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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #392064

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Maize leaf appearance rates: a synthesis from the United States corn belt

item DOS SANTOS, CAIO - Iowa State University
item Abendroth, Lori
item COULTER, JEFFREY - University Of Minnesota
item NAFZIGER, EMERSON - University Of Illinois
item SUYKER, ANDY - University Of Nebraska
item YU, JIANMING - Iowa State University
item SCHNABLE, PAT - Iowa State University
item ARCHONTOULIS, SOTIRIOS - Iowa State University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2022
Publication Date: 4/5/2022
Citation: Dos Santos, C.L., Abendroth, L.J., Coulter, J.A., Nafziger, E.D., Suyker, A.E., Yu, J., Schnable, P.S., Archontoulis, S.V. 2022. Maize leaf appearance rates: a synthesis from the United States corn belt. Frontiers in Plant Science. 13. Article 872738.

Interpretive Summary: The rate of leaf appearance in corn is a critical parameter used for modeling in-season crop development and making management decisions. Historically, leaf appearance has been predicted by the amount of heat experienced by the plant to that point in the growing season. Although the rate of leaf appearance has been based on a small subset of experiments, these findings have been applied across the U.S. Midwest. This research seeks to determine if the rate of leaf appearance is consistent across the region and the degree to which climatic factors influence it. A decade of data were compiled from studies in four states to evaluate the rate of leaf appearance across hybrids, management practices, and weather. Although the rate of leaf appearance was primarily driven by amount of heat accumulated, the rate differed based on the crop life cycle. Secondary variables in determining this rate of leaf appearance were solar radiation in vegetative stages, length of day (photoperiod) in grain filling stages, and plant growth rate. These secondary factors are important in understanding how to best model crop development across different latitudes in the US Midwest and across different current and future climatic conditions.

Technical Abstract: The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf-1) or leaf appearance rate (leaf °C-day-1). However, such important parameter values for modern maize hybrids are rare. To fill this gap, we sourced and analyzed experimental datasets from the US Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009 to 2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs GDD relationship more accurately than the linear model (R2=0.99 vs 0.95, n=4694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9±7.5°C-day, 30.9±5.7 °C-day, and 9.8±1.2 leaves, having a coefficient of variation of 13, 19, and 12%, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r=0.69), while photoperiod was positively related to days to flowering or total leaf number (r=0.89). Additionally, a positive nonlinear relationship between maize leaf appearance rate and plant growth rate was found. Present findings provide important information for calibration and optimization of maize crop models and new insights for model enhancement.