Interdisciplinary Process-Based Approach To Identify Nitrogen Limitations and Increase Soybean Yield and Meal Protein

This is a proposal to fund the continuation of this project, to finish processing data from 2024 trials, analyze data, and summarize findings. The overall goal of this proposal is to deliver management recommendations and novel tools that increase productivity, while also ensuring the sustainability of U.S. soybean production, and high quality seed and meal. Our previous research found an opportunity to enhance both soybean productivity and seed composition with management practices that increase N availability during seed growth. We also demonstrated that aerial imaging technology can detect differences in the plant N status in this legume crop. This new proposal aims to quantify an economically optimum N fertilizer input applied during seed growth that increases soybean productivity and seed protein concentration, as well as the environmental implications of this practice (Objective 1). We collected aerial imaging data using well-fertilized reference checks to quantify the potential of aerial imaging to inform nutrient management decisions in soybean. Ideally, nitrogen fertilizer applications to soybean can be avoided through well-informed crop and soil management practices at the systems level that maintain soil health. To evaluate if soybean N limitations may be addressed with sustainable management practices that improve soil health, we also collected seed composition and yield data from ongoing long-term rotation trials in the U.S. (Objective 2). There is not a single management recommendation that fits all. Novel tools that integrate aerial imaging combined with process-based crop modeling offer a tremendous opportunity to assess nutrient limitations, as well as provide early estimates of soybean yield and seed protein concentration. We will utilize data collected from Objective 1 and 2 to develop and evaluate an interdisciplinary and process-based approach that integrates aerial imaging with dynamic crop modeling (Objective 3), which would provide real-time accurate predictions of soybean yield and seed composition and identify crop N limitations. Thanks to the crop modeling component in this approach, we can also quantify environmental impact through water, carbon, and nitrogen use and losses.