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| Filter results6 paper(s) found. |
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1. Where to Put Treatments for On-farm ExperimentationOn-farm experimentation has become more and more popular due to advancements in technology. These experiments are not as costly as before, as current machinery can allocate different levels of treatment to specific plots. The main goal of this kind of experiment is to obtain a site-specific nutrient level. The yield behavior is different based on the researcher’s treatment. One unanswered question for on-farm experimentation is how the treatments should be allocated in the first place such... D. Poursina, W. Brorsen |
2. Analytical and Technological Advancements for Soybean Quality Mapping and Economic DifferentiationIn the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategies. Recent... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga |
3. Soybean Variable Rate Planting Simulator Using Economic ScenariosSoybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implementation.... B. Mcarthor , A. Prestholt, P. Kyveryga |
4. Using Informative Bayesian Priors and On-farm Experimentation to Predict Optimal Site-specific Nitrogen RatesMost U.S. Corn Belt states now recommend the Maximum Return to Nitrogen (MRTN) method for determining optimal nitrogen rates, which is based on 15 years of on-farm yield response to nitrogen trials. The MRTN method recommends a uniform rate for a region of a state. This study combines Illinois MRTN data, Bayesian methods, and on-farm experimentation from the Data Intensive Farm Management (DIFM) project to provide site-specific nitrogen recommendations. On-farm trials are now being used to provide... W. Brorsen, D. Poursina, C. Patterson, T. Mieno, B. Edge, E.D. Nafziger |
5. Spatial Predictive Modeling to Quantify Soybean Seed Quality Using Remote Sensing and Machine LearningIn recent years, the advancement of artificial intelligence technologies combined with satellite technology is revolutionized agriculture through the development of algorithms that help producers become more sustainable. This could improve the conditions of farmers not only by maximizing their production and minimizing environmental impact but also due to better economic benefits by allowing them to access high-value-added markets. Furthermore, the use of predictive tools that could improve the... C. Hernandez, P. Kyveryga, A. Correndo, A. Prestholt, I. Ciampitti |
6. Evaluation of Peanut Response to Soil Water Levels Using the Crop Water Stress Index Generated from Infrared Thermal Sensors and ImageryIn precision agriculture, precise monitoring of crop water stress is crucial for optimizing water use, increasing crop yield, and promoting environmental sustainability. Achieving high water use efficiency in peanut production is key to producing high-quality crop. This study investigates the efficiency of infrared thermal sensors and thermal imagery from satellites and unmanned aerial vehicles (UAVs) for determining peanut crop water stress index (CWSI). Furthermore, this research explores the... B. Parbi, B.V. Ortiz, E. Abban-baidoo , A. Sanz-saez, J.S. Velasco |