Explainable AI for UK agricultural land use decision-making
Lead Research Organisation:
UK CENTRE FOR ECOLOGY & HYDROLOGY
Department Name: Soils and Land Use (Lancaster)
Abstract
Agricultural land use dynamics and their associated driving factors represent highly complex systems of flows that are subject to non-linearities, sensitivities, and uncertainties across spatial and temporal scales. They are therefore challenging to represent using traditional statistical modelling approaches. Existing process-based modelling has enabled advances in understanding of individual biophysical processes underpinning agricultural land use systems (e.g. crop, livestock and biogeochemical models). However, these tend to focus on individual processes in detail or link a limited number of processes at large scales, thereby mostly ignoring the complex interdependencies between the multiple interacting biophysical and socio-economic components of land use systems. Artificial intelligence (AI) techniques offer great potential to complement such modelling approaches by mining the deep knowledge (e.g. farming patterns and behaviours) encapsulated in 'big' data from ground-based sensors (such as frequently used for precision farming) and Earth Observation satellites. This will deliver enhanced insight on the past and current state and spatio-temporal dynamics of agricultural land use system flows and how they can be influenced by decisions on agricultural policies and related farm management practices.
Our proposal aims to develop a novel explainable AI framework that is transparent, data-driven and spatially-explicit by using probabilistic inference and explicit "if-then" rules. We will demonstrate proof-of-concept for two pilot regions of the UK (Oxfordshire and Lincolnshire), and the framework will be set up in a way that can be readily expanded to the whole UK. Specifically, we will draw on time-series of agricultural land use and production datasets (in-kind support from industry project partner SOYL) to identify the key socio-economic and environmental driving factors that have led to historic agricultural land use changes in the pilot regions. We will then establish explainable AI-rules for the characterisation of these agricultural land use changes and refine them within the framework through machine learning and parameter optimisation.
We will demonstrate and test the potential of the explainable AI framework for providing a new and robust method for predicting changing patterns of agricultural land use in the two pilot regions. This will include testing the ability of the AI framework for improving understanding of past and present agricultural land use dynamics across multiple temporal and spatial scales from 'big' data. It will also assess the potential for continually updating the predictions of land use dynamics in real-time using data from sensors. This could provide early warning when certain driving conditions are triggered or used to repeatedly refine short-term projections of land use change and their estimates of uncertainty.
Our proposal aims to develop a novel explainable AI framework that is transparent, data-driven and spatially-explicit by using probabilistic inference and explicit "if-then" rules. We will demonstrate proof-of-concept for two pilot regions of the UK (Oxfordshire and Lincolnshire), and the framework will be set up in a way that can be readily expanded to the whole UK. Specifically, we will draw on time-series of agricultural land use and production datasets (in-kind support from industry project partner SOYL) to identify the key socio-economic and environmental driving factors that have led to historic agricultural land use changes in the pilot regions. We will then establish explainable AI-rules for the characterisation of these agricultural land use changes and refine them within the framework through machine learning and parameter optimisation.
We will demonstrate and test the potential of the explainable AI framework for providing a new and robust method for predicting changing patterns of agricultural land use in the two pilot regions. This will include testing the ability of the AI framework for improving understanding of past and present agricultural land use dynamics across multiple temporal and spatial scales from 'big' data. It will also assess the potential for continually updating the predictions of land use dynamics in real-time using data from sensors. This could provide early warning when certain driving conditions are triggered or used to repeatedly refine short-term projections of land use change and their estimates of uncertainty.
Planned Impact
We will engage with four major types of beneficiaries to maximise the societal impact of this project:
1. UK Government, Devolved Administrations and Policy-makers:
Our project will provide decision-makers with an innovative and integrated knowledge base supporting agricultural land use decision-making. We will engage with policy-makers including Defra, Natural England (a project partner), the Environment Agency, and the devolved administrations through consultations on the development of the explainable AI system to ensure it meets a broad set of decision-making needs. We will also demonstrate the final AI framework at a 1-day policy workshop that explores the potential of AI approaches for supporting landscape decision-making alongside other modelling and information products, including defining future needs beyond the remit of this proposal. This will include exploring how the framework can help inform the objective in the 25 Year Environment Plan to deliver a clear evidence base to promote precision agriculture and land management.
2. Farming Industries and Agri-business:
Through our strategic partnership with SOYL, the leading precision crop production service provider in the UK (see letter of support), we will have access to a unique 'big' dataset of in-situ and EO-based agricultural land use statistics to establish the explainable AI framework. The methodological innovation in the project will be co-created with SOYL (and our other project partner, Natural England) through four meetings throughout the one-year project lifetime. This will ensure that our outcomes, disseminated through a one-week training event at SOYL, are fit-for-purpose in informing the decision-making needs of agri-businesses in order to catalyse business change and innovation.
3. AI Industries and the Economy:
The AI innovations developed in this project will support the socio-economic development of the UK by promoting automation as part of the Industry 4.0 revolution. The AI framework is fully transformative and scalable, such that it can be elaborated or enhanced as required to support decision-making within a growing UK economy that aims to balance economic, environmental and societal aspects of the UK agricultural sector. Continued technological innovations in the agricultural sector are expected through precision farming and Earth Observation, creating an increasing demand for AI and big data-driven models.
4. Land owners and Trusts:
The knowledge and explainable AI rules derived in the system will support the decision-making of land owners and national trusts, by reducing environmental hazards (e.g. pesticides), enhancing ecosystem services and natural capital, and promoting smart and profitable agriculture, as well as building an inclusive society that allows people and nature to thrive. We will engage with relevant land owners and trusts through Natural England (see letter of support) via dissemination meetings and communications (e.g. media).
The 'Pathways to Impact' document details the activities we will undertake to deliver these impacts.
1. UK Government, Devolved Administrations and Policy-makers:
Our project will provide decision-makers with an innovative and integrated knowledge base supporting agricultural land use decision-making. We will engage with policy-makers including Defra, Natural England (a project partner), the Environment Agency, and the devolved administrations through consultations on the development of the explainable AI system to ensure it meets a broad set of decision-making needs. We will also demonstrate the final AI framework at a 1-day policy workshop that explores the potential of AI approaches for supporting landscape decision-making alongside other modelling and information products, including defining future needs beyond the remit of this proposal. This will include exploring how the framework can help inform the objective in the 25 Year Environment Plan to deliver a clear evidence base to promote precision agriculture and land management.
2. Farming Industries and Agri-business:
Through our strategic partnership with SOYL, the leading precision crop production service provider in the UK (see letter of support), we will have access to a unique 'big' dataset of in-situ and EO-based agricultural land use statistics to establish the explainable AI framework. The methodological innovation in the project will be co-created with SOYL (and our other project partner, Natural England) through four meetings throughout the one-year project lifetime. This will ensure that our outcomes, disseminated through a one-week training event at SOYL, are fit-for-purpose in informing the decision-making needs of agri-businesses in order to catalyse business change and innovation.
3. AI Industries and the Economy:
The AI innovations developed in this project will support the socio-economic development of the UK by promoting automation as part of the Industry 4.0 revolution. The AI framework is fully transformative and scalable, such that it can be elaborated or enhanced as required to support decision-making within a growing UK economy that aims to balance economic, environmental and societal aspects of the UK agricultural sector. Continued technological innovations in the agricultural sector are expected through precision farming and Earth Observation, creating an increasing demand for AI and big data-driven models.
4. Land owners and Trusts:
The knowledge and explainable AI rules derived in the system will support the decision-making of land owners and national trusts, by reducing environmental hazards (e.g. pesticides), enhancing ecosystem services and natural capital, and promoting smart and profitable agriculture, as well as building an inclusive society that allows people and nature to thrive. We will engage with relevant land owners and trusts through Natural England (see letter of support) via dissemination meetings and communications (e.g. media).
The 'Pathways to Impact' document details the activities we will undertake to deliver these impacts.
Organisations
- UK CENTRE FOR ECOLOGY & HYDROLOGY (Lead Research Organisation)
- UNIVERSITY OF EDINBURGH (Collaboration)
- Isaac Newton Institute for Mathematical Sciences (Collaboration)
- UNIVERSITY OF LEEDS (Collaboration)
- Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Collaboration)
- Karlsruhe Institute of Technology (Collaboration)
- UNIVERSITY OF EXETER (Collaboration)
- UNIVERSITY OF SOUTHAMPTON (Collaboration)
- FOREST RESEARCH (Collaboration)
People |
ORCID iD |
Paula Harrison (Principal Investigator) |
Publications
Brown C
(2022)
Agent-Based Modeling of Alternative Futures in the British Land Use System
in Earth's Future
John D
(2022)
An attention-based U-Net for detecting deforestation within satellite sensor imagery
in International Journal of Applied Earth Observation and Geoinformation
Guo R
(2022)
Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China
in Remote Sensing
Jones S
(2023)
Integrated modeling to achieve global goals: lessons from the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative
in Sustainability Science
Li R
(2022)
Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
in IEEE Geoscience and Remote Sensing Letters
Smith AC
(2023)
Sustainable pathways towards climate and biodiversity goals in the UK: the importance of managing land-use synergies and trade-offs.
in Sustainability science
Description | Spatial land use models are indispensable for providing scientific evidence that can inform sustainable land use planning. However, understanding and predicting agricultural land use change and the factors that drive such change is difficult due to the highly complex systems of flows that are subject to non-linearities, sensitivities, and uncertainties across spatial and temporal scales. This complexity is challenging to represent using traditional statistical modelling approaches. In this project, we explored and tested the ability of Artificial Intelligence (AI) techniques to complement traditional land use modelling approaches by learning complex spatial and temporal relationships between large geospatial land use datasets and datasets of important driving factors of land use change. We found three categories of driving factors to be particularly important determinants of UK land use change: climatic factors (temperature, precipitation, evapotranspiration), topographic factors (elevation, slope, distance to water courses) and human-related factors (distance to urban areas, distance to major roads, distance to railways). We developed a novel approach that combines the AI deep learning method, known as Generative Adversarial Networks, with a traditional land use modelling approach, known as Cellular Automata models. We tested the improvement in predicting land use changes from 1990 to 2019 using UKCEH Land Cover Map data to assess if the inclusion of the AI method provided better characterisation of spatial patterns and landscape dynamics. The key findings we identified include: 1. The Cellular Automata model was able to predict future land use change. However, it only utilised limited neighbourhood information at fine scales without considering the large-scale heterogeneous environment. The transition between arable and grassland land cover classes was particularly challenging to predict using a Cellular Automata model, with relatively low accuracy (71%). 2. Capturing fine resolution spatial patterns and their context are essential for predicting land use changes. This was demonstrated in both a pilot study in Oxfordshire and wider application of our method for England. We show that in both case studies accuracy of predicting land use change was increased by 8% with the incorporation of spatial and context information. 3. Combining the AI (deep learning) method with the Cellular Automata model provided both fine-scale neighbourhood and large-scale context information to make joint landscape decisions. The spatial patterns among woodland, arable, grassland, freshwater and urban are accurately characterised, with an overall accuracy of 89%. 4. Human-related factors were found to be key drivers of land use changes. In particular, the distance to urban and distance to road were the two most important drivers that have significant impact on land use changes. These drivers are highly related to human activities and road accessibility, and influenced UK landscape dynamics over the past 30 years. |
Exploitation Route | The project has produced a variety of outcomes such as land use maps and their driving factors for England at a 100 m resolution. The project has provided proof of concept that innovative AI methods can add significant precision to land use prediction, but further effort is needed to make the approach (and associated model code) operational at the large-scale so that it can be implemented to inform policy-makers and agri-businesses. In particular, cloud computing and edge computing are required to establish real-time prediction, where farmers and stakeholders can use mobile device for rapid decision-making. In combination with in-situ precision agriculture, this further effort could help increase agricultural productivity and support land management. |
Sectors | Agriculture Food and Drink Digital/Communication/Information Technologies (including Software) Environment |
URL | https://www.ceh.ac.uk/our-science/projects/explainable-ai-uk-agricultural-land-use-decision-making |
Description | Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework |
Amount | £934,689 (GBP) |
Funding ID | NE/T012307/1 |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 01/2020 |
End | 12/2024 |
Description | Land Use for Net Zero Hub |
Amount | £6,500,000 (GBP) |
Organisation | United Kingdom Research and Innovation |
Sector | Public |
Country | United Kingdom |
Start | 11/2023 |
End | 03/2027 |
Description | Modern Approaches to the Monitoring of BiOdiversity (MAMBO) |
Amount | € 370,378,375 (EUR) |
Organisation | European Union |
Sector | Public |
Country | European Union (EU) |
Start | 08/2022 |
End | 08/2026 |
Description | Pilot study: Can we use drone technology for quantifying and monitoring flower resources in field margins? |
Amount | £15,969 (GBP) |
Organisation | Natural England |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2021 |
End | 04/2021 |
Description | Sustainable soil management to unleash soil biodiversity potential and increase environmental, economic and social wellbeing (SOILGUARD) |
Amount | € 7,000,000 (EUR) |
Organisation | European Commission H2020 |
Sector | Public |
Country | Belgium |
Start | 05/2021 |
End | 05/2025 |
Description | Turing AI Fellowship: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) |
Amount | £1,097,294 (GBP) |
Funding ID | EP/V022636/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2021 |
End | 12/2025 |
Description | CRAFTY-GB: Development of an agent-based model of land use change taking account of land owner/manager decision-making for Great Britain |
Organisation | Forest Research |
Country | United Kingdom |
Sector | Public |
PI Contribution | Information on UK datasets and statistical analysis of land use change over time for different agricultural land classes |
Collaborator Contribution | Set-up, calibration and coding on the CRAFTY agent-based model for Great Britain |
Impact | Land use change scenarios for 6 combinations of climate change (RCPs) and socio-economic change (SSPs) scenarios. Collaboration is multi-disciplinary involving environmental scientists, social scientists, computer programmers and statisticians |
Start Year | 2020 |
Description | CRAFTY-GB: Development of an agent-based model of land use change taking account of land owner/manager decision-making for Great Britain |
Organisation | Karlsruhe Institute of Technology |
Country | Germany |
Sector | Academic/University |
PI Contribution | Information on UK datasets and statistical analysis of land use change over time for different agricultural land classes |
Collaborator Contribution | Set-up, calibration and coding on the CRAFTY agent-based model for Great Britain |
Impact | Land use change scenarios for 6 combinations of climate change (RCPs) and socio-economic change (SSPs) scenarios. Collaboration is multi-disciplinary involving environmental scientists, social scientists, computer programmers and statisticians |
Start Year | 2020 |
Description | CRAFTY-GB: Development of an agent-based model of land use change taking account of land owner/manager decision-making for Great Britain |
Organisation | University of Edinburgh |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Information on UK datasets and statistical analysis of land use change over time for different agricultural land classes |
Collaborator Contribution | Set-up, calibration and coding on the CRAFTY agent-based model for Great Britain |
Impact | Land use change scenarios for 6 combinations of climate change (RCPs) and socio-economic change (SSPs) scenarios. Collaboration is multi-disciplinary involving environmental scientists, social scientists, computer programmers and statisticians |
Start Year | 2020 |
Description | Co-chair of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Nexus Assessment |
Organisation | Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services |
Country | Germany |
Sector | Charity/Non Profit |
PI Contribution | The IPBES Nexus Assessment is collating, synthesizing and assessing published evidence on the interlinkages among biodiversity, water, food, health and climate change. The role of the co-chair is to assume responsibility for overseeing the preparation of an assessment report, including its summary for policymakers (SPM) and ensuring that the report is completed to the highest scientific standard and addresses the key scoping questions. This includes responsibility for coordinating with the > 150 expert authors to ensure that the chapters are delivered in a timely manner. Once the assessment has been approved by the 138 IPBES government members, co-chairs engage in the outreach for the assessment deliverables. |
Collaborator Contribution | Over 150 expert authors nominated by IPBES member governments will contribute their time and knowledge to generating the content of the assessment report. Coordinating Lead Authors will coordinate contributions to the 7 chapters of the assessment working with Lead Authors lead author who produce designated sections or parts of chapters in line with the scoping report for the assessment, on the basis of the best scientific, technical and socioeconomic information available. Review Editors for each chapter ensure that all substantive expert and government review comments are afforded appropriate consideration. |
Impact | The main outcome will be the IPBES Nexus Assessment Report and its Summary for Policy-Makers due in later 2024. |
Start Year | 2021 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" |
Organisation | Isaac Newton Institute for Mathematical Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Collaborator Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Impact | Workshop took place, which enabled networking between multiple disciplines around landscape decision-making. Disciplines involved were quantitative social sciences, environmental sciences, mathematics and statistics. |
Start Year | 2020 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" |
Organisation | University of Exeter |
Department | College of Engineering, Mathematics & Physical Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Collaborator Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Impact | Workshop took place, which enabled networking between multiple disciplines around landscape decision-making. Disciplines involved were quantitative social sciences, environmental sciences, mathematics and statistics. |
Start Year | 2020 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" |
Organisation | University of Leeds |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Collaborator Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Impact | Workshop took place, which enabled networking between multiple disciplines around landscape decision-making. Disciplines involved were quantitative social sciences, environmental sciences, mathematics and statistics. |
Start Year | 2020 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" |
Organisation | University of Southampton |
Department | Faculty of Natural and Environmental Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Collaborator Contribution | Contributed to the design, invitations and organisation of the 4-day online workshop |
Impact | Workshop took place, which enabled networking between multiple disciplines around landscape decision-making. Disciplines involved were quantitative social sciences, environmental sciences, mathematics and statistics. |
Start Year | 2020 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Mathematical and statistical challenges in landscape decision-making" |
Organisation | Isaac Newton Institute for Mathematical Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Collaborator Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Impact | Final Scientific Report created from the programme. Collaboration is multi-disciplinary involving a range of different environmental scientists, mathematicians and statisticians |
Start Year | 2019 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Mathematical and statistical challenges in landscape decision-making" |
Organisation | University of Exeter |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Collaborator Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Impact | Final Scientific Report created from the programme. Collaboration is multi-disciplinary involving a range of different environmental scientists, mathematicians and statisticians |
Start Year | 2019 |
Description | Organising committee for UKRI Landscape Decisions Programme series of events on "Mathematical and statistical challenges in landscape decision-making" |
Organisation | University of Southampton |
Department | Faculty of Natural and Environmental Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Collaborator Contribution | Contributed to the organisation of the main 1-month programme and the opening and closing workshops |
Impact | Final Scientific Report created from the programme. Collaboration is multi-disciplinary involving a range of different environmental scientists, mathematicians and statisticians |
Start Year | 2019 |
Description | Blog on "Overcoming barriers to model coupling" |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Blog on the topic of Model coupling (the communication and interchange of information between models) as a key component in meeting the challenges of many of today's complex real-world environmental problems, such as landscape decisions. |
Year(s) Of Engagement Activity | 2020 |
URL | https://ceeds.ac.uk/blogs/overcoming-barriers-model-coupling |
Description | CEEDS Seminar: Machine Learning for the Natural Environment |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | This seminar was attended by over 170 people - a fantastic and amazing attendance across Lancaster University and UK Centre for Ecology and Hydrology (UKCEH). It helps build the strong links between LU and UKCEH, but also the links across UKCEH science areas and different sites. |
Year(s) Of Engagement Activity | 2021 |
URL | https://ceeds.ac.uk/blogs/ceeds-seminar-machine-learning-natural-environment |
Description | CEEDS Seminar: Machine Learning for the Natural Environment |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | The presentation was designed as an introductory tutorial into machine learning techniques. The focus of the talk was to educate environmental scientists on the available machine learning tools that are applicable for analysing environmental data. |
Year(s) Of Engagement Activity | 2020 |
URL | https://ceeds.ac.uk/blogs/ceeds-seminar-machine-learning-natural-environment |
Description | Conference session on "Emerging research on current trends and future scenarios of the nature-water-food-health-climate nexus" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Session organised at the Ecosystem Services Partnership Europe conference to discuss the latest science on integrated modelling of multiple sectors and ecosystem services, sparking lots of discussion including ideas for the IPBES Nexus Assessment and future research |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.espconference.org/europe22 |
Description | Conference session on "Integrated models and ecosystem services: applications in real world case studies" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | This session at the Ecosystem Services Partnership European conference held 7-10 June 2021 discussed whether integrated ecosystem services models are appropriate for different stakeholders and end-user needs? This session focused on improving understanding of actual applications of integrated ecosystem service models to real-world case studies, and the lessons that can be learnt from such case studies. This included co-production of models, good and bad practice examples, barriers and bridges, the importance of transparency in model assumptions and integration components, communication with stakeholders, and evidence of integrated models actually resulting in a policy or management decision. The session will also consider the role of transdisciplinarity and model integration in such real-world applications, and key feedback from stakeholders and policy-makers. The session sparked questions and discussion around examples of what went well and what didn't, exchange of experiences and their implications for researchers interested in further developing and applying integrated ecosystem service modelling approaches. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.es-partnership.org/events/3rd-esp-europe-conference-2021/#:~:text=The%20Ecosystem%20Serv... |
Description | Evidence seminar to Natural Resources Wales |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Policymakers/politicians |
Results and Impact | Presentation on "Integrated modelling approaches for exploring synergies and trade-offs between policy goals in landscapes". Part of a Natural Resources Wales (NRW) External Evidence Seminar to support NRW's Integrated Evidence Group. The seminar provided an excellent opportunity to enhance the flow of evidence from researchers to decision makers across operations and policy roles. |
Year(s) Of Engagement Activity | 2021 |
Description | Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services Scoping Workshop on the Nexus Assessment |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Policymakers/politicians |
Results and Impact | Government nomination to participate in the scoping workshop for the IPBES Nexus Assessment on the interlinkages among biodiversity, water, food and health in the context of climate change. A scoping report was produced following the scoping workshop, which will be considered and hopefully approved at the IPBES-8 plenary in 2021. |
Year(s) Of Engagement Activity | 2020 |
URL | https://ipbes.net/nexus/scoping-experts |
Description | Invited speaker (International Workshop) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentation given on methods for modelling complex systems, such as landscape decisions, including how uncertainty effects decision-making, which sparked debate and capacity building with groups undertaking similar modelling initiatives in South Korea and other parts of Asia |
Year(s) Of Engagement Activity | 2019 |
Description | Machine Learning Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | A Workshop on Machine Learning was held as part of the Centre for Excellence in Environmental Data Science (CEEDS) on 25 June 2021 to initiate new research collaborations between Lancaster University and the UK Centre for Ecology & Hydrology (UKCEH) related to the fundamental and crucial research area of machine learning. The introduction to the workshop covered existing work on machine learning in both organisations. This was followed by a technical session that demonstrated how to get started in machine learning. The workshop then discussed what was needed in order to overcome the entry barrier in terms of getting started with machine learning. The final session of the workshop focused on plans for future activities through CEEDS around machine learning. For example, potential research collaborations, jointly supervised PhD projects, funding applications, etc. |
Year(s) Of Engagement Activity | 2021 |
URL | https://ceeds.ac.uk/blogs/ceeds-workshop-machine-learning |
Description | Panel debate on Ecosystems Untangled: Understanding and Managing Complexity as part of the Ecosystems Entangled Festival |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Science is increasingly interested in understanding and managing ecosystems in all their complexity, understanding individual components and their complex interrelationships, and valuing the services that healthy ecosystems can offer to society. This panel debate focused on how we deal with this complexity and how digital technology can assist in understanding and managing ecosystems. The debate sparked lots of questions and discussion around the nature of complexity in ecosystems and how well existing approaches represent this complexity (in particular ecosystem services and natural capital research), how environmental modelling needs to adapt to better represent this complexity, and whether there are any lessons to be learned from other fields of study including social science and economics. The discussion particularly focused on the role of a more systems thinking approach to understanding and managing the natural environment, with an eye on whether digital technology has the capability to support such a systemic approach. |
Year(s) Of Engagement Activity | 2021 |
Description | Panel member and participant in the "Landscape Decisions Slow Conference" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | The conference was organised by the UKRI Landscape Decisions Programme Office over 4 days (22, 23, 29 and 30 June). Each day covered a different topic related to landscape decisions, including (i) Balancing priorities in land-use decision-making; (ii) Understanding human decision-making; (iii) Facilitating better land-use decisions; and (iv) Forecasting innovative methods and tools. The workshop enabled detailed discussions between researchers involved in the many different Landscape Decision Programme-funded projects about key issues for advancing methods and approaches, leading to wider awareness of the different project activities and potential collaborations across discipines. |
Year(s) Of Engagement Activity | 2021 |
URL | https://landscapedecisions.org/ |
Description | Participation in workshop on sustainable agriculture under climate change in the Czech Republic |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentations and working groups at workshop on climate change adaptation strategies for agriculture, which sparked discussions and ideas for future collaboration |
Year(s) Of Engagement Activity | 2022 |
URL | http://sustes.czechglobe.cz/#features |
Description | Participatory mapping workshop to inform Defra BEIS joint land-use modelling project |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | This participatory mapping workshop was organised by Defra and BEIS to inform their joint land use modelling project. It was part of a series of workshops, with the focus of this workshop on Agriculture (including arable crop and horticultural production) within the context of the larger land-use system. The aim was to capture linkages between different aspects of the agricultural land-use system, including links between agricultural management practices and other systems, e.g. soils and peat. The workshop was participatory and interactive, so sparked a lot of debate about different perspectives on key linkages within land-use systems and how they can (and can not) be represented in models. |
Year(s) Of Engagement Activity | 2021 |
Description | Presentation on "Coupling models to represent interactions within landscape systems" at Workshop on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Isaac Newton Institute (INI) workshop on "Integrating quantitative social, ecological and mathematical sciences into landscape decision-making" organised as part of the UKRI Landscape Decisions Programme. Presentation summarised discussion from INI Programme in 2019. Research priorities were divided into four themes: (i) transparency, reproducibility and communication in coupled models; (ii) model coupling toolbox; (iii) model coupling technicalities; and (iv) taking advantage of the benefits of model coupling. The key insights that emerged in these four themes were captured within short, medium and longer term research roadmaps. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.newton.ac.uk/seminar/20200907142014401 |
Description | Presentation on "Pathways to sustainable land-use and food systems in the UK" at EAT@Home event side session |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Side event at the EAT Foundation online conference on sustainable land-use and food systems. The presentation on the UK landscape decision-making sparked questions and discussion on the method and the importance of assessing the contribution that sustainable land-use change can make to multiple policy targets, e.g. climate change, biodiversity, health diets. |
Year(s) Of Engagement Activity | 2020 |
Description | Presentation on land-use modelling |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | I was asked to provide an overview of existing work on land-use modelling that could help answer questions related to the different demands on land for meeting multiple policy goals, such as net zero, nature recovery, etc. for the Defra Chief Scientists Office. The presentation was highly interactive and sparker many relevant questions and discussion on current land-use modelling approaches and potential gaps/needs. |
Year(s) Of Engagement Activity | 2021 |
Description | Presentation on the "Explainable AI for UK agricultural land-use decision-making" project at the Landscape Decisions Programme virtual networking event |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Landscape Decisions Programme series of online workshops over 4 days covering (i) Capturing people's interaction with landscapes and landscape decision making; (ii) Multi-functional landscapes - focusing on ecosystem services; (iii) What pressures on Landscapes are currently not being considered - i.e. where are the gaps in our understanding of the pressures?; (iv) Can we systematically capture different levels/scales of landscape decision making and which levels/scales does your project interact with? Sparked questions and discussions around these issues and networking opportunities in relation to the project. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.youtube.com/watch?v=sIbc8-_AqUo&list=PLrlZ6FipN5mlS4_lN9PwZAR6HCyppGTDP&index=4&t=149s |
Description | Public consultation workshop on "Living Landscapes" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | Royal Society Workshop on Living Landscapes (provided expert input to a public consultation exercise on landscapes in the UK). |
Year(s) Of Engagement Activity | 2020 |
Description | Seminar on "Model coupling for evaluating complex systems" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | Centre for Excellence in Environmental Data Science seminar that looked at the important topic of model coupling as a means to address interdependencies between different aspects of the environment. The seminar built understanding around the extent to which complicated environmental questions can be better assessed by integrating models, and discussed the advantages, the challenges, as well as the limitations of joined-up modelling for complex systems. |
Year(s) Of Engagement Activity | 2020 |
Description | Session organised on AI for the environment (Natural Capital Initiative Summit) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Session organised on "AI for the environment" at the Natural Capital Initiative Summit on "Valuing our Life Support Systems" in London, which consisted of talks and debate around the potential of AI approaches to deliver natural capital solutions to practitioners and policy-makers |
Year(s) Of Engagement Activity | 2019 |
Description | Workshop (Environmental Data Science) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Workshop at Alan Turing Institute on exploring collaborative opportunities between the computer and environmental sciences, which sparked discussions and ideas around new collaborations and innovations in environmental data science |
Year(s) Of Engagement Activity | 2019 |
Description | Workshop on "Overcoming barriers to model coupling" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | Centre for Excellence in Environmental Data Science workshop on model coupling. Workshop report produced to summarise discussions and research needs |
Year(s) Of Engagement Activity | 2020 |
Description | Workshop on "Seamless integration between natural and built environment modelling" |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Human activity - the buildings and transport systems we create, the energy we generate and consume, the services we use - has an impact on global environmental systems and vice versa. But what role can digital models play in helping decision-makers to navigate these complex interrelationships and to manage the built and natural environment sustainably? And what if we link models of each of the built and natural environments? This workshop seeks to prompt a discussion of the question: What outcomes and opportunities could seamless integration between built and natural environment modelling deliver? After looking at it from the perspective of different stakeholders and exploring opportunities to get better insights from data and models, the desired outcomes are as follows: • The seeds of a discussion community for identifying opportunities for model integration and interdisciplinary work. • Contacts between people pursuing other strands of work happening in the integration of models for decision support; • A report on insights from the workshop (end of March 2021), which will serve as a resource for future interdisciplinary research projects as well as the Information Management Framework (IMF) community of the National Digital Twin programme. |
Year(s) Of Engagement Activity | 2020 |
Description | Workshop on integrated modelling of food, agriculture, land-use, biodiversity and energy |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Policymakers/politicians |
Results and Impact | Around 20 scientists and 10 policymakers met at an EU Policy Workshop on "Farm to Fork: how to make it work for the EU and the planet", sparking interesting debates and ideas on research priorities for supporting policy in agricultural land-use decision-making |
Year(s) Of Engagement Activity | 2022 |