Abstract
Maize (Zea mays L.) grain yield and compositional quality are interrelated and are highly influenced by environmental factors such as temperature, total precipitation, and soil water storage. Our aim was to develop a regression model to account for this relationship among grain yield and compositional quality traits across a large geographical region. Three key growth periods were used to develop algorithms based on the week of emergence, the week of 50% silking, and the week of maturity that enabled collection and modeling of the effect of weather and climatic variables across the major maize growing region of the United States. Principal component analysis (PCA), stepwise linear regression models, and hierarchical clustering analyses were used to evaluate the multivariate relationship between weather, grain quality, and yield. Two PCAs were found that could identify superior grain compositional quality as a result of ideal environmental factors as opposed to low-yielding conditions. Above-average grain protein and oil levels were favored by less nitrogen leaching during early vegetative growth and higher temperatures at flowering, while greater oil than protein concentrations resulted from lower temperatures during flowering and grain fill. Water availability during flowering and grain fill was highly explanatory of grain yield and compositional quality.
| Original language | English (US) |
|---|---|
| Article number | 16 |
| Journal | Agronomy |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2 2019 |
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Keywords
- Climate
- Corn
- Crop models
- Data mining
- Grain quality
- Maize
- Precipitation
- Principal component analysis
- Temperature
- Yield
ASJC Scopus subject areas
- Agronomy and Crop Science
Cite this
Weather during key growth stages explains grain quality and yield of maize. / Butts-Wilmsmeyer, Carrie J.; Seebauer, Juliann R.; Singleton, Lee; Below, Frederick E.
In: Agronomy, Vol. 9, No. 1, 16, 02.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Weather during key growth stages explains grain quality and yield of maize
AU - Butts-Wilmsmeyer, Carrie J.
AU - Seebauer, Juliann R.
AU - Singleton, Lee
AU - Below, Frederick E.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - Maize (Zea mays L.) grain yield and compositional quality are interrelated and are highly influenced by environmental factors such as temperature, total precipitation, and soil water storage. Our aim was to develop a regression model to account for this relationship among grain yield and compositional quality traits across a large geographical region. Three key growth periods were used to develop algorithms based on the week of emergence, the week of 50% silking, and the week of maturity that enabled collection and modeling of the effect of weather and climatic variables across the major maize growing region of the United States. Principal component analysis (PCA), stepwise linear regression models, and hierarchical clustering analyses were used to evaluate the multivariate relationship between weather, grain quality, and yield. Two PCAs were found that could identify superior grain compositional quality as a result of ideal environmental factors as opposed to low-yielding conditions. Above-average grain protein and oil levels were favored by less nitrogen leaching during early vegetative growth and higher temperatures at flowering, while greater oil than protein concentrations resulted from lower temperatures during flowering and grain fill. Water availability during flowering and grain fill was highly explanatory of grain yield and compositional quality.
AB - Maize (Zea mays L.) grain yield and compositional quality are interrelated and are highly influenced by environmental factors such as temperature, total precipitation, and soil water storage. Our aim was to develop a regression model to account for this relationship among grain yield and compositional quality traits across a large geographical region. Three key growth periods were used to develop algorithms based on the week of emergence, the week of 50% silking, and the week of maturity that enabled collection and modeling of the effect of weather and climatic variables across the major maize growing region of the United States. Principal component analysis (PCA), stepwise linear regression models, and hierarchical clustering analyses were used to evaluate the multivariate relationship between weather, grain quality, and yield. Two PCAs were found that could identify superior grain compositional quality as a result of ideal environmental factors as opposed to low-yielding conditions. Above-average grain protein and oil levels were favored by less nitrogen leaching during early vegetative growth and higher temperatures at flowering, while greater oil than protein concentrations resulted from lower temperatures during flowering and grain fill. Water availability during flowering and grain fill was highly explanatory of grain yield and compositional quality.
KW - Climate
KW - Corn
KW - Crop models
KW - Data mining
KW - Grain quality
KW - Maize
KW - Precipitation
KW - Principal component analysis
KW - Temperature
KW - Yield
UR - http://www.scopus.com/inward/record.url?scp=85059949429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059949429&partnerID=8YFLogxK
U2 - 10.3390/agronomy9010016
DO - 10.3390/agronomy9010016
M3 - Article
AN - SCOPUS:85059949429
VL - 9
JO - Agronomy
JF - Agronomy
SN - 2073-4395
IS - 1
M1 - 16
ER -