Abstract
Crop yield in India depends on both the climate in which crops grow and the way in which they are managed, yet the relative importance of these two groups of factors is rarely quantified within a single statistical framework. This study examines the influence of five climatic factors (temperature, rainfall, relative humidity, sunshine duration and wind speed) and five agricultural factors (irrigation, fertilizer use, soil quality, seed quality and pesticide use) on the yields of wheat, rice and soybean. It uses 299 district-season records from six major producing states, with Madhya Pradesh as the focal state. Pearson and partial correlation analysis, hierarchical multiple regression with crop dummy variables, Shapley decomposition, dominance analysis, bootstrap inference and ten-fold cross-validation were applied, and the model assumptions were verified through standard diagnostic tests. The full model explained 88.8 per cent of the variation in yield (adjusted R² = 0.884; F = 189.91, p < 0.001). Temperature had a significant negative effect (?0.114 t/ha per °C). Rainfall, sunshine, irrigation, fertilizer use, soil quality and seed quality had significant positive effects, while humidity, wind speed and pesticide use were not significant. Agricultural factors accounted for about 66 per cent and climatic factors for about 34 per cent of the within-crop variance explained, and the difference was significant. The model predicted yield accurately on independent data (cross-validated RMSE = 0.335 t/ha). The results indicate that improved input management offers substantial scope for raising yields, while adaptation to rising temperatures remains essential.
Introduction
1.1 Agriculture and Food Security
Agriculture remains central to food security and rural livelihoods in India. Although its share in national income has declined, it continues to support a large part of the population, and fluctuations in crop output affect food prices, farm incomes and rural demand. The need to raise agricultural productivity is becoming more urgent as population and incomes grow while the area available for cultivation remains essentially fixed. Since further expansion of cropland is limited, most future growth in production must come from higher yields on existing land. Understanding what determines crop yield, and how much each determinant matters, is therefore a question of direct policy importance.
1.2 Climate Sensitivity of Crop Production
Crop production is highly sensitive to weather and climate. Temperature governs the rate of crop development and the duration of grain filling, rainfall supplies the water on which rainfed crops depend, and humidity and sunshine influence evaporative demand and photosynthesis. The Intergovernmental Panel on Climate Change (IPCC, 2022) has concluded that climate change has already slowed the growth of agricultural productivity, especially in warm regions, and that the risks will increase with further warming. Global analyses estimate that each 1 °C rise in temperature reduces average yields of wheat, rice and soybean by several per cent (Zhao et al., 2017). Extreme weather events, such as droughts and heat waves, have caused significant losses in national cereal production (Lesk et al., 2016). India, with its high temperatures and dependence on the monsoon, is among the most exposed countries.
1.3 Agricultural Inputs and Productivity
Climate sets the environmental conditions for crop growth, but the yield obtained depends heavily on management. Irrigation, fertilizers, improved seed and plant protection were the foundations of the Green Revolution, and they remain the principal means through which farmers raise yields. In India, irrigation has contributed substantially to wheat yield growth and has reduced the sensitivity of the crop to heat (Zaveri & Lobell, 2019). Large yield gaps persist between farmers working under similar climatic conditions, which shows that management differences matter (Jain et al., 2017). Unlike climate, these inputs can be influenced by farmers and governments, so evidence on their effects is directly relevant to agricultural policy.
1.4 Regression Modelling in Agriculture
Because climatic and agricultural factors act together and are often correlated, their separate effects cannot be judged from simple comparisons. Multiple regression analysis estimates the effect of each factor while holding the others constant. It provides tests of significance and measures of goodness of fit, and it can be used to predict yield from observed conditions (Montgomery et al., 2021). Regression-based approaches have been widely used to study climate impacts on crop yields in India and neighbouring countries (Guntukula, 2020; Ali et al., 2017). In addition, techniques such as dominance analysis allow the explained variance to be divided among correlated groups of predictors in a way that does not depend on the order in which they are entered.
1.5 Context of the Present Study
Most earlier studies have analysed either climatic factors or agricultural inputs, often for a single crop or with national aggregate data, and few have compared the contributions of the two groups formally. The present paper addresses this gap. It analyses five climatic and five agricultural factors together for three major crops (wheat, rice and soybean) across six states of India, with Madhya Pradesh as the focal state. The paper examines the relationships between these factors and yield, quantifies the relative contributions of the two groups, develops and validates a prediction model, and identifies the key factors influencing yield. The remainder of the paper is organised as follows: the literature is reviewed, the methodology is described, the results are presented and discussed, and conclusions are drawn.
Conclusion
This study assessed the impact of climatic and agricultural factors on the yields of wheat, rice and soybean in India using hierarchical multiple regression. The model explained 88.8 per cent of the variation in yield, satisfied all diagnostic tests and predicted yields accurately on independent data. Temperature was the most influential single factor, and its effect was adverse. Rainfall and sunshine had positive effects, while humidity and wind speed had no independent effect. Irrigation, fertilizer use, soil quality and seed quality all raised yields significantly. The central finding is that agricultural factors contribute about twice as much as climatic factors to within-crop variation in yield (66 per cent versus 34 per cent). This indicates that productivity is, to a considerable degree, within the reach of management and policy. Priority should be given to balanced fertilizer use guided by soil testing, expansion of efficient irrigation, soil health improvement and wider use of certified seed. At the same time, the adverse effect of temperature means that heat-tolerant varieties and timely sowing are essential adaptation measures. The study relies on district-level secondary data and seasonal climatic averages, so its estimates are associations rather than proven causal effects. Future research could use farm-level data, measures of climate extremes and panel methods to extend these findings.
References
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