Groundnut is one of the significant sources of oil, food, and fodder in India. It is grown in marginal arid and semi-arid agro-ecosystems with wide yield fluctuations due to spatial variability of rainfall and soil. Climate change, which is predicted to increase the intra- and inter-annual rainfall variability will further constrain the groundnut economy in India besides the global and domestic economic, social and policy changes. Through this study we aim to examine the biophysical and social economic impacts of climate change on groundnut production and prices to provide a comprehensive analysis of how agriculture and the food system will be affected. Using projected climate data for India, we estimated the biophysical impacts of climate change on groundnut during mid-century using representative concentration pathway (RCP 8.5) scenario. We examined the impacts of changes in population and income besides environmental factors on groundnut productivity. This is to highlight the importance of holistic assessment of biophysical and socioeconomic factors to better understand climate change impacts. Modelled projections show that by 2050, climate change under an optimistic scenario will result in −2.3 to 43.2% change in groundnut yields across various regions in India when climate alone was factored in. But the change in groundnut yields ranged from −0.9% to 16.2% when economic (population and income) and market variables (elasticities, trade, etc.) were also considered. Similarly, under pessimistic climate change scenario, the percent change in groundnut yields would be −33.7 to 3.4 with only the climate factored in and −11.2 to 4.3 with the additional economic and market variables included. This indicates the sensitivity of climate change impacts to differences in socioeconomic factors. This study highlights the need to take into account market effects to gain a holistic understanding of how economic and environmental factors impact agricultural food systems and economies.

Given the wide use of the multi-climate model mean (MMM) for impact assessment studies, this work examines the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating the features of Indian summer monsoons as well as the post-rainy seasons for assessing the possible impacts of climate change on post-rainy season sorghum crop yields across India. The MMM simulations captured the spatial patterns and annual cycles of rainfall and surface air temperatures. However, bias was observed in the precipitation amounts and daily rainfall intensity. The trends in the simulations of MMM for both precipitation and temperatures were less satisfactory than the observed climate means. The Crop Environment Resource Synthesis (CERES)-sorghum model was used to estimate the potential impacts of future climate change on post-rainy season sorghum yield values. On average, post-rainy season sorghum yields are projected to vary between −4% and +40% as well as +10% and +59% in the near future (2040–2069) for RCP 4.5 and RCP 8.5, respectively, and between +20% and +70% (RCP 4.5) as well as +38% and +89% (RCP 8.5) in the far future (2070–2099). Even though surface air temperatures are increasing in future climate change projections, the findings suggest that an increase in the post-rainy season sorghum yields was due to an increase in the rainfall amounts up to 23% and an increase in the atmospheric CO₂ levels by the end of the 21st century. The results suggest that the projected climate change during the post-rainy season over India is an opportunity for smallholders to capitalize on the increase in rainfall amounts and further increase sorghum yields with appropriate crop management strategies.

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture.