Identifying links between monsoon variability and rice production in India through machine learning

Abstract

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability.

Tapping the Unused Energy Potential of Solar Water Pumps in India

“…Since many farmers previously depended on rainfall for their irrigation (and thus irrigated fields only during monsoons), this result provides additional evidence of irrigation during the non-monsoon season. Given the variability of rainfall during the monsoon season and the historical seasonality of rice production, our results show that solar water pumps are still used during the monsoon season in addition to rainfall. While the use of pumps in winter is less than in the summer and monsoon seasons, non-zero pump usage indicates that the pumps that previously depended solely on rainfall have expanded irrigation to winter months.…”

Analyzing the Best Ways of Optimizing Rice Production through Machine Learning Technologies

“…Optimizing rice production fundamentally entails striking a delicate balance among a number of variables, including crop management, water utilization, pest and disease control, soil health, and socioeconomic issues. [11]Farmers can accurately adapt their operations to the particular requirements of each field by utilizing cutting-edge agricultural technologies such as precision farming, sensor-based irrigation systems, and data-driven decision-making. This strategy not only ensures effective resource use but also reduces the ecological impact of rice farming.…”

Exploring Soil Quality Variations across Upper, Middle, And Lower Ganga Regions: Leveraging GIS & Multivariate Statistical Methods to assess Organic Farming Viability

“…Traditionally known as the "breadbasket of India," the Gangetic basin region has enormous agricultural signi cance in South Asia (Bowden et al, 2023). However, it faces numerous challenges such as rapid urbanization, extensive use of chemical fertilizers and pesticides, and burgeoning population growth, all of which are compromising soil health and, thus, the overall ecosystem of the Gangetic basin as well as the river Ganges (Balkrishna et techniques to investigate the impact of urbanization-induced land use changes on soil health in the Gangetic plains, revealing signi cant differences in soil properties in Uttarakhand, India (Mishra, 2022).…”

“…Organic farming provides a sustainable option for restoring soil health, preserving ecosystem integrity, and ensuring agricultural productivity in the Gangetic region. It would also contribute signi cantly to the Ganga River water quality (Bowden et al, 2023). Organic farming has the potential for sustainable agriculture, but its feasibility depends on the condition of the underlying soil quality (Gamage et Soni et al, 2022).…”