Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt - dos Santos, Caio L.; Abendroth, Lori J.; Coulter, Jeffrey A.; Nafziger, Emerson D.; Suyker, Andy; Yu, Jianming; Schnable, Patrick S.; Archontoulis, Sotirios V. (Frontiers in Plant Science, 2022)
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 (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 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, 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 leaf appearance rate and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the U.S. Corn Belt, as well as new insights to enhance mechanisms in crop models.