Impacts of climate change on spatial wheat yield and nutritional values using hybrid machine learning

Abstract Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled fiel...

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Bibliographic Details
Published in:	Environmental research letters Vol. 19; no. 10; pp. 104049 - 104066
Main Authors:	Kheir, Ahmed M S, Ali, Osama A M, Shawon, Ashifur Rahman, Elrys, Ahmed S, Ali, Marwa G M, Darwish, Mohamed A, Elmahdy, Ahmed M, Abou-Hadid, Ayman Farid, Nóia Júnior, Rogerio de S, Feike, Til
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01-10-2024
Subjects:	Agricultural sciences Agronomy Algorithms automatic machine learning Climate change Climatic conditions CMIP6 Computation and Language Computer Science Crop yield Cultivars downscaled NEX scenarios Environmental impact Field tests Food security Growing season Human nutrition Iron Learning algorithms Life Sciences Machine learning Nitrogen Nutrition nutritional concentrations Nutritive value stacked ensemble model Statistical tests uncertainty Wheat Zinc CMIP6 nutritional concentrations automatic machine learning uncertainty downscaled NEX scenarios stacked ensemble model
Online Access:	Get full text
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