Improving accuracy in climate suitability modelling through indigenous knowledge of farmers
DOI:
10.31219/osf.io/j2xev
Publication Date:
2024-02-17T05:00:44Z
AUTHORS (8)
ABSTRACT
Climate change is a major concern in Malawi, as it is projected to impact the stability of food production by affecting crop-suitable areas and yields. To facilitate the adaptation of local communities, climate suitability models are used to assess current and future crop areas for land use planning. However, these models often fail to consider indigenous knowledge (IK), which may be crucial in informing climate predictors used in the modelling exercises. This study aimed to assess the role of IK in helping to inform the selection of climate predictors for climate suitability modelling studies among macadamia farmers in Malawi. Using qualitative and quantitative data, the study found that local communities possess a wealth of knowledge about the climatic factors affecting macadamia agro-climate suitability. Factors such as moisture stress during the dry season, rainfall seasonality, higher temperatures, and strong winds were identified as crucial predictors. Empirical data analysis of the climate suitability models supported the farmers' perceptions. In addition, the study revealed that smallholder macadamia farmers can use their IK to inform their agricultural adaptation measures, such as mulching for moisture retention and windbreaks to protect macadamia trees from strong winds. Therefore, the study demonstrates the potential of IK in supplementing complex climate suitability models that can make them more meaningful, accurate, and area-specific. Incorporating such knowledge into climate suitability models can increase social, economic, and environmental resilience, mitigate risk, and strengthen the livelihoods of marginalised communities. The study suggests that research scientists and policymakers should revisit, support, and promote IK already present in these communities to foster more sustainable futures.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (0)
CITATIONS (0)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....