PhD Defense: Alexis Hoffman
Department of Meteorology, Penn State
Advisor: Chris E Forest
ABSTRACT: Food security and agriculture productivity assessments require a strong understanding of how climate and other drivers influence regional crop yields. While the effects of temperature, precipitation, and carbon dioxide are relatively well-understood, the effect of dust on crop yields has yet to be thoroughly investigated. We consider two issues that warrant this line of inquiry. Many areas of the world with frequent dust storms and high dust loadings are often food insecure, as well as the prevalence of wind erosion in the High Plains of the United States. Existing research suggests that the effect of dust on yields should be largely negative, but until now this has not been investigated on a regional scale. A major hindrance to understanding the effect of dust on crop yields is insufficient data and inadequate methods of analysis. In this dissertation, we develop data and analysis methods to determine whether dust affects regional crop yields. In the first project, we validate the use of Random Forest (RF), a machine learning technique, as a diagnostic crop model that can be used to assess the yield impact of individual climate predictors. Because we motivate this research by food security issues, this first project analyzed climate signals in the crop yield record of sub-Saharan Africa from 1962-2014. From this work, we determined that RF could function as a statistical crop model, but the data quality and resolution would inhibit the ability to detect the effect of dust on yields in this area of the world. As a result, we shifted the focus to the central region of the United States for its high quality and high resolution data, as well as its importance as a crop-producing region of the world. Because these data are higher quality, we can explore individual components of the growing season. We develop crop-specific algorithms to compute the planting date, establishment phase, critical window, and grain filling phase to investigate yield responses to phase-specific climate predictors. Using these data, the RF is able to identify nuanced phase-specific responses for important predictors and important climate predictors. Finally, we compute dust metrics from three different data sources and merge them with climate and yield data in the central region of the United States to determine the impact of dust on yields. Over the entire central US region, we find that including dust as a predictor in each crop model does not affect yields in any detectable manner. However, when crop models are broken down by state, we find several instances in which dust weakly reduces yields. Although these state-specific results are encouraging, we present them cautiously because we cannot determine whether these yield responses are an artifact of partitioning the data or a true yield response that is obscured when data is spatially aggregated. Although the results of this dissertation were largely inconclusive, we have made significant advances in statistical crop modeling, developed data sets that can be used to move the science forward, and revealed new questions that merit further research.