IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v261y2022ics0378377421006211.html
   My bibliography  Save this article

Multi-model projections of trade-offs between irrigated and rainfed maize yields under changing climate and future emission scenarios

Author

Listed:
  • Irmak, S.
  • Sandhu, R.
  • Kukal, M.S.

Abstract

Eighteen global circulation models (GCMs) were evaluated to determine the potential impacts of future climate change on irrigated and rainfed maize yields using the FAO AquaCrop model on an inter-annual and decadal basis (2020 s until 2090 s). Prior to deemed fit for future simulations, AquaCrop model was subject to comprehensive calibration and validation using extensive field-measured long-term datasets. We observed declines in (decadal) rainfed maize yields, ranging from 2.2% (0.2 t/ha) to 17% (1.4 t/ha) and from 8.1% (0.6 t/ha) to 21.5% (1.7 t/ha) under Representative Concentration Pathways (RCPs) RCP 4.5 and RCP 8.5, respectively. The range of declines was lower for irrigated yields [3.7% (0.5 t/ha) to 6.7% (1.0 t/ha) and 4.3% (0.6 t/ha) to 15.6% (2.2 t/ha) under RCP 4.5 and RCP 8.5, respectively]. Near maximal yield declines were distributed uniformly across the century and almost all decades exhibited > 10% yield declines under at least one emission scenario. Both economic (grain yield) advantage associated with irrigation (difference in irrigated and rainfed yields) and yield stabilizing benefit of irrigation (difference in rainfed and irrigated yield variability) are projected to decrease significantly (p < 0.05) under RCP 8.5. Rainfed maize yield variability was 533% and 200% greater than irrigated yield variability under RCP 4.5 and RCP 8.5, respectively. For RCP 4.5, the long-term mean inter-GCM (2020–2099) standard deviation in rainfed yields (4.6 t/ha) was 460% greater than that in irrigated yields (0.8 t/ha), while for RCP 8.5, this difference was 271% (4.6 t/ha vs. 1.2 t/ha). Tmax and Tmin were able to explain more variability in irrigated than rainfed maize yields, the difference being 229% and 126%, respectively. Precipitation change explained 46% and 50% of the variability in rainfed yield change under RCP 4.5 and RCP 8.5, respectively, and was 100% and 733% greater than what was explained for irrigated yield variability. The research findings hold significance for water allocation considering how dynamics of grain yields vs. availability of irrigation may manifest in the future.

Suggested Citation

  • Irmak, S. & Sandhu, R. & Kukal, M.S., 2022. "Multi-model projections of trade-offs between irrigated and rainfed maize yields under changing climate and future emission scenarios," Agricultural Water Management, Elsevier, vol. 261(C).
  • Handle: RePEc:eee:agiwat:v:261:y:2022:i:c:s0378377421006211
    DOI: 10.1016/j.agwat.2021.107344
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421006211
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2021.107344?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ahmadzadeh Araji, Hamidreza & Wayayok, Aimrun & Massah Bavani, Alireza & Amiri, Ebrahim & Abdullah, Ahmad Fikri & Daneshian, Jahanfar & Teh, C.B.S., 2018. "Impacts of climate change on soybean production under different treatments of field experiments considering the uncertainty of general circulation models," Agricultural Water Management, Elsevier, vol. 205(C), pages 63-71.
    2. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    3. Deines, Jillian M. & Schipanski, Meagan E. & Golden, Bill & Zipper, Samuel C. & Nozari, Soheil & Rottler, Caitlin & Guerrero, Bridget & Sharda, Vaishali, 2020. "Transitions from irrigated to dryland agriculture in the Ogallala Aquifer: Land use suitability and regional economic impacts," Agricultural Water Management, Elsevier, vol. 233(C).
    4. Abedinpour, M. & Sarangi, A. & Rajput, T.B.S. & Singh, Man & Pathak, H. & Ahmad, T., 2012. "Performance evaluation of AquaCrop model for maize crop in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 110(C), pages 55-66.
    5. Deepak K. Ray & James S. Gerber & Graham K. MacDonald & Paul C. West, 2015. "Climate variation explains a third of global crop yield variability," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
    6. Rashid, Muhammad Adil & Jabloun, Mohamed & Andersen, Mathias Neumann & Zhang, Xiying & Olesen, Jørgen Eivind, 2019. "Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 222(C), pages 193-203.
    7. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
    8. Yang, Chenyao & Fraga, Helder & Ieperen, Wim Van & Santos, João Andrade, 2017. "Assessment of irrigated maize yield response to climate change scenarios in Portugal," Agricultural Water Management, Elsevier, vol. 184(C), pages 178-190.
    9. Seyed Ahmadi & Elnaz Mosallaeepour & Ali Kamgar-Haghighi & Ali Sepaskhah, 2015. "Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2837-2853, June.
    10. Catherine L. Kling & Yiannis Panagopoulos & Sergey S. Rabotyagov & Adriana M. Valcu & Philip W. Gassman & Todd Campbell & Michael J. White & Jeffrey G. Arnold & Raghavan Srinivasan & Manoj K. Jha & Je, 2014. "LUMINATE: linking agricultural land use, local water quality and Gulf of Mexico hypoxia," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(3), pages 431-459.
    11. Kukal, M.S. & Irmak, S., 2020. "Impact of irrigation on interannual variability in United States agricultural productivity," Agricultural Water Management, Elsevier, vol. 234(C).
    12. Sandhu, Rupinder & Irmak, Suat, 2019. "Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed cond," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    13. Khalidullin O H, 2018. "Water Circulation and Climate Change," JOJ Wildlife & Biodiversity, Juniper Publishers Inc., vol. 1(1), pages 1-3, November.
    14. Martins, Minella Alves & Tomasella, Javier & Dias, Cássia Gabriele, 2019. "Maize yield under a changing climate in the Brazilian Northeast: Impacts and adaptation," Agricultural Water Management, Elsevier, vol. 216(C), pages 339-350.
    15. Corey Lesk & Ethan Coffel & Radley Horton, 2020. "Net benefits to US soy and maize yields from intensifying hourly rainfall," Nature Climate Change, Nature, vol. 10(9), pages 819-822, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mary Ann Cunningham, 2022. "Climate Change, Agriculture, and Biodiversity: How Does Shifting Agriculture Affect Habitat Availability?," Land, MDPI, vol. 11(8), pages 1-13, August.
    2. Neik, T. X. & Siddique, K. H. M. & Mayes, S. & Edwards, D. & Batley, J. & Mabhaudhi, Tafadzwanashe & Song, B. K. & Massawe, F., 2023. "Diversifying agrifood systems to ensure global food security following the Russia–Ukraine crisis," Papers published in Journals (Open Access), International Water Management Institute, pages 1-7:1124640.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Minghui & Wang, Haidong & Fan, Junliang & Xiang, Youzhen & Liu, Xiaoqiang & Liao, Zhenqi & Abdelghany, Ahmed Elsayed & Zhang, Fucang & Li, Zhijun, 2022. "Evaluation of AquaCrop model for greenhouse cherry tomato with plastic film mulch under various water and nitrogen supplies," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    3. Feng, Dingrui & Li, Guangyong & Wang, Dan & Wulazibieke, Mierguli & Cai, Mingkun & Kang, Jing & Yuan, Zicheng & Xu, Houcheng, 2022. "Evaluation of AquaCrop model performance under mulched drip irrigation for maize in Northeast China," Agricultural Water Management, Elsevier, vol. 261(C).
    4. Sandhu, Rupinder & Irmak, Suat, 2019. "Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed cond," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    5. Dhouib, M. & Zitouna-Chebbi, R. & Prévot, L. & Molénat, J. & Mekki, I. & Jacob, F., 2022. "Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem," Agricultural Water Management, Elsevier, vol. 273(C).
    6. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    7. Kelly, T.D. & Foster, T. & Schultz, David M., 2023. "Assessing the value of adapting irrigation strategies within the season," Agricultural Water Management, Elsevier, vol. 275(C).
    8. Kukal, M.S. & Irmak, S., 2020. "Impact of irrigation on interannual variability in United States agricultural productivity," Agricultural Water Management, Elsevier, vol. 234(C).
    9. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
    10. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    11. Fawen Li & Dong Yu & Yong Zhao, 2019. "Irrigation Scheduling Optimization for Cotton Based on the AquaCrop Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 39-55, January.
    12. Umesh, Barikara & Reddy, K.S. & Polisgowdar, B.S. & Maruthi, V. & Satishkumar, U. & Ayyanagoudar, M.S. & Rao, Sathyanarayan & Veeresh, H., 2022. "Assessment of climate change impact on maize (Zea mays L.) through aquacrop model in semi-arid alfisol of southern Telangana," Agricultural Water Management, Elsevier, vol. 274(C).
    13. Narges Zaredar & Seyed Ali Jozi & Nematollah Khorssani & Seyed Mahmoud Shariat, 2021. "Climate-induced changing environment in semidry lands: a statistical-based simulation approach in Qarasou Sub-basin of Karkheh River Basin," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10416-10431, July.
    14. Martins, Minella A. & Tomasella, Javier & Rodriguez, Daniel A. & Alvalá, Regina C.S. & Giarolla, Angélica & Garofolo, Lucas L. & Júnior, José Lázaro Siqueira & Paolicchi, Luis T.L.C. & Pinto, Gustavo , 2018. "Improving drought management in the Brazilian semiarid through crop forecasting," Agricultural Systems, Elsevier, vol. 160(C), pages 21-30.
    15. Marjan Aziz & Sultan Ahmad Rizvi & Muhammad Sultan & Muhammad Sultan Ali Bazmi & Redmond R. Shamshiri & Sobhy M. Ibrahim & Muhammad A. Imran, 2022. "Simulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    16. Qaisar Saddique & Huanjie Cai & Jiatun Xu & Ali Ajaz & Jianqiang He & Qiang Yu & Yunfei Wang & Hui Chen & Muhammad Imran Khan & De Li Liu & Liang He, 2020. "Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(8), pages 1523-1543, December.
    17. Tinashe Lindel Dirwai & Aidan Senzanje & Tafadzwanashe Mabhaudhi, 2021. "Calibration and Evaluation of the FAO AquaCrop Model for Canola ( Brassica napus ) under Varied Moistube Irrigation Regimes," Agriculture, MDPI, vol. 11(5), pages 1-18, May.
    18. LoPiccalo, Katherine, 2022. "Impact of broadband penetration on U.S. Farm productivity: A panel approach," Telecommunications Policy, Elsevier, vol. 46(9).
    19. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    20. Cao, Juan & Zhang, Zhao & Tao, Fulu & Chen, Yi & Luo, Xiangzhong & Xie, Jun, 2023. "Forecasting global crop yields based on El Nino Southern Oscillation early signals," Agricultural Systems, Elsevier, vol. 205(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:261:y:2022:i:c:s0378377421006211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.