IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-51555-8.html
   My bibliography  Save this article

Preseason maize and wheat yield forecasts for early warning of crop failure

Author

Listed:
  • Weston Anderson

    (University of Maryland
    NASA Goddard Space Flight Center)

  • Shraddhanand Shukla

    (University of California-Santa Barbara)

  • Jim Verdin

    (United States Agency for International Development)

  • Andrew Hoell

    (NOAA Physical Sciences Laboratory)

  • Christina Justice

    (University of Maryland)

  • Brian Barker

    (University of Maryland)

  • Kimberly Slinski

    (University of Maryland
    NASA Goddard Space Flight Center)

  • Nathan Lenssen

    (University of Colorado Boulder
    Colorado School of Mines)

  • Jiale Lou

    (Princeton University)

  • Benjamin I. Cook

    (NASA Goddard Institute for Space Studies
    Lamont-Doherty Earth Observatory)

  • Amy McNally

    (NASA Goddard Space Flight Center)

Abstract

Provided the considerable logistical challenges of anticipatory action and disaster response programs, there is a need for early warning of crop failures at lead times of six to twelve months. But crop yield forecasts at these lead times are virtually nonexistent. By leveraging recent advances in climate forecasting, we demonstrate that global preseason crop yield forecasts are not only possible but are skillful over considerable portions of cropland. Globally, maize and wheat forecasts are skillful at lead times of up to a year ahead of harvest for 15% and 30% of harvested areas, respectively. Forecasts are most skillful in Southeast Africa and Southeast Asia for maize and parts of South and Central Asia, Australia, and Southeast South America for wheat. Wheat forecasts, furthermore, remain skillful at lead times of over 18 months ahead of harvest in some locations. Our results demonstrate that the potential for preseason crop yield forecasts is greater than previously appreciated.

Suggested Citation

  • Weston Anderson & Shraddhanand Shukla & Jim Verdin & Andrew Hoell & Christina Justice & Brian Barker & Kimberly Slinski & Nathan Lenssen & Jiale Lou & Benjamin I. Cook & Amy McNally, 2024. "Preseason maize and wheat yield forecasts for early warning of crop failure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51555-8
    DOI: 10.1038/s41467-024-51555-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-51555-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-51555-8?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
    ---><---

    References listed on IDEAS

    as
    1. Toshichika Iizumi & Hirofumi Sakuma & Masayuki Yokozawa & Jing-Jia Luo & Andrew J. Challinor & Molly E. Brown & Gen Sakurai & Toshio Yamagata, 2013. "Prediction of seasonal climate-induced variations in global food production," Nature Climate Change, Nature, vol. 3(10), pages 904-908, October.
    2. 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).
    3. James S. Risbey & Dougal T. Squire & Amanda S. Black & Timothy DelSole & Chiara Lepore & Richard J. Matear & Didier P. Monselesan & Thomas S. Moore & Doug Richardson & Andrew Schepen & Michael K. Tipp, 2021. "Standard assessments of climate forecast skill can be misleading," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. Yoo-Geun Ham & Jeong-Hwan Kim & Jing-Jia Luo, 2019. "Deep learning for multi-year ENSO forecasts," Nature, Nature, vol. 573(7775), pages 568-572, September.
    5. Mugiyo, H. & Magadzire, T. & Choruma, D. J. & Chimonyo, V. G. P. & Manzou, R. & Jiri, O. & Mabhaudhi, Tafadzwanashe, 2023. "El Niño’s effects on southern African agriculture in 2023/24 and anticipatory action strategies to reduce the impacts in Zimbabwe," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(11):16.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Yuquan Qu & Diego G. Miralles & Sander Veraverbeke & Harry Vereecken & Carsten Montzka, 2023. "Wildfire precursors show complementary predictability in different timescales," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Kung, Chih-Chun & Wu, Tao, 2021. "Influence of water allocation on bioenergy production under climate change: A stochastic mathematical programming approach," Energy, Elsevier, vol. 231(C).
    4. Kukal, M.S. & Irmak, S., 2020. "Impact of irrigation on interannual variability in United States agricultural productivity," Agricultural Water Management, Elsevier, vol. 234(C).
    5. 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.
    6. Dragana Bojovic & Andria Nicodemou & Asun Lera St.Clair & Isadora Christel & Francisco J. Doblas-Reyes, 2022. "Exploring the landscape of seasonal forecast provision by Global Producing Centres," Climatic Change, Springer, vol. 172(1), pages 1-23, May.
    7. Nazan An & Mustafa Tufan Turp & Murat Türkeş & Mehmet Levent Kurnaz, 2020. "Mid-Term Impact of Climate Change on Hazelnut Yield," Agriculture, MDPI, vol. 10(5), pages 1-20, May.
    8. Anwar, Muhuddin Rajin & Liu, De Li & Farquharson, Robert & Macadam, Ian & Abadi, Amir & Finlayson, John & Wang, Bin & Ramilan, Thiagarajah, 2015. "Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia," Agricultural Systems, Elsevier, vol. 132(C), pages 133-144.
    9. Kung, Chih-Chun & Cao, Xiaoyong & Choi, Yongrok & Kung, Shan-Shan, 2019. "A stochastic analysis of cropland utilization and resource allocation under climate change," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    10. Haowei Ni & Han Hu & Constantin M. Zohner & Weigen Huang & Ji Chen & Yishen Sun & Jixian Ding & Jizhong Zhou & Xiaoyuan Yan & Jiabao Zhang & Yuting Liang & Thomas W. Crowther, 2024. "Effects of winter soil warming on crop biomass carbon loss from organic matter degradation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    11. Kim, Daeha & Chun, Jong Ahn & Inthavong, Thavone, 2021. "Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop," Agricultural Water Management, Elsevier, vol. 256(C).
    12. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    13. Nazan An & M Tufan Turp & M Tufan Turp & Murat Türkeş & M Levent Kurnaz & Murat Türkeş & M Levent Kurnaz, 2020. "Climate Change Effects on Agricultural Production- A Short Review," Current Investigations in Agriculture and Current Research, Lupine Publishers, LLC, vol. 8(3), pages 1097-1099, March.
    14. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
    15. Villoria, Nelson B. & Delgado, Michael, 2017. "Worldwide Crop Supply Responses to El Niño Southern Oscillation," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258564, Agricultural and Applied Economics Association.
    16. Akinmutimi Al & Ukonu A, 2020. "Comparative Effects of Wood Ash and Poultry Manure on Soil Ph and Potassium Release from an Acid Ultisol in Umudike, Abia State, Nigeria," Current Investigations in Agriculture and Current Research, Lupine Publishers, LLC, vol. 8(2), pages 1063-1068, February.
    17. Wenxiang, Ding & Caiyun, Zhang & Shaoping, Shang & Xueding, Li, 2022. "Optimization of deep learning model for coastal chlorophyll a dynamic forecast," Ecological Modelling, Elsevier, vol. 467(C).
    18. Kung, Chih-Chun, 2019. "A stochastic evaluation of economic and environmental effects of Taiwan's biofuel development under climate change," Energy, Elsevier, vol. 167(C), pages 1051-1064.
    19. Yumin Liu & Kate Duffy & Jennifer G. Dy & Auroop R. Ganguly, 2023. "Explainable deep learning for insights in El Niño and river flows," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    20. Li, Zhuo-Lin & Yu, Jie & Zhang, Xiao-Lin & Xu, Ling-Yu & Jin, Bao-Gang, 2022. "A Multi-Hierarchical attention-based prediction method on Time Series with spatio-temporal context among variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).

    More about this item

    Statistics

    Access and download statistics

    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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51555-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.