Assessing the performance of natural resource systems

Assessing the performance of management is central to natural resource management, in terms of improving the efficiency of interventions in an adaptive-learning cycle. This is not simple, given that such systems generally have multiple scales of interaction and response; high frequency of non-linearity, uncertainty, and time lags; multiple stakeholders with contrasting objectives; and a high degree of context specificity. The importance of bounding the problem and preparing a conceptual model of the system is highlighted. This article suggests that the capital assets approach to livelihoods may be an appropriate organizing principle for the selection of indicators of system performance. In this approach, five capital assets are recognized: physical, financial, social, natural, and human. A number of principles can be derived for each capital asset; indicators for assessing system performance should cover all of the principles. To cater for multiple stakeholders, participatory selection of indicators is appropriate, although when cross-site comparability is required, some generic indicators are suitable. Because of the high degree of context specificity of natural resource management systems, a typology of landscapes or resource management domains may be useful to allow extrapolation to broader systems. The problems of non-linearities, uncertainty, and time lags in natural resource management systems suggest that systems modeling is crucial for performance assessment, in terms of deriving “what would have happened anyway” scenarios for comparison to the measured trajectory of systems. Given that a number of indicators are necessary for assessing performance, the question becomes whether these can be combined to give an integrative assessment. The article explores five possible approaches: (1) simple additive index, as used for the Human Development Index; (2) derived variables (e.g., principal components) as the indices of performance; (3) two-dimensional plots of indicators and cases emerging from multivariate techniques used to visualize change; (4) graphical representation of the five capital assets using radar diagrams; and (5) canonical correlation analysis to explore indicators at two different scales.