IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2610.00782.html

Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?

Author

Listed:
  • Colin Aitken
  • Michael K. Tippett
  • Pedram Hassanzadeh
  • Katherine Kowal
  • Rendani Mbuvha
  • John H. Marsham
  • Shruti Nath
  • Ousmane Ndiaye
  • Douglas J. Parker
  • Caroline M Wainwright
  • Michael Kremer
  • William R. Boos

Abstract

Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.

Suggested Citation

  • Colin Aitken & Michael K. Tippett & Pedram Hassanzadeh & Katherine Kowal & Rendani Mbuvha & John H. Marsham & Shruti Nath & Ousmane Ndiaye & Douglas J. Parker & Caroline M Wainwright & Michael Kremer , 2026. "Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?," Papers 2610.00782, arXiv.org, revised Oct 2026.
    • Handle: RePEc:arx:papers:2610.00782
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2610.00782
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Author Correction: Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 621(7980), pages 45-45, September.
    2. Shruti Nath & Tim Palmer, 2026. "Can AI models reliably forecast extreme weather events?," Nature, Nature, vol. 651(8106), pages 583-584, March.
    3. Colin Aitken & Rajat Masiwal & Adam Marchakitus & Katherine Kowal & Mayank Gupta & Tyler Yang & Amir Jina & Pedram Hassanzadeh & William R. Boos & Michael Kremer, 2026. "Designing probabilistic AI monsoon forecasts to inform agricultural decision-making," Papers 2603.07893, arXiv.org, revised Mar 2026.
    4. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 619(7970), pages 533-538, July.
    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. repec:rjr:romjef:v::y:2025:i:3:p:5-23 is not listed on IDEAS
    2. Fang, Zhou & Mengaldo, Gianmarco, 2025. "Dynamical errors in machine learning forecasts," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).
    3. Zhenyu Zhao & Yichen Pan & Jinlong Xiang & Yujia Zhang & An He & Yaotian Zhao & Youlve Chen & Yu He & Xinyuan Fang & Yikai Su & Min Gu & Xuhan Guo, 2025. "High computational density nanophotonic media for machine learning inference," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    4. Bai, Huimin & Gong, Zhiqiang & Li, Li & Ma, Junjie & Dogar, Muhammad Mubashar, 2025. "Vegetation coverage variability and its driving factors in the semi-arid to semi-humid transition zone of North China," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    5. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    6. Song Chen & Jiaxu Liu & Pengkai Wang & Chao Xu & Shengze Cai & Jian Chu, 2024. "Accelerated optimization in deep learning with a proportional-integral-derivative controller," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Shan, Shuo & Dou, Weijing & Sreeram, Victor & Li, Chenxi & Wang, Kai & Zhang, Kanjian & Wei, Haikun, 2026. "SolarFM: Spatio-temporal solar irradiance forecasting based on time series foundation model," Applied Energy, Elsevier, vol. 410(C).
    8. Du, Pengwei & Ming, Bo & Hussain, Fiaz & Fang, Wei & Yan, Zihui & Yang, Xuhui & Huang, Qiang & Liu, Pan, 2026. "An explainable deep learning approach for hydropower production forecasting: Evidence from 116 plants in China," Energy, Elsevier, vol. 355(C).
    9. Zhong, Xiaohui & Chen, Lei & Li, Hao & Fan, Xu & Qian, Wenxu & Liu, Jun & Luo, Enwang & Wu, Libo, 2026. "Improving renewable energy forecasting with FuXi-Energy," Applied Energy, Elsevier, vol. 412(C).
    10. Chang, Ling & Yu, Haibo & Yang, Minghan & Zhang, Ziheng & Chen, Shuai & Wang, Jianye, 2026. "NPP-GPT: Forecasting nuclear power plants operating parameters using pre-trained large language model," Applied Energy, Elsevier, vol. 409(C).
    11. Yan, Jie & Han, Xue & Wang, Han & Ge, Chang & Liu, Yongqian, 2025. "An AI-based weather prediction method for wind farms combining global forecast field and wind speed temporal transfer characteristics," Energy, Elsevier, vol. 329(C).
    12. Yuchen Cai & Jia Yang & Yutang Hou & Feng Wang & Lei Yin & Shuhui Li & Yanrong Wang & Tao Yan & Shan Yan & Xueying Zhan & Jun He & Zhenxing Wang, 2025. "8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    13. Li, Kexin & Jiang, Yanan & Li, Ang & Tian, Xiangzhe & Lu, Jiatong & Wei, Tingting & Xiangli, Jiangfeng & Huang, Xifeng & Li, Yongmin & Sun, Shikun, 2026. "An integrated meteorological adaptive simulation-optimization framework for real-time irrigation scheduling considering perfect weather forecasts," Agricultural Systems, Elsevier, vol. 232(C).
    14. Alok Kumar Mishra & Suneet Dwivedi & Shivam Kesarwani, 2026. "Evaluating the performance of Pangu-Weather model for Dana and Remal tropical cyclones over the Bay of Bengal," 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. 122(2), pages 1-13, January.
    15. Kyle Lesinger & Di Tian, 2025. "Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    16. Huaisheng Tu & Haotian Liu & Tuqiang Pan & Wuping Xie & Zihao Ma & Fan Zhang & Pengbai Xu & Leiming Wu & Ou Xu & Yi Xu & Yuwen Qin, 2025. "Deep empirical neural network for optical phase retrieval over a scattering medium," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    17. Chensen Lin & Ruian Tie & Shihong Yi & Dongqing Liu & Xiaohui Zhong & Zixin Hu & Hao Li, 2026. "Reconstructing fine-scale 3D wind fields with terrain-informed machine learning," Nature Communications, Nature, vol. 17(1), pages 1-10, December.
    18. Li Hu Wang & Xue Mei Liu & Yang Liu & Hai Rui Li & Jia QI Liu & Li Bo Yang, 2023. "Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    19. Hsiao-Chung Tsai & Fang-Yi Lin & Yung-Lan Lin & Nai-Ning Hsu & Treng-Shi Huang & Russell L. Elsberry, 2026. "Quantifying situation-dependent uncertainty in tropical cyclone track forecasts with a recurrent neural network approach," 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. 122(6), pages 1-15, March.
    20. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    21. Susantha Wanniarachchi & Ranjan Sarukkalige & H. A. Prasantha Hapuarachchi & Pattiyage I.A. Gomes & Upaka Rathnayake, 2026. "Uncertainty Reduction in Near Real-time Satellite Precipitation Estimates by Integrating Soil Moisture and Potential Evapotranspiration Using a Machine Learning Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(5), pages 1-20, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2610.00782. 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: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    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.