- African Mountains
Coffee is a global commodity now - When I think of coffee, I think of Frank Herbert’s 1946 Dune Trilogy science fantasy novels, in which the most valuable galactic commodity is ‘spice’. Perhaps he was drinking a lot of coffee when he wrote his books. Now, in 2015, coffee is the ‘spice’ of the modern world, and has become the second most traded commodity in the world, after crude oil, and most of the good coffee comes from Ethiopia. Coffee has become an important modern day beverage associated with success and the ‘good life’. In fact, a Tweeter said, the world might begin to take climate change much more seriously when they learn that their coffee crops are being threatened by a too warm climate.
Coffee is the backbone of the Ethiopian economy, and grows in the highlands, providing more than 30 % of the country’s export earnings, a sizeable amount of $350 million. It is estimated that 25 % of the population, around 20 million people, depend directly or indirectly on coffee for their livelihood. So, coffee is very important to Ethiopians. They also drink a lot of coffee.
Global coffee and climate change impact means trouble ahead - In terms of climate change risks to Ethiopia’s coffee industry – there is trouble ahead. The threat of climate change is already causing coffee growers to be concerned about the fate of their industry. Coffee trees like a cooler tropical climate, and if too hot, become stressed and the quality of the coffee beans deteriorates. The Ethiopian coffee industry, along with international researchers, are exploring what full blown climate change would mean to their industry. The industry consists of cultivated plantations and commercial varieties – but a very important resource is the wild populations of Coffea arabica and Coffea canephora and the genes they contain. These wild groves, as well as the commercial plantations are at risk.
A large computer modeling study and field survey (Davis et al, 2012) has indicated a severely negative impact of climate change on wild coffee forests, with predictions of a 65 - 100 % loss of suitable localities for the survival of the wild species, depending on the climate model invoked.
Wild coffee groves and genetic diversity - As part of providing genetic potential for mitigating climate change, indigenous populations are perceived as a key resource for the medium- to long-term sustainability of Arabica coffee production. Modeling is very important to predict where new, suitable areas for new commercial coffee plantations will be, but safeguarding wild trees and localities from climate change is even more important – the question is, how? Ex situ conservation will have to be considered, and perhaps new suitable areas for live plantations of wild plantations will need to be located based on the modeling results of Davies et al (2012) and others.
The largest and most diverse populations of indigenous (wild) Arabica (Coffea arabica) occur in the highlands of south-western Ethiopia, but the native range includes satellite population in south-eastern South Sudan (Boma Plateau) and northern Kenya (Mt Marsabit), at altitudes between 950 and 1950 m, although 1200 m is the most frequent lower altitude limit. The indigenous distribution of Robusta coffee (Coffea canephora) includes much of tropical Africa, from Guinea to western Tanzania, at altitudes of 50–1500 m.
The genetic diversity of wild Arabica populations far exceeds that of cultivated varieties used in crop production and accessions held in germplasm collections. The Ethiopian highlands are a natural repository of coffee germplasm and naturally growing trees contain far more diversity than all the cultivated coffee in the world, including a high genetic diversity in terms of disease, pest and drought tolerance. In fact, it is possible that somewhere in these wild trees, might be genes which enable coffee to endure warmer conditions linked with climate change. If not, bioclimatic unsuitability in a warming world would place wild coffee populations in peril, leading to severe stress and a high risk of extinction. As these natural forests occur within the same bioclimatic areas as most of the commercial coffee produced in Ethiopia, the loss of wild groves will mean that commercial groves will be severely negatively affected too.
Wild coffee, maps and modeling - This study established a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Maps of current and future distributions of wild coffee Arabica were determined through extensive field work (Davies et al, 2012). Using distribution data the researchers performed bioclimatic modeling and examined future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). In their locality analysis, the most favourable (and most conservative) outcome (scenario B2A; all thresholds) would be a circa 65% reduction in the number of bio-climatically suitable localities, and at worst (scenarios A2A, A1B; 68% threshold) an almost 100% reduction, by the year 2080.
It is feasible for plantations to be moved to cooler climes (where also the soil, soil microbiology and other factors also have to be suitable), but this would cost an unknown amount of money to ‘transplant’ an entire farming sector – but it may be necessary. This would bring coffee farmers and farming into conflict with biodiversity conservation areas and other land owners who may or may not want to be bought out by coffee farmers .
Once finalized, the analyses of Davies et al (2012) will be compared with information gathered from farmers and producers, existing weather station data and climate monitoring equipment that we have placed within coffee farms across Ethiopia. The findings will be presented to key stakeholders in Ethiopia, and after this a long term strategy will identify the actions that are required to best sustain the Ethiopian coffee industry in relation to climate change and land-use, and identify what is needed to ensure resilience.
Specifically, the Davies et al (2012) model does the following : a) 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; b) 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; c) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Also, Arabica coffee is confirmed as a climate sensitive species, and this study provided supporting data and inference that existing plantations will be negatively impacted by climate change (Davies et al, 2012).
Bioclimatic- (or niche-) based modeling has been widely used in the last ten years to predict the potential impacts of climate change on species distributions all over the world. This type of modeling has been judged as ecologically naive by some, say Davis et al (2012), as species occurrence is dependent not only on climate, but also on ecological processes such as dispersal, colonization, and complex interactions with other organisms. In this respect, process based modeling, including ecological data, are often favoured on theoretical grounds, by injecting ecological realism into the modeling framework.
In reality, species-specific process-based modeling remains scarce at the continental and regional scale, owing to the difficulties in acquiring and combining data for analysis. It has been pointed out that the use of both approaches, with their own caveats and advantages, are crucial in order to obtain robust results, and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change (Davies, et al, 2012).
Davis, A. P., Gole, T. W. Baena, S. and Moat, J. (2012). The impact of climate change on natural populations of Arabica coffee: predicting future trends and identifying priorities. PLoS ONE 7(11): .e47981. Available online at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0047981