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Uncertainty of climate change impact on crop characteristics: a case study of Moghan plain in Iran


Crop yield is one of the most critical factors in the food security chain. Climate plays a crucial role in crop water productivity in rainfed and irrigated crop productions. Climate changes would significantly impact crop characteristics, especially in Iran, where water is the major constraint of crop production. This study assessed the impact of climate change on crop water productivity with related uncertainty. The global climate model simulations of rainfall and temperature were statistically downscaled using LARS-WG6 for climate projection. The projected climate was used in the FAO AquaCrop model to simulate the variability of crop characteristics (crop cycle length, crop yield, and water productivity) for the assessment of climate change effect on major crops for three future horizons (2021–2040, 2041–2060, 2061–2080). Results revealed an increase in wheat yield by 14 − 54% and a decrease of growth duration by 1 − 12%, leading to an increase in water productivity by 9 − 96% in the future compared to the base period (1985–2016). In contrast, reduction in corn and soybean yield by 1 − 5% and 2 − 6% and growth period by 1 − 5% and 3 − 12%, and thus, an increase in water productivity by 1 − 9% and 2 − 24%, respectively, were projected. The growth duration of all the major crops was projected to decrease due to a rise in temperature and an increase in crop water productivity in the study area. The results indicate a more favorable condition for crop agriculture in the study area under the projected climate.

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  • Alamgir M, Khan N, Shahid S, et al (2020) Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stoch Environ Res Risk Assess 1–18

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome 300:D05109

  • Amouamouha M, Badalians Gholikandi G (2017) Characterization and antibiofouling performance investigation of hydrophobic silver nanocomposite membranes: A comparative study. Membranes (basel) 7:64

    Article  Google Scholar 

  • Amouamouha M, Gholikandi GB (2018) Assessment of anaerobic nanocomposite membrane bioreactor efficiency intensified by biogas backwash. Chem Eng Process Intensif 131:51–58

    Article  Google Scholar 

  • Arikan BB, Jiechen L, Sabbah IID et al (2021) Dew Point Time Series Forecasting at the North Dakota. Knowledge-Based Eng Sci 2:24–34

    Article  Google Scholar 

  • Barrow EM, Sauchyn DJ (2019) Uncertainty in climate projections and time of emergence of climate signals in the western Canadian Prairies. Int J Climatol 39(11):4358–4371

  • Cai X, Rosegrant MW (2003) World water productivity: current situation and future options. In (Kijne JW, Barker R and Molden D, eds.) Water productivity in agriculture: Limits and opportunities for improvement

  • Calzadilla A, Zhu T, Rehdanz K et al (2013) Economywide impacts of climate change on agriculture in sub-Saharan Africa. Ecol Econ 93:150–165

    Article  Google Scholar 

  • Chiotti QP, Johnston T (1995) Extending the boundaries of climate change research: a discussion on agriculture. J Rural Stud 11:335–350

    Article  Google Scholar 

  • Dubey SK, Sharma D (2018) Assessment of climate change impact on yield of major crops in the Banas River Basin, India. Sci Total Environ 635:10–19

    Article  Google Scholar 

  • FAO (2018) The State of Food Security and Nutrition in the World. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • FAO (2019) AquaCrop | Land & Water. In: Food Agric. Organ. United Nations

  • FAOSTAT F (2018) Statistics of the Food and Agriculture Organization of the United Nations. In: Food Agric. Organ. United Nations

  • Field CB, Barros VR (2014) Climate change 2014–Impacts, adaptation and vulnerability: Regional aspects. Cambridge University Press

  • Gholikandi GB, Beklar BI, Amouamouha M (2021) Performance prediction and upgrading of electroanaerobic baffled reactor using neural-fuzzy method. J Environ Chem Eng 9:106029

    Article  Google Scholar 

  • Hamed MM, Nashwan MS, Shahid S et al (2022) Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmos Res 265:105927

    Article  Google Scholar 

  • Hamed MM, Nashwan MS, Shahid S (2021) A novel selection method of CMIP6 GCMs for robust climate projection. Int J Climatol

  • Heinemann AB, de HN Maia A, Dourado-Neto D, Ingram KT, Hoogenboom G (2006) Soybean (Glycine max (L.) Merr.) growth and development response to CO2 enrichment under different temperature regimes. Eur J Agron 24(1):52–61

  • Heinemann AB, Ramirez-Villegas J, Stone LF, Didonet AD (2017) Climate change determined drought stress profiles in rainfed common bean production systems in Brazil. Agric for Meteorol 246:64–77

    Article  Google Scholar 

  • Homsi R, Shiru MS, Shahid S et al (2020) Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria. Eng Appl Comput Fluid Mech 14:90–106

    Google Scholar 

  • Hosseini TSM, Hosseini SA, Ghermezcheshmeh B, Sharafati A (2020) Drought hazard depending on elevation and precipitation in Lorestan. Iran Theor Appl Climatol 142:1369–1377

    Article  Google Scholar 

  • Kang Y, Khan S, Ma X (2009) Climate change impacts on crop yield, crop water productivity and food security–a review. Prog Nat Sci 19:1665–1674

    Article  Google Scholar 

  • Khaleefa O, Kamel AH (2021) On the evaluation of water quality index: case study of Euphrates River, Iraq. Knowledge-Based Eng Sci 2:35–43

    Article  Google Scholar 

  • Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382

    Article  Google Scholar 

  • Leemans R, Solomon AM (1993) Modeling the potential change in yield and distribution of the earth’s crops under a warmed climate. Clim Res 79–96

  • Liu YR, Li YP, Ding YK (2021) Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method. Atmos Res 247:105241

    Article  Google Scholar 

  • Lobell DB, Ortiz-Monasterio JI, Asner GP et al (2005) Analysis of wheat yield and climatic trends in Mexico. F Crop Res 94:250–256

    Article  Google Scholar 

  • Ludwig F, Asseng S (2006) Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agric Syst 90:159–179

    Article  Google Scholar 

  • Maghrebi M, Noori R, Bhattarai R, et al (2020) Iran’s Agriculture in the Anthropocene. Earth’s Futur 8:e2020EF001547

  • Naganna SR, Beyaztas BH, Bokde N, Armanuos AM (2020) On the evaluation of the gradient tree boosting model for groundwater level forecastinG. Knowledge-Based Eng Sci 1:48–57

    Article  Google Scholar 

  • Noor M, Ismail T, Shahid S et al (2019) Development of multi-model ensemble for projection of extreme rainfall events in Peninsular Malaysia. Hydrol Res 50:1772–1788

    Article  Google Scholar 

  • Omeje OE, Maccido HS, Badamasi YA, Abba SI (2021) Performance of hybrid neuro-fuzzy model for solar radiation simulation at Abuja, Nigeria: a correlation based input selection technique. Knowledge-Based Eng Sci 2:54–66

    Google Scholar 

  • Porter JR, Gawith M (1999) Temperatures and the growth and development of wheat: a review. Eur J Agron 10(1):23–36

  • Qin W, Wang L, Lin A et al (2018) Comparison of deterministic and data-driven models for solar radiation estimation in China. Renew Sustain Energy Rev 81:579–594.

    Article  Google Scholar 

  • Reilly J, Paltsev S, Felzer B et al (2007) Global economic effects of changes in crops, pasture, and forests due to changing climate, carbon dioxide, and ozone. Energy Policy 35:5370–5383

    Article  Google Scholar 

  • Sachindra DA, Ahmed K, Rashid MM et al (2018) Statistical downscaling of precipitation using machine learning techniques. Atmos Res 212:240–258

    Article  Google Scholar 

  • Salman SA, Nashwan MS, Ismail T, Shahid S (2020) Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Hydrol Res 51:781–798

    Article  Google Scholar 

  • Sharafati A, Zahabiyoun B (2014) Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arab J Sci Eng 39

  • Sharafati A, Pezeshki E, Shahid S, Motta D (2020) Quantification and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran. J Soils Sediments

  • Sharafati A, Pezeshki E (2020) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theor Appl Climatol 139:

  • Sharma A, Goyal MK (2020) Assessment of the changes in precipitation and temperature in Teesta River basin in Indian Himalayan Region under climate change. Atmos Res 231:104670

    Article  Google Scholar 

  • Shiferaw B, Smale M, Braun H-J et al (2013) Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur 5:291–317

    Article  Google Scholar 

  • Shin Y, Shin Y, Hong J et al (2021) Future projections and uncertainty assessment of precipitation extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere (basel) 12:97

    Article  Google Scholar 

  • Silva V de PR da, Maciel GF, Braga CC, et al (2018) Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil. Ciência Rural 48:

  • Sowers J, Vengosh A, Weinthal E (2011) Climate change, water resources, and the politics of adaptation in the Middle East and North Africa. Clim Change 104:599–627

    Article  Google Scholar 

  • Steduto P, Raes D, Hsiao TC, et al (2009) Concepts and applications of AquaCrop: the FAO crop water productivity model. In: Crop modeling and decision support. Springer, pp 175–191

  • Sulaiman SO, Shiri J, Shiralizadeh H et al (2018) Precipitation pattern modeling using cross-station perception: regional investigation. Environ Earth Sci 77:709

    Article  Google Scholar 

  • Tur R, Yontem S (2021) A comparison of soft computing methods for the prediction of wave height parameters. Knowledge-Based Eng Sci 2:31–46

    Article  Google Scholar 

  • Waha K, Krummenauer L, Adams S et al (2017) Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Reg Environ Chang 17:1623–1638

    Article  Google Scholar 

  • Yang C, Fraga H, Van Ieperen W, Santos JA (2017) Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric Water Manag 184:178–190

    Article  Google Scholar 

  • Yano T, Aydin M, Haraguchi T (2007) Impact of climate change on irrigation demand and crop growth in a Mediterranean environment of Turkey. Sensors 7:2297–2315

    Article  Google Scholar 

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The authors would like to reveal their gratitude and appreciation to the data providers, Iranian Meteorological Organization and Ministry of Agriculture-Jahad.

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Authors and Affiliations



Ahmad Sharafati proposed the topic, participated in coordination, aided in the interpretation of results, and paper editing. Mahmoud Moradi Tayyebi the review analysis, modeling and participated in drafting the manuscript. Elnaz Pezeshki carried out the investigation, and participated in drafting the manuscript. Shamsuddin Shahid carried out the validation, aided in the interpretation of results, and paper editing. All authors read and approved the final manuscript.

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Correspondence to Ahmad Sharafati.

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Sharafati, A., Moradi Tayyebi, M., Pezeshki, E. et al. Uncertainty of climate change impact on crop characteristics: a case study of Moghan plain in Iran. Theor Appl Climatol 149, 603–620 (2022).

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