Proceedings
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| Filter results5 paper(s) found. |
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1. Within-field Spatial Variability in Optimal Sulfur Rates for Corn in Minnesota: Implications for Precision Sulfur ManagementThe ongoing decline in sulfur (S) atmospheric depositions and high yield crop production have resulted in S deficiency and the need for S fertilizer applications in corn cropping systems. Many farmers are applying S fertilizers uniformly across their fields. Little has been reported on the within-field spatial variability in optimal S rates and the potential benefits of variable rate S applications. The objectives of this study were to 1) assess within-field variability of optimal S rates (OSR),... R.P. Negrini, Y. Miao, K. Mizuta, K. Stueve, D. Kaiser, J.A. Coulter |
2. Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial DataOn-farm trials are being conducted by more and more researchers and farmers. On-farm trials are very different to traditional small plot experiments due to the existence of significant within-field variability in soil-landscape conditions. Traditional statistical techniques like analysis of variance (ANOVA) are commonly adopted for on-farm trial analysis to evaluate overall performance of different treatments, assuming uniform environmental and management factors within a field. As a result, the... K. Mizuta, Y. Miao, J. Lu, R.P. Negrini |
3. Optimizing Chloride (Cl) Application for Enhanced Agricultural YieldThe optimization of chloride (Cl-) application rates is crucial for enhancing crop yields and reducing environmental impact in agricultural systems. This study investigates the relationship between chloride application rates and wheat yields, focusing on Club wheat cultivation in a 19.76-hectare field in Washington State. The target yield was set at 3765 kilograms per hectare, with seeding conducted at 67.24 kilograms per hectare using conservation tillage practices. Potassium chloride... F. Pereira De Souza, R.P. Negrini, H. Tao |
4. Evaluating the Potential Benefits of Variable-rate Sulfur Management in Minnesota Corn Using Machine-learning AnalysisSulfur (S) management in corn is complicated by strong within-field variability in crop response, driven by interactions among soil properties, landscape position, and prior management. As a result, uniform S applications can create unnecessary input costs in nonresponsive areas while undersupplying responsive zones. We developed and demonstrated a practical machine-learning (ML) workflow to estimate within-field agronomic optimum sulfur rate (AOSR) and economic optimum sulfur rate (EOSR) on commercial... R.P. Negrini, Y. Miao |
5. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AIAccurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes |