Robust Models for Smallholder Forests

Abstract

Forest management faces a contradiction: superb decision support systems are available, but only for few forests because the necessary requisites (data, computers, advisors) often remain elusive. Thus many forests and managers are hampered by a lack of information to guide management, and this handicap falls particularly heavily on community and smallholder forests. Whilst the best decision support requires detailed and reliable data, useful advice can nonetheless be provided with simple models that are easily calibrated and have been shown to be robust in diverse situations. This paper presents the underlying framework for a set of simple models that are easily calibrated and reliable under plantation scenarios. It illustrates the calibration and use of these models for several tropical plantation species, demonstrates the implications that can be inferred, and discusses the data required to calibrate these models for new situations.