Thesis - Open Access
Master of Science (MS)
Department / School
Agronomy, Horticulture, and Plant Science
4R Nutrient Stewardship Model, Decision Support Systems, Greenhouse Gas Emissons, Machine Learning
Climate Smart Practices are management strategies that focus on increasing soil and crop productivity, utilize site-specific strategies to increase resiliency against the effects of climate change, and mitigate these negative effects by reducing greenhouse gas (GHG) emissions. Decision Support Systems (DSSs) using machine learning (ML) can adjust models based on new information and help farmers make climate smart decisions within their operation. The 4R nutrient management model of right source, rate, location, and time also demonstrates a framework that may be considered climate smart by improving soil and crop productivity. However, when initially conceptualized, the 4R model did not consider GHG emissions. Additionally, the long-term adoption of DSSs has been low in agriculture, reducing the ability of farmers to collect and analyze farm data to the fullest. Therefore, the objective of the first chapter is to examine applications of, and barriers to, DSSs in precision agriculture (PA). The objective of the second chapter evaluates the 4R model to determine the impact of GHG emissions when utilizing near continuous chambers over a two-year period. The GHG emissions were measured by analyzing nitrous oxide and carbon dioxide emissions from a 50/50 split application of 157 kg N/ha that was applied to corn (Zea mays) at pre-emergence and V6 compared to a single application at pre-emergence 157 kg N/ha in a two-year replicated study. Results from the first chapter identify the barriers preventing farmers from using DSSs as well as suggesting solutions to these challenges. Results from the second chapter indicate that the split application can reduce carbon dioxide and carbon equivalent emissions and therefore, may be a useful framework for DSSs to follow in achieving Climate Smart Practices.
South Dakota State University
Brugler, Skye, "Improving The Utility of Precision Agriculture Through Machine Learning and Climate-Smart Practices" (2023). Electronic Theses and Dissertations. 689.