Resilience potential of the Ethiopian coffee sector under climate change

Abstract

Coffee farming provides livelihoods for around 15 million farmers in Ethiopia and generates a quarter of the country's export earnings. Against a backdrop of rapidly increasing temperatures and decreasing rainfall, there is an urgent need to understand the influence of climate change on coffee production. Using a modelling approach in combination with remote sensing, supported by rigorous ground-truthing, we project changes in suitability for coffee farming under various climate change scenarios, specifically by assessing the exposure of coffee farming to future climatic shifts. We show that 39-59% of the current growing area could experience climatic changes that are large enough to render them unsuitable for coffee farming, in the absence of significant interventions or major influencing factors. Conversely, relocation of coffee areas, in combination with forest conservation or re-establishment, could see at least a fourfold (>400%) increase in suitable coffee farming area. We identify key coffee-growing areas that are susceptible to climate change, as well as those that are climatically resilient.

Least concern to endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee

“…Three major spatial datasets were used in our analyses; observational (geo‐located) ground‐point data, SDMs (which cover present‐day to future, under various climate change scenarios and multiple GCMs), and forest cover (from remote sensing (Moat et al, ; Davis et al, ). The SDMs were combined with forest cover and integrated into a 1 km 2 grid for further analysis.…”

“…Data for this study are derived from Davis et al () and Moat et al (). The data from Davis et al () were taken and then ground‐truthed in Ethiopia during 15 dedicated field missions (2013–2016); four inaccurate records were removed via this process.…”

“…For the present‐day modelled distribution of Arabica coffee in Ethiopia (South Sudan was treated separately) we used the final SDM produced by Moat et al (), which was based on an ensemble SDM approach, with nine selected bioclimatic variables and six modelling methods (Generalized Linear Models, Generalized Boosted Regression Models, Generalized Additive Models, Multiple Adaptive Regression Splines (MARS), Random Forest and Maximum Entropy [Maxent]), processed using the Biomod2 R package, version 3.1–64 (Thuiller et al, ). All 19 BIOCLIM variables (Hijmans, Cameron, Parra, Jones, & Jarvis, ) were examined and reduced to nine; the nine remaining variables were significant to the model fit, not highly correlated, and represented the ecological requirements of Arabica coffee (Moat et al, ). The nine BIOCLIM variables used were: Annual Mean Temperature, Isothermality, Temperature Seasonality, Annual Temperature Range, Annual Precipitation, Precipitation of the Wettest Month, Precipitation Seasonality, Precipitation of the Driest Quarter and Precipitation of the Warmest Quarter.…”

“…For the future projections we used two climate scenarios, A1B and A2, three GCMs (based on 23 GCMs examined and reviewed by Moat et al (), see Table ), four date intervals (1960–1990, 2010–2039, 2040–2069, and 2070–2099), and three of the six migration scenarios devised by Moat et al (). These future projections were downloaded from CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (https://ccafs-climate.org/data_spatial_downscaling/ and https://www.ccafs-climate.org/data/).…”

“…Green areas represent the coverage of the humid forest types* where wild Arabica coffee could occur (where there is ≥1% of forest cover in each km 2 ). Map generated from species distribution models (SDMs) and remote sensing (Davis et al, [one SDM]; Moat et al, [SDMs and remote sensing]). *Humid forest represented by Moist Evergreen Afromontane Forest (MAF) and Transitional Rain Forest (TRF) types (Friis et al, ).…”