Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.

International crop‐related research as conducted by the CGIAR uses crop modeling for a variety of purposes. By linking crop models with economic models and approaches, crop model outputs can be effectively used as inputs into socioeconomic modeling efforts for priority setting and policy advice using ex‐ante impact assessment of technologies and scenario analysis. This requires interdisciplinary collaboration and very often collaboration across a variety of research organizations. This study highlights the key topics, purposes, and approaches of socioeconomic analysis within the CGIAR related to cropping systems. Although each CGIAR center has a different mission, all CGIAR centers share a common strategy of striving toward a world free of hunger, poverty, and environmental degradation. This means research is mostly focused toward resource‐constrained smallholder farmers. The review covers global modeling efforts using the IMPACT model to farm household bio‐economic models for assessing the potential impact of new technologies on farming systems and livelihoods. Although the CGIAR addresses all aspects of food systems, the focus of this review is on crop commodities and the economic analysis linked to crop‐growth model results. This study, while not a comprehensive review, provides insights into the richness of the socioeconomic modeling endeavors within the CGIAR. The study highlights the need for interdisciplinary approaches to address the challenges this type of modeling faces.