Spatial distribution of indigenous climate indicator development for rural smallholder farmers in Nkomazi local municipality, South Africa

Nomcebo Ubisi (), Unathi Kolanisi, Obert Jiri and Frederick Tshitangano
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Nomcebo Ubisi: Human Sciences Research Council, HSRC Building
Unathi Kolanisi: University of KwaZulu-Natal
Obert Jiri: University of KwaZulu-Natal
Frederick Tshitangano: Human Sciences Research Council, HSRC Building

Climatic Change, 2023, vol. 176, issue 9, No 9, 12 pages

Abstract: Abstract This study aimed at smallholder farmers to use their indigenous knowledge in combination with modern climate indicators when making farming decisions in Nkomazi Local Municipality, South Africa. Twelve villages were sampled in Nkomazi municipality based on their use of IKS with 100 participants applying both qualitative and quantitative research methods. ArcMap v10.7.1 software was used to map the distribution of indigenous climate indicators in these villages, and SPSS v25.0 software was used to analyse the quantitative data. Qualitative data was analysed through thematic analysis which is used to interpret patterns of meaning within qualitative data. The most used indicators smallholder farmers relied on for weather predictions included animals (31%) followed by plants (26%). The Poynton model predicted negative results with a 50C temperature increase, meaning that if the hot temperatures continue rising, the farmers' indigenous indicators will decline or disappear, making it difficult for rural smallholder farmers to make informed farm-level decisions. Therefore, the study suggests an integration of Indigenous Knowledge Systems (IKS) and local knowledge to adapt to climate change, have access to updated agricultural information and ability to make informed farm-level decisions.

Keywords: Indigenous knowledge; Climate indicators; Smallholder farmers; Spatial distribution (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10584-023-03536-x

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