42 3547.24 3422.28 5261.07 5216.73 7238.87 6984.66 4201.32 4769.71 5350.64
Price of Green gram in Karnataka market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1788.65 1692.20 1889.46 3529.45 2358.84 2887.30 5697.65 4670.98 4024.98 5223.10 6200.66 7642.83 7286.72 4822.80 5338.89 2097.53 2080.27 2949.80 3346.25 2366.09 3119.85 5568.77 4922.34 3783.28 5614.17 6465.79 7375.66 7559.77 4931.16 5181.53 2160.12 2117.39 2951.61 3665.41 2394.49 3008.33 6485.32 4751.11 3947.84 5578.98 6582.50 7143.24 7300.32 4892.99 5339.89 2192.70 2066.13 3047.84 4000.00 2470.82 3573.73 6419.34 5212.02 3791.01 5835.96 6641.94 7747.69 7542.99 5002.93 5056.67
Feb
2229.20 2086.55 2570.30 3832.71 2304.20 2630.27 6169.10 4921.44 3804.08 5464.15 5913.54 8598.20 8944.02 4961.96 5608.40 2171.57 2178.53 2944.04 3749.23 2683.78 3317.27 6492.29 4613.61 4332.39 5529.39 7000.50 8028.44 7759.34 5082.88 6081.64 2186.07 2132.82 2970.44 3077.38 2665.81 3218.86 6264.80 4793.27 4545.78 5788.56 7038.75 8451.79 7578.31 5085.80 5727.96 2218.98 2172.71 2880.42 3194.82 2718.22 3328.39 6272.50 4935.15 5070.88 6159.07 6579.81 8150.78 7213.37 5074.23 5839.07
Mar
2128.72 2256.94 3095.60 3482.59 2795.23 3115.28 6423.94 5152.62 4944.15 6164.63 7792.28 7632.87 7474.17 5331.16 5243.41 2189.38 2319.19 3146.38 3575.12 2392.23 2804.27 6145.33 5089.82 4486.44 6247.27 7055.47 7130.79 7172.10 5371.27 5821.10 2203.40 2126.71 3545.47 3621.39 2372.02 3057.12 6388.10 4828.76 4357.28 6155.53 6951.00 6582.17 7594.11 5387.27 5682.08 2169.82 2265.46 3307.29 2883.87 2588.58 3168.98 6428.99 5620.03 4345.06 6064.23 6958.31 7403.71 7612.13 5397.99 5605.14
Apr
1926.31 2340.35 3669.32 3754.81 2675.03 3323.30 6605.09 4818.41 4413.40 5998.13 7620.82 8513.59 7667.54 5554.48 5644.46 2224.30 2287.87 3254.81 3742.56 2742.74 3564.00 6753.01 5251.21 4871.20 6491.88 7182.49 8557.86 7639.36 5569.08 6008.09 2171.42 2280.29 3559.52 3710.60 2868.24 3541.87 6284.40 5881.78 4484.00 5934.13 7506.44 8672.62 8187.28 5337.67 5705.82 2226.29 2516.86 3895.41 3411.79 2723.32 3117.65 6458.06 5022.76 4094.43 5974.27 6558.83 7423.99 6914.96 5303.92 5842.31
May
2143.18 2720.37 3910.86 3455.85 2732.73 3517.19 6676.38 4844.23 3864.76 5456.38 7070.60 7466.15 7749.33 5275.74 5704.19 2225.00 2921.77 4218.70 3175.41 2318.72 3317.42 6724.47 4577.22 4194.19 5338.22 6901.52 8148.16 7349.74 5251.60 6482.24 2133.29 2916.07 4266.13 3237.36 2698.46 3710.51 6621.76 4746.79 3921.03 6047.66 7512.50 8232.65 7539.39 5075.14 5935.83 2102.38 2810.70 3280.16 2983.42 2296.47 3694.24 7467.67 4593.54 4002.38 5527.37 7148.93 6882.25 7616.94 5073.89 5978.24
Jun
2091.05 2828.85 3921.12 3089.70 2624.90 3579.18 6560.80 4612.07 4276.45 6031.03 6907.77 8499.27 7324.93 4790.85 6190.61 2185.78 2855.13 3969.93 3181.40 2677.31 3733.99 5035.58 4639.51 4706.67 6217.22 6986.04 8343.47 7492.23 5466.90 5466.90 2194.43 2783.67 3884.35 3151.41 2858.77 4027.75 6704.79 4513.97 4678.22 6259.69 6813.58 9324.34 7054.69 5082.65 5082.65 1175.00 2650.11 3040.81 2784.06 2477.80 4034.42 5678.86 4325.22 4573.20 5908.12 6178.68 7533.16 6217.48 4933.20 4933.20
Jul
1806.65 2271.33 2964.06 2593.36 2630.06 3985.92 5431.93 4791.27 4867.01 6167.05 7949.90 6472.95 6437.79 5237.63 5656.97 1548.78 2183.61 3009.70 2477.15 2770.38 4237.76 5555.84 4806.65 4298.16 6105.08 7144.22 6454.94 5969.78 5342.57 5500.51 1714.39 2254.72 3409.98 2471.48 3247.06 4186.53 5265.11 4000.74 5313.98 6266.29 6657.43 6881.42 6328.55 5282.17 5515.32 1832.96 2236.80 2779.87 2418.66 3258.63 4174.84 5188.68 3983.61 5260.17 5772.07 6902.45 6828.80 6082.42 5069.04 5158.10
Aug
1852.78 2504.85 2690.04 2365.03 3338.22 4033.08 5215.14 4673.87 5266.94 5046.75 6747.95 7642.86 5599.44 4924.67 5055.56 1753.81 2493.76 2751.97 2399.18 3207.66 4142.48 5471.95 4893.41 5572.78 5309.67 6746.52 7317.41 5640.26 4723.75 5059.59 1707.28 2117.84 2801.69 2266.22 3401.04 4412.81 4621.01 4416.52 5158.54 5119.98 6380.65 7577.23 4835.54 4818.85 4907.19 1662.18 2079.36 2850.08 2258.85 3322.89 4232.31 3812.29 4428.52 4510.50 4659.83 6517.08 7323.51 4660.51 4996.88 4600.69
Sep
1728.30 2108.29 2968.29 2139.65 3264.15 4200.79 3666.78 4362.06 4250.83 4463.03 6444.43 7564.74 4900.39 4621.08 4693.40 1695.81 2061.36 3078.76 2076.65 3095.85 4252.78 3661.68 4236.39 4357.96 4907.45 6054.68 7578.77 4898.20 4566.10 4893.53 1667.46 2049.23 3134.61 2107.20 3069.39 3909.94 3572.56 4304.96 4493.11 4859.08 5963.36 7529.64 4978.63 4580.37 4737.89 1637.10 2074.50 3163.63 2116.36 2794.83 4137.85 3579.35 4224.24 4300.34 5046.30 5648.48 7959.57 5050.82 4566.40 4609.05
Oct
1640.95 2147.59 3252.19 2080.98 2877.54 5235.29 3826.85 4103.27 4367.76 5114.58 5759.75 8513.59 4926.29 4730.09 4444.13 1667.21 2410.41 3145.46 2058.16 2942.99 5157.97 3542.62 4297.42 4437.07 4887.13 6374.51 8557.86 4983.34 4441.77 4493.24 1650.90 2584.51 3187.96 2045.60 2851.35 5421.11 3508.03 4060.98 4885.07 5514.81 6485.05 8672.62 5073.32 4604.56 4762.04 1671.06 2464.36 3115.70 2012.08 2900.28 5596.70 3638.37 4106.74 5163.65 5690.94 6869.52 7756.31 5105.94 4705.97 4960.35
Nov
1740.55 2464.36 3220.40 2024.88 2896.70 6194.40 3674.21 4310.96 5000.87 5658.39 7086.19 7978.34 4905.85 4586.89 5186.73 2035.34 2400.00 3200.76 2020.01 2896.64 6371.73 3664.25 4185.69 5299.32 5341.99 7488.34 7750.07 4897.98 4619.36 5272.18 1793.54 2609.98 3204.75 2194.68 3017.09 6384.52 3917.60 3862.97 5036.89 5464.03 7109.83 8113.68 4811.36 4698.89 5351.75 1748.44 2739.67 3302.11 2138.95 2863.97 6099.04 4292.35 3884.81 5164.48 5576.71 7213.64 7615.19 4838.43 4946.03 5348.65
Dec
1700.34 2770.92 3393.60 2127.45 2905.18 6115.82 4254.42 3816.59 4935.78 5612.87 7402.43 7769.29 4858.73 5096.24 5170.46 1890.08 2743.34 3545.39 2166.94 2900.23 6379.00 4173.94 4035.25 5167.95 5304.03 7015.63 7487.06 4826.96 5115.00 5218.31 1426.32 2938.14 3515.05 2164.47 2965.07 6442.46 4085.01 3976.46 5018.16 5566.68 7832.94 7332.47 4853.96 5296.71 5332.30 1972.85 2840.74 3432.53 2016.77 2932.65 6574.58 4556.42 4105.20 5136.20 5560.71 7824.49 7289.76 4793.23 5390.47 5468.89
Price of Green gram in Kerala market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2548.71 2500.92 3450.58 4061.90 3242.69 4196.73 7278.56 5744.67 6193.79 6592.80 7915.16 9676.79 9344.27 7885.26 7179.09 2579.75 2480.60 3540.80 3801.57 3096.43 3929.18 7277.44 5911.60 6341.13 6341.18 7713.21 9651.28 9141.63 7797.08 6980.75 2613.33 2449.56 3576.45 3998.27 3195.76 4130.88 7012.63 6272.71 6387.67 6599.30 7766.61 9620.15 9278.40 7947.64 7126.43 2365.89 2482.27 3672.47 4081.97 3158.49 3996.12 7031.26 6288.71 6256.27 6626.93 8167.74 9745.93 9334.38 7961.25 7184.64
Feb
2644.00 2563.51 3523.68 4064.78 3132.06 3983.67 7028.84 6300.67 6180.68 6619.94 8473.81 9572.63 9374.46 7952.93 7140.69 2585.09 2706.20 3458.57 4139.83 2973.67 4000.00 6908.66 6380.67 6321.85 6766.85 8752.35 9430.21 9728.26 7910.56 7093.14 2531.67 2606.56 3564.95 4146.94 3014.39 3937.77 6803.47 6707.55 6379.67 6789.37 8587.69 9366.60 9187.76 7988.14 7140.38 2555.36 2572.89 3645.39 4091.23 3184.40 4030.95 6873.53 6830.85 6336.08 6715.19 8588.97 9298.82 8964.75 7998.00 7301.77
Mar
2502.08 2641.39 3634.26 4163.24 3419.88 4078.21 6676.52 6817.49 6259.48 6528.29 8666.15 9198.05 9189.65 7581.32 7240.02 2579.64 2683.56 3707.39 4164.14 3392.45 4157.41 6753.40 6634.92 6345.20 6491.24 8586.06 9275.36 9144.41 7386.15 7212.52 2566.31 2400.00 3743.44 3898.34 3370.49 4079.98 6641.05 6329.85 6219.57 6484.89 8538.54 9240.28 8917.83 7349.25 7340.45 2562.87 2583.33 4014.29 4008.44 3307.71 4106.39 7071.65 6697.89 6193.42 6357.11 8469.00 9308.68 8876.73 7337.35 7338.76
Apr
2604.74 2804.35 4096.58 3958.18 3267.46 4026.05 6950.28 6356.26 6158.53 6451.13 8586.13 9604.14 8896.63 7451.45 7323.20 2410.00 2955.51 3843.75 3963.95 3462.35 3795.83 7209.09 6230.85 5928.49 6480.79 8766.10 9251.43 8819.76 7666.09 7379.15 2637.04 2944.07 4219.44 3991.98 3469.75 4254.25 7124.51 6923.89 5935.71 6576.19 8810.08 9297.84 8828.99 7613.11 7434.13 2597.49 3050.00 4166.53 3525.00 3524.53 4175.00 7436.68 6903.68 6103.56 6680.18 8580.81 9261.49 9333.31 7450.92 7536.95
May
2584.21 3072.92 4171.70 3954.07 3006.14 4426.78 7639.13 6813.62 6084.27 6666.00 8575.00 9455.42 9273.74 7623.87 7575.12 2489.53 3082.81 4218.39 3904.60 3478.75 4564.37 7547.19 6982.49 5856.83 6640.86 8052.25 9312.76 9157.82 7658.32 7464.58 2604.30 3063.64 4138.35 3821.37 3440.04 4522.41 7547.00 7062.62 5767.80 6753.01 8484.07 9413.87 9125.84 7601.29 7643.98 2500.95 3044.89 4123.70 3843.73 3416.14 4579.62 7593.33 6969.66 5725.08 6726.66 8449.82 9337.42 9020.61 7591.75 7588.27
Jun
2584.90 3059.62 4196.36 3711.33 3376.19 4682.50 7680.06 6877.62 5816.71 6669.73 8477.09 9306.28 9160.26 7478.73 7596.81 2493.73 3059.65 3987.50 3657.63 3535.25 4626.00 7744.09 6883.10 5793.73 6708.98 8306.88 9280.53 9094.84 7403.67 7403.67 2536.18 3114.95 4145.53 3838.28 3407.64 4683.18 7672.23 6713.80 5809.40 6645.63 8154.46 9127.27 9016.68 7500.25 7500.25 2553.25 3086.05 3932.61 3730.34 3453.73 4646.41 7376.00 6535.35 5780.12 6610.47 8201.60 9203.17 8908.52 7600.56 7600.56
Jul
2473.61 3163.42 4076.40 3569.93 3557.53 4523.96 7210.66 6558.06 5847.74 6628.85 8223.59 9438.43 9547.18 7568.76 7610.29 2381.97 3185.23 3976.00 3620.56 3497.67 4508.37 7221.17 6491.08 5968.52 6565.31 8443.81 9283.84 8874.34 7415.93 7439.07 2561.64 3195.95 4003.05 3640.40 3774.65 4744.44 7033.12 6479.68 6068.06 6619.23 8530.19 9183.32 8855.02 7456.77 7466.14 2683.88 3217.68 3835.82 3441.22 4127.59 4781.45 6876.04 6334.05 6170.48 6629.85 8799.34 9210.36 8657.82 7459.74 7545.89
Aug
2358.93 3150.72 3618.30 3536.31 4409.52 4919.24 6823.43 6351.77 6316.61 6676.52 8698.68 9152.40 8539.06 7377.19 7550.23 2439.47 3220.72 3755.40 3475.00 4596.67 5006.82 6705.13 6402.03 6324.37 6850.75 8620.12 9203.25 8281.93 7414.02 7293.54 2479.17 3214.19 3642.64 3483.40 4415.82 4964.25 6434.16 6272.66 6417.27 6892.21 8939.79 9548.50 8371.56 7525.16 7476.64 2356.21 3138.66 3578.17 2171.88 4409.59 5015.34 6657.53 6289.40 6449.71 6935.59 8974.49 8800.00 8238.26 7563.16 7170.34
Sep
2373.95 2905.49 3595.39 3279.13 4407.03 5377.21 5994.20 6320.03 6533.87 6913.31 8738.71 9423.08 7995.34 7392.64 7333.54 2480.45 2710.84 3724.55 3190.02 4339.44 5303.19 5762.86 6251.61 6536.20 6882.20 9211.11 8878.56 7755.56 7515.86 7293.74 2410.19 2768.07 3970.25 3483.97 4247.52 5413.60 5960.77 6367.34 6446.50 6839.71 8800.00 9826.24 7794.36 7603.92 7189.01 2476.92 2774.24 4067.15 3168.30 4158.28 5402.61 5789.66 6331.76 6485.13 6927.87 8765.15 9368.12 7869.90 7569.82 7172.21
Oct
2452.07 2860.23 3997.49 2856.45 4141.34 5687.59 5584.92 6211.79 6470.55 6866.92 8575.56 9604.14 7728.40 7390.47 6983.91 2358.29 2991.88 3999.41 3103.34 4187.61 6004.79 5564.76 6224.48 6500.86 6609.85 8873.77 9251.43 7899.53 7273.12 7356.89 2434.62 3058.51 4025.55 2980.89 4019.10 6175.52 5238.33 6141.44 6497.98 6793.06 8878.53 9297.84 7709.74 7157.07 7321.29 2373.04 3165.54 4056.30 3028.73 4332.03 6402.08 5536.30 5272.28 6570.95 6897.19 8894.04 9441.98 7676.78 7188.32 7439.14
Nov
2985.09 3054.55 4003.26 3011.48 4234.58 6737.08 5403.33 4941.70 6519.65 6882.43 9236.60 9495.31 9096.51 7114.54 7280.67 2373.63 3254.59 4064.66 3085.71 4116.30 7288.73 5243.24 6392.04 6610.06 6984.99 9501.21 11395.36 7924.40 7100.92 7261.86 2518.75 3225.86 3961.43 3077.32 4011.94 7373.08 5232.88 6364.29 6618.88 7139.48 9560.32 9686.87 7947.75 7038.69 7533.14 2526.51 3522.92 4084.47 3107.91 3976.23 7188.64 5378.58 6188.87 6760.08 7204.45 9742.37 9604.90 7847.72 7046.14 7574.57
Dec
2461.67 3623.91 4012.39 3057.25 3986.15 7429.31 5810.38 6271.60 6726.80 7265.52 9590.70 9706.14 7942.69 6935.41 7491.41 1640.95 3518.75 4056.69 3080.70 3967.99 7437.50 5643.91 6282.12 6731.34 7409.13 9652.16 9870.93 7872.12 7000.78 7557.48 1667.21 3437.50 4081.25 3075.36 4182.91 7294.88 5895.87 6221.59 6758.54 7814.61 9571.28 9573.72 7930.88 7188.98 7417.86 1650.90 3530.17 4068.68 3077.78 3433.33 6492.25 5672.58 6229.97 6716.73 7912.04 9617.92 9449.05 8049.43 7659.27 7508.08
Price of Green gram in Madhya Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1261.92 1084.93 1690.30 2252.88 1946.30 1975.34 2110.97 2935.28 3018.16 5001.85 4973.44 6332.01 6314.19 4317.72 4245.16 1243.63 1075.00 1652.39 2642.19 2025.61 2259.57 2181.56 2858.55 3127.76 4670.43 4326.82 6173.22 6086.81 4122.98 3889.18 1245.00 1015.25 1693.43 2394.80 2025.07 2141.92 2029.84 2789.29 2861.37 4331.56 4885.62 6114.72 5934.12 4172.00 3755.21 1290.66 890.14 1661.86 2497.84 2090.00 2059.72 2279.01 2777.94 5703.34 3773.83 5265.76 6183.33 5987.58 4145.86 3768.66
Feb
1301.35 1260.00 1667.71 2395.46 2032.18 2033.62 2305.49 2529.77 4531.63 6165.65 5895.54 5958.70 5826.16 4056.15 3830.00 1323.64 1399.52 1687.38 2218.42 2218.30 1985.55 2262.26 2423.80 3517.52 6138.99 2965.52 5953.13 5437.32 4067.21 3783.25 1414.50 1413.80 1638.87 1856.06 2285.62 2031.21 2020.05 2425.02 3445.38 5683.42 4171.40 6141.05 5019.74 3983.14 3621.69 1350.84 1333.53 1670.25 1890.02 2529.12 1890.82 1754.85 2329.01 6694.19 3762.33 3068.67 6230.33 5609.88 3868.44 3841.41
Mar
1357.16 1321.77 1728.21 1865.22 2050.00 1944.80 1851.37 2455.73 6863.77 5267.66 6233.03 5993.18 5578.13 4087.76 3734.78 1325.82 1431.97 1747.56 1921.09 2250.00 1983.99 1830.20 2231.76 5841.17 5515.85 2936.46 6279.24 4630.78 4155.11 3918.48 1326.05 1439.18 1806.63 2121.47 2477.22 1904.03 1879.88 2202.16 4006.46 5052.01 4486.71 6314.54 5588.30 4687.41 3819.85 1355.84 1420.32 1819.45 2229.86 2560.00 1908.65 1937.97 2135.65 6710.46 4766.73 2836.72 6123.92 5745.77 4708.93 4042.75
Apr
1452.82 1461.05 1938.05 2351.56 2399.60 2038.66 1989.78 2173.56 7029.70 5231.89 2942.63 7382.14 5972.13 4739.01 4336.42 1306.56 1659.64 1952.85 2405.48 2322.31 1973.58 1915.65 2164.64 4556.48 5267.87 4917.06 7227.90 6291.50 4486.12 5050.22 951.21 1558.61 2415.46 2113.91 2220.00 2007.13 1894.88 2177.80 6591.91 4030.28 6882.83 7457.94 6359.91 4232.32 4227.36
1265.57 1444.97 2021.49 2047.81 2228.47 2077.65 1906.55 2225.85 5706.23 4462.32 6637.62 6905.40 6241.71 4138.07 4533.61
May
1270.79 1618.79 2029.59 2240.80 2165.61 1988.94 1930.81 2334.41 5513.21 4636.50 6764.05 7177.41 6081.97 3939.35 4906.57 1280.00 1764.69 2181.74 2134.55 2071.60 1965.47 2036.87 2396.81 5530.23 4804.78 6803.52 6920.13 6034.32 4142.25 4852.19 1300.00 1691.74 2206.22 2042.30 2274.27 1902.60 2215.77 2446.69 4283.18 4970.40 6351.58 7019.56 5681.91 4113.25 4872.14 1248.18 1728.22 2160.59 2026.89 1933.27 1882.85 2088.66 2755.88 3879.31 4551.33 5055.76 6395.53 5287.47 3917.43 4657.86
Jun
1262.74 2082.00 1644.77 2008.89 1970.93 2031.02 2637.45 3309.64 3650.04 4507.27 4652.39 6459.52 5329.31 3879.09 4600.18 1215.50 1905.11 2369.57 2061.10 1981.17 2112.70 4107.50 3410.98 3597.56 4431.87 4722.40 6203.74 5411.40 5135.46 5135.46 1277.76 1588.73 2550.39 2118.14 2209.60 2111.89 3848.36 3450.13 3662.53 4488.35 4793.52 5859.76 5265.63 5199.27 5199.27 1223.90 1418.76 2280.40 2225.38 2200.07 1992.16 2738.23 3637.74 4089.13 4437.26 5072.96 5458.40 5253.39 5199.31 5199.31
Jul
1292.78 1475.05 2149.69 2273.90 2101.19 2253.20 3241.81 3475.32 3940.63 4228.03 5507.96 5638.85 5125.03 5179.23 4373.61 1265.05 1476.21 2119.22 2218.89 2245.19 2415.70 2278.24 2915.32 4006.24 3914.48 5727.28 5876.43 5265.17 5175.93 4338.42 1305.17 1498.63 2168.70 2156.32 2446.55 2389.07 2653.77 3018.67 4647.94 4021.63 5963.14 5878.89 5016.10 5088.85 4410.95 1393.30 1400.79 2180.42 2166.17 2270.08 2086.60 3197.38 3161.06 4779.55 3597.81 5836.29 5524.48 4756.46 4947.14 4209.69
Aug
1284.36 1550.00 2243.18 2123.36 2258.10 2176.40 2214.85 3086.45 4839.67 3770.69 5753.38 5565.98 4736.44 3542.23 4288.56 1331.08 1570.58 2211.80 2179.90 2295.01 2078.02 2195.95 3137.59 5190.59 3902.28 5837.90 5647.45 4777.35 4028.77 4242.42 1331.06 1440.86 2405.80 2243.85 2334.22 2274.51 3019.30 3587.11 4481.58 3892.56 5898.99 5983.67 4475.06 4115.38 4257.97 1385.78 1958.43 2538.55 2149.68 2324.34 3857.72 3430.02 3987.04 4766.90 3550.78 5893.13 5678.39 4001.13 4302.15 4296.84
Sep
1516.90 1523.33 2720.67 2139.53 2191.23 3054.30 3508.94 3600.86 4492.71 3292.47 5622.81 6240.11 4351.03 3908.63 4203.98 1406.15 1916.77 2897.62 1886.54 2265.07 2975.42 3418.78 3750.34 4087.91 4097.07 5830.72 6015.66 4385.51 3865.73 4306.21 1433.93 1701.51 2816.80 2088.80 2154.46 2850.69 3483.31 3768.99 4202.50 3943.76 5622.51 6563.99 4426.75 3947.51 4289.08 1374.26 2026.85 2961.32 2101.57 2238.18 2715.34 3395.33 3088.47 4180.18 3796.82 5517.47 7134.18 4483.61 4002.60 4210.46
Oct
1480.75 1621.89 2894.50 2089.25 2070.24 2765.72 2814.02 3550.16 3905.73 3771.10 5612.58 7382.14 4443.38 3533.64 4396.80 1614.09 1684.11 2814.43 2289.81 2443.69 2574.16 2986.27 3485.26 3373.55 3922.85 6017.50 7227.90 4104.64 3825.18 4393.24 1453.81 1711.51 2844.78 2222.82 1906.06 2494.26 2692.49 3239.44 4469.39 3233.79 5832.73 7457.94 4289.16 3874.68 4391.91 1373.88 1544.26 2779.76 2228.53 1878.89 2413.00 2802.26 3243.27 4358.56 3721.78 6405.20 6593.23 4259.49 3822.08 4690.95
Nov
1348.94 1601.70 2796.84 2008.97 2040.17 2272.92 2420.10 3083.42 5434.28 3784.45 6413.68 6561.58 4346.26 3895.02 3989.48 975.00 1447.90 2627.61 2250.79 1917.33 2179.32 2564.44 3247.54 4602.60 3831.98 6222.79 6414.41 5067.89 3874.33 4658.66
1353.29 1749.77 2527.94 2112.01 2002.05 2203.68 2671.45 2987.97 5049.33 4141.94 6025.62 6513.20 4506.99 3718.12 4697.49 1383.66 1716.30 2418.65 2048.58 1856.59 2383.48 2658.68 3153.84 4203.95 4978.09 6158.09 6278.88 4262.47 3810.99 5290.33
Dec
1401.34 1687.25 2551.45 1776.16 1960.55 2186.84 2505.27 2985.68 3941.79 3676.11 5923.68 6446.65 4273.24 4104.91 5131.35 1090.00 1712.07 2545.21 1475.00 2143.68 2098.95 2551.75 3313.27 3813.95 3154.12 5968.96 6230.77 4198.82 3582.68 3371.77 1245.67 1657.58 2562.31 1625.58 1921.10 2326.39 2488.58 3080.48 4959.61 3807.33 6319.15 6228.58 4213.19 4240.40 4790.55 945.58 1653.90 2310.80 1475.00 1997.31 2258.07 2554.85 3404.76 3771.46 4207.12 6287.88 6318.27 4302.54 4304.33 4930.99
Price of Green gram in Maharashtra market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1479.85 1706.50 2700.11 2893.71 1972.07 3019.17 6005.98 3951.77 3896.97 4407.99 6013.19 7479.59 7858.15 4627.46 4683.21 1508.62 1660.57 2753.43 3038.05 2007.54 3067.37 5951.88 4115.47 4111.49 5377.19 6251.55 7552.43 7590.58 1961.16 4710.37 1493.94 1712.50 2711.92 3021.37 2095.12 3018.49 5860.95 4392.22 4096.85 5177.80 6592.17 7520.09 7180.45 4621.78 4537.37 1523.63 1765.93 2627.43 3049.49 2065.09 3201.06 5743.83 4601.96 4251.65 5038.20 6806.32 7560.65 7289.90 4544.44 4173.99
Feb
1494.38 1790.35 2195.21 3100.51 2137.60 3220.79 5893.70 4553.43 4275.84 4845.07 6864.81 8059.75 7102.68 4886.59 4683.35 1548.58 1848.23 2640.63 3077.94 2190.41 3225.76 5997.85 4342.94 4082.63 6205.16 7065.09 5561.90 7018.14 4622.03 4782.25 1545.76 1822.18 2727.42 2957.19 2266.32 3217.67 5989.20 4495.71 4316.56 5090.59 6777.15 7222.58 6681.74 4495.61 5004.83 1544.14 1833.89 2893.99 2988.35 2362.07 3201.91 5558.54 4315.48 4255.31 4495.56 7210.50 6619.89 6483.92 4463.53 4863.05
Mar
1465.05 1831.86 2804.23 2924.31 2332.01 3227.86 5836.73 4382.88 4283.12 5082.49 6949.36 7337.64 6730.07 4703.42 5122.37 1486.87 1833.53 2743.15 2994.08 1862.57 3492.29 5926.31 4149.75 4262.77 5634.10 7084.54 7391.55 6235.58 4572.00 5036.78 1535.39 1835.70 3170.75 2949.75 2327.23 3308.29 6010.92 4331.73 4212.64 4934.32 6857.99 7507.00 6792.33 4928.19 4818.20 1548.16 1934.47 3359.95 2997.61 2260.40 3367.35 6191.54 4571.65 4356.83 6762.93 7014.61 7230.73 6528.63 5053.96 5165.46
Apr
1572.66 2011.04 3335.10 3091.95 2354.07 3645.18 6685.12 4677.21 4355.27 5039.97 7320.42 8510.30 7109.52 4922.61 5102.19 1572.64 1970.68 3643.88 3100.44 2259.13 3865.06 6783.54 4745.39 4391.52 5805.96 8131.80 8419.53 6432.28 4816.69 4907.25 1579.96 2131.41 3562.52 3102.53 2395.69 3881.33 6680.78 4822.78 4073.66 4745.48 7773.40 8584.06 7006.08 4718.61 4888.42 1519.99 2098.12 3310.39 2999.62 2272.27 3903.58 6889.01 4540.33 4139.11 6206.43 6755.11 7750.18 7181.27 4598.16 5687.91
May
1570.77 2266.71 2580.06 2872.22 2400.25 4054.36 6582.36 4531.23 4276.25 6306.98 7746.36 7402.16 6726.59 4813.78 5395.23 1612.32 2198.38 3541.11 2719.90 2374.14 4049.06 6856.53 4715.35 4447.06 4377.56 7286.51 7021.56 6662.46 4504.87 5167.58 1620.61 2223.74 3391.14 2575.31 2392.14 4137.24 6710.81 4600.14 4418.77 5136.69 6898.42 6952.79 6530.92 4737.67 5014.00 1558.63 2189.92 3272.17 2478.06 2368.61 3921.42 6897.61 4270.56 4250.89 5062.78 6326.69 6562.50 6312.64 4443.21 4790.77
Jun
1502.06 2248.46 3049.02 2427.45 2297.20 3829.62 6569.56 4142.43 4219.55 4546.33 6147.63 6687.96 6288.20 4497.24 4686.67 1510.71 2340.06 2960.55 2548.52 2399.02 3926.13 6543.83 4187.16 4033.53 5470.19 5058.21 6823.56 6235.64 4405.11 4405.11 1466.58 2368.84 2908.35 2546.43 2347.32 3989.38 6774.57 4145.55 5116.22 5100.69 5688.13 7065.46 6117.05 4028.56 4028.56 1509.81 2370.87 2850.47 2439.52 2293.40 4043.49 6483.60 5177.85 4828.39 4881.97 5831.63 6122.25 6026.03 4160.32 4160.32
Jul
1498.88 2364.13 2779.80 2536.33 2449.70 4208.93 6475.17 5080.39 4752.49 4799.80 6397.26 6474.18 5898.19 4373.74 4861.33 1581.82 2368.86 3389.54 2348.14 2841.01 4434.32 6393.67 5023.26 5148.91 6051.31 6206.05 6776.76 7033.43 4263.94 5094.83 1824.17 2412.34 2358.57 2437.31 2975.48 4484.50 6226.63 4275.36 5090.07 5462.01 6825.21 6867.33 6471.88 4999.10 5519.12 1891.65 2387.55 2860.34 2480.42 3048.80 4267.82 5890.52 5943.89 5046.56 5491.34 6671.84 6407.78 6194.24 4123.98 5081.69
Aug
1648.56 2453.63 2731.41 2311.25 2916.44 4207.99 5851.48 5315.97 5318.83 5283.89 6892.80 6268.11 6307.29 4138.85 5290.36 1641.82 2556.76 2609.45 2248.54 2862.19 4541.81 5136.29 4867.18 5354.77 5233.90 6556.25 6760.27 5875.80 5521.02 5285.65 1641.38 2466.71 2360.03 2323.66 2898.96 4675.00 4493.66 4823.27 5285.80 5009.75 6882.08 7283.99 5208.11 5361.64 5123.84 1655.04 2057.60 2694.94 2176.40 3270.26 4095.65 3905.78 4869.10 4685.47 4912.27 7052.60 7214.19 5218.21 5232.22 4748.62
Sep
1721.43 2160.51 2878.87 2010.86 3161.09 3777.62 3610.79 3851.70 4625.42 4891.97 6406.61 7661.92 5168.89 4676.12 4678.85 1794.44 2074.66 2979.45 1973.57 2948.19 3862.37 3617.17 3759.33 4419.39 5081.16 6278.20 7676.97 5271.37 4653.20 4869.39 1743.22 1963.36 3067.79 1926.55 2721.15 4172.75 3565.93 3901.80 4368.17 5069.27 6238.49 7673.20 5259.45 4738.27 4817.06 1709.70 1988.84 3080.66 2029.61 2870.24 4596.37 3518.96 3807.97 4305.47 5095.65 6273.60 8206.47 5269.42 4439.48 4787.07
Oct
1654.45 2171.60 3210.52 2039.31 2904.11 5113.90 3587.79 3820.91 4409.06 5213.56 6429.26 8510.30 5345.38 4422.86 4779.41 1670.32 2270.66 3227.25 2056.74 2875.76 5085.23 3562.75 3909.28 4508.54 5222.97 6870.42 8419.53 5413.50 4262.41 4837.69 1718.59 2437.70 3135.15 2118.63 2875.49 5455.17 3470.96 3940.28 4868.59 5179.47 6875.09 8584.06 4788.57 4340.66 4754.69 1746.84 2458.81 3106.20 2075.03 2869.86 5712.88 3831.12 4079.21 4801.15 5449.81 7251.32 8123.73 4746.84 4413.25 4885.56
Nov
1720.62 2435.56 3095.30 2053.35 2833.94 5805.22 3497.79 4098.87 4865.95 5448.21 7775.79 7920.81 4727.28 4338.32 5004.94 1767.52 2561.31 3088.65 2075.67 2912.41 6002.64 3659.50 3993.08 4799.64 5466.75 7622.80 7935.80 4709.05 4290.40 5068.24 1739.25 2491.33 3093.02 2070.39 2819.27 6028.92 3563.27 3956.13 4850.05 5710.48 7514.63 7714.55 4761.34 4344.86 5091.46 1688.84 2501.85 3082.44 2041.33 2789.63 6047.96 3589.34 3876.92 4519.63 5752.63 7433.60 7520.13 4648.96 4402.96 5109.18
Dec
1728.09 2535.75 3067.50 1982.10 2845.24 5879.53 3586.00 3900.00 4900.03 5848.55 7227.56 7461.16 4601.33 4629.83 5208.31 1680.93 2386.83 3084.40 1977.86 2930.45 5927.59 3723.64 4003.51 5115.26 5816.48 7435.20 7460.84 4638.69 4625.53 5082.13 1706.78 2589.43 3033.23 2009.98 3082.80 6161.56 3722.89 4124.98 5320.19 6049.24 7536.47 7492.53 4539.86 4635.80 5288.61 1716.86 2520.98 3060.29 1980.32 3001.20 6222.34 3825.66 3997.41 5460.39 6028.67 7604.63 7084.93 4462.89 4818.16 5030.53
Price of Green gram in Rajasthan market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1403.99 1714.39 2596.58 3035.90 2120.99 2736.91 6173.28 3819.47 3398.79 5095.49 5654.00 7660.62 6965.60 4454.64 4904.73 1383.14 1701.91 2370.89 3059.79 2112.52 2821.46 5969.93 3867.84 3432.19 5094.93 5950.00 7672.66 6859.13 4416.41 4773.18 1422.26 1715.01 2597.39 3169.20 2117.84 2965.52 5942.91 4118.05 3351.26 5137.05 6080.37 7653.28 6763.12 4444.97 4697.64 1393.90 1726.28 2613.29 3231.55 2090.39 3020.76 5719.34 4031.58 3188.07 5087.19 5706.77 7527.97 6641.06 4392.84 4613.18
Feb
1395.33 1774.61 2575.02 3219.11 2089.90 3187.89 5749.23 3999.88 3185.81 5013.25 5484.42 7310.69 6226.92 4315.76 4562.23 1362.79 1718.58 2656.89 3156.25 2175.23 3241.97 4827.80 3688.26 3134.07 4897.13 6229.96 7123.16 6561.49 4380.55 4487.79 1354.35 1851.17 2510.84 3099.33 2202.05 3116.43 5857.84 3744.54 3428.14 4936.26 6456.70 5795.91 6590.49 4289.49 4502.24 1338.27 1897.48 1945.56 3102.67 2398.31 3110.35 5412.87 3504.17 3203.37 5015.16 5809.61 7429.45 6469.85 4230.12 4462.71
Mar
1345.38 1787.79 2222.19 2822.64 2398.47 2764.27 5615.52 3769.09 3149.03 4914.65 6084.87 7323.09 6477.63 4274.45 4499.26 1364.98 1755.21 1769.38 3090.97 2347.42 3553.45 5775.52 4036.71 2990.69 4849.75 6289.55 7465.81 6563.12 4459.03 4489.21 1329.20 1751.43 1912.23 3088.50 2289.55 3580.50 5753.10 2839.60 3200.60 4039.37 4993.94 7571.56 6544.56 4695.01 4468.19 1306.32 1810.00 1904.09 3246.81 2415.20 3259.56 6437.56 2562.83 3260.84 3733.18 3268.81 7547.39 6468.67 4790.18 4465.39
Apr
1381.78 2017.30 2215.55 3153.52 2267.03 2657.27 3734.61 3816.56 3269.89 4096.22 3961.02 8028.89 6627.39 4880.53 4583.29 1385.06 2084.04 2946.55 3038.48 2233.88 1191.99 4909.55 3798.12 3204.02 4231.03 4012.50 7856.03 6797.52 4749.22 4571.05 1330.62 1613.23 2148.49 2770.26 2180.87 2692.96 3292.70 3610.78 3165.11 3987.34 3471.38 7837.98 6750.94 4664.64 4626.11 1329.82 2019.72 2094.81 2687.92 2187.12 2409.84 4840.18 3607.65 3094.12 4028.57 3671.19 7038.92 6609.29 4502.49 4540.25
May
1362.88 1974.57 2165.27 3077.50 2234.15 3258.26 6489.45 3272.95 3287.94 4010.62 3544.76 7676.62 6405.18 4555.27 4594.24 1337.68 1307.34 2662.86 2893.25 2270.95 3811.72 6416.19 3223.05 3332.73 4554.98 4248.23 7499.80 6336.88 4565.74 4608.15 1318.04 2020.39 3056.96 2594.02 2225.72 1737.72 6412.47 2992.71 3259.24 4379.29 5043.03 7214.64 6055.22 4445.23 4605.30 1356.78 2042.18 3098.68 2466.13 2198.33 3725.26 5990.33 3109.73 3227.45 4490.47 3590.55 7077.33 5738.32 4352.59 4220.62
Jun
1325.00 2101.63 3011.95 2508.17 2225.70 3662.44 5781.27 3292.45 3349.06 4038.62 3555.71 7227.30 5676.19 4201.11 4501.20 1403.23 2267.39 2987.62 2568.01 2268.26 3822.22 5080.29 3391.20 3357.42 4343.17 3961.58 7005.71 5998.99 4255.10 4255.10 1179.48 2264.23 3081.01 2645.02 2436.89 3997.26 5683.55 3240.56 3348.53 4517.48 4433.47 7405.52 5781.64 4406.21 4406.21 1411.72 2204.64 2983.41 2635.79 2392.60 3669.64 5386.95 3474.31 3655.78 4293.70 4911.64 6559.65 5727.13 4301.76 4301.76
Jul
1442.25 2151.43 2580.64 2502.95 2419.99 2950.14 5171.97 3414.93 3644.57 4450.09 4854.97 6390.82 5550.06 3995.07 4745.78 1582.79 2230.99 2633.04 2413.90 2522.74 4095.83 4819.59 3523.99 3741.39 4204.14 4843.49 6080.90 5572.81 4366.24 4826.18 1590.06 2215.89 2515.85 2431.92 2896.89 4252.11 4776.97 3385.42 4317.18 4742.94 4051.95 6032.08 5399.46 4540.08 4826.21 1619.66 2149.83 2711.36 2318.77 2931.21 4002.73 4476.18 3543.19 4451.30 4445.70 4493.25 5826.58 5299.46 4332.98 4823.76
Aug
1535.81 2160.92 2360.50 2152.31 2745.94 4024.55 4328.54 3475.10 4761.19 4677.63 3959.42 5799.50 4894.41 4506.55 4840.02 1515.77 2153.39 2428.82 2141.00 2542.47 4140.49 4538.65 3776.48 4598.42 4751.15 3502.53 5960.20 4928.32 4752.81 4666.65 1516.56 2197.95 2618.42 2156.74 2491.46 4041.24 4448.63 3827.97 4538.65 4754.65 3324.47 6116.83 4516.19 4776.60 4563.52 1540.89 2181.13 2645.67 2093.52 2781.74 4015.67 3701.80 3782.47 4333.35 4234.56 4180.33 6079.39 4284.01 4739.88 4455.55
Sep
1505.73 2208.97 2792.14 2013.12 2854.12 4028.21 3868.36 3692.84 4147.97 4311.83 4896.11 6617.34 4524.57 4130.90 4470.23 1625.16 2143.36 2621.69 2007.07 2785.79 4099.20 4141.05 3692.38 4124.03 4868.16 5023.20 6647.11 4609.00 4286.33 4520.89 1579.36 2072.35 2860.82 1992.47 2859.11 4376.49 3484.20 3754.53 4284.97 4915.52 5446.84 6948.55 4817.11 4298.20 4353.43 1596.45 1986.93 3006.67 2140.71 2723.50 4782.07 3624.63 3669.04 4271.14 5059.24 5935.90 7505.49 4900.83 4160.30 4380.13
Oct
1626.24 2092.56 3089.73 2125.93 2738.85 5313.13 3821.19 3643.99 5072.80 4936.17 6186.30 8028.89 4781.08 4209.91 4473.88 1699.32 2278.25 3173.43 2171.67 2707.88 5216.95 3801.25 3741.94 5144.33 4865.26 6611.12 7856.03 4448.33 4148.36 4913.41 1698.35 2334.25 3114.89 2246.18 2729.80 5580.28 3598.94 3633.30 5368.66 4811.65 6479.74 7837.98 4524.35 4240.31 4995.46 1743.97 2331.15 3119.42 2160.20 2579.92 5800.35 3467.87 3613.39 5243.07 4884.14 6798.48 7241.15 4525.93 4249.96 5039.72
Nov
1769.82 2363.40 3112.31 2067.07 2649.63 6173.47 3305.86 3631.82 5210.41 5023.24 6944.77 6985.75 4529.44 4187.70 4963.65 1750.15 2279.06 3038.39 2106.59 2705.84 6290.39 3820.43 3564.05 5133.52 5081.90 6943.63 7341.29 4629.54 4180.15 4959.02 1729.49 2298.25 3054.96 2123.23 2694.99 6120.03 3631.27 3505.48 5245.35 5067.57 6940.63 7112.46 4527.25 4277.45 4971.10 1732.98 2280.94 3006.65 2072.53 2644.48 5952.70 3740.21 3337.34 5108.48 5141.72 6981.41 6932.85 4448.49 4521.06 5115.56
Dec
1705.49 2281.69 2984.53 2060.20 2664.66 5972.06 3664.21 3386.93 4987.98 4972.44 6587.73 7107.26 4342.82 4670.55 5557.66 1689.73 2224.62 3021.28 2045.83 2869.96 5921.57 3725.66 3453.35 5090.68 4989.69 6878.22 6942.79 4381.53 4754.61 5123.23 1704.62 2405.02 2911.32 2084.32 2789.68 6279.72 3500.59 3377.34 5079.43 5047.49 7164.48 6924.06 4270.85 4878.31 5463.05 1690.39 2455.64 3006.54 2016.91 2816.72 6248.44 3442.25 3240.05 5082.98 5364.15 7572.78 6899.30 4391.36 4875.38 5642.20
Price of Green gram in Tamilnadu market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1526.09 1419.41 1788.62 2992.86 3505.43 2412.00 4453.05 2758.30 3458.49 4336.33 4526.33 6334.07 6142.61 4393.56 4126.16 1531.14 1426.86 2103.86 2898.01 3795.80 2089.00 5021.36 3181.00 3243.85 2582.69 5519.59 6593.31 6227.26 5159.06 4532.83 1566.55 1483.78 2365.91 2922.44 3573.64 2560.54 4933.47 3846.56 3887.16 4361.74 6370.26 6074.69 6166.10 4565.81 4845.46 1574.55 1479.54 2271.50 3147.83 2628.88 3194.28 5210.27 4530.29 3701.08 5060.58 6247.31 6378.09 6403.59 4557.21 4882.81
Feb
1533.80 1451.83 2370.14 3157.40 2625.13 3049.71 4949.39 5166.94 3526.63 5125.34 6084.63 6474.56 6235.09 4735.99 4916.97 1503.61 1513.81 2410.21 3168.98 2429.89 3307.21 5381.25 4403.44 3470.47 4847.38 6196.16 6097.57 6179.76 4513.81 4847.91 1509.53 1593.35 2444.77 2872.80 2298.11 3248.31 5335.27 4133.44 3627.99 5054.07 6352.94 6323.98 6120.61 6296.86 4472.23 1439.26 1595.80 2460.33 2781.71 2281.48 3086.73 5183.78 4607.80 3560.22 5089.93 6153.01 6439.38 6086.56 4815.41 4606.82
Mar
1382.91 1570.02 2347.26 2684.54 2199.37 3426.62 5285.70 4631.65 3653.54 5035.90 6074.12 6026.37 6404.02 4457.66 4691.38 1326.27 1580.88 2653.28 2642.20 2174.67 3205.14 5367.00 4398.09 3520.23 4877.69 6168.27 6408.40 6069.14 4967.67 4695.39 1385.45 1566.02 2779.35 2556.59 2347.93 3218.30 5609.21 4641.48 3570.93 4807.94 5448.35 6243.21 6219.13 5267.36 4575.66 1318.72 1628.02 2915.24 2506.13 2428.88 3205.22 5830.93 4318.95 3541.04 4631.15 6149.66 6291.42 5831.24 5490.84 4192.40
Apr
1369.12 1506.54 2819.28 2584.17 2441.79 3301.87 6129.89 4345.06 3652.31 4575.83 6180.74 6947.41 5808.94 5364.08 4671.47 1389.71 1836.89 2944.87 2544.08 2321.31 3485.79 6043.53 4000.00 3403.79 4661.94 5889.89 7574.67 5307.43 5349.94 4671.10 1373.57 1786.33 2695.35 2632.41 2302.93 3662.72 5922.31 4236.22 3447.63 4217.24 5799.87 7586.62 6082.51 5270.41 4574.73 1314.03 1608.15 2586.56 2492.17 2315.00 3458.88 5378.38 4000.92 3287.65 4488.39 5840.65 5682.68 5839.01 5152.26 4368.12
May
1348.31 1690.95 2772.40 2531.55 2308.97 3394.97 5967.88 4089.35 3273.49 4579.98 5847.59 6444.78 5722.07 4628.26 4333.15 1430.57 1573.67 2991.94 2457.66 2311.98 3403.44 6079.92 3800.06 1783.87 4348.77 5766.60 6335.45 5506.33 4370.44 4306.84 1426.73 1343.44 2540.98 3001.21 2320.59 3273.70 5914.34 3779.70 2913.20 4444.39 5694.98 6099.26 5320.80 4341.83 4456.16 1349.60 1947.07 2690.45 2252.26 2271.17 3289.98 6181.84 3468.14 2948.55 4210.00 5757.58 6046.16 5517.56 4488.15 4776.54
Jun
1174.79 1618.00 2512.12 2233.88 2472.00 3417.07 5430.62 3158.94 2998.99 4491.76 5536.23 6381.54 3026.38 4105.24 4215.23 1111.15 1652.22 2405.00 2615.27 2100.00 3273.70 5984.95 2750.85 3080.86 4570.11 5142.35 5017.56 2046.65 4153.97 4153.97 1191.00 1860.57 2053.00 2129.26 1500.00 3289.98 5744.47 2851.72 3272.93 4854.59 5234.33 5857.59 2592.25 3458.45 3458.45 1157.58 1712.20 2164.49 2385.15 1800.00 3417.07 5371.98 2967.28 3367.81 4523.00 4926.02 5229.46 2535.57 3519.88 3519.88
Jul
1145.82 1879.43 2222.70 2369.16 1650.00 3463.61 5060.08 2930.17 3562.52 3091.59 5069.86 3864.11 4983.24 4026.16 3666.66 1223.73 2145.00 2066.15 2471.87 2900.00 3777.85 5319.12 2815.15 3485.33 3373.81 5606.48 5301.00 5053.76 3938.00 3930.28 1163.00 1845.64 2294.64 2308.53 2275.00 4061.66 4916.78 2911.46 3870.51 4712.76 5219.58 5838.71 5036.54 4142.97 4124.49 1147.86 1899.70 2090.80 2323.40 2587.50 3922.26 4405.90 2828.42 3986.56 4514.69 5875.78 6048.55 5422.80 4032.67 4251.17
Aug
1332.17 2290.86 1997.91 2077.16 2431.25 2750.53 4305.16 3036.09 4028.46 4314.79 6090.36 5617.35 5732.92 3982.93 4083.22 1190.25 1667.62 1913.33 2319.20 2509.38 3629.39 4394.55 3059.37 2928.10 4514.70 5955.62 5704.47 4766.84 3985.33 4032.38 1246.14 1672.17 2221.60 2078.05 2470.31 3680.30 3855.45 2897.73 3698.88 4510.08 5661.00 5650.80 4688.86 4226.53 3286.06 2660.10 1793.60 2207.13 1930.35 2489.84 3430.36 3580.85 3185.33 3698.14 4337.08 5322.28 5491.74 4968.53 4357.63 4197.21
Sep
1037.00 1871.00 2048.00 1852.74 2480.08 3587.74 3575.38 3147.68 3516.36 4180.34 5531.44 5837.62 4381.99 4470.95 3604.70 859.00 1886.08 2516.67 1908.37 2484.96 3768.16 3159.67 3428.74 3731.59 3991.10 5532.15 5682.26 4304.78 4338.86 4127.67
1171.50 1839.32 2554.09 1763.72 2482.52 3970.82 3649.54 3472.82 3680.22 4484.46 5919.97 6504.90 4766.18 4534.08 5189.29 1015.25 1830.50 2847.91 1988.30 1225.39 3792.27 3530.58 3774.94 3968.42 5202.48 5915.59 7860.38 5052.14 4565.55 4218.03
Oct
1069.00 1901.60 2786.18 2090.08 2590.00 4597.62 3537.77 4010.09 3363.90 5048.78 5879.45 6947.41 5081.69 4595.88 4437.00 1550.00 1867.41 2586.74 2020.06 2753.74 4697.88 3311.54 3962.26 4423.28 5293.20 6527.22 7574.67 4623.77 4404.28 4226.88 1239.50 2065.14 2833.33 2153.40 2786.21 4283.08 3347.88 4082.93 4559.70 5062.86 6187.49 7586.62 4656.78 4196.11 4121.11 1279.86 1895.49 2961.04 1942.29 2771.44 5665.57 2859.40 4083.45 4287.44 4482.75 6412.44 6610.26 4495.48 4132.15 4568.57
Nov
1168.00 2366.16 2325.27 2110.96 2745.97 5962.59 3029.38 4059.53 4691.98 3608.71 6227.44 6267.01 4432.37 3927.04 4799.31 1370.00 2190.09 2605.80 1877.57 2732.48 6152.41 2902.22 3874.96 4307.89 4829.37 7011.44 5886.00 4296.55 4115.47 4675.00 1328.67 2053.60 2438.64 1966.14 2242.18 5780.61 3281.57 3775.58 4333.33 4786.32 6342.84 6097.80 3953.09 4030.75 4618.55 1317.36 2196.27 2124.03 1785.12 2366.21 5305.87 3332.91 3719.77 4430.63 4983.15 4879.17 6493.13 4800.00 3532.88 4629.48
Dec
1092.03 2151.58 2154.23 2265.54 2448.58 5354.83 3450.00 3035.37 2625.96 4678.49 4946.95 5678.65 4800.00 3920.42 4712.79 867.06 2148.55 2270.12 1997.67 2355.17 5084.55 2068.34 3420.10 4045.23 4171.60 5328.43 6084.19 4477.63 3591.06 5209.04
1163.67 1836.70 1741.25 1683.75 2144.62 4340.16 2759.17 3385.01 4251.13 4306.72 4927.34 5939.12 4793.77 3791.97 4379.23 1187.10 1793.51 2187.80 1923.61 2476.12 5533.20 3377.52 3451.14 4037.89 4216.87 6128.78 2001.14 3944.68 3927.59 4271.45
Price of Green gram in Telengana market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1415.52 1605.94 2419.51 2926.29 1919.57 2647.92 5745.45 3489.84 3208.22 4604.14 4757.16 5029.47 6422.58 4282.45 3929.84 1374.58 1648.11 2307.17 2988.65 1987.14 2750.00 5415.31 3537.34 3240.46 3654.96 4580.87 7023.17 6207.65 4115.77 3980.31 1415.06 1686.36 2531.15 3193.95 2005.00 2902.47 5452.39 3600.81 3148.11 4769.18 5805.16 5502.66 6567.63 4181.77 3739.81 1413.84 1689.65 2684.00 3144.52 2087.14 2995.65 5252.87 2863.00 3164.26 4906.52 4952.91 6561.60 6143.94 4330.96 3925.59
Feb
1371.52 1500.25 2554.53 3088.50 1998.75 3071.24 4781.84 4039.83 3057.89 4642.17 5877.89 6480.72 5960.89 4242.33 3470.37 1331.33 1743.18 2548.72 3033.64 2104.17 2994.38 5125.65 4115.60 3053.91 4642.53 4837.51 6667.34 5980.37 4340.08 4467.12 1339.58 1794.29 2769.34 2272.80 2153.00 3139.66 5370.14 3708.79 3429.10 4585.49 5087.00 6755.19 5888.19 4297.83 4201.96 1308.85 1779.54 2659.03 2767.13 2250.00 3125.00 5202.78 4262.50 3179.42 4799.51 6139.28 6512.95 6063.48 4179.58 4731.05
Mar
1318.17 1754.58 2869.57 2915.83 2225.00 2618.16 5107.90 3130.73 3064.10 4579.98 4687.26 4949.08 5946.13 4454.18 4064.94 1385.06 1648.36 3031.84 2912.29 2150.00 2806.66 5155.45 3706.44 3111.45 4516.05 3948.35 6446.51 5965.96 4390.15 3042.57 1454.40 1710.89 3224.72 2850.98 2187.50 2920.74 5401.47 4141.53 3276.70 4926.87 6132.74 6305.50 5933.86 4823.14 4511.04 1407.64 1745.98 3426.79 2824.43 2168.75 3181.36 5945.26 4108.88 2982.42 4721.46 5079.53 6763.08 7183.80 5019.44 4457.92
Apr
1425.42 1883.90 3176.52 2838.14 2178.13 3293.23 5884.69 4382.08 2908.86 4824.17 6555.46 7589.74 6155.40 5118.11 3568.65 1420.00 1954.49 2996.43 2723.91 2173.44 3508.12 6411.29 4562.50 3441.70 4772.81 6593.51 7649.30 6425.53 4889.46 5047.73 1375.00 1953.33 2845.39 2758.44 2175.78 3091.67 5990.26 4536.06 3887.01 4798.49 5788.08 7936.34 6408.51 4657.85 4508.39 1293.44 1966.95 2630.99 2663.36 2174.61 3744.64 4528.93 4183.37 3758.49 4785.65 6581.32 6966.45 6348.95 4320.03 4479.53
May
1420.00 1855.81 2858.98 2749.49 2175.20 3685.70 6346.28 4466.53 3617.00 4792.07 6255.65 6887.33 6157.60 4230.24 4533.07 1136.11 1824.94 3124.52 2649.40 2174.90 3712.00 6138.46 4209.52 3679.38 4788.86 6164.27 6896.23 5694.60 4245.81 2206.19 1475.00 1540.41 2992.61 2500.89 1950.00 3200.00 6163.00 4273.95 3256.18 4790.46 6532.86 6749.12 5975.25 4016.76 4031.65 1440.00 1785.71 2992.52 2369.59 1995.00 3660.86 6106.39 3924.47 3120.13 5000.00 5828.42 6564.13 5387.76 3926.37 4083.96
Jun
1496.75 1890.53 2568.55 2565.14 1972.50 3564.44 6145.64 3743.15 3099.84 4895.23 5331.19 6199.09 5187.90 3739.21 4296.24 1570.00 1932.33 2248.30 2610.30 1983.75 3317.71 6179.73 3582.25 3143.62 4696.38 5241.32 7612.56 5194.76 3743.29 3743.29 1222.62 1995.50 2700.06 2474.51 1978.13 3453.08 5675.17 3637.77 3075.28 4629.42 5107.87 5916.53 4895.33 3873.99 3873.99 1265.00 1963.92 2655.29 2662.23 2050.00 3342.42 5506.63 3720.55 3457.67 4408.73 5135.82 6283.59 4964.31 3862.40 3862.40
Jul
1193.41 2130.00 2483.78 2656.47 2382.00 3883.43 5408.24 3688.90 3497.96 4259.45 5523.89 5161.10 4528.68 3928.02 4360.81 1237.30 2004.67 2352.77 2479.61 2325.00 3975.51 5545.36 3627.51 3279.43 4681.53 5991.18 5913.41 5033.91 3942.08 3969.74 1350.00 2030.00 2552.47 2573.51 2353.50 3861.43 5167.41 3417.86 3670.43 3888.97 6067.94 5947.03 4578.58 3901.80 4381.69 1328.75 1960.00 2523.70 2138.48 2610.00 3724.20 4635.92 3405.76 4038.23 4481.33 5861.67 5921.00 4818.11 3845.19 4017.22
Aug
1299.07 1940.11 2423.38 2302.59 2690.00 4145.51 3800.82 3394.48 3919.15 4316.23 5947.18 5923.42 4488.22 4004.88 4660.13 1254.02 2037.43 2483.60 2220.41 2650.00 4390.80 3476.83 3318.22 3733.54 4316.62 5905.68 6347.12 4499.24 4534.31 4693.50 1403.79 1929.22 2594.06 2203.64 2483.50 4447.22 3824.72 3277.51 4071.30 4100.47 6095.57 6537.49 4285.67 4498.33 4471.81 1564.64 1756.65 2625.20 2190.02 2427.77 4435.39 3680.70 3416.16 4013.15 3906.15 6002.04 6414.30 4356.22 4285.82 4155.47
Sep
1551.61 1797.32 2811.65 2045.85 2471.97 4445.89 3538.16 3336.97 3972.47 3869.44 5722.77 6892.50 4539.43 4005.42 4081.17 1684.75 1825.23 3010.51 1964.94 2449.87 4483.05 3439.41 3374.91 4013.40 4182.58 5482.65 6810.82 4196.91 3667.77 4399.67 1655.28 1852.14 3015.35 1932.36 2412.50 4458.27 3549.72 3765.08 3944.18 4444.89 5754.85 6971.90 4726.45 3720.13 3750.29 1643.30 1824.54 2984.67 1947.59 2467.92 4574.27 3239.69 3625.12 3836.52 4483.75 5465.15 7109.25 4727.99 3620.86 4279.88
Oct
1579.02 1939.14 2956.16 2083.59 2647.08 4922.52 3326.50 3609.21 4073.21 4537.25 5629.07 7589.74 4649.18 3493.01 4278.88 1594.71 2128.24 2995.52 2148.80 2648.58 5067.57 2963.71 3573.49 4154.64 4518.92 5838.04 7649.30 4596.89 3437.52 3900.34 1615.24 2204.35 3018.56 1926.93 2593.79 5365.40 2965.53 3598.88 4663.34 4685.27 6120.62 7936.34 4370.41 3189.49 4321.74 1644.05 2203.66 2898.10 2050.62 2672.31 5719.08 2927.68 3684.68 4771.33 4872.20 6396.02 7195.19 4316.77 3486.86 4615.25
Nov
1612.21 2374.95 2859.24 1803.69 2648.15 6201.71 2852.15 3608.16 4889.73 5035.86 6596.08 7437.30 4380.31 3322.59 4468.50 1632.42 2411.29 2800.51 1895.77 2660.23 6278.27 3157.89 3620.54 4638.52 5250.07 6865.12 7396.61 4440.83 3612.25 4541.87 1509.32 2434.65 2898.45 1920.97 2654.19 5943.80 3336.97 3523.08 4865.23 5053.30 6567.82 7281.63 4307.19 3283.89 4505.18 1604.28 2261.31 2887.41 1887.71 2586.22 5503.23 3488.77 3358.14 4832.48 5155.13 6568.54 6953.52 4425.78 3738.48 4523.53
Dec
1578.92 2279.89 2838.22 1858.55 2610.74 5064.92 3584.70 3290.45 4727.55 5274.32 6262.57 7006.44 4176.55 3666.54 4514.36 1579.66 2349.26 2903.50 1850.26 2749.38 5434.83 3292.57 3450.22 4266.24 5096.55 6569.65 6845.11 4090.84 3645.96 4640.00 1616.69 2384.63 2886.50 1881.49 2621.74 5723.24 3396.80 3047.72 4562.92 4809.64 6944.26 6536.65 4024.68 3411.12 4033.33 1606.34 2477.14 2938.72 1861.77 2681.34 5820.50 3491.28 3103.44 4537.49 4783.21 6390.07 6216.78 4152.60 3449.01 4094.90
Price of Green gram in Uttar Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1423.35 1995.77 2738.29 2777.84 2845.81 3070.60 5426.07 4772.63 3806.89 4774.47 5612.40 6923.00 6744.31 4949.84 4851.35 1423.72 2045.92 2819.13 2756.07 3594.16 2936.50 5440.46 4995.43 3833.11 4849.01 5600.48 6982.45 6946.85 5576.92 4954.56 1423.50 2026.52 2882.59 2797.83 3449.26 3197.00 5483.47 4817.97 3892.17 4853.99 5745.85 7000.69 6778.95 5002.46 5071.99 1422.83 2041.81 2848.80 2859.96 2460.56 3072.76 5517.78 4837.24 3949.32 4906.78 6203.29 7213.75 6673.58 5108.73 4906.10
Feb
1424.83 2075.80 2899.04 2836.75 3378.43 3407.89 5091.20 4823.46 3674.59 4939.80 6225.72 7261.67 6531.57 5097.69 5043.46 1422.85 2111.32 2854.37 3023.58 3500.19 2673.70 5092.68 4769.84 3904.40 5028.92 6156.16 7241.57 6728.87 5328.21 4884.97 1420.80 1846.43 2806.28 3195.70 3438.10 2952.50 5274.24 4787.57 3864.18 5114.59 6229.87 7277.97 6529.31 5210.51 5049.12 1430.85 1995.88 2829.99 3205.87 3414.81 3600.67 4366.24 4797.43 3886.46 5217.71 6341.53 7141.62 7013.37 5093.46 5201.95
Mar
1416.89 2058.50 3595.35 3186.58 3391.43 3472.34 4855.48 4809.69 3942.58 5007.43 6529.42 7164.13 6299.62 4387.04 5142.80 1414.67 1977.11 3608.34 3192.14 3419.15 3487.50 4461.29 4526.46 3903.90 5210.27 6506.42 7316.60 6392.58 4373.39 4947.33 1461.00 1973.75 3583.24 3135.57 3414.81 3528.12 4701.79 4984.50 3961.24 5225.85 6572.18 7353.86 6526.25 4593.96 4860.95 1375.00 1827.50 3594.49 3091.58 3400.00 3581.60 4532.70 4880.79 3983.99 5190.29 6504.45 7383.92 6503.68 5040.24 5002.44
Apr
1408.00 2210.00 3647.28 3026.33 3403.89 3584.12 4916.16 5079.81 3996.27 5234.26 6632.40 6622.18 6630.55 5069.23 4553.65 1600.00 2700.00 3507.94 3055.58 3406.06 3610.81 5028.82 4938.37 3984.28 5214.95 6768.33 6669.77 6648.39 4903.69 4707.50 1558.71 2024.29 3628.25 3041.23 3414.29 3670.15 4904.89 5418.29 3987.87 5199.13 6888.64 7410.34 9038.83 4997.04 4384.72 1558.58 1929.55 3805.65 3102.02 3427.44 3732.97 5074.33 5500.00 4062.48 5199.84 6919.85 7451.82 6812.34 4702.64 4405.09
May
1558.85 1953.14 3769.48 3165.97 3513.61 3869.42 5132.94 3961.46 4044.60 5371.34 6832.94 7592.62 6945.49 4728.79 4477.44 1558.32 1994.85 3517.39 3312.16 3600.97 4047.73 5307.26 1783.99 4024.49 5313.65 6935.82 7579.68 6907.99 4759.28 4586.74 1559.37 1988.52 3349.25 3246.61 3590.34 4078.81 5229.50 3205.77 4072.15 5318.29 7114.64 7659.99 6816.52 5300.26 4559.28 1557.27 2101.92 3342.50 2267.42 3440.52 3175.00 5534.01 1872.52 3867.84 5461.65 7099.88 7779.85 6443.24 4884.28 4738.50
Jun
1561.47 2509.09 3416.99 3216.24 3220.98 4019.60 5548.13 4168.82 3946.31 4969.23 6285.51 7529.70 6284.08 4673.83 4763.90 1553.07 2524.55 3345.90 3117.48 3218.75 4078.87 5781.04 4216.70 3986.60 4948.68 5732.46 7576.26 6026.92 4286.87 4286.87 1557.12 2535.45 3275.46 3035.93 2490.57 4210.14 5389.34 3959.89 3944.04 5421.18 5839.95 7500.69 6097.07 4227.95 4227.95 1500.00 2319.76 3315.99 3018.64 3182.81 4197.20 5522.70 3770.79 3930.25 5201.20 5645.18 6974.92 5824.88 4029.72 4029.72
Jul
1207.55 2605.33 2811.69 3159.46 2804.33 4374.08 5847.61 4038.49 4298.27 5088.04 5718.67 6758.56 5615.53 4092.57 4689.01 1410.53 2617.86 2891.57 3410.00 2849.15 4457.34 6091.74 4357.01 4380.21 4842.36 6070.26 6549.91 6059.85 3953.04 4811.66 1373.41 1881.76 2737.56 2973.61 3039.73 4288.57 5136.08 4175.92 4376.82 4974.04 6264.79 6664.78 6244.98 3627.19 4921.39 1079.15 2644.29 2800.00 3420.00 2895.67 3905.12 4693.01 4266.14 4504.07 4801.35 6229.79 6506.67 5824.15 3590.73 4968.31
Aug
2173.33 2654.00 2831.68 2941.95 2871.74 3460.17 6170.55 3846.09 4587.77 4475.80 6335.02 6404.75 5444.05 3756.49 4731.70 1389.69 2675.00 2832.35 3305.52 2974.26 3603.33 4303.85 3908.74 4629.78 4818.54 6394.43 6485.29 5155.50 4061.82 4987.38 1686.06 2706.92 2701.32 3576.34 2679.01 3700.93 5890.93 3666.75 4564.26 5040.15 6584.37 6325.15 5242.50 4314.39 4947.19 1802.13 2757.14 2695.13 3578.90 2539.50 3610.53 5816.01 3725.18 4581.89 4956.02 6469.37 5924.94 5617.11 4436.54 4960.31
Sep
1855.31 1959.52 2737.50 3448.05 2846.21 2835.00 3409.40 3877.47 4594.71 4221.49 6553.25 6158.15 5097.38 4525.33 4936.90 1898.16 1975.89 2791.72 3580.24 2817.57 3484.33 5408.15 3559.88 4531.96 4290.25 6262.14 6336.29 5561.80 4549.07 4830.48 1920.74 2090.68 2789.64 3440.15 2866.02 2958.50 4345.40 3771.43 4211.41 4407.87 6551.28 6404.47 5217.38 4479.32 4807.34 1897.31 2644.55 2749.43 3599.56 2828.65 3605.10 4606.95 3555.87 4050.21 4747.10 6583.67 6523.44 5255.55 4391.47 4677.08
Oct
1777.50 2640.00 2662.07 3664.93 2732.13 3891.24 5359.52 3657.26 4346.51 4705.38 6571.18 6622.18 6016.99 4359.54 4195.57 1772.50 2650.00 2716.88 3004.23 2727.33 3921.76 5155.65 3499.13 4417.63 4809.36 6637.08 6669.77 5780.93 4423.45 4205.91 1966.40 2645.00 2738.29 3404.30 2795.67 3448.96 5252.16 3611.49 4315.38 5014.81 6570.03 7410.34 5602.42 4340.49 3982.96 2080.71 2647.50 2819.13 2600.37 2733.96 3917.46 4991.27 3673.18 4627.28 5137.26 6373.45 6919.70 4930.68 4663.60 4273.45
Nov
1886.55 2646.25 2882.59 2788.66 2941.12 3743.89 6056.10 3973.45 4613.36 5274.86 6364.58 6780.51 5438.72 5776.78 4630.91 1880.00 2646.88 2848.80 2278.30 2789.78 3240.22 5026.33 4089.18 4594.51 5187.25 6483.56 6830.68 5553.66 4987.80 4612.69 1919.83 2646.56 2899.04 2544.93 3017.58 3655.32 5621.26 4091.32 4713.62 5176.66 6606.94 6837.98 5296.97 5122.69 4893.54 1955.00 2646.72 2854.37 2964.05 2997.90 4662.48 5257.02 3627.71 4615.97 5400.41 6605.25 6757.15 5317.59 4688.18 5173.64
Dec
1964.44 2646.64 2806.28 2409.85 2446.87 4437.24 4987.61 3740.52 4735.49 5075.59 6706.28 6757.60 5036.03 4994.87 5040.09 1999.53 2646.68 2829.99 2266.61 2752.13 4236.31 4947.30 3682.46 4752.82 5102.80 6703.13 6628.74 5100.47 5055.41 5121.79 2021.36 2646.66 2817.38 2190.49 3055.03 4238.29 5213.90 3645.73 4673.66 5377.52 6753.16 6636.71 5058.15 4881.62 5353.39 2014.41 2646.67 2767.07 2157.29 3089.17 3842.22 4901.50 3216.00 4698.25 5448.36 6754.99 6598.70 5212.42 4980.92 5375.76
Annexure IV: Price of Groundnut in Selected State markets of India Price of Groundnut in Odisha market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1500.00 1910.73 1666.93 1790.21 1940.43 2830.51 2480.00 4750.00 2670.97 3996.48 4433.86 4200.00 4552.09 4300.00 5033.33 1577.00 1735.82 1564.12 1857.87 2450.00 3067.99 2800.00 4400.00 3000.00 3525.00 3045.45 4040.00 4526.04 4300.00 4384.62 1654.00 1560.91 1233.47 1675.62 2071.52 2317.23 2218.60 2771.63 3470.83 3784.74 3072.73 4666.67 4500.00 4250.00 5200.00 1693.47 1476.56 1347.83 1628.72 1978.13 2039.58 2166.90 2760.87 3599.90 3541.71 3100.00 4200.00 4513.02 4275.00 4792.31
Feb
1262.27 1527.60 1426.86 1605.33 2176.02 2031.68 2420.66 2957.00 3442.03 3883.52 2780.77 4433.34 4448.28 4262.50 4996.16 1295.81 1479.77 1519.71 1684.54 1851.66 2121.61 2348.90 2306.47 3320.11 3537.50 2704.55 4316.67 4480.65 4300.00 5000.00 1567.96 1572.80 1536.14 1764.86 2200.81 2191.32 2894.27 2336.30 3327.81 3282.01 3225.00 4375.00 4107.25 4484.62 4800.00 1502.09 1497.49 1487.81 2060.87 2156.12 2165.36 2255.96 3036.39 3895.05 3066.67 2900.00 4345.83 4124.35 4120.00 4900.00
Mar
1541.84 1848.09 1489.26 2186.21 2546.89 2168.52 1737.68 2718.20 2301.65 3075.95 2660.00 4142.38 4115.31 4364.90 4466.04 1600.00 1678.73 1534.95 2100.00 2149.07 2400.00 2500.00 2400.00 4292.83 3006.98 3200.00 4000.00 4274.30 4242.45 4450.00 1660.59 1629.00 2026.48 1810.89 2237.65 2847.80 3000.00 3997.29 4885.53 4808.43 3152.00 4546.96 4173.30 4120.00 4800.00 1695.00 1717.42 1762.49 1830.10 2631.02 2876.44 2966.35 2957.14 4750.00 3210.82 3214.49 4231.64 4234.91 4800.00 5409.09
Apr
1458.00 1865.00 1680.80 1833.81 2076.74 2760.71 2800.00 4961.54 3000.00 3441.18 3020.00 4085.37 4389.97 4800.00 5106.51 1464.62 1932.50 1650.66 1749.49 2700.00 2916.01 1846.43 3200.00 3358.53 2861.72 3228.57 4066.47 4811.98 4800.00 4803.92 1471.25 2000.00 1517.46 1702.35 2040.74 2312.93 2080.42 2589.79 3439.94 3755.62 3169.43 4117.71 4711.27 5976.47 4497.22 1500.00 1504.87 1425.06 1594.31 1931.33 2246.95 2375.68 2721.75 3238.81 3510.56 3374.65 4170.45 4834.96 4504.88 4500.00
May
1284.47 1523.37 1469.31 1693.60 2124.58 2113.76 2338.93 2518.91 3424.92 3906.82 3936.73 4687.77 4590.31 4752.44 4500.00 1370.69 1470.96 1520.64 1775.64 1991.03 2128.75 2148.25 2363.33 3231.69 3585.08 3750.57 4980.39 4343.10 5000.00 4500.00 1557.56 1578.95 1500.98 1928.66 2101.53 2077.56 2700.00 2701.05 3869.01 3214.56 3773.38 4395.14 4214.07 5000.00 5934.78 1514.39 1540.57 1412.27 2085.14 2070.42 2180.03 2556.53 3625.00 3960.84 3128.23 3780.00 4366.81 4114.23 4500.00 5558.82
Jun
1568.81 1626.86 1513.08 2109.30 2338.75 2153.92 2225.27 2733.33 3171.88 2870.02 3766.87 4906.63 4211.72 4996.08 4500.00 1598.02 1682.71 1514.43 1911.37 2124.41 2766.81 2814.72 2932.09 3500.00 3071.59 3983.33 4326.37 4191.20 4731.13 6000.00 1700.00 1578.61 1710.80 1713.43 2363.13 2690.42 2500.00 5327.18 4104.55 5205.61 3688.53 4454.55 4176.30 4863.61 5846.15 1785.00 1708.84 1803.90 1810.37 2587.89 2800.00 4116.61 3000.00 4961.54 3258.45 3576.07 4304.45 4354.29 4797.37 4452.19
Jul
1204.88 1650.00 1540.64 1789.04 2700.00 3061.28 2000.00 3200.00 3400.00 4000.00 3102.78 4461.54 4577.36 4830.49 4000.00 1350.22 1570.51 1653.99 1808.81 1892.45 2729.37 2179.17 3000.00 3200.00 3637.50 2972.95 4000.00 4367.03 4813.93 3975.00 1495.56 1491.03 1264.43 1655.27 1472.67 2164.34 2168.20 2333.25 3430.10 3755.61 3050.07 4230.77 4316.47 6000.00 3500.00 1767.38 1505.78 1374.24 1653.82 1965.35 1982.44 2439.69 3112.26 3669.02 3645.94 3654.46 4200.00 4522.62 6796.36 4859.91
Aug
1499.05 1499.72 1539.59 1711.03 2042.79 2030.01 2200.00 2374.71 3402.31 3621.30 4070.89 4215.39 4030.00 6000.00 3500.00 1516.33 1516.57 1515.47 1573.72 1974.76 2116.48 2364.58 3109.09 3310.82 3489.01 4127.98 4000.00 5537.37 6000.00 3500.00 1561.35 1609.53 1529.91 1815.98 2013.95 2108.16 2611.11 3402.39 3803.57 2859.28 4194.06 4161.29 5416.67 5000.00 5309.86 1556.07 1526.16 1577.64 2192.88 2192.39 2307.74 1904.47 3433.33 3951.26 3097.33 4096.94 4080.65 5063.64 5000.00 3940.00
Sep
1590.93 1635.52 1432.83 2428.42 2204.78 2145.45 2457.41 2673.97 2406.90 3175.02 4110.97 4120.97 4800.00 4800.00 3940.00 1600.18 1614.08 1426.39 2201.25 2251.49 2551.38 2902.44 3506.61 4867.77 2975.15 4145.83 4100.00 4800.00 4600.00 4500.00 1668.00 1621.14 1640.70 1812.15 2474.95 2533.33 4184.55 4566.81 4183.63 4993.21 4127.50 5000.00 4800.00 4776.56 4500.00 1450.00 1541.30 1815.09 1819.35 2807.22 2977.10 5466.67 3100.00 3200.00 3627.91 4201.92 4335.68 4628.13 4776.56 4500.00
Oct
1450.00 1789.00 1820.19 1842.73 2525.81 3082.98 2438.46 4276.92 3301.31 4000.00 4106.80 5000.00 4456.25 5228.28 5003.84 1461.78 1644.87 1950.55 1738.21 2045.45 2435.37 2157.25 4433.33 3000.00 3961.76 4000.00 4000.00 6000.00 5680.00 5200.00 1473.57 1500.74 1416.89 1665.06 2117.31 2049.32 2284.34 2665.27 3460.23 3724.44 4036.52 3960.00 5172.81 5586.30 5200.00 1400.00 1500.09 1404.94 1599.25 2119.49 1909.40 2363.16 3062.07 3648.39 3852.64 4062.50 4186.67 5057.70 6000.00 5000.00
Nov
1501.16 1529.84 1514.17 1715.23 1942.69 2113.73 2414.71 2272.28 3384.68 3675.81 4046.46 4073.34 5257.10 5793.15 4800.00 1489.36 1514.75 1524.47 1853.61 2211.64 2135.77 2513.49 2360.10 3946.37 3319.72 4008.51 4833.33 4778.55 5896.58 4500.00 1557.01 1449.55 1502.44 1983.12 2121.55 2116.77 2806.60 2647.34 3525.22 3116.16 4370.69 4500.00 4300.00 5844.86 4200.00 1523.08 1554.88 1551.78 2172.97 2275.77 2271.99 1894.12 4075.00 3119.05 2982.60 4433.33 4916.67 4300.00 4828.57 4350.00
Dec
1564.82 1627.86 1518.35 2040.00 2172.03 2337.88 2361.36 2339.39 3092.59 2772.91 4170.73 4708.34 4300.00 5336.72 4275.00 1600.00 1599.45 1683.69 2222.27 2283.18 2838.19 2829.36 3565.57 4909.37 3640.50 4189.58 4500.00 4300.00 5082.64 4750.00 1600.00 1631.35 1804.44 1829.89 2612.61 2381.82 3803.57 3544.26 4200.00 4698.31 4122.22 4604.17 4200.00 4828.57 4512.50 1650.00 1748.14 1817.60 1800.00 2905.00 2892.68 4777.78 3425.00 4276.92 4169.41 4275.79 4500.00 5716.67 4955.61 4840.00
Price of Groundnut in Andhra Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1616.18 1571.85 1411.90 1849.08 2036.96 2099.09 2577.01 2727.34 3656.48 4579.09 3019.32 3637.51 4629.54 4881.09 3530.27 1616.36 1588.37 1423.03 1938.07 2128.78 2191.65 2517.54 2571.29 3399.84 4736.56 3059.09 3689.82 4504.30 4693.07 3630.88 1667.68 1672.65 1415.54 1890.18 2139.01 2093.35 2451.46 2807.99 3608.17 4538.76 3041.64 3933.89 4474.59 4617.69 3745.43 1686.99 1630.51 1456.97 1976.33 2269.26 2157.56 2465.65 2848.54 3517.65 4545.40 3119.53 4126.55 4412.36 4829.72 3596.20
Feb
1616.54 1534.33 1492.79 2023.27 2217.23 2044.96 2542.90 3050.77 3908.38 4686.40 3006.13 4663.70 4501.67 4617.21 3895.73 1719.00 1588.76 1500.82 2075.03 2342.49 2153.92 2530.48 3364.74 3949.75 4714.98 3152.58 4610.09 4532.17 4542.02 4150.81 1706.31 1581.44 1533.08 2063.60 2291.80 2250.22 2544.78 3483.37 3814.61 4535.18 3173.28 3885.86 4667.61 4723.15 3875.98 1736.10 1635.14 1516.95 2071.52 2242.02 2269.99 2625.44 3572.13 4189.06 4333.89 2980.85 4117.53 4760.87 4673.36 3909.76
Mar
1500.00 1919.58 1559.44 2161.48 2487.48 2300.53 2584.65 3630.46 4394.75 4312.15 3063.17 4362.32 4811.54 4919.10 3953.89 1527.85 1777.36 1548.04 2174.17 2456.42 2358.82 2561.05 3670.30 4392.62 4271.25 3070.25 4202.22 4978.70 5272.32 3819.89 1350.00 1559.28 1532.57 2160.01 2475.41 2377.41 2708.62 3769.53 4376.08 4112.54 3288.54 4093.79 4886.82 5292.91 3545.18 1450.00 1601.24 1519.63 2130.11 2384.07 2478.81 2905.97 3817.35 4206.64 4300.00 3353.59 4198.02 4849.39 5382.79 3682.54
Apr
1555.71 1604.39 1556.28 2222.36 2310.78 2603.20 2870.35 3867.56 4125.40 4206.27 3453.25 3847.58 4822.24 5366.48 3613.86 1627.85 1688.61 1604.18 2202.90 2374.04 2628.86 2855.64 3871.97 4097.12 4253.14 3381.92 4421.70 4804.79 5132.18 3648.20 1690.00 1634.40 1581.11 2202.94 2404.86 2549.16 2772.45 3868.54 4183.75 4229.70 3300.21 4623.19 5015.77 5038.30 3631.03 1661.41 1588.95 1616.44 2239.07 2432.66 2550.36 2749.86 3784.90 4081.36 4241.42 3208.58 4175.09 5036.76 4995.81 3639.61
May
1700.00 1648.39 1588.69 2239.59 2363.02 2468.28 2793.53 3881.79 4189.89 4235.56 3236.33 4291.03 5087.47 4766.31 3635.32 1707.50 1675.17 1571.57 2255.98 2481.18 2564.02 2833.19 3863.00 4023.80 4238.49 3264.68 4349.55 5215.13 4945.07 3637.47 1632.82 1730.15 1623.13 2299.81 2491.09 2528.07 2848.46 3374.85 4268.36 4237.03 3527.95 4588.04 4824.49 4636.95 3636.39 1691.41 1731.97 1620.19 2333.23 2528.61 2504.10 2731.63 3696.08 4056.51 4237.76 3609.06 4543.83 4734.39 4704.91 3636.93
Jun
1715.00 1645.07 1677.11 2408.66 2534.28 2454.51 2948.66 3616.11 4501.63 4237.39 3662.06 5037.68 4559.53 4464.20 3636.66 1725.50 1678.57 1677.11 2266.19 2554.97 2568.35 3063.32 3242.36 4208.02 3725.16 3386.55 4447.53 4646.79 5001.14 3636.79 1750.00 1690.92 1740.22 2345.99 2568.69 2463.92 3221.23 3832.92 3897.21 3898.50 3558.40 4462.30 5211.02 4758.15 3636.73 1738.59 1685.20 1646.41 2448.69 2633.72 2400.88 3134.89 3649.67 3911.31 3626.62 3455.67 4405.00 4407.88 3937.95 3636.76
Jul
1736.00 1711.72 1692.83 2317.27 2664.29 2339.72 3157.72 3769.20 3958.97 3630.20 3256.54 4280.03 3593.22 4726.23 3636.74 1718.67 1010.11 1654.20 2321.92 2427.53 2314.36 3208.69 3429.13 3990.30 3739.94 3246.86 4095.56 4219.02 3358.38 3636.75 1727.17 1707.16 1666.16 2244.14 2623.50 2391.79 3360.18 3298.59 3978.07 3826.08 3321.26 4090.15 4339.91 3278.72 3930.78 1718.51 1863.34 1668.76 2200.06 2594.14 2245.42 2975.82 3335.32 4239.81 3559.05 3404.94 4696.84 4230.84 3954.96 3646.16
Aug
1610.69 1578.86 1717.52 1892.24 2414.97 2448.29 2816.08 3346.81 4042.61 3603.89 3529.97 4373.61 4493.23 4167.67 3765.91 1776.70 1539.75 1745.23 2090.21 2623.83 2341.14 2912.50 2875.64 4034.86 3589.83 3687.10 4365.84 4297.48 4201.22 4010.64 1658.19 1495.48 1756.66 2104.05 2337.60 2398.38 3020.49 2803.09 4606.86 3624.29 3904.69 4821.67 4894.11 3813.75 4336.82 1653.12 1676.73 1711.77 2069.88 2592.77 2379.00 2839.07 3280.17 4547.51 3495.90 3840.04 4817.39 3644.36 3893.16 4028.93
Sep
1638.74 1655.20 1641.50 2226.18 2475.27 2349.47 3063.82 3184.59 4668.20 3898.52 3831.65 4884.82 3409.88 3659.26 4407.30 1573.75 1672.00 1550.39 2283.86 2534.02 2498.48 3144.21 3582.04 4879.04 3621.19 3825.26 4579.60 3830.40 4335.39 4028.97 1671.46 1673.86 1661.38 2154.15 2504.65 2232.69 2802.26 3665.74 4873.48 3659.14 3912.08 4630.88 3829.84 3633.80 4176.65 1540.43 1672.93 1507.29 2192.96 2182.14 2431.07 2684.04 3670.04 5093.94 3795.98 3998.98 4965.74 3410.37 3527.41 4118.31
Oct
1777.19 1595.56 1886.34 2228.71 2490.14 2443.24 2885.58 3491.46 5210.80 3793.81 4239.38 5672.13 3900.42 3431.59 3881.57 1451.76 1565.96 1651.98 2205.29 2600.00 2622.26 2977.66 3556.72 4902.01 3660.12 3782.37 4314.58 3885.89 3549.54 4535.34 1440.17 1568.87 1563.47 2220.06 2288.32 2516.20 3040.18 3544.52 4567.35 3619.32 3727.83 3791.75 4033.77 3056.04 4445.90 1577.44 1541.35 1890.67 2123.34 2199.52 2465.47 2849.43 3519.67 4663.83 3482.28 3780.32 3711.92 3721.41 3094.23 4521.99
Nov
1531.84 1524.19 1857.00 2050.69 2161.34 2425.83 2698.62 3555.57 4745.83 3466.04 3843.16 3679.54 3904.97 2939.81 4545.94 1454.57 1592.25 1803.51 2045.88 2100.00 2599.64 3040.34 3488.86 4666.78 3553.73 3707.02 3768.15 4369.20 3277.90 4410.83 1558.58 1574.81 1724.70 2011.44 2130.67 2814.55 2992.06 3483.44 4703.34 3632.73 3716.47 4197.30 4253.07 3926.77 4307.42 1555.02 1591.54 1906.83 1974.71 2115.34 2807.52 2975.48 3450.21 4789.62 3511.20 3516.27 4395.19 4160.21 3819.47 4162.09
Dec
1603.84 1521.79 1783.55 1983.48 2083.58 2711.83 2770.55 3434.76 4798.02 3208.74 3287.08 4466.24 4160.31 3798.36 4129.99 1591.70 1516.35 1648.08 1870.95 2232.54 2479.93 2744.89 3323.29 4511.10 3223.73 3406.52 4261.99 4336.45 3557.13 4301.08 1568.78 1519.03 1875.04 1867.24 2170.41 2544.65 2781.80 3386.11 4645.52 3127.97 3402.61 4277.31 4541.87 3647.39 4410.27 1581.09 1459.26 1957.78 1968.25 2074.96 2488.46 2849.21 3432.48 4536.39 3174.11 3548.21 4483.66 4762.78 3517.94 4430.73
Price of Groundnut in Gujarat market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1995.30 1784.52 1681.88 2364.54 2626.58 2217.79 2753.54 2813.07 4115.86 4981.53 3344.87 4279.66 4530.61 4152.83 3829.62 1877.41 1751.07 1693.25 2242.78 2679.71 2294.92 2704.78 2798.35 4036.80 4862.03 3350.79 4404.38 4603.59 4103.19 3805.27 1786.04 1751.80 1677.55 2482.77 2614.14 2261.42 2733.99 2908.65 4030.36 4940.27 3415.72 4265.80 4399.82 4158.03 3832.31 1801.50 1712.23 1666.56 2382.39 2599.63 2251.04 2657.00 2818.14 4068.46 4913.52 3371.26 4292.51 4316.46 4124.75 3671.46
Feb
2030.73 1750.17 1662.38 2275.84 2619.38 2172.38 2666.88 2786.29 4115.32 4886.22 3367.56 4202.45 4390.31 4015.39 3570.34 2044.97 1791.97 1648.96 2307.60 2676.28 2185.00 2722.70 2803.79 4120.89 4886.13 3358.33 4182.72 4311.64 4062.33 3602.26 1837.16 1730.43 1641.56 2382.87 2638.88 2122.45 2703.58 2804.83 4346.39 4860.33 3292.97 4161.02 4366.48 4104.90 3602.28 1975.70 1725.09 1661.78 2376.08 2707.18 2089.32 2721.29 2832.51 4425.64 4912.69 3151.65 4108.60 4383.76 4099.26 3531.26
Mar
2050.82 1688.41 1644.86 2369.31 2839.99 2152.34 2712.78 2815.53 4481.40 4657.59 3264.79 4061.45 4454.95 4106.04 3805.90 2053.69 1701.78 1630.62 2351.15 2792.08 2112.74 2734.58 2818.23 4646.56 4775.03 3337.25 4246.22 4530.74 4156.69 3657.56 2049.00 1693.32 1662.63 2340.81 2722.39 2191.85 2718.41 2811.22 4673.96 4710.16 3313.48 4197.66 4608.37 4186.38 3416.73 1901.91 1717.10 1620.85 2453.18 2721.01 2265.33 2856.30 2860.23 4885.07 4544.94 3381.90 4264.06 4454.96 4390.01 3567.06
Apr
1888.91 1731.25 1671.37 2446.05 2724.71 2417.65 2825.79 3005.20 5027.07 4812.23 3380.00 4324.58 5356.38 4510.65 3516.23 1846.58 1742.67 1704.54 2462.36 2688.11 2494.83 2847.71 3054.69 4721.03 4876.46 3274.63 4459.51 5121.79 4485.58 3481.22 2048.87 1719.92 1810.64 2507.96 2712.95 2468.75 2838.15 3154.78 4772.91 4834.21 3428.82 4305.52 5465.23 4632.44 3473.61 1887.73 1706.04 1707.07 2486.33 2740.65 2437.27 2823.96 3254.94 4929.68 4816.29 3512.42 4413.89 5543.34 4397.81 3483.39
May
1887.71 1674.66 1699.80 2493.07 2688.07 2478.36 2735.41 3196.31 5225.38 4679.66 3579.72 4512.38 5605.93 4443.41 3523.41 1910.72 1732.09 1686.21 2621.54 2698.47 2482.14 2790.07 3294.37 4806.24 4696.62 3498.40 4698.24 5513.76 4324.84 3519.42 2005.01 1670.89 1730.65 2545.42 2716.95 2475.82 2841.98 3331.80 4946.66 4618.87 3470.19 4682.18 5659.01 4356.44 3592.25 1966.77 1823.91 1694.66 2588.08 2664.89 2397.90 2906.38 3364.83 4721.26 4413.70 3434.31 4691.95 5689.11 4154.38 3635.41
Jun
1872.65 1829.60 1812.17 2558.07 2599.26 2401.64 2904.40 3446.48 4677.10 4345.46 3362.95 4923.85 5763.09 4240.11 3673.79 1913.21 1786.93 1896.22 2574.04 2658.58 2391.16 3040.12 3617.17 4658.26 4252.47 3326.99 5141.52 5868.26 4166.21 3758.10 1870.82 1726.36 1909.86 2675.89 2649.18 2396.16 3043.94 3697.46 4570.10 4149.95 3296.05 5058.75 5864.23 4037.42 3754.15 1900.79 1785.25 1869.83 2783.05 2626.20 2475.90 3051.36 3799.49 4595.82 3962.25 3457.49 5062.58 5877.70 3994.75 3703.17
Jul
1957.30 1858.13 1954.20 2761.76 2646.38 2520.59 3107.32 3739.05 4741.68 3773.16 3524.01 5073.59 6040.56 4261.94 3769.23 1962.50 1889.89 1983.30 2853.42 2673.54 2480.17 3207.80 3493.55 4887.23 3983.35 3529.23 5069.89 6073.86 4343.47 3776.17 1933.98 1883.67 2009.20 2762.37 2732.70 2499.47 3367.68 3474.52 4892.26 3886.03 3629.87 5172.47 6459.04 3953.88 3752.19 2155.89 1877.21 2017.93 2647.06 2720.64 2459.93 3256.08 3530.86 5413.04 3535.10 3659.31 5090.76 6435.03 3536.71 3819.98
Aug
2062.25 1857.92 2144.86 2692.74 2714.75 2432.03 3108.27 3696.40 5566.20 3735.53 3539.90 5124.02 6514.91 3434.15 3840.09 2018.83 1836.23 2153.54 2602.53 2617.48 2509.02 3169.36 3554.49 4856.86 3763.41 3464.87 5101.94 6156.53 3313.39 3961.93 2067.98 1820.76 2269.03 2628.22 2584.53 2578.44 3060.88 3543.61 5307.80 3252.55 3675.36 5060.51 6173.54 3351.34 3968.93 2066.11 1941.77 2232.99 2628.98 2593.06 2635.03 2936.62 3633.08 5299.70 3157.57 3653.50 4908.83 5756.02 3374.30 3728.16
Sep
2020.61 1992.66 2056.03 2622.69 2524.51 2472.44 3251.25 3676.05 5036.54 3115.31 3477.25 4979.01 5460.43 3134.50 3705.14 2069.14 1969.52 1947.64 2618.34 2312.14 2462.22 3316.42 3547.03 4901.31 2981.74 3505.50 5146.61 4739.73 3145.28 3737.32 2050.49 1962.14 1999.53 2663.47 2336.56 2438.89 3092.66 3442.73 4355.51 3084.50 3561.21 5021.64 4746.86 3256.04 3795.19 2023.10 1901.34 2007.93 2576.69 2239.31 2423.66 3071.67 3184.00 4149.01 3150.78 3732.42 4609.63 4918.53 3427.82 3995.22
Oct
1907.42 1856.97 2072.45 2145.34 2267.94 2492.42 3087.66 3208.71 4235.59 3080.09 3814.00 4415.06 4669.03 3569.82 4148.01 1814.45 1853.62 1970.27 2302.19 2314.70 2532.09 3169.95 3275.04 4719.48 3090.79 3887.32 4290.49 4184.48 3704.92 4248.16 1838.69 1841.95 1932.31 2208.18 2429.27 2480.93 2932.59 3183.00 4998.34 3132.94 3933.43 3972.67 4024.21 3837.29 4302.75 1846.82 1781.51 1969.60 2325.65 2347.66 2526.91 2817.24 3261.08 4943.28 3283.77 3886.97 3859.13 3848.11 3880.11 4299.38
Nov
1870.49 1736.33 1999.19 2279.92 2342.71 2627.20 2715.82 3303.88 5006.69 3259.09 3837.07 3858.76 3939.95 3781.92 4296.35 1779.47 1692.55 2135.34 2306.49 2217.34 638.94 2888.27 3305.35 4968.21 3596.69 3891.37 3831.38 4103.79 3787.59 4296.30 1849.82 1678.70 2167.33 2420.02 2108.78 2660.54 2943.62 3099.90 5007.11 3530.41 3740.12 4062.18 4098.76 3755.66 4167.02 1815.19 1731.53 2213.64 2472.22 2273.25 2677.41 3038.02 3131.72 5155.92 3485.53 3802.67 4306.77 4122.10 3863.42 4037.60
Dec
1814.68 1718.06 2193.45 2453.82 2253.46 2709.23 2978.44 3224.07 5078.74 3446.24 3736.55 4334.49 4037.03 3906.33 3964.83 1787.25 1666.78 2114.77 2500.28 2314.20 2707.29 2825.68 3359.32 4973.04 3439.56 3692.20 4333.51 4096.62 3866.59 3978.88 1770.43 1651.62 2246.78 2520.66 2273.71 2708.85 2847.87 3735.56 5135.89 3439.89 3594.56 4497.00 4134.28 3880.77 3986.59 1791.83 1674.82 2305.64 2535.22 2294.18 2718.65 2878.17 3870.14 5057.32 3636.82 3929.60 4562.12 4148.76 3946.86 4049.24
Price of Groundnut in Karnataka market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1633.56 1586.16 1491.01 1892.99 2094.26 2133.72 2577.95 2815.32 3462.90 4430.19 3159.94 3494.79 4459.56 4410.57 3514.87 1690.19 1643.94 1469.56 1891.66 2149.40 2175.01 2529.31 2706.81 3528.86 4489.19 2940.74 3632.46 4434.30 4553.68 3487.20 1595.45 1570.80 1455.81 1920.81 2226.60 2251.82 2460.32 2817.70 3458.34 4453.06 3073.37 3890.15 4604.79 4466.15 3655.03 1571.23 1520.98 1452.20 1701.30 2227.91 2279.24 2462.77 2982.79 3941.19 4331.61 3212.61 4289.18 4580.88 4979.13 3758.50
Feb
1536.40 1469.92 1473.85 1811.06 2332.99 2338.53 2476.37 3183.96 3994.83 4456.20 3148.63 4498.05 4045.91 4783.95 3959.84 3315.23 1466.05 1514.00 2159.07 2414.35 2432.16 2322.66 3491.22 3974.21 4437.09 3157.51 4505.97 4610.19 4882.12 4152.88 1666.11 1569.40 1550.53 2205.17 2371.74 2342.75 2608.80 3600.70 4167.85 4233.88 3028.17 4457.62 4643.61 4854.07 4002.03 1650.86 1550.22 1469.77 2177.71 2356.34 2143.63 2726.84 3465.37 4320.21 4430.55 2924.08 4483.32 4807.33 4802.62 3910.53
Mar
1621.28 1568.95 1570.98 2157.98 2585.99 2128.21 2642.66 3486.20 4531.67 4345.55 3144.77 4545.66 4981.24 4986.45 4025.69 1567.04 1538.43 1498.63 2172.88 2566.23 2228.55 2705.83 3697.62 4497.29 4247.80 3102.20 4646.33 5137.96 5183.00 3761.66 1630.95 1511.70 1611.62 2290.00 2482.48 2324.56 2724.81 3613.82 4418.49 4015.43 3291.10 4545.98 5095.85 5090.39 3803.56 1597.91 1522.26 1601.39 2237.95 2478.46 2373.91 2775.28 3682.16 4408.37 4098.40 3466.58 4481.35 5108.35 5262.08 3834.91
Apr
1632.18 1543.99 1590.03 2180.15 2374.87 2411.16 2676.12 3528.94 4246.94 4345.87 3442.83 4730.85 5052.97 5125.21 3789.08 1522.53 1566.21 1624.69 2183.96 2469.26 2433.97 2587.45 3629.03 4211.38 4147.33 3235.90 4529.92 4847.22 5042.54 3555.31 1553.90 1539.95 1680.50 2110.99 2223.67 2495.48 2645.49 3441.16 4012.84 4072.29 3179.54 4680.83 4856.50 4934.59 3333.61 1562.58 1532.38 1651.87 2189.63 2134.00 2378.41 2534.56 3405.41 4058.08 4090.78 3117.02 4737.52 4807.56 5058.34 3272.88
May
1680.56 1522.10 1660.91 2095.27 2191.31 2370.91 2465.36 3331.73 4104.23 4085.90 3110.19 4886.77 4817.91 4766.87 3265.07 1592.05 1492.09 1686.15 2153.10 2333.44 1983.11 2504.18 3285.11 4156.14 4103.28 3180.50 5084.92 4859.03 4674.41 3272.72 1609.74 1510.94 1595.75 2124.01 2374.34 2404.46 2600.23 3360.33 4292.82 4073.21 3448.68 4997.11 4855.74 5217.68 3483.12 1600.32 1526.90 1557.19 2225.82 2429.93 2362.58 2696.22 3406.54 4164.85 4021.05 3498.60 5276.24 4874.91 4318.39 3361.39
Jun
1615.37 1548.31 1535.36 2278.42 2409.13 2436.64 3748.01 3425.67 4267.51 3988.65 3402.25 5445.21 4925.08 4204.94 3321.02 1577.52 1578.81 1567.12 2291.97 2381.69 2454.32 2864.53 3425.43 4379.71 3842.81 3392.27 5407.14 4973.83 4161.31 3251.14 1560.74 1598.94 1553.77 2452.57 2492.10 2425.95 2846.74 3526.99 4247.49 3772.45 3290.26 5295.75 4831.76 4092.38 3363.22 1605.73 1544.19 1566.62 2470.51 2439.22 2383.63 2855.19 3625.94 4201.41 3818.02 3441.10 5351.16 4900.59 4005.16 3306.72
Jul
1607.78 1606.36 1569.38 2391.02 2443.40 2390.18 4118.68 3677.86 4438.15 3805.34 3555.05 5436.30 4637.88 3749.83 3425.85 1658.32 1568.28 1626.28 2188.72 2876.61 2336.80 2981.07 3610.47 4474.20 4285.12 3610.32 5456.99 4864.46 4073.66 3440.97 1590.23 1597.80 1708.80 2281.13 2454.17 2395.06 2965.82 3567.53 4229.76 3955.48 3565.57 5531.28 4951.28 4032.67 2558.64 1507.37 1639.36 1538.85 2063.82 2442.97 2268.27 3602.50 3514.81 4295.87 3871.73 3746.04 5032.02 4786.83 3971.12 3763.67
Aug
1515.24 1620.38 1659.77 2208.18 2476.91 2165.33 2784.89 3508.85 4232.54 3910.28 3307.94 3999.70 4499.65 4164.97 3866.40 1344.34 1563.00 1587.97 2039.28 2412.22 2172.92 2850.56 3359.06 4328.68 4055.06 3480.68 4664.11 4749.24 4283.73 3806.67 1366.38 1350.74 1618.01 2063.24 2369.32 2257.82 2880.14 3370.28 4407.64 3682.46 3642.34 4823.04 4189.30 4359.62 4042.21 1525.06 1620.99 1663.13 1926.48 2258.77 2399.15 2949.14 3324.11 4481.46 3731.45 3385.35 4612.05 4271.53 4037.66 4272.52
Sep
1474.97 1580.34 1593.57 1855.87 2279.52 2145.63 2760.08 3278.88 4414.15 3830.95 3473.58 4798.43 4256.96 3505.09 3801.05 1525.82 1498.30 1528.43 1826.60 2058.59 2072.97 2616.78 2968.76 4524.21 4010.10 3430.68 4696.52 4161.00 3695.99 3648.44 1443.65 1547.06 1513.96 1932.25 1886.96 1907.10 3238.75 3025.58 4206.57 3315.44 3448.16 4616.14 4081.54 3736.61 3870.89 1469.84 1533.10 1648.60 1879.88 1984.31 2035.43 3143.28 3141.27 4527.62 3130.16 3541.60 4778.20 3893.84 3902.32 3880.73
Oct
2542.74 1468.92 1552.26 1865.19 2179.13 2325.31 3113.64 3099.30 4820.06 3205.27 3547.71 4907.33 4481.19 3913.55 3850.58 1537.12 1283.40 1605.52 1956.02 2148.62 2455.31 3224.93 3386.83 5130.33 3285.53 3697.10 4715.09 4551.34 3816.22 3867.58 1612.01 1482.41 1654.03 1823.14 2344.57 2372.57 3134.38 3458.73 4995.94 3267.69 3314.87 4288.28 5011.59 3600.91 3855.08 1648.88 1544.25 1686.24 2008.19 2242.62 2382.54 3023.82 3463.23 5101.97 3253.86 3475.79 4264.38 4544.25 3708.18 4102.79
Nov
1661.08 1436.69 1828.25 1973.78 2299.98 2386.46 2941.91 3428.78 5189.98 3565.13 3664.56 4374.29 4375.88 3550.89 4066.07 1612.38 1487.78 1884.79 1912.82 2204.42 2481.96 2888.81 3461.56 5220.24 3920.81 3483.66 4089.56 4216.83 3637.06 4180.45 1590.34 1548.05 1888.90 1990.55 2134.82 2699.89 3091.74 3476.39 5154.69 3895.42 3563.60 4217.96 4269.46 3782.84 4132.53 1587.85 1530.14 1976.67 2014.69 2194.41 2697.67 3083.04 3455.82 4941.15 3704.87 3448.27 4163.82 4368.12 3607.81 3884.65
Dec
1579.70 1521.76 1987.87 1982.26 2215.41 2674.89 2981.08 3298.45 4950.12 3432.14 3335.41 4178.00 4284.55 3512.20 3776.21 1565.76 1443.26 1890.17 1943.73 2264.36 2621.01 2735.53 3283.38 4817.03 3823.14 3374.57 4155.74 4199.46 3445.90 3678.69 1562.94 1466.61 1869.20 2010.60 2363.53 2570.10 2801.22 3306.54 4653.57 3644.83 3439.54 4205.73 4384.31 3381.09 3888.13 1582.67 1477.21 1893.06 1979.25 2281.10 2537.47 2878.92 3397.63 4541.51 3320.48 3622.04 4177.32 4310.89 3336.71 3723.47
Price of Groundnut in Madhya Pradesh market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1680.64 1595.51 1591.02 1987.85 2278.96 2220.49 2348.49 2358.31 3911.28 4935.34 2884.82 4128.85 4337.05 3862.64 3528.38 1689.23 1593.94 1641.97 2042.09 2291.82 2463.46 2373.94 2468.04 3801.99 5014.79 2867.58 4248.50 4407.91 3929.40 3335.90 1721.25 1582.05 1655.72 2003.29 2401.30 2301.10 2280.30 2464.84 3748.01 4934.75 2993.86 4342.93 4421.50 3864.20 3556.90 1723.21 1534.45 1542.25 2009.24 2306.36 2111.62 2184.32 2609.17 3654.39 4718.55 2891.56 3710.64 3955.30 3868.21 3540.86
Feb
1722.45 1524.97 1552.92 2026.70 2391.44 2182.26 2209.12 2748.26 3764.35 4738.80 2807.76 3609.42 3982.23 3814.66 3436.74 1776.85 1577.16 1572.91 2237.21 2395.62 2041.50 2103.69 2874.56 3971.31 4677.78 2776.93 3979.14 4078.77 3970.58 3328.18 1738.32 1446.80 1600.18 2037.81 2515.35 2247.22 2249.01 2971.44 4141.75 4526.49 2772.67 4013.50 4321.04 3583.06 3421.61 1734.62 1200.42 1466.95 2159.46 2498.67 2111.62 2070.83 3017.84 4382.30 4425.03 2738.11 3824.59 4062.39 3560.77 3222.11
Mar
1622.71 1656.48 1532.07 2043.82 2769.93 2206.00 1974.55 3880.59 4252.45 4480.02 2926.63 4056.90 4122.10 3997.40 3237.23 1553.44 1653.82 1499.51 1951.08 2726.56 2201.00 1934.41 2921.46 4255.64 4577.92 3281.18 3929.43 4463.21 3991.63 3270.77 1608.56 1440.36 1515.79 1972.86 2695.75 2279.33 2004.69 3069.06 4558.21 4631.87 2845.82 3991.62 4143.62 4261.59 3258.13 1651.96 1547.09 1507.65 1886.62 2518.94 1991.00 2034.34 2762.36 3744.17 3959.62 2918.33 4032.16 3982.40 4326.95 3187.48
Apr
1474.75 1493.73 1511.72 2105.60 2535.22 2464.76 2019.27 3110.95 5034.24 4674.25 2996.14 3847.27 4558.53 4316.03 3147.55 1501.00 1680.40 1595.00 2009.46 2440.67 2081.96 2092.27 3245.01 5035.09 4690.98 2941.61 4389.54 4776.29 4234.25 3160.01 1001.00 1587.06 1700.00 2073.04 2404.78 2420.04 2198.06 3193.57 4464.47 4674.21 2944.17 4290.15 4881.61 4161.74 2974.20 1663.64 1511.00 1868.00 2160.64 2458.85 2174.23 2200.32 3308.85 4552.61 4623.44 2941.02 4448.95 4624.27 4180.37 3162.66
May
1410.10 1467.49 1410.49 2198.82 2477.33 2193.10 2356.02 3046.46 4743.84 4567.75 2934.09 4421.15 4888.57 4274.69 3259.17 1500.00 1580.90 1809.88 2139.26 2699.75 2077.50 2447.19 2357.25 4415.70 4604.42 3015.38 3343.73 6069.32 4124.53 3125.25 1552.43 1619.34 1663.06 2283.70 2717.38 2219.18 2530.52 3339.29 4418.73 4572.14 2946.34 4397.44 4753.96 4008.95 3115.22 1488.40 894.90 1819.77 2216.24 2485.26 2340.15 2714.22 3440.31 4039.62 4248.89 3092.37 3094.14 4392.87 4132.50 3350.21
Jun
1491.18 1696.75 1823.39 2226.96 2671.78 2517.11 2743.30 2184.23 4445.28 4130.11 2977.04 4410.76 4497.81 4092.83 3445.37 1518.24 1645.35 1802.63 2158.86 2446.97 1978.92 2569.35 3254.79 4426.19 4062.23 2823.71 4487.49 5248.74 4055.63 1183.07 1464.31 1645.63 1801.26 2309.21 2435.83 2143.00 2303.94 3251.74 4236.96 3794.57 2771.04 3945.05 4682.06 3312.04 3155.54 1633.33 1639.93 1821.97 2166.09 2497.67 2006.95 1823.43 3060.71 3454.14 3537.21 2760.75 4231.78 4444.62 3486.16 3299.11
Jul
1570.59 1752.97 1713.88 2082.79 2516.06 1978.92 1681.79 2463.44 3555.03 3525.52 2898.69 4111.59 4196.85 3032.33 2932.75 1479.56 1901.00 1906.36 2330.00 2494.64 2143.00 2660.29 2309.59 3565.74 3960.52 2839.37 4096.85 4332.31 3465.83 3120.57 1557.00 1826.99 1490.81 2095.31 2450.04 2006.95 2624.94 2203.80 3334.18 3847.41 2763.13 4225.75 4542.03 3007.33 3065.10 1293.71 1803.00 1581.57 2405.14 2391.50 2014.66 2995.53 2428.50 4340.41 3535.81 3093.46 4343.87 4795.05 3406.83 3634.58
Aug
1612.00 1814.99 1100.00 2418.06 2267.50 2923.16 1818.41 2290.29 4186.76 3545.11 3685.21 4262.32 4604.16 2794.43 3289.73 1461.25 1100.00 1781.00 2164.85 2230.00 2200.00 2893.75 2461.10 4218.08 3632.82 3763.86 4440.74 4364.22 3036.36 3591.34 1536.63 1550.00 1476.89 2219.82 2335.50 2250.71 3569.57 2335.25 4137.50 3446.96 3411.05 4452.32 4393.78 2752.91 3540.98 1498.94 1325.00 1861.00 2188.78 2229.50 2246.58 2774.64 2328.21 4379.76 3293.78 3980.82 2965.09 4434.28 3203.48 3758.30
Sep
1668.95 1900.00 1920.00 2317.72 2200.00 2332.26 2709.09 2257.03 2241.13 3061.94 4023.09 2151.58 4532.83 2892.05 3263.56 1583.94 1415.00 1326.67 1655.00 2151.00 2073.47 1670.41 2128.73 4004.78 2648.95 3798.77 1707.95 4815.14 2584.87 3345.92 1034.00 1100.00 1300.00 1575.33 1708.86 2115.12 1618.62 1559.54 2230.13 2879.02 3976.62 2215.52 2730.18 2428.09 3091.29 1421.51 1257.50 1392.63 1568.35 1803.83 1891.88 2191.28 2296.49 2747.20 2596.39 2768.36 2005.36 2848.98 2932.78 2763.32
Oct
1483.01 1608.13 1445.38 2154.78 2098.42 1719.58 2987.59 2787.02 3426.19 2937.97 3501.79 3717.31 3681.08 3565.26 4651.42 1535.43 1415.75 1777.95 2064.65 2109.39 2133.01 3141.68 2935.54 4362.79 2889.16 3809.95 4186.65 3522.23 3768.90 3979.24 1497.83 1552.09 1725.46 2112.06 2097.00 2366.37 2829.73 2865.80 4552.87 3348.63 3800.05 4101.73 3639.72 3586.26 3977.12 1494.52 1591.83 1746.05 2173.06 2037.45 2471.68 2710.54 2878.50 4465.85 3450.01 4058.77 3913.32 3719.83 3367.74 3963.90
Nov
1540.16 1504.65 1808.98 2143.40 2107.16 2504.53 2365.54 3002.94 4677.94 3510.88 3911.81 4046.29 3697.14 3306.60 3836.37 1586.78 1432.95 1892.78 2310.24 2177.71 2495.26 2502.68 2840.33 4574.82 3711.93 3907.08 3166.53 4592.23 3185.53 3988.42 1576.22 1513.98 1886.29 2291.39 2202.40 2541.94 2524.45 2935.85 4782.78 3611.32 3923.55 4014.27 4061.89 3291.82 3807.35 1658.30 1536.88 1906.53 2273.61 2204.61 2531.33 2617.02 3009.14 4800.03 3342.45 3910.14 4039.25 3960.70 3313.34 3624.39
Dec
1618.09 1548.80 2006.77 2324.28 2330.29 2371.60 2586.72 3087.96 4729.01 3699.96 3813.24 3977.44 3677.80 3263.39 3718.04 1587.14 1557.94 1988.55 2282.17 2306.13 2173.63 2384.87 3286.55 4764.23 3539.05 3793.49 3964.33 3712.97 3205.63 3680.05 1565.52 1581.89 2112.92 2329.53 2244.00 2173.64 2321.12 3647.37 4937.81 3290.07 3803.20 4169.29 3882.96 3464.01 3717.60 1600.10 1615.23 2188.61 2319.88 2327.42 2299.20 2264.52 3682.03 4596.01 3160.28 3827.36 4272.53 3583.65 3599.95 3881.92
Price of Groundnut in Maharashtra market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2376.70 2691.30 2794.33 3565.17 3836.81 4002.45 4750.72 5074.54 5742.63 8671.77 6146.82 6865.81 7923.65 8326.32 6692.80 2859.13 2734.32 2827.49 3566.52 3824.39 4028.12 4762.25 5092.91 5715.38 8971.41 6071.53 7029.21 7922.19 8176.07 6785.08 2827.40 2668.89 2804.47 3497.01 3989.02 3970.04 4761.31 5008.28 5727.30 8981.82 6028.62 6771.58 7738.57 8012.20 6890.57 2788.87 2717.46 2779.30 3723.15 3870.90 3829.11 4738.48 5121.87 5776.31 8972.14 5854.79 7109.17 7685.56 8048.78 6958.48
Feb
1086.50 2744.05 2779.85 3661.95 3997.33 3870.58 4736.32 5229.97 5733.41 8826.25 6046.65 7281.35 7347.98 7734.09 6707.32 2788.71 2593.68 2837.04 3686.12 3984.78 3798.31 4744.64 5237.80 5756.52 8691.33 5748.32 6986.49 7548.81 7916.46 3478.00 2759.30 2608.32 2800.00 3650.36 3986.86 3798.39 4757.17 5312.44 5727.33 8677.64 5530.49 6986.49 7544.82 8094.44 3530.40 2683.80 2645.25 2793.09 3756.00 4050.00 3680.45 4722.90 5395.05 5087.04 8769.97 5505.53 7350.00 7356.45 7920.00 3611.48
Mar
2707.35 2660.00 2750.04 3723.08 4109.55 3636.56 4724.14 5388.74 5360.00 8707.83 5346.77 7300.00 7585.39 7728.75 3694.05 2685.08 2678.08 2766.63 3658.96 4086.26 3658.88 4688.39 5334.71 5774.84 8645.75 5185.64 7187.50 7659.21 7941.67 3518.27 2183.28 2398.79 2793.50 3744.69 4041.06 3716.05 4757.57 5346.57 5765.19 8669.36 5711.43 7258.09 7923.91 8210.59 3325.36 2711.30 2430.49 2740.79 3536.94 4092.02 3735.97 4751.89 5368.45 5810.14 7708.89 5640.78 7200.00 7775.00 8608.47 3633.37
Apr
2587.08 2613.61 2741.63 3608.62 3998.04 4199.33 4740.04 5502.02 5715.35 8610.86 6209.64 7207.50 8334.38 7900.00 3384.38 2625.00 2699.41 2871.93 3730.50 3888.67 4212.48 4749.15 5670.72 5710.19 8482.93 6205.21 7287.50 7840.48 7290.32 3483.77 2625.00 2669.64 2744.50 3615.66 3960.81 4244.64 4734.30 5822.74 5861.52 8368.44 6199.12 7650.00 7782.61 7916.67 3463.18 2674.39 2656.86 2768.55 3758.96 3922.40 4261.69 4747.84 5829.29 5785.53 8287.81 6178.85 7600.00 8594.44 8563.16 3440.30
May
2706.43 2656.95 2784.76 3777.99 3877.34 4290.00 4644.85 5842.20 6079.65 8245.37 6090.73 8000.00 8057.56 8728.31 3574.23 2750.00 2737.92 2789.18 3835.25 3376.98 4276.99 4697.53 5268.52 6065.96 7645.93 6139.57 8000.00 8282.82 8642.95 3524.30 2333.94 2745.64 2769.74 3850.09 4067.52 4282.81 4591.58 6141.73 5856.68 6734.20 6209.24 8069.83 8224.80 8375.65 3587.68 2691.00 2355.16 2809.28 3931.68 4107.49 3996.27 4764.62 6329.91 5764.37 6377.06 6244.83 8258.33 8344.66 8177.05 3507.45
Jun
2650.00 2760.18 2827.93 3913.93 4110.71 3904.71 4566.70 6311.22 5634.13 7030.30 6179.22 8679.17 8840.53 8031.75 3571.66 2666.96 2776.51 2885.79 3981.47 4149.18 4439.64 4909.77 6425.65 6294.64 7683.25 5748.78 8240.62 8652.50 8001.22 3482.14 2717.72 2786.19 2888.85 4030.90 4223.18 4499.21 5089.86 6350.97 6138.83 7909.01 5944.80 8550.00 9039.61 7890.85 3603.72 2722.92 2588.15 2901.45 4306.83 3009.89 4489.92 5074.49 6389.47 6441.29 7618.64 5984.16 8553.85 8901.81 7950.82 3610.11
Jul
2406.84 2901.49 2896.62 4268.22 4300.87 4439.42 5200.04 6433.03 6487.55 7684.52 5983.50 8650.00 8810.29 7845.95 3635.36 2506.04 2921.08 2960.85 4259.76 4260.32 4463.32 5198.09 6541.82 6514.28 7866.21 5927.70 8628.57 9104.17 8130.42 3789.48 2758.88 2922.79 3163.70 4242.51 4406.45 4424.48 5429.62 6315.85 6549.69 6846.03 5951.93 8372.83 9047.30 7841.85 3963.08 2837.99 2973.98 3131.69 4391.16 4383.13 4438.88 5749.81 6292.00 6759.30 6188.15 6105.43 8372.83 9243.45 8156.21 4122.04
Aug
2800.00 2999.49 3125.47 4387.96 4351.28 4200.00 5765.23 6354.36 7129.49 6663.41 6207.34 9060.00 9131.32 8088.06 4432.01 2800.00 3049.61 3168.76 4412.83 4384.72 4190.81 5877.10 6349.67 7036.20 4700.00 6245.21 9009.62 9212.32 7723.58 4408.19 2863.06 3071.86 3236.34 4397.63 4335.73 4210.81 6052.17 6433.74 7015.18 6099.40 6565.16 9328.57 9023.02 7777.78 4282.64 2926.70 3035.75 3218.98 4510.38 4312.91 4380.05 6042.10 6495.62 7054.45 6650.00 6480.86 8968.67 8989.47 7775.00 3794.44
Sep
2887.56 2928.57 3218.21 4268.16 4372.22 4285.90 6280.56 6725.42 8116.10 7678.12 6510.01 9030.56 9278.92 7683.33 3383.17 2899.69 2866.45 3236.24 4527.26 4166.74 4358.70 6410.47 6430.89 8521.32 7635.29 6401.84 9553.85 8536.07 7504.62 3719.19 3075.46 2945.68 3345.74 4647.57 4156.89 4374.48 6373.73 6333.91 8490.22 316.16 6390.03 9553.85 8537.50 7436.54 4125.60 2916.78 2959.49 3397.26 4696.81 4172.23 4375.20 6229.44 6093.92 8243.14 7320.33 6384.40 9345.00 8504.69 7373.17 4103.95
Oct
2975.45 3065.47 3437.05 4623.23 4081.23 4384.09 6305.21 6229.29 8235.74 3818.25 6392.52 9195.67 8723.21 6996.05 4683.07 2950.00 3229.41 3429.10 4466.67 4039.19 4431.36 5887.06 5923.06 8271.54 6562.58 6306.39 8781.48 8422.22 7043.75 4474.98 2977.85 3056.05 3408.79 3851.34 4099.70 4420.64 5918.22 5410.43 8184.54 7155.52 6237.00 8416.67 8493.52 7425.19 4585.06 2934.01 3101.73 3446.81 3917.39 4074.21 4403.82 5285.58 5594.16 8227.38 7181.43 4902.13 7824.00 8217.02 7624.03 4607.62
Nov
3062.23 2951.77 3319.98 3899.03 3997.40 4433.79 5160.24 5303.07 8530.45 6477.11 3597.89 7938.10 7780.51 7359.38 4656.67 3027.78 2810.10 3559.75 3817.16 4012.80 4707.28 5180.99 5895.56 9443.36 6262.04 3723.46 8250.00 8048.73 7500.48 4743.53 2937.31 2908.77 3524.63 3870.78 3961.22 4781.48 5060.52 5809.71 9301.90 6427.90 6995.74 7807.14 8252.84 7451.29 4873.31 2625.00 2848.29 3562.67 3926.71 3960.42 4838.64 5036.69 5582.36 8707.15 6413.12 6582.91 7845.00 8165.46 7516.90 4554.76
Dec
2767.53 2889.91 3457.19 3869.00 3924.66 4786.64 5039.88 5580.83 8895.60 5352.73 6795.64 7985.82 8194.00 7510.93 4685.95 2804.96 2901.27 3415.12 3775.06 4049.11 4795.43 5084.05 5845.50 8984.30 5636.71 6747.14 8097.08 8221.14 7400.85 4760.17 2713.21 2115.19 3449.46 3805.75 4088.79 4794.28 5025.52 5872.88 8956.59 5904.37 6750.00 8007.32 8074.04 7338.59 4613.94 2675.88 2808.16 3458.84 3500.00 3982.17 4791.77 5047.70 5715.95 5227.51 6155.88 6858.81 7791.30 8191.33 6887.27 4757.48
Price of Groundnut in Rajasthan market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
1780.32 1602.20 1483.60 2120.70 2467.77 2228.77 2993.28 2598.81 3675.94 5214.63 3171.46 3894.03 3999.09 4188.60 3499.06 1724.09 1550.52 1517.62 2056.52 2504.10 2140.04 2938.26 2592.69 3702.95 4584.57 3183.57 3990.41 3975.31 4120.51 3461.45 1685.72 1506.74 1461.50 2053.34 2530.33 2119.48 2920.64 2559.92 3690.76 4845.95 3273.78 3506.98 4057.42 4100.97 3374.14 1610.18 1474.68 1480.61 2096.43 2536.62 2084.26 2900.88 2588.56 3776.34 4475.11 3188.72 3972.49 4057.25 4077.87 3504.42
Feb
1578.89 1451.32 1441.26 2027.72 2484.14 2054.00 2784.36 2506.19 3716.50 4192.43 3178.39 3901.00 4009.69 4017.65 3503.65 1541.81 1447.94 1485.96 2134.84 2457.74 2067.04 2653.95 2488.10 3814.72 4244.25 3122.77 3737.54 4070.19 3994.74 3564.74 1593.38 1438.26 1449.27 2103.18 2362.37 2078.13 2668.35 2778.29 4171.67 4256.86 3261.39 3669.78 4089.48 3974.45 3563.41 1662.88 1517.87 1422.88 2162.29 2275.10 2117.73 2635.59 2873.51 4218.53 4125.08 3294.72 3560.46 4074.64 3963.98 3526.59
Mar
1700.70 1557.02 1468.21 1919.12 2454.48 2082.44 2605.45 2964.44 4540.73 4209.75 3425.43 3588.78 4157.06 3980.55 3478.88 1607.25 1621.23 1499.48 2016.01 2527.77 2052.33 2602.83 2481.32 4558.22 4193.28 3284.71 3697.85 4347.40 3957.24 3499.61 1656.94 1565.26 1491.28 1975.58 2388.98 2012.25 2572.40 2890.98 4331.08 4251.85 3159.40 3552.77 4338.34 4094.27 3420.43 1654.96 1406.40 1479.60 1939.03 1973.22 2204.03 2909.97 2692.48 4590.87 3359.88 3304.90 3514.24 4762.06 4372.65 3688.35
Apr
1744.38 1393.29 1609.44 2019.78 1982.30 2358.36 2707.41 2956.69 4634.09 4527.16 3350.84 3558.74 4856.37 4403.04 3340.32 1682.12 1712.22 1611.79 2090.51 2535.69 2534.50 2904.26 2638.38 4375.49 4545.03 3108.55 3540.60 4715.41 4227.97 3305.85 1598.88 1689.08 1660.85 2205.92 2488.28 2427.56 2666.92 3039.37 3481.54 4550.99 2973.51 4390.31 4779.52 4158.36 3268.90 1720.24 1700.45 1625.54 2273.80 2513.04 2479.32 2626.30 2966.76 4406.14 4516.39 3155.06 3496.54 4782.64 4098.22 3286.75
May
1758.97 1718.64 1584.74 2173.31 2532.35 2451.05 2672.69 2947.21 4269.87 4240.00 3124.82 3526.34 4728.98 3999.92 3216.22 1675.00 1703.98 1620.45 2134.66 2530.54 2428.88 2645.23 3031.89 4187.16 4100.41 3014.52 3838.73 4988.06 3963.39 3210.17 1632.00 1753.88 1632.24 2110.04 2635.25 2464.33 2580.80 2994.36 4252.30 4304.99 3184.06 3753.98 4730.19 3913.79 3341.40 1819.75 1543.96 1633.06 2098.37 2583.80 2236.52 2591.08 2977.17 4056.22 4017.57 3022.74 3594.09 4863.31 3820.44 3322.53
Jun
1605.24 1566.31 1592.48 2255.64 2525.20 2287.15 2693.26 2790.00 4194.30 4018.83 3007.94 3646.24 4826.13 3682.59 3241.63 1557.17 1546.26 1640.67 2142.74 2576.49 2277.23 2411.27 2925.77 4118.90 3881.66 2916.92 3530.57 4844.32 3550.43 3161.26 1362.57 1524.07 1601.20 2052.12 2465.70 2200.61 2564.35 2979.23 4063.13 3651.07 2768.20 3570.04 4899.86 3480.27 3358.05 1126.42 1476.39 1854.50 2118.58 2520.29 2191.77 2735.95 3391.86 4036.58 3255.31 2860.12 3522.65 4715.48 3077.12 3416.16
Jul
1498.30 1408.19 1808.67 2209.56 2386.90 2177.68 2625.25 3255.93 3942.14 3052.20 2940.76 3565.28 4562.69 3309.53 3311.17 1684.46 1550.74 1503.74 2006.65 2160.49 2238.02 2683.23 3090.64 4158.78 3347.28 3159.87 3546.28 4975.22 3452.44 3280.30 1573.50 1581.06 1619.99 2280.41 2282.24 2401.34 2991.52 3051.89 4016.74 3285.33 3276.35 3551.34 5043.28 3502.61 3326.05 1663.82 1582.73 1612.38 2243.94 2176.44 2326.43 2944.75 2764.00 4397.21 3163.83 3168.96 3560.43 4950.64 3453.59 3532.94
Aug
1692.50 1568.24 1642.42 2300.69 2121.28 2369.14 2890.86 2385.57 4466.66 3239.07 3165.71 3584.81 5288.02 3465.51 3342.20 1750.00 1482.91 1612.32 2417.54 2180.77 2610.42 2966.66 2643.06 4499.30 3164.36 3306.01 3592.00 5226.11 3466.29 3039.56 1746.59 1501.52 1609.67 2479.53 2121.40 2664.86 3058.78 1558.28 4384.50 2964.07 3243.51 3500.26 4898.79 3553.23 3300.29 1652.80 1618.34 1640.50 2362.50 2089.17 2525.56 1770.75 2720.83 4537.95 3045.95 3184.29 3499.83 4395.05 3472.82 3852.80
Sep
1779.10 1535.51 1726.52 2402.57 2092.64 2502.87 3253.17 2614.94 4741.86 2947.96 3572.29 3509.43 4588.00 3581.03 4888.10 1730.97 1503.20 1739.83 2556.65 2278.56 2470.54 2879.53 2664.02 4537.30 3105.17 4392.60 3587.21 4581.13 3658.45 3489.31 1734.14 1647.48 1803.34 2332.78 2340.82 2759.24 2857.18 3273.86 4019.16 3323.56 3578.64 4211.33 4417.69 4094.09 3711.34 1705.17 1668.87 1800.89 2708.58 2439.15 2868.18 3113.69 3239.91 3742.67 3793.87 3729.72 4569.88 4576.84 4174.77 3870.97
Oct
1621.87 1980.42 1718.84 2450.93 2414.82 2757.44 3079.07 3106.96 4125.09 4154.05 3929.23 4381.93 4727.47 4094.91 4654.67 1727.45 1914.82 1655.00 2289.34 2357.41 2786.23 3174.46 2971.43 4282.40 4211.24 3895.36 4308.03 4589.01 3934.12 4820.10 1696.54 1661.62 1669.96 2167.69 2275.76 2775.48 2845.34 2624.14 4492.89 3926.76 3633.90 4057.94 4237.64 3882.68 4559.42 1657.40 1568.88 1671.88 2207.83 2258.79 2874.22 2588.79 2703.49 4516.52 3726.65 3475.21 3910.86 3867.19 3845.22 4147.21
Nov
1617.93 1547.72 2078.35 2152.69 2247.49 3043.76 2403.95 2780.05 4687.79 3578.56 3458.92 4036.80 3955.09 3677.83 4125.16 1558.68 1483.62 2171.95 2276.94 2187.46 2950.16 2612.14 2727.31 4579.96 3568.59 3477.75 3996.44 3879.05 3633.97 4177.34 1656.60 1498.52 2205.59 2370.54 2182.98 2996.03 2405.19 2720.42 4801.43 3488.71 3505.45 4025.38 4011.16 3512.50 4179.13 1653.50 1472.10 2170.80 2451.38 2263.53 2897.67 2566.35 2768.05 4966.31 3337.60 3705.44 4083.73 4048.00 3618.03 3996.29
Dec
1613.61 1474.10 2201.58 2390.19 2292.07 2830.55 2582.97 2798.63 4963.20 3443.19 3616.16 4057.82 4031.54 3547.60 4146.29 1592.64 1486.05 2273.77 2359.38 2301.29 2782.85 2434.17 3054.61 4822.32 3284.57 3442.92 3969.23 4024.65 3603.70 4119.95 1560.36 1498.48 2180.31 2445.60 2509.65 2879.77 2480.04 3526.89 5319.91 2979.85 3515.39 4029.30 4074.31 3577.48 3923.60 1613.41 1485.15 2228.29 2469.98 2422.00 2878.54 2513.39 3566.47 5084.80 3133.35 3652.82 4081.40 4122.31 3491.57 3883.73
Price of Groundnut in Tamilnadu market
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jan
2512.26 2120.24 2063.38 2795.05 2960.16 2868.29 3498.33 4172.05 4006.92 6180.73 4252.45 5186.78 5592.41 6328.22 4851.85 2196.09 2178.09 2046.03 2780.66 2983.57 2700.97 3377.02 4460.51 4575.55 6527.37 4316.61 4806.43 5625.00 6039.94 4537.01 2399.24 2034.77 2205.60 2852.78 3000.73 2689.37 3363.28 3949.93 4211.54 6124.04 4044.15 4864.08 5537.30 6196.79 4357.29 2378.79 2113.65 2227.18 2424.72 3026.13 2704.92 3164.85 3949.85 4313.62 6232.03 4152.87 4784.39 5301.35 6181.97 4213.32
Feb
2211.86 2107.86 2089.15 2881.00 3124.50 2742.20 3333.81 3697.07 4509.36 6727.80 3499.85 5041.07 5349.27 6371.55 5000.55 2304.28 2077.97 1950.26 2709.25 3198.72 2572.48 3783.48 3220.44 4534.19 6503.56 4516.54 5636.91 5233.21 6720.84 4981.19 2267.40 2152.94 2258.27 2628.81 3191.95 2787.16 3609.08 3825.76 4758.30 6152.51 3791.45 5792.38 5429.54 6882.82 5141.49 2235.52 2030.80 2235.05 2840.73 3007.82 2772.75 3509.45 3790.40 4744.17 5654.50 4577.21 5589.35 6014.33 7009.61 5193.23
Mar
2217.89 2040.50 2168.15 2832.76 3132.45 2945.76 3632.82 4220.13 4492.69 6051.30 4824.31 5563.99 4056.58 7469.08 5388.86 2184.12 1951.27 2129.75 2365.77 2920.67 2764.33 3451.20 3365.39 4675.35 5993.59 4323.94 5312.19 4978.70 7280.39 5023.41 2224.25 1924.45 2068.17 2498.16 2858.42 2927.77 3257.75 2500.29 4361.18 5544.46 4312.87 5348.67 4886.82 7363.20 5255.40 2234.44 1663.49 2145.78 2604.37 2941.31 2988.21 3337.49 3095.75 4619.83 5586.78 4741.33 4644.10 4849.39 7392.20 5206.78
Apr
2222.77 1840.53 2211.25 2652.85 3038.40 2901.09 3368.98 3067.33 4539.28 6010.11 4724.70 5054.64 6407.52 7330.34 5392.22 2203.00 2053.44 2194.37 2675.74 3180.62 2888.23 3244.62 4155.58 4571.34 5834.17 4630.01 5359.71 6418.40 6996.90 5025.63 2212.79 2029.87 2060.57 2746.94 3139.14 2836.14 3427.97 4572.39 4602.09 5769.43 4585.55 5551.75 6235.79 7216.19 4901.50 2291.50 2016.20 2110.14 2740.95 3133.72 2787.07 3462.21 4679.57 4665.84 5665.03 4868.10 5510.80 6091.56 6992.86 4770.27
May
2368.87 2005.60 2170.90 2737.90 2888.52 2852.05 3331.17 4751.10 4704.45 5440.68 4691.09 5901.80 6748.67 6940.24 4688.36 2395.76 2038.98 2177.95 2817.97 3358.82 2821.00 3273.87 4269.75 4702.32 5856.22 4778.96 6559.09 6734.11 6858.93 4543.66 2425.84 2014.32 2182.67 2778.20 3297.09 2770.42 3345.95 4772.56 5202.91 5805.68 5149.93 6086.46 6625.66 6784.30 4798.14 2331.76 2645.11 2271.64 2983.23 3230.02 3017.42 3599.08 4559.45 4841.32 6187.29 5280.97 6210.81 7089.82 7083.33 5519.22
Jun
2262.48 2027.97 2330.32 3051.52 2846.91 2867.24 3748.88 4875.52 5206.09 5716.74 5191.52 7059.78 7154.54 6785.56 5054.03 2171.72 2159.28 2278.85 3011.30 3332.19 3329.22 3810.14 5274.70 5133.16 5529.12 5666.32 7615.74 6996.54 5052.32 5047.13 2151.50 1990.36 2242.83 3170.84 2668.26 3274.75 4264.07 5143.58 5061.88 5570.96 4968.68 7329.81 6904.87 6483.30 4586.30 2174.83 1713.33 2236.06 3392.06 3240.36 3236.87 4201.53 5213.17 5261.50 2836.47 4989.76 6611.92 7197.54 6380.42 4921.13
Jul
2337.02 2164.41 2276.39 3417.95 3438.68 3263.05 4349.48 5020.27 5589.18 5113.44 5023.16 6910.32 7124.43 7444.82 4961.61 2192.10 2249.71 2275.08 3401.81 3316.96 3264.21 3982.07 5074.28 5497.20 4901.27 5084.54 6731.87 7104.03 6090.39 5166.80 2276.03 2345.25 2368.67 3375.43 3354.70 3232.07 4408.59 4913.20 5701.81 5280.76 5021.52 7030.31 6534.16 6387.61 5160.96 2251.42 2236.61 2338.47 3345.25 2722.04 3349.10 4866.64 4717.65 5542.64 5172.72 5120.35 6863.89 6661.91 6391.91 5365.76
Aug
2286.28 2285.64 2392.04 3406.54 2991.47 3334.14 4798.90 4463.43 5809.45 5077.23 5446.71 7102.13 7321.28 6338.40 5931.24 2302.92 2295.11 2586.90 3348.24 2743.39 3307.96 4673.29 4269.90 5954.94 5274.06 5827.87 7253.67 6941.58 6458.02 5845.78 2530.59 2284.72 2551.20 3151.16 2387.08 3314.98 4893.78 4717.16 6131.63 5193.10 6645.30 7964.98 7051.77 6279.12 5904.86 2545.01 2269.58 2505.80 3088.23 3034.66 3294.11 4288.39 4213.82 6360.71 5157.39 6172.27 7767.38 6638.96 5955.66 5605.06
Sep
2498.32 2288.56 2618.57 2719.87 3796.58 3345.71 4472.97 4155.51 6022.66 5170.79 6060.81 7259.33 6008.66 5789.71 5650.11 2452.41 2253.68 2556.47 2385.43 2552.04 3146.28 4152.59 3990.60 5718.79 4961.05 6224.02 6992.22 6131.48 6131.34 5355.41 2397.04 2278.08 2677.48 2802.85 2882.42 3217.92 4134.49 4614.20 5494.20 5096.88 5630.86 7314.91 6206.55 5682.00 5233.76 2151.53 2228.96 2499.21 3244.31 2482.65 3188.99 4076.30 4649.81 4974.77 4970.98 5789.55 6095.78 5855.97 5564.05 5326.14
Oct
2444.64 2317.89 2623.15 3180.73 2368.69 3276.42 4080.81 4683.78 5101.13 4338.37 5412.43 6583.36 6140.46 5449.43 5234.14 2532.07 2361.92 2342.84 2701.76 3064.18 3147.82 4077.98 4213.31 5292.73 4880.29 5549.57 6644.04 6348.30 5066.76 5745.51 2541.44 2363.18 2297.28 2845.30 2502.86 3242.87 3931.88 4070.01 5470.31 4701.81 5518.73 5662.64 5946.87 5106.61 5415.90 2632.08 2303.15 2359.69 2707.26 2991.69 3015.18 3821.36 3931.62 5646.76 4834.44 5045.92 5222.08 5721.99 4845.62 5515.02
Nov
2501.87 2058.10 2521.77 2742.05 2744.15 3127.87 3647.89 3497.32 6395.64 4832.91 4815.12 5541.34 5792.27 4854.91 5218.17 2331.14 2358.87 2463.26 2734.55 2822.88 3083.02 3566.90 3775.89 6137.22 4852.14 4683.36 4932.95 5971.61 4830.04 4894.32 2213.49 2079.85 2481.41 2997.51 2665.34 3348.87 3878.45 3941.98 6061.19 4858.44 5431.74 5114.04 5972.89 4776.46 5619.18 2145.68 1995.45 2557.09 3004.18 2582.40 3277.22 4154.89 3989.60 5721.39 4744.38 4519.90 4809.81 5797.38 5048.74 5545.03
Dec
2193.58 2065.15 2688.34 2964.89 3032.49 3450.58 4051.57 3767.28 6334.62 4536.75 4357.65 4832.23 5791.84 4936.68 5830.85 2156.74 1904.45 2567.55 2757.77 2752.32 3339.73 3831.86 3990.22 4975.23 4581.48 4178.98 5248.86 5968.00 4888.33 5686.29 2129.71 2025.75 2752.55 2706.89 2794.28 3438.91 4147.43 4022.12 6243.25 4615.12 4451.36 5299.30 6447.32 4969.34 5114.82 2107.49 2071.09 2773.83 2850.27 2788.07 3130.59 4145.02 4363.58 6011.66 4459.35 4652.33 5553.12 6198.46 4744.71 6306.94
