Project Monitoring, Evaluation, and Learning

To truly transform agricultural supply chains, we need to know what works, and what doesn’t. Project Monitoring, Evaluation, and Learning (PMEL) is the backbone of our operations. For Solidaridad, PMEL is not merely for accountability purposes and to keep our donors informed. It provides the essential framework for deepening our impact, enhancing efficiency, improving our learning and strengthening our programming while remaining truly accountable to all our stakeholders.

By systematically tracking our progress and understanding the context of our work, we can make data-driven decisions. A robust and solid PMEL system allows us to be flexible, adapting our strategies when challenges arise, and scaling up interventions when evidence proves they work. Most importantly, it ensures that our programs remain inclusive and truly cater to the needs and realities of the farmers we work with.

Our overarching vision for PMEL is to move from “data to intelligence”. This means moving beyond extracting data solely for accountability, and instead using it to generate actionable insights that support institutional learning and adaptive management. We believe that numbers alone do not tell the full story. We therefore always complement quantitative data with qualitative stories of change. By interpreting data deeply, we aim to validate our strategic assumptions, learn from our successes and failures, and empower our beneficiaries with knowledge. Ultimately, our philosophy is that we evaluate not only to prove what we have achieved, but to continuously improve how we work.

Project Cycle Management

PMEL in every phase of the project

To achieve this vision, our PMEL strategy is deeply embedded in every phase of our Project Cycle Management (PCM). Our approach is built on several key pillars:

To measure our global impact consistently, we utilize a standardized Network Data Model comprised of 8 Global Key Performance Indicators (KPIs). These indicators allow us to track impact across different regions and commodities.

We prioritize the collection of granular data using digital tools. In doing so, we strictly adhere to “Fair Data” and GDPR and/or local laws and principles—ensuring we obtain informed consent, protect privacy, and respect that farmers remain in control of their own data.

Because supply chains are complex systems, we explicitly combine quantitative data with qualitative insights, such as “Most Significant Change” or Focus Group Discussions (FGD). For advocacy and policy work, we use methodologies like Outcome Harvesting and Contribution Analysis to capture unintended consequences and verify our true contribution to systemic change.

We strive to co-design projects with local stakeholders and apply participatory methods in our monitoring and evaluation processes. Through regular “sense-making” sessions, we invite farmers, workers, and local partners to interpret the data with us, ensuring our findings reflect local realities and that our learnings are shared back with the communities we work with.

PMEL in practice

Assuming the PMEL Challenge

In this article, Mariana Alves of Solidaridad in Brazil and Nathalia Ramos at Solidaridad in Colombia, share how Solidaridad approaches complex PMEL challenges in programmes that span borders, commodities, and teams.

CREATING A FAIR GOLD SUPPLY CHAIN IN UGANDAN MINES

Together with project partners, Solidaridad has recently finalized the project Joint Forces to Tackle Child Labour: from Gold Mines to Electronics, implemented in artisanal and small-scale mines and mining (ASM) communities in Busia, Uganda. The aim of our project was to establish a sustainable, traceable gold supply chain that creates a better future for miners and their families, and to contribute to preventing and reducing child labour in the ASM communities in Busia.

Learn more

Solidaridad’s approach to PMEL in 10 Questions

A deeper dive into Solidaridad’s Project Management, Evaluation, and Learning.

Monitoring, Evaluation and Learning (PMEL) enables Solidaridad to manage programmes effectively, demonstrate results, and continuously improve interventions.

Monitoring involves the systematic collection of data on activities, outputs and outcomes against agreed indicators. This allows Solidaridad to track progress, identify implementation challenges early, and adjust interventions or budgets when necessary. Monitoring data also supports programme participants—for example by providing farmers with insights to improve farm management.

Evaluation assesses whether programmes achieved their intended outcomes and impact, and why. Evaluations help determine the relevance, effectiveness and sustainability of interventions, and identify the factors that contribute to success or failure.

PMEL also supports adaptive management and organisational learning. Evidence from monitoring and evaluation informs programme improvements, guides future programme design, and helps identify interventions that can be replicated or scaled.

Finally, PMEL strengthens accountability and transparency by providing credible evidence of results to donors, partners and programme participants.

Solidaridad measures results through a global Results Framework linked to its organisational strategy and Global Theory of Change. The framework explains how programme activities contribute to outcomes and long-term impact.

The framework includes eight global Key Performance Indicators (KPIs) used across the Solidaridad network. Each KPI has a detailed monitoring protocol that defines indicator definitions, data collection methods, and standardised data points.

These KPIs focus primarily on outcomes and impact, rather than only outputs. They are aligned with recognised methodologies such as the Women’s Empowerment in Agriculture Index (WEAI) and the Agricultural Viability Index.

Solidaridad collects both quantitative and qualitative data. Quantitative data measures progress against targets, while qualitative data—such as stakeholder feedback or stories of change—helps explain how change occurs.

Measurement takes place throughout the project cycle:

baseline assessments at the start of projects
continuous monitoring during implementation
mid-term reviews
final internal or external evaluations.

This approach enables Solidaridad to assess both results and its contribution to change in agricultural value chains.

Solidaridad has dedicated PMEL specialists in each region and most countries of operation. These specialists work closely with programme teams to design appropriate monitoring systems.

At the start of each project, teams identify key information needs and develop a measurement plan that defines indicators, data sources, and data collection methods.

Solidaridad uses a mixed-methods approach, combining quantitative and qualitative data collection techniques. These include:

surveys conducted in person, online or by phone
key informant interviews
focus group discussions
field observations and farm visits
project documentation review
stories of change.

Primary data is complemented with secondary data, such as sector statistics, research reports or market information.

Data collection is typically conducted by trained enumerators using digital data collection tools such as ODK or KoboCollect on mobile devices. After collection, data is cleaned, verified and stored in central databases. Data is analysed and visualised using platforms such as Power BI or Tableau.

Solidaridad integrates data quality assurance into its project management cycle.

During project design, teams develop a Theory of Change and Measurement Plan that clearly define indicators, data sources, collection methods and responsibilities. This ensures monitoring focuses on relevant and measurable results.

Dedicated PMEL staff at country and regional level support programme teams in implementing monitoring systems and ensuring consistent application of methodologies.

Several mechanisms ensure data reliability:

Data quality assessments conducted by PMEL staff
Real-time monitoring dashboards that track survey duration, GPS locations and enumerator activity
Outlier detection and follow-up verification during data cleaning
Double verification procedures for critical indicators.

Data is also triangulated by comparing multiple sources—for example farmer surveys with cooperative records or sector statistics.

To ensure representativeness, Solidaridad uses appropriate sampling strategies, such as stratified random sampling.

External evaluations and alignment with recognised methodologies, such as the Multidimensional Poverty Index (MPI) and Women’s Empowerment in Agriculture Index (WEAI), further strengthen credibility.

Collecting data in agricultural value chains can be challenging due to factors such as remote locations, limited connectivity, and varying literacy levels.

Solidaridad addresses these challenges through context-appropriate monitoring approaches.

One example is the lead farmer model, where selected farmers or community members receive training to collect data within their communities using digital tools. This approach reduces travel costs and improves data collection in areas with limited connectivity.

Solidaridad also applies human-centered design when developing digital tools, ensuring that surveys and applications are accessible and relevant for users.

For certain indicators—such as deforestation monitoring or carbon accounting—Solidaridad combines field data with satellite imagery and geospatial data provided by specialised partners.

To manage monitoring costs, Solidaridad often uses representative sampling rather than surveying the entire population. This allows reliable conclusions while keeping monitoring proportional to project size and duration.

Where project timelines are short, additional feedback may be collected through rapid methods such as phone or online surveys.

Solidaridad applies strict standards for data protection and privacy.

Before data collection, respondents receive a clear explanation of the purpose of the data collection and how their information will be used. Participation is voluntary and informed consent is recorded.

Additional safeguards include:

anonymisation of personal data before sharing
compliance with GDPR and relevant national data protection laws
data retention policies limiting how long data is stored
non-disclosure agreements when data is shared with external researchers or evaluators.

Data privacy and security are included in staff onboarding and training. These measures ensure that personal data is handled responsibly while still allowing meaningful analysis of programme results.

Solidaridad utilizes data as a strategic asset to drive adaptive management, evidence-based advocacy, and accountability. By transforming raw information into actionable insights, we reduce uncertainty in decision-making and foster transparency across the entire value chain. Our data utilization is categorized into four primary pillars:

1. Enhancing Program Efficiency and Adaptive Management

Data is the foundation for optimizing project performance and resource allocation. At the field level, we analyze crop yields, soil conditions, and adoption rates of sustainable practices to tailor technical assistance to the specific needs of producers. This granular insight allows for “precision development,” where climate-smart solutions are deployed only where they are most effective. Furthermore, our digital data platforms create a cost-effective model for scaling support, allowing us to reach a larger number of producers without a linear increase in costs. Through continuous monitoring of Key Performance Indicators (KPIs), we apply organizational learning to refine methodologies in real-time, avoiding the replication of past inefficiencies.

2. Evidence-Based Policy and Advocacy

Project-level data provides the empirical evidence required to influence global and national policy. We synthesize field outcomes into high-level knowledge products—such as the Coffee Barometer—which serve as technical benchmarks for engaging governments and the private sector. This evidence is critical for advocating for smallholder-inclusive regulations within frameworks like the EU Deforestation Regulation (EUDR) or the Corporate Sustainability Due Diligence Directive (CSDDD). By quantifying the potential impacts of these regulations on small-scale producers, we ensure that civil society voices are represented in high-level policy dialogues, holding large institutions accountable to the realities on the ground.

3. Donor Accountability and Transparent Reporting

Data is essential for demonstrating the responsible use of funds and the achievement of measurable outcomes. Using our robust PMEL processes, we track progress against the specific targets outlined in our Global Theory of Change. This includes both internal monitoring and independent external impact assessments. The resulting data is used to generate comprehensive, transparent reports that align with donor requirements, providing a clear audit trail from investment to impact.

4. Empowering Beneficiaries through Data Feedback Loops

Solidaridad rejects the “extractive” model of data collection, where information flows only upwards to donors. Instead, we prioritize returning data to beneficiaries to empower their own decision-making. This “Fair Data” approach provides value in three specific ways:

5. Participatory Sense-Making: We share findings with local partners and producers to interpret results together. This collaborative “sense-making” ensures that our conclusions reflect local realities and that project adjustments are truly representative and actionable. This downward accountability builds the deep trust necessary for long-term project sustainability.

Solidaridad operates through a global network of regional offices. To ensure consistent reporting, programmes use a shared global measurement framework with eight global KPIs.

Projects collect data using standardised definitions and data points integrated into digital monitoring tools. After verification at regional level, data is stored in regional databases.

Each year, regions submit their KPI data to Solidaridad’s central portfolio management system, Plaza, which is built on Salesforce.

At global level, data specialists review submissions, verify consistency and aggregate results across regions. This process produces consolidated global indicators for each KPI.

These aggregated results allow Solidaridad to track progress against its global strategic targets and are published annually in the organisation’s results reporting.

Solidaridad’s PMEL approach is designed to capture diverse perspectives and ensure that programme participants are involved in the monitoring process.

Results are disaggregated by gender, age (including youth) and other relevant socio-economic characteristics. This helps identify differences in outcomes between groups and ensures that interventions are inclusive.

Solidaridad also applies participatory approaches. Producers, workers and local partners are engaged in data collection, validation and interpretation of results. This improves data accuracy and ensures that findings reflect local realities.

Monitoring tools are translated into local languages and adapted to the local context to ensure accessibility.

Finally, Solidaridad’s local presence allows continuous engagement with programme participants. Monitoring results are shared back with communities whenever possible so that data can support their own decision-making processes.

Solidaridad aligns its Global Results Framework with the United Nations Sustainable Development Goals (SDGs). Each of the organisation’s global KPIs contributes to one or more SDG targets.

For example:

Farmer resilience and livelihoods contribute to SDG 1 (No Poverty) and SDG 2 (Zero Hunger).
Farm viability and employment relate to SDG 8 (Decent Work and Economic Growth).
Greenhouse gas mitigation contributes to SDG 13 (Climate Action).
Access to services and entrepreneurship relates to SDG 9 (Industry, Innovation and Infrastructure).
Corporate sustainability and responsible sourcing contribute to SDG 12 (Responsible Consumption and Production).
Improved governance frameworks relate to SDG 16 (Peace, Justice and Strong Institutions).

In addition, disaggregation by gender and age contributes to monitoring progress towards SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities).

This alignment allows Solidaridad’s programme results to be understood within the broader international development agenda.

KPI Category	Solidaridad Global KPI	Primary SDG Alignment

Overall Impact	Number of farmers with enhanced resilience (disaggregated by M/F/Youth)	SDG 1 & 2: Targets 1.5 (Resilience to shocks) and 2.4 (Sustainable food production systems). 2.3 Agricultural productivity and incomes of small-scale food producers
Production	Number of farmers with improved farm viability	SDG 8: Target 8.5 (Decent work and productive employment for all). SDG 8.2 Achieve higher levels of economic productivity through diversification, technological upgrading and innovation.2.3 Agricultural productivity and incomes of small-scale food producers SDG 2.4 Increase area under productive and sustainable agriculture
Production	Amount of GHG equivalents mitigated per year	SDG 2.4 Increase area under productive and sustainable agriculture SDG 13: Target 13.2 (Integrate climate change measures into national policies/strategies).
Services	Number of farmers recognized as (co)-owners of value addition or service businesses	SDG 9: Target 9.3 (Increase access of small-scale enterprises to financial services and value chains).SDG 1.4 Ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance
Services	Number of farmers accessing new/improved services from supported providers	SDG 2.a. Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countries SDG 17: Target 17.6 (Enhance North-South, South-South and regional international cooperation).
Governance	Number of regulations/frameworks improved to protect smallholder interests	SDG 16: Target 16.7 (Ensure responsive, inclusive, participatory and representative decision-making). SDG 12.1 Implement the 10-year framework of programmes on sustainable consumption and production, all countries taking action, with developed countries taking the lead, taking into account the development and capabilities of developing countries
Markets	Number of partner companies directly rewarding farmers for sustainable practices	SDG 12: Target 12.6 (Encourage companies to adopt sustainable practices and integrate sustainability info).
Markets	Number of partner companies adopting sustainable sourcing policies/action plans	SDG 12: Target 12.a (Support developing countries to move towards more sustainable patterns of consumption).

FOR MORE INFORMATION

Victoria Graham

victoria@solidaridadnetwork.org