Simulation of Phonological Development and Growth Duration in Sorghum (Sorghum bicolor L.) using SSM-Sorghum Model (Case Study: Gorgan County)

Document Type : Scientific - Research


1 Department of Plant Production, Gonbad University, Gonbad, Iran.

2 Department of Agricultural and Horticultural Research, Center for Research and Education of Agriculture and Resources Golestan Province Natural, Agricultural Research, Education and Promotion Organization, Gorgan, Iran

3 Department of Agriculture, Payame Noor University, Tehran, Iran


 Sorghum (Sorghum bicolor L.) belongs to the cereal family and was domesticated at the same time as other cereals around three thousand years ago. Sorghum is the fifth most important cereal in the world after wheat, rice, corn and barley. Crop modeling is one of the branches of agriculture and crop physiology that has been around for 40 years and the development of powerful and efficient computers has played a major role in the development of this field. SSM model is a simple simulator to estimate the yield of different crops and the effects of climate change. This model requires limited input and easily accessible information. The above model has been evaluated for more than 30 Iranian crop species using various evaluation parameters. Evaluation of SSM_iSorghum model to predict different stages of phenology and yield of grain sorghum under temperate humid climate conditions in Gorgan.
Materials and methods
SSM-Sorghum is a model to simulate sorghum crop phenology, growth and yield formation. The model was derived from SSM-Wheat model described by Soltani and Sinclair (2013). The sorghum model structure is the same as that wheat model except for phenology submodel. Phenology submodel is based on Alagarswamy and Ritchie (1990) with some modifications. The growth and development of sorghum were simulated using the SSM_iSorghum model based on meteorological data of Gorgan city during the years 2010 to 2011, including minimum and maximum temperature, amount of sunlight and rainfall on Kimia cultivar using different scenarios. For model input parameters including meteorological data, soil properties, crop management (using the values obtained from field experiments of 2010-2011 in the research farm of Gorgan Agricultural Research Station) was used. These experiments were performed under favorable agronomic conditions. Experiments at four different densities (17, 21, 28 and 35 plants/m2) and five different planting dates (May 10th and May 15th of 2010, May 10th and May 15th and June 15th 2011) during two growing years in total with 20 different scenarios were performed.
Results and Discussion 
The results showed that model predicted day from planting to emergence, day from planting to ripening, harvest index, biological yield and grain yield with high accuracy (R2=0.91-0.94%). In using the models to predict the performance, it has been reported that the value of the explanation coefficient should be more than 60%, which is a condition in this model. Regarding the 1:1 line between the observed yield and the simulated yield, it was found that values are in the range of 15% up and down the 1: 1 line. So the model has a suitable simulation of Gorgan conditions for grain yield.
Evaluation of SSM-Sorghum model showed that this model simulates phenological stages, including harvest index, leaf area index, and biological yield with different planting dates and different densities with appropriate accuracy in Gorgan climatic conditions. Most of the points were in the reliable range (± 15%) of the 1:1 line, which indicates an accurate estimate of the model parameters or confirms the simulation efficiency of the model process steps. Therefore, this model can be used in farm management planning including selection of appropriate planting date and appropriate density. Although the model well simulates the phenology , it seems necessary to retest the accuracy of the model with data from various experiments and to use it in the equations if the results of this study are confirmed. Obviously, models are effective when used by analyzing physiological and ecological conditions and based on experiments and experimental measurements of the system.


Main Subjects

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Volume 14, Issue 4 - Serial Number 54
December 2023
Pages 713-729
  • Receive Date: 19 November 2020
  • Revise Date: 06 August 2021
  • Accept Date: 16 August 2021
  • First Publish Date: 16 August 2021