Revisiting the Determinants Of Agricultural Productivity Of Food Grains In India: An Econometric Approach

This paper tries to recheck the determinants of food grains production in India. After the Green revolution & New economic policy reforms India has been self suﬃcient in food grain production. It is evident from the available literature that, there is a need of rechecking the causality amongst the factors in details. This paper uses VECM method to examine such relationships. Datas collected here are mostly of secondary nature.

Introduction: The Vedas have vividly articulated the concept of "Annamayakosa", which basic exemplifies the importance of food and food grains.Food grains are primarily consumed by all individuals as staple food to feed the basic necessity called Food.Unlike other crops they form a sizable portion of any Indian plate and simultaneously contribute significantly in terms of calories and quantity of food consumed.In countries like India they also bear the popularity of being the traditional staples and funding the average Indian body with enough energy and carbohydrate to engage in the age-old tradition of agriculture.
The production of food grains in India has several inbuilt advantages towards the society and economy as a whole.In India they occupy 2/3 rd of the total cropped area, which amounts to be 219.33 million hectors in 2019.This evidently proves the agricultural dependency of farmers on production of food grains.The reasons of such significance can be attributed to requirement of less expertise in the production process, more use of surplus manpower and most importantly the tradition of allocating some portion of land for production of food grains in order to avail the minimal food requirement for a year.So after using to feed the family the surplus amount is spent on commercial purposes.Evidently it has contribution in both subsistence and commercial level of agriculture.The employment of rural population as a prominent of rural livelihood is hugely contributed by food grain production as this gives routine agricultural employment to the largest portion of rural agricultural workforce.This contribution lies independent of extensive operation of Lewis's theory of Unlimited supply of labor in rural India.This fact is highly influenced by the basic nature of technology and labour mix required for food grain production.Though it is evident from every ordinary agricultural field in India that technology has started taking over manpower and substituting it, but the human contribution id yet to be called as insignificant.
After independence during the Green revolution only, absolute focus was centralized around food grain production in order to make India self-sufficient and to ensure food on every Indian plate.After that by observing the current situation of food grains production in India, it can be inferred the need for further robust research on the ground of food grain production.So this paper tries to analyse some determinants of the same.
Literature Review: Though voluminous research has been conducted o the determinants of agricultural productivity but very insignificant dive has been given to this particular site of agriculture i.e. food grains production.Followings are some of the most important research literatures that we went through and that inspired this piece of exploration: Nayak & Priyadarshini (2017): This paper specifies the long run simulation between various factors of land productivity and suggests for non-product specific support from the Government Side.By taking variables like irrigation, electricity consumption, private expenditure on agriculture, fertilizers consumption and nonproduct specific inputs it tries to elaborate the long run and short run impacts of these factors on agricultural productivity.
V. M Rao (1992): This paper primarily implied towards further reorientation of agricultural pricing policy & the government should concern itself with the major determinants of growth and stability and with the task of ensuring a market framework with a reasonable degree of efficiency.
Parikh, Kumar & Darbha (2003): This paper yielded some interesting findings like, increase in the MSP of wheat and rice leads to decline in overall GDP, increase in aggregate price index and reduction in investments.Even the increase in agricultural GDP resulting from higher MSP dwindles rapidly and only a minuscule positive impact on agricultural GDP remains by the third year.
Chand (2007): This paper titled "Wheat Supply, Price and Food Security", found that wheat production on average has not grown at the pace equivalent to growth of its demand.While focusing primarily on the production of wheat, this paper vividly analysed the pricing policy of wheat and roll of its determinants in ensuring food security in India.From the above table it can be observed that • On productivity of food grains irrigation has a delayed impact as compared to electricity in short run.This basically signifies the nature of influence of electricity to food grains production to be indirect.• Though consumer expectations (in terms of CPI) and Producer expectation (in terms of WPI) doesn't directly influence FGPD but they influence each other.This is because of the fact that consumer expectation and producer expectations are formed in long run and they don't have any instant impact on the production process.• Consumption of electricity is influenced by production and consumer expectation of last year, signifying income dependency of electricity consumption in agricultural sector.This specifically demands for governmental attention towards supply of electricity to agricultural uses for technological advancement and productivity.
• Irrigation in the current year is highly influenced by Electricity consumption of the previous years.It shows technological dependence of irrigation in food grains production in India as increasingly climatic conditions of rainfall has become uncertain and the monsoon rainfall is not dependable, leading to reduction in rainfed agriculture.• Irrigation of the current year is influenced by previous year irrigation as well.That means if irrigated previously it will be irrigated this year also and vice-versa.• Producers' expectation in not-naive i.e. previous year WPI influences current years WPI and thereby the producer expectations.
Below we have shown the results of Wald Test for checking joint influence of factors: Reject H 0 *Source: author's own Eviews calculations From the significant coefficients of Wald test following policy implications in nutshell; • Though ELEC (-2) doesn't individually influence FGPD but along with its forward value of ELEC (-1) it jointly influences FGPD.• Producer expectations of previous two years jointly influence consumer expectations of the current year.• Electricity consumption is jointly influenced by production of the previous years i.e. more income leads more electrification.• Irrigation is highly dependent upon the electricity consumption of previous years.

Diagnostic Checks of Short Run Causality:
Here is the diagnostic check of the model that shows the model under consideration is a robust model and the policy suggestions of the model can be used practically.Here * = 1% level of significance and **= 5% level of significance.

Statistics
The results of Unit root test show that all variables are non-stationary at level and stationary at first difference.So after running Johansen Cointegration test we got at-least 2 cointegrating equations using the trace statistic values and 1cointegrating equations using the Max-eigen values.This model is now eligible for running VECM.For running VECM we have the following system equation: Result of running OLS on the above-mentioned system equation is given below: Hosted file image2.emfavailable at https://authorea.com/users/718238/articles/703498-revisitingthe-determinants-of-agricultural-productivity-of-food-grains-in-india-an-econometricapproach The above table shows the VECM results of the model.Here our first coefficient of the system equation is negative with a value of -0.643707 and the Probability value is 0.0376 is also significant.This derives the conclusion that there exists long run relationship among the factors affecting food grains productivity and they influence food grain production collectively.
Diagnostic Checks for Long-run Causality: Just like short-run causality we also tried to check the robustness of the model by using the normality, heteroscedasticity, autocorrelation tests.The results of the values provided above confirms the model to be robust and reliable.
Exogenous Observations: While considering different variables of the model we came across several variables that couldn't be included due to several issues like modelling problems, lack of adequate number of observation and etc.Those variables are Minimum Support Price (MSP) of food grains, Issue price and economic cost of production of food grains, food stock of food grains, government outlay on agriculture and agricultural research and development.On the basis of these variables following observations are being made which are exogenous to the existing model of the paper.

Hosted file
More outlay towards agricultural R&D.This is evident from all other sectors of India that there is a need for more spending in research and development in order to bring in Innovation and thereby productivity.The left sided diagramme shows the trend of percentage of total outlay to agricultural R&D which has reduced from 6.0 in 2005 to 4.8 in 2017, thus there is a need more allocation of funds towards this definitely.The right side diagramme shows percentage growth of agricultural R&D which almost equal to 0. So more planned action in this regard is highly suggested.

Conclusion:
Based on the findings of both short and long run causality tests it is evident that short-run individual influences of food grain productivity are primarily dependent on factors proving to be influencing enough to have an impact in the short period e.g.irrigation and electricity.Yet the joint test estimations stand at the fact that factors having no individual influence bear a high chance of influencing if another lag is added.But in the long run all the factors influence production of food-grains vehemently. References: • 1)*( FGPD(-1) + 0.273690898491*CPI AL (-1) -10.2107213895*ELEC(-1) + 0.288505364095*PGIA(-1) -2.33614222992*WPI FG(-1) -1255.08511823 ) + C(2)*D(FGPD(-1)) + C(3)*D(FGPD(-2)) + C(4)*D(CPI AL (-1)) + C(5)*D(CPI AL (-2)) + C(6)*D(ELEC(-1)) + C(7)*D(ELEC(-2)) + C(8)*D(PGIA(-1)) + C(9)*D(PGIA(-2)) + C(10)*D(WPI FG(-1)) + C(11)*D(WPI -FG(-2)) + C(12) Data Sources & Methodology: This paper basically used timeseries data of 30 consecutive years, starting from 1989-90 to 2018-19.The datas collected here are of secondary nature which are collected from various sources like RBI Statistical Handbook on Indian Economy, Food & Agriculture Organisation (FAO) and Census and Economic Information Center (CEIC).Following is the table of all the variables taken under consideration.This paper has used Standard VAR model to assess the individual influence of factors of food grain production in the short-run.Similarly, in order assess the joint influence this paper used Wald Test in the short-run causality test.On the other hand, we used the Vector Error Correction Model (VECM) for long-run causality test.Towards each of the Short and long run tests we have gone through several diagnostic tests like LM Autocorrelation test, Normality test &White Heteroscedasticity test to check the robustness of the model.
Objectives: Followings are the objectives of the study: 1.To assess short-run individual and collective influence of factors affecting food-grains productivity.2.To assess long run influence of factors determining productivity on food-grains production.3.To check possible influence among different factors.Results & Discussion: By using the standard VAR model for short run and VECM model for long run the paper upholds following results.Short Results Analysis: Here the table below shows all the coefficients that came significant by concerning their respective P-values.*Source:author's own Eviews calculations.
The long-run causality between the factors and their influence on food-grain production in this paper is assessed by the help of VECM model.As a precondition to that the lag selection test suggests two lags following the majoritarian principle as Akaike Information Criteria (AIC) and Hannan -Quinon (HQ) criteria both suggest the same.In order to check the possible long run association we use Johansen Cointegration test.As a pretesting for Johansen all the variables are required to be integrated of order one i.e.I(1) nature.Below is the results of Unit Root test analysis of all the variables following Augmented Dicky fuller (ADF) test and Phillips-Peron (PP) test of stationarity.