Research Articles
Assessing the effects of senescence on mesic subtropical grass quality and quantity using in situ and remotely sensed data
DOI:
10.2989/10220119.2026.2649134
Author(s):
Lwando RoyimaniDirectorate of Sustainable Resource Use and Management, Eastern Cape Department of Agriculture, South Africa, Onisimo MutangaDiscipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, South Africa, Mbulisi SibandaGeography, Environmental Studies, and Tourism, University of the Western Cape, South Africa, Timothy DubeInstitute for Water Studies, Department of Earth Sciences, University of the Western Cape, South Africa, Rob SlotowSchool of Life Sciences, University of KwaZulu-Natal, South Africa,
Abstract
Forage quality is one of the most important factors that determines the distribution and grazing patterns of livestock. During senescence, herbaceous plants redistribute nutrients from leaves to roots. This study quantified the decline in nGongoni (Aristida junciformis) grass quality and quantity due to senescence using in situ and Sentinel-2 data. Forage availability status was assessed based on dry matter (DM) content. Chlorophyll content and biomass were used as surrogates for pre-senescent and senescent grass quality and quantity, while average biomass was used to determine the DM. The Random Forest regression yielded coefficients of determination (R2) of 91.3% and 96.6% plus root mean square errors (RMSEs) of 2.12 and 0.55 µg Chl cm−2 for pre-senescent and senescent grass qualities, respectively. The model further yielded R2 values of 70.9% and 94.2%, and RMSEs of 0.34 and 0.02 kg m−2, for pre-senescent and senescent grass quantities, respectively. Optimal predictions of pre-senescent and senescent grass qualities and quantities were achieved using the red-edge and its associated indices, as well as the near-infrared and red band derivatives. The results also showed a decrease in grass quality (17.2 µg Chl cm−2) and quantity (0.89 kg m−2) following senescence. This study helps to understand variability in forage quality and quantity through space and time.
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