EXPLORING PERCEIVED IMPACTS OF SHIFTING MOPANE WOODLAND ON MEDICINAL PLANTS IN VHEMBE, SOUTH AFRICA

Exploring perceived impacts of shifting Mopane woodland on medicinal plants in Vhembe, South Africa

Exploring perceived impacts of shifting Mopane woodland on medicinal plants in Vhembe, South Africa

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Introduction.Shifting ecosystems, driven by climate change and human activities, are affecting various ecosystems around the globe.This is a phenomenon which unfolds spatially and temporally.Shifting ecosystems have Ott-Antonsen ansatz truncation of a circular cumulant series significant consequences for communities that rely on ecosystem services.

This is particularly true in rural areas where people depend on indigenous medicinal plants.Objective.This study investigates the impacts of shifting Mopane woodlands on medicinal plants used by local communities in Ward 12 of the Musina local municipality, Vhembe district, Limpopo province, South Africa, focusing on the availability and effectiveness of these plants.Methods.

A mixed study approach was used combining semi-structured interviews with a qualitative focus group discussion (FGD).These methods aimed to assess perceived changes in the availability and effectiveness of medicinal plants due to ecosystem shifts in Ward 12 of Musina local municipality in the Vhembe district of the Limpopo province.Results.The study identified 64 medicinal plants, of which 25 species are becoming extinct and rare, while some species are perceived to be less effective for treating ailments and other problems owing to diminishing quality of leaves (31 species) and roots (18 species).

Implications.We recommend that community tree planting projects should integrate the growing of indigenous trees of medicinal value to minimise the threat on natural species and to perpetuate their use.The results provide actionable insights for local authorities, environmental policymakers, and health practitioners engaged Improvement the estimation of reference evapotranspiration by combining different types of meteorological data Using machine learning models in the conservation of natural resources and traditional medicine.

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