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Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments

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Abstract

Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders. Nevertheless, there exists inherent uncertainty in such cases. General Circulation Models (GCMs) and future climate change scenarios (different Representative Concentration Pathways (RCPs) in different future time periods) are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield. This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments (Shiraz, Hamedan, Sanandaj, Kermanshah and Khorramabad) in eastern and southern Iran. These five representative locations can be categorized into three climate classes: arid cold (Shiraz), semi-arid cold (Hamedan and Sanandaj) and semi-arid cool (Kermanshah and Khorramabad). Accordingly, the downscaled daily outputs of 29 GCMs under two RCPs (RCP4.5 and RCP8.5) in the near future (2030s), middle future (2050s) and far future (2080s) were used as inputs for the Agricultural Production Systems sIMulator (APSIM)-wheat model. Analysis of variance (ANOVA) was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield. Years from 1980 to 2009 were regarded as the baseline period. The projection results indicated that wheat grain yield was expected to increase by 12.30%, 17.10%, and 17.70% in the near future (2030s), middle future (2050s) and far future (2080s), respectively. The increases differed under different RCPs in different future time periods, ranging from 11.70% (under RCP4.5 in the 2030s) to 20.20% (under RCP8.5 in the 2080s) by averaging all GCMs and locations, implying that future wheat grain yield depended largely upon the rising CO2 concentrations. ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations, followed by scenarios, GCMs, and their interactions. Specifically, at the semi-arid climate locations (Hamedan, Sanandaj, Kermanshah and Khorramabad), most of the variations arose from the scenarios (77.25%), while at the arid climate location (Shiraz), GCMs (54.00%) accounted for the greatest variation. Overall, the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions, particularly in dryland environments characterized by large fluctuations in rainfall and temperature. Moreover, the current research suggested some GCMs (e.g., the IPSL-CM5B-LR, CCSM4, and BNU-ESM) that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.

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References

  • AgMIP. 2013. Guide for running AgMIP climate scenario generation tools with R in Windows Version 2.3. [2022-04-18]. https://raw.githubusercontent.com/agmip/Climate-Scenarios-Generator/master/Guide-for-Running-AgMIP-Climate-Scenario-Generation-with-R-v2.0.pdf

  • Ahmad I, Ahmad B, Boote K, et al. 2020. Adaptation strategies for maize production under climate change for semi-arid environments. European Journal of Agronomy, 115: 126040, doi: https://doi.org/10.1016/j.eja.2020.126040.

    CAS  Google Scholar 

  • Ahmad Q U A, Biemans H, Moors E, et al. 2020. The impacts of climate variability on crop yields and irrigation water demand in South Asia. Water, 13(1): 50, doi: https://doi.org/10.3390/w13010050.

    Google Scholar 

  • Amiri S, Eyni-Nargeseh H, Rahimi-Moghaddam S, et al. 2021. Water use efficiency of chickpea agro-ecosystems will be boosted by positive effects of CO2 and using suitable genotype×environment×management under climate change conditions. Agricultural Water Management, 252: 106928, doi: https://doi.org/10.1016/j.agwat.2021.106928.

    Google Scholar 

  • Amiri S R, Deihimfard R, Soltani A. 2016. A single supplementary irrigation can boost chickpea grain yield and water use efficiency in arid and semiarid conditions: a modeling study. Agronomy Journal, 108(6): 2406–2416.

    Google Scholar 

  • Araya A, Hoogenboom G, Luedeling E, et al. 2015. Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia. Agricultural and Forest Meteorology, 214: 252–265.

    Google Scholar 

  • Asseng S, Foster I, Turner N C. 2011. The impact of temperature variability on wheat yields. Global Change Biology, 17(2): 997–1012.

    Google Scholar 

  • Asseng S, Ewert F, Rosenzweig C, et al. 2013. Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3(9): 827–832.

    CAS  Google Scholar 

  • Asseng S, Ewert F, Martre P, et al. 2014. Rising temperatures reduce global wheat production. Nature Climate Change, 5: 143–147.

    Google Scholar 

  • Boughton W C. 1989. A review of the USDA SCS curve number method. Soil Research, 27(3): 511–523.

    Google Scholar 

  • Chapagain R, Remenyi T A, Harris R M, et al. 2022. Decomposing crop model uncertainty: A systematic review. Field Crops Research, 279: 108448, doi: https://doi.org/10.1016/j.fcr.2022.108448.

    Google Scholar 

  • Cheng L, Phillips T J, AghaKouchak A. 2015. Non-stationary return levels of CMIP5 multi-model temperature extremes. Climate Dynamics, 44(11): 2947–2963.

    Google Scholar 

  • Collins B, Najeeb U, Luo Q, et al. 2022. Contribution of climate models and APSIM phenological parameters to uncertainties in spring wheat simulations: Application of SUFI-2 algorithm in northeast Australia. Journal of Agronomy and Crop Science, 208(2): 225–242.

    Google Scholar 

  • De Pauw E, Ghasemi Dehkordi V R, Ghaffari A. 2018. Agroecological zones. In: Roozitalab M, Siadat H, Farshad A. The Soils of Iran. Enschede: Springer, Cham, 163–173.

    Google Scholar 

  • Ding Z L, Ali E F, Elmahdy A M, et al. 2021. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agricultural Water Management, 244: 106626, doi: https://doi.org/10.1016/j.agwat.2020.106626.

    Google Scholar 

  • Edenhofer O. 2014. Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the IPCC Fifth Assessment Report. New York: Cambridge University Press, 1–1435.

    Google Scholar 

  • Eghdamirad S, Johnson F, Sharma A. 2017. Using second-order approximation to incorporate GCM uncertainty in climate change impact assessments. Climate Change, 142(1–2): 37–52.

    Google Scholar 

  • Engebretsen A, Vogt R D, Bechmann M. 2019. SWAT model uncertainties and cumulative probability for decreased phosphorus loading by agricultural Best Management Practices. CATENA, 175: 154–166.

    CAS  Google Scholar 

  • FAO (Food and Agriculture organization of the United Nations). 2020. FAOSTAT Data. [2022-04-18]. http://www.fao.org/faostat/en/#data/QC.

  • Freychet N, Hegerl G, Mitchell D, et al. 2021. Future changes in the frequency of temperature extremes may be underestimated in tropical and subtropical regions. Communications Earth & Environment, 2(1): 1–8.

    Google Scholar 

  • Gupta R, Mishra A. 2019. Climate change induced impact and uncertainty of rice yield of agro-ecological zones of India. Agricultural Systems, 173: 1–11.

    Google Scholar 

  • Hao S R, Ryu D, Western A, et al. 2021. Sensitivity analysis of APSIM wheat yield predictions. American Geophysical Union, New Orleans (USA). 2021-12-12-2021-12-17. New Orleans, USA.

    Google Scholar 

  • Hawkins E, Smith R S, Gregory J M, et al. 2016. Irreducible uncertainty in near-term climate projections. Climate Dynamics, 46(11): 3807–3819.

    Google Scholar 

  • Hawkins R H, Ward T J, Woodward D, et al. 2008. Curve Number Hydrology: State of Practice. Reston: American Society of Civil Engineers, 1–104.

    Google Scholar 

  • Holzworth D P, Huth N I, deVoil P G, et al. 2014. APSIM-evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62: 327–350.

    Google Scholar 

  • Hosseinzadehtalaei P, Tabari H, Willems P. 2017. Uncertainty assessment for climate change impact on intense precipitation: how many model runs do we need? International Journal of Climatology, 37(S1): 1105–1117.

    Google Scholar 

  • Kamali B, Lorite I J, Webber H A, et al. 2022. Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain. Scientific Reports, 12: 4049, doi: https://doi.org/10.1038/s41598-022-08056-9.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kassie B T, Asseng S, Rotter R P, et al. 2015. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Climatic Change, 129(1): 145–158.

    Google Scholar 

  • Khan M S, Coulibaly P, Dibike Y. 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319(1–4): 357–382.

    Google Scholar 

  • Kidanemariam S, Goitom H, Desta Y. 2021. Coupled application of R and WetSpa models for assessment of climate change impact on streamflow of Werie Catchment, Tigray, Ethiopia. Journal of Water and Climate Change, 12(3): 916–936.

    Google Scholar 

  • Liu W H, Ye T, Jägermeyr J, et al. 2021. Future climate change significantly alters interannual wheat yield variability over half of harvested areas. Environmental Research Letters, 16(9): 094045, doi: https://doi.org/10.1088/1748-9326/ac1fbb.

    CAS  Google Scholar 

  • Lobell D B, Hammer G L, Chenu K, et al. 2015. The shifting influence of drought and heat stress for crops in northeast Australia. Global Change Biology, 21(11): 4115–4127.

    PubMed  Google Scholar 

  • Lv Z F, Liu X J, Cao W X, et al. 2013. Climate change impacts on regional winter wheat production in main wheat production regions of China. Agricultural and Forest Meteorology, 171–172: 234–248.

    Google Scholar 

  • Masutomi Y, Takahashi K, Harasawa H, et al. 2009. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models. Agriculture, Ecosystems & Environment, 131(3–4): 281–291.

    Google Scholar 

  • Obembe O S, Hendricks N P, Tack J. 2021. Decreased wheat production in the USA from climate change driven by yield losses rather than crop abandonment. PLOS ONE, 16(6): e0252067, doi: https://doi.org/10.1371/journal.pone.0252067.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Ojeda J J, Rezaei E E, Kamali B, et al. 2021. Impact of crop management and environment on the spatio-temporal variance of potato yield at regional scale. Field Crops Research, 270: 108213, doi: https://doi.org/10.1016/j.fcr.2021.108213.

    Google Scholar 

  • Olesen J E, Carter T R, Diaz-Ambrona C H, et al. 2007. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Climatic Change, 81(1): 123–143.

    Google Scholar 

  • Osborne T, Rose G, Wheeler T. 2013. Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation. Agricultural and Forest Meteorology, 170: 183–194.

    Google Scholar 

  • Ponce V M, Hawkins R H. 1996. Runoff curve number: Has it reached maturity? Journal of Hydrologic Engineering, 1(1): 11–19.

    Google Scholar 

  • R Core Team. 2017. R: A Language and Environment for Statistical Computing. Online: R Foundation for Statistical Computing, Vienna, Austria. [2022-04-18]. http://www.R-project.orgs.

    Google Scholar 

  • Rahimi-Moghaddam S, Kambouzia J, Deihimfard R. 2019. Optimal genotype×environment×management as a strategy to increase grain maize productivity and water use efficiency in water-limited environments and rising temperature. Ecological Indicators, 107: 105570, doi: https://doi.org/10.1016/j.ecolind.2019.105570.

    Google Scholar 

  • Rahimi-Moghaddam S, Deihimfard R, Azizi K, et al. 2021. Characterizing spatial and temporal trends in drought patterns of rainfed wheat (Triticum aestivum L.) across various climatic conditions: A modelling approach. European Journal of Agronomy, 129: 126333, doi: https://doi.org/10.1016/j.eja.2021.126333.

    Google Scholar 

  • Rahman M H, Ahmad A, Wang X C, et al. 2018. Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agricultural and Forest Meteorology, 253–254: 94–113.

    Google Scholar 

  • Rettie F M, Gayler S, Weber T K D, et al. 2022. Climate change impact on wheat and maize growth in Ethiopia: A multi-model uncertainty analysis. PLOS ONE, 17(1): e0262951, doi: https://doi.org/10.1371/journal.pone.0262951.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Reyenga P J, Howden S M, Meinke H, et al. 1999. Modelling global change impacts on wheat cropping in south-east Queensland, Australia. Environmental Modelling & Software, 14(4): 297–306.

    Google Scholar 

  • Ruane A C, Cecil L D, Horton R M, et al. 2013. Climate change impact uncertainties for maize in Panama: Farm information, climate projections, and yield sensitivities. Agricultural and Forest Meteorology, 170: 132–145.

    Google Scholar 

  • Ruane A C, McDermid S P. 2017. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspectives, 4(1): 1–20.

    Google Scholar 

  • Ruiz-Ramos M, Rodríguez A, Dosio A, et al. 2016. Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century. Climatic Change, 134(1–2): 283–297.

    Google Scholar 

  • Saddique Q, Liu D L, Wang B, et al. 2020. Modelling future climate change impacts on winter wheat yield and water use: A case study in Guanzhong Plain, northwestern China. European Journal of Agronomy, 119: 126113, doi: https://doi.org/10.1016/j.eja.2020.126113.

    Google Scholar 

  • Saxton K E, Willey P H. 2005. The SPAW model for agricultural field and pond hydrologic simulation. In: Singh V P, Frevert D K. Watershed Models. Boca Raton: CRC Press, 400–435.

    Google Scholar 

  • Schierhorn F, Hofmann M, Adrian I, et al. 2020. Spatially varying impacts of climate change on wheat and barley yields in Kazakhstan. Journal of Arid Environments, 178: 104164, doi: https://doi.org/10.1016/j.jaridenv.2020.104164.

    Google Scholar 

  • Shi L J, Feng P Y, Wang B, et al. 2020. Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models. Journal of Hydrology, 590: 125394, doi: https://doi.org/10.1016/j.jhydrol.2020.125394.

    Google Scholar 

  • Tao F L, Rötter R P, Palosuo T, et al. 2018. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Global Change Biology, 24(3): 1291–1307.

    PubMed  Google Scholar 

  • UNEP (United Nations Environment Programme). 1992. World Atlas of Desertification. [2022-04-18]. https://wedocs.unep.org/20.500.11822/42137.

  • UNESCO (United Nations Educational, Scientific and Cultural Organization). 1979. Map of the World Distribution of Arid Regions: Explanatory Note. Paris: UNESCO, 1–54.

    Google Scholar 

  • Vogeler I, Sharp J, Cichota R, et al. 2022. Sensitivity analysis of soil parameters in the Agricultural Production Systems sIMulator (APSIM). Soil Research, 61(2): 176–186.

    Google Scholar 

  • Wang B, Liu D L, Waters C, et al. 2018. Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Climatic Change, 151(2): 259–273.

    Google Scholar 

  • Zhang Y, Zhao Y X, Feng L P. 2019. Higher contributions of uncertainty from global climate models than crop models in maize-yield simulations under climate change. Meteorological Applications, 26(1): 74–82.

    CAS  Google Scholar 

  • Zhao G, Bryan B A, Song X D. 2014. Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters. Ecological Modelling, 279: 1–11.

    CAS  Google Scholar 

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Acknowledgements

The project was funded by the Deputy of Research Affairs, Lorestan University, Iran (Contract No. 1400-6-02-5-18-1402).

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Correspondence to Sajjad Rahimi-Moghaddam.

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Deihimfard, R., Rahimi-Moghaddam, S., Javanshir, F. et al. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments. J. Arid Land 15, 545–561 (2023). https://doi.org/10.1007/s40333-023-0056-x

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