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University of KwaZulu-Natal (2018)

Analysis and integration of regional scale temperature datasets into a seasonal crop monitoring system.

Magadzire, Tinomutenda Tamuka.

Titre : Analysis and integration of regional scale temperature datasets into a seasonal crop monitoring system.

Auteur : Magadzire, Tinomutenda Tamuka.

Université de soutenance : University of KwaZulu-Natal

Grade : Doctor of Philosophy in Agrometeorology 2018

Résumé partiel
Populations in excess of 20 million people in southern Africa annually face food insecurity. This number increases appreciably when detrimental seasonal climate conditions lead to widespread reductions in crop harvests. This situation has led to the development of regionalscale crop monitoring systems that incorporate crop-specific water balance (CSWB) models for early detection and warning of impending weather-related crop production shortfalls. Early warning of anticipated reductions in crop harvests facilitates early action in responding to potential crises. One such system, used by the Famine Early Warning Systems Network (FEWS NET) in southern Africa for crop monitoring, calculates the water requirements satisfaction index (WRSI) using a CSWB model. Operationally, CSWB models for calculating WRSI have used a static length of growing period (LGP) to bracket the period over which rainfall and evapotranspiration variations can affect crop yields. In the long term, concerns have been raised by some studies on the impact of rising air temperatures on crop production. There is therefore a need to incorporate the impacts of air temperature on crops directly into food security monitoring systems, in order to improve the accuracy of these monitoring systems in identifying and locating weather-related crop production shortfalls. This study sought to assess the potential improvements that can be introduced to the crop monitoring system in general and the WRSI in particular, by incorporating air temperature data into the CSWB model. To address this objective, daily maximum and minimum air temperature grids derived from a general circulation model reanalysis were used to generate thermal time estimates, expressed as growing degree day (GDD) grids for a maize crop. The GDDs were used to estimate the LGP of maize for each pixel of each summer season (which typically runs between around October and March) from 1982/1983 to 2016/2017 in southern Africa. The variable, temperature-driven LGP estimates compared favourably with LGP values obtained from literature for a few sample locations. The variable LGP was used to calculate the WRSI for 35 seasons, and the resultant WRSI showed improved correlation with historical yield estimates compared to the static-LGP WRSI, particularly after the farming practice of planting on multiple dates was taken into consideration. Various expressions of WRSI were considered in the analysis, including WRSI calculated assuming planting at the onset of rains, WRSI aggregated from varying number of separate planting dates, including three and six planting dates as test examples, and WRSI calculated using a modified soil water holding capacity to better capture local soil management practices.


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