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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #373144

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Soil-nitrogen, potentially mineralizable-nitrogen, and field condition information marginally improves corn nitrogen management

item CLARK, JASON - South Dakota State University
item FERNANDEZ, FABIAN - University Of Minnesota
item Veum, Kristen
item CAMBERATO, JAMES - Purdue University
item CARTER, PAUL - Dupont Pioneer Hi-Bred
item FERGUSON, RICHARD - University Of Nebraska
item FRANZEN, DAVID - North Dakota State University
item KAISER, DANIEL - University Of Minnesota
item Kitchen, Newell
item LABOSKI, CARRIE - University Of Wisconsin
item NAFZIGER, EMERSON - University Of Illinois
item ROSEN, CARL - University Of Minnesota
item SAWYER, JOHN - Iowa State University
item SHANAHAN, JOHN - Fortigen

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/18/2020
Publication Date: 6/28/2020
Citation: Clark, J.D., Fernandez, F.G., Veum, K.S., Camberato, J.J., Carter, P., Ferguson, R.B., Franzen, D.W., Kaiser, D.E., Kitchen, N.R., Laboski, C.A., Nafziger, E.D., Rosen, C.J., Sawyer, J.E., Shanahan, J.F. 2020. Soil-nitrogen, potentially mineralizable-nitrogen, and field condition information marginally improves corn nitrogen management. Agronomy Journal. 112(5):4332–4343.

Interpretive Summary: Improved prediction of the optimal nitrogen fertilizer rate could provide economic and environmental benefits to producers. Laboratory tests that estimate the amount of organic nitrogen provided by the soil to plants during the growing season, such as potentially mineralizable nitrogen (PMN), may help improve fertilizer decisions and nitrogen use efficiency. However, a full understanding of the utility of PMN is still lacking. In this study, soil and weather characteristics, including texture, soil nitrate, and temperature, improved relative yield predictability using PMN data across a range of climate and soil conditions. This study establishes that several factors need to be considered when using PMN tests to help make nitrogen fertilizer decisions or improve relative yield predictability. Ultimately, tests such as PMN may help producers improve crop nitrogen use efficiency, but only when combined with other important soil and weather information.

Technical Abstract: The anaerobic potentially mineralizable N (PMNan) test combined with the pre-plant (PPNT) and pre-sidedress (PSNT) nitrate tests may improve predictions of corn (Zea mays L.) N fertilization needs. This potential improvement has not been evaluated in depth in the US Midwest. Forty-nine corn N response experiments (mostly corn following soybean [Glycine max (L.) Merr.]) were conducted in the US Midwest from 2014-2016 to evaluate the effect of combining PMNan values from different soil sample timings and N fertilizer rates with the PPNT and PSNT values on relative yield (RY) predictability and over- and under-application of N fertilizer failure rates under contrasting soil and weather conditions. Soil was sampled (0-30 cm) for PMNan analysis before pre-plant N application and at the V5 development stage from the 0 and 180 kg-N ha-1 pre-plant N applications. Grain yield was not optimized to estimate the critical soil nitrate content (CSNC) using the PPNT and PSNT alone or combined with PMNan. Including N application rate with PPNT or PSNT improved RY predictability (R2 = 0.49-0.62). Including PMNan (pre-plant only) with both nitrate tests improved RY predictability (R2 = 0.60-0.65) only for coarse- and medium-textured soils. Little improvement was found in estimating N fertilizer needs regardless of the variables used with PMNan in conjunction with PPNT or PSNT (fertilization, sampling depth, and textural and growing degree-day categories) to improve RY predictions and reduce N fertilizer over- and under-application failure rates.