نوع مقاله : کاربردی
نویسندگان
1 دانشجوی دکتری خاک‌شناسی، دانشکدۀ کشاورزی، دانشگاه گیلان، رشت، ایران
2 دانشیار گروه خاک‌شناسی، دانشکدۀ کشاورزی، دانشگاه گیلان، رشت، ایران
3 دانشیار گروه مهندسی آب، دانشکدۀ کشاورزی، دانشگاه گیلان، رشت، ایران
4 دانشیار، موسسه تحقیقات آب و خاک، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران
کلیدواژه‌ها
عنوان مقاله English
نویسندگان English
Introduction: Food security relies on access to safe, affordable food and meeting nutritional needs. Rice is a staple crop, feeding over a third of the global population. Simulation models are essential tools for optimizing agricultural systems and forecasting crop performance under various environmental conditionsDeveloped by the FAO, the AquaCrop model evaluates the interplay of soil, water, climate, and management practices, offering user-friendly and robust features for simulating irrigated and rainfed farming systems. Central to such models is simplifying complex biological dynamics to accurately predict yield and biomass while minimizing environmental degradation. By simplifying complex biological processes, AquaCrop enables the accurate prediction of crop yield and biomass. This study uses AquaCrop to simulate the growth and productivity of Hashemi rice across 92 paddy fields in Sowme’eh Sara County. By calibrating and validating the model with site-specific data, this research showcases AquaCrop’s effectiveness in addressing regional challenges and its potential to improve agricultural sustainability through enhanced resource management and informed decision-making.
Materials and Methods: This study was conducted in 92 paddy fields within Sowme’eh Sara County, aiming to assess the AquaCrop model’s reliability for simulating rice yield and biomass under real-world conditions. Soil samples were collected via stratified random sampling from depths of up to 60 cm across different soil layers (surface, compacted plow layer, and subsoil) and analyzed for parameters such as texture (loamy to clayey), organic carbon content, field capacity, wilting point, pH, and electrical conductivity. Meteorological data, including daily temperatures, solar radiation, precipitation, and wind speed, were gathered during the rice growth period to calibrate and validate the model. Farmer surveys documented management practices, including fertilization methods, irrigation strategies (alternate vs. continuous), and pest control techniques. The AquaCrop model was calibrated by adjusting region-specific parameters, such as initial canopy cover, harvest index, and water-use efficiency, using soil, crop, and meteorological data. Validation involved comparing observed yield and biomass data with AquaCrop-simulated results. The model’s performance was evaluated using statistical indices such as normalized root mean square error (nRMSE), root mean square error (RMSE), coefficient of determination (R²), and model efficiency (EF). Finally, sensitivity analysis identified key parameters influencing simulation accuracy, ensuring comprehensive applicability to diverse management scenarios.
Results and Discussion: The observed yield values for grain across the studied paddy fields ranged from 2.54 ton/ha to 5.97 ton/ha, with a mean yield of 3.92 ton/ha. Biomass production varied from 6.20 ton/ha to 12.62 ton/ha, averaging 8.46 ton/ha. The AquaCrop model successfully replicated these trends, achieving R² values of 0.70 for grain yield and 0.66 for biomass, with nRMSE values of 10% and 20%, respectively. Model efficiency indices further confirmed AquaCrop’s reliability, with EF = 0.70 for grain yield and EF = 0.97 for biomass yield. Sensitivity analysis identified several critical parameters influencing simulation accuracy, such as harvest index, initial canopy cover, and saturated hydraulic conductivity. Fine-tuning these inputs enhanced the model’s predictive capabilities. While minor discrepancies were noted in biomass accumulation under specific stress conditions, these results aligned with findings from previous research. The AquaCrop model demonstrated its adaptability for agricultural decision-making by evaluating outcomes under diverse management practices across 92 paddy fields. Simulated results also provided effective strategies for optimizing irrigation water usage, nutrient management, and reducing environmental impacts. The study highlights AquaCrop’s role as a robust decision-support tool capable of offering actionable insights into improving agricultural sustainability. It successfully accommodated variations in soil structure, agroecological settings, and crop rotation practices, underscoring its potential for large-scale applications in heterogeneous farming systems.
Conclusion: The AquaCrop model exhibits high potential for sustainable resource management in agriculture and serves as an advanced and effective tool for the agricultural community. This study demonstrated that AquaCrop is highly accurate and effective in simulating rice grain yield and biomass under real-world farming conditions. With simple input requirements and adaptable algorithms, AquaCrop offers robust solutions for sustainable agricultural resource management. The model proved capable of optimizing irrigation practices and resource inputs like water and nutrients, thereby reducing environmental impact while maintaining productivity. Its application across varied agroecosystems highlights its scalability and relevance to regions facing challenges like water scarcity and climate change. AquaCrop’s role as a proactive tool for agricultural sustainability strongly supports its utility in future farming research and policymaking. Extending this framework to additional crops and geographical areas could further enhance global food security and resource efficiency.
کلیدواژه‌ها English
References
DOI 10.1007/978-981-10-4796-1_6
