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Full-Text Articles in Agriculture
Integration Of Cover Crops Into Midwest Corn-Soybean Cropping Systems And Potential For Weed Suppression, Joshua S. Wehrbein
Integration Of Cover Crops Into Midwest Corn-Soybean Cropping Systems And Potential For Weed Suppression, Joshua S. Wehrbein
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
Cover crops have potential to provide benefits to agricultural systems, such as improved soil productivity, nutrient scavenging, weed suppression, and livestock forage. There are several challenges associated with cover crop integration into traditional Midwest corn-soybean cropping systems. One of these challenges is timely establishment in the fall, which is limited by the relatively late harvest of corn and soybean. Cover crop effectiveness is related to the amount of biomass produced, thus maximizing the growth period in the fall is desired. To address this challenge, we evaluated the potential to utilize early-season soybean maturity groups (MGs) to allow for earlier soybean …
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
John E. Sawyer
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …
Development Of A Nitrogen Recommendation Tool For Corn Considering Static And Dynamic Variables, Laila A. Puntel, Agustin Pagani, Sotirios V. Archontoulis
Development Of A Nitrogen Recommendation Tool For Corn Considering Static And Dynamic Variables, Laila A. Puntel, Agustin Pagani, Sotirios V. Archontoulis
Department of Agronomy and Horticulture: Faculty Publications
Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used …
Evaluation Of Stabilized Fertilizer And Crop Canopy Sensors As Next-Generation Nitrogen Management Technologies In Irrigated Corn, Leonardo Mendes Bastos
Evaluation Of Stabilized Fertilizer And Crop Canopy Sensors As Next-Generation Nitrogen Management Technologies In Irrigated Corn, Leonardo Mendes Bastos
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
Nitrogen (N) is often the most limiting nutrient to corn. Once applied to the field, N can be lost through different pathways, which contributes to low N use efficiency (NUE) by plants. Increases in NUE and decreases in N losses can be potentially achieved by using management options that allow a better synchrony between N supply and demand, such as stabilized fertilizers, and spatially-variable sensor-derived in-season N application. Three studies were conducted in order to assess the effects of different stabilized fertilizers and crop canopy sensors on irrigated corn yield. The first study evaluated the effect of urease inhibitor on …
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Department of Agronomy and Horticulture: Faculty Publications
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …