Abstract:
Changes in phenology can be used as a proxy to elucidate the short and long term trends
in climate change and variability. Such phenological changes are driven by weather and climate
as well as environmental and ecological factors. Climate change affects plant phenology largely
during the vegetative and reproductive stages. The focus of this study was to investigate the changes
in phenological parameters of maize as well as to assess their causal factors across the selected
maize-producing Provinces (viz: North West, Free State, Mpumalanga and KwaZulu-Natal) of South
Africa. For this purpose, five phenological parameters i.e., the length of season (LOS), start of season
(SOS), end of season (EOS), position of peak value (POP), and position of trough value (POT) derived
from the MODIS NDVI data (MOD13Q1) were analysed. In addition, climatic variables (Potential
Evapotranspiration (PET), Precipitation (PRE), Maximum (TMX) and Minimum (TMN) Temperatures
spanning from 2000 to 2015 were also analysed. Based on the results, the maize-producing Provinces
considered exhibit a decreasing trend in NDVI values. The results further show that Mpumalanga
and Free State Provinces have SOS and EOS in December and April respectively. In terms of the
LOS, KwaZulu-Natal Province had the highest days (194), followed by Mpumalanga with 177 days,
while NorthWest and Free State Provinces had 149 and 148 days, respectively. Our results further
demonstrate that the influences of climate variables on phenological parameters exhibit a strong
space-time and common covariate dependence. For instance, TMN dominated in North West
and Free State, PET and TMX are the main dominant factors in KwaZulu-Natal Province whereas
PRE highly dominated in Mpumalanga. Furthermore, the result of the Partial Least Square Path
Modeling (PLS-PM) analysis indicates that climatic variables predict about 46% of the variability
of phenology indicators and about 63% of the variability of yield indicators for the entire study
area. The goodness of fit index indicates that the model has a prediction power of 75% over the
entire study area. This study contributes towards enhancing the knowledge of the dynamics in the phenological parameters and the results can assist farmers to make the necessary adjustment in
order to have an optimal production and thereby enhance food security for both human and livestock.
