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Optimising Agricultural Land Use Choices in Ireland Using a Spatial Decision-Support Framework

Maynooth University Department of Geography
✓ Fully Funded ⏰ Closing Soon 🎓 Environmental Science 🎓 Geography agriculture gis spatial decision support land use modelling climate analysis environmental policy soil management

Explore how spatial modelling can optimise agricultural land use in Ireland's changing climate. Develop tools integrating GIS and climate data to support sustainable farming and policy decisions.

AI-generated overview

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Why This Research Matters

This research advances sustainable agricultural land management by integrating climate and spatial data to optimize land use. It offers practical tools to balance productivity with environmental stewardship, crucial for Ireland’s agricultural sector facing climate change.

Spatial Analysis GIS agriculture environment policy analysis

Project Description

Project Overview

This project focuses on optimising agricultural land use in Ireland by developing a spatial decision-support framework. It integrates GIS development, spatial modelling, regional climate analysis, and land-atmosphere interactions to address agricultural, environmental, and policy challenges in a changing climate.

What You Will Do

You will combine applied GIS techniques and advanced climate data analysis to model land use scenarios that support sustainable agriculture. The research involves spatial multi-criteria decision analysis, modelling soil functions, and assessing trade-offs in agricultural productivity and environmental outcomes.

Expected Outcomes

A robust spatial decision-support tool tailored for Ireland’s agricultural landscapes. The tool will inform policy and land management decisions balancing productivity, carbon storage, and ecosystem services. Contributions also include enhanced methodologies for integrating climate and land use data.

Why This Matters

With increasing climate pressures on agriculture, optimising land use is critical for sustainability. This research supports effective management of soil-based ecosystem services, sustainable intensification of agriculture, and environmental protection at local and national levels.

How to Apply

Full details and application instructions are available on the Maynooth University website. Shortlisted candidates will be interviewed in early to mid-April 2026, with online interviews possible. Non-native English speakers must provide competency evidence as per the University's requirements.

Eligibility

UK/Home
EU
International

Supervisor Profile

DR
Dr. Réamonn Fealy
Maynooth University, Department of Geography
1810 Citations
21 h-index
Google Scholar

Dr. Réamonn Fealy is a senior researcher at Teagasc specialising in spatial analysis, GIS development, and spatial modelling applied to agriculture, environment, and policy. His work integrates advanced spatial techniques with climate studies, focusing on Ireland. He has a strong publication track record in functional land management and environmental science.

Key Publications

2014 403 citations
Functional land management: A framework for managing soil-based ecosystem services for the sustainable intensification of agriculture
Provided a framework that supports sustainable intensification of agriculture through soil-based ecosystem services management.
2013 177 citations
Are fine resolution digital elevation models always the best choice in digital soil mapping?
Evaluated the effectiveness of fine resolution digital elevation models for soil mapping, influencing digital soil mapping practices.
2012 113 citations
Local‐scale factors structure wild bee communities in protected areas
Identified how local factors influence wild bee communities, informing conservation efforts in protected areas.
2015 109 citations
Making the most of our land: Managing soil functions from local to continental scale
Explored soil function management at various scales, informing land management policies.
2010 107 citations
The Irish Agricultural Catchments Programme: catchment selection using spatial multi‐criteria decision analysis
Introduced a spatial multi-criteria approach for catchment selection, improving agricultural catchment management.

Research Contributions

Developed a framework for functional land management optimizing soil-based ecosystem services.
Supports sustainable agricultural intensification and informed land use policy decisions.
Demonstrated key local-scale environmental factors affecting wild bee biodiversity in protected areas.
Supports conservation biology and biodiversity protection strategies.
Applied spatial multi-criteria decision analysis for environmental catchment selection and management.
Improves effectiveness of agricultural catchment practices and environmental policy planning.

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