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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha–1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R2 ≤ 0.08; RMSE, ≥67 kg N ha–1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine-tex-tured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R2 increased ≤0.18, and RMSE was reduced ≤7 kg N ha–1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N were relatively low (R2 ≤ 0.33; RMSE ≥ 68 kg N ha–1) compared with other tools for N fertilizer recommendations.

Original languageEnglish (US)
Pages (from-to)3329-3338
Number of pages10
JournalAgronomy Journal
Volume111
Issue number6
DOIs
StatePublished - Nov 1 2019

Bibliographical note

Funding Information:
This work was supported by funds from DuPont Pioneer. The authors thank the graduate students [Matt Schafer (IA), Curtis Ransom, Gregory Bean (MO), and Christopher Bandura (WI)], supporting scientists [Matt Yost; Dan Barker (IA); Lakesh Sharma, Amitava Chatterjee, and Norm Cattanach (ND); Todd Andraski (WI); and Tim Hart (DuPont Pioneer)], field technicians [Matt Volkmann (MO); Jason Niekamp and Joshua Vonk (IL); Glen Slater (NE); Andrew Scobbie, Thor Sellie, Nicholas Severson, Darby Martin, and Erik Joerres (MN)], and cooperating farmers and research farm personnel for their help in completing this project. Mention of trade names or commercial products in this publication is solely for the purpose of providing information and does not imply recommendation or endorsement by the affiliated Universities or the US Department of Agriculture.

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