Essays in yield modeling and basis price forecasting

Abstract

Concerns over the sustainability of small-holder commercial agriculture in the face of extended periods of extreme weather have increased across much of the country in the past two decades. A growing body of research has linked interannual variation in growing season weather to regional and national yields and prices, however relatively less work exists delineating the impacts of weather on disaggregated farm-level yield and elevator-level price outcomes. Here I make use of fine-scaled weather datasets tied to historic farm yields and elevator harvest basis levels to explicitly model these weather impacts using panel fixed-effects regressions. Results suggest that sorghum yields are quite sensitive to warming temperatures - as moderate increases of 2°C in growing season temperatures lead to an average 24% reduction in yields – thereby raising doubts about its potential for offsetting climate change impacts relative to other crops. We also consider whether warming impacts can be lessened through adjustment of the growing season (i.e. shifting up, shortening, or extending the growing season) and find very little support for this form of adaptation. Inclusion of soil moisture data into base price forecasts indicate that improvement in forecasts can be made by inclusion of within-season weather data – as inclusion of weekly cubic precipitation improves naïve harvest basis forecasts for Kansas grain elevators by 14%. This result improves to upward of 36% when the same model is applied to the other states in the Southern Great Plains.