Parameterization and Evaluation of a Simple Simulation Model (SSM-iCrop2) for Alfalfa (Medicago sativa L.) Growth and Yield in Iran

Crop simulation models are very useful tools for the evaluation of plant growth and development processes. Crop-simulating models may be used to estimate yield and evaluate climatic, plant, and management parameters on yield. Also, it may be used to predict crop water requirements under different conditions. Crop models should be evaluated and parameterized to simulate crop growth and development. Parameterization is used for precise simulation of crop growth and development and can estimate the best and most appropriate values for model parameters obtained via observed data or calibration. The objectives of this study were to describe the SSM-iCrop2 model, determine plant parameters, and evaluate alfalfa (Medicago sativa L.) in its major production regions using the SSM-iCrop2 model in Iran.

SSM-iCrop2 crop simulation model is a simplified form of SSM crop models which is suitable for the simulation of growth, development, and yield of different crops under different environmental conditions and large-scale estimation of crop production, especially in the evaluation of nutrient availability and climatic effects. This model includes sub-models of phenology, leaf expansion and senescence, dry matter production and distribution, and soil water balance. Daily weather data, agronomic management, soil properties, and plant parameters are required for simulation in this model. The present study investigates the performance of the SSM-iCrop2 model regarding the prediction of single cuts and overall cuts, phonologic stages, and water requirement of alfalfa under changing climatic conditions in Iran. To simulate the growth, development and yield of alfalfa using SSM-iCrop2 model in Iran, the major irrigated alfalfa production provinces, including East Azarbaijan, Hamedan, West Azarbaijan, Sistan and Baluchestan, Khorasan Razavi, Esfahan, Kordestan, Ghazvin, Ardabil, Markazi, Fars, Zanjan, Chaharmahal and Bakhtiyari and Tehran were identified based on the data available in Ministry of Agriculture statistics. Then, field experiment data required for model parameterization and estimation were collected from these provinces.

According to the results of the SSM-iCrop2 model parameterization, two cultivars with different leaf area indices (high-yielding and low-yielding) were identified in major alfalfa production provinces. The model was evaluated using independent experimental data that had not been used for parameterization. The evaluation results for alfalfa yield showed that the observed single-cut forage yield ranged from 112 to 640 g.m-2 with an average of 330 g.m-2; the observed total annual forage yield ranged from 646 to 4042 g.m-2 with an average of 1717 g.m-2; and the water requirement of alfalfa obtained from the NETWAT software was between 5140 to 12690 m3 ha-1 with an average of 8746 m3 ha-1. The predicted single-cut forage yield, predicted total annual forage yield, and alfalfa water requirement ranged from 189 to 457 g.m-2 with an average of 351 g.m-2, 693 to 3296 g.m-2 with an average of 1654 g.m-2, and 4093 to 16874 m3 ha-1 with an average of 10940 m3.ha-1, respectively. Overall, in the evaluation of observed versus simulated alfalfa forage yield, 31 points were obtained for single-cut yield with a correlation coefficient (r) of 0.79, root mean square error (RMSE) of 88.3 g.m-2, and coefficient of variation (CV) of 26.78%; and 21 points were obtained for annual yield with an r of 0.90, RMSE of 344.4 g.m-2, and CV of 20.05%. The evaluation results also showed that the observed versus simulated alfalfa water requirement had an r of 0.43, RMSE of 3503 m3 ha-1, and CV of 40%.

The results obtained from parameterization and evaluation of the SSM-iCrop2 model show that the mentioned model presents a logical prediction and accurate estimation of model parameters for yield and water requirement of alfalfa crops in Iran. Thus, this model may be used for the prediction of alfalfa yield under different climates and management conditions.