A scoping review of literature on adoption and impact of climate smart agricultural technologies by smallholder farmers in Africa

This scoping review aims to map the literature on empirical evidence on CSA technology adoption among smallholder farmers, to identify critical gaps, and propose future research directions, with a focus on outcomes related to productivity, food security, and resilience. Its unique contribution is in methodically uncovering understudied behavioral and gender gaps in Sub-Saharan Africa, demonstrating oversights that undermine equitable and transformative CSA technologies and impact. Using the PRISMA-ScR framework, the review analyzes 54 peer-reviewed empirical studies (published 2013–2025) selected from an initial 598 articles searched in June 2025 across multiple databases. Inclusion criteria prioritized quantitative and mixed-methods studies employing inferential statistics, excluding qualitative-only works and gray literature. Key findings show binary adoption as the dominant outcome variable (52% of studies), followed by adoption intensity (26%) and decision-making factors (11%). Geographically, research clusters in Ethiopia, Kenya, and Ghana, with scant attention to countries like Tanzania, DRC, Senegal, and Mali. Theoretically, adoption and diffusion models prevail (57.5%), while behavioral (22.5%) and economic frameworks (10%) are underrepresented. Contextually, studies emphasize crop-based practices, largely overlooking livestock systems, gender dynamics, agroecological diversity, and dis-adoption processes. Behavioral factors—such as risk perceptions and environmental attitudes—and gender intersectionality, including intra-household power imbalances, remain underexplored. Methodologically, cross-sectional designs dominate, constraining causal inference and generalizability. Climate change intensifies vulnerabilities for SSA smallholder farmers, where rainfed systems heighten exposure to erratic weather, yield losses, and threats to food systems and livelihoods. CSA emerges as a vital strategy to bolster productivity, resilience, and sustainability, aligning with SDG 13 on climate action. By addressing these evidentiary gaps, the review advocates for inclusive, longitudinal research that links adoption to tangible outcomes like yield stability and income resilience, ultimately informing policies to realize CSA's potential for equitable smallholder farming amid escalating climate extremes.

1 Introduction and background

This paper undertakes a scoping review of the empirical evidence on the adoption of CSA practices, technologies, and innovations among smallholder farmers, with a particular focus on establishing evidence on their utility on productivity, resilience, and food security outcomes. It seeks to establish whether the research reflects the promise of CSA technology adoption for smallholder farmers, the gaps in research, and areas of further research to support evidence-based policy design and implementation strategies that respond to on the ground realities of smallholder farmers. The study contributes to the growing body of literature that seeks to understand how adoption of CSA technologies can be effectively implemented to support sustainable livelihoods for smallholder farmers in a changing climate.

A substantial and expanding literature underscores the transformative potential of CSA adoption, positioning it as a critical pathway for smallholder farmers to confront climate-induced vulnerabilities while enhancing agricultural outcomes. Empirical studies consistently demonstrate that CSA technologies—ranging from drought-resistant crop varieties and conservation tillage to integrated soil fertility management—can boost yields by 20%−50% under variable climatic conditions, strengthen resilience against shocks such as droughts and floods, and contribute to household food security by stabilizing production and incomes (Ewulo et al., 2025; Makate et al., 2019; Khatri-Chhetri et al., 2017; Issahaku and Abdulai, 2020; Abdureman Omer and Hassen, 2020). For instance, research across sub-Saharan Africa highlights how CSA practices like agroforestry and improved water harvesting not only mitigate greenhouse gas emissions but also improve adaptive capacity, with adoption rates linked to significant reductions in crop failure risks and enhanced nutritional diversity (Nyadzi et al., 2020; Amadu et al., 2020; Ogisi and Begho, 2023). This body of work aligns with broader findings that technology adoption in rainfed systems can promote sustainable resource management, counteracting soil degradation and biodiversity loss amid rising temperatures and erratic rainfall (Lipper et al., 2014; Tesfaye et al., 2019). Yet, despite these promising results, the literature reveals persistent barriers to widespread uptake, including limited access to extension services, credit constraints, and knowledge gaps, which underscore the need for context-specific interventions (Ouédraogo et al., 2019; Neufeldt et al., 2015; Ragasa et al., 2019; Mmapatla et al., 2021). By synthesizing this evidence, the review illuminates how CSA adoption can fulfill its triple-win objectives, while identifying evidentiary shortfalls in long-term impacts and gender-disaggregated outcomes to inform scalable, equitable strategies (Mutenje et al., 2019).

CSA technology adoption literature considers climate change to be a complex challenge of the 21st century, with profound implications for agriculture and food systems globally. Smallholder farmers form the backbone of rural economies in the Global South but are among the most vulnerable to climate variability and extremes due to limited adaptive capacity and exposure to climatic risks (Ricciardi et al., 2018; Teklu et al., 2022, 2023; Petros et al., 2024). Considering most of Africa's agricultural systems are dependent on rainfed agriculture, the unpredictable rainfall, soaring temperatures, and relentless droughts are not just environmental concerns: they are economic saboteurs, jeopardizing livelihoods, destabilizing food security, and demanding urgent action (Ewulo et al., 2025). Technology and innovation can be a gamechanger in supporting smallholder farmers to adapt to these climate change realities and can play a crucial role in addressing the challenges facing agriculture currently and in the future. Studies have indicated that adopting improved agricultural technologies can significantly enhance productivity, improve livelihoods, and promote sustainable resource management (Ewulo et al., 2025). It is from this view the literature on climate-smart agriculture (CSA) positions it as a strategic framework, integrating climate adaptation, mitigation, and productivity goals to promote sustainable agricultural transformation. It is an approach for transforming and reorienting agricultural systems to support food security under the new realities of climate change (Lipper et al., 2014).

As countries seek to implement the Paris Agreement and the Sustainable Development Goals (SDGs), Climate-Smart Agricultural (CSA) practices, technologies, and innovations are progressively being integrated into global, regional, and national policy frameworks as a strategic approach to fostering resilient and equitable food systems. CSA brings together established sustainable agricultural practices with enabling policy instruments and institutional mechanisms to address the multifaceted challenges posed by climate change. This potentially makes it an efficient model to address multiple challenges faced by agriculture and food systems simultaneously and holistically, which helps avoid counterproductive or conflicting policies, legislation, and/or financing (Finizola e Silva et al., 2024).

1.1 Global

Globally, CSA is conceptualized as an integrated approach to managing agricultural landscapes to achieve three interrelated objectives: sustainably increasing productivity and incomes, enhancing resilience to climate variability and shocks, and reducing or removing greenhouse gas emissions (FAO, 2010). It is not a single set of technologies, but a context-specific portfolio of practices and institutional innovations tailored to local biophysical and socio-economic conditions. CSA is advanced through international platforms such as the Global Alliance for Climate-Smart Agriculture (GACSA) and policy frameworks including the Koronivia Joint Work on Agriculture under the United Nations Framework Convention on Climate Change (UNFCCC) and the SDGs particularly SDG 13 on climate action.

The changing climate has multiple effects on smallholder farmers. It disrupts food markets, posing population-wide risks to food supply (Lipper et al., 2014) and puts the livelihoods of smallholder farming communities at risk, constraining their access to essential basic services such as education, healthcare, and decent housing. Without transformative adaptation strategies, climate change is projected to reduce global crop yields by 3%−12% by mid-century and 11%−25% by the end of the century under an intensified warming scenario (Kabato et al., 2025). To counter this eventuality, climate smart agriculture has been promoted as a potential pathway to avert a climate change crisis on smallholder farmers. Empirical evidence at the global level indicates that CSA technology adoption contributes positively to smallholder agricultural outcomes. It simultaneously addresses the challenges of climate change while supporting economic growth in the agricultural sector (Gikonyo et al., 2022; Bezner Kerr et al., 2022; FAO, 2017). Studies also indicate that CSA practices—such as agroforestry, improved seeds, conservation agriculture, and integrated soil fertility management—can improve yields by 20%−70% depending on context, enhance farmers' adaptive capacity, and promote food and nutritional security. When implemented together, various CSA practices can significantly improve food security in vulnerable regions by increasing crop yields, strengthening household resilience, and aiding communities in adapting to climate change impacts (Kabato et al., 2025).

However, the effectiveness of CSA varies across agroecological zones and farming systems, and benefits are often moderated by access to finance, extension services, markets, and land rights. Studies also establish some key drivers of CSA adoption globally that include access to technical information, institutional support, incentives for risk reduction (e.g., weather insurance), and strong social networks (Bayala et al., 2021; Zougmoré et al., 2014). Yet several barriers persist in uptake such as the high upfront costs of technologies, limited awareness, gender inequalities, insecure tenure, and institutional fragmentation (Rosenstock et al., 2016; Thornton et al., 2018). Moreover, gender dimensions remain underexplored in many settings, despite growing recognition that gender-responsive CSA interventions—such as targeted extension services or inclusive farmer organizations—can enhance both adoption and equity.

Beyond productivity and adaptation, CSA can also be a lever for achieving mitigation co-benefits. Practices such as agroforestry, conservation tillage, and organic soil amendments contribute to carbon sequestration and reduced emissions. These synergies between adaptation and mitigation are increasingly recognized in climate finance mechanisms such as the Green Climate Fund (GCF) and voluntary carbon markets, which are beginning to fund CSA initiatives in low- and middle-income countries.

Another emerging area of global CSA research involves the role of digital agriculture. Technologies such as remote sensing, mobile applications, and AI-based advisory tools are transforming the dissemination and uptake of CSA practices. For instance, apps like PlantVillage Nuru (Penn State University, 2018) and Weather Info for All have been piloted in various African and Asian countries to improve farmers; access to climate forecasts and agronomic advice. While promising, digital tools also raise issues of digital exclusion, particularly among women, the elderly, and marginalized groups.

The global momentum around CSA suggests growing alignment between climate policy and agricultural transformation. However, equitable adoption of CSA technologies requires deliberate efforts to address power asymmetries in access to knowledge, finance, and decision-making. This is especially critical in SSA, where the impacts of climate change are intensifying, and smallholder systems remain structurally marginalized.

1.2 Regional context and relevance

In Sub Sahara Africa, where agriculture accounts for 20%−30% of GDP and employs over 60% of the population, CSA has gained significant traction as a means to address structural vulnerabilities. Policymakers and development practitioners have paid close attention to the adoption of CSA technologies to ensure that as many farmers as possible practice low-emission, climate-resilient agriculture while increasing agricultural productivity (Gikonyo et al., 2022). The African Union (AU; FAO; IFAD; World Bank, 2015) has championed CSA through initiatives such as the Comprehensive Africa Agriculture Development Programme (CAADP), the Malabo Declaration, and the African CSA Alliance, aiming to reach 25 million farmers by 2025. Many countries have mainstreamed CSA into National Adaptation Plans (NAPs) and Agricultural Sector Investment Plans, often supported by development partners and regional organizations.

Empirical studies across SSA demonstrate that CSA adoption improves smallholder productivity, by 30%−60% in some contexts, enhances resilience to droughts and floods, and supports household food security (Diro et al., 2022; Sisay et al., 2023; Odoom et al., 2023; Daudu and Robele, 2025). For instance, in Zambia, conservation agriculture increased maize yields by up to 80% in dry years (Akinyi et al., 2022), while in Ethiopia, land management interventions under CSA enhanced soil fertility and rural incomes (Adimassu et al., 2024; Diro et al., 2022; Sisay et al., 2023). In West Africa, CSA practices such as agroforestry and drought-tolerant crops have reduced seasonal food gaps among smallholder households (The World Bank, 2021). In the livestock sector, the demand for animal protein in Sub-Saharan Africa is rising steadily, driven by rapid population growth, urbanization, and a growing middle class. According to the Malabo Montpellier Panel's (2020) report the region's population is projected to double from 1.3 billion in 2020 to 2.5 billion by 2050, significantly increasing the need for livestock-derived foods such as meat, milk, and eggs. CSA practices such as improved animal breeds, zero-grazing systems, and manure management are being promoted to enhance productivity while reducing greenhouse gas emissions. These interventions not only potentially support adaptation but also contribute to mitigation goals. As the African animal protein market is projected to grow from USD 0.8 billion in 2025 to USD 1.01 billion by 2030 at a CAGR of 4.81% (Mordor Intelligence, 2024), integrating CSA into livestock systems is essential to ensure sustainable growth that aligns with climate and development objectives.

Key enabling conditions in SSA include agroecological suitability, donor and NGO support, government policy coherence, and access to climate information (Adu-Baffour et al., 2023; Nkiaka et al., 2019). Drivers of adoption vary across zones: in humid regions like Uganda and Rwanda, CSA focuses on soil erosion and fertility, whereas in arid areas like the Sahel, emphasis is placed on water harvesting and climate-smart livestock systems (World Bank; CIAT, 2016). Nonetheless, multiple barriers inhibit wider adoption, including financial constraints, limited extension coverage, tenure insecurity, and gender-based inequalities (Maguza-Tembo et al., 2017; Antwi-Agyei and Amanor, 2023). Many CSA practices are labor-intensive, making them less appealing to resource- constrained households. In some countries, elite capture of CSA programs has resulted in uneven benefits.

However, within the SSA region gender is a critical axis of inclusion in SSA's adoption of CSA technologies. Women are key participants in food production but often lack land ownership, decision-making power, and access to training. While adoption of CSA technologies has the potential to empower women, this is only realized when programs are deliberately gender sensitive. Examples of success include participatory varietal selection and women-led home gardens that enhance nutrition and income. However, CSA projects that overlook intra-household dynamics risk exacerbating women's labor burdens or excluding them from benefits. Without inclusive strategies, the region risks entrenching inequalities and misaligning CSA goals with broader development outcomes (Etwire et al., 2013).

1.3 National context—Kenya

Kenya has emerged as a regional leader in CSA policy integration, having developed comprehensive frameworks to support climate-resilient agriculture. The Climate-Smart Agriculture Strategy (2017–2026; Kenya Ministry of Agriculture, Livestock and Fisheries, 2017) and its Implementation Framework (2018–2027) outline Kenya's roadmap for promoting CSA among smallholder farmers. These are complemented by broader climate and agricultural strategies, including the National Climate Change Action Plan and the Agricultural Sector Transformation and Growth Strategy (ASTGS 2019–2029). It has also been in the lead in developing a climate change adaptation strategy. It launched the National Climate Change Action Plan (NCCAP 2013–2017) in 2013, and the National Climate Change Response Strategy (NCCRS) in 2010 (Waaswa et al., 2024). CSA is thus embedded within Kenya's national development priorities, reflecting the country's high vulnerability to climate risks and its reliance on rainfed agriculture.

Evidence from CSA implementation in Kenya indicates positive impacts on productivity and food security, though outcomes are regionally differentiated (Muriithi et al., 2021; Andati et al., 2022; Muluki et al., 2022). In semi-arid counties such as Machakos, Kitui, and Makueni, CSA practices like zai pits, water pans, and terracing have improved water retention and yields during dry seasons. In highland regions, conservation agriculture and agroforestry have led to 15%−30% yield gains and improved soil health. Farmer-managed irrigation schemes and integration of drought-tolerant crops have also enhanced resilience.

The livestock sector in Kenya contributes about 12% of GDP and supports over 60% of rural households—particularly in arid and semi-arid lands (ASALs). This sector is central to food security, cultural identity, and economic resilience. Pastoralist communities, who manage over 70% of Kenya's livestock, are especially vulnerable to climate shocks such as droughts, which threaten grazing resources and water availability. In response, CSA innovations tailored to the livestock sector are gaining traction. These include climate-resilient fodder crops, mobile-based early warning systems, livestock insurance schemes, and integrated rangeland management. For instance, the Index-Based Livestock Insurance (IBLI) program in northern Kenya has helped pastoralists buffer against drought-induced losses by linking payouts to satellite-derived vegetation indices. Nonetheless, challenges remain in quantifying CSA's long- term benefits due to limited impact evaluations and variation in adoption levels. Empirical studies across counties reveal diverse adoption patterns. In Kisii County, farmers reported climate change impacts and adopted practices like crop diversification, early planting, and mixed- cropping. In Laikipia, crop diversification and mixed farming were most common, while agroforestry had high non-adoption rates. In Taita Taveta, systemic constraints such as land tenure insecurity, capital scarcity, and human-wildlife conflict limited CSA uptake (Kenduiwa et al., 2024; Musyoki et al., 2022).

Drivers of CSA adoption in Kenya include county government support, donor-funded projects such as The World Bank's Kenya Climate-Smart Agriculture Project (KCSAP; The World Bank, 2017), ICT platforms offering climate advisories, and strong partnerships with research bodies like KALRO (Republic of Kenya, 2019; Waaswa et al., 2024). Kenya has different agroecological zones from highlands and arid and semi-arid areas (ASALs). Thus, enabling conditions differ across these zones: highlands prioritize intensification, while ASALs focus on risk reduction and adaptation. However, barriers persist—especially for smallholders in remote or underserved areas—ranging from lack of credit, market volatility, and gendered access to resources. CSA technologies are often perceived as costly or labor-intensive, particularly by women and older farmers who face multiple livelihood burdens (Gikonyo et al., 2022).

Gender dynamics in Kenya mirror broader regional patterns. Although women contribute significantly to agricultural labor, their participation in CSA programs is often constrained by structural inequalities in land ownership, decision-making, and access to extension services. Gender-sensitive interventions—such as climate-smart poultry, kitchen gardens, and women-targeted training in counties like Bungoma and Kakamega—have shown promise in improving nutrition and income (Musafiri et al., 2022; Leta et al., 2023). Yet many projects lack gender-disaggregated data, limiting the ability to track differential impacts. Without proactive inclusion, adoption may fail to reach the most vulnerable populations.

Despite the utility of CSA technologies, adoption rates have remained low in Kenya even while being promoted by the Kenyan government and its development partners (Waaswa et al., 2024). Non-adoption or maladaptation of CSA in Kenya has serious implications. Farmers who do not adopt CSA face declining yields, increased exposure to climate shocks, and persistent food insecurity. Furthermore, whereas there are potential benefits of adaptation, mitigation and productivity of CSA interventions to small-scale farmers, their adoption requires farmers to acquire new knowledge, and to invest a considerable amount of their time, labor and cash in the Process (Gikonyo et al., 2022). With prevailing high levels of poverty among smallholders farmers, these requirements cause the adoption of CSAs to slow down or cause non-adoption. Conversely, poorly implemented CSA—such as top-down technology transfers or exclusionary targeting—can lead to maladaptation, undermining both ecological sustainability and social cohesion. With 80% of the country classified as ASAL and agricultural GDP projected to decline by 2.4% annually due to climate change, implementing inclusive and context-appropriate CSA remains a national imperative.

2 Statement of the problem

Despite the promise of climate-smart agricultural (CSA) technologies—such as improved seed varieties, conservation agriculture, and agroforestry—to enhance productivity, build resilience, and curb emissions, empirical research on their adoption among smallholder farmers remains skewed and fragmented. Studies predominantly focus on binary adoption of crop-based practices, often sidelining livestock systems, systemic approaches like agroforestry, and emerging digital tools critical for holistic resilience (Mthethwa et al., 2022; Serote et al., 2021, 2023; Makamane et al., 2023). Behavioral dimensions, including risk perceptions and intra-household dynamics, and gender considerations, such as women's access to resources and decision-making power, are consistently underexplored, limiting insights into inclusive adoption strategies. Furthermore, the overreliance on cross-sectional designs and geographic concentration in East and Southern Africa restricts causal inference and generalizability, overlooking diverse agroecological and socio-political contexts where vulnerabilities are acute.

These methodological and thematic gaps hinder the development of robust, context-sensitive CSA practices, technologies and innovation strategies, risking ineffective interventions that fail to address food insecurity, inequality, and unsustainable farming practices. This scoping review addresses these gaps by systematically mapping empirical evidence, barriers and drivers of CSA adoption, and identifying opportunities to strengthen equitable and transformative agricultural systems for smallholder farmers in Africa.

3 Purpose of the scoping review

The review sought to answer the following overarching questions:

“What is the nature and scope of empirical evidence on CSA adoption among smallholder farmers, and how does this evidence link to productivity, resilience, and food security under climate change?”

The specific questions were the following:

1. What is the empirical evidence on how CSA technology adoption influences agricultural productivity, resilience to climate variability, and food security outcomes among smallholder farmers across diverse agroecological zones, and how do these impacts vary by gender?

2. How does the intensity of adoption of CSA practices, technologies and innovations affect smallholder farmers' productivity and resilience, and what are the contextual factors shaping these outcomes?

3. What are the key barriers and enabling factors influencing the adoption and effectiveness of CSA technologies, including behavioral, socio-economic, institutional, and environmental determinants?

This review seeks to provide a comprehensive synthesis of CSA adoption dynamics, addressing methodological and thematic gaps to support equitable and transformative agricultural systems in Sub-Saharan Africa and beyond. The specific objectives were to:

1. Systematically review and appraise empirical studies to assess the effects of CSA adoption on agricultural productivity, resilience, and food security, with attention to gender-differentiated impacts.

2. Synthesize evidence on the intensity of CSA adoption and evaluate its role in enhancing productivity and resilience across diverse agroecological and socio-economic contexts.

3. Identify and analyze the drivers and barriers affecting CSA adoption, focusing on behavioral, socio-economic, institutional, and environmental factors to inform inclusive and effective interventions.

4 Methodology

The objective of this review was to map the existing evidence of climate smart agricultural technologies in building resilience, and raising productivity for smallholder farmers. It took on a scoping review which is an evidence synthesis that aims to systematically identify and map the breadth of evidence available on a particular topic, field, concept, or issue, often irrespective of source (Munn et al., 2022). This would help to detail which practices, technologies, and innovations are used, and their impact on smallholder farmers.

These questions, together with the selection process followed, help to meet the purpose of mapping the extent, range, and nature of the literature, as well as to determine possible gaps in the literature on a topic as put forward by Mak and Thomas (2022). The decision to undertake a scoping review and not a systematic review is guided by the intent. It followed the distinction laid out by Tricco et al. (2018) that whereas systematic reviews are useful for answering clearly defined questions, scoping reviews are useful for answering much broader questions such as “What is the nature of the evidence for this intervention?” This review applied the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework (Page et al., 2021) to guide a rigorous and transparent process of literature selection, inclusion, analysis, and synthesis. The review explored the adoption of climate-smart agricultural practices, technologies, and innovations among smallholder farmers.

4.1 Research strategy and data sources

A comprehensive search was conducted across multiple databases including Google Scholar, ResearchGate, EBSCOhost, Taylor & Francis, MDPI, Perlego, Frontiers, JSTOR, Paperity, PLOS, and Wiley Open Access. The search was conducted in June 2025 using broad and inclusive search terms such as “Climate Smart Agriculture,” Adoption OR Uptake AND agricultural technologies,” “climate-smart agriculture” OR “climate-resilient agriculture” AND adoption OR uptake AND “smallholder farmers” OR “small-scale producers,” “climate-smart agriculture” AND adaptation OR resilience AND “technology uptake” AND “smallholder farmers,” “climate-smart agriculture” AND innovation OR “digital tools” AND adoption AND “sustainable livelihoods” “smallholder farmers,” “Technology AND Innovation in Agriculture,” “CSA technologies AND women smallholder farmers,” “Climate Change Adaptation,” “CSA Technologies” OR “Climate Friendly Agriculture” AND Food security,” “CSA Technologies” OR “Sustainable Agriculture” AND Productivity,” “CSA Technologies AND agroecological zones” alongside relevant synonyms. No restrictions were applied based on geography or agroecological zone to ensure global coverage of CSA adoption literature.

The study selection followed four main PRISMA-ScR phases:

Phase 1: Identification

The initial search retrieved approximately 598 articles. These were imported into Zotero and subsequently filtered for peer-reviewed journal articles. Non-journal publications (e.g., theses, dissertations, reports, policy briefs, and reviews) were excluded, yielding 66 articles for the screening phase.

Phase 2: Screening

A two-tiered screening process was applied:

• Step 1: abstracts and titles were scanned to remove qualitative-only studies, non-peer-reviewed articles, those using secondary data, and those missing relevant keywords.

• Step 2: full texts were reviewed with attention to the methodology and data analysis sections. Articles were retained if they employed quantitative or mixed methods approaches and utilized inferential statistics. Color coding was used in an Excel sheet to categorize studies.

Phase 3: Eligibility

Articles were assessed against rigorous inclusion/exclusion criteria to ensure methodological robustness and relevance to the objectives of the scoping review. Only peer-reviewed, empirical studies employing inferential statistical methods were included, as these enable generalizability, quantify relationships, test hypotheses, and ensure replicable, statistically significant findings critical for evidence-based insights into CSA technology adoption. Mixed-methods studies were included if they applied inferential statistics to quantitative data, complementing qualitative insights with statistical rigor. Excluded were qualitative-only studies, which lack generalizability and quantifiable effect sizes, and gray literature, due to inconsistent methodological transparency. This process yielded 54 articles (32 quantitative, 22 mixed methods) from an initial pool of 598, searched in June 2025 across various databases.

Phase 4: Inclusion

The final 54 articles formed the evidence base for analysis. These studies were published between 2013 and 2025, offering a 12-year outlook to assess emerging trends and longitudinal shifts in CSA technology adoption literature. The selection was reviewed by two peers to ensure all 54 aligned with the laid out criteria on the types of articles required.

4.2 Inclusion and exclusion criteria

Table 1.

Table 1

Table 1. Inclusion and exclusion criteria.

4.3 Data extraction and coding procedure

An Excel spreadsheet was developed to guide data extraction from the 54 retained studies. Key information recorded included a variety of data:

• Research design and method

• Target population and context

• Data collection tools and variables measured

• Type of CSA practices, technologies, or innovations assessed

• Inferential statistics applied

To support analysis, the dependent (outcome) variable from each article was identified and similar DVs grouped together thematically. Thematic coding and grouping also went through peer review by two other reviewers to ensure accuracy. Articles were categorized under major themes using a content analysis approach. This process yielded three dominant thematics: adoption of climate smart agricultural technology (binary), Intensity and Use, and decision-making factors. Further coding was applied to all metadata columns in the Excel file to uncover trends, patterns, and gaps.

4.4 Thematic analysis

The outcome variables were coded and clustered under three dominant themes:

1. Adoption as binary outcome: whether or not CSA technologies/practices were adopted.

2. Adoption intensity: number and combinations of CSA practices adopted.

3. Adoption decision-making: behavioral, socioeconomic, and institutional determinants of adoption.

For each theme, a frequency analysis was performed to identify the most common methodologies, data sources, and statistical tools. This enabled an in-depth understanding of CSA adoption dynamics, the scope and scale of research engagement, and the methodological robustness of existing empirical work. Tables and figures (e.g., frequency tables, PRISMA flow diagram) are included in the study to summarize key findings.

4.5 PRISMA flow diagram

Figure 1 summarizes the PRISMA-ScR four-stage process used to identify, screen, and include studies:

Figure 1

Figure 1. PRISMA Flow Diagram (adapted from Page et al., 2021).

This structured and replicable methodology ensures that the review is both systematic and transparent, aligning with best practices for evidence synthesis in agricultural and climate change research.

4.6 Limitations

Exclusion of Non-Peer-Reviewed and Gray Literature: the review was restricted to peer-reviewed empirical journal articles, excluding theses, dissertations, reports, policy briefs, and other gray literature sources. This may have omitted practical, context-specific insights from development organizations and non-academic entities, potentially limiting the comprehensiveness of evidence on CSA adoption in real-world settings.

Omission of Qualitative-Only Studies: by screening out qualitative-only research and focusing on quantitative or mixed-methods studies with inferential statistics, the review may have underrepresented nuanced explorations of behavioral attitudes, perceptions, and socio-cultural drivers of CSA adoption, thereby constraining depth in understanding farmer decision-making processes.

Language and Source Bias: the search for articles was confined to specific academic databases possibly missing publications in less indexed or regional journals. In addition, only English-language, peer-reviewed articles included. Excludes insights from Francophone, Lusophone, and Arabic-speaking regions.

5 Synthesis findings for three outcome variables

5.1 Overview

The findings of this scoping review reveal the current spread of empirical research on the adoption of climate-smart agricultural (CSA) technologies among smallholder farmers (Figures 2–8). The analysis indicates a complex picture shaped by the interplay of adoption of CSA technology, intensity of use, and decision-making processes across diverse agroecological and socio-economic contexts. The evidence underscores the predominance of crop-based practices, the uneven exploration of livestock and systemic innovations, and the critical gaps in linking adoption to measurable outcomes such as yield stability, income, and resilience. These findings, grounded in quantitative and mixed-methods studies, offer valuable insights into the opportunities and challenges of CSA implementation, while highlighting areas where research and policy must evolve to support sustainable, equitable, and climate-resilient agricultural systems.

Figure 2

Figure 2. Countries of research across three outcome variables.

Figure 3

Figure 3. Distribution of research questions across three outcome variables.

Figure 4

Figure 4. Theories used across the outcome variables.

Figure 5

Figure 5. Target population across three outcome variables.

Figure 6

Figure 6. Research design across three outcome variables.

Figure 7

Figure 7. Independent variables across three outcome variables.

Figure 8

Figure 8. Moderating variables across the three outcome variables.

5.1.1 Countries of research

The countries of research across the three outcome variables reveal a geographic concentration of CSA technology adoption studies in Ethiopia, Kenya, and Ghana, which collectively dominate research across binary adoption (Ethiopia 28.6%, Kenya 25%), intensity (Ghana 43%, Kenya 21%), and decision-making influences (Ghana 43%, Kenya 21%). South Africa also features prominently, while countries such as Tanzania, Uganda, DRC, Senegal, and Mali are significantly underrepresented across all dependent variables.

The implications of this geographic spread are significant for equity in adoption of CSA technologies. Concentration in Ethiopia and Kenya restricts generalizability, potentially overlooking unique challenges in underrepresented areas like the DRC's conflict-prone regions or Mali's desertification hotspots. This could lead to policies that are ill-suited for diverse agroecological zones, exacerbating vulnerabilities and hindering SDG 13 progress. Future research must include neglected countries, enabling context-specific insights that address localized climate risks and foster inclusive adaptation. Broader coverage would enhance knowledge transfer, prevent maladaptation, and support regional frameworks like the AU's CAADP, ultimately unlocking the potential of CSA technologies for global food system resilience and sustainability in marginalized farming communities.

5.1.2 Research questions

Collectively, 89% of questions target adoption drivers, utilizing inferential statistics to quantify influences, while only a few of them link to productivity, resilience, or food security metrics. Dis-adoption dynamics, gender intersectionality, and long-term impacts under escalating climate extremes are underexplored. Questions rarely incorporate agroecological specificity, with just 13% addressing zone-based variations.

This focus of research questions implies a determinant-heavy evidence base that may overemphasize uptake without evaluating value, risking promotion of ineffective CSA practices and inefficient policy interventions. The lack of outcome-oriented questions limits causal insights into how adoption translates to yield stability or income gains, potentially perpetuating food insecurity in vulnerable smallholder systems. The implications call for an expanded set of questions integrating longitudinal outcomes, behavioral and equity dimensions to capture intra-household dynamics and risks of maladaptation. By reframing questions to bridge adoption with transformative impacts, future studies can inform evidence-based strategies and align with global agendas like the Paris Agreement and SDGs. This shift would enhance the role of CSA in building resilient food systems, and empowering policymakers to design interventions that effectively address on-ground realities of smallholder farmers in a changing climate.

5.1.3 Theories

The theoretical frameworks employed in the reviewed studies on CSA adoption are predominantly technology adoption and diffusion models (57.5%), integrated across binary, intensity, and decision-making outcomes. For binary adoption, the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) dominate and primarily focus on rational decision-making, perceived utility, and cost-benefit analysis in Ethiopian and Kenyan contexts. Intensity studies apply economic theories (10%), such as Utility Maximization, to assess scaling decisions in Ghana, while behavioral models (22.5%), including the Theory of Planned Behavior (TPB) feature, probing attitudes and norms. There is limited integration of broader systemic, institutional, and socio-cultural dimensions. Development and livelihood theories (7.5%) and communication/extension models (2.5%) are rarely used, despite their relevance in understanding knowledge dissemination and community-level dynamics. Underrepresented also are gender theories, environmental models like Resilience Theory, and intersectional approaches, comprising less than 10% combined. Theories rarely incorporated contextual moderators like agroecological specificities, with only implicit use in 13% of zone-specific studies.

The theoretical bias toward adoption-diffusion models implies an overreliance on individual-level factors of uptake, potentially overlooking systemic barriers such as institutional fragmentation or gender power dynamics, and this can lead to policy recommendations that could be technically sound but socially and contextually inappropriate. Underrepresentation of behavioral and gender theories risks ignoring intra-household inequalities, exacerbating inequities in CSA benefits for women and marginalized groups. The scarcity of environmental theories limits understanding of resilience under climate extremes, hindering adaptive strategies. Future research should diversify theories, integrating intersectional and resilience frameworks to capture multifaceted drivers, enhancing causal inference and equity. This would strengthen the alignment of CSA with SDGs, fostering holistic policies that promote sustainable, inclusive agricultural transformation and mitigate maladaptation in diverse smallholder contexts.

5.1.4 Target population

The reviewed studies predominantly focus on smallholder farmers (59.4% across binary adoption, 63% for intensity and decision-making), reflecting their central role in food production and vulnerability to climate change. Crop-specific studies (25%−31.3%) offer more tailored insights, while only 13% explicitly examine farmers in distinct agroecological zones such as arid/semi-arid regions, highlands, or humid zones. Livestock-focused populations are sidelined (less than 5%), despite their relevance in pastoral systems, and gender-disaggregated analyses are rare, with minimal attention to intra-household roles, youth, peri-urban farmers or other marginalized groups.

Implications of this narrow targeting underscore gaps in inclusivity, potentially marginalizing livestock-dependent communities and perpetuating gender disparities in CSA benefits. The predominant focus on crop-based smallholders limits insights into integrated farming systems, risking ineffective interventions in mixed agro-pastoral zones. The scant zone-specific targeting ignores biophysical diversity, reducing applicability of CSA amid climate variability. Future studies should broaden populations to include livestock keepers, women-led households, and intersectional groups, employing stratified sampling for equity. This would reveal tailored drivers, enhancing the transformative impact of CSA technologies on resilience and food security, aligning with SDG 13, and ensuring sustainable adaptation policies empower all smallholders. A more inclusive and differentiated approach to target populations will also support better tracking of adoption impacts, particularly in terms of resilience, productivity, and food security across varied contexts and social groups.

5.1.5 Research design

The scoping review reveals a dominant reliance on cross-sectional research designs across all dependent variables—binary adoption (89.3%), intensity and decision-making influences (100%). These designs offer valuable snapshots of CSA technology adoption patterns but inherently limit causal inference, temporal analysis, and understanding of dynamic processes such as scaling, dis-adoption, or technology switching. Alternative designs such as longitudinal studies, quasi-experiments, and mixed-methods approaches are rare, despite their potential to enrich explanatory depth and policy relevance.

This methodological homogeneity has significant implications. First, it constrains the ability to assess the sustainability and long-term impacts of CSA technologies. Without tracking adoption over time, it is difficult to determine whether practices are retained, scaled, or abandoned—and why. Second, the lack of experimental or quasi-experimental designs weakens causal claims about the effectiveness of interventions such as extension services, credit access, or climate advisories. Third, the absence of mixed-methods integration limits insights into behavioral, cultural, and institutional drivers that are not easily captured through surveys alone.

To strengthen the evidence base, future research should diversify the methodological toolkit. Longitudinal panel studies can illuminate adoption trajectories and resilience-building over time.

Experimental designs such as randomized controlled trials or natural experiments can provide robust causal evidence on what works and for whom. Mixed-methods approaches can uncover nuanced socio-cultural dynamics and validate quantitative findings. Additionally, spatial analysis tools like GIS and remote sensing can enhance measurement accuracy and link adoption to environmental outcomes.

This shift would enhance generalizability, inform evidence-based interventions, and align research with global priorities like the Paris Agreement, ultimately fostering equitable, resilient food systems for smallholder farmers in the Global South.

5.1.6 Independent variables

Independent variables in CSA technology adoption studies are mostly socio-economic and demographic factors (63.2%), across binary, intensity, and decision-making. Binary adoption emphasizes household income, education, age, and farm size as key predictors in Ethiopian and Kenyan contexts. Intensity incorporates institutional access (19.5%), like extension services and credit, influencing scale in Ghana. Decision-making highlights farmer perceptions (10%), such as risk attitudes, alongside environmental variables (7.3%) like soil quality or rainfall variability. Overall, socio-demographics dominate, with institutional factors secondary, and perceptions and environmental dimensions underrepresented, often measured via surveys.

This emphasis reflects a rational-choice framework where adoption is influenced by perceived benefits and resource availability. It also reveals an overemphasis on individual traits, potentially ignoring systemic enablers, leading to policies that undervalue institutional reforms or perceptual shifts.

The limited inclusion of behavioral and environmental variables constrains understanding of psychological drivers and ecological fit. For instance, risk perception, trust in institutions, and climate awareness are rarely modeled, despite their relevance in shaping farmer decisions under uncertainty. Similarly, environmental factors—such as soil quality, rainfall variability, and agroecological zone—are underexplored, even though CSA practices are inherently context-dependent.

The low focus on environmental variables limits climate-specific insights and can lead to maladaptation. Overreliance on socio-economic variables risks oversimplifying adoption dynamics and overlooking critical behavioral and environmental enablers or constraints. This can lead to interventions that are technically sound but socially or ecologically misaligned. Moreover, the lack of multidimensional modeling limits the ability to design integrated CSA packages that respond to both farmer capacities and environmental realities.

Future research should adopt a more holistic approach to variable selection, integrating behavioral, institutional, and environmental dimensions alongside socio-economic factors. This would enable richer analysis of adoption drivers and support the design of CSA technology interventions that are both effective and equitable. Additionally, modeling interactions such as the moderating effect of education on extension access, can reveal complex pathways and inform more targeted policy and programming.

5.1.7 Moderating variables

Moderating variables are underexplored across CSA technology adoption studies, yet they hold significant potential for deepening understanding of how contextual and individual factors shape adoption outcomes. Location emerges as the most frequently used moderator (42.9%), reflecting the influence of agroecological zones, infrastructure, and climate variability. Gender (28.6%) and age (14.3%) are also included in some models, while interaction terms—such as education extension access—are rare but can reveal complex adoption pathways. Overall, moderators like intra-household power dynamics and conflict-sensitivity are underexplored, often inferred rather than tested, highlighting gaps in behavioral and equity lenses.

The limited use of moderation analysis has important implications. First, it restricts the ability to tailor CSA interventions to specific sub-populations or environments. For example, gender as a moderator can reveal how access to land, labor, and information for women affects adoption differently than men, informing gender-sensitive programming. Neglecting gender moderation exacerbates inequalities and can overburden women without benefits while intersectional gaps miss compounded vulnerabilities, limiting resilience building. Similarly, location-based moderation can guide strategies that align CSA technologies with local biophysical and socio-economic conditions. Without these insights, interventions risk leading to policies that fail vulnerable subgroups and cause maladaptation.

Second, the absence of interaction terms and multi-level moderation models limits the explanatory power of CSA adoption. It is rarely driven by single factors; but rather results from the interplay of factors such as education, access to services, climate risk perception, and social norms. Moderation analysis can uncover these layered dynamics, enabling more precise targeting and effective scaling of CSA technologies.

Future research should prioritize the inclusion of moderating variables in adoption models, particularly those reflecting gender, age, location, and institutional access. Advanced statistical techniques as structural equation modeling can help capture these interactions. This will align CSA technology interventions with equity goals and SDGs, empowering smallholders through contextualized, impactful and transformative agricultural adaptations in diverse Global South settings.

6 Synthesis discussion of overall gaps on adoption of CSA technologies

6.1 Theoretical gaps

The theoretical underpinning of Climate-Smart Agriculture (CSA) adoption remains conceptually disjointed, with most studies adopting one or two established frameworks without synthesizing them into a holistic explanatory model. The dominant theories are Theory of Planned Behavior (TPB; Ajwang et al., 2024; Khumalo et al., 2024), Diffusion of Innovations (DOI; Abegunde et al., 2019; Anuga et al., 2019), Sustainable Livelihoods Approach (SLA; Kirungi et al., 2023), Technology Acceptance Model (TAM), and utility maximization models tend to assume that farmers make decisions rationally based on perceived utility, risk, and available resources. This assumption neglects bounded rationality (where decisions are made under imperfect information and cognitive constraints), path dependency (where previous choices constrain future ones), and iterative adaptation (where adoption may occur in stages with feedback loops; Bwiza et al., 2024; Alemayehu et al., 2024).

Behavioral economics concepts such as loss aversion, time preferences, and framing effects are absent in most adoption models, despite their relevance in explaining why farmers sometimes avoid high-potential CSA technologies due to perceived short-term costs (Waaswa et al., 2024). Similarly, socio-cultural norms including gendered division of labor, community resource-sharing traditions, and local risk perceptions are typically relegated to control variables rather than treated as central determinants (Musafiri et al., 2022; Nyang'au et al., 2021). The complementarity and substitution between CSA technologies is mentioned in multi-technology studies (Zeleke et al., 2024; Ogada et al., 2021), but these relationships are not theorized within a systems-based decision framework. This gap weakens the ability to explain how farmers create bundles of mutually reinforcing practices while avoiding others due to labor, capital, or knowledge constraints.

Gender and intersectionality remain under-theorized despite consistent empirical evidence of disparities in resource access, decision-making autonomy, and benefit capture (Alemayehu et al., 2024; Ewulo et al., 2025). No widely used CSA adoption framework fully embeds intra-household bargaining models, youth aspirations, or intergenerational perspectives into its structure. Likewise, climate justice and sustainability transitions theory, which could bridge household-level decisions with structural transformation and equity, are rarely applied.

While resilience theory is frequently referenced as an overarching goal of CSA, it is rarely operationalized into measurable adoption pathways connecting farm-level changes to long-term adaptive capacity and socio-ecological stability (Nyasimi et al., 2017; Anuga et al., 2019). Similarly, social learning theory and agricultural innovation systems perspectives are underutilized despite clear evidence that peer-to-peer influence and cooperative structures are pivotal to CSA uptake (Ajwang et al., 2024; Waaswa et al., 2024).

Overall, there is a pressing need for a unified CSA-specific adoption framework that merges socio-ecological resilience thinking, behavioral decision theory, innovation diffusion models, political economy analysis, and equity-centered approaches. Such a framework would better reflect the complex, multi-layered, and context-specific nature of CSA adoption, moving beyond linear and individualistic decision-making models.

6.2 Empirical gaps

Empirical research on Climate-Smart Agriculture (CSA) adoption is geographically, thematically, and methodologically uneven, limiting the scope for robust generalizations and context-specific policy design. Geographically, there is a pronounced overconcentration of studies in East and Southern Africa, particularly in Kenya, Ethiopia, Ghana, Tanzania, Malawi, and South Africa (Utonga et al., 2024; Waaswa et al., 2024; Abegunde et al., 2019; Kurgat et al., 2020; Senyolo et al., 2021). By contrast, Central Africa, North Africa, small island developing states, and fragile or conflict-affected contexts remain critically underrepresented (Ouédraogo et al., 2019; Waaswa et al., 2024). This geographical skew hinders the development of cross-regional comparative analyses and constrains the generation of policy prescriptions suited to diverse agroecological and socio-political conditions, especially in settings where climate vulnerabilities are acute but under-researched.

Thematically, CSA adoption research remains heavily crop-centric, dominated by staple cereals particularly maize (Kirungi et al., 2023; Asante et al., 2024a,b; Alemayehu et al., 2024), rice (Fiawoo et al., 2024), wheat (Alemayehu et al., 2024), and coffee (Bwiza et al., 2024). Sectors such as high-value horticulture, livestock-integrated systems, agroforestry-based mixed farming, and urban or peri-urban agriculture are conspicuously absent despite their potential to deliver significant climate resilience and livelihood benefits (Meshesha et al., 2022; Hebsale Mallappa and Pathak, 2023). Likewise, the technological portfolio examined in empirical studies is relatively narrow. Dominant focus areas include drought-tolerant seed varieties, zero or minimum tillage, and crop rotation (Abegunde et al., 2019; Negera et al., 2022). In contrast, integrated pest management, advanced irrigation systems, biochar application, livestock-based CSA practices, energy-efficient post-harvest technologies, and ICT-enabled decision-support tools remain under-researched, even though they could address both adaptation and mitigation imperatives (Ogada et al., 2021; Negera et al., 2022).

A notable empirical shortcoming in the CSA literature is the tendency to analyze single CSA practices in isolation, rather than within the multi-technology adoption bundles that characterize real-world farm decision-making. This analytical simplification neglects the complementarities and trade-offs inherent in farmers' integrated management strategies. For example, combining soil conservation measures with drought-tolerant seed varieties can produce synergistic effects, amplifying yield stability under variable rainfall conditions through improved soil moisture retention and enhanced plant stress tolerance (Zeleke et al., 2024; Negera et al., 2022). Conversely, the adoption of multiple labor-intensive CSA practices, such as minimum tillage coupled with manual compost application, can create competing demands for household labor, reducing the feasibility of full-scale implementation, particularly in smallholder systems where household members also engage in off-farm employment or care responsibilities (Ogada et al., 2021; Alemayehu et al., 2024).

Ignoring these interactions leads to a misrepresentation of adoption behavior by implying that each technology is considered independently, when in practice, farmers make portfolio decisions that weigh the relative benefits, costs, and risks of combinations of practices (Meshesha et al., 2022; Kirungi et al., 2023). Such portfolio choices are influenced not only by biophysical compatibility but also by economic and institutional constraints, such as access to multiple complementary inputs, extension advice on integrated packages, or coordinated credit and subsidy programmes (Asante et al., 2024a,b; Alemayehu et al., 2024).

The absence of multi-practice adoption modeling also prevents an adequate understanding of substitution effects, where the uptake of one CSA practice displaces another due to limited land, labor, or financial capital. For instance, investment in drip irrigation may reduce a farmer's capacity to adopt agroforestry because both require upfront capital and long-term commitment. Likewise, climate variability can prompt adaptive bundling, yet these dynamic reconfigurations are rarely documented (Bwiza et al., 2024; Utonga et al., 2024).

Methodologically, single-practice studies constrain the use of more sophisticated econometric and systems modeling approaches that can explicitly account for interdependencies among practices. Without such modeling, the sector risks overestimating the impact of individual CSA components and underestimating the potential of integrated, synergistic technology packages to deliver CSA's promised triple wins of productivity, adaptation, and mitigation.

Reliance on cross-sectional survey data limits the ability to track adoption over time and understand adoption trajectories, persistence, scaling, and dis-adoption (Negera et al., 2022; Waaswa et al., 2024). Dis-adoption and technology switching in response to market changes or climatic variability remain almost entirely neglected, despite their critical importance for sustainability assessments (Meshesha et al., 2022; Alemayehu et al., 2024).

While gender-disaggregated analyses are increasingly included, they often lack depth. Studies seldom unpack intra-household decision-making processes, intergenerational transfer of farming knowledge, or the compounded effects of intersectional barriers related to ethnicity, social class, or insecure land tenure (Alemayehu et al., 2024; Ewulo et al., 2025). Likewise, the roles of youth aspirations and migration dynamics remain underexplored. Social networks, trust in information sources, and informal peer-to-peer learning are recognized as influential adoption drivers (Ajwang et al., 2024; University for Development Studies Ghana and Anang, 2022), yet their influence is rarely quantified or modeled causally.

Outcome measurement is another weak point. Most CSA adoption studies focus narrowly on productivity and income indicators, with limited integration of broader sustainability metrics such as resilience capacity, food and nutrition security, ecosystem service enhancement, or greenhouse gas (GHG) mitigation potential (Khumalo et al., 2024; Bwiza et al., 2024). Even fewer studies combine adoption data with biophysical measurements such as soil fertility, water-use efficiency, or biodiversity indicators even though such integration is essential for verifying whether CSA truly delivers its promised triple wins of productivity, adaptation, and mitigation (Negera et al., 2022; Fiawoo et al., 2024).

6.3 Contextual gaps

Methodological shortcomings in the CSA adoption literature span study design, data collection, analytical approaches, and outcome measurement, collectively constraining the strength and applicability of findings. Most studies rely on cross-sectional household surveys (Abegunde et al., 2019; Alemayehu et al., 2024; Waaswa et al., 2024), which capture only a static snapshot of adoption behavior. This limits understanding of dynamic processes such as technology trial, scaling, persistence, dis-adoption, and switching to alternative practices. Longitudinal panel data essential for tracing adoption trajectories and assessing the influence of climate variability over time remains rare (Negera et al., 2022; Zeleke et al., 2024). Experimental and quasi-experimental designs, including randomized controlled trials, natural experiments, and difference-in-differences approaches, are underutilized despite their potential to strengthen causal inference. As a result, much of the evidence remains correlational and vulnerable to bias from unobserved heterogeneity and reverse causality.

Data collection practices also present significant gaps. Most adoption metrics are based on self-reported information, which is susceptible to recall and social desirability biases, particularly when farmers believe certain responses are favored by extension agents or researchers. Objective biophysical measurements such as soil organic carbon content, water use efficiency, crop biomass, or greenhouse gas emissions are seldom integrated into adoption studies (Khumalo et al., 2024), limiting the ability to validate claimed environmental benefits. Gender-disaggregated data collection is inconsistent, often focusing only on the sex of the household head rather than intra-household decision-making dynamics. Youth-specific data, critical for understanding intergenerational transitions in farming, are even less common.

Analytical approaches also have limitations. Many studies employ binary choice models without considering adoption intensity or partial uptake beyond a simple yes/no dichotomy (Ajwang et al., 2024; Ogada et al., 2021). Even where adoption intensity is analyzed, there is no standardized measurement approach, making cross-study comparisons difficult. Endogeneity issues, such as omitted variable bias and simultaneity, are often insufficiently addressed; advanced econometric techniques such as instrumental variables, control functions, or structural equation modeling are rarely applied (Zeleke et al., 2024). Spatial econometric and geostatistical methods, which could illuminate diffusion patterns and context-specific adoption hotspots, remain largely absent, as do systems modeling approaches that could simulate the interactions of multiple CSA practices under varying market, policy, and climate conditions.

Mixed-methods integration is limited. While qualitative tools such as focus group discussions and key informant interviews are occasionally used, they are often treated as supplementary rather than systematically integrated with quantitative findings (Musafiri et al., 2022). This parallel approach limits the ability to capture complex socio-cultural and institutional dimensions of adoption. Participatory action research, which could foster co-creation of CSA solutions and farmer empowerment, is rare, with most farmer involvement confined to data provision rather than collaborative problem-solving.

Finally, outcome measurement is narrowly focused on yield and income, with limited attention to resilience indicators such as yield stability, livelihood diversification, or adaptive capacity (Bwiza et al., 2024). Environmental outcomes, particularly mitigation metrics such as carbon sequestration or emissions reduction, are seldom assessed outside of agronomic trials. Few studies link adoption data to weather and climate records, missing opportunities to evaluate whether CSA practices genuinely buffer against climate shocks. Likewise, spillover and diffusion effects are rarely measured, leaving a gap in understanding the broader community and landscape-level impacts of CSA adoption.

6.4 Methodological gaps

Methodologically, the CSA adoption literature remains heavily dominated by cross-sectional household survey designs analyzed primarily through binary or multinomial logit and probit models (Abegunde et al., 2019; Senyolo et al., 2021; Waaswa et al., 2024). While these approaches are relatively easy to implement and interpret, their reliance on single-point-in-time data prevents meaningful analysis of temporal adoption dynamics such as technology trial, scaling, persistence, dis-adoption, and switching to alternative practices. Longitudinal and panel datasets are exceedingly rare (Negera et al., 2022; Zeleke et al., 2024), as are experimental and quasi-experimental designs, including randomized controlled trials, difference-in-differences estimators, and propensity score matching which could substantially strengthen causal claims about the impacts of CSA interventions (University for Development Studies Ghana and Anang, 2022; Ajwang et al., 2024). The absence of such designs means most evidence remains correlational and is prone to omitted variable bias and reverse causality.

Data quality also represents a significant challenge. Self-reported adoption data dominate the field, raising risks of recall bias and social desirability bias, particularly where respondents are aware of researchers' or extension agents' expectations. Few studies triangulate survey responses with field observations, remote sensing imagery, or input transaction records to validate reported adoption (Khumalo et al., 2024; Musafiri et al., 2022). Additionally, definitions and measurement of “adoption” vary widely: some studies employ binary indicators (adopted/not adopted), others count the number of CSA practices adopted, while very few develop a standardized CSA adoption intensity index. This lack of methodological harmonization makes cross-study comparability difficult and complicates meta-analyses.

Sampling strategies are another weakness. Many studies are geographically narrow, often focusing on one or two districts or counties (Abegunde et al., 2019; Alemayehu et al., 2024), and frequently lack statistical power for broader generalization. Stratification by gender, age, farm size, or agroecological zone is inconsistent, leading to limited insights into heterogeneity in adoption drivers. Medium-scale commercial farmers remain underrepresented (Kassa and Abdi, 2022), despite their potential to influence technology diffusion through demonstration effects and market participation.

Econometric models also tend to omit key behavioral and cognitive determinants of adoption such as trust in institutions, innovativeness, risk perception, and time preferences (Meshesha et al., 2022; Kirungi et al., 2023), despite mounting evidence that these factors moderate the effects of structural enablers like access to credit, training, or extension services. Few models adequately account for endogeneity and selection bias, particularly in cases where farmers self-select into extension programs or are targeted for interventions based on unobserved characteristics correlated with adoption (Ogada et al., 2021; Zeleke et al., 2024).

Mixed-methods approaches remain underutilized, and when qualitative data are collected through focus groups, interviews, or participatory rural appraisals they are rarely integrated into the statistical modeling process in a systematic way (Musafiri et al., 2022). This limits the explanatory richness of adoption studies, particularly regarding socio-cultural and institutional contexts. Furthermore, spatial and geostatistical tools capable of linking adoption behavior to micro-climatic variability, market proximity, or agroecological zoning, are seldom employed, despite their potential to improve targeting and scaling strategies (Utonga et al., 2024).

Finally, the scope of impact evaluations remains narrow. The overwhelming emphasis is on yield and income outcomes, with far fewer studies systematically measuring CSA's effects on resilience (e.g., yield stability, livelihood diversification), environmental indicators (e.g., soil organic carbon, water-use efficiency, GHG mitigation), or nutritional outcomes (e.g., dietary diversity, micronutrient availability; Bwiza et al., 2024; Fiawoo et al., 2024). The lack of integrated socio-economic and biophysical datasets limits the ability to holistically assess the “triple win” promise of CSA on productivity, adaptation, and mitigation.

7 Conclusion and recommendations for further research

Based on the gaps identified across the reviewed literature, three key directions emerge for future research on CSA adoption. First, there is a clear need for longitudinal, multi-country comparative studies that can track both adoption and dis-adoption of CSA practices over time. Such studies should span diverse agroecological, cultural, and policy contexts to capture the dynamics of sustainability and scalability, allowing researchers to distinguish between short-term uptake driven by external interventions and long-term integration of practices into farming systems.

Second, researchers should focus on developing integrated theoretical frameworks that move beyond single-discipline perspectives. By combining insights from behavioral science, socio-ecological systems theory, and political economy, future work could better capture the complex interactions between individual decision-making, community-level processes, and macro-level institutional and policy structures. This would enable a more nuanced understanding of how CSA technology adoption is influenced by factors ranging from farmers' risk perceptions and social networks to land tenure arrangements, market access, and government incentives.

Finally, methodological innovation is essential. Future research should embrace advanced and mixed-methods approaches that integrate geospatial tools, participatory methods, and experimental or quasi-experimental designs. Geospatial data could help validate self-reported adoption and reveal spatial diffusion patterns, while participatory approaches can uncover local knowledge systems and socio-cultural drivers often missed by surveys. Experimental designs, in turn, can strengthen causal inference regarding the impacts of specific interventions. By combining these methods, future studies can produce richer, more reliable, and context-sensitive insights into CSA adoption dynamics. Together, these research directions offer a pathway toward building a more comprehensive, empirically robust, and theoretically grounded understanding of CSA adoption that is both globally relevant and locally applicable in climate-vulnerable agricultural systems.

8 Conclusion

This scoping review highlights the potential of climate-smart agricultural (CSA) technologies to enhance smallholder productivity, resilience, and food security in the Global South. It bridges global climate imperatives—such as SDG 13 and UNFCCC frameworks—with SSA's regional vulnerabilities and Kenya's national policy landscape to reveal critical gaps in CSA adoption research (Alidu and Man, 2025; Bishibura Erick et al., 2025; Tran et al., 2019), including geographic concentration, underexplored behavioral and gender dimensions, and methodological constraints that limit causal insights into productivity, resilience, and food security (FAO, 2010; FAO; IFAD; World Bank, 2015; Republic of Kenya, 2018). By systematically mapping empirical evidence and synthesizing drivers like extension services with barriers such as tenure insecurity, the review addresses the problem of fragmented knowledge that risks ineffective interventions amid escalating extremes (Ewulo et al., 2025; Gikonyo et al., 2022).

However, critical gaps persist in theory, methodology, and geographic coverage. The dominance of crop-based studies, limited attention to livestock systems, and underrepresentation of gender and intersectional dynamics constrain the inclusivity and scalability of CSA interventions. Methodologically, overreliance on cross-sectional designs limits causal inference and fails to capture adoption trajectories, dis-adoption, or long-term impacts. Empirical studies rarely link CSA adoption to measurable outcomes such as yield stability, income resilience, or food security, weakening the evidence base for policy formulation. These gaps highlight the need for inclusive studies to unlock the transformative potential of adoption of CSA technology, ultimately informing equitable policies that enhance smallholder sustainability across scales and align with global agendas for climate-resilient agriculture.

Future research can directly address the identified gaps by prioritizing multi-country comparative designs that span underrepresented regions like the DRC and Mali, thereby filling geographic voids and tailoring interventions to diverse agroecologies (Rabine, 2024; Huang et al., 2025) in alignment with regional frameworks such as CAADP and the African CSA Alliance, while supporting national strategies like Kenya's Climate-Smart Agriculture Strategy through localized evidence on yield stability and income resilience (FAO; IFAD; World Bank, 2015; Republic of Kenya, 2018). Integrating advanced methodologies and intersectional analyses of gender, age, and socio-economic status will ensure equity by unpacking intra-household dynamics and power imbalances, fostering gender-responsive innovations that close yield gaps (20%−30% in women-led households) and enhance adaptive capacity (FAO, 2011; Huyer and Chanana, 2021). Incorporating digital tools and geospatial analysis can enhance contextual understanding and validate adoption claims. Policy must prioritize the need for gender-responsive extension services, locally grounded implementation strategies, and robust monitoring systems that reflect farmers' realities, and institutional reforms to boost CSA technologies uptake to enhance productivity, resilience against climate shocks, and food security. This approach will bolster resilience through quantified metrics like shock recovery rates and asset diversification, while securing food systems by linking adoption bundles (e.g., agroforestry with livestock integration) to outcomes like reduced seasonal hunger gaps. This will ultimately contribute to global goals under SDG 2 (zero hunger), SDG 13 (climate action), and SDG 5 (Gender Equality) by promoting sustainable, inclusive transformations for smallholder farmers amid climate extremes.

Author contributions

MR: Visualization, Formal analysis, Methodology, Conceptualization, Writing – original draft, Data curation, Writing – review & editing. DM: Validation, Formal analysis, Supervision, Methodology, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fclim.2025.1692929/full#supplementary-material

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