Bringing Agroforestry Technology to Farmers in the Philippines: Identifying constraints to success using systems modelling
Jack Baynes, John Herbohn, I. W. Russell, Carl S. Smith · 2008
Bayesian Belief Network (BBN) modelling may be applied in rural extension in situations where program outputs are influenced by variables which are sequentially influenced by other variables. For a recently completed agroforestry extension program in Leyte the Philippines, BBN modelling of site factors, establishment practices and risk, predicted widely different program outputs for different levels of extension assistance and farmer inputs. In a situation where very little was known about how farmers would respond to offers of extension assistance, monitoring of the program over a period of three years revealed that extended extension assistance was crucial in determining the likely survival and growth of trees. Extended extension assistance was also important for the elimination of unsuitable sites and the use of appropriate establishment procedures. Where extension support was not available, farmers displayed a poor knowledge of the principles of tree growth, planting trees underneath complete canopies and adjacent to mature coconut palms even though they could have been expected to have extensive local knowledge of raising and growing plants. Approximately one third of planting sites were infertile and eroded and growth of newly planted trees on these sites was poor, often because site preparation and maintenance was minimal. Newly established trees were also found to be at risk from fire, typhoon, and grazing and in situations where plantations were destroyed, farmers became antagonistic towards the program. The implications of the BBN modelling for a hypothetically expanded program are that extended assistance and site inspections are necessary to eliminate planting trees on inappropriate and unsuitable sites and to improve establishment practices and weed control in order to avoid plantations of suppressed and chlorotic trees which fail to meet the expectations of farmers, thus impinging on the success of the program.