Monitoring by communities – what makes it cost-effective?
A number of studies have found that ground-based forest monitoring by local communities is more cost-effective than monitoring by professional foresters and academic researchers. But how big is the difference in cost, and is it really so straightforward?
In a new article, we discuss some of the key findings of studies comparing the cost of community monitoring with that of external experts, who may need to travel far (sometimes by aeroplane) to carry out the work, and are often paid higher salaries.
The results show that, while community monitoring often requires higher initial start-up costs, especially due to the need for training, it can quickly become more cost-effective than monitoring by external experts. These costs can continue to decline as communities become more familiar with the monitoring methods and routes.
The difference in cost varies from project to project, and also depends on the methods used to calculate and compare them, but different studies point in the same direction. For example, one study covering 289 plots across four countries in Southeast Asia revealed that, in the second year, community monitoring was up to 20% less expensive than professional monitoring. Other studies have found that community monitoring can be as much as 50% less expensive.
Ultimately, however, a monitoring initiative can only be said to be truly cost-effective if it is not only cheaper than other methods, but that it is also able to generate useful information for as long as monitoring is required – i.e. that it can be sustained. In order to achieve this, and to avoid monitoring initiatives faltering after external funding and expertise are withdrawn, it is vital that they are designed with local information needs and interests at their core, so that monitoring is clearly beneficial to communities, and not felt as a burden on their time and resources.
Read the full article for details and to find out more about the research on cost-effectiveness of community-based forest monitoring, and follow our blog as we explore other issues such as sustainability and data accuracy.