Worldwide, over 400 million hectares of tropical forests are set aside for timber production. Several certification schemes exist to ensure more sustainable exploitation and large areas of production forests are currently certified. Under such schemes, logging companies are required to evaluate whether species are not overexploited and, if necessary, adapt their logging activities. However, the data needed to project exploitation intensities – growth, mortality and regeneration rates of trees – are scarce or non-existent. Tree-ring analysis provides lifetime species-specific growth data that can be used to allow or improve the projections of timber availability during following logging cycles. In this study, we integrated growth data from tree rings with logging inventory data to forecast timber yields in the next harvest round for four timber species in Cameroon. We compared projections using tree-ring data with projections using fixed growth rates, as set by law and customarily applied in Cameroon. Additionally, we assessed the effect of increasing logging cycles and of using filed-based species-specific logging intensities on the next cycle's yield projections. Under current logging practices, timber volumes available at next logging cycles are projected to be 21–36% of the volumes obtained at first harvest. Simulations using fixed rates often resulted in lower yields with lower volume ingrowth from trees that were below minimum cutting diameters in the first harvest. Lengthening the logging cycle increased yield predictions during the next harvests, but yields were still not sustained over time. This problem can be resolved by using species-specific logging intensities, which led to projected yields of up to 73% of the initial harvested volume. The growth data provided by tree-ring analysis allows conducting such species-specific projections and thus helps to provide the knowledge base necessary for sustainable forest management. Yet, the low overall yields are a concern to forest conservation, as loss of economic value may lead to conversion of forests to other land uses.