نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Increasing water demand due to population growth and climate change has led to the depletion of agricultural water resources in water-scarce regions. Accordingly, a field experiment was conducted over two consecutive seasons (2022 and 2023) at the Medicinal Plants Research Center, Islamic Azad University, Shahr-e Qods Branch. The objective of this study was to evaluate the effects of surface drip irrigation (SDI) and partial root zone drying (PRD), combined with three irrigation treatments (100%, 75%, and 55% of crop evapotranspiration), on lentil yield, and to assess the SALTMED model's performance in simulating soil moisture, total dry matter, and yield. Although the highest yields were obtained under full irrigation (100% ETc), no significant difference was observed between the 100% and 75% treatments. This suggests that a 25% reduction in water use can be achieved without compromising yield. Yield and water productivity under PRD were slightly higher than those under SDI, as PRD maintained soil moisture in the root zone for a longer period without exposing the crop to water stress. The SALTMED model accurately simulated soil moisture, total dry matter, yield, and water use efficiency. Therefore, it can serve as a valuable tool for crop, water, and land management under Iran's climatic conditions.
کلیدواژهها [English]
Water scarcity is one of the major challenges facing agriculture today. This study evaluated the effects of surface drip irrigation (SDI) and partial root-zone drying (PRD) under different irrigation levels (100%, 75%, and 55% of crop evapotranspiration) on lentil growth, yield, and water productivity. The SALTMED model was used to simulate soil moisture, dry matter, and yield. Results aimed to identify a water-saving, cost-effective irrigation strategy suitable for drought-prone regions.
Field experiments were conducted during the 2022 and 2023 growing seasons at the research farm of Islamic Azad University, Shahre Ghods Branch, under a hot and dry climate. Soil samples were collected to 60 cm depth, and irrigation treatments were applied using surface drip and partial root-zone drying methods with three irrigation levels (100%, 75%, and 55% of crop water requirement) in a randomized complete block design. Meteorological data were recorded by a nearby weather station and ET₀ was calculated using the FAO Penman-Monteith equation. Lentil was sown in mid-September and harvested in early December. Soil moisture was measured using calibrated EnviroSCAN sensors, and crop yield and water productivity were assessed. Irrigation scheduling was based on a 3-day interval using climatic data, soil properties, and FAO recommendations for effective rainfall and leaching requirements. The SALTMED model was calibrated using observed soil moisture, dry matter, and yield under full irrigation, then validated using remaining treatments. Model performance was evaluated using R², RMSE, NRMSE, NSE, and CRM indices. Statistical analysis was conducted using SPSS and Duncan's test.
A strong correlation was observed between measured and simulated soil moisture in both 0.00–0.25 m and 0.25–0.50 m layers under full irrigation with surface drip and PRD methods, with simulated values closely matching the observed data. A high agreement was observed between measured and simulated soil moisture in both 0–25 cm and 25–50 cm layers under surface drip and partial root-zone drying irrigation. The minimal variation in soil moisture was attributed to frequent irrigation events that maintained high moisture levels in the root zone. Statistical performance indicators confirmed the model's accuracy and calibration quality, indicating its suitability for validation and application in soil water dynamics modeling under different irrigation strategies. Yield and total dry matter were successfully calibrated in both surface drip and PRD systems by adjusting crop growth parameters, showing strong agreement between observed and simulated values. Model validation for other treatments confirmed its accuracy, with good alignment between simulated and measured soil moisture, yield, and biomass. Soil moisture was lower in the upper layer due to surface evaporation, and PRD maintained higher moisture levels than surface drip across both seasons, likely due to reduced wetted area and lower evaporation losses. Statistical indices confirmed a strong correlation between observed and simulated soil moisture across all irrigation treatments and soil layers under both surface drip and PRD systems. The validation results demonstrated the model’s reliability and effectiveness in simulating soil water content under different irrigation methods. The irrigation system and water treatment levels had consistent effects on lentil yield and total dry matter in both seasons. The highest values were observed under full irrigation, while the lowest occurred under severe deficit irrigation in both surface drip and PRD systems. Yield and biomass reductions were mainly due to soil moisture deficiency, which limited photosynthesis and overall plant performance. In the 2022 and 2023 growing seasons, the model showed a strong correlation between observed and simulated yield and total dry matter under full irrigation for both surface drip and PRD systems, with minimal deviations. Across all irrigation treatments, simulated values closely matched observed data, indicating high model accuracy. Statistical indicators confirmed excellent agreement and low error rates, placing the model's performance in yield and biomass simulation within a highly reliable range. Water productivity, defined as crop yield per unit of irrigation water, showed similar trends across two seasons. The highest water productivity occurred under deficit irrigation treatments, with the partial root-zone drying method outperforming surface drip irrigation. Statistical analyses confirmed a strong correlation between observed and simulated water productivity, with low error margins and good model efficiency. Overall, the SALTMED model accurately simulated soil moisture, yield, dry matter, and water productivity for lentil under different irrigation levels in both irrigation systems.
The highest yield was obtained under the 100% crop water requirement treatment. However, the difference in yield between the 100% and 75% treatments was not statistically significant, indicating that irrigation with 75% of the plant’s water requirement does not impose significant water stress on lentil. Therefore, a 25% reduction in irrigation water can be achieved without a notable decline in yield. In the 2022–2023 growing season, partial root-zone drying (PRD) irrigation increased yield by 11.743%, 13.76%, and 15.55% compared to surface drip irrigation for the 100%, 75%, and 55% water requirement treatments, respectively, with an average increase of 13.59%. In the second season, PRD irrigation resulted in 10.47%, 13.80%, and 15.43% higher yields under the same treatments, averaging 13.05% higher than surface drip irrigation. Overall, across both years, PRD improved yield by an average of 13.32%. Water productivity under PRD was also higher, as water remained longer in the root zone, reducing moisture stress and enhancing yield. In the first season, water productivity in PRD improved by 11.59%, 13.74%, and 15.59% for the 100%, 75%, and 55% treatments, respectively, with a mean increase of 13.75%. In the second season, it increased by 10.49%, 13.65%, and 15.61%, respectively, with an average improvement of 13.61%. Over both years, water productivity under PRD was 13.68% higher than under surface drip irrigation. Although PRD slightly outperformed surface drip in yield and water productivity, the main benefit lies in saving 25% of irrigation water by applying 75% of the crop water requirement. The SALTMED model accurately simulated soil moisture, dry matter production, and yield under all three treatments and both irrigation systems. It proved effective for predicting crop growth, yield, and water productivity under various scenarios, with minimal differences between observed and simulated data. The highest water productivity occurred under the 55% treatment, followed by the 75% and 100% treatments.
Conceptualization, M.A. and A.N.GH.; methodology, M.A. and A.N.GH.; software, M.A. and A.N.GH.; validation, M.A. and A.N.GH.; formal analysis, M.A. and A.N.GH.; investigation, M.A. and A.N.GH.; resources, M.A. and A.N.GH.; data curation, M.A.; writing-original draft preparation, M.A. and A.N.GH.; writing-review and editing, M.A. and A.N.GH.; visualization, M.A. and A.N.GH.; supervision, M.A. and A.N.GH.; project administration, M.A. and A.N.GH.; funding acquisition, M.A. and A.N.GH. All authors have read and agreed to the published version of the manuscript.
Data is available on reasonable request from the authors.
The authors would like to thank the reviewers and editor for their critical comments that helped to improve the paper
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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