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ارزیابی یک سامانۀ هوشمند آبیاری برای مدیریت آبیاری گندم در منطقۀ مشهد | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 26، تابستان - شماره پیاپی 99، تیر 1404، صفحه 54-37 اصل مقاله (811.62 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2025.369010.1613 | ||
| نویسندگان | ||
| سید ابو القاسم حقایقی مقدم* 1؛ مجید کرامتی2؛ اردلان ذوالفقاران1 | ||
| 1استادیار پژوهشی بخش فنی و مهندسی، مرکز تحقیقات کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران. | ||
| 2مربی پژوهشی بخش فنی و مهندسی، مرکز تحقیقات کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران. | ||
| چکیده | ||
| این پژوهش با هدف ارزیابی عملکرد سامانۀ هوشمند «هوشاب» در برنامه‌ریزی آبیاری گندم با استفاده از داده‌های بهنگام هواشناسی، طی دو سال زراعی در منطقۀ مشهد اجرا شد. تیمارهای آبی شامل: (1) آبیاری بر اساس توصیۀ سامانۀ هوشاب (100 درصد)، (2) آبیاری با 80 درصد و (3) آبیاری با 120 درصد حجم آب محاسبه‌شده توسط سامانه بودند. پس از برداشت محصول، حجم آب مصرفی، میزان بارش مؤثر، عملکرد گندم و بهره‌وری فیزیکی آب (بر اساس حجم آب آبیاری و مجموع آب آبیاری به‌همراه بارش مؤثر) اندازه‌گیری شد. داده‌ها با استفاده از آزمون آماری t تحلیل شدند. نتایج تحقیق نشان داد در سامانۀ آبیاری سطحی، عملکرد گندم در تیمار هوشاب به‌طور معنی‌داری از تیمار کاهش‌یافته (80 درصد) بیشتر است اما مصرف آب در تیمار هوشاب تا 4560 مترمکعب در هکتار افزایش یافته است که بیش از میانگین استانی است. در آبیاری قطره‌ای، تفاوت آماری معناداری میان تیمارهای 100 و 120 درصد مشاهده نشد. بیشترین بهره‌وری آب در آبیاری قطره‌ای و در تیمار 80 درصد به میزان 1/63 کیلوگرم بر مترمکعب به‌دست آمد. برآورد نیاز آبی توسط سامانه هوشاب نیز حدود دو برابر مقدار محاسبه‌شده با داده‌های ایستگاه هواشناسی مشهد بود. اگرچه استفاده از داده‌های بهنگام سامانه موجب بهبود عملکرد محصول شد، اما بهره‌وری آب به شکل معنادار افزایش نیافت و مصرف آب بیشتر از حد انتظار بود. به‌طور کلی، سامانۀ هوشاب پتانسیل بالایی در به‌کارگیری داده‌های لحظه‌ای در مدیریت آبیاری دارد، اما برای دستیابی به هدف بهینه‌سازی مصرف آب، نیازمند بازنگری در الگوریتم تخمین نیاز آبی است. | ||
| کلیدواژه‌ها | ||
| آبیاری هوشمند؛ برنامه‌ریزی آبیاری؛ برنامه ریزی آبیاری؛ بهره‌وری آب | ||
| عنوان مقاله [English] | ||
| Performance Evaluation of a Smart Irrigation System for Efficient Wheat Water Management in the Mashhad Region | ||
| نویسندگان [English] | ||
| Abolghasem Haghayeghi1؛ Majid Keramati2؛ Ardalan Zolfagharan1 | ||
| 1Assistant professor, Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran | ||
| 22. Master’s degree, Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran | ||
| چکیده [English] | ||
| Introduction Water, as a vital and limited resource in agriculture, plays a decisive role in crop production. With the growing population and water resource constraints, optimizing water use in agriculture has become a major challenge. The use of modern technologies and real-time meteorological data can improve irrigation management and enhance water productivity. In recent years, efforts have been made to develop irrigation systems based on meteorological data. These systems use information such as temperature, humidity, precipitation, and evapotranspiration to estimate crop water requirements more accurately. However, many of these systems are still in the experimental and developmental stages, requiring field evaluations to assess their performance and efficiency. This study aimed to evaluate an intelligent irrigation planning system for wheat cultivation in the Mashhad region using real-time meteorological data. Methodology The study was conducted over two years in the Mashhad region, focusing on wheat cultivation. The irrigation treatments included: (1) irrigation based on real-time meteorological data from the intelligent system, (2) irrigation supplying 80% of the water volume used in the first treatment, and (3) irrigation supplying 120% of the water volume used in the first treatment. Water productivity was calculated based on irrigation volume and effective rainfall. A paired T-test was used to compare the treatments. Data on crop yield, water consumption, and water productivity were collected and analyzed. Results and Discussion The results showed that wheat yield in the treatment based on the intelligent system increased compared to the average yield in Mashhad farms. However, this treatment did not show an advantage in reducing water consumption, as it used more water than the regional average. Water productivity in this treatment was also not superior to the average wheat farms in the region. The intelligent system estimated water requirements to be more than double those calculated using data from the Mashhad meteorological station. In terms of usability, the system was simple and user-friendly for experts but required further optimization for farmers with limited technical knowledge. In the first year, surface irrigation showed that the 80% treatment had a significantly lower yield (4890 kg/ha) compared to the intelligent system (6600 kg/ha). The 120% treatment (6285 kg/ha) did not differ significantly from the intelligent system. In drip irrigation, the 120% treatment had the highest yield (6345 kg/ha), but it was not significantly different from the intelligent system (6170 kg/ha). In the second year, a 60% treatment was added. Surface irrigation showed that the 80% treatment (3550 kg/ha) had a significantly lower yield than the intelligent system (4680 kg/ha), while the 60% treatment (4600 kg/ha) did not differ significantly. The 120% treatment (6000 kg/ha) showed a significant difference compared to the intelligent system. Water consumption in the first year for surface irrigation was highest in the intelligent system (100% treatment), while the 80% and 120% treatments used 67% and 90% of this volume, respectively. In drip irrigation, the 100% treatment used the most water (4560 m³/ha), while the 80% and 120% treatments used 2840 and 4180 m³/ha, respectively. Water productivity in surface irrigation was highest in the 120% treatment (0.723 kg/m³), while in drip irrigation, the 80% treatment had the highest productivity (1.63 kg/m³). The intelligent system overestimated water requirements compared to data from the Mashhad meteorological station, primarily due to differences in minimum and maximum temperatures, relative humidity, and sunshine hours. The system's performance was rated as good for maximum temperature, moderate for relative humidity, and poor for minimum temperature and sunshine hours. Conclusion The intelligent irrigation scheduling system showed potential for improving wheat yield but did not reduce water consumption or enhance water productivity significantly. The system's overestimation of water requirements highlights the need for algorithmic improvements. While the system was user-friendly for experts, it requires further optimization for farmers with limited technical knowledge. This study serves as a preliminary step toward developing more efficient irrigation systems based on real-time meteorological data. | ||
| کلیدواژه‌ها [English] | ||
| Water productivity, Irrigation scheduling, Real-time meteorological data, Wheat, Mashhad region | ||
| مراجع | ||
|
Abbasi, N. & Abbasi, F. (2020). Overview of Water Resources and Consumption in Iran. Technical Report No. 57384, Agricultural Engineering Research Institute, Karaj, Iran. (in Persian). Al-Ghobari, H. M. (2014). The assessment of automatic irrigation scheduling techniques on tomato yield and water productivity under a subsurface drip irrigation system in a hyper arid region. WIT Transactions on Ecology and The Environment, Vol. 185. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO. Aydin, Ö., Kandemir, C. A., Kiraç, U., & Dalkiliç, F. (2021). Development of a smart irrigation system based on artificial intelligence and IoT. Sustainability, 13(14), 7805. Bwambale, E., Abagale, F.K. & Anornu, G.K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, Volume 260, 107324. Çetin, O., Üzen, N., Temiz, M.G. & Altunten, H. (2021). Improving Cotton Yield, Water Use and Net Income in Different Drip Irrigation Systems Using Real-Time Crop Evapotranspiration. Pol. J. Environ. Stud. Vol. 30, No. 5, 4463-4474. Evans, R. G., & Sadler, E. J. (2008). Methods and technologies to improve efficiency of water use. Water Resources Research, 44(7). https://doi.org/10.1029/2007WR006200 FAO. (2012). ETo calculator, Land and water digital media series N° 36. FAO, Rome, Italy. Gutiérrez, J., Villa-Medina, J.F., Nieto-Garibay, A. & Porta-Gándara, M.A. (2015). Automated irrigation system using a wireless sensor network and GPRS module. Transaction on instrumentation and measurement, Vol. 63, No. 1. Incrocci. L., Marzialettib, P., Incroccia, G., Di Vitaa, A., Balendonckc, J., Bibbianid, C., Spagnole, S., & Pardossia, A. (2014). Substrate water status and evapotranspiration irrigation scheduling in heterogenous container nursery crops. Agricultural Water Management, 131, 30-40. Jensen, M. E., & Allen, R. G. (2016). Evaporation, evapotranspiration, and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70. American Society of Civil Engineers. Jones, H. G. (2004). Irrigation scheduling: Advantages and pitfalls of plant-based methods. Journal of Experimental Botany, 55(407), 2427–2436. https://doi.org/10.1093/jxb/erh213 Karar, M. E., Al-Rasheed, M. F., Al-Rasheed, A. F., & Reyad, O. (2020). Smart water pumping system using IoT and artificial neural networks. International Journal of Electrical and Computer Engineering, 10(5), 5017–5024. Kehui, X., Deqin, X. & Xiwen, L. (2010). Smart water-saving irrigation system in precision agriculture based on wireless sensor network. Transactions of the CSAE, Vol.26, No.11. Khaleghi, N. (2015). Comparison of Effective Rainfall Estimation Methods in Agriculture. Journal of Water and Sustainable Development, Vol. 2, No. 2, pp. 51-58. (in Persian). Kiani, A. & Abbasi, F. (2021). Barresi-ye tasir-e fanavari-haye novin dar kahnash-e masraf-e ab-e keshavarzi [Investigating the impact of modern technologies on reducing agricultural water consumption]. Ab va Tose'e-ye Paydar, 2(2), 77–84. (in Persian). Kijne, J. W., Barker, R., & Molden, D. (2003). Water productivity in agriculture: Limits and opportunities for improvement. CABI Publishing. Liakos, V., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48. Masseroni, D., Moller, P., Tyrell, R., Romani, R., Lasagna, A., Sali, G., Facchi, A. & Gandolfi, C. (2018). Evaluating performances of the first automatic system for paddy irrigation in Europe. Agricultural water management, 201, pp. 58–69. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. Navarro-Hellin, H., Martinez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F. & Torres-Sanchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124 (1), pp. 121-131. Raes, D., Steduto, P., Hsiao, T. C., & Fereres, E. (2011). FAO crop water productivity model to simulate yield response to water. FAO Irrigation and Drainage Paper No. 66. Rogers, D. H., Alam, M., & Aguilar, J. (2015). Irrigation management for agricultural producers. Kansas State University Agricultural Experiment Station and Cooperative Extension Service. Shahrokhnia, M. A., Zare, A., & Dehghani Sanij, H. (2015). Comparison of Different Tools for Drip Irrigation Planning of Citrus in Medium and Heavy Soils. Journal of Irrigation and Drainage of Iran, 3(9): 448-458. (in Persian). Shekhar, Y., Dagur, E., & Mishra, S. (2017). Smart irrigation system using machine learning and IoT. International Journal of Engineering Sciences & Research Technology, 6(7), 132–138. Smith, M., Muñoz, G., & van Wijk, M. T. (2010). Water management for sustainable agriculture. FAO Water Reports, 36. Vallejo-Gómez, D., Osorio, M., & Hincapié, C. A. (2023). A review of intelligent irrigation systems in agriculture: Technologies and challenges. Computers and Electronics in Agriculture, 205, 107616. Zhang, X., Chen, S., Sun, H., Wang, Y., & Shao, L. (2019). Root size, distribution and soil water depletion as affected by cultivars and environmental factors. Field Crops Research, 234, 1-10. Zhang, X., Lin, X., & Luo, Y. (2019). Smart irrigation systems: A review of the latest technologies and their applications. Agricultural Water Management, 215, 48–62. https://doi.org/10.1016/j.agwat.2019.01.001 Zwart, S. J., & Bastiaanssen, W. G. M. (2004). Review of measured crop water productivity values for irrigated wheat, rice, cotton, and maize. Agricultural Water Management, 69(2), 115-133. | ||
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