The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States 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 (2009–2019), 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 = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, 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 LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.
Bibliographical noteFunding Information:
This work was sponsored by NSF (#1830478, #1842097), USDA Hatch project (IOW10480), the Iowa State University Plant Sciences Institute, Stine Seed, and Pioneer Crop Management research award.
PS is a co-lead of the Genomes to Fields Initiative and PI of the USDA-NIFA funded Agricultural Genome to Phenome Initiative. He is co-founder of Data2Bio, LLC; Dryland Genetics, Inc.; EnGeniousAg, LLC; and LookAhead Breeding, LLC. He is a member of the scientific advisory board and a shareholder of Hi-Fidelity Genetics, Inc., and a member of the scientific advisory boards of Kemin Industries and Centro de Tecnologia Canavieira. He is a recipient of research funding from Iowa Corn and Bayer Crop Science.
Copyright © 2022 dos Santos, Abendroth, Coulter, Nafziger, Suyker, Yu, Schnable and Archontoulis.
- crop models
- leaf appearance rate
PubMed: MeSH publication types
- Journal Article