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Addressing the effect of meteorological factors and agricultural subsidy on agricultural productivity in India: a roadmap toward environmental sustainability.

Imran Ali BaigMuhammad IrfanMd Abdus SalamCem Işik
Published in: Environmental science and pollution research international (2022)
In the last two decades, the extensive literature that has measured agricultural productivity and growth rate remains controversial and provides few strategies about its main determinants. The present study aims to find out the key determinants of food grain yield (FGY) and examine the role of climate change and agricultural subsidy (SUB) in the context of India using annually data spanning from 1991 to 2018. The current study applied the ARDL modelling to investigate the impacts of climatic factors (average rainfall (RF), mean temperature (AT), and carbon emission (CO 2 ) and agricultural subsidy (SUB) on food grain yield (FGY) in the short-and long-term in India. The estimated outcomes indicate that climatic factors such as RF have a positive impact while AT and CO 2 have a negative effect on FGY. Similarly, non-climate variables such as gross capital formation (GCF) and fertilizer usage (FERT) positively contributed to FGY, while the area under crop (LUC), SUB, and employment (AL) negatively affected FGY in India. The results from Granger causality divulge that climatic and non-climatic elements are the main determinants of food grain yield, which have been playing play a significant role in enhancing food grain production and ensuring food security in India. Based on empirical outcomes and findings, some key policy implications emerged. Precisely, government and policy developers should focus on technological innovation and precision agriculture to increase agriculture production and productivity. Government should create funds to curb the climate change problem and promote eco-friendly renewable energy.
Keyphrases
  • climate change
  • human health
  • public health
  • healthcare
  • risk assessment
  • systematic review
  • mental health
  • type diabetes
  • emergency department
  • machine learning
  • big data
  • children with cerebral palsy
  • data analysis