Abstract
SUMMARYThis research aimed to develop a typology of agriculture in Timor-Leste using national census data at the village level. Although Timor-Leste is a relatively small nation, its varied topography contains a rich diversity in agricultural livelihoods, from coffee covered mountains, to dryland-swidden agriculture. Each of the livelihoods are very complex, with a single household often managing more than 10 crop and 4–5 animal species in very small holdings. Using census data from each village only, statistical clustering analysis was used to group villages with similar levels of participation in crop and livestock production. The clustered village groups were then mapped, and it was seen that villages in each cluster, occupied particular locations. Using expert knowledge about the locations of each cluster, livelihood zones based on a small number of rules were defined to mimic the output of the clustering. Seven livelihood zones were identified from mapping the livelihood systems. These included three zones with irrigation (rice-based), two highland zones (coffee-based) and two lowland zones based on rain-fed agriculture. Government and development agencies have endorsed the typology of livelihood zones, which is now in use for planning and decision-making. The technique of using national census data to define agricultural zones through statistical clustering can be replicated wherever there is reliable village-level census data.
Highlights
SUMMA RY This research aimed to develop a typology of agriculture in Timor-Leste using national census data at the village level
Census data and cluster analysis This study used agricultural variables from national census data to develop a typology for agricultural livelihoods in Timor-Leste
A summary of the rule based zones is presented in Table 2 and a summary of zoning typology rules follows: 1. Villages with more than 35% of households involved in rice farming. 1b
Summary
SUMMA RY This research aimed to develop a typology of agriculture in Timor-Leste using national census data at the village level. Efficient resource allocation for agriculture development is hindered by the highly variable landscape, and diverse farming systems, even in small areas. An approach that could group villages into particular agricultural zones – based on their agricultural systems – would allow more targeted rural development nationally. Efficient rural development requires a simple method to define the location and description of livelihood zones across the country. Such a method needs to be easy to apply but to be consistent with the quantitative data available for each village
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