Integrating perennial plant, such as Falcataria moluccana, in farming system can provide economic and environmental benefits, especially in marginal areas. Indonesian governments at all levels have been employing a number of efforts to speed-up adoption of tree planting on farm. However, the establishment of farm forestry on private land in Indonesia, especially in Java, is widely varied. While the farm forestry in some locations has been well adopted, the farmers or land users in other location are reluctant to adopt them, although the traits of farmers and farm land in both locations are similar. Most adoption studies have employed cross-sectional data in a static discrete choice modeling framework to analyze why some farmers adopt at a certain point in time. The static approach does not consider the dynamic environment in which the adoption decision is made and thus does not incorporate speed of adoption. The information of adoption speed of an innovation is important in designing extension policies as well as reengineering innovations in order to align with socio-economic conditions of the farmers. Based on data from a survey of a random sample of 117 smallholder households in Wonosobo Regency, Central Java, Indonesia, this study investigated determinants of time to adoption of farm forestry using duration analysis. Results revealed that factors that accelerate the adoption varied include age of household head, level of education of household head, off-farm employment and output price. Older farmers tend to adopt faster than the younger farmers. The other interesting findings are that off-farm employment and membership to farmers group are two most influential factors in speeding-up adoption of Falcataria-based farm forestry. The policy implications of this research are that government should design policies that promote farmers’ participation in off-farm income activities and strengthening farmer groups in addition to extension services and timber markets.
Application of a Simple Nonparametric Conditional Quantile Function Estimator in Unemployment Duration Analysis
