Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

The Impact of Climate Change on Groundwater and Crop Yield in Asia: A Comprehensive Review

Version 1 : Received: 23 December 2023 / Approved: 25 December 2023 / Online: 25 December 2023 (09:21:55 CET)

How to cite: Santhosh, A.; Prabhakaran, S.; Veeraswamy, D.; Srinivasulu, A.; Lal, A.; Naidu, R. The Impact of Climate Change on Groundwater and Crop Yield in Asia: A Comprehensive Review. Preprints 2023, 2023121777. https://doi.org/10.20944/preprints202312.1777.v1 Santhosh, A.; Prabhakaran, S.; Veeraswamy, D.; Srinivasulu, A.; Lal, A.; Naidu, R. The Impact of Climate Change on Groundwater and Crop Yield in Asia: A Comprehensive Review. Preprints 2023, 2023121777. https://doi.org/10.20944/preprints202312.1777.v1

Abstract

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

Keywords

climate change; groundwater; crop yield; machine learning; artificial neural network

Subject

Environmental and Earth Sciences, Environmental Science

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