Secure land tenure in rural landscapes is widely recognized as an essential foundation for achieving a range of economic development goals. However, forest areas in low and middle-income countries face particular challenges in strengthening the security of land and resource tenure. Forest peoples are often among the poorest and most politically marginalized communities in their national contexts, and their tenure systems are often based on customary, collective rights that have insufficient formal legal protection.
A household-level switching regression model is implemented to examine potential selectivity bias for rural households under high and low levels of investments in soil conservation in El Salvador and Honduras. In the presence of selectivity bias, separate stochastic production frontiers are estimated for low and high adopters. The main results indicate that households with higher levels of investments in soil conservation show higher average TE than those with a lower level of investments. Constrains in the rural land and credit markets are likely explanations for these differences.
This study evaluates technical efficiency (TE) levels for rural households under high and low levels of investments in soil conservation in El Salvador and Honduras. To correct for potential self-selectivity bias a household-level switching regression framework is implemented to estimate separate stochastic production frontiers for the two groups of households under analysis. The main results indicate that a systematic difference exists between the two studied groups.
This paper posits that deforestation and poverty levels are related through an inverted-U shape --the environmental Kuznets-- curve and that access to credit shifts this curve downwards, thus positively impacting natural resource uses. This hypothesis is tested using a household panel data set from El Salvador.
Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated.
This training manual focuses on how to manage and resolve conflicts over land tenure rights, security of tenure and land access in the field of rural development. It results from complementary activities undertaken within FAO's Livelihood Support Programme (LSP) and the Land Tenure and Management Unit and with the International Land Coalition. It addresses the specific issues of land tenure identified in the volume Negotiation and Mediation Techniques for Natural Resource Management published by the LSP.