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Full-Text Articles in Agriculture
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 …
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 …
Winter Wheat Grain Yield Response To Fungicide Application Is Influenced By Cultivar And Rainfall, Emmanuel Byamukama, Shaukat Ali, Jonathan Kleinjan, Dalitso N. Yabwalo, Christopher Graham, Melanie Caffe-Treml, Nathan D. Mueller, John Rickertsen, William A. Berzonsky
Winter Wheat Grain Yield Response To Fungicide Application Is Influenced By Cultivar And Rainfall, Emmanuel Byamukama, Shaukat Ali, Jonathan Kleinjan, Dalitso N. Yabwalo, Christopher Graham, Melanie Caffe-Treml, Nathan D. Mueller, John Rickertsen, William A. Berzonsky
Department of Agronomy and Horticulture: Faculty Publications
Winter wheat is susceptible to several fungal pathogens throughout the growing season and foliar fungicide application is one of the strategies used in the management of fungal diseases in winter wheat. However, for fungicides to be profitable, weather conditions conducive to fungal disease development should be present. To determine if winter wheat yield response to fungicide application at the flowering growth stage (Feekes 10.5.1) was related to the growing season precipitation, grain yield from fungicide treated plots was compared to non-treated plots for 19 to 30 hard red winter wheat cultivars planted at 8 site years from 2011 through 2015. …
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 …