Future-oriented agricultural water management with scenario-based evaluation: Case study of Maize in Khuzestan

Document Type : Special Issue: New Approaches to Water and Soil Management and Modeling

Authors

1 Ph.D. Candidate in Irrigation and Drainage, Department of Irrigation and Reclamation, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Irrigation and Reclamation, University of Tehran; and Faculty Member at Imam Khomeini International University, Qazvin, Iran

3 Professor, Department of Irrigation and Reclamation, University of Tehran, Karaj, Iran

Abstract

Amidst intensifying climatic and management pressures on water resources in Iran, this research focuses on exploring the desirable and effective future of water use in agriculture, with a case study of maize in Khuzestan Province. The LARS-WG 8 was used to project climate data up to the horizon year 2040. The biophysical crop yield was examined using AquaCrop 7.1. According to the results, the LARS-WG model generated temperature data (NRMSE≈1%) with greater accuracy than precipitation (NRMSE≤13%) at Ahvaz and Dezful stations. In addition, the AquaCrop model (R²=0.96, RMSE<0.5 t/ha, NSE≈0.98) confirmed the high accuracy of maize yield simulation. Moreover, structural scenarios developed with the ScenarioWizard software and the MICMAC matrix included 13 significant drivers from the policy, technology, and climate domains. The findings indicate that the effect of climate change on water productivity is incremental, and shaping a desirable future is largely influenced by management. The results showed that, when moving from SSP1-2.6 to SSP5-8.5, grain maize yield and water productivity increased in both spring and summer maize. In spring, yield increased from 6.26 t/ha in SSP1-2.6 to 6.79 t/ha in SSP5-8.5, and water productivity increased from 1.13 to 1.22 kg/m3. In summer, this trend was more pronounced, with yield rising from 8.32 to 9.15 t/ha and water productivity from 1.29 to 1.42 kg/m3. These results indicated that under the more severe climate change scenario (SSP5-8.5), crop growth and yield were more affected, especially in summer. Furthermore, this study provides a picture of the desired future of water use in agriculture. According to the Total Impact Score in ScenarioWizard, (381, 405, 408) a desirable and effective future was identified when political and technological measures were taken at a high level of intervention. According to these results, achieving a desirable future depends on the full implementation of decentralization policies, data transparency, and the use of advanced statistical systems.

Keywords

Main Subjects


Abdulsahib, S. M., Zubaidi, S. L., Almamalachy, Y., & Dulaimi, A. (2024). Temperature and precipitation change assessment in the North of Iraq using LARS-WG and CMIP6 models. Water, 16(19), 2869.‏ doi: 10.3390/w16192869
Adib, A., Kalantarzadeh, S. S. O., Shoushtari, M. M., Lotfirad, M., Liaghat, A., & Oulapour, M. (2023). Sensitive analysis of meteorological data and selecting appropriate machine learning model for estimation of reference evapotranspiration. Applied Water Science, 13(3), 83.‏ doi: 10.1007/s13201-023-01895-5
Agricultural Yearbook 2022–2023 Field Crops. 2024. Planning and Economic Affairs Deputy, Ministry of Agriculture. https://get.agrodl.ir/statistics/field-crops/401-402.pdf. [In Persian]
Doorenbos, J., & Kassam, A (1979). Yield response to water. Irrigation and drainage paper, 33, 498.‏
Ahmadi, M., Etedali, H. R., Salem, A., Al-Mukhtar, M., & Elbeltagi, A. (2024). Simulation of wheat water footprint using AquaCrop model under the climate change, case study in Qazvin plain. Applied Water Science, 14(12), 264.‏ doi: 10.1007/s13201-024-02305-0
Alexandra, C., & Wyborn, C. (2023). Foresight in natural resource management: A case study in Australia. Futures, 154, 103259.‏ doi: 10.1016/j.futures.2023.103259
Almazroui, M., Islam, M.N., Saeed, F., Saeed, S., Ismail, M., Ehsan, M.A., Diallo, I., O’Brien, E., Ashfaq, M., Martínez-Castro, D. and Cavazos, T. (2021). Projected changes in temperature and precipitation over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Systems and Environment, 5(1), 1-24. doi: 10.1007/s41748-021-00199-5
Bagheri Khaneghahi, M., HezarJaribi, A., Kamali, M. I., & Zamani, F. (2025). Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways scenarios. Modeling and Management of Water and Soil, 5, 32-48.‏ doi: 10.22098/mmws.2025.17004.1568.
Baluch, S. M., Wang, L., Faiz, M. A., Li, H., Chen, Y., Wang, L., & Li, M. (2025). Adaptation simulation and planning for crop yield under climate change: Integrating AquaCrop and DSSAT to project drought-induced yield risks in the Sanjiang Plain. Agricultural Water Management, 319, 109818.‏ doi: 10.1016/j.agwat.2025.109818
Barati, A. A., Azadi, H., Dehghani Pour, M., Lebailly, P., & Qafori, M. (2019). Determining key agricultural strategic factors using AHP-MICMAC. Sustainability, 11(14), 3947.‏ doi: 10.3390/su11143947
Correia, P. M., da Silva, A. B., Vaz, M., Carmo-Silva, E., & Marques da Silva, J. (2021). Efficient regulation of CO2 assimilation enables greater resilience to high temperature and drought in maize. Frontiers in plant science, 12, 675546. doi: 10.3389/fpls.2021.675546.
Dehaghi, B. F., Amini, M., Rangkooy, H. A., & Ghavamabadi, L. I. (2022). Estimation of farmworkers’ exposure to heat extremes in upcoming years in the southern part of Iran. Air Quality, Atmosphere & Health, 15(8), 1489-1495.‏ doi: 10.1007/s11869-022-01194-z
Dehghani, T., Liaghat, A., Nazari, B. (2025a). Analysis of the Inefficiencies in Agricultural Water Productivity Policies in Iran. Iranian Water Resources Research Article e228903 (Article in press). doi: 10.22034/iwrr.2025.534241.2916 [In Persian]
Dehghani, T., Liaghat, A., Nazari, B. (2025b). Analysis of Climate Scenario Impacts on Irrigated Wheat Water Productivity in Alborz Province Using the AquaCrop Model. Iranian Water Research Journal (Article in press). doi: 10.22034/iwrj.2025.15462.2717 [In Persian]
Dehghani, T., Rahimikhoob, A., & Arab, M. (2019). Investigating the effect of Basil planting date on AquaCrop’s normalized water productivity. Iranian Journal of Soil and Water Research, 49(6), 1299-1307.‏ doi: 10.22059/ijswr.2018.252253.667850 [In Persian]
Demneh, M. T., Zackery, A., & Nouraei, A. (2023). Using corporate foresight to enhance strategic management practices. European Journal of Futures Research, 11(1), 5.‏ doi: 10.1186/s40309-023-00217-x
Ebrahimi Pak, N.A., Agdarnajad, A., & Khodadadi-Dehkordi, D. (2018). Evaluation of the AquaCrop model in simulating maize yield under deficit irrigation treatments and different levels of superabsorbent application. Iranian Journal of Irrigation and Water Engineering, 8(31), 166–183. https://www.waterjournal.ir/article_74092.html [In Persian]
Ednie, G., Kapoor, T., Koppel, O., Piczak, M.L., Reid, J.L., Murdoch, A.D., Cook, C.N., Sutherland, W.J. and Cooke, S.J. (2023). Foresight science in conservation: Tools, barriers, and mainstreaming opportunities. Ambio, 52(2), 411-424.‏ doi: 10.1007/s13280-022-01786-0
Farahza, M.N., Nazari, B., Liaghat, A., Alizadeh, H.A. (2019). Irrigation water productivity of agricultural crops in Bushehr Province. Water Management in Agriculture 6(1): 95–104. https://wmaj.iaid.ir/article_100577.html [In Persian]
Flores-Marquez, R., Vera-Vílchez, J., Verástegui-Martínez, P., Lastra, S., & Solórzano-Acosta, R. (2024). An evaluation of dryland ulluco cultivation yields in the face of climate change scenarios in the Central Andes of Peru by using the Aquacrop model. Sustainability, 16(13), 5428.‏ doi: 10.3390/su16135428
George-Williams, H. E., Hunt, D. V., & Rogers, C. D. (2024). Foresight for Sustainable Water Futures in Sub-Saharan Africa: A Systematic Review. Sustainability, 16(20), 8874.‏ doi: 10.3390/su16208874
Ghazizadeh, A., Omidvar, K., Mozaffari,. G.H., Mazidi, A. (2025). Detection of climate change impacts on precipitation and temperature parameters in the Kabul River Basin, Afghanistan using the LARS model. Geography and Environmental Hazards 14 no. 2: 68–87. doi: 10.22067/geoeh.2025.88181.1488 [In Persian]
Godet, M. (2007). Manuel de prospective stratégique-Tome 2-3ème édition-L'Art et la méthode. dunod.‏ https://documentation.insp.gouv.fr/insp/doc/SYRACUSE/130967/manuel-de-prospective-strategique-tome-2-l-art-et-la-methode-michel-godet
Habibi, A., Jahantigh, F. F., & Sarafrazi, A. (2015). Fuzzy Delphi technique for forecasting and screening items. Asian Journal of Research in Business Economics and Management, 5(2), 130-143.‏ https://www.researchgate.net/publication/271964020_Fuzzy_Delphi_Technique_for_Forecasting_and_Screening_Items.
Hajivand Paydar, S., Yazdanpanah, H., Andarzian, S.B. (2023). Assessment of climate change impacts on growth and yield of grain maize in northern Khuzestan Province using the AquaCrop model. Agricultural Meteorology Journal 11(2): 40–50. doi: 10.22125/agmj.2023.330985.1132 [In Persian]
Hosseini, SM., Khoshrosh, M., Gholami-Sefidkoohi, M.A., Norouz-Valashadi, R. (2024). Assessment of climate change impacts and planting dates under IPCC scenarios on green pea yield using the AquaCrop model. Water Research in Agriculture 38(4): 341–355. doi: 10.22092/jwra.2025.366835.1053 [In Persian]
Jahangir, M., & Rouzbahani, F. (2024). Simulation of climatic parameters using statistical downscaling models SDSM and LARS in West Azerbaijan Province. Ecohydrology Journal 11 no. 3: 374–394. doi: 10.22059/ije.2024.373803.1805 [In Persian]
Jorrehnoosh, M., Agdarnajad, A., Shahrokhnia, M., Ebrahimi Pak, NA. (2024). Evaluation of the AquaCrop model for simulating wheat yield under different agricultural management scenarios in Qazvin. Modeling and Management of Water and Soil, 4 (2): 1–16. doi: 10.22098/mmws.2023.12533.1248 [In Persian]
Khordadi, M. J., Olesen, J. E., Alizadeh, A., Nassiri Mahallati, M., Ansari, H., & Sanaeinejad, H. (2019). Climate change impacts and adaptation for crop management of winter wheat and maize in the semi‐arid region of iran. Irrigation and Drainage, 68(5), 841-856.‏ Irrigation and Drainage 68 no. 5: 841-856.‏ doi: 10.1002/ird.2373
Khorsand, A., Dehghanisanij, H., Heris, A. M., Asgarzadeh, H., & Rezaverdinejad, V. (2024). Calibration and evaluation of the FAO AquaCrop model for canola (Brassica napus) under full and deficit irrigation in a semi-arid region. Applied Water Science, 14(3), 56. doi: 10.1007/s13201-024-02108-3.
Khoshsirat, A. M., Najarchi, M., Jafarinia, R., & Mokhtari, S. (2022). Sensitivity analysis and determination of the optimal level of water use efficiency for winter wheat and barley under different irrigation scenarios using the AquaCrop Model in arid and semiarid climatic conditions (Case Study: Dehloran Plain, Iran). Water, 14(21), 3455. doi: 10.3390/w14213455.
Kimball, B. A. (2016). Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Current opinion in plant biology, 31, 36-43. doi: 10.1016/j.pbi.2016.03.006
Kipkulei, H. K., Boitt, M., Eshetu, S. B., Sieber, S., & Rotich, B. (2025). Modelling Maize Yield Sensitivity to Abiotic Stresses in East Africa: Integration of Crop Modelling and Synthetic Climate Change Scenarios. International Journal of Plant Production, 1-11.‏ doi: 10.1007/s42106-025-00341-7
Kosow, H., Brauner, S., Brumme, A., et al. (2024). Uncharted water conflicts ahead: mapping the scenario space for Germany in the year 2050. Frontiers in Water, 6, 1492336.‏  doi: 10.3389/frwa.2024.1492336
Lazurko, A., Schweizer, V., & Armitage, D. (2023). Exploring “big picture” scenarios for resilience in social–ecological systems: transdisciplinary cross-impact balances modeling in the Red River Basin. Sustainability Science, 18(4), 1773-1794.‏ doi: 10.1007/s11625-023-01308-1
Li, Y., Li, N., Javed, T., Pulatov, A. S., & Yang, Q. (2024). Cotton yield responses to climate change and adaptability of sowing date simulated by AquaCrop model. Industrial Crops and Products, 212, 118319. doi: 10.1016/j.indcrop.2024.118319
Lotfi, M., Kamali, G. A., Meshkatee, A. H., and Varshavian, V. 2022. Performance analysis of LARS-WG and SDSM downscaling models in simulating temperature and precipitation changes in the West of Iran. Modeling Earth Systems and Environment, 8(4), pp. 4649-4659.‏ doi: 10.1007/s40808-022-01393-8
Manzano-Solís, L. R., Díaz-Delgado, C., Gómez-Albores, M. A., Mastachi-Loza, C. A., & Soares, D. (2019). Use of structural systems analysis for the integrated water resources management in the Nenetzingo river watershed, Mexico. Land Use Policy, 87, 104029.‏ doi: 10.1016/j.landusepol.2019.104029
Marei, G., Agdarnajad, A., Ebrahimi-Pak, N.A. (2024). Simulation of climate change impacts on yield, water productivity, and growth duration of sugar beet in Qazvin using the AquaCrop model. Sugar Beet Journal 40(2): 165–184. doi: 10.22092/jsb.2025.365259.1351 [In Persian]
Maryanji, Z, Sotoudeh, F., Toulabi-Nejad, M., Zarrin. Z., (2025). Modeling and forecasting temperature change trends in Hamedan County. Applied Research in Geographical Sciences 25(77): 152–173. doi: 10.61186/jgs.25.77.17 [In Persian]
Maucieri, C., Borin, M., Morbidini, F., et al. (2025). Projecting the impacts of climate change on soybean production and water requirements using AquaCrop model. European Journal of Agronomy, 165, 127538.‏ doi: 10.1016/j.eja.2025.127538
Mirgol, B., Nazari, M., Eteghadipour, M. (2020). Modelling climate change impact on irrigation water requirement and yield of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and fodder maize (Zea mays L.) in the semi-arid Qazvin Plateau, Iran. Agriculture 10(3): 60.‏ doi: 10.3390/agriculture10030060
Moazazi, F., Yavari, G., Mousavi, S.H., Bagheri, M. (2021). Assessment of climate change impacts on agriculture in the Hamedan–Bahar Plain with emphasis on water productivity and food security. Agricultural Economics and Development 34(3): 305–323. doi: 10.22067/jead.2020.17793.0 [In Persian]
Moradi R, Koocheki, A., Nassiri Mahallati, M. (2013). Impact of climate change on maize production and evaluation of planting date adjustment as an adaptation strategy under Mashhad climatic conditions. Journal of Agricultural Science and Sustainable Production 23(4): 111–130. https://sustainagriculture.tabrizu.ac.ir/article_786.html [In Persian]
Nawir, D., Bakri, M. D., & Syarif, I. A. (2023). Central government role in road infrastructure development and economic growth in the form of future study: the case of Indonesia. City, Territory and Architecture, 10(1), 12.‏ doi: 10.1186/s40410-022-00188-9
Nazari, M., Mirgol, B., & Salehi, H. (2021). Climate change impact assessment and adaptation strategies for rainfed wheat in contrasting climatic regions of Iran. Frontiers in Agronomy, 3, 806146.‏ doi: 10.3389/fagro.2021.806146
Nejadrekabi, M., Eslamian, S., & Zareian, M. J. (2022). Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology, 19(7), 6573-6594.‏ doi: 10.1007/s13762-021-03852-8
Neysi, K., Egdernezhad, A., Abbasi, F. (2023). Evaluation of AquaCrop model for corn simulation under different management of nitrogen fertilizer in Karaj. Modeling and Management of Water and Soil, 3(1), 26–41. doi: 10.22098/mmws.2022.10969.1093. [In Persian]
Paziresh, H., Nazari, B., Sotoudehnia, A., (2023). Evaluation of strategies for improving water productivity in upstream and downstream areas of the Qazvin irrigation network. Water and Irrigation Management 13 no. 1: 141–156. doi: 10.22059/jwim.2022.342528.987 [In Persian]
Qin, M., Zheng, E., Hou, D., et al. (2023). Response of wheat, maize, and rice to changes in temperature, precipitation, CO2 concentration, and uncertainty based on crop simulation approaches. Plants, 12(14), 2709.‏ doi: 10.3390/plants12142709
Rezaei, E. E., & Lashkari, A. (2019). The consequences of change in management practices on maize yield under climate warming in Iran. Theoretical and Applied Climatology, 137(1), 1001-1013.‏ doi: 10.1007/s00704-018-2637-8
Semenov, M. A., & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic change, 35(4), 397-414.‏ doi: 10.1023/A:1005342632279
Sokolova, A. (2022). Pre-foresight integrative methodology for STI policy: Increasing coherence and impact. Futures, 135, 102875.‏ doi: 10.1016/j.futures.2021.102875
Soltani, A., Nahbandani, A., Zeinali, A. et al. (2019). “Development of an atlas of yield gaps and production potential of major crops in Iran under current and future climate conditions. Vazhegan Sirang, Tehran. https://agrilib.areeo.ac.ir/book_9002.pdf [In Persian]
Steduto, P., & Albrizio, R. (2005). Resource use efficiency of field-grown sunflower, sorghum, wheat and chickpea: II. Water use efficiency and comparison with radiation use efficiency. Agricultural and Forest Meteorology, 130(3-4), 269-281.‏ doi: 10.1016/j.agrformet.2005.04.003
Steduto, P., Hsiao, T. C., & Fereres, E. (2007). On the conservative behavior of biomass water productivity. Irrigation Science, 25(3), 189-207.‏ doi: 10.1007/s00271-007-0064-1
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
Tori, S., Te Boveldt, G., & Keseru, I. (2023). Building scenarios for urban mobility in 2030: The combination of cross-impact balance analysis with participatory stakeholder workshops. Futures, 150, 103160.‏ doi: 10.1016/j.futures.2023.103160
Van Bussel, L. G., Grassini, P., Van Wart, J., et al. (2015). From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research, 177, 98-108.‏ doi: 10.1016/j.fcr.2015.03.005
Wang, X., Luo, N., Zhu, Y., Yan, Y., Wang, H., Xie, H., Wang, P. and Meng, Q. (2023). Water replenishment to maize under heat stress improves canopy temperature and grain filling traits during the reproductive stage. Agricultural and Forest Meteorology, 340, 109627. doi: 10.1016/j.agrformet.2023.109627.
Wang, X., Wang, L., Chen, Y., Hu, Y., Guan, R., Li, M., & Zhang, Y. (2024). Mitigating the negative effect of warming on crop yield: assessing the carbon fertilization and organic amendment application effect. Field Crops Research, 311, 109370. doi: 10.1016/j.fcr.2024.109370.
Weimer-Jehle, W. (2009). Properties of cross-impact balance analysis. arXiv preprint arXiv:0912.5352.‏ doi: 10.48550/arXiv.0912.5352
Weimer-Jehle, W. (2023). Cross-Impact Balances (CIB) for Scenario Analysis. Cham, Switzerland: Springer.‏ doi: 10.1007/978-3-031-27230-1
Yaraghi Fard, M., & Shokouhibidhendi, M. S. (2025). Futuristic research of resilience of water resources with scenario planning approach based on a case study: Zayandeh Rood watershed. Modeling and Management of Water and Soil, 5(3), 1–19. doi: 10.22098/mmws.2024.15086.1460. [In Persian]
Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250, 105369. doi: 10.1016/j.atmosres.2020.105369
Zhang, M., Xu, M., & Li, F. (2025). Drought risk assessment of summer maize based on aquacrop model. Sustainable Water Resources Management, 11(3), 57.‏ doi: 10.1007/s40899-025-01239-y
Zhang, Y., Niu, H., & Yu, Q. (2021). Impacts of climate change and increasing carbon dioxide levels on yield changes of major crops in suitable planting areas in China by the 2050s. Ecological Indicators, 125, 107588.‏ doi: 10.1016/j.ecolind.2021.107588
Volume 6, Issue 2
Special Issue (Guest Editor: Dr. Raoof Mostafazadeh)
May 2026
Pages 232-251
  • Receive Date: 01 December 2025
  • Revise Date: 29 December 2025
  • Accept Date: 29 December 2025