Modelling Maize Yield Vulnerability to Climate Variability

The effect of climate variability on maize yield has been the subject of numerous studies globally, but very few of these studies have focused on the local scale in Africa. As a result, the focus of this work is on creating a vulnerability index that combines sensitivity, exposure, and adaptive capa...

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Veröffentlicht in:IDRiM Journal 2023-12, Vol.13 (2)
Hauptverfasser: Hazzan, Oluwadamilola .O., Lawal, Olanrewaju, C. Elendu, Collins, I. Abdulraheem, Mukhtar, Pei-Gao, Duan
Format: Artikel
Sprache:eng
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Zusammenfassung:The effect of climate variability on maize yield has been the subject of numerous studies globally, but very few of these studies have focused on the local scale in Africa. As a result, the focus of this work is on creating a vulnerability index that combines sensitivity, exposure, and adaptive capacity to assess the degree of vulnerability of Maize yield to climate variability in the south-south region of Nigeria in West Africa. The ratio between the actual maize yield and the projected yield was used to calculate yield sensitivity. Adaptive capacity examines some of the socioeconomic and demographic factors in the study area. A fuzzy function was employed to derive the aggregation of the determinants of Adaptive Capacity (adult literacy, poverty prevalence, accessibility to the settlements of people, and dependency ratio). Exposure was expressed as the average of the long-term and short-term climatic factors (Rainfall and Temperature). Yield sensitivity ranges between 0.471 to 0.698 with moderate to high sensitivity observed in almost the entire growing region. Exposure values indicate a very high level of climate variability with the North of Edo to the Southeastern and Southwestern parts of the State being more exposed. Adaptive capacity is highly variable ranging from 0.174 to 1. The vulnerability index ranges from 0.393 to 0.698. The result indicates a very high to extremely high vulnerability on maize yield across the majority of growing regions in the south-south, which is an indication of a probable yield drop due to changing climate. This model provides a structure for decision-making and planning on climate variability mitigation needs assessment.
ISSN:2185-8322
2185-8322
DOI:10.5595/001c.91437