evaluating non-linear regression models for use in growth analysis of wheat

growth analysis is a valuable method in the quantitative analysis of crop growth, development and crop production. there are many regression models to describe the sigmoid growth patterns. by considering that, the parameters of non-linear regression models have physiological meanings, they are preferable relation to linear regression models. the aim of this study was to collect and evaluate the high visibility non-linear regression models in the growth analysis studies (logistic, gompertz, richards, weibull, truncated expolinear, symetrical expolinear and two kinds of beta model to describe the biomass accumulation, and logistic and beta models to describe the leaf area index variation patterns). an experiment was conducted using 7 wheat cultivars (arya, darya, kuhdasht, shiroudi, tajan, taro and zagros) in 2 conditions, irrigated and rainfed, in randomized complete block design with 4 replications in 2008-2009. all models were fitted to the dry matter and lai data of two cultivars (arya and zagros). results shoed that all of the used models at this study described well the variation pattern of dry matter accumulation and lai by time (day after planting). and these models can be used in the growth analysis studies.