Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.

This study investigates the impact of urban form and landscaping type on the mid-afternoon microclimate in semi-arid Phoenix, Arizona. The goal is to find effective urban form and design strategies to ameliorate temperatures during the summer months. We simulated near-ground air temperatures for typical residential neighborhoods in Phoenix using the three-dimensional microclimate model ENVI-met. The model was validated using weather observations from the North Desert Village (NDV) landscape experiment, located on the Arizona State University's Polytechnic campus. The NDV is an ideal site to determine the model's input parameters, since it is a controlled environment recreating three prevailing residential landscape types in the Phoenix metropolitan area (mesic, oasis, and xeric). After validation, we designed five neighborhoods with different urban forms that represent a realistic cross-section of typical residential neighborhoods in Phoenix. The scenarios follow the Local Climate Zone (LCZ) classification scheme after Stewart and Oke. We then combined the neighborhoods with three landscape designs and, using ENVI-met, simulated microclimate conditions for these neighborhoods for a typical summer day. Results were analyzed in terms of mid-afternoon air temperature distribution and variation, ventilation, surface temperatures, and shading. Findings show that advection is important for the distribution of within-design temperatures and that spatial differences in cooling are strongly related to solar radiation and local shading patterns. In mid-afternoon, dense urban forms can create local cool islands. Our approach suggests that the LCZ concept is useful for planning and design purposes.