When the Rains Don't Come: A Mixed Methods Approach to Assess the Impact of Climate Change on Agriculture in Southern Zambia

Keywords: climate change, agriculture, Sub-Saharan Africa, mixed methods, decolonial methods, machine learning, remote sensing
Abstract Type: Paper Abstract

Abstract

Tropical regions face a myriad of concerns as global temperatures rise. Many of these areas are predicted to shift into novel climate regimes (Rurinda et al., 2014, 2015) as the Intertropical Convergence Zone (ITCZ) continues to intensify and narrow (Byrne et al., 2018). These changes in the ITCZ will cause more erratic rainfall, especially at the margins of the rainy season. Smallholder farmers will bear the brunt of these changes, as slight shifts in crop production can have devastating impacts on their livelihoods (Rojas et al., 2011). The impact of climate change on small-scale farmers is especially relevant in Sub-Saharan Africa, where 80% of agricultural production takes place on smallholder farms (FAO, 2018) and only 5% have access to irrigation systems (World Bank, 2013).

The purpose of this study is to investigate how climate change has impacted and will impact agricultural yields and farmer experiences in southern Zambia. I will employ a sequential mixed methods research design with multilevel data analysis, first utilizing qualitative data gathered from in-depth interviews with farmers to determine the quantitative data that will be used in a predictive machine learning model. In addition, this study will incorporate community-based decolonial methods around transformation and emancipation first outlined in Freire (1978) and later developed by Thambinathan & Kinsella (2021). Using the praxes outlined by these authors, I will incorporate data that is relevant to the communities I work with and seek to create products with the communities that are helpful for them.