Document Type : Research Article
Authors
Department of Plant Production and Genetics, Razi University, Kermanshah, Iran
Abstract
Introduction
Lentil is the third most important legume in the cold-season legume family. Legumes, as the second most important food source after cereals, account for about 613000 hectares of the annual cultivated area of crops in Iran. In terms of cultivated area, lentil is in third place among legumes after chickpeas and beans, and the provinces of Hamedan, Lorestan, Kerman, Fars and Bushehr are the main centers of production of this plant. Crop growth simulation models can play an important role in allowing farmers and planners to make decisions about the feasibility of systems (crops and technologies). These useful tools greatly facilitate the optimization of crop production and management strategies. Crop growth models also provide a useful tool in researches to simulate potential growth and yield. The aim of this study was to calibrate and validate the SSM-iCrop2 model for simulation of growth and yield of lentil in order to investigate the effect of sowing date and different irrigation under Kermanshah and Azna regions using the sub models associated with phenology, dry matter production and distribution and the changes in leaf area.
Materials and Methods
This study was conducted at Agricultural and Natural Resources, Razi University and in a leading farmer's farm in the Azna region. The SSM-iCrop2 model was used. This model has been tested and proved for a wide range of crops. This model requires limited and easily available input information. In order to collect the necessary information for model calibration and evaluation, experiments were conducted using a split factorial design with three replications in 2022 and 2023. In this way, one experiment was conducted to extract the parameters required for model calibration under the Kermanshah region in 2022, and then two other experiments were conducted to extract the information required to model validation under the Kermanshah and Azna regions in 2023. Treatments included supplementary irrigation (no irrigation, one irrigation at flowering stage, and two irrigations at flowering and seed filling stages) as the main factor, sowing date (February 17, March 21, March 20 for Kermanshah, March 6, 16, and 24 for Azna), and cultivar (Bile Savar, Kimia, and Gachsaran) as secondary factors. Measured data of phenological development stages and grain yield were used to evaluate the model. Model evaluation indices included linear regression fitting between observed and simulated data and their comparison with the slope of the 1:1 line, root mean square error (RMSE), normalized root mean square error (nRMSE), mean error of approximation (MBE), and Wilmot's agreement index (d).
Results and Discussion
The model calibration results indicated appropriate accuracy of the plant parameter values used in the model structure, which led to very accurate prediction of growth and yield characteristics. The evaluation results showed acceptable accuracy of SSM-iCrop2 in simulating the process of changes in developmental stages and grain yield of cultivars in the studied treatments. The RMSE value of the developmental stage from sowing to physiological maturity in Kermanshah for the cultivars Bileh Savar, Kimia and Gachsaran was 2.5, 2.5 and 3.4 days, respectively, and in Azna it was 12.9, 12.5 and 11.8 days, respectively. The RMSE value of grain yield in Kermanshah ranged from 1.4 to 2.15 and from 4 to 7 g m-2 in Azna. Several studies have been conducted on the evaluation of the SSM-iCrop2 model in simulating grain yield in different crops. Among them is the research on soybean by Nehbandani et al. (2015), who reported satisfactory results regarding the model evaluation. They stated that this model can be used to determine the most suitable sowing and harvesting dates, grain yield, and other phenological stages of soybean.
Conclusion
The results indicated that the SSM-iCrop2 model accurately estimated the phenological development and grain yield of lentil cultivars. Despite variations in sowing dates and irrigation regimes, the model successfully simulated lentil growth and yield using a relatively small number of input parameters. Model evaluation demonstrated that SSM-iCrop2 can accurately simulate the developmental stages and grain yield of lentil in the Kermanshah and Azna regions. Based on these findings, the model is suitable for predicting lentil growth and yield and can be applied to studies assessing lentil performance under diverse climatic conditions and management practices.
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