University of Tehran perss Journal of Crops Improvement 2008-8337 20 1 2018 05 22 Prediction of dry matter production and leaf area index in faba bean under different planting dates and densities Prediction of dry matter production and leaf area index in faba bean under different planting dates and densities 283 298 66209 10.22059/jci.2018.237540.1791 FA Najebullah Ebrahimi Agronomy, Gorgan University of Agricultural Sciences and Natural Resource , Gorgan, Iran Benjamin Torabi null 0000-0002-9788-3828 Afshin Soltani null Ebrahim Zenali null Journal Article 2017 07 11 <strong>To analyze the growth, it is necessary to access to accurate and well-arranged data obtained from measuring leaf area and dry matter accumulation. The purpose of this study was to evaluate different nonlinear regression models to study the trend of changes in leaf area index and dry matter production and to estimate the parameters related to the growth analysis. The experiment was conducted on </strong>faba<strong> bean "cv. Barkat" in a split-plot experiment based on randomized complete block design with three planting dates and four densities in four replications. In this study, the beta and logistic models were fitted to the leaf surface data and the beta, Gompertz and logistic models to dry matter production. AICc benchmark showed that the beta model was fitted to the leaf surface data the better than the logistic model. LAI_max in different densities varied between 2.3 to 5.3, </strong>t<strong>_m between 131.9 and 144.2, and t_e between 158.7 and 163.5 days after planting. AICc benchmark showed that the beta model was fitted to the dry matter accumulation data the better than the Gompertz and logistic models. W_max in different densities varied between 725.1 and 1484.3 g/m², </strong>t<strong>_m between 138.3 and 146.4 and t_e between 162.60 and 179.0 days after planting. Grain yield varied from 231 to 2744 g/m², and with increasing density in each planting date, grain yield showed the increased trend. The results showed that yield changes were directly affected by maximum leaf area index, maximum dry matter accumulation and crop growth rate.</strong> <strong>To analyze the growth, it is necessary to access to accurate and well-arranged data obtained from measuring leaf area and dry matter accumulation. The purpose of this study was to evaluate different nonlinear regression models to study the trend of changes in leaf area index and dry matter production and to estimate the parameters related to the growth analysis. The experiment was conducted on </strong>faba<strong> bean "cv. Barkat" in a split-plot experiment based on randomized complete block design with three planting dates and four densities in four replications. In this study, the beta and logistic models were fitted to the leaf surface data and the beta, Gompertz and logistic models to dry matter production. AICc benchmark showed that the beta model was fitted to the leaf surface data the better than the logistic model. LAI_max in different densities varied between 2.3 to 5.3, </strong>t<strong>_m between 131.9 and 144.2, and t_e between 158.7 and 163.5 days after planting. AICc benchmark showed that the beta model was fitted to the dry matter accumulation data the better than the Gompertz and logistic models. W_max in different densities varied between 725.1 and 1484.3 g/m², </strong>t<strong>_m between 138.3 and 146.4 and t_e between 162.60 and 179.0 days after planting. Grain yield varied from 231 to 2744 g/m², and with increasing density in each planting date, grain yield showed the increased trend. The results showed that yield changes were directly affected by maximum leaf area index, maximum dry matter accumulation and crop growth rate.</strong> https://jci.ut.ac.ir/article_66209_41afcf0f4146cebdceed1088402852d7.pdf