Characterizing the impact of climatic and price anomalies on agrosystems in the northwest United States

14 Pags.- 11 Figs.

We present an analysis of the sensitivity of three key crops (alfalfa, barley and winter wheat) produced in the northwestern United States to climatic and agricultural market anomalies using widely used standardized indices. Rather than investigating sensitivity of crop yields (production per unit area), we focus on agricultural production (yield * harvested area) anomalies, which captures both variations in yield and the effect of decision-making factors such as allocation of cropping area. We used two well-known standardized precipitation and reference evapotranspiration (ETo) indices (SPI and EDDI, respectively) and a standardized crop value index in a multivariate linear regression analysis to determine the characteristic timing and time-scales of precipitation and ETo anomalies that best explain annual crop production anomalies. Since climatic and market factors are standardized, regression coefficients are interpreted as a sensitivity measure that captures the relative effect of climatic and agricultural markets on agricultural production. Results show that alfalfa production was most sensitive climatic anomalies while barley and wheat production was more responsive to crop prices. Sensitivity to precipitation anomalies followed gradients in precipitation, temperature, and soil moisture regimes across the study area where drier and warmer climates were associated with increased sensitivity to climatic anomalies. We found that irrigation decoupled alfalfa production from climatic variability, but the effect of irrigation on decoupling barley production was less clear. Winter wheat production was most sensitive to price anomalies, and alfalfa was least sensitive. Omitting agricultural market conditions and other farmer incentives may introduce biases in our understanding of how drought and climate change impact agricultural production.

This work has been supported by the United States Department of Agriculture, USDA-NIFA research grant 2016-67026-25067, and by the NASA EPSCoR program research grant 80NSSC18M0025. P.W. also acknowledges partial support from the NSF EPSCoR cooperative agreement EPS-1101342.

Peer reviewed