Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data
Author:
Funder
National Science and Technology Council
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11356-024-34652-5.pdf
Reference123 articles.
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2. Acevedo M, Pixley K, Zinyengere N et al (2020) A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nat Plants 6:1231–1241. https://doi.org/10.1038/s41477-020-00783-z
3. Agbo EP, Nkajoe U, Edet CO (2023) Comparison of Mann-Kendall and Şen’s innovative trend method for climatic parameters over Nigeria’s climatic zones. Clim Dyn 60(11):3385–3397. https://doi.org/10.1007/s00382-022-06521-9
4. Ali S, Eum H, Cho J, Dan L, Khan F, Dairaku K, Shrestha ML, Hwang S, Nasim W, Khan IA, Fahad S (2019) Assessment of climate extremes in future projections downscaled by multiple statistical downscaling methods over Pakistan. Atmos Res 222:114–133. https://doi.org/10.1016/j.atmosres.2019.02.009
5. Alidoost F, Stein ZS (2019) Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: a new copula application. Weather Clim Extrem 26:100227. https://doi.org/10.1016/j.wace.2019.100227
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