Evaluating heterogeneity in agroforestry adoption and practices within smallholder farms in Trans-Nzoia County, Kenya uri icon

abstract

  • We hypothesize that understanding the structure and densities of tree populations in these agricultural landscapes is useful in determining the viability of trees for tree diversity conservation and in determining the dominant tree species influencing the agroecosystems. The study investigated the dominant agroforestry practices in agricultural landscapes of Trans Nzoia, Kenya. The study used ground based methods and semi structured questionnaires to enumerate tree species present in each farm. Tree basal area mensuration (tree cross-sectional area measured at breast height) was undertaken. All trees greater than or equal to 5 cm diameter at breast height (DBH) were enumerated. DBH's was measured using calibrated tree diameter tapes. Local names of tallied trees were recorded from farmer interviews. All enumerated trees were identified to the species level according to Beentje (1994) or Maundu and Tengnäs (2005). Tree inventories during farm walks involved simultaneous recording of species presence counts and DBH readings. Key informants' interviews were conducted at each of the selected settlements. The key informants were elderly people, village/settlement heads and other knowledge holders who had lived in the area for a period greater than 40 years, selected after informal discussions with the inhabitants. The information from key informants' survey was used to classify farmers into different wealth categories. Wealth ranking was carried out by adapting the technique of Crowley (1997). The purpose of wealth ranking in relation to current study was to investigate whether differences in household's income levels have an influence on the adoption and management of tree species. At every settlement, the key informants were requested to list wealth indicators in their order of importance in classification of social classes. The main indicators identified were; 1) land area owned, 2) house status (quality and size, permanent, semi-permanent or grass thatched), 3) form of employment; formal or casual employment (off-farm employment) and also the frequency of seeking casual employment, 4) amount of annual crop production; yield and ability to purchase inputs such as fertilizer and, 5) cow; quantity and quality(improved or local breeds), and 6) children remittance or external source of income. A combination of two key approaches was used to group farmers. First, the information or characteristics obtained from questionnaire about each farmer was used against the wealth indicators taking into account the importance of indicator in the wealth categorization list. This was by an independent researcher who allocated each farmer into one of the 3 established categories; wealthier, moderate or poor category. Second, at each settlement, the chairman and the key informants were requested to rank the farmers according to their own criteria and also taking into account previously list of wealth indicators ordered by importance. The two criteria were merged to obtain the final list. Since the key informants were from the area, farmers were grouped using their local knowledge. This was in an effort to avoid an overly strict interpretation of the data

publication date

  • 2014