Challenges and Lessons Learned from Integrated Landscape Management (ILM) Projects

The report analyzes 10 ILM projects across North America and Europe, identifying a general framework consisting of baseline assessment, future scenario envisioning, collaborative planning, and monitoring. Key findings highlight the difficulty of integrating socio-economic and biophysical data, the challenge of translating scientific models into usable policy tools, and the necessity of early stakeholder engagement to ensure local relevance and ownership of project outcomes.

• ILM projects typically follow a four-step general framework: assessing current conditions to create a baseline, envisioning future pathways, establishing collaborative interjurisdictional plans and objectives, and monitoring changes to adapt actions.
• Most analyzed ILM projects focused more heavily on environmental, land-use, and biodiversity issues than on human development factors such as population growth, equity, infrastructure, and economic performance.
• Scenario development is a central tool in ILM, with most projects using extrapolatory forecasting based on current trends, while a few, such as the Georgia Basin Futures Project and the Corn Belt project, utilized a 'backcasting' approach that works backward from a desired future vision.
• Data gathering is a significant hurdle, characterized by a general lack of accessible data, inappropriate temporal or spatial scales, and inconsistent frequencies of collection across social, economic, and environmental domains.
• Integrating diverse biophysical and socio-economic data is a major challenge; effective integration requires attention in the early stages of project development to avoid structural mismatches in the modelling framework.
• There is a persistent gap between scientific outputs and policy needs; policy-makers often find complex model outputs difficult to use and prefer targeted documents that highlight main results, provide clear visual information, and offer direct recommendations.
• The high specificity of ILM models to their local geography often results in low transferability to other regions, although some tools like INGRID aim for transferability by focusing on specific ecosystem types like grasslands.

APA

Bizikova, L. (2009). Challenges and Lessons Learned from Integrated Landscape Management (ILM) Projects. International Institute for Sustainable Development. https://www.iisd.org/system/files/publications/challenges_lessons_ilm.pdf

Chicago

Bizikova, Livia. Challenges and Lessons Learned from Integrated Landscape Management (ILM) Projects. International Institute for Sustainable Development, 2009. https://www.iisd.org/system/files/publications/challenges_lessons_ilm.pdf.

Wikipedia

{{cite report |last1=Bizikova |first1=Livia |title=Challenges and Lessons Learned from Integrated Landscape Management (ILM) Projects |publisher=International Institute for Sustainable Development |date=March 2009 |url=https://www.iisd.org/system/files/publications/challenges_lessons_ilm.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{bizikova2009challenges, author = {Bizikova, Livia}, title = {{Challenges and Lessons Learned from Integrated Landscape Management (ILM) Projects}}, institution = {International Institute for Sustainable Development}, year = {2009}, month = mar, url = {https://www.iisd.org/system/files/publications/challenges_lessons_ilm.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }