Savimbo began when five medical doctors and a community organization of small farm conservationists in Colombia asked an innovative technology company to help them stop the logging activity on their lands. The project was created, and began developing methodologies to document carbon sequestration through forest conservation.
The project began by showing farmers how to track their own offsets using trusted off-grid methods, and assessment toolkits soon included drone photography, satellite monitoring, machine learning, GPS, and blockchain to track offsets from creation to sale. The same tools were later used to develop water and biodiversity credits.
Savimbo pays subsistence farmers direct monthly payments to preserve their local ecosystems. Savimbo’s nonprofit arm subsidizes new communities wishing to certify and sell their credits and then equitably splits gross revenues .
Savimbo’s goal is to reverse and halt encroachment on pristine jungle. Savimbo supports a sustainable regional economy that measurably expands tropical forests and native biodiversity. It addresses multiple levels from smallholder farms of less than 5 hectare, to petroleum and mining interests and narco trafficking.
Savimbo directly specializes in using emerging technology to repurpose remote jungle economies from deforestation to reforesting.
This pack focuses on the principles, standards, and components that enable different data systems, tools, and platforms to work together seamlessly. It includes concepts such as data harmonization, metadata standards, APIs, and system integration within CGIAR MELIAF ecosystems.
The pack supports the development of interoperable MELIA systems across programs and geographies, including integration with national systems such as those in Kenya. It is particularly useful to spread a mindset of collaboration and integration, allowing consistent data exchange between partners, improving efficiency, transparency, and comparability of results across projects.
Stage of development describing how well MELIAF standards, systems and practices are adopted and consistently applied, typically ranging from ad hoc use to fully embedded, continuously improved alignment.
Stage of development describing how well MELIAF standards, systems and practices are adopted and consistently applied, typically ranging from ad hoc use to fully embedded, continuously improved alignment.
Consistency between ToCs, indicators, evaluations and impact models regarding how change is expected to occur, ensuring all MELIAF components reflect the same underlying causal logic.
Consistency between ToCs, indicators, evaluations and impact models regarding how change is expected to occur, ensuring all MELIAF components reflect the same underlying causal logic.
Sequenced chain linking activities, outputs, outcomes and impacts, including intermediate changes and feedbacks, used to visualize and test how interventions are expected to generate results.
Sequenced chain linking activities, outputs, outcomes and impacts, including intermediate changes and feedbacks, used to visualize and test how interventions are expected to generate results.
Structured hierarchy of impact areas, outcomes and outputs used to organize, monitor and report contributions, ensuring MELIAF data and evaluations align with agreed strategic results architecture.
Structured hierarchy of impact areas, outcomes and outputs used to organize, monitor and report contributions, ensuring MELIAF data and evaluations align with agreed strategic results architecture.
Conformance with agreed standards, protocols, procedures and governance rules that regulate how MELIAF data, indicators, models and evaluations are designed, implemented, shared and reported.
Conformance with agreed standards, protocols, procedures and governance rules that regulate how MELIAF data, indicators, models and evaluations are designed, implemented, shared and reported.
Reasoned explanation of how an intervention influences observed changes, distinguishing contribution from attribution and drawing on evidence and counterfactual thinking in MELIAF.
Reasoned explanation of how an intervention influences observed changes, distinguishing contribution from attribution and drawing on evidence and counterfactual thinking in MELIAF.
Curated list of allowed terms and definitions for concepts, indicators, themes or tags, used to standardize language and reduce ambiguity in MELIAF data and documentation.
Curated list of allowed terms and definitions for concepts, indicators, themes or tags, used to standardize language and reduce ambiguity in MELIAF data and documentation.
Capacity of datasets from different sources to be combined, compared and reused without manual rework, thanks to shared formats, identifiers, metadata, semantics and access protocols across MELIAF functions.
Capacity of datasets from different sources to be combined, compared and reused without manual rework, thanks to shared formats, identifiers, metadata, semantics and access protocols across MELIAF functions.
Degree to which plans, ToCs, indicators, data standards and modules are intentionally specified to fit a common reference model, enabling coherent MELIAF implementation across projects and portfolios.
Degree to which plans, ToCs, indicators, data standards and modules are intentionally specified to fit a common reference model, enabling coherent MELIAF implementation across projects and portfolios.
Ability to follow reported figures, findings and claims back through models, analyses and datasets to original sources, enabling verification and strengthening credibility of MELIAF products.
Ability to follow reported figures, findings and claims back through models, analyses and datasets to original sources, enabling verification and strengthening credibility of MELIAF products.
Compatibility of mandates, decision rules, accountability arrangements and oversight processes that enables multiple institutions to jointly steward MELIAF data, standards and evaluations.
Compatibility of mandates, decision rules, accountability arrangements and oversight processes that enables multiple institutions to jointly steward MELIAF data, standards and evaluations.
Bringing together data, tools, workflows and governance arrangements so MELIAF activities function as a connected system rather than fragmented, stand‑alone components.
Bringing together data, tools, workflows and governance arrangements so MELIAF activities function as a connected system rather than fragmented, stand‑alone components.
Ability of diverse systems, processes, and actors to exchange, interpret, and use data, concepts, and insights so that monitoring, evaluation, learning, impact assessment and foresight activities operate as a coherent whole.
Ability of diverse systems, processes, and actors to exchange, interpret, and use data, concepts, and insights so that monitoring, evaluation, learning, impact assessment and foresight activities operate as a coherent whole.
Foundational elements—standards, identifiers, schemas, APIs, governance rules and capacities—required to make MELIAF data, processes and tools connect, communicate and evolve together.
Foundational elements—standards, identifiers, schemas, APIs, governance rules and capacities—required to make MELIAF data, processes and tools connect, communicate and evolve together.
Relationship between level of interoperability achieved and the cumulative resources required, highlighting how early investment in standards and alignment can reduce marginal costs of connecting additional MELIAF systems.
Relationship between level of interoperability achieved and the cumulative resources required, highlighting how early investment in standards and alignment can reduce marginal costs of connecting additional MELIAF systems.
Networked representation linking entities, indicators, documents, ToC elements and outcomes via typed relationships, enabling MELIAF users and algorithms to explore and infer connections.
Networked representation linking entities, indicators, documents, ToC elements and outcomes via typed relationships, enabling MELIAF users and algorithms to explore and infer connections.
Organization of data and documents into explicit, standardized formats that software can reliably parse, validate and process without manual intervention in MELIAF workflows.
Organization of data and documents into explicit, standardized formats that software can reliably parse, validate and process without manual intervention in MELIAF workflows.
Agreed set of fields, formats and vocabularies describing datasets, indicators, models and documents so MELIAF assets are findable, understandable, and reusable by humans and machines.
Agreed set of fields, formats and vocabularies describing datasets, indicators, models and documents so MELIAF assets are findable, understandable, and reusable by humans and machines.
Baseline requirements for articulating ToCs, causal pathways, assumptions and evidence links so that MELIAF activities consistently express and test shared causal logic.
Baseline requirements for articulating ToCs, causal pathways, assumptions and evidence links so that MELIAF activities consistently express and test shared causal logic.
Core structure, definitions, disaggregations and metadata required for key performance indicators to ensure comparability, integrity and machine‑readability across MELIAF systems.
Core structure, definitions, disaggregations and metadata required for key performance indicators to ensure comparability, integrity and machine‑readability across MELIAF systems.
Lean specification of variables, formats and metadata required for reporting results so that monitoring and evaluation data can be aggregated, compared and reused across initiatives.
Lean specification of variables, formats and metadata required for reporting results so that monitoring and evaluation data can be aggregated, compared and reused across initiatives.
Formally specified set of concepts, relationships and rules describing a domain, providing a machine‑interpretable backbone for MELIAF data integration, reasoning and advanced queries.
Formally specified set of concepts, relationships and rules describing a domain, providing a machine‑interpretable backbone for MELIAF data integration, reasoning and advanced queries.
Extent to which MELIAF workflows, timelines and decision procedures can interact and synchronize across initiatives, enabling joint planning, shared reviews and consistent reporting with minimal friction.
Extent to which MELIAF workflows, timelines and decision procedures can interact and synchronize across initiatives, enabling joint planning, shared reviews and consistent reporting with minimal friction.
Ability of tools, dashboards, models, reports and evaluation products to plug into each other’s data structures, semantics and interfaces, allowing reuse and composition in MELIAF workflows.
Ability of tools, dashboards, models, reports and evaluation products to plug into each other’s data structures, semantics and interfaces, allowing reuse and composition in MELIAF workflows.
Category describing a point along the results chain—such as input, activity, output, outcome or impact—used to classify indicators, data and findings in MELIAF systems.
Category describing a point along the results chain—such as input, activity, output, outcome or impact—used to classify indicators, data and findings in MELIAF systems.
Formal structural blueprint describing entities, fields, relationships and constraints in a dataset or database, providing a shared template for MELIAF data creation, storage and exchange.
Formal structural blueprint describing entities, fields, relationships and constraints in a dataset or database, providing a shared template for MELIAF data creation, storage and exchange.
Records that follow an agreed schema’s field definitions, types, relationships and constraints, enabling automated validation, integration and analysis across MELIAF systems.
Records that follow an agreed schema’s field definitions, types, relationships and constraints, enabling automated validation, integration and analysis across MELIAF systems.
Shared conceptual representation that maps data fields, indicators and entities to well‑defined meanings, enabling consistent interpretation and integration across MELIAF tools and datasets.
Shared conceptual representation that maps data fields, indicators and entities to well‑defined meanings, enabling consistent interpretation and integration across MELIAF tools and datasets.
Commonly agreed narrative and diagram of how activities, outputs and outcomes are expected to contribute to long‑term impacts, serving as a reference for MELIAF planning and assessment.
Commonly agreed narrative and diagram of how activities, outputs and outcomes are expected to contribute to long‑term impacts, serving as a reference for MELIAF planning and assessment.
Specific, time‑bound value set for an indicator, expressing expected performance and guiding planning, monitoring and accountability within MELIAF.
Specific, time‑bound value set for an indicator, expressing expected performance and guiding planning, monitoring and accountability within MELIAF.
Consistency and non‑overlap of terms, categories and indicator labels so that MELIAF data, ToCs and reports use a shared conceptual vocabulary across scales and disciplines.
Consistency and non‑overlap of terms, categories and indicator labels so that MELIAF data, ToCs and reports use a shared conceptual vocabulary across scales and disciplines.
High‑level, often system‑wide metric that tracks progress on priority outcomes or impacts and is mandatory for aggregation and external accountability within MELIAF.
High‑level, often system‑wide metric that tracks progress on priority outcomes or impacts and is mandatory for aggregation and external accountability within MELIAF.
Intermediate‑level metric that supports comparative analysis across programs or regions, offering flexibility while retaining alignment with the overarching results framework.
Intermediate‑level metric that supports comparative analysis across programs or regions, offering flexibility while retaining alignment with the overarching results framework.
Context‑specific metric tailored to project or initiative needs, supporting local learning and management while remaining mappable to higher‑tier indicators when needed.
Context‑specific metric tailored to project or initiative needs, supporting local learning and management while remaining mappable to higher‑tier indicators when needed.
Explicit mapping between elements of a Theory of Change and projected benefits in quantitative models, ensuring consistency between narrative causal logic and forward‑looking analysis.
Explicit mapping between elements of a Theory of Change and projected benefits in quantitative models, ensuring consistency between narrative causal logic and forward‑looking analysis.
This thematic pack covers concepts, terminology, and visual elements related to the design, testing, and scaling of innovations within agrifood systems. It includes terms such as innovation bundles, TRL, scaling pathways, and usage models, with attention to enabling environments and partnerships. The pack supports users in illustrating how innovations move from research to widespread uptake across diverse contexts.
It can be used in MELIAF-aligned reporting, proposal design, and learning products to show how innovations are introduced, refined, and expanded. It is also useful in interactive dashboards and pathway visualizations that track adoption levels, scaling readiness, and systemic change over time.
Innovations, solutions or conditions that are necessary to scale core innovations.
Innovations, solutions or conditions that are necessary to scale core innovations.
Innovations that are at the heart of initiatives/ interventions and that are expected to contribute to transformation and impact at scale.
Innovations that are at the heart of initiatives/ interventions and that are expected to contribute to transformation and impact at scale.
New, improved, or adapted outputs or groups of outputs such as products, technologies, services, organizational and institutional arrangements with high potential to contribute to positive impacts when used at scale.
New, improved, or adapted outputs or groups of outputs such as products, technologies, services, organizational and institutional arrangements with high potential to contribute to positive impacts when used at scale.
Combinations of interrelated innovations and enabling conditions that, together, can lead to transformation and impact at scale in the CGIAR research delivery hierarchy.
Combinations of interrelated innovations and enabling conditions that, together, can lead to transformation and impact at scale in the CGIAR research delivery hierarchy.
Organizations or entities that CGIAR collaborates and co-invests with to improve the readiness of innovations to contribute to impact at scale.
Organizations or entities that CGIAR collaborates and co-invests with to improve the readiness of innovations to contribute to impact at scale.
A metric used to assess the maturity of an innovation, with a scale ranging from the idea (lowest level) to validated under uncontrolled conditions (highest level).
A metric used to assess the maturity of an innovation, with a scale ranging from the idea (lowest level) to validated under uncontrolled conditions (highest level).
Number of research and development innovations by stage, where the stages are: i) end of research phase (discovery/proof of concept); ii) end of piloting phase (if relevant); iii) available for uptake; iv) uptake by next user.
Number of research and development innovations by stage, where the stages are: i) end of research phase (discovery/proof of concept); ii) end of piloting phase (if relevant); iii) available for uptake; iv) uptake by next user.
A metric used to assess the extent to which an innovation is already being used, by which type of users and under which conditions, with a scale ranging from no use (lowest level) to common use (highest level).
A metric used to assess the extent to which an innovation is already being used, by which type of users and under which conditions, with a scale ranging from no use (lowest level) to common use (highest level).
Investments, strategies and processes aimed at increasing innovation readiness and/ or innovation use to contribute to positive impacts at scale.
Investments, strategies and processes aimed at increasing innovation readiness and/ or innovation use to contribute to positive impacts at scale.
Tracks how innovations, evidence, or policy advice move through next user/boundary partner institutions and systems to support long-term impact pathways.
Tracks how innovations, evidence, or policy advice move through next user/boundary partner institutions and systems to support long-term impact pathways.
Organizations or entities that CGIAR collaborates with to advance the uptake and use of innovations at scale.
Organizations or entities that CGIAR collaborates with to advance the uptake and use of innovations at scale.
Organizations or entities that CGIAR collaborates with to advance the uptake and use of innovations at scale.
Organizations or entities that CGIAR collaborates with to advance the uptake and use of innovations at scale.
Metric that combines single or average innovation readiness and innovation use scores at innovation package or portfolio level.
Metric that combines single or average innovation readiness and innovation use scores at innovation package or portfolio level.
Evidence-based approach to support the design, implementation, monitoring and evaluation of strategies to increase readiness and use of innovations at innovation package and/or portfolio level.
Evidence-based approach to support the design, implementation, monitoring and evaluation of strategies to increase readiness and use of innovations at innovation package and/or portfolio level.
These are the major domains of long-term change targeted by CGIAR. It includes standardized terminology and icons that align with CGIAR impact frameworks.
The pack is useful for showing strategies, aligning projects with CGIAR-wide priorities, and communicating contributions to global goals. It can be used in reports, proposals, and visual tools to map how activities and outcomes contribute to specific impact areas.
Improving small-scale producers’ resilience and reducing greenhouse gas emissions from food systems.
Improving small-scale producers’ resilience and reducing greenhouse gas emissions from food systems.
Increasing productivity in food systems while staying within environmental boundaries and maintaining biodiversity.
Increasing productivity in food systems while staying within environmental boundaries and maintaining biodiversity.
Closing the gender gap and enhancing opportunities for youth in food, land, and water systems.
Closing the gender gap and enhancing opportunities for youth in food, land, and water systems.
Ending hunger and enabling safe, affordable, healthy diets for the world’s most vulnerable people.
Ending hunger and enabling safe, affordable, healthy diets for the world’s most vulnerable people.
Building on a 50-year track record of lifting millions out of poverty.
Building on a 50-year track record of lifting millions out of poverty.
This pack focuses on the types, sources, and management of data used within MELIAF systems, including datasets, data governance, and quality standards. It includes terminology and icons related to data collection, storage, sharing, and analysis.
This collection can be used in MEL Plans, dashboards, and documentation to represent data flows and assets.
Documentation kept by farmers or field staff recording agronomic activities, inputs, and outputs over time to generate detailed panel data at the plot level.
Documentation kept by farmers or field staff recording agronomic activities, inputs, and outputs over time to generate detailed panel data at the plot level.
Structured listing of existing datasets, their characteristics, and accessibility that guide new data collection and help researchers avoid duplication.
Structured listing of existing datasets, their characteristics, and accessibility that guide new data collection and help researchers avoid duplication.
A ready-for-analysis dataset that has been checked and corrected for errors, inconsistencies, and missing values.
A ready-for-analysis dataset that has been checked and corrected for errors, inconsistencies, and missing values.
Ongoing verification of incoming data for completeness, consistency, and plausibility throughout collection and entry. Continuous data validation can be used to safeguard data quality before analysis.
Ongoing verification of incoming data for completeness, consistency, and plausibility throughout collection and entry. Continuous data validation can be used to safeguard data quality before analysis.
Systematic processing and examination of collected qualitative, quantitative, and administrative data to identify and organize patterns within sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems).
Systematic processing and examination of collected qualitative, quantitative, and administrative data to identify and organize patterns within sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems).
An outline of required datasets, base parameter values, resolution scales, and scenario assumptions, including sources, normalization methods, validation, and quality checks that serves as the basis for implementing simulation model runs. Data and assumptions plan plans help modeling teams coordinate data acquisition and support transparency and replicability when developing simulation models and generating scenarios.
An outline of required datasets, base parameter values, resolution scales, and scenario assumptions, including sources, normalization methods, validation, and quality checks that serves as the basis for implementing simulation model runs. Data and assumptions plan plans help modeling teams coordinate data acquisition and support transparency and replicability when developing simulation models and generating scenarios.
Intellectual Assets that are raw or manipulated datasets and information products produced by CGIAR, as outputs of CGIAR’s research and development activities.
Intellectual Assets that are raw or manipulated datasets and information products produced by CGIAR, as outputs of CGIAR’s research and development activities.
The process of using defined tools and protocols to systematically gather both measurements and observations about study system variables like people, plants, animals, crops, soils, economics, climate, or water systems.
The process of using defined tools and protocols to systematically gather both measurements and observations about study system variables like people, plants, animals, crops, soils, economics, climate, or water systems.
Techniques or approaches determined through methodological design that define how information is/will be gathered from different sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems).
Techniques or approaches determined through methodological design that define how information is/will be gathered from different sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems).
Specific instruments, devices, or guidelines that are used to to capture and record data from sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems) is gathered. Data collection tools operationalize data collection methods and may include questionnaires, interview guides, observation checklists, sensors, and data.
Specific instruments, devices, or guidelines that are used to to capture and record data from sources or samples (such as people, plants, animals, crops, soils, economics, climate, or water systems) is gathered. Data collection tools operationalize data collection methods and may include questionnaires, interview guides, observation checklists, sensors, and data.
Activities concerned with assessing and improving the merit or worth of data collection, aggregation, cleaning, and management, as well as its compliance with given standards.
Activities concerned with assessing and improving the merit or worth of data collection, aggregation, cleaning, and management, as well as its compliance with given standards.
Aligning variables, formats, units, and coding schemes across datasets to create interoperability and allow for combination or comparison. Harmonization is crucial for meta‑analysis and global or regional modeling.
Aligning variables, formats, units, and coding schemes across datasets to create interoperability and allow for combination or comparison. Harmonization is crucial for meta‑analysis and global or regional modeling.
Socio-economic data collected on farmer characteristics, decisions, perceptions, and outcomes, often at the household level.
Socio-economic data collected on farmer characteristics, decisions, perceptions, and outcomes, often at the household level.
Socio-economic data collected on farmer characteristics, decisions, perceptions, and outcomes, often at the household level.
Socio-economic data collected on farmer characteristics, decisions, perceptions, and outcomes, often at the household level.
High‑quality, location‑specific observations collected on the ground used to validate or calibrate remote‑sensing products and models. Ground‑truth data ensures spatial and modeled estimates reflect reality.
High‑quality, location‑specific observations collected on the ground used to validate or calibrate remote‑sensing products and models. Ground‑truth data ensures spatial and modeled estimates reflect reality.
Documentation on harvest quantity, quality, losses, and timing. Harvest records are central to yield measurements and economic analysis.
Documentation on harvest quantity, quality, losses, and timing. Harvest records are central to yield measurements and economic analysis.
Existing datasets from past time periods, including surveys, administrative records, and gridded climate or yield series for use in trend analysis, baselines, and model calibration.
Existing datasets from past time periods, including surveys, administrative records, and gridded climate or yield series for use in trend analysis, baselines, and model calibration.
Data collected without experimental manipulation, such as administrative records or routine surveys. These data are widely used and require careful causal reasoning when used for impact estimates.
Data collected without experimental manipulation, such as administrative records or routine surveys. These data are widely used and require careful causal reasoning when used for impact estimates.
All data collected at the plot level, including inputs, management, yield, and environmental conditions.
All data collected at the plot level, including inputs, management, yield, and environmental conditions.
The data observed or collected by a researcher, evaluator, or M&E professional from first-hand experience, specifically for the research project or area of interest.
The data observed or collected by a researcher, evaluator, or M&E professional from first-hand experience, specifically for the research project or area of interest.
Assembled datasets and visualizations that present modeled trajectories of key indicators under different scenarios. Foresight approaches often use such projections to communicate possible futures and impacts of options.
Assembled datasets and visualizations that present modeled trajectories of key indicators under different scenarios. Foresight approaches often use such projections to communicate possible futures and impacts of options.
Data collection conducted temporally proximal to the events of interest, minimizing recall issues and allowing near‑real‑time monitoring.
Data collection conducted temporally proximal to the events of interest, minimizing recall issues and allowing near‑real‑time monitoring.
Types of surveys that collect information about past events or practices, relying on recall.
Types of surveys that collect information about past events or practices, relying on recall.
Data products derived from satellite observations, such as land cover, vegetation indices, and weather proxies. Satellite datasets complement ground data and support large‑scale environmental and agricultural analysis.
Data products derived from satellite observations, such as land cover, vegetation indices, and weather proxies. Satellite datasets complement ground data and support large‑scale environmental and agricultural analysis.
Data that has been collected for another purpose but may be reanalyzed in a subsequent study.
Data that has been collected for another purpose but may be reanalyzed in a subsequent study.
Information differentiated on the basis of what pertains to females and their roles and to males and their roles.
Information differentiated on the basis of what pertains to females and their roles and to males and their roles.
Detailed documentation of planting dates, seed types, and planting densities. Sowing records help researchers and farmers interpret yield outcomes and assess interactions between climate and management.
Detailed documentation of planting dates, seed types, and planting densities. Sowing records help researchers and farmers interpret yield outcomes and assess interactions between climate and management.
The collection of information using (1) a predefined sampling strategy, and (2) a survey instrument.
The collection of information using (1) a predefined sampling strategy, and (2) a survey instrument.
A pre-designed form (questionnaire) used to collect data during a survey.
A pre-designed form (questionnaire) used to collect data during a survey.
This pack provides a standardized set of core terms and definitions used across MELIAF that are not process-specific but can apply to all MELIAF functions.
It is useful for onboarding, training, and harmonizing language across teams and partners. The pack can be applied in guidelines, reports, and digital tools to ensure shared understanding of MELIAF concepts.
In governance and management is the duty to ensure and report that the use of authority is aligned with rules, standards, policy and interests of the program, organization and the broader group of stakeholders.
In governance and management is the duty to ensure and report that the use of authority is aligned with rules, standards, policy and interests of the program, organization and the broader group of stakeholders.
A generic term to mean any difference. Specific changes resulting from a research output are to be characterized as outcomes or impacts.
A generic term to mean any difference. Specific changes resulting from a research output are to be characterized as outcomes or impacts.
The term refers more broadly to the role and mandate of the CGIAR in producing international public goods where there are no alternative research suppliers that are better positioned to produce those goods.
The term refers more broadly to the role and mandate of the CGIAR in producing international public goods where there are no alternative research suppliers that are better positioned to produce those goods.
Scientific credibility requires that research findings be robust and that sources of knowledge be dependable and sound.
Scientific credibility requires that research findings be robust and that sources of knowledge be dependable and sound.
The evaluation criteria provide a normative framework used to determine the merit or worth of an intervention… CGIAR evaluation policy defines these to include relevance, efficiency, quality of science, effectiveness, impact and sustainability in line with CGIAR context and the OECD DAC.
The evaluation criteria provide a normative framework used to determine the merit or worth of an intervention… CGIAR evaluation policy defines these to include relevance, efficiency, quality of science, effectiveness, impact and sustainability in line with CGIAR context and the OECD DAC.
An evaluation policy outlines the definition, concept, role and use of evaluation within an organization.
An evaluation policy outlines the definition, concept, role and use of evaluation within an organization.
A structure set up to work with the evaluation managers to ensure good communication with, learning by, and appropriate accountability to primary evaluation clients and key stakeholders, while preserving the independence of researchers/evaluators.
A structure set up to work with the evaluation managers to ensure good communication with, learning by, and appropriate accountability to primary evaluation clients and key stakeholders, while preserving the independence of researchers/evaluators.
An evaluation that is performed before implementation of a development intervention.
An evaluation that is performed before implementation of a development intervention.
An evaluation of a development intervention after it has been completed… The intention is to identify the factors of success or failure, to assess the sustainability of results and impacts, and to draw conclusions that may inform other interventions.
An evaluation of a development intervention after it has been completed… The intention is to identify the factors of success or failure, to assess the sustainability of results and impacts, and to draw conclusions that may inform other interventions.
A structured and explicit exploration of multiple futures in order to inform decision-making. Foresight studies use a range of methodologies, such as scanning the horizon for emerging changes, analyzing megatrends and developing multiple scenarios, to reveal and discuss useful ideas about the future.
A structured and explicit exploration of multiple futures in order to inform decision-making. Foresight studies use a range of methodologies, such as scanning the horizon for emerging changes, analyzing megatrends and developing multiple scenarios, to reveal and discuss useful ideas about the future.
A framework document outlines high level principles to frame to take forward relevant field.
A framework document outlines high level principles to frame to take forward relevant field.
Principles from ‘Integrating Human Rights and Gender Equality in Evaluation: Towards UNEG Guidance’ to improve human rights and gender equality responsive evaluations in the UN system and beyond.
Principles from ‘Integrating Human Rights and Gender Equality in Evaluation: Towards UNEG Guidance’ to improve human rights and gender equality responsive evaluations in the UN system and beyond.
A durable change in the condition of people and their environment brought about by a chain of events or change to which research, innovations and related activities have contributed.
A durable change in the condition of people and their environment brought about by a chain of events or change to which research, innovations and related activities have contributed.
An assessment that estimates the causal effects of research outputs and related activities and assesses the magnitude of impact achieved. Impact assessments are conducted long after an intervention has been completed and are used to substantiate claims of sustained results.
An assessment that estimates the causal effects of research outputs and related activities and assesses the magnitude of impact achieved. Impact assessments are conducted long after an intervention has been completed and are used to substantiate claims of sustained results.
In conducting an evaluation, the absence of bias in due process, in the scope and methodology, and in considering and presenting achievements and challenges.
In conducting an evaluation, the absence of bias in due process, in the scope and methodology, and in considering and presenting achievements and challenges.
A standing panel of impartial, world-class scientific experts providing rigorous, independent strategic advice to the CGIAR System Council and other stakeholders.
A standing panel of impartial, world-class scientific experts providing rigorous, independent strategic advice to the CGIAR System Council and other stakeholders.
A quantitative or qualitative variable that represents an approximation of the characteristic, phenomenon or change of interest (for instance, efficiency, quality or outcome).
A quantitative or qualitative variable that represents an approximation of the characteristic, phenomenon or change of interest (for instance, efficiency, quality or outcome).
New, improved, or adapted outputs or groups of outputs such as products, technologies, services, organizational and institutional arrangements with high potential to contribute to positive impacts when used at scale.
New, improved, or adapted outputs or groups of outputs such as products, technologies, services, organizational and institutional arrangements with high potential to contribute to positive impacts when used at scale.
Learning means that evidence and lessons are drawn from experience, accepted and internalized in new practices, thereby building on success to make improvements and avoiding past mistakes.
Learning means that evidence and lessons are drawn from experience, accepted and internalized in new practices, thereby building on success to make improvements and avoiding past mistakes.
Means that the research process is fair and ethical and perceived as such. This encompasses the ethical and fair representation of all involved and consideration of interests and perspectives of intended users.
Means that the research process is fair and ethical and perceived as such. This encompasses the ethical and fair representation of all involved and consideration of interests and perspectives of intended users.
Defines an organization’s primary objectives and its approach to reaching those objectives.
Defines an organization’s primary objectives and its approach to reaching those objectives.
To deliver science and innovation that advance transformation of food, land and water systems in a climate crisis.
To deliver science and innovation that advance transformation of food, land and water systems in a climate crisis.
A system/framework for project/program management including tools, approaches and indicators to be used to assess results, integrate lessons and improve impacts related to strengthen capacities.
A system/framework for project/program management including tools, approaches and indicators to be used to assess results, integrate lessons and improve impacts related to strengthen capacities.
In the context of the CGIAR, this refers to the accountability of all partners, including donors, for the efficiency of outputs, outcomes and impacts of a program, institution or policy and sustainability of research.
In the context of the CGIAR, this refers to the accountability of all partners, including donors, for the efficiency of outputs, outcomes and impacts of a program, institution or policy and sustainability of research.
Includes organizations and institutions created and/or funded by the government as a support for the national program of agricultural development.
Includes organizations and institutions created and/or funded by the government as a support for the national program of agricultural development.
OneCGIAR research delivery hierarchy to date: OneCGIAR >Action Area > Initiative > Work Package > Innovation Package.
OneCGIAR research delivery hierarchy to date: OneCGIAR >Action Area > Initiative > Work Package > Innovation Package.
A change in knowledge, skills, attitudes and/or relationships, which manifests as a change in behavior in particular actors, to which research outputs and related activities have contributed.
A change in knowledge, skills, attitudes and/or relationships, which manifests as a change in behavior in particular actors, to which research outputs and related activities have contributed.
Knowledge, technical or institutional advancement produced by CGIAR research, engagement and/or capacity development activities.
Knowledge, technical or institutional advancement produced by CGIAR research, engagement and/or capacity development activities.
“CGIAR Portfolio” means the research programs and/or platforms carried out by the Centers and the CGIAR System Partners in support of the CGIAR Strategy and Results Framework.
“CGIAR Portfolio” means the research programs and/or platforms carried out by the Centers and the CGIAR System Partners in support of the CGIAR Strategy and Results Framework.
These are defined as goods with the three following economic properties: ‘non-rival’… ‘non-excludable’… and available worldwide.
These are defined as goods with the three following economic properties: ‘non-rival’… ‘non-excludable’… and available worldwide.
QoR4D is a framework that facilitated CGIAR System-wide agreement on the nature and assessment of the quality of science, a concept broadened beyond scientific credibility to include the likelihood of achieving development outcomes.
QoR4D is a framework that facilitated CGIAR System-wide agreement on the nature and assessment of the quality of science, a concept broadened beyond scientific credibility to include the likelihood of achieving development outcomes.
Generation and communication of data, information and knowledge on an empirical basis.
Generation and communication of data, information and knowledge on an empirical basis.
The positive/negative, direct/indirect, and intended/unintended change or consequence of a planned project/program activity or intervention in the form of output, outcome or impact.
The positive/negative, direct/indirect, and intended/unintended change or consequence of a planned project/program activity or intervention in the form of output, outcome or impact.
A management strategy by which all contributing stakeholders within a given intervention, project or program ensure that their intended results (i.e., outputs, outcomes, and impact) align with the results achieved.
A management strategy by which all contributing stakeholders within a given intervention, project or program ensure that their intended results (i.e., outputs, outcomes, and impact) align with the results achieved.
CGIAR’s Strategy and Results Framework 2016-2030 sets out three System Level Outcomes (SLOs): reducing poverty; ensuring food and nutrition security; and improving natural resources and ecosystem services.
CGIAR’s Strategy and Results Framework 2016-2030 sets out three System Level Outcomes (SLOs): reducing poverty; ensuring food and nutrition security; and improving natural resources and ecosystem services.
A major shift that results from significant positive, intended change in the governance and functioning of a system (structure entailing actors, processes and interactions making a meaningful whole with a common aim/purpose).
A major shift that results from significant positive, intended change in the governance and functioning of a system (structure entailing actors, processes and interactions making a meaningful whole with a common aim/purpose).
A world with sustainable and resilient food, land and water systems that deliver diverse, healthy, safe, sufficient and affordable diets, and ensure improved livelihoods and greater social equality, within planetary and regional environmental boundaries.
A world with sustainable and resilient food, land and water systems that deliver diverse, healthy, safe, sufficient and affordable diets, and ensure improved livelihoods and greater social equality, within planetary and regional environmental boundaries.
Statement to define the desired future state of a project, program or organization.
Statement to define the desired future state of a project, program or organization.
Concepts and methods for developing and analyzing future trends and scenarios in agrifood systems. It covers foresight key terms such as scenario modeling, horizon scanning, and trend analysis.
The pack can help scientists and funders speak the same language and be used in interactive tools and reports to communicate potential futures and inform decision-making.
In governance and management is the duty to ensure and report that the use of authority is aligned with rules, standards, policy and interests of the program, organization and the broader group of stakeholders.
In governance and management is the duty to ensure and report that the use of authority is aligned with rules, standards, policy and interests of the program, organization and the broader group of stakeholders.
A planning method that starts with a desirable future and works backward to identify the conditions required to reach it. Foresight and impact assessment functions apply back casting to align research and strategies with long‑term climate and food‑system goals.
A planning method that starts with a desirable future and works backward to identify the conditions required to reach it. Foresight and impact assessment functions apply back casting to align research and strategies with long‑term climate and food‑system goals.
The process of setting back casting model parameters to be consistent with a desired result, and then exploring trajectories to determine whether pathways are feasible with biophysical and economic constraints.
The process of setting back casting model parameters to be consistent with a desired result, and then exploring trajectories to determine whether pathways are feasible with biophysical and economic constraints.
Textual description of the baseline scenario, explaining current conditions and expected “business‑as‑usual” outcomes. The baseline narrative provides context for interpreting deviations in alternative scenarios and for communicating to non‑technical audiences.
Textual description of the baseline scenario, explaining current conditions and expected “business‑as‑usual” outcomes. The baseline narrative provides context for interpreting deviations in alternative scenarios and for communicating to non‑technical audiences.
Scenario in which current trends and policies continue as they are, with no major changes in direction or magnitude of trends in the drivers of a model. They serve to highlight the events that would occur without intervention and determine or estimate the impact of alternative parameter values or context conditions. Baseline scenarios can be compared to scenarios in which mitigation strategies are implemented.
Scenario in which current trends and policies continue as they are, with no major changes in direction or magnitude of trends in the drivers of a model. They serve to highlight the events that would occur without intervention and determine or estimate the impact of alternative parameter values or context conditions. Baseline scenarios can be compared to scenarios in which mitigation strategies are implemented.
Use of models to simulate past and future climate conditions and their effects on crop, hydrological, and economic systems to assess risks and adaptation and mitigation options.
Use of models to simulate past and future climate conditions and their effects on crop, hydrological, and economic systems to assess risks and adaptation and mitigation options.
Simulation models that represent how multiple sectors, production factors, and institutions interact and how they jointly respond to policy, technology, or external shocks while maintaining overall supply and demand balance. They are used to trace direct and indirect effects of shocks on prices, production, incomes, trade, and welfare across the economy.
Simulation models that represent how multiple sectors, production factors, and institutions interact and how they jointly respond to policy, technology, or external shocks while maintaining overall supply and demand balance. They are used to trace direct and indirect effects of shocks on prices, production, incomes, trade, and welfare across the economy.
Models simulating crop growth, yield, nutrient requirements, crop management, and water using data from weather, soil characteristics, land management practices, and crop varietal types.
Models simulating crop growth, yield, nutrient requirements, crop management, and water using data from weather, soil characteristics, land management practices, and crop varietal types.
An outline of required datasets, base parameter values, resolution scales, and scenario assumptions, including sources, normalization methods, validation, and quality checks that serves as the basis for implementing simulation model runs. Data and assumptions plan plans help modeling teams coordinate data acquisition and support transparency and replicability when developing simulation models and generating scenarios.
An outline of required datasets, base parameter values, resolution scales, and scenario assumptions, including sources, normalization methods, validation, and quality checks that serves as the basis for implementing simulation model runs. Data and assumptions plan plans help modeling teams coordinate data acquisition and support transparency and replicability when developing simulation models and generating scenarios.
Foresight models that simulate epidemiological variables, like severity and incidence, associated with crop-related pests and diseases.
Foresight models that simulate epidemiological variables, like severity and incidence, associated with crop-related pests and diseases.
A parameter that strongly influences a system’s plausible future state, shaping how trends unfold and interact under high uncertainty. General drivers are seen as uni-directional and highly influential on systems. They are marked by contested, complex, or poorly understood impacts, making them central to exploring foresight scenarios.
A parameter that strongly influences a system’s plausible future state, shaping how trends unfold and interact under high uncertainty. General drivers are seen as uni-directional and highly influential on systems. They are marked by contested, complex, or poorly understood impacts, making them central to exploring foresight scenarios.
Models that represent how natural systems behave and interact by simulating physical, chemical, and biological processes.
Models that represent how natural systems behave and interact by simulating physical, chemical, and biological processes.
Models that integrate integrate biophysical processes, farm or household-level economic decision‑making, and management processes to simulate responses to technologies and policies.
Models that integrate integrate biophysical processes, farm or household-level economic decision‑making, and management processes to simulate responses to technologies and policies.
Exploration of plausible medium or long-term futures to inform strategy, priority-setting, and risk-aware decision-making under uncertainty.
Exploration of plausible medium or long-term futures to inform strategy, priority-setting, and risk-aware decision-making under uncertainty.
A structured and explicit exploration of multiple futures in order to inform decision-making. Foresight studies use a range of methodologies, such as scanning the horizon for emerging changes, analyzing megatrends and developing multiple scenarios, to reveal and discuss useful ideas about the future.
A structured and explicit exploration of multiple futures in order to inform decision-making. Foresight studies use a range of methodologies, such as scanning the horizon for emerging changes, analyzing megatrends and developing multiple scenarios, to reveal and discuss useful ideas about the future.
The use of multiple, linked models and datasets to analyze how food, land, and water systems may co‑evolve under alternative futures and policy or investment options, and to quantify cross‑sector synergies and trade‑offs.
The use of multiple, linked models and datasets to analyze how food, land, and water systems may co‑evolve under alternative futures and policy or investment options, and to quantify cross‑sector synergies and trade‑offs.
Structured templates used to frame and prioritize the questions a foresight exercise should answer. They help participants in a scenario development workshop capture issues such as what futures to explore, which drivers and uncertainties matter most, whose perspectives to consider, what decisions the work should inform, and what indicators or evidence will be needed.
Structured templates used to frame and prioritize the questions a foresight exercise should answer. They help participants in a scenario development workshop capture issues such as what futures to explore, which drivers and uncertainties matter most, whose perspectives to consider, what decisions the work should inform, and what indicators or evidence will be needed.
Quantitative tools that simulate land use and land cover change over time to monitor transitions between land uses, attribute these changes to key drivers, and produce metrics to be used as baselines, targets, and outcome/impact indicators.
Quantitative tools that simulate land use and land cover change over time to monitor transitions between land uses, attribute these changes to key drivers, and produce metrics to be used as baselines, targets, and outcome/impact indicators.
Scenario narrative consistent with “middle‑of‑the‑road” socio‑economic pathways, where trends continue without extreme challenges or breakthroughs. Foresight studies sometimes use such narratives to compare moderate futures against more optimistic or pessimistic ones. See also: baseline narrative.
Scenario narrative consistent with “middle‑of‑the‑road” socio‑economic pathways, where trends continue without extreme challenges or breakthroughs. Foresight studies sometimes use such narratives to compare moderate futures against more optimistic or pessimistic ones. See also: baseline narrative.
The iterative process of updating and improving foresight, ex‑ante impact assessment, and simulation models in response to new data, methods, parameters, and user needs. Refinement allows models and tools to remain decision‑relevant.
The iterative process of updating and improving foresight, ex‑ante impact assessment, and simulation models in response to new data, methods, parameters, and user needs. Refinement allows models and tools to remain decision‑relevant.
The process of choosing an appropriate model based on evaluation questions, theory of change, scale, and data. Good model selection should balance the complexity of parameters with predictive performance.
The process of choosing an appropriate model based on evaluation questions, theory of change, scale, and data. Good model selection should balance the complexity of parameters with predictive performance.
The process of testing a calibrated model against independent data and alternative indicators to assess whether it reproduces observed patterns with acceptable accuracy for its intended purpose, thereby increasing confidence that its projections and scenario analyses are credible and fit for decision‑making. In order for validation to occur, there must be independent data with which to validate the model for geographies/periods.
The process of testing a calibrated model against independent data and alternative indicators to assess whether it reproduces observed patterns with acceptable accuracy for its intended purpose, thereby increasing confidence that its projections and scenario analyses are credible and fit for decision‑making. In order for validation to occur, there must be independent data with which to validate the model for geographies/periods.
Method used to systematically compare future options or scenarios against several explicit criteria to support transparent, participatory choice among uncertain futures. Multi‑criteria analysis helps stakeholders assess and rank scenarios, strategies, or investments using weighted criteria, making value judgements and trade‑offs visible in long‑term decision‑making.
Method used to systematically compare future options or scenarios against several explicit criteria to support transparent, participatory choice among uncertain futures. Multi‑criteria analysis helps stakeholders assess and rank scenarios, strategies, or investments using weighted criteria, making value judgements and trade‑offs visible in long‑term decision‑making.
Scenario narrative consistent with best case shared socio‑economic pathways. Outcomes reflect minimal challenges, ideal policy change(s), beneficial conditions, or optimal intervention(s).
Scenario narrative consistent with best case shared socio‑economic pathways. Outcomes reflect minimal challenges, ideal policy change(s), beneficial conditions, or optimal intervention(s).
Translating qualitative assumptions, drivers, and system relationships into numerical values, rules, and ranges that a model can use. Parameterization specifies how drivers of change are represented as parameters with set ranges that can be simulated and compared, and assumptions are declared.
Translating qualitative assumptions, drivers, and system relationships into numerical values, rules, and ranges that a model can use. Parameterization specifies how drivers of change are represented as parameters with set ranges that can be simulated and compared, and assumptions are declared.
Facilitated workshops where stakeholders co‑develop scenarios, discussing drivers, system dynamics, and implications. This ensures scenarios are co-created and grounded in stakeholder knowledge, and gives participants ownership of resulting strategies and research agendas.
Facilitated workshops where stakeholders co‑develop scenarios, discussing drivers, system dynamics, and implications. This ensures scenarios are co-created and grounded in stakeholder knowledge, and gives participants ownership of resulting strategies and research agendas.
Scenario narrative consistent with worst-case socio‑economic and/or environmental pathways. Outcomes reflect extreme challenges, adverse conditions, or negative intervention(s).
Scenario narrative consistent with worst-case socio‑economic and/or environmental pathways. Outcomes reflect extreme challenges, adverse conditions, or negative intervention(s).
Graphical tool mapping a focal problem and the relationships between underlying causes and observable effects.
Graphical tool mapping a focal problem and the relationships between underlying causes and observable effects.
Structured approaches for clarifying underlying causes, pathways, and boundaries of issues. Problem‑framing tools can be used to align stakeholders and develop interventions.
Structured approaches for clarifying underlying causes, pathways, and boundaries of issues. Problem‑framing tools can be used to align stakeholders and develop interventions.
A specific knowledge gap that a body of research seeks to answer, framed to address knowledge gaps and inform decisions. Research questions are often linked explicitly to theories of change and are the basis for more specific hypotheses.
A specific knowledge gap that a body of research seeks to answer, framed to address knowledge gaps and inform decisions. Research questions are often linked explicitly to theories of change and are the basis for more specific hypotheses.
Structured descriptions of possible future outcomes, like the state of food, land, socioeconomic, water, and climate systems, based on specific assumptions about drivers and policies. In foresight, scenarios combine narratives and quantitative model outputs to explore risks and opportunities for research and scaling strategies. They also provide policy advice.
Structured descriptions of possible future outcomes, like the state of food, land, socioeconomic, water, and climate systems, based on specific assumptions about drivers and policies. In foresight, scenarios combine narratives and quantitative model outputs to explore risks and opportunities for research and scaling strategies. They also provide policy advice.
A story-like description of a potential future, based on a systematic set of assumptions and projections about drivers of change that influence outcomes. Scenario narratives may describe optimistic, middle-of-the-road, or pessimistic futures.
A story-like description of a potential future, based on a systematic set of assumptions and projections about drivers of change that influence outcomes. Scenario narratives may describe optimistic, middle-of-the-road, or pessimistic futures.
Specific quantitative or qualitative values which define how a given scenario differs from others. They translate narratives about future change into concrete assumptions for model use. Scenario parameters include population growth rates, GDP growth, technology adoption rates, input and output prices, policy variables, land‑use change rates, and emissions trajectories.
Specific quantitative or qualitative values which define how a given scenario differs from others. They translate narratives about future change into concrete assumptions for model use. Scenario parameters include population growth rates, GDP growth, technology adoption rates, input and output prices, policy variables, land‑use change rates, and emissions trajectories.
Standardized narratives and quantitative pathways created by the IPCC for climate research which describe how global society, demographics, economies, technology, and policies might evolve over the 21st century. Shared socioeconomic pathways demonstrate how these factors may influence greenhouse gas emissions and the capacity to mitigate and adapt to climate change.
Standardized narratives and quantitative pathways created by the IPCC for climate research which describe how global society, demographics, economies, technology, and policies might evolve over the 21st century. Shared socioeconomic pathways demonstrate how these factors may influence greenhouse gas emissions and the capacity to mitigate and adapt to climate change.
Biophysical models simulating hydrological balances and competing water usage. They are used to assess water productivity, drought, allocation, and heat stress risks.
Biophysical models simulating hydrological balances and competing water usage. They are used to assess water productivity, drought, allocation, and heat stress risks.
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1. Grant of License
By downloading the [bundle name] package, you are granted a non-exclusive, non-transferable right to use these Lex Icons™ in all CGIAR and CG Center knowledge products (Websites, Reports, PPTs, Apps, and Manuals). This license is valid for the period of [validity period].
2. Integrity
Each icon in [bundle name] package is a scientific token. You agree to:
3. Technical & Visual Standards
To maintain brand synchronization, you must adhere to following the Usage Guidelines described in the MELIAF HUB and Lex Icons™ Library.
4. Governance and Evolution
5. Recognition
Correct usage qualifies your project for the MELIAF Compliant Badge. Consistent high-integrity use earns MELIAF Compliant status, providing priority visibility in the MELIAF HUB and competitive advantages in donors’ reporting.
6. Warranty Disclaimer
CGIAR provides these icons “as is.” The presence of a Lex Icons™does not constitute a financial audit or endorsement of your data by the Office of the Chief Scientist. The Licensee assumes all risk for the accuracy of claims made.