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University of Cape Town (2011)

Assessing maize water requirements in the context of climate change uncertainties over southern Africa

Hachigonta, Sepo Promise

Titre : Assessing maize water requirements in the context of climate change uncertainties over southern Africa

Auteur : Hachigonta, Sepo Promise

Université de soutenance : University of Cape Town.

Grade : Doctor of Philosophy (PhD) 2011

Résumé
Climate change studies are subject to high uncertainties partly resulting from data reliability. This study investigates the challenges of using statistical downscaled climate data to examine the likely impacts of climate change on maize growth in southern Africa in the context of these uncertainties. Daily downscaled data from five General Circulation Models (GCMs) were used to investigate changes (between the future and recent past) in rainfall totals, evapotranspiration, crop sowing dates, as well as the number and length of dry spells during critical periods for growing maize. A crop model is used together with the downscaled climate data to simulate maize water requirement satisfaction index at 176 stations in southern Africa. A new sensitivity approach that investigates the contribution of sowing decisions to the variation in the maize water requirement satisfaction index is used to develop adaptation options. The projected climate change results show that there is a strong likelihood for increased precipitation over south eastern South Africa. This is characterised by an increase in specific humidity as well as anticyclonic wind anomalies centred along the border between South Africa and Mozambique during the summer rainfall season. In addition, late sowing dates are projected over Botswana, Zimbabwe, central parts of Zambia, the Limpopo region of South Africa and the region bordering Mozambique and South Africa, while earlier sowing are projected over the central and eastern parts of South Africa. The maize water requirement satisfaction index simulations across the five GCMs are more consistent in projecting future changes than the rainfall totals. It suggests that consistent responses may be better detected in crop model outputs as opposed to changes in seasonal or monthly rainfall characteristics. Expected rainfall in the first sowing dekad is the most significant factor to the yield variation in most regions over southern Africa, and as such is an important component of future adaptation in maize growth.

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Page publiée le 12 février 2019