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Adjusting corn nitrogen management by including a mineralizable‐nitrogen test with the preplant and presidedress nitrate tests.

Authors :
Clark, Jason D.
Fernández, Fabián G.
Veum, Kristen S.
Camberato, James J.
Carter, Paul R.
Ferguson, Richard B.
Franzen, David W.
Kaiser, Daniel E.
Kitchen, Newell R.
Laboski, Carrie A. M.
Nafziger, Emerson D.
Rosen, Carl J.
Sawyer, John E.
Shanahan, John F.
Source :
Agronomy Journal; Jul/Aug2020, Vol. 112 Issue 4, p3050-3064, 15p
Publication Year :
2020

Abstract

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea mays L.) N fertilization predictions. Forty‐nine corn N response experiments (mostly corn following soybean [Glycine max (L.) Merr.]) were conducted in the U.S. Midwest from 2014–2016 to evaluate the ability of the PPNT and PSNT to predict corn relative yield (RY) and N fertilizer over‐ and under‐application rates when adjusted by PMN. Before planting and N fertilization, PPNT (0–30, 30–60, and 60–90 cm) and PMN (0–30 cm) samples were obtained. In‐season soil samples were obtained at the V5 development stage for PSNT (0–30, 30–60 cm) in all N rate treatments and PMN (0–30 cm) in only the 0 and 180 kg N ha−1 preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2 increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (preplant only) with PPNT or PSNT improved RY predictability minimally (R2 increase up to 0.10) only for coarse‐ and medium‐textured soils, but N fertilizer over‐ and under‐application frequencies were not substantially reduced (≤12%). These marginal improvements in RY predictability and N fertilizer over‐ and under‐application frequencies, regardless of the variables used (e.g., fertilization, sampling depth, soil texture, and growing degree‐day categories), demonstrate that including PMN with soil NO3–N alone does not improve corn N fertilization need predictions enough to recommend their use. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00021962
Volume :
112
Issue :
4
Database :
Complementary Index
Journal :
Agronomy Journal
Publication Type :
Academic Journal
Accession number :
144369895
Full Text :
https://doi.org/10.1002/agj2.20228