Understanding the food water nexus: Characterizing the impact of climatological anomalies on agrosystems

Climate variability at global and regional scales is escalating with increased atmospheric carbon and is expected to magnify the intensity and duration of meteorological extremes, especially droughts. From the many environmental stresses that diminish crop production (e.g., soil salinity, frost, soil erosion) drought is one of the most prevalent. This study focuses on the sensitivity of three key crops produced in the northwestern United States to climatological anomalies, while controlling for attribution using anomalies in price. The study differs from similar studies in that we focus on variability in production which captures both yield (tonnes/ha) and cropping area (ha), as opposed to only yield. We use multivariate linear regression to determine the timing and time-scale of precipitation and PET anomalies most correlated with annual crop production anomalies, and develop sensitivity coefficients using Markov chain Monte- Carlo. Counties with similar sensitivity to precipitation, PET, and price were then clustered using k-means analysis. Alfalfa was most sensitive to both precipitation and PET anomalies, with as much as 93% and 81% of the precipitation and PET anomalies translating to the production anomaly. Barley was least sensitive. The timing of precipitation and PET anomalies were generally most important in June- August. The time-scale of precipitation and PET anomaly best correlated to production was variable, but generally greater than similar studies focusing on yield. Sensitivity to precipitation anomalies followed gradients in precipitation, temperature, and soil moisture regimes present across the study area. Our research provides simple models of climate effects on production at the county scale using public data which can be implemented by agricultural producers and decision makers to quantify the impacts of climatological and economic fluctuations on annual crop production