بررسی زراعی- اقتصادی تغییرات کمی و کیفی آب آبیاری در دشت ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای اقتصاد کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

2 نویسندة مسئول و دانشیار گروه اقتصاد کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار گروه اقتصاد کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

4 دانشیار گروه مهندسی و مدیریت آب، دانشگاه تربیت مدرس، تهران، ایران.

چکیده

سطح آبخوان­‌ها، در نتیجة کاهش سطح آب دریاچة ارومیه، کاهش یافته و از سوی دیگر، نفوذ آب شور و نفوذ املاح مازاد به‏ ویژه نیترات ناشی از مصرف کودهای شیمیایی موجب کاهش کیفیت آب­های زیرزمینی در دشت ارومیه شده است. در مطالعة حاضر، با هدف بررسی زراعی- اقتصادی تغییرات کمی و کیفی آب آبیاری، به شبیه ‏سازی عملکرد محصولات گندم آبی، ذرت علوفه‌ای، آفتابگردان و گوجه­ فرنگی با استفاده از مدل آکواکراپ (AquaCrop) در سال زراعی 1401-1400 پرداخته و سپس، گزینه‌­های کم ­آبیاری، کاهش کوددهی و افزایش شوری آب زیرزمینی در تمامی مراحل رشد محصولات اعمال شد؛ همچنین، از چهار گروه داد­ه­‌های اقلیمی، گیاهی، خاک‌‏شناسی و مدیریتی برای انجام شبیه­‌سازی استفاده شد. نتایج نشان داد که کاهش کوددهی بیشترین درصد تغییرات و افزایش شوری آب‏های آبیاری و زیرزمینی کمترین درصد تأثیر را روی عملکرد محصولات دارند. از آنجا که کاهش عملکرد محصولات با اثرات اقتصادی نیز همراه است، این اثرات با محاسبة شاخص‌­های بهره­‌وری فیزیکی و اقتصادی آب و کود نیتروژن بررسی شدند که از آن میان، شاخص بهره­‌وری اقتصادی آب و کود انتخاب شد و بر اساس آن، مقادیر حداکثر شاخص بهره­‌وری اقتصادی آب (بر حسب ریال بر مترمکعب) و شاخص بهره‌وری اقتصادی کود (بر حسب ریال بر کیلوگرم)، به­ ترتیب، برای گوجه ­فرنگی 209580 و 3005000 ریال و آفتابگردان 177630 و 2115720 ریال در منطقه پنج و ذرت علوفه ­ای 94900 و 1323500 ریال و گندم آبی 56620 و 454570 ریال در مناطق سه و شش بود. با توجه به تأثیر زیاد گزینة کاهش کوددهی و شاخص بهره­‌وری اقتصادی کود، باید امکان جایگزینی کودهای ارگانیک با کودهای نیتراته از نظر فنی و اقتصادی بررسی و قیمت بالا برای مصارف بیش از مقدار توصیه ‏شدة کودهای نیتراته تعیین شود. این سیاست‌ها برای کاهش شوری نیز موثرند، زیرا منبع اصلی شوری آب زیرزمینی کوددهی بی­‌رویه است. همچنین، پیشنهاد می‌­شود که فناوری­‌های مناسب آبیاری به کشاورزان شناسانده و الگوی کشت صحیح در منطقه ارائه شود. در نهایت، باید اقدامات سخت‌­گیرانة مدیریتی صورت گیرد تا هم‏زمان، از اثرات مخرب کوددهی زیاد و برداشت بی‌­رویه آب‏های سطحی و زیرزمینی جلوگیری شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Agro-Economic Survey on Quantitative and Qualitative Changes of Irrigation Water in Urmia Plain of Iran

نویسندگان [English]

  • Shabnam Karari 1
  • Hamed Najafi Alamdarlo 2
  • Sadegh Khalilian 3
  • Majid Delavar 4
1 PhD Student in Agricultural Economics Departmet, Tarbiat Modares University.
2 Corresponding Author and Associate Professor, Department of Agricultural Economics, Tarbiat Modares University, Tehran, Iran
3 Associate Professor, Department of Agricultural Economics, Tarbiat Modares University, Tehran, Iran.
4 Associate Professor, Department of Water Engineering and Management, Tarbiat Modares University, Tehran, Iran.
چکیده [English]

Introduction: The level of aquifers has decreased as a result of the decrease in the water level of Urmia Lake in Iran. In addition, the infiltration of both salt water and excess salts, especially nitrates, caused by the use of chemical fertilizers have reduced the quality of groundwater in Urmia plain. Saline waste water, return water, agricultural activities, evaporation and infiltration of sea water are among the salinity factors of water resources. On the other hand, in modern agriculture, the yield and productivity of the product is improved by the use of chemical fertilizers. For this reason, agriculture is known as the most important source of non-point pollution of groundwater nitrates caused by human activities. The nitrate range of plain varies from 0 to 115 mg/l. Also, the level of EC varies from 0.3 to 2.9 ds/m. Both ranges are in warning points, which should be prevented from increasing excessively by adopting management measures. This study mainly aimed at investigating quantitative and qualitative changes of irrigation water in agro-economic terms.
Materials and Methods: The present study was conducted in Urmia plain located in Urmia city of Iran. The area of the plain is 962 km2 and is located on the western side of the lake. The yields of irrigated wheat, forage corn, sunflower and tomato were simulated using the AquaCrop model in the cropping year of 2022-2023; then, the options of reduced irrigation, reduced fertilization and increased groundwater salinity were applied in all stages of crop growth. Also, four groups of climatic, crop, soil and management data were used for the simulation. In proportion to the fact that decreasing crop yield has economic effects, the effects were investigated by calculating water and nitrogen fertilizer physical and economic productivity indexes. Both consumption amount of each input per hectare and the purchase cost of each input were obtained through the interviews with 140 farmers using random classification sampling method. By adding all the costs, the cost of each product was calculated and the farmers' income was obtained according to the yield and price of the products. Then, changes in costs were examined by changing the amount of irrigation and fertilization. Changes in income were also calculated by applying each option, and net profit was obtained by changing the costs and revenues. By carrying out yield simulation and applying options, the impacts of yield reduction on farmers' productivity and profit were calculated and accordingly, the potential areas for growing each crop in each region were determined.
Results and Discussion: Decreased fertilization demonstrated the utmost effect and increased salinity of irrigation water and groundwater revealed the least effect on the yield of crops. Among the two indexes, the economic productivity index of water IRI rials/m3 and fertilizer IRI rials/kg was chosen, based on which tomato (209580, 3005000) and sunflower (177630, 2115720) in region 5, forage corn (94900, 1323500) and irrigated wheat (5620, 454570) in regions 3 and 6 had the highest rate, respectively. Furthermore, the rate of fertilizer economic productivity index was higher than water productivity. After calculating these indicators, the value of the water and fertilizer economic productivity index was recalculated after applying the options and the changes were analyzed. With the reduction of irrigation, the water economic efficiency index for some crops decreased in some areas and increased in some other areas, but with the decrease in fertilization, the economic efficiency of fertilizer decreased in all areas and all crops. In both options, tomato and wheat had the highest and lowest amount, respectively.
 Conclusions: Considering the obtained results and the quantitative and qualitative effects of water reduction in the region, it is necessary to reduce the effects of excessive use of chemical fertilizers before reaching a critical stage. In addition, reducing the excessive use of surface and underground water is vital. If the current trend continues, the cultivation pattern and the type of land use should be changed according to the conditions, or additional costs may be imposed on the farmers to eliminate the effects of excessive consumption, and all these cases require proper management of water resources to maintain their quantity and quality. Given the high impacts of the fertilizer reduction option and the economic productivity index of fertilizer, the possibility of replacing organic fertilizers with nitrate ones should be technically and economically investigated and the high price for using more than the recommended amount of nitrate fertilizers should be determined. These policies are also effective for reducing salinity because the main source of groundwater salinity is excessive fertilization. It is also suggested that appropriate irrigation technologies are introduced to the farmers and the correct cultivation patterns are presented in each region of the whole area. Finally, strict management actions should be taken to moderate the destructive effects of excessive fertilization and to reduce the over-exploitation of surface and groundwater.

کلیدواژه‌ها [English]

  • AquaCrop Model
  • Deficit Irrigation
  • Nitrate Pollution
  • Productivity
  • Salinity
  • Abbasi, F., Abbasi, N. & Tavakoli, A.R. (2017). Water productivity in agriculture: challenges and perspectives. Journal of Water and Sustainable Development, 4(1), 141-144. DOI: 10.22067/jwsd.v4i1.67121. [In Persian]
  • Adeboye, O.B., Schultz, B., Adeboye, A.P., Adekalu, K.O. & Osunbitan, J.A. (2021). Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Information Process in Agriculture, 8(3), 419-436. DOI: 10.1016/j.inpa.2020.10.002.
  • Aelion, C.M., & Conte, B.C. (2004). Susceptibility of residential wells to VOC and nitrate Environmental Science and Technology, 38(6), 1648-1653. DOI: 10.1021/es030401p.
  • Akumaga, U., Tarhule, A. & Yusuf, A. A. (2017). Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa. Agricultural and Forest Meteorology, 232, 225-234. DOI: 10. 1016/j. agrformet. 2016. 08. 011.
  • Alizadeh, A. (2015). Principles of applied hydrology. Mashhad: Ferdowsi University. [In Persian]
  • Alizadeh, H., & Abbasi, F. (2017). Investigation of grain yield response to different levels of water and fertilizer application using Aquacrop model. Irrigation Science and Engineering, 40(2), 119-134. DOI:22055/jise.2017.13166. [In Persian]
  • Ayers, R. S., & Westcot, D. W. (1994). Water quality for agriculture. FAO Irrigation and Drainage, 29(1), 1-130.
  •       Bameri, A., Piri, H. & Ganji, F. (2015). Assessment of groundwater pollution in Bajestan plains for agricultural purposes using indicator of kriging. Journal of Water and Soil Conservation, 22(1), 211-229. [In Persian]
  •        Doorenbos, J., & Kassam, A.H. (1979). Yield response to water. The United Nations: Food and Agriculture Organization (FAO). DOI: 10.1016/B978-0-08-025675-7.50021-2.
  •   Emdad, M.R., Tafteh, A. & Jafarnejadi, A.R. (2018). Evaluation of AquaCrop model for predicting wheat yield indifferent fertilizer management. Research in Agriculture, 10(2), 41-61. [In Persian]
  • Eskandaripour, R., Khorsand, A., Rezaverdinejad, V., Zeinalzadeh, K. & Norjoo, A. (2020). Investigation of polyethylene mulch on improvement of tomato water use efficiency using AquaCrop model. Plant Ecophysiology (Arsanjan Branch), 11(39), 71-85. [In Persian]
  • Gregory, P.J., Ingram, J.S.I., Anderson, R., Betts R.A., Brovkin, V. & Chase, T.N. (2002). Environmental consequences of alternative practices for intensifying crop Agriculture, Ecosystems and Environment, 88(3), 279-290. DOI: 10.1016/S0167-8809(01)00263-8.
  • Guendouz, A., Hafsi, M., Khebbat, Z. & Achiri A. (2014). Performance evaluation of AquaCrop model for durum wheat (Triticum durum desf.) crop in semi-arid conditions in Eastern Algeria. International Journal of Microbiology and Applied Sciences, 3(2), 168-176.
  • Heng, L.K., Evett, S.R., Howell, T.A. & Hsiao, T.C. (2009). Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal, 101(3), 448-459. DOI: 2134/agronj2008.0029xs.
  • Horrigan, L., Lawrence, R.S. & Walker, P. (2002). How sustainable agriculture can address the environmental and human health harms of industrial agriculture. Environmental Health Perspectives, 110(5), 445- DOI: 10.1289/ehp.02110445.
  • Hsiao, T.C., Heng, L.K., Steduto, P., Raes, D. & Fereres, E. (2009). AquaCrop model parameterization and testing for maize. Agronomy Journal, 101(3), 448-459. DOI: 10.2134/agronj2008.0218s.
  •    Jamali, S., Ansari, H. & Salehnia, N. (2022). Economic productivity analysis of water and nitrogen in alternate furrow irrigation for quinoa. Journal of Water Management in Agriculture, 8(2), 1-14. [In Persian]
  •    Karimi, M., & Jolaini, M. (2017). Evaluation of agricultural water productivity indices in major field crops in Mashhad plain. Journal of Water and Sustainable Development, 4(1), 133-138. DOI: 10.22067/jwsd.v4i1.52783. [In Persian]
  •    Khaleghi, M. (2019). Evaluation of the sunflower yield, water productivity and soil salinity simulation under water and salinity stresses using the AquaCrop model. Journal of Water and Soil Resources Conservation, 8(2), 15-38. [In Persian]
  •    Khavazi, K., Balali, M., Bazargan, K., Tehrani, M.M., Rezaei, H., Asadi Rahmani, H., Gheybi, M.N., Davoudi, M.H., Saadat, S., Moshiri, F. & Davatgar, N. (2015). Comprehensive program of soil fertility and plant nutrition (2015- 2026). Karaj, Iran: Soil and Water Research Institute. [In Persian]
  • Kumar, P., Sarangi, A., Singh, D.K. & Parihar, S.S. (2014). Evaluation of AquaCrop model in predicting wheat yield and water productivity under irrigated saline regimes. Journal of Irrigation and Drainage, 63(4), 474-487. DOI: 10.1002/ird.1841.
  • Li, S., & Zhang, Q. (2008). Geochemistry of upper Han River basin, China, 1: Spatial distribution of major ion compositions and their controlling factors. Applied Geochemistry, 23(12), 3535-3544. DOI: 10.1016/j.apgeochem.2008.08.012.
  • Livingston, M.L., & Cory, D.C. (1998). Agricultural nitrate contamination of groundwater: an evaluation of environmental policy. Journal of the American Water Resources Association (JAWRA), 34(6), 1311-1317. DOI: 10.1111/j.1752-1688.1998.tb05433.x.
  • MacQuarrie, K.T.B., Sudicky, E. & Robertson, W.D. (2001). Numerical simulation of a fine-grained denitrification layer for removing septic system nitrate from shallow groundwater. Journal of Contaminant Hydrology, 52(1-4), 29-55. DOI: 10.1016/S0169-7722(01)00152-8.
  • Mansour, H.A., Gaballah, M.S. & Nofal, O.A. (2020). Evaluating the water productivity by AquaCrop model of wheat under irrigation systems and
  •  
  • Open Agriculture, 5(1), 262-270. DOI: 10.1515/opag-2020-0029.
  •  
  • Mehdizadeh Mahalli, S.S., & Vafaei, F. (2016). Experimental and numerical investigation on saltwater intrusion into unconfined coastal aquifers. Journal of Oceanography, 7(25), 67-76. [In Persian]
  • Mehrazar, A., Soltani, J. & Rahmati, O. (2016). Evaluation of the Aqua‎Crop model to simulate maize yield response under salinity stress. Journal of Water and Soil, 30(5), 1426-1439. DOI: 10.22067/jsw.v0i0.43858. [In Persian]
  •  Mousavifazl, S.H., & Akhyani, A. (2020). Effect of irrigation water and nitrogen fertilizer on the yield, quality and water productivity of potato crop in drip irrigation (tape) method. Irrigation and Drainage Journal, 14(4), 1227-1239. [In Persian]
  •    Nasr, M., & Zahran, H.F. (2014). Using of PH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. The Egyptian Journal of Aquatic Research, 40(2), 111-115. DOI: 10.1016/j.ejar.2014.06.005.
  • Raes, D., Steduto, P., Hsiao, T.C. & Fereres, E. (2012). Refrence manual, AquaCrop (Chapter 3). Rome, Italy: FAO, Land and Water Division.
  •    RWCWAP (2021). Level of EC in Urmia plain. Urmia: Regional Water Company of West Azerbaijan Province (RWCWAP). Available at www.agrw.ir. [In Persian]
  •    Saeidi, R., Ramezani Etedali, H., Sotoodehnia, A., Nazari, B. & Kaviani, A. (2021). Evaluation of AquaCrop model for estimating of changes process of soil moisture, evapotranspiration and yield of maize under salinity and fertility stresses. Environmental Stresses in Crop Sciences, 14(1), 195-210. DOI: 10.22077/escs.2020.2473.1652. [In Persian]
  • Singh, R., Van Dam, J.C. & Feddes, R.A. (2006). Water productivity analysis of irrigated crops in Sirsa district. Indian Agricultural Water Management, 82(3), 253-278. DOI: 10.1016/j.agwat.2005.07.027.
  • Soler, C.M.T., Sentelhas, P.C. & Hoogenboom, G. (2007). Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal Agronomy, 27(2-4), 165-177. DOI: 10.1016/j.eja.2007.03.002.
  • Steduto, P., Hsiao, T.C., Raes, D. & Fereres E. (2009). AquaCrop-The FAO crop model to simulate yield response to water: I. concepts and underlying principles. Agronomy Journal. 101(3), 426-437. DOI: 10.2134/agronj2008.0139s.
  • Stricevic, R., Dzeletovic, Z., Djurovic, N. & Cosic, M. (2014). Application of the AquaCrop model to simulate the biomass of Miscanthus x giganteus under different nutrient supply conditions. Global Change Biology Bioenergy (CGB-Bioenergy). DOI: 10.111/gcbb.12206.
  • Taifeh Rezaei, H. (2014). Irrigation planning of agricultural and garden crops. Urmia, Iran: Agriculture-Jahad Organization of West Azerbaijan Province. [In Persian]
  •    WAPWWC (2021). Nitrate range of Urmia plain. Urmia, Iran: West Azerbaijan Province Water and Wastewater Company (WAPWWC). Available at https://www.abfaazgharbi.ir. [In Persian]
  • Wellman, T.P., & Rupert, M.G. (2016). Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, upper black squirrel Creek basin, Colorado, 2013. Scientific Investigations Report. DOI: 10.3133/sir20165020.
  • WHO (2011). Guidelines for drinking-water quality. Geneva, Switzerland: World Health Organization (WHO). Available at https://www.who.int/docs/default-source/food-safety/arsenic/9789241549950-eng.pdf?sfvrsn=bad6319a_2.
  • Zamani, O., Mortazavi, S.A. & Balali, H. (2014). Economical water productivity of agricultural products in Bahar plain, Hamadan. Journal of Water Research in Agriculture, 28(1), 51-62. DOI: 10.22092/jwra.2015.101065. [In Persian]
  •   Zhang, C., Xie, Z., Wang, Q., Tang, M., Feng, S. & Cai, H. (2022). AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity. Agricultural Water Management, 266, 107580. DOI: 10.1016/j.agwat.2022.107580.