Trees on farms: ecological and socioeconomic analyses of tropical agroforestry landscapes using remote sensing

Abstract

Covering roughly a third of the Earth’s surface, agricultural land is central to livelihoods, food security, biodiversity and climate. The need for food and materials, declining soil fertility in agricultural systems, and climate change have all led to wide-scale agricultural expansion into natural habitats. More than 90% of deforestation across the tropics is estimated to be driven by agriculture. Deforestation and degradation damage the ecosystem services that many rural and forest-proximate people rely on. Agriculture, forestry and other land use account for nearly a quarter of anthropogenic greenhouse gas emissions. How we use land is critical to the future trajectories of biodiversity, climate change and poverty alleviation. The global agricultural system needs transformation, and trees on farms have been identified as an important tool in this transformation. Trees on farms can improve biodiversity, sequester carbon, provide ecosystem services and improve livelihoods in agricultural landscapes.

To understand the state of, and changes in, biodiversity in these agricultural landscapes with trees, they must be monitored in a consistent and timely manner. However, efficient wide-scale monitoring methods are lacking, and it is not yet clear how Earth Observation (EO) data can be used for the landscape-level analyses needed to monitor biodiversity. In chapter 2, I test a novel approach to mapping tree species assemblages by mapping ordination axes of floristic composition using EO data. Existing methods for modelling floristic gradients have been confined to more homogeneous landscapes or using hyperspectral imagery and have limited wider utility. The models were tested in three complex agricultural-forest landscapes (in Uganda, Rwanda and Honduras) using a fusion of optical and radar imagery alongside other geospatial datasets. Nonmetric Multidimensional Scaling ordination scores describing floristic composition were modelled using random forest regression (with the remote sensing data as predictors) and mapped across the study sites, testing the approach’s applicability in multiple contexts. EO data were able to predict some of the variations in floristic composition: model fits varied from R2=0.56 - 0.77 and RMSE from 9 - 19% across sites. The resultant maps capture the main landscape features of tree floristic gradients. The results show that this novel approach using a fusion of optical and radar EO data alongside geospatial data in a machine learning model can map the tree floristic gradients in complex agricultural systems. The floristic gradient map provides more detailed spatial assessments of floristic composition for understanding biodiversity in agricultural landscapes than were previously possible with satellite data and is a step towards monitoring biodiversity in these systems.

EO data can be used to scale up field data to assess aspects of biodiversity at landscape scales, but this is not feasible at national scales. A lack of systematic data on the biodiversity in agricultural land at national scales means monitoring global targets is difficult. There is a need for indicators of agricultural biodiversity applicable at widescale and across different landscapes. In chapter 3, I develop and present the proof of concept for an indicator of the biodiversity value of agricultural landscapes by assessing the properties of their trees. The tool uses freely available satellite data products to estimate wooded area, structural diversity and spectral diversity of agricultural lands. It combines them to ascribe a score that can be mapped at national scales. Qualitative photointerpretation validation shows promising results in four case studies in various agricultural contexts. Ideas for developing the indicator to ensure indicator continuity are discussed and include improvements in data that can come from upcoming satellite products, further qualitative and quantitative validation from those with on-site expertise, and testing the applicability of the indicator for change detection and quantification. The indicator should be a valuable tool for planners and decision-makers to monitor agricultural land, report on biodiversity, and plan informed conservation strategies. It has the potential to be a much-needed indicator for the post-2020 agenda for measuring and monitoring agricultural biodiversity.

In order to realise the potential that trees on farms have, it must be adopted widely by farmers. Promoting agroforestry for all its benefits requires an understanding of the determinants of adoption. We know the adoption of agroforestry depends on many factors, including a number of socioeconomic and biophysical conditions. Current research in understanding these determinants is focused on context-specific case studies and is inconsistent between studies. More generalisable information is needed to ensure effective and informed policy and action. Chapter 4 takes a regional approach to exploring these determinants to see how they vary from region to region across Uganda. The results show that, on average, across all regions, travel time to cities was the most important factor, but there is significant regional disparity in which factors are most important as well as inconsistent directions of the relationships. This spatially explicit information can help improve agroforestry adoption through better extension services and interventions tailored to regional circumstances, tackling the most important barriers in each region.