Benjamin Torabi; Najebullah Ebrahimi; Afshin Soltani; Ebrahim Zeinali
Abstract
The present study was conducted to parameterize the SSM_iCrop model and evaluate the prediction of growth and development of faba bean in Gorgan climate condition. This study was carried out on faba bean cv."Barkat" as split-plot in randomized complete block design with four replications at Gorgan University ...
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The present study was conducted to parameterize the SSM_iCrop model and evaluate the prediction of growth and development of faba bean in Gorgan climate condition. This study was carried out on faba bean cv."Barkat" as split-plot in randomized complete block design with four replications at Gorgan University of Agricultural Sciences and Natural Resources in 2015-2016. The experimental factors consisted of planting date (27 November, 25 December and 31 January) and plant density (5, 15, 25 and 35 plants/m2). The parameters of phonological stages, leaf expansion and senescence, production and distribution of dry matter and water balance were estimated using the present data experiment and other data. The results of model evaluation showed that, it can well predict, days to flowering (RMSE = 3.8 and CV =4.1), days to maturity (RMSE = 11.9 and CV= 8.1), node number on main stem (RMSE = 1.7 and CV = 10.0), leaf area index (RMSE =0.8 CV =28.8), biological yield (RMSE = 158.5 and CV =21.6) and seed yield (RMSE = 118.6 and CV = 24.7). Therefore, the SSM_iCrop model can be used to evaluate the agronomic management and analyze the growth and yield of faba bean in Gorgan conditions.
Najebullah Ebrahimi; Benjamin Torabi; Afshin Soltani; Ebrahim Zenali
Abstract
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 ...
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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 faba 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. LAImax in different densities varied between 2.3 to 5.3, tm between 131.9 and 144.2, and te 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. Wmax in different densities varied between 725.1 and 1484.3 g/m2, tm between 138.3 and 146.4 and te 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.