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Effect and side-effect assessment of different agricultural water saving measures in an integrated framework

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  • Raeisi, Leila Goli
  • Morid, Saeed
  • Delavar, Majid
  • Srinivasan, Raghavan

Abstract

Water scarcity has always been a serious challenge for agriculture and food security. Hence, different strategies are considered to save more water and increase water productivity. However, the effectiveness of such water saving measures have not been sufficiently evaluated. In addition, they may have side effects like salt accumulation, which will be exacerbated in dry and semi-arid zones. So, any evaluation needs to be done in an integrated framework. Thus, this work aims to evaluate some of the most popular agricultural water management measures from the points of view of real water saving, water productivity, soil salinity and environmental flow. Such an evaluation requires both field- and basin-scale simulations. To this aim, a modelling setup was defined to estimate the components of crop growth (yield, evaporation and transpiration) as well as water and salt budgets, using the SWAT and AquaCrop models. The evaluated measures include: deficit irrigation, replacing surface irrigation with drip irrigation and greenhouse cultivation. The study area of this research work is a part of the Tashk-Bakhtegan Basin, located in the Fars province of Iran. The results show that deficit irrigation can save water up to 12%, but it would also cause up to 94% more salt accumulation than surface irrigation. Notably, implementation of improved irrigation systems like drip irrigation can increase water consumption up to 8% and still have nearly the same problem with salt accumulation. Among the assessed measures, greenhouse cultivation resulted in an up to 24% reduction in water consumption, with no serious salt accumulation. While it was assumed that the rebound effects of measures are controlled, it was seen in the study area that these measures can lead to an illusion of reducing water consumption due to consequent increases in cultivated area or changing to more water consuming crop patterns. This ultimately reduces groundwater recharge or environmental flow that have been traditionally supplied by irrigation return flows.

Suggested Citation

  • Raeisi, Leila Goli & Morid, Saeed & Delavar, Majid & Srinivasan, Raghavan, 2019. "Effect and side-effect assessment of different agricultural water saving measures in an integrated framework," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    • Handle: RePEc:eee:agiwat:v:223:y:2019:i:c:48
    DOI: 10.1016/j.agwat.2019.105685
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    References listed on IDEAS

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    2. Jeyrani, F. & Morid, S. & Srinivasan, R., 2021. "Assessing basin blue–green available water components under different management and climate scenarios using SWAT," Agricultural Water Management, Elsevier, vol. 256(C).
    3. Wang, Rong & Huang, Guanhua & Xu, Xu & Ren, Dongyang & Gou, Jiachao & Wu, Zhangsheng, 2022. "Significant differences in agro-hydrological processes and water productivity between canal- and well-irrigated areas in an arid region," Agricultural Water Management, Elsevier, vol. 267(C).
    4. Gao, Jie & Zhuo, La & Duan, Ximing & Wu, Pute, 2023. "Agricultural water-saving potentials with water footprint benchmarking under different tillage practices for crop production in an irrigation district," Agricultural Water Management, Elsevier, vol. 282(C).
    5. Wang, Yanyun & Long, Aihua & Xiang, Liyun & Deng, Xiaoya & Zhang, Pei & Hai, Yang & Wang, Jie & Li, Yang, 2020. "The verification of Jevons’ paradox of agricultural Water conservation in Tianshan District of China based on Water footprint," Agricultural Water Management, Elsevier, vol. 239(C).
    6. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    7. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.
    8. Yan, Zhenxing & Zhang, Wenying & Liu, Xiuwei & Wang, Qingsuo & Liu, Binhui & Mei, Xurong, 2024. "Grain yield and water productivity of winter wheat controlled by irrigation regime and manure substitution in the North China Plain," Agricultural Water Management, Elsevier, vol. 295(C).
    9. Babaeian, Fariba & Delavar, Majid & Morid, Saeed & Srinivasan, Raghavan, 2021. "Robust climate change adaptation pathways in agricultural water management," Agricultural Water Management, Elsevier, vol. 252(C).
    10. Nouri, Milad & Homaee, Mehdi & Pereira, Luis S. & Bybordi, Mohammad, 2023. "Water management dilemma in the agricultural sector of Iran: A review focusing on water governance," Agricultural Water Management, Elsevier, vol. 288(C).

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