Identifying potential areas of understorey coffee in Ethiopia’s highlands using predictive modelling

Abstract

Coffee production is one of the main economic activities in Ethiopia, representing about 40% of the country's economy. Coffee is particularly important in the Ethiopian highlands, where appropriate climate allows higher productivity and quality. The Ethiopian highlands also host an outstanding biodiversity, being considered one of the world's most important biodiversity hotspots. In this context, conciliating agricultural practices with biodiversity conservation is a priority task for researchers and other stakeholders. However, identifying and mapping understorey coffee plantations in Ethiopian highlands is particularly challenging due to the presence of scattered exotic trees and the characteristics of understorey cultivation. In this research, we mapped potential areas of understorey coffee using predictive modelling and evaluated how projected changes in climate would affect the suitability of coffee production in the study area. Landscape maps, which were mapped using remote-sensing data based on object-based image analysis, remotely sensed spectral vegetation indices, and climatic variables were used to delineate probability maps showing the most likely location of understorey coffee. Normalized difference vegetation index and maximum temperature and precipitation were considered the best predictors for explaining the spatial distribution of understorey coffee. The accuracy of the probability map was validated based on existing understorey coffee areas mapped during field surveys. In addition, we show that potential changes in temperature and precipitation by 2050 are likely to shift suitable areas of understorey coffee to higher altitudes, affecting the landscape changes dynamics in the region.

Waiving the extinction debt: Can shade from coffee prevent extinctions of epiphytic plants from isolated trees?

“…Coffee is a primary local livelihood crop and grows under shade trees in home gardens, as well as in forest margins and in forests (Aerts et al., ; Hylander et al., ; Teketay, ). The coffee cover could actually be rather dense under what is seen as forests from satellite images (Hailu, Maeda, Pellikka, & Pfeifer, ).…”

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

“…A forecast based on fewer observations (mono-temporal, bi-temporal) is possible but is only made in few and special cases. We observed mono-temporal forecasts only in studies, in which projected non-EO data is combined with EO-derived recent or future LULC maps [65,78,134,138,143,[156][157][158]168]. The same occurs in two bi-temporal projection-based studies by Patil et al [175] and Yao et al [177], while others employ transition probabilities or transition vectors between two time steps to forecast future conditions [65,81,83,174].…”

“…In general, however, next to OLS, machine learning methods are used much less frequently in this forecast model category. Of the 22 projection-based forecasting studies identified in this review, 15 were based on climate projections [78,85,130,131,134,142,[154][155][156][168][169][170]175,177,178], six on LULC projections [65,143,144,157,158,167], and one study on both [118].…”

“…Other studies forecast LULC change in a less specialized manner and at a greater spatial scale [65][66][67][68][69][70][71][72][73][74][75][76][77]. Some studies, in contrast, focus on the loss of valuable or protected ecosystems such as forests or wetlands to agricultural or built-up LULC classes [78][79][80][81][82][83]. By altering or updating some of the input variables, some studies simulate future LULC under different scenarios to reflect different land planning policies or change trajectories [42,46,61,64,68,72,75,77,81].…”

“…By altering or updating some of the input variables, some studies simulate future LULC under different scenarios to reflect different land planning policies or change trajectories [42,46,61,64,68,72,75,77,81]. Almost all of these LULC forecasts are acquired by the application of CA-like methods, except for Hailu et al [78], who forecast the future probability of coffee abundance in Ethiopia based on climate projections.…”