dc.contributor.advisor |
Corstanje, Ronald |
|
dc.contributor.advisor |
Hannam, Jacqueline A. |
|
dc.contributor.advisor |
Black, H. I. J. |
|
dc.contributor.advisor |
Lilly, A. |
|
dc.contributor.author |
Campbell, Grant |
|
dc.date.accessioned |
2023-10-17T15:26:06Z |
|
dc.date.available |
2023-10-17T15:26:06Z |
|
dc.date.issued |
2018-10 |
|
dc.identifier.uri |
https://dspace.lib.cranfield.ac.uk/handle/1826/20390 |
|
dc.description |
Black, H. I. J. (Associate supervisor The James Hutton Institute), Lilly, A. (Associate supervisor The James Hutton Institute) |
|
dc.description.abstract |
Many countries have created soil maps to illustrate the variety of soil properties and support
how soils can be used. Traditional soil mapping by field survey and interpretation has been
the most recognised form of soil mapping for many years and an effective way to capture a
variable soil landscape. Such maps have enabled scientists and stakeholders to improve their
understanding of relationships between soils and other landscape factors such as geology
and land cover. However, with the amount of soil information growing and technology
improving, Digital Soil Mapping (DSM) has been developed as an alternative approach to
generate soil property predictions and to produce finer resolution soils data. Currently, DSM
produces maps based on training of models with observed soils data and environmental
covariates and then releases these to stakeholders to evaluate their utility. This PhD has taken
a different approach by addressing stakeholder needs at the beginning of the process.
The overall aim of this PhD was to improve the spatial resolution of soil properties across
Great Britain (GB), as informed by stakeholders. Three main aims were identified. The first
assessed what current soils data and information stakeholders currently use, and what
improvements they want to see from future soil-related products. The second aim, using
information from the questionnaire survey and a comparison of laboratory and analytical
methods, is to develop DSM which could be applied across the whole of GB. This was done
by comparing two modelling approaches: Boosted Regression Trees (BRTs) and Multivariate
Adaptive Regression Splines (MARS) for mapping soil properties (loss-on-ignition, texture and
pH) across two pilot areas. The characteristics of MARS and BRT models at both training and
deployment stages are examined. The third outcome investigated how well the soil properties
mapped across GB, building on the development of DSM in the pilot areas and whether they
reflect expert pedological knowledge. This section also focusses on how suitable an
independent validation dataset is at evaluating soil property predictions.
This PhD has shown that stakeholders are aware of what soils data and information they are
using and could clearly express what is needed to improve current maps. Wider use of soil
information by non-soil experts would be improved by increasing data accessibility and user-
friendly supporting materials. Fundamentally, most stakeholders require finer resolution than
what is currently available which identifies an opportunity for DSM to fill some of this gap.
To address these gaps and develop DSM across GB, this PhD focussed on mapping soil
properties that were directly comparable across Scotland and England & Wales and also key
to many stakeholder information needs. After investigation of laboratory and analytical
methods from the two national soil surveys of Scotland and England & Wales, soil loss on
ignition, soil texture and soil pH were chosen for developing DSM for GB.
From the development of DSM, results showed that MARS models produced better statistical
performances than BRTs for predicting soil properties within a training environment. However,
when MARS models are deployed to larger areas, they extrapolate beyond their means and
BRTs performed better. This is because MARS models perform more consistently when many
variables are required. Furthermore, MARS models struggle with overfitting and missing data
which subsequently leads to incorrect and unfeasible pedological relationships between soil
properties. BRT models, despite not performing as well statistically, produce more consistent
relationships between pedology and mapped soil properties. This is because BRT models
introduce randomness in the boosting which reduces overfitting and improves the predictive
performance. BRTs have shown to be more consistent in the mapping outputs than MARS
because regressing to the mean is more favourable when most data matches up with one
another. However, this does not necessarily mean that the full range of soils in these areas
were being captured by the BRT model. This led to scaling up from the pilot areas to modelling
soil properties across GB using a single regional BRT model and evaluating its performance.
BRT modelling results for GB at 2D and 3D predict well for pH and LOI but less so for texture.
Going forward, more data are required to produce more consistent modelling outputs
especially for areas across GB where soil properties are not currently being predicted well.
The GB modelling results also highlighted a poor performance of the model against an
independent validation dataset. This is because the original data for both GB training and
validation datasets were analysed and collected for different purposes. These datasets were
taken at different time periods under a different sampling design. Furthermore, the data for
both training and validation GB datasets were collected at different scales.
At present, these first versions of soil property DSM maps for GB have produced variable
results. However, this exercise has shown that the development of reliable DSM maps would
benefit from interaction between pedologists, modellers and stakeholders to ensure that
mapped outputs are of sufficient quality at adequate finer resolution and can be usable. Such
DSM maps, alongside management recommendations, will help to address many global
challenges associated to soils. However, DSM is not the panacea for all mapping needs. Until
such time that DSM fully develops into DSA and adequately incorporates the breadth of
information available in traditional soil maps, mapping from field survey and observation will
continue to be necessary for stakeholders. |
en_UK |
dc.description.sponsorship |
Engineering and Physical Sciences (EPSRC) |
en_UK |
dc.language.iso |
en |
en_UK |
dc.publisher |
Cranfield University |
en_UK |
dc.rights |
© Cranfield University, 2018. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. |
en_UK |
dc.subject |
Digital soil mapping |
en_UK |
dc.subject |
stakeholders |
en_UK |
dc.subject |
soil properties |
en_UK |
dc.subject |
finer resolution |
en_UK |
dc.subject |
pedology |
en_UK |
dc.subject |
Boosted Regression Trees (BGRTs) |
en_UK |
dc.subject |
Multivariate Adaptive Regressive Splines (MARS) |
en_UK |
dc.subject |
Great Britain |
en_UK |
dc.subject |
validation |
en_UK |
dc.title |
The application of digital soil mapping to improve the resolution of national soil properties across Great Britain. |
en_UK |
dc.type |
Thesis or dissertation |
en_UK |
dc.type.qualificationlevel |
Doctoral |
en_UK |
dc.type.qualificationname |
PhD |
en_UK |
dc.publisher.department |
SWEE |
en_UK |
dc.description.coursename |
PhD in Environment and Agrifood |
en_UK |