Comparing two versions of a non-linear model for simulating leaf number and developmental stages in maize based on air temperature

• Autor(a) principal: Streck,Nereu Augusto
• Data de Publicação: 2009
• Outros Autores: Gabriel,Luana Fernandes, Samboranha,Flavia Kaufmann, Lago,Isabel, Schwantes,Ana Paula, Schons,Alfredo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Ciência Rural
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782009000300003
• topic: crop models; Zea mays; leaf number; phenology

The Wang and Engel (WE) model simulates crop development considering the non-linear response of plant development to temperature. Daily air temperature is the input for the temperature response function [f(T)] in the WE model, and because there are several approaches for computing daily temperatures, there are several ways to calculate the f(T). The objective of this study was to compare two versions of the WE model for simulating leaf number and developmental stages in maize, considering two approaches for imputing daily air temperature (daily mean air temperature and daily minimum/maximum air temperature). A two-year field experiment with the maize variety BRS Missões sown in several sowing dates was conducted in Santa Maria, Rio Grande do Sul State, Brazil, during the 2005-2006 and 2006-2007 growing seasons. The f(T) in the WE model was calculated using daily mean air temperature calculated as the arithmetic average of daily minimum (TN) and maximum (TX) air temperatures (WE Tmean), and calculating an f(T) using TN and an f(T) using TX and then averaging the two f(T)s (WE Tmm). Ligule and tip leaf number, and silking and physiological maturity developmental stages measured in the 2005-2006 growing season were used to estimate model coefficients and the ones measured in the 2006-2007 growing season were used as independent data sets to evaluate models. Predictions of ligule and tip leaf number, silking and physiological maturity of the maize variety BRS Missões were better with the WE Tmm model than with the WE Tmean model.