Accurate and timely maps of tree cover attributes are important tools for environmental research and natural resource management. We evaluate the utility of Landsat 8 for mapping tree canopy cover (TCC) and aboveground biomass (AGB) in a woodland landscape in Burkina Faso. Field data and WorldView-2 imagery were used to assemble the reference dataset. Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models. RF models based on multi-temporal and single date imagery were compared to determine the influence of phenology predictor variables. The effect of reducing the number of predictor variables on the RF predictions was also investigated. The model error was assessed using 10-fold cross validation. The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables). The coefficient of determination of predicted versus observed values was 0.77 for TCC (RMSE = 8.9%) and 0.57 for AGB (RMSE = 17.6 tons∙ha⁻¹). This mapping approach is based on freely available Landsat 8 data and relatively simple analytical methods, and is therefore applicable in woodland areas where sufficient reference data are available.

Global population growth, especially in developing countries, will most likely require an increase in agricultural production, but the sustainability of this production cannot be achieved without the preservation of ecosystem functions. Therefore, farmers need to know about, and deal with, the trade-offs between agricultural productivity and ecosystem functions and services. This review aims to assess practical science-based tools that can be used to make decisions for sustainable agricultural production. We reviewed 184 articles and divided them into categories depending on whether they describe tools, practices, ecosystem services, models, or other topics. Although many studies were global in scope, the approach to analyzing and assessing trade-offs appears to vary geographically. The review showed that trade-offs between agricultural productivity and ecosystem functions are most commonly studied in Europe and Asia, while few studies have been conducted in sub-Saharan Africa. Most tools in the review addressed only one or a bundle of ecosystem services, related to water, biodiversity, or climate regulation, and were designed for different types of land use and ecosystems and applicable at different scales. More practical tools for trade-off analysis have mainly been developed and applied by development organizations with support from science. Closer collaboration between practitioners, development organizations, and scientists is suggested to foster co-development of tools useful for identifying sustainable strategies for closing the yield gap, increasing productivity and for balancing ecosystem services, building on the Sustainable Development Goal’s framework and its targets for agricultural productivity and ecosystem services for trade-off analysis. We recommend the development and fine-tuning of the identified tools to specific contexts and landscapes through innovation platforms bringing together farmers, extension workers, scientists, and local decision-makers.

Small-scale farmers on marginal land in the Loess Plateau of China are adapting their livelihood to new situations and changes such as varying climate, new land-use policies, changing employment opportunities and new market situations. To avoid generalising explanations with regard to land-use change, interactions between proximate causes and underlying driving forces adopted from a meta-analysis model are explored through 23 in-depth interviews. This was done through collaborative work with farmers in northern Shaanxi Province and focused on the land-use situation between 1982 and 2005. The result reveals five categories of land-use change. The interaction pattern is broken down into eight proximate causes and four underlying driving forces. The dominant underlying driving forces are economic forces with short time horizons arising partly from compensation through policies, changes in crop demand from an expanding nearby market and a need for cash because of an increasingly cash-based lifestyle. The direct proximate causes were vividly described by the farmers as tools or means by which they are adapting to more abstract and indirect factors. These factors were identified as underlying driving forces. Hence, the knowledge and ability to separate the interaction into proximate causes and underlying driving forces are crucial in policy-making.