Variability of satellite derived phenological parameters across maize producing areas of South Africa

Show simple item record Adisa, O.M. (Omolola) Botai, Joel Ongego Hassen, Abubeker Darkey, Daniel Adeola, Abiodun Morakinyo Tesfamariam, Eyob Habte Botai, Christina M. Adisa, Abidemi T. 2018-10-22T11:16:35Z 2018-10-22T11:16:35Z 2018-08-27
dc.description.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. en_ZA
dc.description.department Animal and Wildlife Sciences en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.department Plant Production and Soil Science en_ZA
dc.description.librarian am2018 en_ZA
dc.description.sponsorship The Department of Science and Technology (DST) and The National Research Foundation (NRF). en_ZA
dc.description.uri en_ZA
dc.identifier.citation Adisa, O.M., Botai, J.O., Hassen, A. et al. 2018, 'Variability of satellite derived phenological parameters across maize producing areas of South Africa', Sustainability, vol. 10, art. 3003, pp. 1-20. en_ZA
dc.identifier.issn 2071-1050 (online)
dc.identifier.other 10.3390/su10093033
dc.language.iso en en_ZA
dc.publisher MDPI Publishing en_ZA
dc.rights © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_ZA
dc.subject Phenology en_ZA
dc.subject Maize en_ZA
dc.subject Climate en_ZA
dc.subject Variation en_ZA
dc.subject Zea mays en_ZA
dc.subject Moderate resolution imaging spectroradiometer (MODIS) en_ZA
dc.subject Normalized difference vegetation index (NDVI) en_ZA
dc.title Variability of satellite derived phenological parameters across maize producing areas of South Africa en_ZA
dc.type Article en_ZA

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