No abstract

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 CO 2 , 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 -017-1950-z Climatic Change (2018) 146:247-261 DOI 10.1007/s10584

No abstract

Irrigated agriculture in snow-dependent regions contributes significantly to global food production. This study quantifies the impacts of climate change on irrigated agriculture in the snow-dependent Yakima River Basin (YRB) in the Pacific Northwest United States. Here we show that increasingly severe droughts and temperature driven reductions in growing season significantly reduces expected annual agricultural productivity. The overall reduction in mean annual productivity also dampens interannual yield variability, limiting yield-driven revenue fluctuations. Our findings show that farmers who adapt to climate change by planting improved crop varieties may potentially increase their expected mean annaul productivity in an altered climate, but remain strongly vulnerable to irrigation water shortages that substantially increase interannual yield variability (i.e., increasing revenue volatility). Our results underscore the importance for crop adaptation strategies to simultaneously capture the biophysical effects of warming as well as the institutional controls on water availability.

Despite the prevalence of climate change assessments of crop yields, there are significant limits to our understanding of how parametric uncertainty in the underlying agro‐hydrologic models as well as the stationarity assumptions tacit to their commonly employed calibration procedures are influencing projections. This study addresses this knowledge gap by clarifying how parametric uncertainty in agro‐hydrologic models influences yield projections under changing future climate. We focus on rain‐fed winter wheat systems in the drylands of United States Pacific Northwest. We use a tightly coupled agro‐hydrologic model, VIC‐CropSyst, as a representative of this class of models. Our contributed diagnostic global sensitivity analysis framework identifies differences in how influential factors (e.g., temperature during early growth stages or the growing degree‐day required to reach peak leaf area index) vary across zones during historical and future periods. Our results show that the dominant parametric controls for yield projection and their sensitivities change subject to agro‐climatic zones and differences in the specific temperature‐precipitation trends in future climate scenarios. Our results also indicate that the stationarity assumptions tacit to using historical observations to calibrate agro‐hydrologic model parameters and their subsequent use in future yield projections may introduce significant bias. Employing the stationarity assumption in future projections problematically ignores how shifts in climate influence the relative dominance of underlying agro‐hydrologic processes in the model. This study's contributed diagnostic exploratory modeling framework has promise for advancing our understanding of how calibration, parametric uncertainties, and climate induced changes in the dominance of model biophysical processes shape crop yield projections.