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

Bibliographic Data

ID15544461
AuthorsAhmed M S Kheir (0000-0001-9569-5420, Julius Kühn-Institut, corresponding author), Osama Ali (0000-0002-7121-4220, Menoufia University), Ashifur Rahman Shawon (0000-0002-0276-7497, Julius Kühn-Institut), Ahmed S Elrys (0000-0003-0373-888X, Zagazig University, corresponding author), Marwa G M Ali (0000-0003-2615-8327, Cairo University), Mohamed A Darwish (Cairo University), Ahmed Elmahdy (0000-0003-4063-0499, Cairo University, corresponding author), Ahmed M Elmahdy, A F Abou-Hadid (0000-0003-0548-7447, Ain Shams University), Rogério de Souza Nóia Júnior (0000-0002-4096-7588, Université de Montpellier), Til Feike (0000-0002-1978-9473, Julius Kühn-Institut)
Year2024
Volume19
Issue10
Pages104049-104049
Publication date2024-08-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad75ab
OpenAlexW4402029870
LanguageEN
Citations received3
References cited67

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 field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) ( R 2 > 0.78), N ( R 2 > 0.75), Fe ( R 2 > 0.71) and Zn ( R 2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition

Agricultural engineering · Agriculture · Agronomy · Biology · Climate change · Context (archaeology · Crop · Crop yield · Food security · Geography · Grain yield · Machine learning · Nitrogen · Yield (engineering · Chemistry · Climate change impacts on agriculture · Computer Science · Crop Yield and Soil Fertility · Environmental Science · Materials Science · Mathematics · Soil Carbon and Nitrogen Dynamics · Ecology

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