Trees on Farms

Ecological and socioeconomic analyses of Tropical agroforestry landscapes using remote sensing

Agricultural land is central to livelihoods, food security, biodiversity and climate. Wide-scale expansion of agriculture into natural habitats has led to forest loss, damaging biodiversity and releasing stored carbon. More than 90% of deforestation across the tropics is estimated to be driven by agriculture. The global agricultural system needs transforming, and trees on farms have been identified as an important tool in this transformation. Trees on farms can improve biodiversity, sequester carbon, provide materials and food for people, and improve people’s livelihoods in agricultural landscapes.

To understand the biodiversity in these agricultural landscapes with trees, they must be monitored. In chapter 1, I test an approach to mapping the gradual changes in tree species groups on agricultural land using satellite data. This method has been used previously, but not on complex landscapes like agriculture. The method was tested in three agricultural-forest landscapes (in Uganda, Rwanda and Honduras). The satellite data were able to predict some of the variations in tree species groups. The maps created by the method captured the main tree species changes in the landscape. This method provides more detailed assessments of trees on farms for understanding biodiversity in agricultural landscapes than was previously possible with satellite data.

Satellite data can be used to scale up field measurements of trees to assess aspects of biodiversity in landscapes, but this is not realistic at national scales. There is a need for indicators of agricultural biodiversity that can be used at broad scales and across different landscapes. In chapter 2, I develop and present an indicator of the biodiversity value of agricultural landscapes by assessing the properties of their trees. The tool uses freely available satellite data to estimate tree cover, structural diversity and diversity of spectral responses measured by the satellite. It combines them to create a score that can be mapped at national scales. Validation shows promising results in four case study countries: Uganda, Rwanda, Honduras and Indonesia. This tool has the potential to be a valuable and much-needed indicator for measuring and monitoring agricultural biodiversity for the post-2020 agenda.

In order to realise the potential that trees on farms have, farmers must adopt it widely. Facilitating sustainable land management through agroforestry is difficult, and so understanding the links between socioeconomic characteristics of farming communities and the adoption of agroforestry practices is critical for promoting agroforestry. Promoting trees on farms for all these benefits requires understanding what determines if a farmer adopts the practice. We know it depends on many factors, including the farmer’s characteristics and the area’s climate. Current research in understanding these factors focuses on case studies and shows that factors are inconsistent. More generalisable information is needed to ensure policy and action are informed. Chapter 3 takes a regional approach to exploring these factors to see how they vary from region to region across Uganda. The results show that, on average, across all regions, travel time was the most important factor. However, there are significant regional differences in which factors are most important and varying directions of the relationships. This information can help improve the adoption of trees on farms through better services promoting it that are tailored to regional circumstances, tackling the most important barriers in these regions.