Trees on farms: ecological and socioeconomic analyses of tropical agroforestry landscapes using remote sensing
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Date
20/06/2023Item status
Restricted AccessEmbargo end date
20/06/2024Author
Harrison, Sam B.
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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.
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