A review of effectiveness (including cost- effectiveness) of climate information systems, early warnings, and agro- advisories in reducing climate change- related risks for smallholder producers Project Inception Report Contents | Page 1 of 24 CGIAR Contents Contents 1 Executive Summary 3 Background and Rationale 5 Project Management and Team Structure 13 Stakeholder Engagement Plan 13 Work Plan and Timeline 15 References 16 Annex 18 CGIAR Contents | Page 2 of 24 Authors Suchi Malhotra¹,² Ashrita Saran ² , Ashima Mohan ¹,² , Varsha Nair ¹,² Affiliations: 1. Campbell South Asia 2. Global Development Network Corresponding Author: Suchi Malhotra (skmalhotra@campbellsouthasia.org) Suggested citation: Malhotra, S. (2025). A review of effectiveness (including cost- effectiveness) of climate information systems, early warnings, and agro-advisories in reducing climate change-related risks for smallholder producers: Project Inception Report. CGIAR Climate Action. CGSpace link: https://hdl.handle.net/10568/180860 Cover photo: CGIAR Copyright: © 2025. ILRI. This publication is licensed for use under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view this license, visit https://creativecommons.org/licenses/by/4.0. Disclaimers: This publication has been prepared as an output of the CGIAR Climate Action Program. Any views and opinions expressed in this publication are those of the author(s) and are not necessarily representative of or endorsed by the CGIAR System Organization. Acknowledgements: This work was conducted by ILRI as part of the CGIAR Climate Program. CGIAR research is supported by contributions to the CGIAR Trust Fund. CGIAR is a global research partnership for a food-secure future dedicated to transforming food, land, and water systems in a climate crisis. We gratefully acknowledge Aditi Mukherji and Neal Haddaway for their valuable support and methodological training. https://hdl.handle.net/10568/180860 https://creativecommons.org/licenses/by/4.0. https://www.cgiar.org/funders/ Executive Summary | Page 3 of 24 CGIAR Executive Summary The rationale for the review Climate change is intensifying risks to agriculture in low- and middle-income countries (LMICs), where smallholder farmers responsible for a significant share of food production face increasing vulnerability due to dependence on rain-fed systems, limited adaptive capacity, and inadequate access to timely, actionable information (FAO, 2016; IPCC, 2022). Rising temperatures, erratic rainfall, and the frequency of extreme weather events are already disrupting food systems, threatening livelihoods, and undermining development gains (Thornton et al., 2014; Vermeulen et al., 2018). Climate Information Services (CIS) including seasonal forecasts, early warning systems, and agro-advisory services have emerged as critical tools for risk management, adaptive decision-making, and resilience-building among these producers (Hansen et al., 2011; Vaughan & Hansen, 2017). While emerging studies report promising benefits of CIS for improving agricultural productivity, income, and risk management (Djido et al., 2021; Lu et al., 2021), critical questions remain regarding their effectiveness, cost-efficiency, and equity of access—especially for women and marginalized groups (Ngigi, 2022; Madhuri, 2022). Despite rapid scaling of CIS in national climate strategies and donor programs, no systematic review has consolidated evidence on their real-world impacts or value-for-money. This review will evaluate the effectiveness and cost-effectiveness of CIS for smallholder crop producers in LMICs. Findings will support evidence-informed policymaking, investment decisions, and inclusive climate adaptation planning. This evidence map will systematically identify, categorize, and assess the existing research on the effectiveness of Climate Information Services (CIS) for smallholder crop producers in LMICs. The primary research question(s): What evidence exists on the effectiveness (including cost-effectiveness)) of climate information systems such as early warning mechanisms and agro-advisory services in reducing climate- related risks and enhancing adaptive capacity, productivity, and resilience among smallholder crop producers in low- and middle-income countries (LMICs)? The proposed scope and objectives: The primary objective of this evidence mapping exercise is to identify, map, and describe existing research on the effectiveness of climate information systems (CIS) including early warning mechanisms and agro-advisory services in reducing climate-related risks and enhancing adaptive capacity, productivity, and resilience among smallholder crop producers in low- and middle-income countries (LMICs). Expected outputs and timelines: • Deliverable 1: Inception Report that includes the final RQs, and overall approach to the review, including proposed search strings, volume of literature surfaced and detailed Gannt chart showing different steps of the review (interim) • Deliverable 2: First draft of protocol for scoping review and systematic maps (interim) • Deliverable 3: Final protocol for a scoping review and a systematic map published on either CGSpace (CGIAR's document repository) or any other publicly available specialized repository (final) CGIAR Executive Summary | Page 4 of 24 • Deliverable 4: First draft of the scoping review and systematic map including a searchable data base of evidence and evidence gap map (interim) • Deliverable 5: Final draft of the scoping review and a systematic map, including a searchable database of evidence and evidence gap map published either on CGSpace or any other public repository or submitted to a journal for publication (final) • Deliverable 6: First draft of protocol for systematic review protocol (interim) • Deliverable 7: Final protocol for systematic review published on CGSpace (CGIAR's document repository) or any other publicly available specialized repository (final) • Deliverable 8: First draft of systematic review ready for submission to a journal (interim) • Deliverable 9: Final draft of systematic review ready for submission to a journal (final) Background and Rationale | Page 5 of 24 CGIAR Background and Rationale Topic overview: Brief description of the subject area and why a systematic review is needed. Climate change is an increasingly urgent global threat, with rising temperatures, shifting rainfall patterns, and the growing frequency and intensity of extreme weather events posing serious risks to food security, livelihoods, and poverty reduction efforts—particularly in developing regions (Ngigi, 2022; Alidu, 2022; IPCC, 2021; FAO et al., 2020). Smallholder farmers, who constitute the backbone of agricultural production in sub-Saharan Africa and many other low- and middle-income countries, are especially vulnerable due to their dependence on rain-fed agriculture, limited access to resources, and constrained adaptive capacity (FAO, 2015; UNDP, 2017; IPCC, 2014; Wheeler & von Braun, 2013). In response to these challenges, Climate Information Services (CIS) including early warning systems and agro-advisories have gained increasing policy relevance. These services aim to provide timely, localized, and actionable climate data that enables smallholder producers and decision-makers to anticipate and manage climate-related risks more effectively (Hansen et al., 2019; Vincent et al., 2018; Bizo et al., 2019; Madhuri, 2022). CIS are thus critical tools for building resilience, supporting climate-smart agriculture, and promoting evidence-based planning and early action. Their strategic value aligns closely with key Sustainable Development Goals (SDGs), notably SDG 13 (Climate Action), SDG 2 (Zero Hunger), SDG 1 (No Poverty), and SDG 5 (Gender Equality). While studies have demonstrated that CIS can improve agricultural decision-making, boost productivity, and reduce vulnerability to climatic shocks (Lu et al., 2021; Djido et al., 2021; Madhuri, 2022), significant challenges remain—particularly concerning accessibility, equity, and cost-effectiveness among marginalized groups such as women and youth (Ofori et al., 2021; Ngigi & Muange, 2022). Although systematic reviews exist on broader topics such as climate-smart agriculture technologies (Rosenstock et al., 2016; Bui & Vu, 2020), agricultural innovation (Waddington et al., 2014), and evidence gap maps on agricultural innovation (Lopez-Avila et al., 2017), and one review addresses the impact and challenges of climate information on farming (Madhuri, 2022), no comprehensive evidence map or synthesis has systematically organized and assessed the effectiveness of CIS for smallholder crop producers in in low- and middle-income countries (LMICs). This project addresses this gap through combined evidence mapping and synthesis approach. The evidence map will locate, categorize, and describe studies on CIS interventions including early warning systems and agro-advisories and their impacts on productivity, resilience, adaptive capacity, and related outcomes. Building on this map, the synthesis will critically evaluate the effectiveness and cost-efficiency of these interventions, including differential impacts across gender and socio-economic groups. Together, the mapping and synthesis will provide a structured overview of the current evidence base and highlight priority areas for research and policy. Problem statement: Identify the knowledge gap or policy/research issue the review addresses. Despite numerous studies and systematic reviews on climate-smart agriculture and agricultural innovation, no consolidated evidence exists that systematically maps and synthesizes CIS interventions for smallholder crop producers in LMICs. This gap limits the ability of policymakers, practitioners, and researchers to make informed decisions or to identify evidence-based priorities for future interventions. The proposed review will fill this gap by providing a comprehensive evidence map and synthesis, offering a clear understanding of what works, for whom, and under what conditions. CGIAR Background and Rationale | Page 6 of 24 Objectives: Clear, specific aims aligned with the problem statement. The overall aim of this project is to map and synthesize the available evidence on Climate Information Services (CIS) interventions including early warning systems and agro-advisories for smallholder crop producers in low- and middle-income countries (LMICs). The evidence map will identify, catalogue, and categorize existing studies, making research on CIS more discoverable and supporting evidence-based decision-making in climate resilience, agricultural policy, risk management, and development programming. Building on this map, the synthesis will critically evaluate the effectiveness and cost-effectiveness of CIS interventions in reducing climate-related risks, enhancing adaptive capacity, improving agricultural productivity, and strengthening resilience to climate shocks. It will also examine equity in CIS delivery, exploring differential impacts across gender and socio-economic groups, with particular attention to accessibility, usability, and inclusivity. Together, the mapping and synthesis will provide a comprehensive, structured overview of the current evidence, highlight priority gaps for future research, and inform policy and programming decisions in climate-smart agriculture and climate risk management. Research Questions List and explain the finalized research question(s) and sub-questions following the PICO/PEO framework (Population, Intervention/Exposure, Comparison, Outcome): Main Research Question(s) What is the effectiveness and cost-effectiveness of climate information systems (including early warning mechanisms and agro-advisories) in reducing climate-related risks and improving adaptive capacity, productivity, and resilience among smallholder crop producers in low- and middle-income countries? Sub- questions- evidence mapping RQ1. What is the extent of the available evidence on the effectiveness of Climate Information Services including early warning systems and agro-advisories on agricultural productivity, resilience, adaptive capacity, and related outcomes for smallholder crop producers in L&MICs? RQ2. How does the coverage of evidence vary by geography and contextual factors such as the type of CIS intervention, delivery channels, climate risk context, and target groups? RQ3. Where are the major gaps, limitations, and opportunities for future primary studies and evidence syntheses to strengthen understanding of the impacts of CIS interventions in L&MICs? Sub- questions- evidence synthesis RQ1 What is the effectiveness of CIS interventions improve agricultural decision-making, productivity, and income stability among smallholder producers? RQ2 What is the cost-effectiveness of CIS and early warning systems compared to traditional or informal sources of climate information? Background and Rationale | Page 7 of 24 CGIAR RQ3 What socio-economic or gender-based disparities exist in access to, and effective use of, climate information services among smallholder producers? Scope and boundaries Scope (Using PICO Framework) Scope (Using PICO Framework) Population Smallholder crop producers engaged in agriculture, primarily in low- and middle-income countries (LMICs), especially in climate-vulnerable regions. According to the FAO, smallholder producers typically manage up to 10 hectares of land, use mainly family labor, and rely partially on production for household consumption. They often face constraints in accessing inputs, markets, and climate information, making them highly vulnerable to climate- related risks. Intervention Climate Information Systems (CIS), including: • Weather and seasonal forecasts • Early warning systems for extreme events (e.g., droughts, floods) • Agro-advisory services integrating climate, weather, and agronomic data • Decision-support tools for farm-level climate risk management Comparator Business-as-usual approaches Outcomes Primary outcomes: • Reduction in climate-related agricultural risks (e.g., crop loss, income variability) • Enhanced adaptive capacity and resilience Secondary outcomes: • Improved decision-making on agricultural practices (e.g., planting time, input use) • Increased productivity and/or income • Cost-effectiveness of CIS interventions • Adoption of climate-smart or risk-reducing practices Study Design We will include completed empirical primary studies measuring the impacts. Eligible designs include randomised controlled trials (RCTs) and quasi- experimental designs (QEDs). QEDs are studies that use statistical methods in the absence of randomised assignment to intervention, such as discontinuity design, interrupted time series design, difference-in-differences analysis applied to controlled pre-test post-test data, and statistical methods applied to post-test cross-sectional data only (e.g. synthetic controls, statistical matching, adjusted regression analysis). To measure the cost-effectiveness of interventions, we included economic and financial evaluations. Economic evaluations provide a measurement of economic efficiency. To be an eligible economic evaluation, a study needed to have two essential features: costs and outcomes must be analysed, and more than one alternative strategy must be compared. Cost-effectiveness studies (including cost-efficacy, or studies comparing costs with numbers reached), cost-utility, and cost-benefit analyses were included. Systematic evidence syntheses, including systematic reviews and meta- analyses, systematic scoping reviews and rigorous literature reviews and rapid evidence assessments. We will exclude before–after study designs and qualitative studies. CGIAR Background and Rationale | Page 8 of 24 Inclusion and Exclusion Criteria Eligibility will be determined using the PICOS framework and relevance to climate risk reduction, adaptation, and decision-making support in agri-food systems within low- and middle-income countries (LMICs). Inclusion Criteria: • Empirical studies examining the impact of climate information services, weather forecasts, agro-advisories, or early warning systems on smallholder crop farmers in LMICs • Studies reporting outcomes related to adaptation, resilience, agricultural productivity, decision-making, or climate risk reduction • Evaluations conducted in low- and middle-income countries (as classified by the World Bank). • Quantitative studies and reviews with transparent and documented methodology, including randomized controlled trials (RCTs), quasi-experimental designs, systematic review and meta-analysis. Exclusion Criteria: • Conceptual or theoretical papers without empirical evidence or outcome data • Studies not focused on smallholder crop producers or those conducted outside of LMIC contexts • Interventions unrelated to climate information systems or not addressing climate- related agricultural risk • Studies lacking sufficient methodological detail for assessment or replicability Methodological Approach Review Framework This evidence map will follow to the methodological standards of the Campbell Collaboration to ensure rigor, transparency, and reproducibility throughout the review process. The protocol will be framed using a PICO framework and will be registered with the Open Science Framework (OSF). The mapping will be conducted in phases: (1) scoping and protocol development, (2) searching and screening of studies, (3) data extraction and coding, (4) analysis and presentation and (5) dissemination of findings. EPPI-Reviewer will be used to manage all stages of the review, including screening, coding, and critical appraisal. The map will be generated using EPPI-Mapper, a specialised EGM app commissioned by the Campbell Collaboration which uses the data exported from EPPI-Reviewer Search Strategy Dabases and Sources Background and Rationale | Page 9 of 24 CGIAR The search strategy for this systematic review will include both peer-reviewed and grey literature to ensure comprehensive coverage across geographic regions and thematic areas. Searches will be conducted in key academic databases: • Scopus • Web of Science Core Collection (Social Sciences Citation Index, Science Citation Index) • AGRIS • PubMed Medline (Ovid) • 3ie Development Evidence Portal • GreenFILE • AGRIS • LILACS • Campbell Evidence Library. To capture relevant grey literature, we will explore institutional repositories including the CGIAR, FAO Document Repository, IFPRI, IFAD, World Bank, and UNFCCC portals, as well as national-level repositories in LMICs such as the Indian Council for Agricultural Research and African Journals Online. Additional targeted sources will include conference proceedings (e.g., UN Food Systems Summit, COP side events), the Campbell Evidence Library, OSF Protocols, and outputs from South-South research consortia like SANDEE and AERC. In parallel, we will identify project-level evaluations from organizations such as OECD DEReC, UNDP ERC (including GEF projects), the Green Climate Fund (GCF), IFAD, PEDRR, Landesa, Stockholm Environment Institute, UN Volunteers, and the United Nations Office for Disaster Risk Reduction (UNDRR) for the last five years. To further enhance retrieval, we will use machine learning-assisted searches in OpenAlex via EPPI-Reviewer. The studies identified through the peer-reviewed and grey literature searches will form the training dataset, which will be matched to citation records in OpenAlex to create a citation network that supports the identification of additional relevant literature. A list of benchmark studies will be identified to test the search and screening tools. The search and screening process will be reported following the PRISMA guidelines. https://www.globalindexmedicus.net/biblioteca/lilacs/ CGIAR Background and Rationale | Page 10 of 24 Search Terms and Strings A preliminary set of keywords will be defined based on the PICO components, following logic model mapping and existing systematic reviews. The detailed search strategy (annexure1) Core keywords: • “climate information services smallholder farmers LMIC” • “early warning systems agriculture developing countries” • “agro-advisories climate resilience” • “weather forecast decision-making smallholder agriculture” • “cost-effectiveness climate services rural livelihoods” • “climate-smart agriculture adaptation information access” Search strategy approach: • Boolean operators (AND/OR), truncations (e.g., farm*), and proximity operators (e.g., NEAR/3 or ADJ3) will be used to build complex search strings tailored to each database. • Synonyms and regional variations (e.g., “global south”, “developing countries”, “low- income countries”) will be included to capture diverse terminologies. • Search strings will combine population terms (e.g., “smallholder farmers”, “rural producers”), intervention terms (e.g., “climate information”, “agro-advisory”, “weather forecast”), outcome terms (e.g., “resilience”, “adaptation”, “productivity”), and economic evaluation terms (e.g., “cost-effectiveness”, “economic evaluation”, “impact assessment”). Language and Date Limits The review will prioritize English-language studies ONLY. Studies published post-2000 will be included to ensure relevance to current policy environments. Screening and Inclusion Criteria A standardized screening tool will be developed and piloted to ensure consistency in applying the eligibility criteria based on the PICOS elements (Population, Intervention, Comparator, Outcome, and Study Design). We will conduct a pilot screening of 40–50 studies as a team to test and refine the screening tool and to ensure shared understanding of the eligibility criteria. Title and abstract screening will be conducted independently by two reviewers for a sub-set of records. To further enhance the efficiency of the title and abstract screening process, we will employ, machine learning-assisted priority screening in EPPI- Reviewer. This feature analyzes patterns in records that have already been screened and predicts which unscreened records are most likely to be relevant. These records are placed Background and Rationale | Page 11 of 24 CGIAR at the top of the screening queue, enabling reviewers to focus on studies that are more likely to meet the inclusion criteria earlier in the process. While priority screening facilitates the ranking of likely relevant studies, all records will still be screened to ensure comprehensive coverage. At this stage, we will implement single screening, in which one reviewer screens each record after the machine learning model has prioritized it. This approach improves efficiency while maintaining thoroughness and ensuring that all potentially relevant studies are reviewed. Articles flagged as potentially relevant will be advanced to full-text review. During the full-text screening phase, two teams of reviewers will independently assess each manuscript for eligibility. Discrepancies will be resolved through discussion and consensus. The primary reasons for exclusion at this stage will be documented systematically to ensure transparency. Data Extraction and Synthesis Screening and Data Extraction/ Coding A standardized screening tool will be developed and piloted to ensure consistency in applying the eligibility criteria based on the PICOS elements (Population, Intervention, Comparator, Outcome, and Study Design). Title and abstract screening will be conducted independently by two reviewers. To enhance efficiency and prioritize relevant studies, machine learning-assisted screening will be employed at this stage. Articles flagged as potentially relevant will be advanced to full-text review. During the full-text screening phase, two teams of reviewers will independently assess each manuscript for eligibility. Discrepancies will be resolved through discussion and consensus. The primary reasons for exclusion at this stage will be documented systematically to ensure transparency. For all included studies, data will be extracted using a structured coding framework to capture key information on study context, design, intervention characteristics, outcome measures, and findings related to effectiveness and cost-effectiveness. Where available, data will be disaggregated by gender and socio-economic groups to assess differential impacts and equity considerations. Double coding will be implemented on a subset of the included studies to ensure consistency and reliability of data extraction. Any discrepancies will be discussed and resolved collaboratively among the review team. In the next phase, a narrative synthesis will be conducted to examine the effectiveness and cost-effectiveness of the included studies. Critical Appraisal and Risk of Bias All included studies will be assessed using a pre-validated tool drafted by the Campbell Collaboration team. This critical appraisal tool is based on a review of existing critical appraisal instruments from SURE (Sure, 2015), CASP (Buccheri & Sharifi, 2017) and Joanna Briggs Institute (Munn et al., 2023). We will appraise systematic review using AMSTAR, while economic evaluations will be assessed using a checklist adapted from Drummond et al. (2011) and the CHEERS reporting standards. CGIAR Background and Rationale | Page 12 of 24 We will use tools tailored to effectiveness reviews: Study Designs Description Key Study design Use the study design coding High: Experimental Medium: Non-experimental Low: Before versus after Intervention Is the intervention clearly named and described, including all relevant components. See examples below. High: full and clear description, so that the main components and how they are delivered are clear Medium: Partial description Low: Little or no description Outcomes Are the outcomes clearly defined? Where appropriate do they use an existing, validated measurement tool? See examples below. High: full and clear definition using validated instruments where available (a researcher wishing to use these outcomes would have sufficient information to do so) Medium: Partial definition. May use validated instruments but without sufficient references to source. Low: Little or no definition Sample size (power calculation) Do the authors report a power calculation as the basis for sample size? High: Power calculation report and sample size meets necessary sample size Medium: Power calculation mentioned and sample size meets necessary sample size Low: No mention of power calculation. Attrition Reported for endline and longest follow up. Calculate overall attrition and differential attrition (see example below). It is often necessary to calculate from table of results. If sample size varies by outcome calculate for highest attrition. High: Attrition within IES conservative standard Medium: Attrition within IE liberal standard Low: Attrition outside IES liberal standard Overall The overall score uses the weakest link in the chain principle i.e. is the lowest score on any item High: High on all items Medium: No lower than medium on any item Low: At least one low Note IES Attrition Brief https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_brief_attrition_080715.pdf https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_brief_attrition_080715.pdf Project Management and Team Structure | Page 13 of 24 CGIAR Project Management and Team Structure • Suchi Malhotra, Director Research, Campbell South Asia, India, Principal Investigator • Ashrita Saran, Director, Evaluation and Evidence Synthesis, Global Development Network, India, Co-principal Investigator • Ashima Mohan, Director, Strategic Communication and Policy Advocacy, Research support • Varsha Nair, Research Associate, Campbell South Asia, India, Research support Stakeholder engagement will occur at two key stages. During the design phase, the advisory group will help refine research questions, prioritize policy-relevant outcomes, and validate the logic model used to guide the review strategy. During dissemination, they will support the development of region-specific policy briefs, co-host webinars with local institutions, and promote findings at platforms, as well as through practitioner networks and open-access repositories. Stakeholder Engagement Plan This review recognizes that evidence uptake and use depend on early and sustained engagement with key stakeholder groups. To ensure the review reflects current thinking, draws on a broad range of evidence, and maximizes relevance for the development community, we will establish a Review Advisory Group comprising external researchers and practitioners. Advisory Group Members: • Ranjitha Puskur – Lead, Evidence Module, CGIAR GENDER Impact Platform; Principal Scientist – Gender and Livelihoods • Hugh Sharma Waddington – Assistant Professor, London School of Hygiene and Tropical Medicine (LSHTM); works on climate and health topics with Pathfinder, DESTINY, HEARTH, and HPRU3 • Dr. Gavin Stewart – Reader in Evidence Synthesis, Newcastle University; internationally recognized expert in meta-analysis and evidence synthesis across health, environment, and policy; contributor to Campbell, Cochrane, and Collaboration for Environmental Evidence • Tarun Khanna – Assistant Professor at the School of Public Policy and Global Affairs (SPPGA), University of British Columbia (UBC) • Peninah Murage – Assistant Prof., Environmental Epidemiology, LSHTM • Aniruddha Ghosh – Senior Scientist, Alliance of Bioversity International and CIAT • Carlos Navarro – Research Specialist, Central America Climate Action Focal Point, Alliance of Bioversity International and CIAT • Ajit Govind – Senior Climate Scientist and Systems Modeler, ICARDA CGIAR Project Management and Team Structure | Page 14 of 24 • Issa Ouedraogo – Senior Scientist, Country Representative for Senegal, Alliance of Bioversity International and CIAT • Jagdish Jaba – Scientist & Head, Entomology, ICRISAT • Emmanuel Zapata – Environmental scientist, Alliance of Bioversity International and CIAT • Jacob Joseph – Agrometeorologist, ILRI • Omonlola Worou Nadine – Scientific coordinator, AICCRA in Senegal, ILRI • Peerzadi Rumana Hossain – Climate Change Research Scientist, WorldFish CGIAR Work Plan and Timeline | Page 0 of 24 Work Plan and Timeline Aug-25 Sep-25 Oct-25 Nov-25 Dec-25 Jan-26 Feb-26 Mar-26 Apr-26 May-26 Jun-26 Jul-26 Finalise review protocol and registration Full database search and stakeholder workshop Screening (title/abstract & full text) Data extraction and appraisal Synthesis Stakeholder briefs Final review draft and journal write-ups Dissemination planning and COP30 coord CGIAR References | Page 0 of 24 References • Alidu, A.-F., Man, N., Ramli, N. N., Mohd Haris, N. B., & Alhassan, A. (2022). Smallholder farmers’ access to climate information and climate smart adaptation practices in the Northern Region of Ghana. Heliyon, 8(5), e09513. https://doi.org/10.1016/j.heliyon.2022.e09513 • Bizo, I. M., Traore, B., Sidibé, A., & Soulé, M. (2023). Effectiveness of climate information services: An evaluation of the accuracy and socio-economic benefits for smallholder farmers in Niger and Mali. Climate Services, 30, 100373. https://doi.org/10.1016/j.cliser.2023.100373 • Bui, L. V., & Vu, T. B. (2020). A systematic review of Climate‐Smart Agriculture (CSA) practices and its potential for adoption in the implementation of Nong thon moi in the 2021‐2030 Strategy. CGSpace: A Repository of Agricultural Research Outputs, 154– 166. • Djido, A., Zougmoré, R. B., Houessionon, P., Ouédraogo, M., Ouédraogo, I., & Diouf, N. S. (2021). To what extent do weather and climate information services drive the adoption of climate-smart agriculture practices in Ghana? Climate Risk Management, 32, 100309. https://doi.org/10.1016/j.crm.2021.100309 • FAO. (2015). The state of food and agriculture: Social protection and agriculture— Breaking the cycle of rural poverty. Food and Agriculture Organization of the United Nations. • FAO, IFAD, UNICEF, WFP, & WHO. (2020). The state of food security and nutrition in the world 2020: Transforming food systems for affordable healthy diets. https://doi.org/10.4060/ca9692en • IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. • Lu, X., Yu, H., Ying, M., Zhao, B., Zhang, S., Lin, L., Bai, L., & Wan, R. (2021). Western North Pacific tropical cyclone database created by the China Meteorological Administration. Advances in Atmospheric Sciences, 38, 690–699. https://doi.org/10.1007/s00376-020-0211-7 • Lopez-Avila, D., Husain, S., Bhatia, R., Nath, M., & Vinaygyam, R. (2017). Agricultural innovation: An evidence gap map (3ie Evidence Gap Map Report 12). International Initiative for Impact Evaluation (3ie). • Madhuri. (2022). Growing smarter: A systematic review of the impact and challenges of climate information on farming. Natural Hazards, 111(1), 599–624. https://doi.org/10.1007/s11069-021-05169-3 • Ngigi, M. W., & Muange, E. N. (2022). Access to climate information services and climate-smart agriculture in Kenya: A gender-based analysis. Climate and Development. Advance online publication. https://doi.org/10.1007/s10584-022-03432- 6 • Ngigi, M. W., & Muange, E. N. (2022). Access to climate information services and climate-smart agriculture in Kenya: A gender-based analysis. Climatic Change, 174(3). https://doi.org/10.1007/s10584-022-03445-5 https://doi.org/10.1016/j.heliyon.2022.e09513 https://doi.org/10.1016/j.cliser.2023.100373 https://doi.org/10.1016/j.crm.2021.100309 https://doi.org/10.1007/s00376-020-0211-7 https://doi.org/10.1007/s11069-021-05169-3 https://doi.org/10.1007/s10584-022-03432-6 https://doi.org/10.1007/s10584-022-03432-6 References | Page 1 of 24 CGIAR • Rosenstock, T. S., Lamanna, C., Chesterman, S., Bell, P., Arslan, A., Richards, M., Rioux, J., Akinleye, A., Champalle, C., & Cheng, Z. (2016). The scientific basis of climate-smart agriculture: A systematic review protocol (CCAFS Working Paper No. 138). CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). • Waddington, H., Snilstveit, B., Hombrados, J., Vojtkova, M., Phillips, D., Davies, P., & White, H. (2014). Farmer field schools for improving farming practices and farmer outcomes: A systematic review. Campbell Systematic Reviews, 10(1), i–335. • Wheeler, T., & von Braun, J. (2013). Climate change impacts on global food security. Science, 341(6145), 508–513. https://doi.org/10.1126/science.1239402 • UNDP. (2017). Strengthening climate information and early warning systems for climate resilient development and adaptation to climate change in Africa. United Nations Development Programme. https://doi.org/10.1126/science.1239402 CGIAR Annex | Page 2 of 24 Annex Annexure 1 – Search strategy Search Strategy Drafting Project Name: Effectiveness (including cost-effectiveness) of climate information systems, early warnings, and agro-advisories in reducing climate change-related risks for smallholder producers Database Platform Scopus Date searched November 23, 2025 1 smallholder TITLE-ABS(("smallhold*" OR "small hold*" OR "small farm*" OR "microfarm*" OR "micro-farm*" OR "family-managed farm*" OR "family-run farm*" OR "family-owned farm*" OR "family farm*" OR "pastoral*" OR "agropastoral" OR "agro-pastoral" OR "ejido" OR "silvopastoral") OR (("small-scale" OR "smallscale" OR "low-income" OR "poor-income" OR "subsistence" OR "semi-subsistence" OR "resource-poor" OR "resource-limited" OR "small-size*" OR "low- income" OR "peasant*" OR "village*" OR "household*") W/3 ("farm*" OR "agriculture" OR "producer*" OR "grower*" OR "agronomy" OR "husbandry" OR "aquaculture" OR "floriculture" OR "horticulture"))) OR AUTHKEY(("smallhold*" OR "small hold*" OR "small farm*" OR "microfarm*" OR "micro-farm*" OR "family-managed farm*" OR "family-run farm*" OR "family-owned farm*" OR "family farm*" OR "pastoral*" OR "agropastoral" OR "agro-pastoral" OR "ejido" OR "silvopastoral") OR (("small-scale" OR "smallscale" OR "low-income" OR "poor-income" OR "subsistence" OR "semi-subsistence" OR "resource-poor" OR "resource-limited" OR "small-size*" OR "low- income" OR "peasant*" OR "village*" OR "household*") W/3 ("farm*" OR "agriculture" OR "producer*" OR "grower*" OR "agronomy" OR "husbandry" OR "aquaculture" OR "floriculture" OR "horticulture"))) 109,337 2 Agro-advisory and early warning systems TITLE-ABS((((agricultur* OR farm* OR crop* OR weather OR meteorolog* OR climat* OR drought* OR heat OR heatwave* OR rain* OR precipitation OR temperature* OR "forest fire*" OR wildfire* OR landslide* OR flood* OR hurricane* OR monsoon* OR "severe storm*" OR "natural disaster*" OR hazard* OR ((ocean OR sea OR coastal OR surface OR subsurface OR tidal) W/1 current*)) OR ((environmental W/1 (extreme* OR disaster*)))) AND (forecast* OR predict* OR outlook* OR trajector* OR alert* OR notif* OR advis* OR warn* OR "information delivery" OR "disaster preparedness information" OR "information system*" OR "information service*")) OR "early warning system*" OR "agro-advisor*" OR "agri-advisor*" OR agrometeorolog* OR "climate information" OR "weather information" OR "climate-informed") OR AUTHKEY((((agricultur* OR farm* OR crop* OR weather OR meteorolog* OR climat* OR drought* OR heat OR heatwave* OR rain* OR precipitation OR temperature* OR "forest fire*" OR wildfire* OR landslide* OR flood* OR hurricane* OR monsoon* OR "severe storm*" OR "natural disaster*" OR hazard* OR ((ocean OR sea OR coastal OR surface OR subsurface OR tidal) W/1 current*)) OR ((environmental W/1 (extreme* OR disaster*)))) AND (forecast* OR predict* OR outlook* OR trajector* OR alert* OR notif* OR advis* OR warn* OR "information delivery" OR "disaster preparedness information" OR "information system*" OR "information service*")) OR "early warning system*" OR "agro-advisor*" OR "agri-advisor*" OR agrometeorolog* OR "climate information" OR "weather information" OR "climate-informed") 1,360,805 Annex | Page 3 of 24 CGIAR 3 LMICs TITLE-ABS-KEY(afghanistan or albania or algeria or "american samoa" or angola or "antigua and barbuda" or antigua or barbuda or argentina or armenia or armenian or aruba or azerbaijan or bahrain or bangladesh or barbados or belarus or byelarus or belorussia or byelorussian or belize or "british honduras" or benin or dahomey or bhutan or bolivia or "bosnia and herzegovina" or bosnia or herzegovina or botswana or bechuanaland or brazil or brasil or bulgaria or "burkina faso" or "burkina fasso" or "upper volta" or burundi or urundi or "cabo verde" or "cape verde" or cambodia or kampuchea or "khmer republic" or cameroon or cameron or cameroun or "central african republic" or "ubangi shari" or chad or chile or china or colombia or comoros or "comoro islands" or "iles comores" or mayotte or "democratic republic of the congo" or "democratic republic congo" or congo or zaire or "costa rica" or "cote d'ivoire" or "cote d' ivoire" or "cote divoire" or "cote d ivoire" or "ivory coast" or croatia or cuba or cyprus or czech or czechoslovakia or djibouti or "french somaliland" or dominica or dominican or ecuador or egypt or "united arab republic" or "el salvador" or "equatorial guinea" or "spanish guinea" or eritrea or estonia or eswatini or swaziland or ethiopia or fiji or gabon or gabonese or gambia or "georgia republic" or georgian or ghana or "gold coast" or gibraltar or greece or grenada or guam or guatemala or guinea or "guinea bissau" or guyana or "british guiana" or haiti or hispaniola or honduras or hungary or india or indonesia or timor or iran or iraq or "isle of man" or jamaica or jordan or kazakhstan or kazakh or kenya or korea or kosovo or kyrgyzstan or kirghizia or kirgizstan or "kyrgyz republic" or kirghiz or laos or "lao pdr" or "lao people's democratic republic" or latvia or lebanon or "lebanese republic" or lesotho or basutoland or liberia or libya or lithuania or macau or macao or "macedonia republic" or macedonia or madagascar or "malagasy republic" or malawi or nyasaland or malaysia or "malay federation" or "malaya federation" or maldives or "indian ocean islands" or "indian ocean" or mali or malta or micronesia or "federated states of micronesia" or kiribati or "marshall islands" or nauru or "northern mariana islands" or palau or tuvalu or mauritania or mauritius or mexico or moldova or moldovian or mongolia or montenegro or morocco or ifni or mozambique or "portuguese east africa" or myanmar or burma or namibia or nepal or "netherlands antilles" or nicaragua or niger or nigeria or oman or muscat or pakistan or panama or "papua new guinea" or "new guinea" or paraguay or peru or philippines or philipines or phillipines or phillippines or poland or "polish people's republic" or portugal or "portuguese republic" or "puerto rico" or romania or russia or "russian federation" or ussr or "soviet union" or "union of soviet socialist republics" or rwanda or ruanda or samoa or "pacific islands" or polynesia or "samoan islands" or "navigator island" or "navigator islands" or "sao tome and principe" or "saudi arabia" or senegal or serbia or seychelles or "sierra leone" or slovakia or "slovak republic" or slovenia or melanesia or "solomon island" or "solomon islands" or "norfolk island" or "norfolk islands" or somalia or "south africa" or "south sudan" or "sri lanka" or ceylon or "saint kitts and nevis" or "st. kitts and nevis" or "saint lucia" or "st. lucia" or "saint vincent and the grenadines" or "saint vincent" or "st. vincent" or grenadines or sudan or suriname or surinam or "dutch guiana" or "netherlands guiana" or syria or "syrian arab republic" or tajikistan or tadjikistan or tadzhikistan or tadzhik or tanzania or tanganyika or thailand or siam or "timor leste" or "east timor" or togo or "togolese republic" or tonga or "trinidad and tobago" or trinidad or tobago or tunisia or turkey or turkmenistan or turkmen or uganda or ukraine or uruguay or uzbekistan or uzbek or vanuatu or "new hebrides" or venezuela or vietnam or "viet nam" or "middle east" or "west bank" or gaza or palestine or yemen or yugoslavia or zambia or zimbabwe or "northern rhodesia" or "global south" or "africa south of the sahara" or "sub-saharan africa" or "subsaharan africa" or "africa, central" or "central africa" or "africa, northern" or "north africa" or "northern africa" or magreb or maghrib or sahara or "africa, southern" or "southern africa" or "africa, eastern" or "east africa" or "eastern 11,040,265 CGIAR Annex | Page 4 of 24 africa" or "africa, western" or "west africa" or "western africa" or "west indies" or "indian ocean islands" or caribbean or "central america" or "latin america" or "south and central america" or "south america" or "asia, central" or "central asia" or "asia, northern" or "north asia" or "northern asia" or "asia, southeastern" or "southeastern asia" or "south eastern asia" or "southeast asia" or "south east asia" or "asia, western" or "western asia" or "europe, eastern" or "east europe" or "eastern europe" or "developing country" or "developing countries" or "developing nation*" or "developing population*" or "developing world" or "less developed countr*" or "less developed nation*" or "less developed population*" or "less developed world" or "lesser developed countr*" or "lesser developed nation*" or "lesser developed population*" or "lesser developed world" or "under developed countr*" or "under developed nation*" or "under developed population*" or "under developed world" or "underdeveloped countr*" or "underdeveloped nation*" or "underdeveloped population*" or "underdeveloped world" or "middle income countr*" or "middle income nation*" or "middle income population*" or "low income countr*" or "low income nation*" or "low income population*" or "lower income countr*" or "lower income nation*" or "lower income population*" or "underserved countr*" or "underserved nation*" or "underserved population*" or "underserved world" or "under served countr*" or "under served nation*" or "under served population*" or "under served world" or "deprived countr*" or "deprived nation*" or "deprived population*" or "deprived world" or "poor countr*" or "poor nation*" or "poor population*" or "poor world" or "poorer countr*" or "poorer nation*" or "poorer population*" or "poorer world" or "developing econom*" or "less developed econom*" or "lesser developed econom*" or "under developed econom*" or "underdeveloped econom*" or "middle income econom*" or "low income econom*" or "lower income econom*" or "low gdp" or "low gnp" or "low gross domestic" or "low gross national" or "lower gdp" or "lower gnp" or "lower gross domestic" or "lower gross national" or lmic or lmics or "third world" or "lami countr*" or "transitional countr*" or "emerging econom*" or "emerging nation*" OR afghan or afghani or albanian or algerian or "american samoan" or angolan or antiguan or barbudan or argentine or argentinian or argentinean or armenian or aruban or azerbaijani or bahraini or bangladeshi or bangalees or bajan or belarusian or byelorussian or belizean or beninese or bhutanese or bolivian or bosnian or botswana or batswana or brazilian or brasilian or bulgarian or burkinabe or burkinese or burundian or "cape verdean*" or "cabo verdean*" or cambodian or khmer or cameroonian or "central african*" or chadian or chilean or chinese or colombian or comorian or congolese or "costa rican*" or ivorian or croatian or cuban or cypriot or czech or djiboutian or ecuadorian or egyptian or salvadoran or "equatorial guinean*" or equatoguinean or eritrean or estonian or swazi or swati or ethiopian or fijian or gabonaise or gambian or georgian or ghanaian or gibraltarian or greek or grenadian or guamanian or guatemalan or guinean or "bissau guinean" or guyanese or haitian or honduran or hungarian or indian or indonesian or iranian or iraqian or iraqi or manx or jamaican or jordanian or kazakhstani or kenyan or kirabati or kirabatian or "north korean*" or korean or kosovar or kosovan or kyrgyz or lao or laotian or latvian or lebanese or lesothan or lesothonian or mosotho or basotho or liberian or libyan or lithuanian or macanese or macedonian or malagas or madagascan or malawian or malaysian or maldivian or malian or maltese or marshallese or mauritanian or mauritian or mexican or micronesian or moldovan or mongolian or mongol or moroccan or mozambican or burmese or myanma or namibian or nauruan or nepali or nepalese or "netherlands antillean*" or nicaraguan or nigerien or nigerian or "northern mariana islander*" or mariana or omani or pakistani or palauan or panamanian or "papua new guinean*" or paraguayan or peruvian or philippine or philipine or phillipine or phillippine or filipino or filipina or polish or pole or poles or portuguese or "puerto rican*" or romanian or russian or "soviet people" or "soviet population" or rwandan or Annex | Page 5 of 24 CGIAR rwandese or ruandan or ruandese or samoan or "sao tomean" or santomean or "saudi arabian*" or saudi or senegalese or serbian or montenegrin or seychellois or seychelloise or "sierra leonean*" or slovak or slovene or "solomon islander*" or somali or "south african*" or "south sudanese" or "sri lankan*" or ceylonese or kittitian or nevisian or "saint lucian*" or vincentian or sudanese or surinamese or syrian or tajik or tajikistani or tanzanian or tanganyikan or thai or timorese or togolese or tongan or trinidadian or tobagonian or tunisian or turk or turkish or turkmen or tuvaluan or ugandan or ukrainian or uruguayan or uzbek or vanuatu or venezuelan or vietnamese or yemeni or yemenite or yemenese or yugoslav or yugoslavian or zambian or zimbabwean or african or asian or "pacific islander*" or "latin american*" or "central american*" or "south american*" or caribbean or "west indian*" or "iberoamerican*" or "middle eastern*") 4 RCTs and QEDs TITLE-ABS-KEY(((experiment*) W/2 (design or study or research or evaluation or evidence or vary or varies or variation)) or ((random or randomi?ed or randomly) W/2 (trial or assign* or treatment or control* or allocat* or experiment* or vary or varies or variation or choose or chose*)) OR ((impact* or effect*) W/2 (evaluat* or assess or assessing or assessment or analyze or analyse or analyzing or analysing or analysis or analytical or estimate or estimating or estimation or cause or causal)) OR "program* evaluation" or "project evaluation" or "evaluation research" or "natural experiment*" or "program* effectiveness" or "outcome assessment" or "evaluation study" or "field experiment" OR ((match*) W/2 (propensity or coarsened or covariate or neighbor)) or "propensity score" or "difference* in difference*" or "difference-in-difference*" or "differences-in-difference*" or "double difference*" or quasi- experiment* or "quasi experiment*" or (estimator and evaluat*) or "instrumental variable*" or ((IV) W/2 (estimation or approach)) or ((Heckman) W/3 (model* or approach*)) or ((two-stage or "two stage") W/3 (control* or function* or "least squares")) or "regression discontinuity" or "synthetic control*" or "time series" or counterfactual or "segment* regression" or (non W/2 participant*) or ((control or comparison) W/2 (group* or condition* or area* or village* or household* or intervention* OR farm*)) or ((panel) W/2 (data or household* or model*)) or ((exploit* or "tak* advantage") W/3 (variation* or variety or exogen* or heterogen*)) or ((econometric) W/2 (model* or adjust*)) or ((select*) W/2 (bias* or self))) 7,836,593 5 Economic evaluations TITLE-ABS(economic* OR cost OR costs OR costly OR costing OR price OR prices OR pricing OR expenditure OR expenditures OR expense OR expenses OR financial OR finance OR finances OR financed OR ((cost*) W/2 (effective* OR utilit* OR benefit* OR minimi* OR analy* OR outcome OR outcomes)) OR ((value) W/2 (money OR monetary))) OR AUTHKEY(economic* OR cost OR costs OR costly OR costing OR price OR prices OR pricing OR expenditure OR expenditures OR expense OR expenses OR financial OR finance OR finances OR financed OR ((cost*) W/2 (effective* OR utilit* OR benefit* OR minimi* OR analy* OR outcome OR outcomes)) OR ((value) W/2 (money OR monetary))) 7,112,348 6 #4 OR #5 14,107,178 7 #1 AND #2 AND #3 AND #6 2626 8 Publication years 2000-present 2536 9 Language: English 2446 CGIAR Annex | Page 6 of 24 CGIAR is a global research partnership for a food-secure future. CGIAR science is dedicated to transforming food, land, and water systems in a climate crisis. Its research is carried out by 13 CGIAR Centers/Alliances in close collaboration with hundreds of partners, including national and regional research institutes, civil society organizations, academia, development organizations and the private sector. www.cgiar.org We would like to thank all funders who support this research through their contributions to the CGIAR Trust Fund: www.cgiar.org/funders. To learn more about the Climate Action Program, please visit this webpage. To learn more about this and other Science Programs and Accelerators in the CGIAR Research Portfolio 2025–2030, please visit www.cgiar.org/cgiar-research-porfolio-2025-2030/ Copyright: © 2025. ILRI This publication is licensed for use under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view this license, visit https://creativecommons.org/licenses/by/4.0. | | | http://www.cgiar.org/funders https://www.cgiar.org/cgiar-research-portfolio-2025-2030/climate-action https://creativecommons.org/licenses/by/4.0. https://x.com/CGIAR_SAAF https://www.facebook.com/CGIARAnimalAqua https://www.linkedin.com/showcase/cgiar-sustainable-animal-and-aquatic-food/about https://www.youtube.com/channel/UCYuSEwWKAsoNwg6MJEI-qeA