Impact of environmental degradation on agricultural efficiency in India: evidence from robust econometric models

Abstract

Using annual time-series data from 1970 to 2016, this study attempts to examine the effects of environmental degradation on agricultural efficiency in India. Autoregressive distributed lag (ARDL), fully modified OLS (FMOLS) models are used to explore the long-run influence of environmental degradation and other control variables such as labor force, metabolic energy, fertilizer usage, and farm machinery on agricultural efficiency. Also, dynamic OLS (DOLS) and canonical cointegration regression (CCR) methods are used to validate the robustness of the estimated ARDL and FMOLS results. The Granger causality approach is used to examine causation among variables. According to ARDL and FMOLS estimates, fertiliser use, farm machinery, and metabolic energy improve India's agricultural efficiency. Agricultural methane emissions, on the other hand, reduce agricultural production. Furthermore, the DOLS and CCR approaches validate the estimated ARDL and FMOLS outcomes, indicating that the results are robust and consistent. The study suggest that the government should modernize agricultural research and seed systems and market and input diversification to help farmers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.