Allometric models to predict plant biomass in maize: a comprehensive analysis using data from published field studies

ROTILI, DIEGO HERNÁN; TOGNETTI, PM; MADDONNI, GUSTAVO ÁNGEL

Simposio; Predictive Agriculture: turning Data into Decisions; 2020

Kansas State University y Corteva Agriscience

modelling. Since Andrade et al. (1999), allometric models have allowed to predict vegetative (stem + leaves + tassel) and reproductive (ear) biomass of individual maize plants. While linear models have been locally adjusted, cross-experimental estimation of fixed predictors and random effects originating from genotype (G), environment (E) and their interaction (GxE) are lacking. Using a comprehensive experimental maize database (21 site-x-year environments and 231 genotypes from 20 published papers), we estimated the fixed allometric predictors and random G, E and GxE effects for vegetative and reproductive biomass. We run linear and nonlinear mixed-effects models, with 70/30 estimation/validation proportion. For vegetative biomass, the best model included stem volume as a fixed-effect predictor, with a quadratic term (R2marg./cond.=0.84/0.96; NRMSEmarg./cond.=0.33/0.21; n = 6630) which reduced slope variance by 34% (E), 12% (G) and 26% (GxE) compared to a linear model. In the quadratic model, E (74%), more than G (15%) or GxE (11%) explained slope variance. For reproductive biomass, the best model was an exponential regression with maximum ear diameter as a fixed effect (R2marg./cond. = 0.77/0.86; NRMSEmarg./cond. = 0.44/0.30; n = 2894). Using data?s central 80% to exclude extreme values, models did not improve with N-addition or hybrid/inbred information (𝚫AIC