Projected Food-grain Production and Yield in India: An Evidence from Statewise Panel Data Investigation during 1977-2014

Abstract

Purpose : This study examines the impact of climatic change on food-grain production and yield using state-wise panel data during 1977-2014 in fifteen Indian states. Accordingly, it estimates the projected food-grain production and yield by the years of 2040, 2040, 2060, 2080 and 2100. Finally, it provides the effective practical and effective policy suggestions to reduce the climate change impact on food-grain farming based on existing studies. Research Method : Regression coefficients of food-grain production and yield with climatic and non-climatic factors are estimated using Cobb-Douglas production function model. Marginal impact analysis method is used to examine the projected food-grain production and yield. Findings : Empirical results infer that most climatic factors have a negative impact on food-grain production and yield in different weather seasons. Projected results are suggested that food-grain production is expected to be declined by 5.25%, 6.64%, 8.03% and 9.57% by 2040, 2060, 2080 and 2100 respectively. Food-grain yield is likely to be decreased by 1.05%, 1.96%, 2.87% and 5.07% by the aforesaid years. Research Limitations : This study could not capture impact of inter-states disparities in socio-economic condition of farmers, geographical conditions, agriculture policies and public spending on agriculture and rural development on food-grain production and yield. It also could not include factors such as solar radiation, sun intensity, wind speed and heat wave in empirical investigation. Originality/ Value : It compiles state-wise panel of food-grain production and yield as dependent variables, and climatic variables and non-climatic factors as explanatory variables during 1977-2014. It examines the projected food-grain production and yield.

Implications of Farmer's Adaptation Strategies to Climate Change in Agricultural Sector of Gujarat: Experience from Farm Level Data

“…In this regard, existing studies estimated the impact of climate change in the Indian agricultural sector in several ways. Most studies have focused to examine the climate change impact on production and yield of food-grain and commercial crops in India [1,5,8,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] . Other studies also assessed the influence of climatic and non-climatic factors on productivity and performance of agricultural sector in India [31][32][33][34][35][36][37][38] .…”

Impact of Technological Change on Growth and Agricultural Sector in Gujarat State of India: A Time-Series Data Study

“…Existing studies have used different factors such as time trend factor (TTF), fertilizer intensity, tractor intensity, education of labour force, improved seed, fertilizer, pesticide and herbicide, farm management practices, and farmer's experience as a proxy variable to examine the influence of technological change on production and yield of various crops in different countries (Gebeyehu, 2016;Khan & Anwar, 2009;Kumar, Sharma, & Joshi, 2015b;Siddick, 2019;Singh & Jyoti, 2021a, 2021bSingh & Sharma, 2018;Singh et al, 2017). TTF is highly effective to assess the impact of technological change on agricultural production.…”

“…The statistical interpretation of regression coefficients of independent variables which are estimated using C-D production model is easy and simple (Kumar, Singh, & Sharma, 2020;Singh & Sharma, 2018;Singh et al, 2017). Furthermore, regression results based on this model can be used to predict the future values of output (Jyoti & Singh, 2020;Kumar et al, 2017;Singh & Jyoti, 2021a, 2021b.…”

Assessment of Global Sustainable Development, Environmental Sustainability, Economic Development and Social Development Index in Selected Economies

“…As this study used country-wise panel of various indicators during 2000-2016. Thus, the regression coefficients of explanatory variables are estimated using linear regression correlated panels corrected standard errors model to reduce the presence of cross-sectional dependency, heteroskedasticity, serial-correlation and auto-correlation in the panel data [3,4,12,24,[51][52][53][54][55][56]. Also, Ramsay RESET test [37,55,56], and Akaike Information Criterion (AIC) and Schwarz Information Criteria (BIC) are used to check the validity of model and consistency of regression coefficients of explanatory variables in the proposed models [3,4,[54][55][56].…”

“…Hence, it is compulsory to check the credibility of regression coefficients to make the rationality in the prediction of results. For this, existing researchers like Singh et al [54], Jyoti and Singh [55], Singh and Jyoti [56] estimated the correlation coefficients of error-term with its various lags under a well-defined regression model to check the credibility of regression coefficients. Further, above-mentioned studies claimed that if the correlation coefficients of error-term with its at least first two lags are statistically significant then the regression coefficient may be valid and have credibility as well.…”