Context-dependency of climate mitigation, biodiversity and ecosystem services in regenerative agriculture


Driven by the need to limit global warming to below 1.5°C above pre-industrial levels and halt biodiversity decline, numerous policies and incentives across the UK and worldwide are convincing land managers to adopt “green” or “carbon farming” practices. In recent years, farmers in particular are joining a trend to adopt “regenerative agriculture” on their land. While a definition for regenerative agriculture is still lacking[1], most scholars refer to either the use of specific processes (avoid soil disturbance, keep soil cover, keep living roots, crop diversification, livestock in arable rotation), outcomes (e.g., to improve soil health, sequester carbon, and to increase biodiversity) or a combination of the two.

Whilst the importance of understanding the efficacy of such practices on climate mitigation, biodiversity and other ecosystem services (e.g. flood mitigation) is clear, especially in the context of countries such as the UK, there are significant challenges to do so. In particular, the complexity of farm environments, including land managers’ decisions throughout the year, make the efficacy of those practices (e.g. cover crops) dependent on (a) local context e.g. soil type, climate, (b) landscape context e.g. composition, configuration and (c) details of farm practices e.g. sowing date.  The majority of existing meta-analyses on regenerative agriculture (i) focus on a single objective (e.g. change in soil carbon) (ii) only assess mean effects (iii) do not take into account contextual drivers. Indeed, for those reasons the literature includes conflicting “conclusions” about the benefits of commonly applied farming practices, or make unsubstaintiated claims with poor statistical power.

In this PhD, the student will build on initial efforts by the iCASP Public Goods[2], and the iCASP Soil Carbon projects, and augment a large collection of existing empirical data from published meta-analysis, augmented by systematic review of recent literature. Using statistical analysis and/or “explainable machine-learning algorithms”, an exploratory analysis of the context-dependency common practices efficacy will be performed.

Objective 1:

As a first step, the project will compile secondary data on context-dependency (epseically pedo-climate and management specifics) of the impact of the dominant land-use/cover/use intensity/management practice practice in UK regenerative agriculture. The analyses will cover aspects including soil carbon/GHG emissions, biodiversity and farming productivity/costs. Sources will include published literature as well as results from Defra science projects and international efforts (e.g. EPJ-SOIL). This part of the project will help deciper what are the gaps in the empirical evidence, especially in relation to combined effects of regenerative practices.

Objective 2:

The collated database will be queried with statistical/Bayesian and/or machine learning approaches to generalise the (combined) climate, biodiversity, economic and environmental impact for different soil, climatic and geographic conditions persisting across the UK. This work will help determine the (proximate and distal) controlling factors determining the efficacy of practices applied by farmers.

Objective 3:

Using the pedo-climate dependent models, an exploratory analysis of the impact of future climate scenarios on efficacy of common practices will be performed. This would answer what we might expect to see by 2050 in terms of overall climate mitigation and biodiversity contribution from current and future adoption of regenerative practices.

Potential for high impact outcome:

The outputs of this research project will be of benefit to academics, conservation organisations and groups, farmers and landowners, policy development, government and society. This project will significantly advance our understanding of how current regenerative practices impact climate, soils, biodiversity and other ecosystem services. This will help inform government policy on agri-environment schemes for regenerative practices, including those in the upcoming Environmental Land Management Schemes in England. In addition, the findings will be useful for private initiatives, such as the voluntary carbon markets and developing UK Farm Soil Carbon Code. The project will lead to several outputs, including the publication of 3-4 academic papers and policy briefings.

Training: The student will work under the supervision of Professor Guy Ziv & Professor Pippa Chapman within the School of Geography, University of Leeds. The successful candidate will develop a range of research skills, including data analysis and curation, statistical analysis and modelling, data interpretation, academic writing skills and giving presentations. Training will be provided to help develop these skills.

The student will be supported throughout the studentship by a comprehensive PGR skills training programme. Training needs will be assessed at the beginning of the project and at key stages throughout the project and the student will be encouraged to participate in the numerous training and development courses that are run within the NERC DTP and the University of Leeds to support PGR students, including statistics training, academic writing skills, grant writing, how to ensure your research has impact, etc. Supervision will involve regular meetings with all of the supervisors. The student will also be part of different research groups across the School of Geography and University of Leeds such as the Priestly Centre – a major interdisciplinary research centre that delivers climate change research that underpins robust and timely solutions and the Global Food and Environment Institute.

Student profile: The student should have a keen interest in environmental issues with a strong background in one or more of physical geography, environmental sciences, ecology, biology, soil science or related discipline.  Some statistical modelling skills are desirable but not essential, as full training will be provided during the PhD.


[1] Peter Newton et al., ‘What Is Regenerative Agriculture? A Review of Scholar and Practitioner Definitions Based on Processes and Outcomes’, Frontiers in Sustainable Food Systems 4 (2020),

[2] See