Soil, climate, and management impacts on regional wheat productivity in Mexico from remote sensing

Abstract

Understanding sources of variability in net primary productivity is critical for projecting ecosystem responses to global change, as well as for improving management in agricultural systems. However, the processes controlling productivity cannot be fully addressed with field- or global-scale observations. In this study, we performed a regional observational experiment using remote sensing to analyze sources of yield variability in an irrigated wheat system in Northwest Mexico. Four different soil types and 3 years with contrasting weather served as the two main experimental factors, while remotely sensed yields provided thousands of observations within each treatment. Analysis of variance revealed that 6.6 and 4.6% of the variability in yields could be explained by soil type and climate, respectively, with a negligible fraction explained by soil-type–climate interactions. The majority of the variability in yields (88.6%) was observed within treatments and was attributed mainly to variations in management. The impacts of management were observed to depend significantly on both soil type and climate, as revealed by distributions of yields within each treatment. The results indicate that changes in management will have the greatest impact on regional production, and will also play a large role in determining the impact of any changes in climate or soil. This work also demonstrates the use of consistent remote sensing estimates to perform regional studies unfeasible with field-based approaches.