Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region

Fatima A.M. Tenorio, Alison J. Eagle, Eileen L. McLellan, Kenneth G. Cassman, Reka Howard, Frederick E Below, David E. Clay, Jeffrey A. Coulter, Allen B. Geyer, Darin K. Joos, Joseph G. Lauer, Mark A. Licht, Alexander J. Lindsey, Bijesh Maharjan, Cameron M Pittelkow, Peter R. Thomison, Charles S. Wortmann, Victor O. Sadras, Patricio Grassini

Research output: Contribution to journalArticle

Abstract

Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years.

Original languageEnglish (US)
Pages (from-to)185-193
Number of pages9
JournalField Crops Research
Volume240
DOIs
StatePublished - Jul 1 2019

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nitrogen balance
maize
corn
nitrogen
removal
water budget
management practice
air temperature
fertilizer
climate
fertilizer rates

Keywords

  • Grain nitrogen concentration
  • Grain nitrogen removal
  • Maize
  • Nitrogen balance

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Soil Science

Cite this

Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region. / Tenorio, Fatima A.M.; Eagle, Alison J.; McLellan, Eileen L.; Cassman, Kenneth G.; Howard, Reka; Below, Frederick E; Clay, David E.; Coulter, Jeffrey A.; Geyer, Allen B.; Joos, Darin K.; Lauer, Joseph G.; Licht, Mark A.; Lindsey, Alexander J.; Maharjan, Bijesh; Pittelkow, Cameron M; Thomison, Peter R.; Wortmann, Charles S.; Sadras, Victor O.; Grassini, Patricio.

In: Field Crops Research, Vol. 240, 01.07.2019, p. 185-193.

Research output: Contribution to journalArticle

Tenorio, FAM, Eagle, AJ, McLellan, EL, Cassman, KG, Howard, R, Below, FE, Clay, DE, Coulter, JA, Geyer, AB, Joos, DK, Lauer, JG, Licht, MA, Lindsey, AJ, Maharjan, B, Pittelkow, CM, Thomison, PR, Wortmann, CS, Sadras, VO & Grassini, P 2019, 'Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region', Field Crops Research, vol. 240, pp. 185-193. https://doi.org/10.1016/j.fcr.2018.10.017
Tenorio, Fatima A.M. ; Eagle, Alison J. ; McLellan, Eileen L. ; Cassman, Kenneth G. ; Howard, Reka ; Below, Frederick E ; Clay, David E. ; Coulter, Jeffrey A. ; Geyer, Allen B. ; Joos, Darin K. ; Lauer, Joseph G. ; Licht, Mark A. ; Lindsey, Alexander J. ; Maharjan, Bijesh ; Pittelkow, Cameron M ; Thomison, Peter R. ; Wortmann, Charles S. ; Sadras, Victor O. ; Grassini, Patricio. / Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region. In: Field Crops Research. 2019 ; Vol. 240. pp. 185-193.
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AU - Cassman, Kenneth G.

AU - Howard, Reka

AU - Below, Frederick E

AU - Clay, David E.

AU - Coulter, Jeffrey A.

AU - Geyer, Allen B.

AU - Joos, Darin K.

AU - Lauer, Joseph G.

AU - Licht, Mark A.

AU - Lindsey, Alexander J.

AU - Maharjan, Bijesh

AU - Pittelkow, Cameron M

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AU - Sadras, Victor O.

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N2 - Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years.

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