How can we evaluate whether an intervention truly improves natural resources—like water, air, soil, and biodiversity—while also responding to societal demands?

Scientific literature offers a wealth of methodologies, and assessment tools abound. Yet few frameworks can compare impact across different ecosystems and scales, while balancing rigor, cost, and relevance to both ecosystem services and social priorities. Even fewer are grounded in a persuasive theory of change.

This article outlines two sections:

1- The key challenges any robust evaluation framework must address

2- A proposal to meet those challenges with a practical, scalable solution

🔍 Challenges in Evaluating Landscape-Level Interventions

🌱 1. Ecosystem Specificity

Ecosystem services depend on biodiversity—but biodiversity is highly context-specific. A species that supports a key function in one ecosystem may be irrelevant in another. For example, earthworms drive organic matter decomposition in humid zones, while beetles fulfill that role in dry areas. An effective framework must find creative ways to generalize across ecosystems without losing ecological nuance.

🗺️ 2. Multiple Scales of Influence

Interventions often target a specific group—such as cocoa farmers—but other actors in the same landscape also affect outcomes. For instance, while cocoa farmers may adopt regenerative practices, upstream cattle ranchers might deforest, and downstream banana growers may use aerial pesticides. A framework must assess both farm-level and landscape-level dynamics to capture the full picture.

🧭 3. Contextual Societal Demands

Societal well-being is shaped by both universal needs (e.g. food, shelter) and locally constructed expectations (e.g. gender equity, youth empowerment). These demands vary by landscape and community. An evaluation tool must be able to read and compare these context-specific social priorities.

💸 4. Cost and Feasibility

Scientific rigor is essential—but so is affordability. Some tools rely on satellite algorithms, others on costly fieldwork. The challenge is to combine secondary and primary data sources in a way that balances quality and cost.

🔄 5. Theory of Change

Most tools assess resource conditions or predefined indicators (e.g. living income), but lack a clear causal logic. A strong framework must link people’s subjective demands to resource use drivers, and those drivers to the capacity of ecosystems to deliver services.

🧪 Our Proposal: A Scalable Framework for Socio-Environmental Assessment

We propose a framework that compares impact across ecosystems and scales, integrates both ecological and social indicators, and operates at a reasonable cost—all grounded in a clear theory of change.

🌿 Ecosystem Comparison Through Functional Biodiversity

We use standardized lists of functional agrobiodiversity by agroecosystem. Each ecosystem function (e.g. decomposition) is linked to keystone species (e.g. earthworms in wet zones, beetles in dry zones). We calculate a standardized value by comparing species abundance/diversity in disturbed vs. undisturbed areas:

Example:

• 40 worms in disturbed area / 100 worms in undisturbed area = 0.4

• This means the disturbed area retains 40% of its potential to deliver decomposition services

This method allows:

• Diachronic comparison (before, during, after intervention)

• Synchronic comparison across ecosystems using equivalent functional species

• Generalization of ecosystem-specific data through functional proxies

🏞️ Landscape-Level Assessment

We define landscapes as interconnected resource systems (e.g. cocoa, cattle, banana linked by a micro-basin). We:

• Identify ecosystem and societal demands per system

• Map drivers (e.g. deforestation, pesticide use) that affect service provision

• Co-design instruments to shift those drivers (e.g. silvopastoral incentives, buffer zones)

This approach ensures that interventions address not just isolated farms, but the broader landscape dynamics.

👥 Standardizing Societal Demands

We identify societal demands through legal and political claims within each resource system (e.g. bans on exotic cocoa varieties signal reputational concerns; campaigns for women’s savings groups reflect gender equity goals). We then assess provision using tailored questionnaires:

Example:

• 50 out of 100 farmers avoid exotic varieties = 0.5

• The landscape meets 50% of its potential for the societal demand of reputation

By averaging standardized scores across demands, we can compare landscapes both over time and across regions.

💰 Cost-Efficient Data Collection

We propose community-led monitoring as the primary data source, supported by visual interpretation of satellite imagery. This approach:

• Reduces costs

• Builds local capacity

• Enhances data credibility through participatory verification

🔧 Theory of Change in Practice

Our framework defines the landscape as the unit of analysis and resource systems as the link between subjective demands and ecological outcomes. It follows a four-step process:

1- Baseline assessment of drivers and service provision

2- Intervention design to shift resource use through negotiated instruments

3- Ongoing monitoring of both driver change and service delivery

4- Final evaluation of impact on ecosystem and societal services

✅ Conclusion

This framework offers a practical, participatory, and scalable way to evaluate interventions that aim to regenerate landscapes and respond to community priorities. By integrating ecological and social indicators, and grounding them in a clear theory of change, it enables funders, practitioners, and communities to track progress, adapt strategies, and communicate impact with credibility.