تحلیل چندمعیاره سطوح حد مجاز تخلیه رطوبت خاک در چغندرقند (رقم شکوفا)

نوع مقاله : مقاله پژوهشی

نویسنده

محقق، موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی

چکیده

هدف این پژوهش، بررسی اثر تیمارهای مختلف آبیاری مبتنی بر حد مجاز تخلیه رطوبت خاک (MAD) بر عملکرد، کیفیت و بهره‌وری آب چغندرقند رقم «شکوفا» بود. آزمایش مزرعه‌ای در کرج طی دو سال زراعی (1398–1396) با سه سطح حد مجاز تخلیه رطوبت خاک 40%، 60% و 80% به­عنوان تیمارهای آبیاری در چهار تکرار انجام شد. صفات کمی و کیفی شامل تبخیر –تعرق، عملکرد ریشه و قند، درصد قند خالص، ضریب استحصال و قلیائیت اندازه‌گیری شد و بهره‌وری آب محاسبه گردید. آنالیز واریانس داده­ها، تأثیر تیمارها و سال را بر صفات نشان داد و تحلیل چندمعیاره تاپسیس(TOPSIS) با تحلیل حساسیت وزن معیارها تصمیم­گیری شد و شبیه‌سازی مونت کارلو، رتبه­بندی و ثبات رتبه‌بندی تیمارها را ارزیابی کرد. بر اساس نتایج روش تاپسیس، تیمار با ضریب تخلیه مجاز رطوبت خاک 60% با ضریب نزدیکی 0.557 در رتبه نخست قرار گرفت و بهترین گزینه شد. تیمار با ضریب تخلیه مجاز رطوبت خاک 40% با ضریب نزدیکی 0.479 در رتبه دوم قرار گرفت. تیمار با ضریب تخلیه مجاز رطوبت خاک 80% با ضریب نزدیکی 0.373 کمترین رتبه را کسب کرد، که بیانگر فاصله بیشتر آن از شرایط ایده‌آل در ارزیابی هم­زمان معیارهای مورد بررسی بود. نتایج شبیه­سازی مونت کارلو با ۱۰٪± نوسان در وزن‌ معیارها نشان داد تیمار با ضریب تخلیه مجاز رطوبت خاک 60% در 99.87% از شبیه ­سازی­ ها رتبه اول را کسب کرد. این امر بیانگر ثبات بالای رتبه‌بندی و مقاومت تصمیم‌گیری نسبت به عدم قطعیت وزن‌ها است و اطمینان بالایی می‌دهد که انتخاب این تیمار در شرایط مختلف مدیریتی و وزن‌دهی معیارها، بهترین گزینه برای بهینه‌سازی عملکرد و بهره‌وری آب چغندرقند است.

کلیدواژه‌ها

عنوان مقاله [English]

Multi-Criteria Analysis of Management Allowed Moisture Deficit in Sugar Beet (Shokoufa Variety)

نویسنده [English]

  • Reza Mohammadikia
Researcher, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO)
چکیده [English]

The objective of this study was to investigate the effects of different irrigation treatments based on the Management Allowable Depletion (MAD) of soil moisture on the yield, quality, and water productivity of sugar beet. A field experiment was conducted in Karaj, Iran, during two growing seasons (2017–2019) using a randomized complete block design with three irrigation treatments (MAD=40%, 60%, and 80%) and four replications. Quantitative and qualitative traits, including evapotranspiration, root and sugar yield, pure sugar percentage, extraction and alkalinity coefficient, were measured, and water productivity was calculated. Analysis of variance indicated significant effects of the treatments and year on the measured traits. Multi-criteria decision analysis using the TOPSIS method, coupled with sensitivity analysis of criterion weights and Monte Carlo simulation, was employed to evaluate the ranking and stability of the treatments. According to the TOPSIS results, the treatment with a MAD of 60% achieved the highest rank with a closeness coefficient of 0.577, and was identified as the optimal option. The treatment with MAD=40% ranked second with a closeness coefficient of 0.479. Conversely, the treatment with MAD=80% obtained the lowest rank with a closeness coefficient of 0.373, indicating a greater distance from the ideal conditions in the simultaneous evaluation of the considered criteria. Monte Carlo simulation results, incorporating a ±10% fluctuation in criterion weights, revealed that MAD= 60% retained the first rank in 99.87% of the simulations. This demonstrates high stability in the ranking and robustness of the decision against weight uncertainty, providing strong confidence that the selection of this treatment, under various management conditions and criterion weighting scenarios, represents the best option for optimizing both yield and water productivity of sugar beet.

کلیدواژه‌ها [English]

  • Irrigation management
  • Multi-criteria decision making
  • Sensitivity analysis
  • Water productivity

مراجع

  1. متکان، عبدالرضا، درویش‌زاده، رضا، حسینی‌اصل، احمد، ابراهیمی‌خفسی، محمد و ابراهیمی‌خفسی، زهرا، ۱۳۹۱. خشکسالی‌پهنه‌بندی در مناطق خشک با استفاده از الگوریتم‌های مبتنی بر دانش در محیط GIS (مطالعه موردی: شیتور، یزد). فصلنامه پژوهش‌های اقلیم‌شناسی، ۲(۵–۶)، ۱۰۳–۱۱۶.
  2. سازمان هواشناسی کشور، ۱۴۰۰. داده‌های اقلیم‌شناسی ایستگاه کرج (2020–1990). تهران، سازمان هواشناسی کشور
  3. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M., 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome.
  4. Al-Wabel, M. I., Al-Ghamdi, A. A., & Al-Garni, S. Z., 2014. Effect of deficit irrigation on sugar beet (Beta vulgaris L.) yield and water use efficiency under Saudi Arabian conditions. Journal of Arid Environments, 108, pp.62–68.

DOI: 10.1016/j.jaridenv.2014.05.006

  1. Ayers, R. S., & Westcot, D. W., 1985. Water quality for agriculture. FAO Irrigation and Drainage Paper No. 29, Rev. 1. Rome. https://www.fao.org/3/t0234e/T0234E00.htm
  2. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J., 2012. A state-of-the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), pp.13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
  3. Blum, A., 2017. Osmotic adjustment is a prime drought stress adaptive engine in support of plant production. Plant, Cell & Environment, 40(1), pp.4–10,

 https://doi.org/10.1111/pce.12813

  1. Bodner, G., & Alsalem, W., 2023. Drought stress effects on root growth and sugar accumulation in Beta vulgaris L. Agronomy, 13(10), 2519.

 https://doi.org/10.3390/agronomy13102519

  1. Chaves, M. M., & Oliveira, M. M., 2021. Mechanisms underlying plant resilience to drought: Effects on photosynthesis and crop productivity. Journal of Experimental Botany, 72(10), pp.3481–3499. https://doi.org/10.1093/jxb/erab208
  2. Cheng, M., Wang, H., Fan, J., Zhang, S., Liao, Z., Zhang, F., & Wang, Y. A., 2021. Global meta-analysis of yield and water use efficiency of crops, vegetables and fruits under full, deficit and alternate partial root-zone irrigation. Agricultural Water Management, 248, 106778. https://doi.org/10.1016/j.agwat.2021.106778
  3. Chiche, J., Ferchichi, A., & Rhouma, A., 2021. Application of multi-criteria decision-making methods for irrigation management under water scarcity conditions. Water, 13(9), 1234. https://doi.org/10.3390/w13091234
  4. Chieb, M., & Gachomo, E. W., 2023. The role of plant growth promoting rhizobacteria in plant drought stress responses. BMC Plant Biology, 23, Article 407.

https://doi.org/10.1186/s12870-023-04403-8

  1. Doorenbos, J., & Kassam, A. H., 1979. Yield response to water. FAO Irrigation and Drainage Paper No. 33. Rome.
  2. Dutton, J. and Bowler, G., 1984. Money is still being wasted on nitrogen fertilizer. British Sugar Beet Review, 52(4), pp.74-77.
  3. 2003. Deficit irrigation practices. Rome:

https://www.fao.org/4/Y3655E/y3655e03.htm

  1. 2003. Irrigation water quality guidelines. FAO Irrigation and Drainage Paper No. 29, Rev. Rome. https://www.fao.org/3/y3674e/y3674e00.htm
  2. 2022. Water productivity in agriculture: From water savings to increased value. Rome.
  3. Farooq, M., Hussain, M., & Siddique, K. H. M., 2019. Drought stress in plants: An overview. Plant Physiology and Biochemistry, 147, pp.1–19.

https://doi.org/10.1016/j.plaphy.2019.01.001

  1. Farooq, M., Wahid, A., Kobayashi, N., Fujita, D., & Basra, S. M. A., 2009. Plant drought stress: Effects, mechanisms and management. Agronomy for Sustainable Development, 29(1), pp.185–212. https://doi.org/10.1051/agro:2008021
  2. Gee, G. W., & Bauder, J. W., 1986. Particle-size analysis, in A. Klute (Ed.) Methods of Soil Analysis. Part 1: Physical and Mineralogical Methods. 2nd edn. Madison, Soil Science Society of America, pp.383-411.
  3. Geerts, S., & Raes, D., 2009. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management, 96(9), pp.1275–1284. https://doi.org/10.1016/j.agwat.2009.03.009
  4. Grimes, D. W., & Yamada, H., 1982. Relation of cotton growth and yield to minimum leaf water potential. Crop Science, 22(1), pp.134–139.

https://doi.org/10.2135/cropsci1982.0011183X002200010032x

  1. Gupta, A., Rico Machado, J. L., de Oliveira, A. C., & Sivasakthi, K., 2021. Physiological and molecular responses of plants under drought stress. Plant Physiology and Biochemistry, 163, pp.90–105. https://doi.org/10.1016/j.plaphy.2021.04.032
  2. Hajkowicz, S., & Higgins, A., 2008. A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research, 184(1), pp.255–265. https://doi.org/10.1016/j.ejor.2006.10.045
  3. Han, F., Alkhawaji, R. N., & Shafieezadeh, M. M., 2025. Evaluating sustainable water management strategies using TOPSIS and fuzzy TOPSIS methods. Applied Water Science, 15, 4. https://doi.org/10.1007/s13201-024-02336-7
  4. Howell, T. A., Schneider, A. D., & Jensen, M. E., 1991. History of lysimeter design and use for evapotranspiration measurements. In R. G. Allen et al. (Eds.), Advances in evapotranspiration (pp.1–9). St. Joseph: ASAE.
  5. Huang, I. B., Keisler, J., & Linkov, I., 2011. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of the Total Environment, 409(19), pp.3578–3594. https://doi.org/10.1016/j.scitotenv.2011.06.022
  6. Hwang, C. L., & Yoon, K., 1981. Multiple attribute decision making: Methods and applications — A state of the art survey. Lecture Notes in Economics and Mathematical Systems, 186. Springer.
  7. Karam, M. A., El-Hattab, A. M., & El-Baky, H. A., 2016. Response of sugar beet (Beta vulgaris L.) to different irrigation regimes under semi-arid conditions in Egypt. International Journal of Agricultural Biology, 18(4), pp.741–748.

DOI: 10.17957/IJAB/61832

  1. Kirda, C., 2002. Deficit irrigation scheduling based on plant growth stages showing water stress tolerance. In Deficit Irrigation Practices (FAO Water Report No. 22), pp.102–114. FAO.
  2. Kiymaz, S., & Ertek, A., 2015. Yield and quality of sugar beet (Beta vulgaris L.) under deficit irrigation. Agricultural Water Management, 152, pp.91–98.

https://doi.org/10.1016/j.agwat.2015.01.012

  1. Kordrostami, F., Nazari, M., & Jafari, M., 2026. Genotypic variability of sugar beet under water deficit conditions: Implications for sustainable production. Scientific Reports, 16, 33240. https://doi.org/10.1038/s41598-025-33240-y
  2. Lee, W. K., 2025. Comparative study of TOPSIS and Fuzzy TOPSIS for the determination of water allocation in an urbanized river basin. Journal of Advanced Research Design, 144(1), pp.90–102.
  3. Li, P., Wu, J., & Chen, J., 2013. Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights. Environmental Monitoring and Assessment, 185, pp.2453–2461. https://doi.org/10.1007/s10661-012-2723-9
  4. Liu, B., Dai, T., & Zhang, D., 2022. Climate change impacts on agricultural water management and crop productivity. Journal of Agricultural Science, 160(4), pp.547–560. https://doi.org/10.1017/S0021859622000158
  5. Love, D. R., Smith, J., & Johnson, P., 2023. Quantity and quality changes in sugar beet (Beta vulgaris L.) induced by different sources of biostimulants. Scientific Reports. https://www.nature.com/articles/s41598-023-42182-2
  6. Manavalan, L. P., Guttikonda, S. K., Phan Tran, L. S., & Nguyen, H. T., 2009. Physiological and molecular approaches to improve drought resistance in soybean. Plant and Cell Physiology, 50(7), pp.1260–1276. https://doi.org/10.1093/pcp/pcp080
  7. Mobedi, A., & Karimi, H., 2020. Management allowed depletion (MAD) levels for deficit irrigation in sugar beet. International Journal of Crop Sciences, 18(3), pp.210–224.
  8. Moghaddam, M., Najafi, A., & Khoshraftar, H. R., 2018. Evaluation of water productivity and economic return of sugar beet under different irrigation levels in central Iran. Journal of Agricultural Science and Technology, 20(5), pp.1027–1040.
  9. Musick, J. T., Dusek, D. A., & Martin, D. L., 1994. Deficit irrigation for grain sorghum in the Southern High Plains. Agronomy Journal, 86(6), pp.934–941.

https://doi.org/10.2134/agronj1994.00021962008600060015x

  1. Payero, J. O., Melvin, S. R., & Irmak, S., 2006. Soybean and corn water productivity under deficit irrigation. Agricultural Water Management, 83(3), pp.233–243.

https://doi.org/10.1016/j.agwat.2005.10.010

  1. Pollach, G., 1984. Tests on improvement of a rizomania diagnosis based on conventional beet analysis (Rhizomania, beet necrotic yellow vein virus). German. Zuckerindustrie.
  2. Rinaldi, M., & Vonella, A., 2006. Water requirements and irrigation scheduling of sugar beet. Agricultural Water Management, 85(1–2), pp.15–24.

 https://doi.org/10.1016/j.agwat.2006.03.002

  1. Rousta, B., & Araghinejad, S., 2015. Development of a multi-criteria decision making tool for a water resources decision support system. Water Resources Management, 29(15), pp.5713–5727. https://doi.org/10.1007/s11269-015-1142-4
  2. Saremi, H., & Maknoon, R., 2020. Monte Carlo simulation for sensitivity analysis in TOPSIS: An agricultural water management case study. Agricultural Water Management, 240, 106303. https://doi.org/10.1016/j.agwat.2020.106303
  3. Sari, S., & Kiymaz, S., 2021. Evaluation of irrigation strategies using multi-criteria decision analysis methods. Agricultural Water Management, 243, 106447.

https://doi.org/10.1016/j.agwat.2020.106447

  1. Sinclair, T. R., 2011. Challenges in breeding for increased water use efficiency. Journal of Experimental Botany, 62(13), pp.4391–4402. https://doi.org/10.1093/jxb/err141
  2. Teulat, B., Borries, C., & This, D., 2001. QTL analysis of stay-green in two recombinant inbred line populations of durum wheat. Plant Physiology, 125(1), pp.364–378. https://doi.org/10.1104/pp.125.1.364
  3. Wang, Z., & Kong, D., 2025. The effect of irrigation and fertilization reduction on yield, quality, and resource use efficiency of drip fertilized sugar beet (Beta vulgaris L.) in Northern China. Plants, 14(4), 536. https://doi.org/10.3390/plants14040536
  4. Xu, X., et al., 2025. Optimizing water-efficient agriculture: Evaluating sustainability of soil management and irrigation synergies using fuzzy extent analysis. Scientific Reports.
  5. Zhang, Y., & Wang, L. (2021)., Optimization of irrigation scheduling for sugar beet based on soil moisture depletion levels. Agricultural Water Management, 250, 106789. https://doi.org/10.1016/j.agwat.2021.106789
  6. Zhang, Y., Li, S., Wang, J., et al., 2021. Optimal irrigation scheduling for sugar beet under deficit irrigation: Yield, water use efficiency and economic returns. Agricultural Water Management, 247, 106689.
پژوهش آب در کشاورزی
دوره 39، شماره 4
اسفند 1404
صفحه 341-357

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