{{menu_nowledge_desc}}.

CIFOR–ICRAF publishes over 750 publications every year on agroforestry, forests and climate change, landscape restoration, rights, forest policy and much more – in multiple languages.

CIFOR–ICRAF addresses local challenges and opportunities while providing solutions to global problems for forests, landscapes, people and the planet.

We deliver actionable evidence and solutions to transform how land is used and how food is produced: conserving and restoring ecosystems, responding to the global climate, malnutrition, biodiversity and desertification crises. In short, improving people’s lives.

Multidisciplinary Landscape Assessment

The characteristics of forested landscapes are usually critical to their inhabitants, but the significance of these relationships is largely hidden from the outsider. The challenge is to understand what aspects of the landscape local people care about, why they matter and how much. The groundbreaking approach reported in this book was developed during a study of seven communities in the forest-rich upper portion of the Malinau watershed in East Kalimantan, Indonesian Borneo. A village-based survey collected a wide range of qualitative and quantitative information about the judgments, needs, culture, institutions and aspirations of the communities, and examined general perceptions of the local landscape. A parallel field survey assessed sample sites and recorded soil, vegetation and other site characteristics through both 'scientific' and indigenous approaches. These field methods emphasized landscape-scale characterization through high replication of small data-rich samples, and assessments of community territories based on these samples. Two hundred research plots were established and about 2000 plant species recorded, representing a 'baseline', 'exploratory' or 'diagnostic' phase within a longer- term research strategy. Decision makers require guidance on how to deal with the needs of local communities and biodiversity in landscapes.

Dataset's Files

exploring_bio.pdf
MD5: 51e76bcbdf366d3aae9509f8148288e4
This is not intended as a manual. We would rather it viewed as a summary of lessons learned.

Other datasets you might be interested in