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Enhancing Agricultural Research Quality, Comparability, and Impact

Overview of the AR-QCI initiative connecting agricultural research quality, comparability, and impact through a voluntary, non-binding framework integrating international standards and improving research synthesis and application.

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NENA GNC FLAGSHIP PROJECT Agricultural Research Quality, Comparability and Impact (AR-QCI) Initiative AR-QCI INITIATIVE A voluntary initiative that connects research quality, comparability of results and practical impact. DR. DIDEM KÖKDEN NENA Coordinator 22 July 2026 AARINENA TAGEM QUALITY • COMPARABILITY • IMPACT VOLUNTARY • NON-BINDING • INTEGRATING EXISTING INTERNATIONAL REFERENCES
Presentation Structure The presentation moves from the conceptual problem to the AR-QCI response, its architecture, development pathway and longer- term horizon. 1 SCIENCE, RESEARCH AND AGRICULTURAL RESEARCH Clarify the difference between science as a verified knowledge system and research as the process that develops, tests and revises knowledge. 2 THE CURRENT CONDITION AND MISSING FOUNDATION Show how agricultural research operates across many domains and outputs, but lacks one shared foundation for quality, comparability and impact. 3 INTRODUCING THE AR-QCI INITIATIVE Present AR-QCI as a voluntary, non-binding initiative connecting research quality, comparability of results and practical impact. 4 FRAMEWORK ARCHITECTURE AND COVERAGE Explain the seven-level architecture and how a common core is combined with research-domain-specific technical modules. 5 INTERNATIONAL REFERENCES AND FRAGMENTATION Map the international reference landscape and show that the problem is fragmentation—not absence of standards, methodologies and guidance. 6 AR-QCI ACROSS THE RESEARCH CYCLE Show how relevant references, minimum expectations and practical tools connect each stage of the agricultural research cycle. 7 DEVELOPMENT PROCESS AND EXPECTED GAINS Explain how the Framework is being developed and what countries, NARS, research institutions and researchers would gain. 8 CURRENT STATUS, GAP MAP AND DEVELOPMENT HORIZON Present what has been completed, the remaining gaps and why full Framework development extends through 2028 or 2029.
Science vs. Research Difference and relationship Science is the system. Research is the process. S SCIENCE A system of verified knowledge and rules for judging reliability. Role: validates, organizes, explains R RESEARCH The process of searching for or creating new knowledge. Role: investigates, tests, discovers RELATIONSHIP Science gives methods, criteria, theories Research brings evidence, findings, questions SCIENCE updates and disciplines RESEARCH Core idea: Research generates possible knowledge; Science tests and integrates what proves reliable.
Agricultural Science vs. Agricultural Research Core difference: system vs. process Science is the system. Research is the process. SYSTEM AGRICULTURAL SCIENCE An organized system of understanding agriculture: concepts, theories, methods, standards of evidence, and established knowledge. PROCESS AGRICULTURAL RESEARCH The systematic activity of developing, testing, and revising knowledge about agriculture. Key distinction: Agricultural Science is a verified knowledge system; Agricultural Research is a knowledge-building process.
Agricultural Research Today A process view with current condition Agricultural Research is not a sector list; it is a structured process for producing and validating knowledge. MANY DOMAINS ONE RESEARCH LOGIC MANY OUTPUTS CURRENT CONDITION Biological systems Natural resources Food systems Technology Markets Policy Institutions Rural systems Innovation systems Knowledge Methods Models Technologies Practices Datasets Policy options 1 Many parallel research streams 2 Diverse bodies of knowledge 3 Limited synthesis into one coherent Agricultural Science Key transition: Agricultural Research is broad and productive, but its knowledge base remains parallel, diverse, and weakly synthesized. SCOPE METHOD EVIDENCE ANALYSIS VALIDATION OUTPUT USE
The Missing Foundation Agricultural Science as a unified system Agricultural Research is extensive; but is not sufficiently grounded in one shared system of concepts, principles, methods, evidence standards, and validation logic. AGRICULTURAL RESEARCH Produces studies, evidence, methods, models and applied outputs. It is extensive knowledge- building work. MISSING CONSOLIDATION Agricultural Research is not organized by a shared foundation of concepts, principles, methods, evidence standards, validation logic, and established knowledge. Weak point: synthesis, integration, and scientific grounding.. AGRICULTURAL SCIENCE Should be the unified system of concepts, theories, methods, evidence standards and established knowledge. Current state: weak / unfinished as one system. Core conclusion: The fundamental problem today is not the absence of Agricultural Research, but the weakness of Agricultural Science as the integrated foundation for that research. Agricultural Research is not fundamentally built on and not sufficiently consolidated into one coherent Agricultural Science.
INTRODUCING AR-QCI Agricultural Research Quality, Comparability and Impact Initiative AR-QCI INITIATIVE A voluntary initiative that connects research quality, comparability of results and practical impact. QUALITY Credible, rigorous, transparent and useful Agricultural Research. COMPARABILITY Results that can be understood and compared across countries, institutions and research domains. IMPACT Research connected to practical use, policy, innovation, investment and public value. VOLUNTARY • NON-BINDING • INTEGRATING EXISTING INTERNATIONAL REFERENCES Organizing and connecting existing references so they can be used as one coherent framework for Agricultural Research.
AR-QCI Architecture of Coverage Architecture of Coverage: The categories are organized into seven levels Level Purpose Main coverage Why this level is needed Level 1 — Conceptual and System Foundations Defines Agricultural Research and how research systems are understood and organized. Scope, priorities, institutional capacity, governance and research portfolios. Creates common language and enables system-level understanding and comparison. Level 2 — Research Process Quality Defines how credible agricultural knowledge is designed, produced and validated. Methodology, field and on-farm research, laboratories, modelling, documentation and quality assurance. Ensures evidence is credible, transparent, reproducible and appropriate to context. Level 3 — Evidence, Data, Reporting and Visibility Defines how evidence is documented, governed, shared, compared and communicated. Data quality, metadata, interoperability, open science, reporting and knowledge visibility. Makes results findable, reusable, understandable and comparable. Level 4 — Technical Domains and Regulatory Interfaces Covers diverse scientific, technical and regulatory contexts. Crops, livestock, health, soils, water, climate, food, digital agriculture, engineering and socioeconomic research. Agricultural Research is multi-domain; one technical standard cannot cover every field. Level 5 — Ethics, Safeguards, Integrity and Partnerships Defines whether research is conducted responsibly, fairly and legitimately. Research integrity, biosafety, data rights, inclusion, participation, safeguards and partnership quality. Research credibility depends on both scientific quality and responsible conduct. Level 6 — Research-to- Use, Impact, Policy and Learning Connects evidence to application, innovation, policy, scaling and investment. Extension, adoption, policy use, impact, public value and learning. Agricultural Research must demonstrate pathways beyond outputs to use and value. Level 7 — Implementation and Maturity Pathway Makes the architecture usable by systems and institutions with different capacities. Common quality core, modular mapping, self- assessment, maturity levels, improvement planning and capacity support. Turns the architecture into a practical system for continuous improvement. Together, the seven levels connect research priorities and system readiness to credible evidence, responsible partnerships, practical use, impact and continuous improvement.
The International Reference Landscape Behind AR-QCI International reference instruments identified across the scope of Agricultural Research 71 NAMED REFERENCES STANDARDS • METHODOLOGIES • GUIDELINES • PRINCIPLES • INDICATORS • TREATIES • REFERENCE SYSTEMS 1. Research Systems, Quality, Evaluation and Governance OECD Frascati Manual • FAO- ASTI / Agricultural Science and Technology Indicators • OECD/Eurostat Oslo Manual • CGIAR Quality of Research for Development (QoR4D) • IDRC Research Quality Plus (RQ+) • OECD-DAC Evaluation Criteria • ISO 31000 Risk Management 2. Research Methods, Reporting, Laboratories and Testing PRISMA Statement • STROBE Statement • CONSORT/SPIRIT • ARRIVE Guidelines • EQUATOR Network • ISO/IEC 17025 • OECD Good Laboratory Practice (GLP) • OECD Test Guidelines • EPPO PP1 Standards / Good Experimental Practice 3. Seeds, Genetic Resources, SPS, Animal Health and Food Safety ISTA International Rules for Seed Testing • OECD Seed Schemes • UPOV DUS Test Guidelines • UPOV TGP Documents • FAO Genebank Standards • International Treaty on Plant Genetic Resources for Food and Agriculture • Nagoya Protocol • Cartagena Protocol on Biosafety • WIPO Treaty on Intellectual Property, Genetic Resources and Associated Traditional Knowledge (WIPO GRATK Treaty) • IPPC International Standards for Phytosanitary Measures (ISPMs) • WTO Agreement on the Application of Sanitary and Phytosanitary Measures (WTO SPS Agreement) • WOAH Codes and Manuals • Codex Alimentarius • One Health Joint Plan of Action 4. Open Science, Data, Interoperability and Digital Agriculture UNESCO Recommendation on Open Science • FAIR Principles • CARE Principles for Indigenous Data Governance • FAO AGRIS • FAO AGROVOC • MIAPPE – Minimum Information About a Plant Phenotyping Experiment • BrAPI – Breeding API • Crop Ontology • OECD Recommendation on Enhancing Access to and Sharing of Data • FAO E-Agriculture Strategy Guide • UNESCO Recommendation on the Ethics of Artificial Intelligence • OECD AI Principles • Principles for Digital Development 5. Soil, Water, Climate, Natural Resources, Fisheries, Forestry and Engineering ISO 28258 Soil Quality – Digital Exchange of Soil-Related Data • FAO Global Soil Partnership / Soil Information Systems • FAO Irrigation and Drainage Paper 56 • IPCC 2006 Guidelines and 2019 Refinement – AFOLU • ISO 14040 Life-Cycle Assessment – Principles and Framework • ISO 14044 Life-Cycle Assessment – Requirements and Guidelines • GHG Protocol Land Sector and Removals Standard • FAO Code of Conduct for Responsible Fisheries • FAO Technical Guidelines on Aquaculture Certification • FAO Global Forest Resources Assessment • FAO Responsible Management of Planted Forests • ISO Agricultural Machinery Standards • ASABE Published Standards 6. Socioeconomics, Ethics, Partnerships, Inclusion and Safeguards FAO AGRISurvey • FAOSTAT • FAO/UNIDO Sustainable Food Value Chain Guidance • Singapore Statement on Research Integrity • Montreal Statement on Research Integrity in Cross-Boundary Research Collaborations • TRUST Code / Global Code of Conduct for Research in Resource-Poor Settings • COPE Core Practices • GFAiR Partnership Principles • CFS Voluntary Guidelines on Gender Equality and Women’s and Girls’ Empowerment • FAO Voluntary Guidelines on the Responsible Governance of Tenure (VGGT) • CFS Principles for Responsible Investment in Agriculture and Food Systems (CFS-RAI) • ISO 26000 Social Responsibility 7. Extension, Innovation, Scaling and Capacity Development FAO/TAP Common Framework on Capacity Development for Agricultural Innovation Systems • GFRAS New Extensionist Learning Kit • World Bank Agricultural Innovation Systems Sourcebook AR-QCI does not replace these instruments. It provides the missing architecture for organizing, connecting and translating them into practical tools for countries and research institutions. Grouped by primary function; many instruments contribute to more than one AR-QCI level. The reference base will expand as mapping continues.
The Problem Is Fragmentation, Not Absence Valuable international references already exist, but they were developed by different organizations for different functions and do not operate as one coherent system. SCALE FRAGMENTED INSTRUMENTS SYSTEM-LEVEL GAP 71 NAMED REFERENCES IDENTIFIED SO FAR STANDARDS METHODOLOGIES GUIDANCE INDICATORS PRINCIPLES TREATIES NO COMMON ARCHITECTURE No shared logic shows how the instruments connect, what applies where, or how they should be used together. CONSEQUENCES Results are harder to compare across countries, institutions and contexts. Evidence is harder to reuse, combine and make visible. Research quality is harder to connect to practical impact. The instruments exist. What is missing is a coherent way to organize, connect and use them across Agricultural Research.
What Quality, Comparability and Impact Mean AR-QCI Each word in the name defines a distinct purpose of AR-QCI—and the word Initiative defines the nature of the effort. QUALITY Credible, rigorous, transparent and useful Trans-disciplinary Agricultural Research. COMPARABILITY Methods, data, outputs and evidence that can be correctly understood and compared across sites, research disciplines, programs, sectors, research cycle, countries and institutions—with context taken into account. IMPACT Measurable Impact. Research connected not only to publications and financial / finding opportunities, but also to the development agenda, practical use, policy, innovation, investment and public value. INITIATIVE A voluntary, non-binding and developmental effort—not a new mandatory standard or compliance regime. Together, the four terms define both the ambition of AR-QCI and the boundaries of what the initiative is intended to be.
AR-QCI Connects the Full Agricultural Research Cycle At each stage, AR-QCI answers two practical questions: what must be addressed, and how can existing references make it operational? → → → → → → → 1 NEEDS & PRIORITIES What should be studied—and why? HOW AR-QCI HELPS Common criteria for needs assessment, agenda-setting and transparent prioritization. 2 SYSTEM READINESS Can the system deliver credible research? HOW AR-QCI HELPS Assessment of institutional capacity, governance, resources and maturity. 3 RESEARCH DESIGN How should the study be conducted? HOW AR-QCI HELPS Relevant methods, protocols, trials and minimum quality criteria. 4 EVIDENCE GENERATION & VALIDATION Can the findings be trusted? HOW AR-QCI HELPS Documentation, validation, reproducibility and quality assurance. 5 DATA & REPORTING Can others find, understand and compare the results? HOW AR-QCI HELPS Data quality, metadata, interoperability and transparent reporting. 6 RESPONSIBLE PARTNERSHIPS Is the research ethical, fair and transparent? HOW AR-QCI HELPS Integrity, safeguards, benefit- sharing, inclusion and participation. 7 USE How will evidence reach practice and decisions? HOW AR-QCI HELPS Extension, stakeholder engagement, policy pathways, adoption and scaling. 8 IMPACT & LEARNING What changed, for whom, and what should improve? HOW AR-QCI HELPS Impact assessment, public value, feedback and continuous learning. AR-QCI connects the stages by mapping relevant references, defining minimum quality and comparability expectations, supporting self-assessment and turning identified gaps into improvement actions. The connection is system-level; each technical domain continues to use the standards and methods appropriate to its context.
One Framework, Different Technical Content AR-QCI uses one seven-level structure, while technical modules identify what is relevant for each research domain or function. COMMON ARCHITECTURE = THE SEVEN LEVELS 1 CONCEPTUAL AND SYSTEM LEVEL 2 RESEARCH PROCESS QUALITY 3 EVIDENCE, DATA AND KNOWLEDGE VISIBILITY 4 TECHNICAL AGRICULTURAL DOMAINS 5 RESPONSIBLE RESEARCH AND PARTNERSHIP 6 RESEARCH-TO-USE, IMPACT AND PUBLIC VALUE 7 IMPLEMENTATION AND MATURITY TECHNICAL MODULES For each research domain or function, a module identifies: RELEVANT STANDARDS AND METHODOLOGIES RELEVANT GUIDANCE AND PRINCIPLES RELEVANT INDICATORS GAPS — WHAT IS INCOMPLETE OR MISSING The module organizes this content within the seven-level structure. It does not create one identical technical protocol for all research. + THE SEVEN-LEVEL STRUCTURE IS COMMON. THE TECHNICAL CONTENT DIFFERS ACCORDING TO THE RESEARCH DOMAIN OR FUNCTION.
Two Analogies: One Overall Structure, Different Technical Requirements One overall structure can coordinate different kinds of work without making them follow the same technical requirements. MEDICINE — ONE PATIENT-CARE SYSTEM The hospital coordinates diagnosis, evidence, decisions, treatment and follow-up. Laboratory medicine, radiology, cardiology and surgery each use the standards and procedures for their work. CONSTRUCTION — ONE HOSPITAL BUILDING One architectural plan coordinates structure, electricity, water, ventilation, fire safety and medical systems. Each system follows the standards for that system. AR-QCI follows the same principle: the seven-level structure is shared, while the technical content differs according to the research domain or function.
How the AR-QCI Framework Is Being Developed AR-QCI is the overall voluntary initiative. Its main product—the Framework—is developed through the sequence below. 1 DEFINE AGRICULTURAL RESEARCH Define Agricultural Research as a structured knowledge-production process—from needs and priorities through methods, evidence and validation to communication, use, impact and learning. 2 BUILD THE CATEGORY-FIRST ARCHITECTURE Create the complete A–G category structure covering system foundations, research-process quality, evidence and data, technical domains, ethics and partnerships, use and impact, and implementation. 3 VALIDATE AND CORRECT THE CATEGORY ARCHITECTURE Check that every major issue has a clear place; remove overlaps, correct inconsistencies and add missing or underdeveloped categories before the next stage. 4 CLASSIFY CATEGORIES BY REQUIRED INSTRUMENT TYPE(S) For each category, identify the required combination of definitions, standards or protocols, methodologies, guidance, principles, indicators, reference systems, assessment tools, legal or regulatory instruments and development needs. 5 MAP REFERENCES AND PRODUCE THE FULL GAP MAP Map every international reference to the category and research function it supports; then decide whether it should be used directly, aligned with, adapted, converted into a tool or treated as a gap signal. Record what is strong, partial, fragmented, difficult or missing. 6 DEFINE THE FOUR-LAYER FRAMEWORK ARCHITECTURE Turn the diagnosis into four connected layers: a common core, technical domain modules, cross-cutting principles, and an implementation and maturity pathway. The Framework remains voluntary, modular and non-binding. 7 BUILD THE FRAMEWORK DESIGN MATRIX AND TOOLS For each Framework component, show its purpose, category, required instrument type, existing references, gap and proposed tool. Use this Design Matrix to develop the reference-use map, quality and comparability packages, checklists, maturity model and gap-to-roadmap tools. 8 CONSULT, TEST, REFINE, SUPPORT AND UPDATE Test the Framework with countries, NARS, research institutions, regional organizations and partners. Use feedback to refine it, support voluntary application through guidance and training, and review and update it over time.
What Countries, Research Institutions and Researchers Would Gain COUNTRIES & NATIONAL RESEARCH SYSTEMS A clear national picture of research priorities, capacities, evidence and gaps. Why AR-QCI can enable this Self-assessment and maturity tools bring these elements into one structure. More comparable evidence for national planning and policy decisions. Why AR-QCI can enable this Common descriptors, metadata and reporting expectations make comparison more reliable. A stronger basis for capacity support, regional cooperation and public investment. Why AR-QCI can enable this Gap-to-roadmap and impact logic connect diagnosed needs to improvement priorities and demonstrated value. RESEARCH INSTITUTIONS Clearer and more consistent quality requirements across research projects. Why AR-QCI can enable this The Framework turns existing references into design, data, documentation, reporting and quality-assurance criteria. Less uncertainty about which standards, methods and guidance apply. Why AR-QCI can enable this The Reference Use Map links each reference to a research function and context. More responsible partnerships and a practical basis for institutional improvement. Why AR-QCI can enable this Partnership criteria and maturity tools support self-assessment, gap diagnosis and capacity priorities. RESEARCHERS Clear expectations for design, methods, validation, documentation, data and reporting. Why AR-QCI can enable this Minimum quality criteria make the requirements explicit. Research results that are easier to assess, compare, reproduce, reuse and make visible. Why AR-QCI can enable this Comparability and visibility requirements align documentation, metadata and reporting. Clearer routes from research results to extension, innovation, policy, scaling and impact. Why AR-QCI can enable this The research-to-use pathway connects outputs with application and learning. SHARED GAIN | Clearer expectations • Comparable and reusable evidence • More visible results • Responsible partnerships • Stronger links to use and impact Each practical gain follows from a specific AR-QCI function: organizing relevant references, defining common expectations, supporting self-assessment and connecting research to use and impact.
AR-QCI Framework: Current Status and Development Horizon CURRENT STATUS The Framework is not yet complete and is not an operating certification system. MAY–JUNE 2026 FOUNDATION AND ARCHITECTURE • Conceptual foundation and definition • Category architecture validated and corrected • Instrument types classified • Reference mapping and Gap Map • Framework architecture JUNE–JULY 2026 REVIEW AND REGIONAL CONSULTATION • Review, validation and revision • Regional consultation on the emerging Framework • Confirmation of priority development areas JULY–AUGUST 2026 IMMEDIATE FOCUS ONLY • Priority thematic areas • Technical groups This period does not cover development of the entire Framework. FULL FRAMEWORK DEVELOPMENT | AUGUST 2026–2028 OR 2029 A multi-year process extending beyond harmonization • Develop the Framework Design Matrix, common core, technical modules and practical tools. • Conduct broader multi-institutional consultations, testing and technical elaboration across the full Framework. • Determine which references can be used directly, aligned with, adapted or translated into tools, and update or fine- tune the reference base where needed. • Refine the Framework through feedback and maintain it through phased implementation and periodic review.
The Gap Map Confirms a Multi-Year Development Horizon Across 44 categories in seven architecture groups, coverage is partial, fragmented, uneven or mostly missing—and must be converted into practical Framework components and tools. A CONCEPTUAL AND SYSTEM FOUNDATIONS • 4 CATEGORIES • PARTIAL Common vocabulary • readiness model • portfolio-quality guidance B RESEARCH PROCESS QUALITY • 5 CATEGORIES • PARTIAL / FRAGMENTED Design core • field/on-farm guide • tiered lab QA • reporting package C EVIDENCE, DATA, REPORTING AND VISIBILITY • 5 CATEGORIES • PARTIAL Data package • interoperability map • governance • comparability reporting D TECHNICAL DOMAINS AND REGULATORY INTERFACES • 14 CATEGORIES • UNEVEN Modular standards/guidance map • targeted technical-domain modules E ETHICS, SAFEGUARDS, INTEGRITY AND PARTNERSHIPS • 6 CATEGORIES • OPERATIONAL GAP Ethics/safeguards • partnership quality • inclusion and participation F USE, IMPACT, POLICY AND LEARNING • 5 CATEGORIES • WEAK INTEGRATION Research-to-use • scaling • impact/public value • policy and learning G IMPLEMENTATION AND MATURITY PATHWAY • 5 CATEGORIES • MOSTLY MISSING Quality core • modular map • maturity model • roadmaps • capacity support 44 CATEGORIES mapped across seven architecture groups 10 CRITICAL CROSS-CUTTING GAPS including quality core, comparability and maturity 12 PRACTICAL DELIVERABLES needed to turn diagnosis into an operational Framework WHY THE HORIZON EXTENDS TO 2028 OR 2029 The next phase is not only harmonization: it requires domain-by-domain mapping, adaptation, tool development, broader multi- institutional consultation, testing and repeated refinement.