Diversity of Useful Plants in the Coffee Forests of Ethiopia

Plant use diversity and their forms of use and management were studied in four coffee forests of Ethiopia. A coffee forest is a segment of moist montane forest with occurrence of wild Arabica coffee populations. The present study was conducted in four forest fragments located in the southwestern and southeastern parts of the country. These forests represent three different indigenous ethnic groups that live in and around the coffee forests. On the bases of ethnobotanical and floristic studies, a total of 143 useful plant species representing 54 families were identified in all study areas. Nearly all species are native except one which is naturalized. The identified use categories include medicine, food, honey, material sources, social services, animal fodder and environmental uses. Overall, Yayu and Harenna shared a high number of useful plant species in common. Of the total, about 25 species (19%) were similarly used across three or more studied ethnic groups. The implication is that there is a difference between and among the four communities studied for general plant knowledge and uses. As observed, deforestation, over-harvesting, cultivation of marginal lands and overgrazing appear to be threatening the plant resources and their habitats in the studied areas. Ecosystem conservation will ensure in situ conservation of many useful plant species by applying sustainable harvesting methods for collecting plants for any type of use from wild habitats

The role of traditional coffee management in forest conservation and carbon storage in the Jimma Highlands, Ethiopia

Ethiopia has lost 90% of its forest extent. Remnant patches in the southwest are often semi-forest coffee (SFC), a system whereby coffee is managed beneath the canopy. Here, we (1) quantify aboveground live carbon (AGC) stored by trees in SFC and other land use types in the Jimma Highlands; and (2) determine coffee farmers’ preference for canopy shade trees, and the resulting differences in carbon storage. We surveyed twenty coffee farmers and assessed thirty-one 1-ha vegetation plots across a 23.6-km transect. The most preferred shade species were Albizia gummifera, Acacia abyssinica, Millettia ferruginea and Cordia africana, which together accounted for 42% AGC in SFC and 12% in natural forests. These species had broad size class distributions, while the least preferred had scant representation in lower size classes. SFC stores significantly more AGC (61.5 ± 25.0 t ha−1, mean ± SE) than woodland, pasture and cropland, significantly less than plantation and slightly less than natural forest (82.0 ± 32.1 t ha−1). If SFC was converted to cropland, then 59.5 t ha−1 would be released, at a social cost of US$2892–4225 ha−1. Carbon-payment schemes (e.g. REDD+) may, therefore, play a role in conserving these forests and associated biodiversity and livelihoods into the future

Landscape and management influences on smallholder agroforestry yields show shifts during a climate shock

Sustaining yields for smallholder perennial agriculture under a rapidly changing climate regime may require consideration of landscape features and on-farm management decisions in tandem. Optimising landscape and management may not be possible for maximising yields in any one year but maintaining heterogeneous landscapes could be an important climate adaptation strategy. In this study, we observed elevation, forest patch and shade management gradients affecting smallholder coffee (Coffea arabica) yields in a ‘normal’ year versus the 2015/16 El Niño. We generally found a benefit to yields from having leguminous shade trees and low canopy openness, while maintaining diverse shade or varying canopy openness had more complex influences during a climate shock. The two years of observed climate shock were dominated by either drought or high temperatures, with yield responses generally negative. Climate projections for East Africa predict more erratic rainfall and higher temperatures, which will disproportionately impact smallholder farmers.</p

The structures underpinning vulnerability: examining landscape-society interactions in a smallholder coffee agroforestry system

Smallholder farmers dependent on rain-fed agriculture are particularly vulnerable to extreme climate events and, therefore, it is necessary to identify adaptive measures that would increase farmer resilience to these shocks. The management options in a low-input system, like forest coffee (Coffea arabica), are limited and there are several factors out of farmers' control driving their vulnerability to changing climatic conditions. These can relate to social structures and landscape factors, which can interact to reduce farmers' adaptive capacity, creating a state of contextual vulnerability. We explored the potential synergies of this interaction across elevation, patch area and shade management gradients for smallholder coffee farms around the UNESCO Yayu Coffee Forest Biosphere Reserve in Ethiopia before, during and immediately following the 2015/16 El Niño. We documented a dramatic collapse in coffee yields across all farms, resulting in coffee incomes 29.5 ± 18.0 % and 19.5 ± 10.0 % of 2014 incomes in 2015 and 2016, respectively. We identified farms at elevations between 1500-1600 m with canopy openness between 40-45 % as being consistently low yielding over our study period. We found these farmers had the highest rates of income diversification and, therefore, were already exhibiting adaptive capacity. Farmers with the largest income losses were spatially concentrated between 1600-1700 m, located in larger patch areas with lower canopy openness. Farmers at this elevation have access to poor infrastructure, restrictions on shade management and reported higher dependence on income from coffee, indicating an interaction of biotic and social factors exacerbating their vulnerability. Unfortunately, due to a nationally declared state of emergency, we were unable to survey farmers on the adaptive measures they undertook; therefore, we are limited in assessing their resilience. However, we do show the importance of considering both biotically and socially-mediated influences for assessing smallholder vulnerability, particularly barriers to diversifying incomes

The impact of climate change on indigenous Arabica coffee (Coffea arabica): predicting future trends and identifying priorities.

Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020-2050), representing assessment priorities for ex situ conservation; (2) identifies 'core localities' that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing plantations will be neagtively impacted by climate change

Locality analysis overview II.

<p>Predicted climate change outcomes for indigenous Arabica localities (349 in total) for the year intervals 2000, 2020, 2050 and 2080. Histograms for actual predicted values, under each scenario. Dashed line and red text indicate thresholds (68%, 95%, 100%, of the 2000 models). This figure provides finer-scale detail than <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g003" target="_blank">Figure 3</a>, including the subtle shifts around the thresholds that are evident in the locality analysis. For example in scenario B2A, in 2000 there are a high proportion of localities in optimum bioclimatic space (0.6 and 0.65), but by 2080 most of the localities are outside of all suitable bioclimatic space, with a small number of localities (‘core localites’) still occupying optimal bioclimatic space.</p

Locality analysis predictions superimposed on main protected areas in the study region.

<p>Point size and colour represent the total predicted score for each locality across all scenarios and time intervals (until 2080). Large dots (high score) represent ‘core localities’, i.e. those that predicted to withstand climate change until at least 2080. See internal legend for further details. Protected area data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone.0047981-IUCN1" target="_blank">[70]</a>. Dedicated coffee reserves: Yayu = Yayu Coffee Forest UNESCO MAN Biosphere Reserve (<a href="http://www.unesco.org/mabdb/br/brdir/directory/biores.asp?code=ETH02&mode=all" target="_blank">http://www.unesco.org/mabdb/br/brdir/directory/biores.asp?code=ETH02&mode=all</a>. Accessed 2012 May 10), included within the Yayu National Forest Priority Area (NFPA) (<a href="http://www.ecff.org.et/component/content/article/10-yayu/6-yayu-coffee-forest-biosphere-reserve.html" target="_blank">http://www.ecff.org.et/component/content/article/10-yayu/6-yayu-coffee-forest-biosphere-reserve.html</a>. Accessed 2012 May 10); Kafa = Kafa Coffee Biosphere Reserve UNESCO Biosphere Reserve (<a href="http://www.kafa-biosphere.com/" target="_blank">http://www.kafa-biosphere.com/</a>. Accessed 2012 May 10). Note. Controlled Hunting Areas not shown.</p

Predicted and actual distribution of indigenous Arabica.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g002" target="_blank">Figure 2A</a>. Green dots show recorded data-points. Coloured areas (yellow to red) show predicted distribution based on MaxEnt modelling (see internal legend). Highest predicted area (dark red) indicates ‘core region’. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g002" target="_blank">Figure 2B</a>. The same map with thresholds of bioclimatic suitability applied at 68% (optimal), 95% (intermediate) and 100% (marginal) to the localities. Prediction values for each locality are represented by colour and size (see internal legend) with values for low predictions labelled in red, superimposed on predicted surface (space) according to the area analysis. Localities with the smallest (dark green) circles represent ‘core localities’; highest (optimal) predicted area (green) indicates the ‘core region’. See main text for further details.</p

Predicted and actual distribution of indigenous Arabica.

<p>Green dots show recorded data-points. Coloured areas (yellow to red) show predicted distribution based on MaxEnt modelling (see internal legend). A context map is given in the top left hand corner.</p

Locality analysis overview I.

<p>Predicted climate change outcomes for indigenous Arabica localities for the year interval 2000, 2020, 2050 and 2080. Stacked bar-charts based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-t001" target="_blank">Table 1</a>. Green = optimal [bioclimatic] localities (68%); yellow = intermediate (suboptimal) [bioclimatic] localities (95%); red = marginal (extreme) [bioclimatic] localities (100%); grey = unsuitable bioclimatic localities.</p