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      LOCAL KNOWLEDGE OF TREE ATTRIBUTES UNDERPINS SPECIES SELECTION ON COFFEE FARMS

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          SUMMARY

          The extent to which coffee agroforestry systems provide ecosystem services depends on local context and management practices. There is a paucity of information about how and why farmers manage their coffee farms in the way that they do and the local knowledge that underpins this. The present research documents local agro-ecological knowledge from a coffee growing region within the vicinity of the Aberdare Forest Reserve in Central Kenya. Knowledge was acquired from over 60 coffee farmers in a purposive sample, using a knowledge-based systems approach, and tested with a stratified random sample of 125 farmers using an attribute ranking survey. Farmers had varying degrees of explanatory knowledge about how trees affected provisioning and regulating ecosystem services. Trees were described as suitable or unsuitable for growing with coffee according to tree attributes such as crown density and spread, root depth and spread, growth rate and their economic benefit. Farmers were concerned that too high a level of shade and competition for water and nutrients would decrease coffee yields, but they were also interested in diversifying production from their coffee farms to include fruits, timber, firewood and other tree products as a response to fluctuating coffee prices. A range of trees were maintained in coffee plots and along their boundaries but most were at very low abundances. Promoting tree diversity rather than focussing on one or two high value exotic species represents a change of approach for extension systems, the coffee industry and farmers alike, but is important if the coffee dominated landscapes of the region are to retain their tree species richness and the resilience this confers.

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          Most cited references 25

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            Advantages, disadvantages and desirable characteristics of shade trees for coffee, cacao and tea

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                Author and article information

                Journal
                applab
                Experimental Agriculture
                Ex. Agric.
                Cambridge University Press (CUP)
                0014-4797
                1469-4441
                May 31 2016
                : 1-15
                Article
                10.1017/S0014479716000168
                © 2016

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