Document Type : Research Article
Authors
Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Abstract
Introduction: The nation's substantial corn requirements are currently met through imports. Given the scarcity of arable land and water resources, enhancing yield per unit area is the only viable solution to augment domestic production. Significant progress can be made toward this goal by optimizing production management to narrow the yield gap. Utilizing a reliable simulation model can play a pivotal role in reducing the yield gap and achieving optimal yields through evaluating various management practices. Effective management strategies are essential to optimize maize production and ensure food security. While field experiments can provide valuable insights, they can be resource-intensive, time-consuming, and even impractical due to complex interactions between environmental and management factors. Crop simulation models offer a powerful alternative, enabling the exploration of various scenarios and the identification of optimal management practices. These models simulate plant growth and development in response to climate variables, soil conditions, management inputs, and genetic traits, providing valuable information for decision-making. Accurate parameterization is crucial for reliable crop model predictions. This study aims to parameterize and evaluate the SSM-iCrop model for predicting grain maize yield and nitrogen dynamics in Iran.
Materials and Methods: Simple Simulation Models (SSM), were initially developed for soybean yield prediction in 1986. The model has since been refined to simulate various crops, including maize. SSM-iCrop simulates daily plant growth and development processes, such as phenology, leaf area development, dry matter production, yield formation, and water and nitrogen dynamics. The model has been successfully used in various studies for different plants. Moreover, comparisons of this model with other crop models have shown its effectiveness in simulating yield. The SSM-iCrop model requires input data on weather parameters (minimum and maximum temperature, precipitation, and solar radiation), soil properties, cultivar-specific parameters, and management practices (planting date, plant density, irrigation, and nitrogen fertilization). The SSM-iCrop model was parameterized and calibrated in this study using data from various studies conducted in Iran between 2001 and 2022. The calibration process involved adjusting plant parameters within a reasonable range, as determined by scientific literature, to minimize the difference between simulated and observed data. The parameters that yielded the best fit were selected as the final estimates. Lastly, the model was evaluated using different studies.
Results and Discussion: The SSM-iCrop model accurately simulated key maize growth stages, including emergence, tasseling, silking, and physiological maturity (RMSE = 13.22, CV = 17.4, r = 0.97). The model also accurately predicted leaf area index (RMSE = 0.4, CV = 6.2, r = 0.98), biological yield (RMSE = 431.3, CV = 23.9, r = 0.66), and grain yield (RMSE = 161.9, CV = 16.9, r = 0.81).
Compared to previous studies, such as Zeinali et al. (2016), the current study demonstrated superior performance in grain yield prediction. While Zeinali et al. (2016) did not explicitly simulate nitrogen dynamics, the current study considered nitrogen processes. Manschadi et al. (2021) reported high accuracy in simulating grain maize yield in Austrian conditions. The difference in accuracy between the two studies may be attributed to the quality of the observed data, as accurate parameterization is crucial for model performance. Although this study incorporated nitrogen fertilization treatments and examined the impact of nitrogen-related parameters on yield, dry matter production, and leaf area, specific nitrogen-related traits were not evaluated. This is because existing research has primarily focused on the overall effects of nitrogen fertilization on yield and yield components, often neglecting the measurement of nitrogen content in critical plant tissues such as leaves and grains.
Conclusion: The performance of the SSM-iCrop model for simulating key maize growth stages, leaf area index, and biological and grain yield was suitable. This model is a valuable tool for simulating maize yield and optimizing management practices in Iran. By simulating the impact of different environmental factors and management strategies, the model can help farmers and policymakers make informed decisions to improve maize production and ensure food security.
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