Spatial distribution of indigenous climate indicator development for rural smallholder farmers

Abstract

This study aimed to help smallholder farmers to use their indigenous knowledge in combination with modern climate indicators when making farming decisions in Nkomazi Local Municipality. The Poynton model was used to predict the distribution of the plant and animal indicators if the temperature continues to rise by at 50C. Twelve villages were sampled with 100 participants applying both qualitative and quantitative research methods. ArcMap 10.7.1 was used to map the distribution of indigenous climate indicators in these villages, and SPSS 25.0 was used to analyse the quantitative data. Qualitative data was analysed through thematic analysis. Mostly 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 the rural smallholder farmers to make informed farm-level decisions. These are the negative effects climate change has on rural smallholder farmers. Therefore, the study suggests an integration of Indigenous Knowledge Systems (IKS) and modern science to adapt to climate change, have access to updated agricultural information and ability to make informed farm-level decisions.