Simulating maize phenology as a function of air temperature with a linear and a nonlinear model

Abstract

The objective of this study was to adapt a nonlinear model (Wang and Engel - WE) for simulating the phenology of maize (Zea mays L.), and to evaluate this model and a linear one (thermal time), in order to predict developmental stages of a field-grown maize variety. A field experiment, during 2005/2006 and 2006/2007 was conducted in Santa Maria, RS, Brazil, in two growing seasons, with seven sowing dates each. Dates of emergence, silking, and physiological maturity of the maize variety BRS Missões were recorded in six replications in each sowing date. Data collected in 2005/2006 growing season were used to estimate the coefficients of the two models, and data collected in the 2006/2007 growing season were used as independent data set for model evaluations. The nonlinear WE model accurately predicted the date of silking and physiological maturity, and had a lower root mean square error (RMSE) than the linear (thermal time) model. The overall RMSE for silking and physiological maturity was 2.7 and 4.8 days with WE model, and 5.6 and 8.3 days with thermal time model, respectively.

Simulating maize yield in sub‑tropical conditions of southern Brazil using Glam model

“…The occurrence of milky maturity (102 days) and physiological maturity (132 days) was predicted five and one day later, respectively, than the observed. The agreement in timing (days after sowing) between estimated and observed phenological stages can be explained by the high dependence of maize phenology on the degree-days accumulation, as observed by Streck et al (2008). However, leaf area expansion showed a consistent lag between the simulated and observed data, which may be attributed to other factors.…”

Estimating the development of landrace and improved maize cultivars as a function of air temperature

“…Air temperature is the main meteorological variable that drives development in maize (STRECK et al, 2008;. Models based on air temperature can be divided into two groups: linear models, which use the concept of thermal time or degree days, and nonlinear models, such as the Wang and Engel (WE) model (WANG & ENGEL, 1998).…”

“…Models based on air temperature can be divided into two groups: linear models, which use the concept of thermal time or degree days, and nonlinear models, such as the Wang and Engel (WE) model (WANG & ENGEL, 1998). The former ones are simpler from an operational point of view, but they assume a linear relationship between temperature and crop development, which is not entirely realistic (STRECK et al, 2007). In the WE model, the temperature response function [f(T)] is non-linear and described by a beta function varying from zero to one, with three coefficients which have biological meaning (lower base, optimal, and upper base cardinal temperatures) and operational definition (ALBERTO et al, 2009).…”

“…In the WE model, the temperature response function [f(T)] is non-linear and described by a beta function varying from zero to one, with three coefficients which have biological meaning (lower base, optimal, and upper base cardinal temperatures) and operational definition (ALBERTO et al, 2009). The f(T) is zero when temperature is lower base or upper base, and one when temperature is equal to the optimal temperature (STRECK et al, 2008). The f(T) is multiplied by the maximum development rate (r max ) to calculate the daily development rate (STRECK et al, 2008;.…”

“…The f(T) is zero when temperature is lower base or upper base, and one when temperature is equal to the optimal temperature (STRECK et al, 2008). The f(T) is multiplied by the maximum development rate (r max ) to calculate the daily development rate (STRECK et al, 2008;.…”