Spatial and Temporal Assessment of Smallholder Vulnerability and Resilience in Diverse Landscapes

Food security, especially in arid, climate-sensitive regions, is becoming increasingly unstable. Understanding the human and bio-physical components of agricultural and environmental systems, is crucial for mitigating negative climate impacts on food security. However, bio-physical and agricultural systems are complex, non-linear, and interconnected, and current methods cannot fully capture all the dynamic and static elements of these integrated systems (Inwood et al., 2018).

The aim of this PhD is to develop a novel methodology for the simultaneous landscape-scale assessment of agricultural and environmental systems and their interactions, using housheold surveys and open-source geo-spatial data. This research has three main objectives:

1. Assess and adapt the methodology for understanding the multivariate spatial-temporal interplay between elements of a complex system;
2. Apply the method to real-world data from two active study sites and evaluate its effectiveness;
3. Conduct 'what-if?' scenarios to identify levers of change and tipping points within the system.

The research focuses on the Taita-Taveta Hills, southeast Kenya, and the Borana, southern Ethiopia. The methodological efficacy is established through mathematical proofs and real-world testing via ground-truthing in each study site. Ground-truthing will be informed by site visits and over 1,400 household surveys.

The 'what-if?' scenarios will explore changes in environmental factors (e.g., meteorological activity, wildlife populations, forest cover) and human factors (e.g., population growth, policy interventions). Simultaneous multivariate spatial-temporal systems analysis remains an unsolved problem (Chen et al., 2020), though existing models in latent variable, neural network, and Bayesian hierarchical approaches show promise. A comprehensive literature review will be conducted to identify the most viable path forward.

The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.