Date of Award
Doctor of Philosophy
From a general vantage point, mechanized agriculture cropping system fields such as those producing maize, wheat, and soybean appear to homogenous in terms of yield and economic return. However, the availability of precision agriculture data has revealed subfield variability in yield and economic performance as well as environmental impact. While subfield spatial and temporal variability of yields is known to exist and can be characterized using yield monitor or remote sensing technology, how to best use these data sources to better improve both economic and environmental performance remains challenging. The following dissertation describes the integration of environmental and economic modeling tools with precision agriculture data and public databases to identify subfield areas where the adoption of more sustainable practices is both environmentally impactful as well as cost-effective.
Chapter 1 of the dissertation describes the development of a precision agro-economic and environmental performance tool. To assess the tool performance, modeled data was compared with empirical NO3--N leaching data obtained from a long-term experiment. Results of the comparison showed the model captured spatial variability of NO3--N leaching at the subfield spatial scale with an average RMSE of 21.5 kg ha-1 and an r2 of 0.19. A case study analysis of a cropping system field using the modeling framework revealed estimated NO3--N leaching and ROI were correlated, and high priority zones with low ROI and high NO3- leaching were found to represent approximately 6% of the total field area.
Chapter 2 focuses on the application of the precision agro-economic and environmental modeling framework described in Chapter 1. Analysis of 15 fields showed a significant correlation between N-loss and economic return indicating a majority of fields contain areas susceptible to limited ROI and high NO3- leaching and/or N2O emissions. Simulating the targeted integration of switchgrass in these areas was estimated to reduce field-scale NO3--N leaching by up to 21.1% and , however the economic impacts were dependent on potential biomass prices which were predicted to approximately $93 t-1 yr-1 in order to reach relative break-even compared with maize and soybean cropping.
Chapter 3 describes the novel use of the ApSIM agriculture system simulator and public data sources as a tool for estimating economically optimum seeding and N-fertilizer application rates at field to subfield scales. Maximum crop productivity typically corresponded with maximum seeding and N-fertilizer rates, however maximum ROI often corresponded with reduced input resources, particularly seeding density. Modeled crop production loss between maximum yield and maximum ROI seeding and N-management scenarios ranged from 313.2 to 538.7 kg ha-1 and corresponded with an ROI increases ranging from 5.5 to 11.0%. Results indicated yield-oriented seeding and N-fertilizer recommendations decrease potential ROI.
Gabriel Sean McNunn
McNunn, Gabriel Sean, "Integrating environmental models and precision agriculture data to identify spatially explicit subfield opportunities for increased sustainability and economic return" (2018). Graduate Theses and Dissertations. 16855.