Rice is a staple food for over half the world's population, and India, the world's second-largest rice producer, plays a crucial role in global food security. Kharif monsoon rice, accounting for 85% of India's total rice production, is particularly vulnerable to climate variability. Extreme climate events significantly impact crop yields globally, and India's Kharif rice production is no exception, with yields declining in 65% of regions due to climate variability. Monsoon-dependent crops are more sensitive to rainfall changes than other crops. While studies have shown negative impacts of excessive rainfall on rice yields, particularly in Northeast India, and the influence of El Niño-Southern Oscillation (ENSO) events, there's a lack of comprehensive understanding of the optimal rainfall threshold for Kharif monsoon rice across India and its spatial and temporal variations. This study aims to address this gap by analyzing the impact of rainfall on Kharif monsoon rice yield across all districts of India from 1990 to 2017, identifying the optimal rainfall threshold (ORT) nationwide and regionally, and exploring the influence of ENSO events. Understanding these relationships is vital for improving food security management in a changing climate.

Existing literature acknowledges the nonlinear relationship between rice yield and rainfall, with yield declining when rainfall deviates from an optimal range. Most previous research has focused on quantifying yield losses under excessive rainfall, neglecting the impact of rainfall deficits and the optimal rainfall threshold. Some studies have examined wheat production in the US, but the specific threshold beyond which excessive rainfall negatively impacts Kharif monsoon rice yield in India remains unknown. Studies highlight the dependence of rice production on climate variability and the impact of extreme weather events. The sensitivity of Indian monsoon rainfall to ENSO events and their subsequent effects on rice production have also been documented. However, a comprehensive analysis combining spatial and temporal variations of optimal rainfall thresholds with the influence of ENSO events on Kharif monsoon rice yield in India is lacking. This study bridges this gap by providing a detailed analysis of the rainfall-yield relationship at the district level across India.

This study utilized district-level data on harvested area, crop production, and yield of Kharif monsoon rice in India from 1990 to 2017, obtained from the ICRISAT database. Daily gridded rainfall and air temperature data from the India Meteorological Department (IMD) were used, with spatial resolutions of 0.25° x 0.25° and 1° x 1°, respectively. The data were rescaled to the district level using area-weighted averages. Data on the Oceanic Niño Index (ONI) were used to assess the influence of ENSO events. A multivariate log-linear regression mixed-effects model was employed to analyze the impact of rainfall and temperature on rice yield, considering both long-run trends and rainfall variables. The model included year, rainfall, rainfall squared, temperature, and temperature squared as fixed-effect variables, while location was considered as a random effect variable. The percentage of relative yield change (RYC) was calculated to quantify the impact of rainfall trends on rice yield. To determine the optimal rainfall threshold (ORT), a segmented regression method was employed, identifying the breakpoint where the relationship between rainfall and RYC shifts from positive to negative. The sensitivity of RYC to rainfall was calculated by comparing the slopes of the regression lines on either side of the ORT. Finally, the relationship between rice yield, rainfall, and ONI was analyzed to assess the impact of ENSO events on rice production.

The analysis revealed a significant positive effect of rainfall on rice yield, but extreme rainfall (both excessive and deficient) negatively impacted yields. Nationwide, the average yearly relative yield change (RYC) showed yield losses exceeding 3% in 2003, 2009, and 2014, and significant gains in 2008, 2012, and 2015. The overall optimal rainfall threshold (ORT) for India was estimated to be 1621 ± 34 mm. Above this threshold, yield declined at a rate of 6.41 kg ha⁻¹ per 100 mm increase in rainfall. Below the ORT, yield decreased at a rate of 17 kg ha⁻¹ per 100 mm decrease in rainfall. Spatial heterogeneity in ORTs was observed across 14 states, ranging from 544 mm (Haryana) to 2775 mm (Kerala). The sensitivity of RYC to rainfall varied across states, with some states showing stronger negative impacts from excessive rainfall than from rainfall deficits. Analysis of temporal variations showed that ORTs increased with increasing mean rainfall. El Niño events were associated with reduced rainfall and lower rice yields, while La Niña events were associated with increased rainfall and higher yields. The strong negative correlation between ONI and RYC further supported the impact of ENSO on rice yield.

The findings highlight the complex and spatially variable relationship between rainfall and Kharif monsoon rice yield in India. The identification of the nationwide and state-specific ORTs provides valuable insights for water management and agricultural planning. The nonlinear response of rice yield to rainfall emphasizes the need for strategies to mitigate the negative impacts of both excessive and deficient rainfall. The influence of ENSO events on rainfall patterns and rice yields underscores the importance of incorporating climate forecasts into agricultural planning. The observed spatial variations in ORTs reflect the diverse agro-ecological conditions across India and the need for region-specific adaptation strategies. The study's findings can inform agricultural policies and practices aimed at improving resilience to climate change and enhancing food security.

This study provides crucial information on the optimal rainfall thresholds for Kharif monsoon rice production in India, revealing significant spatial and temporal variations influenced by ENSO events. The nonlinear relationship between rainfall and yield highlights the risks associated with both excessive and deficient rainfall. These findings can inform the development of region-specific adaptation strategies to enhance rice production resilience to climate change and improve food security. Future research could focus on exploring the impact of other climate variables and socio-economic factors on rice yield and investigate the effectiveness of different adaptation strategies in various agro-ecological regions.

The model primarily focuses on the influence of rainfall, while other factors like temperature, soil conditions, fertilizer use, pest infestation, and technological advancements could also affect rice yield. Data limitations at the district level might introduce uncertainties in the analysis. The study does not explicitly consider the impacts of different rice varieties or farming practices on the optimal rainfall thresholds.