2. NFA OrganizationThere is no simple and clear-cut answer to the question how an NFA should be organized in a country. Our point of departure is that the NFA should be operational over a longer period of time, and thus that there is a need to establish a permanent organization. In case the NFA is only carried out within a shorter period, consultants may be hired to conduct the assessment. However, we do not consider this being an option here, although the role of consultants may be important during the establishment of the NFA and during other phases of development of the organization.
We start by discussing the role of an NFA and continue by treating the trade-offs between inventory complexity and organizational requirements. Then, key issues and components of the NFA organization are considered.
No specific references on NFA organisation appear to be available in the literature. On the other hand, there is a vast general literature on organisation and management, see e.g. Dawson (1996) and Morgan (1997).
The role of the NFA
The suitable organizational structure of an NFA must be discussed in relation to the tasks of the NFA. These tasks may vary considerably. Considering the objectives of the NFA in relation to the degree of geographical and topical resolution in data and analyses, a number of broad cases can be identified. The objectives of the NFA may be:
- National level analyses of sustainable forestry;
- National level analyses of sustainable land use;
- National and sub-national level analyses of sustainable forestry;
- National and sub-national level analyses of sustainable land use;
- National, sub-national, and local level analyses of sustainable forestry;
- National, sub-national, and local level analyses of sustainable land use.
- Assessment of current state in terms of areas of different land-use categories and available forest resources, e.g. the total biomass or volume available within a certain region of a country.
- Assessment of trends, i.e. estimating changes between the current and past conditions.
- Indirect inferences of cause-effect relationships. For policy purposes, in many cases it is of interest to know the response by forest owners and other stakeholders to changes in legislation or institutional arrangements. For example, it may be of interest to study the extent of a certain kind of land-use practice before and after a change in the legislation.
- Model based scenario analyses, aiming at forecasting future forest conditions, assessing resource utilization potentials and evaluating optional management strategies given different land-use scenarios (Lundström & Söderberg 1996, Sandewall & Nilsson, 2001, Lämås & Eriksson 2003).
For sustainable forestry or land-use, the above types of analyses need to consider a broad range of goods and services, e.g. round-wood for industrial use, fuel wood, non-timber forest products, biodiversity, removals and emissions of carbon dioxide from the atmosphere, recreation, water protection, etc. In policy development and strategic planning, NFA data also will need to be co-analysed with data and information from other sources, e.g. socio-economic data, market data and changes in legislation and policies.
The decisions being based on NFA data typically concern national and sub-national forest or land-use policies. As a basis for such decisions, all kinds of analyses listed above may be needed for providing background information. In particular, the analyses under (4) are important in trying to identify the different options available, while the analyses under (2) and (3) are important during the work of evaluating the effects of past and present policy.
In the subsequent sections, our treatment of NFA organizational issues assumes that the organization be responsible for both data acquisition and analyses. We believe that this is often a suitable arrangement, where people who use the data also have a chance to oversee the procedures for acquiring them, and thus gain insight into data quality issues, important for correct interpretation of results. In case the NFA organization will only be responsible for data acquisition, and the analysis part is left to some other organization, the link between data capture and analysis will obviously be weaker.