Despite the great potential of agricultural innovations, the uptake by smallholder farmers in sub-Saharan Africa seems to be slow. We reviewed existing theories and frameworks for the uptake of agricultural innovations and found that these tend to emphasize the role of extrinsic factors such as the characteristics of the adopter and the external environment in the decision-making process. In this paper, we argue that intrinsic factors such as the knowledge, perceptions and attitudes of the potential adopter towards the innovation play a key role, but this has been less studied. We present an analytical framework that combines both extrinsic and intrinsic factors in farmers' decisions to adopt new agricultural technologies and apply the framework to agroforestry adoption as a case study. We review the literature on agroforestry adoption in sub-Saharan Africa and identify the extrinsic and intrinsic variables affecting the uptake of agroforestry technologies. We conclude that the uptake of agricultural technologies is a complex process influenced by both extrinsic and intrinsic variables, and recommend that future studies aiming to understand the adoption process of agricultural innovations take into account both sets of variables. A mechanistic understanding of how intrinsic and extrinsic factors interact and drive adoption can help in targeting technologies appropriately to ensure sustainability.

In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.

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While sustainable forestry in Europe is characterized by the provision of a multitude of forest ecosystem services, there exists no comprehensive study that scrutinizes their sensitivity to forest management on a pan-European scale, so far. We compile scenario runs from regionally tailored forest growth models and Decision Support Systems (DSS) from 20 case studies throughout Europe and analyze whether the ecosystem service provision depends on management intensity and other co-variables, comprising regional affiliation, social environment, and tree species composition. The simulation runs provide information about the case-specifically most important ecosystem services in terms of appropriate indicators. We found a strong positive correlation between management intensity and wood production, but only weak correlation with protective and socioeconomic forest functions. Interestingly, depending on the forest region, we found that biodiversity can react in both ways, positively and negatively, to increased management intensity. Thus, it may be in tradeoff or in synergy with wood production and forest resource maintenance. The covariables species composition and social environment are of punctual interest only, while the affiliation to a certain region often makes an important difference in terms of an ecosystem service's treatment sensitivity.

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