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Open AccessArticle

Weather During Key Growth Stages Explains Grain Quality and Yield of Maize

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Department of Crop Sciences, University of Illinois at Urbana-Champaign, 1102 S. Goodwin Ave., Urbana, IL 61801, USA
2
Centrec Consulting Group, 3 College Park Ct., Savoy, IL 61874, USA
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(1), 16; https://doi.org/10.3390/agronomy9010016
Received: 14 November 2018 / Revised: 24 December 2018 / Accepted: 25 December 2018 / Published: 2 January 2019
(This article belongs to the Special Issue Environmental and Management Factor Contributions to Maize Yield)
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. View Full-Text
Keywords: maize; grain quality; yield; climate; temperature; precipitation; data mining; principal component analysis; crop models; corn maize; grain quality; yield; climate; temperature; precipitation; data mining; principal component analysis; crop models; corn
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MDPI and ACS Style

Butts-Wilmsmeyer, C.J.; Seebauer, J.R.; Singleton, L.; Below, F.E. Weather During Key Growth Stages Explains Grain Quality and Yield of Maize. Agronomy 2019, 9, 16.

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