Climate change and associated uncertainties have serious direct and indirect consequences for crop production and food security in agriculture-based developing regions. Long-term climate data analysis can identify climate risks and anticipate new ones for planning appropriate adaptation and mitigation options. The aim of this study was to identify near-term (2030) and mid-term (2050) climate risks and/or opportunities in the state of Bihar, one of India’s most populous and poorest states, using weather data for 30 years (1980–2009) as a baseline. Rainfall, maximum and minimum temperatures, and evapotranspiration will all increase in the near- and mid-term periods relative to the baseline period, with the magnitude of the change varying with time, season and location within the state. Bihar’s major climate risks for crop production will be heat stress due to increasing minimum temperatures in the rabi (winter) season and high minimum and maximum temperatures in the spring season; and intense rainfall and longer dry spells in the kharif (monsoon) season. The increase in annual and seasonal rainfall amounts, and extended crop growing period in the kharif season generally provide opportunities; but increasing temperature across the state will have considerable negative consequences on (staple) crops by affecting crop phenology, physiology and plant-water relations. The study helps develop site-specific adaptation and mitigation options that minimize the negative effects of climate change while maximizing the opportunities.

Climate change impacts on agriculture have become evident, and threaten the achievement of global food security. On the other hand, the agricultural sector itself is a cause of climate change, and if actions are not taken, the sector might impede the achievement of global climate goals. Science-policy engagement efforts are crucial to ensure that scientific findings from agricultural research for development inform actions of governments, private sector, non-governmental organizations (NGOs) and international development partners, accelerating progress toward global goals. However, knowledge gaps on what works limit progress. In this paper, we analyzed 34 case studies of science-policy engagement efforts, drawn from six years of agricultural research for development efforts around climate-smart agriculture by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Based on lessons derived from these case studies, we critically assessed and refined the program theory of the CCAFS program, leading to a revised and improved program theory for science-policy engagement for agriculture research for development under climate change. This program theory offers a pragmatic pathway to enhance credibility, salience and legitimacy of research, which relies on engagement (participatory and demand-driven research processes), evidence (building scientific credibility while adopting an opportunistic and flexible approach) and outreach (effective communication and capacity building).

Water footprint (WF), a comprehensive indicator of water resources appropriation, has evolved as an efficient tool to improve the management and sustainability of water resources. This study quantifies the blue and green WF of major cereals crops in India using high resolution soil and climatic datasets. A comprehensive modelling framework, consisting of Evapotranspiration based Irrigation Requirement (ETIR) tool, was developed for WF assessment. For assessing climate change impact on WF, multi-model ensemble climate change scenarios were generated using the hybrid-delta ensemble method for RCP4.5 and RCP6.0 and future period of 2030s and 2050s. The total WF of the cereal crops are projected to change in the range of − 3.2 to 6.3% under different RCPs in future periods. Although, the national level green and blue WF is projected to change marginally, distinct trends were observed for Kharif (rainy season—June to September) and rabi (winter season—October to February) crops. The blue WF of paddy is likely to decrease by 9.6%, while for wheat it may increase by 4.4% under RCP4.5 during 2050s. The green WF of rabi crops viz. wheat and maize is likely to increase in the range of 20.0 to 24.1% and 9.9 to 16.2%, respectively. This study provides insights into the influences of climate change on future water footprints of crop production and puts forth regional strategies for future water resource management. In view of future variability in the WFs, a water footprint-based optimization for relocation of crop cultivation areas with the aim of minimising the blue water use would be possible management alternative.

Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9.

Increasing agricultural production with current resources and technology may lead to increased GHG emissions. Additionally, large population countries like India face substantial challenges in terms of food demand, agro-ecological heterogeneity, carbon footprint and depleting natural resources, thus increasing the decision complexities for policymakers and planners. We aim to examine the potential of producing more food from available agricultural land with low-carbon (reduced GHG emissions) and resource-conscious (optimal resource use) options. The current study develops multiple calorie production and emission-centric land use using a land use optimization model wherein the calorie production and emission objective, resource and emissions constraints, and food production targets interact across multiple spatial levels. The capabilities of the developed model are demonstrated with a case study in India targeting ten crops (grown over two seasons) covering three food groups (cereals, legumes, and oilseeds). Three hypothetical scenarios for each objective of maximizing calories production (Calories-nation, Calories-group, Calories-crop) and minimizing GHG emissions (Emissions-nation, Emissions-group, Emissions-crop) are developed concerning targets of national crop production (Calories-nation, Emissions-nation), state food groups production (Calories-group, Emissions-group), and state crop production(Calories-crop, Emissions-crop), with different spatial levels of constraints. A maximum growth of 11% in calorie production is observed in Calories-nation while mitigating 2.5% emissions. Besides, the highest emission reduction of around 30% is observed in Emissions-group but with no change in calorie production. Emission scenarios can spare up to 14.8% land and 18.2% water, while calorie production-maximization scenarios can spare a maximum of 4.7% land and 6.5% water. The optimization-based methodology identifies the regions of altered land use by proposing appropriate crop substitution strategies, such as increasing oilseeds in Rajasthan and soybean in east Maharashtra. Many states show conservative production growth and emission reduction with state-level crop production targets (Calories-crop), suggesting crop redistribution within the state alone will not be sufficient unless improved technologies are introduced. The maximum growth and mitigation potential estimated in this study may be affected by climate shocks; therefore, introducing the improved technologies needs to be coupled with a crop redistribution mechanism to design climate-resilient and futuristic land use systems. The proposed land use model can be modified to incorporate climate change effects through consideration of scenarios of changed crop yields or through direct/indirect coupling with dynamic crop simulation models.