The computational analysis of COVID-19-induced socio-economic, environmental, and climatic disruptions on the Indian food production system

Abstract

COVID-19 dominantly affected all the sectors of the Indian economy, surprisingly the impact is much lower with respect to the agricultural production (−2.7%) in India. The increase in yield of the crops can be attributed to the variables such as environmental, climatic, and socio-demographic factors. The study illustrates the relationship of the increase in crop yield in India during the first wave of COVID-19 along with the increase in the infection count and the land under cultivation attributed to supporting factors during the year 2020. The relation is explained by the method of ordinary least square (OLS) and geographically weighted regression (GWR). The distribution of the increase in crop yield across India is analyzed against COVID-19 infections along with other dominant factors. Useful intuitions against crop yield can be generated by studying the spatial relationships between them. The geographically weighted regression method depicted an increase in R2 value as compared to the global ordinary least regression method. The Akaike information criterion in the geographically weighted regression method is also lower as compared to the ordinary least square therefore explaining GWR as a better model as compared to OLS. The combination of the various variables affecting agricultural yield in India is found to vary geographically as well as with the type of crops.