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University of KwaZulu-Natal (2019)

Integration of indigenous knowledge systems and modern climate science : development of a mobile application to improve smallholder agricultural production.

Ubisi, Nomcebo Rhulani.

Titre : Integration of indigenous knowledge systems and modern climate science : development of a mobile application to improve smallholder agricultural production.

Auteur : Ubisi, Nomcebo Rhulani.

Université de soutenance : University of KwaZulu-Natal

Grade : Doctor of Philosophy (Food Security) 2019

Résumé partiel
In sub Saharan Africa, subsistence agriculture underpins rural livelihoods. However, climate change has negatively affected rural smallholder farming due to over-dependence on climate-sensitive rain-fed agriculture. The effects of climate change have become the most critical issue for rural smallholder farmers. Rural smallholder farmers are greatly impacted by climate change and variability, leading to reduced crop yields, crop failure, loss of assets and livelihood opportunities. However, despite such challenges, farming continued to sustain livelihoods in rural areas over the years. Traditionally, African rural smallholder farmers have relied on their indigenous knowledge (IK) to sustain themselves, maintain their cultural identity as well as understanding climate and weather patterns for their decision-making at a farm level. However, the increase in rainfall variability in the past few years associated with climate change has reduced the reliability of IK. To address such challenges, the study suggests the integration of indigenous knowledge with modern climate science at a local level, to enhance the resilience of smallholder farmers to climate change. The aim of the study was to establish commonly used indigenous knowledge indicators for climate and weather forecasts predictions and smallholder farmers’ perceptions on the integration of the two knowledge systems as well as the use of mobile app technology to improve agricultural production in Nkomazi Local Municipality, South Africa. The study information was collected through both qualitative and quantitative research methods. Data were collected from twelve villages, sampling 100 participants, 8 key informant interviews, transact walk conducted with a small group of farmers (maximum 5) and eight focus group discussions in Nkomazi Local Municipality. ArcMap 10.7.1 was used to map the distribution of indigenous indicators used by Nkomazi smallholder farmers and the Poynton model was used to predict the impact of the increasing temperature on smallholder farmers’ production using the plant and animal indigenous climate indicators in these villages, and SPSS 25 was used to analyse the quantitative data as well as Excel 2016. Qualitative data was analysed through thematic analysis. From the transect walks and focus group discussions, the study findings revealed that many of the Nkomazi smallholder farmers relied more on their indigenous knowledge (IK) than on scientific weather forecasts (SWFs) for farm level decision-making.

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