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Yield loss due to natural disasters, such as storms with high-speed winds and rainfall, can significantly damage standing corn (Zea mays L.) plants and yield. Using a geospatial approach, the study aimed to estimate green snap wind damage to corn and assess potential yield and economic loss in the Mississippi Delta. Midseason corn (V12–V14) snapping occurred on 8 June 2022. We recorded green snap damage in 13 fields [1.0 to 2.0 hectares (ha−1)] with low (224 kg ha−1) and high (336 kg ha−1) N rates and two different row orientations (north–south and east–west) after the damage. The results indicated no nitrogen rates or row orientation effect on green snap damage. The average yield loss could be ~29.25 kg ha−1, with every 1% increase in green snap wind damage causing significant economic loss to producers. Research methods can help scientists to estimate potential green snap yield loss due to severe winds in the larger fields. Research results can also help estimate potential yield and economic loss to assist producers and other stakeholders in decision-making to prepare for changing weather patterns and unprecedented severe windstorms in the future.

Abstract Cover crops can be effective in minimizing nutrient losses from agricultural fields. The objective of this study was to determine the impact of cover crop (rye, Secale cereale L.) and winter manure application on nutrient loss in simulated rainfall runoff. A split block design study with manure (as vertical block) and cover crops (as horizontal block) was established in 2009. Two rain simulations (the first defined as “dry” and the second “wet”) using sixteen 4 m2 steel frames were conducted in May 2010. The runoff volume collected from each plot was analyzed for nitrate–nitrogen (NO3–N), total suspended solids, total Kjeldahl nitrogen, total phosphorus, and total dissolved phosphorus. In the dry run, the concentration and load of NO3–N were significantly lower (p = 0.05) in runoff with the cover crop than in no‐cover crop treatment. Overall, cover crops reduced nutrient loss in concentration by 6%–48% in the dry and 8%–40% in the wet run than with no‐cover crops. The concentration and load of NO3–N were significantly higher under manure treatments in both “dry” and “wet” runoff runs compared to no‐manure application. Manure application increased nutrient loss in concentration by 6%–58% in the dry and 10%–69% in the wet run than with no‐manure application. This study helps us to understand the complexity of winter manure application with cover crops and potential risks of nutrient loss to surface runoff during spring in the Northern Great plains of the Dakotas.

Abstract Tractor guidance (TG) improve environmental gains relative to nonprecision technologies; however, studies evaluating how tractor operator experience for nonguidance comparisons affect gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0–1, 2–3, 6+ yr) during fertilizer and herbicide applications based on terrain attributes. Tractor paths recorded by global navigation satellite systems were used to create overlap polygons. Results illustrate operator experience level is critical for better efficiency gains estimation (for non‐TG comparisons). Operators with 6+ yr of experience reduced overlap by 7.7 and 20.6% compared with operators with 2–3 and 0–1 yr of experience, respectively. New operators had consistently higher overlap across all slope (1). A low interpersonal reliability value of 0.02–0.03 indicates operator experience is crucial to estimate TG efficiency gains and consistent drivers experience levels are needed when evaluating economic and environmental gains from TG.

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

Abstract Tractor overlaps and gaps during field operations reduce production efficiencies; however, studies evaluating linkages between overlaps and field terrain attributes are limited. Our objective is to explore relationships between tractor guidance (TG) systems and field shape and terrain attributes on tractor path overlap. Fields with varying attributes were used for fertilizer (six fields) and herbicide (four fields) applications (with and without TG). Overlap polygon and terrain attributes were overlaid and sampled per 50‐ × 50‐m grid cell. Overlap increased with increasing field irregularities (6 to 11% field area), shorter pass lengths (2 to 19 m2), greater slope (17 to 53 m2), and roughness index (13 to 38 m2) per grid cell. Averaged across slope classes, overlap area decreased 41 to 17 m2 (58% reduction) per grid cell with TG. These results are important for future design of machinery, sensors, and improved TG efficiencies for automated field operations.

Abstract Tractor guidance (TG) systems presumably improve agricultural production efficiencies by reducing overlaps and gaps during field operations. However, replicated field studies that calculate TG gains are limited but necessary to estimate efficiencies on small farms (82% of U.S. producers). We applied fertilizer with a wheel spreader and herbicides with a boom sprayer on six and four fields, respectively, with and without TG. This study developed novel postprocessing methodology to calculate spatial coverage efficiency from TG systems. Overlap and gap estimation was implemented using the individual pass polygon method (IPPM). We propose a correction factor to adjust biased estimation of overlap area by IPPM. Results from IPPM suggest TG systems reduced overlaps (up to 6% of field area) and gaps (up to 16%) during field operations and improved overall efficiency up to 20%. Consequently, TG systems lead to reduced input use and in‐field operation time, as well as improved equipment efficiency, leading to potential economic and environmental savings for small‐scale producers.

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels −1 (126% greater grazing hours per AU), soil Cr −1 (108%), and a SAGA wetness index of Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.