Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments and management practices, i.e., the genetic × environment × management interaction (GEMI), can help understand suitability of genotype and agronomic practices, and possibly accelerate turnaround in plant breeding programs. However, the readiness of models to support these tasks can be debated. In this article, we point out modeling and data limitations and argue the need for evaluation and improvement of relevant process algorithms as well as model convergence. Under conditions suitable for plant growth, without meteorological extremes or soil limitation to root exploration, models can simulate resource capture, growth, and yield with relative ease. As stresses accumulate, the plant species‐ and genotype-specific attributes and their interactions with the soil and atmospheric environment generate a large range of responses, including conditions where resources become so limiting as to make yields very low. The space in between high and low yields is where most rainfed production occurs, and where the current model and user skill at representing GEMI varies. We also review studies comparing the performance of a large number of crop models and the lessons learned. The overall message is that improvement of models appears as a necessary condition for progress, and perhaps relevancy. Model ensembles help mitigate data input, model, and user-driven uncertainty for some but not all applications, sometimes at a very high cost. Successful model-based assessment of GEMI not only requires better crop models and knowledgeable users, but also a realistic representation of the environmental conditions of the landscape where crops are grown, which is not trivial given the 3D nature of water and nutrient transport. Models remain the best quantitative repository of our knowledge on crop functioning; they contain a narrative of plant, soil, and atmospheric functioning in computer language and train the mind to couple processes. But in our quest to tame GEMI, will they lead the way or just ride along history?

The long-term sustainability of wheat-based dryland cropping systems in the Inland Pacific Northwest (IPNW) of the United States depends on how these systems adapt to climate change. Climate models project warming with slight increases in winter precipitation but drier summers for the IPNW. These conditions combined with elevated atmospheric CO₂, which promote crop growth and improve transpiration-use efficiency, may be beneficial for cropping systems in the IPNW and may provide regional opportunities for agricultural diversification and intensification. Crop modeling simulation under future climatic conditions showed increased wheat productivity for the IPNW for most of the century. Water use by winter wheat was projected to decrease significantly in higher and intermediate precipitation zones and increase slightly in drier locations, but with winter crops utilizing significantly more water overall than spring crops. Crop diversification with inclusion of winter crops other than wheat is a possibility depending on agronomic and economic considerations, while substitution of winter for spring crops appeared feasible only in high precipitation areas. Increased weed pressure, higher pest populations, expanded ranges of biotic stressors, and agronomic, plant breeding, economic, technology, and other factors will influence what production systems eventually prevail under future climatic conditions in the region.

Simulations of crop yields under climate change are subject to uncertainties whose quantification is important for effective use of projected results for adaptation and mitigation strategies. In the US Pacific Northwest (PNW), studies based on single crop models and weather projections downscaled from a few general circulation models (GCM) have indicated mostly beneficial effects of climate change on winter wheat production for most of the 21st century. In this study we evaluated the uncertainty in the projection of winter wheat yields at 7 sites in the PNW using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and daily weather data downscaled from 14 GCMs for 2 representative concentration pathways (RCP) of atmospheric CO2 (RCP4.5 and 8.5). All crop models were calibrated for high, medium, and low precipitation dryland sites and one irrigated site using 1979–2010 as the baseline period. All five models were run from years 2000 to 2100 to evaluate the effect of future conditions (precipitation, temperature and atmospheric CO2) on winter wheat grain yield. Simulations of future climatic conditions and impacts were organized into three 31-year periods centered around the years 2030, 2050 and 2070. All models predicted a decrease of the growing season length and crop transpiration, and increase in transpiration-use efficiency, biomass production, and yields, but with substantial variation that increased from the 2030s to 2070s. Most of the uncertainty (up to 85%) associated with predictions of yield was due to variation among the crop models. Maximum uncertainty due to GCMs was 15% which was less than the maximum uncertainty associated with the interaction between the crop model effect and GCM effect (25%). Large uncertainty associated with the interaction between crop models and GCMs indicated that the effect of GCM on yield varied among the 5 models. The mean of the ensemble of all crop models and GCMs provided a robust indication of positive effects of future environmental conditions on winter wheat yield during this century at all sites studied, with greater beneficial effect under water stressed conditions than under well-watered conditions, and under RCP8.5 than RCP4.5.

Agriculture in the dryland region of the Inland Pacific Northwest (IPNW, including northern Idaho, eastern Washington and northern Oregon) is typically characterized based on annual rainfall and associated distribution of cropping systems that have evolved in response to biophysical and socio-economic factors. Three agro-ecological classes (AEC) have been proposed for the region: a) crop/fallow (CF), b) annual crop/fallow transition (CCF), and c) continuous cropping (CC). AECs attempt to associate land use into relatively homogeneous areas that result in common production systems. Although there is an interest in sustainable intensification of cropping systems (e.g., reduction of fallow), the question remains whether climate change will preclude intensification or shift the borders of existing AECs toward greater fallow utilization. A simulation study was conducted to address this question, with the aim of classifying 4x4 km pixels throughout the region into one of the three AECs for baseline (1979-2010) and future periods (2030s, 2015-2045; 2050s, 2035-2065; 2070s, 2055-2085). Baseline data were derived from traditional rotations and historical climate records. Data for future projections were derived from atmospheric CO2 concentration considering daily weather downloaded from 12 global circulation models and 2 representative concentration pathways (RCP 4.5 and 8.5). Due to the direct effect of atmospheric CO2 on photosynthesis and stomatal conductance, the transpiration use efficiency of crops (TUE; g above-ground biomass kg water-1) showed an increasing trend, with winter wheat TUE changing from 4.76 in the historical period to 6.17 and 7.08 g kg-1 in 2070s, depending on AEC. Compared to the baseline, total grain yield by the 2070s in the region was projected to increase in the range of 18% to 48% (RCP 4.5) and 30% to 65% (RCP 8.5), depending on AEC. As a consequence of these changes, compared to the historical baseline period, the future fraction of the area classified as CF decreased from 50% to 39-36%, CC increased from 16% to 24-28%, and CCF decreased slightly (~1%), with the greater change projected for the RCP 8.5 scenario.