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Physical Sciences and Mathematics Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Predicting Economic Optimal Nitrogen Rate With The Anaerobic Potentially Mineralizable Nitrogen Test, Jason D. Clark, Fabián G. Fernández, Kristen S. Veum, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Daniel E. Kaiser, Newell R. Kitchen, Carrie A. M. Laboski, Emerson D. Nafziger, Carl J. Rosen, John E. Sawyer, John F. Shanahan
Predicting Economic Optimal Nitrogen Rate With The Anaerobic Potentially Mineralizable Nitrogen Test, Jason D. Clark, Fabián G. Fernández, Kristen S. Veum, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Daniel E. Kaiser, Newell R. Kitchen, Carrie A. M. Laboski, Emerson D. Nafziger, Carl J. Rosen, John E. Sawyer, John F. Shanahan
John E. Sawyer
Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha−1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled …
United States Midwest Soil And Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen, Jason D. Clark, Kristen S. Veum, Fabián G. Fernández, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Daniel E. Kaiser, Newell R. Kitchen, Carrie A. M. Laboski, Emerson D. Nafziger, Carl J. Rosen, John E. Sawyer, John F. Shanahan
United States Midwest Soil And Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen, Jason D. Clark, Kristen S. Veum, Fabián G. Fernández, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Daniel E. Kaiser, Newell R. Kitchen, Carrie A. M. Laboski, Emerson D. Nafziger, Carl J. Rosen, John E. Sawyer, John F. Shanahan
John E. Sawyer
Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, …
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 …
Nitrogen Fertilizer Suppresses Mineralization Of Soil Organic Matter In Maize Agroecosystems, Navreet K. Mahal, William R. Osterholz, Fernando E. Miguez, Hanna J. Poffenbarger, John E. Sawyer, Daniel C. Olk, Sotirios V. Archontoulis, Michael J. Castellano
Nitrogen Fertilizer Suppresses Mineralization Of Soil Organic Matter In Maize Agroecosystems, Navreet K. Mahal, William R. Osterholz, Fernando E. Miguez, Hanna J. Poffenbarger, John E. Sawyer, Daniel C. Olk, Sotirios V. Archontoulis, Michael J. Castellano
John E. Sawyer
The possibility that N fertilizer increases soil organic matter (SOM) mineralization and, as a result, reduces SOM stocks has led to a great debate about the long-term sustainability of maize-based agroecosystems as well as the best method to estimate fertilizer N use efficiency (FNUE). Much of this debate is because synthetic N fertilizer can positively or negatively affect SOM mineralization via several direct and indirect pathways. Here, we test a series of hypotheses to determine the direction, magnitude, and mechanism of N fertilizer effect on SOM mineralization and discuss the implications for methods to estimate FNUE.Wemeasured the effect of synthetic …