Climate-smart agriculture for sustainable farming: a bibliometric, TCM, and ADO framework based systematic review

1 Introduction

Global climate change, primarily driven by greenhouse gas (GHG) emissions such as carbon dioxide (CO₂), poses severe risk to human life, economic development, environment stability, and sustainability (Raihan, 2023). It is a current exigency that requires crucial mitigation and remediation. Agriculture, being one of the significant contributors of the emissions makes it one of the most challenging sectors under changing climate conditions (Sadiq et al., 2024). Empirical evidence shows that a 1% rise in economic growth, fossil fuel energy use, urbanization, and tourism will correspondingly raise CO₂ emissions by 0.45%, 1.77%, 0.10% and 0.04%. Moreover, a 1% increase in renewable energy consumption and agricultural productivity is associated with reductions in CO₂ emissions of 0.66% and 0.20% (Raihan and Tuspekova, 2022). Agriculture production also relies heavily on energy intensive inputs like Fuel and Fertilizers. According to USDA economic research service, nearly 40% of farm operation expenses go to energy use dominated by fuel, fertilizer, and pesticide [U. S. Department of Agriculture (USDA), (2014)]. While policy frameworks have prioritized decarbonization of fuel and transport, little attention has been directed toward decarbonization of fertilizers and pesticides (Muratori et al., 2023). This necessitates the need for innovative technology and policy solutions that go beyond traditional limits as climate change imposes unprecedented risks on agricultural productivity and energy systems worldwide (Lin et al., 2024). Currently, agriculture sustains the livelihood of more than 2.5 billion people and provides food for 8.1 billion globally, with the population expected to reach around 9.7 billion by 2050. Climate projections, however, indicate that the yield of major cereals like rice, wheat and maize will decline 25-30% by mid-century. The most severe impacts are observed in tropical and subtropical regions where food insecurity is already most severe.

Hence, Climate-Smart Agriculture (CSA) has emerged as a transformative framework to attain food security under climate change. Adoption of technologies, chemicals and fertilizers has gradually dismantled the agricultural ecosystem and food webs from microorganisms to meet the global food demand. CSA ensures better productivity, enhanced ecosystem functions and carbon sequestration by balancing the soil microbiome. The approach emphasizes the role of soil microbial communities as key drivers of nutrient cycling, ecosystem functioning and conservation of soil organic matter considering CSA as a holistic resilience strategy for sustainable agricultural development under climate change (Purohit et al., 2024). This evidence suggests that adoption of CSA practices is imperative to simultaneously alleviate the impacts of climate change and enhance structural sustainability within the food systems. Existing reviews on CSA and Carbon sequestration have primarily focused on how CSA represents a sustainable farming framework aimed at improving food security and productivity, while lowering greenhouse gas emissions and enhancing agricultural resilience to climate change (Begna and Wakweya, 2026). Studies also highlight the role of soil in mitigating climate change and emphasize how agricultural practices can enhance this contribution (Tóth et al., 2025). CSA promotes resource recycling and carbon sequestration while reducing greenhouse gas emissions. Its effectiveness is directly linked to soil health regulated by soil microbiome that performs diverse physiological functions (Purohit et al., 2024). For instance Purohit et al. (2024) highlights how CSA strengthens its pillars namely adaptation, mitigation and productivity by regulating soil organic carbon and supporting plant-microbe symbiosis. Similarly. Tóth et al. (2025) provides an insight at focusing on practices such as cover crops, conservation tillage, crop rotation and agroforestry stressing that their effectiveness depends on local climate, geographic and soil conditions in Hungary. Kabato et al. (2025) broadens the scope by highlighting the need for integrated practices like precision agriculture, regenerative practices and biochar application to enhance soil health and mitigate GHG emissions. Begna and Wakweya (2026) further connects CSA to food security emphasizing that adoption of stress tolerant crops and conservation practices improves the yield by 15-30% and reducing GHG emissions up to 25%. In addition, Maqbool et al. (2025) highlights about conservation Agriculture and its potential to preserve soil organic carbon (SOC), improve soil quality, reducing agriculture’s environmental footprint and enhancing climate change adaptation in agricultural systems.

However, despite these findings, gaps remain. First, the global evolution and distribution of CSA research lack a systematic overview, most of the reviews are highly localized. Second, current CSA studies use too many different methods from studying soil microbes to mapping SOC haven’t been brought together and compared in one place. Third, CSA practices are recognized for their potential to improve food security, resilience, and income but, there is limited study of how and why farmers make the decision to adopt them and what are key drivers of adoption. Finally, only a few reviews suggest future directions combining new technologies, supportive policies, and practical way for farmers to use CSA. This leaves a gap in showing how CSA practices as a whole act as a long-term resilience strategy.

This SLR synthesizes empirical evidence from existing literature on CSA, and therefore addresses the following research questions,

RQ1: What are the publication trends in Climate-Smart Agriculture (CSA) research?

RQ2: What are the most prevalent theories, contexts, and methodologies have commonly been employed in CSA?

RQ3: What are the key antecedents, decisions, and outcomes of CSA implementation for sustainable farming?

RQ4: What are the critical gaps in the CSA literature and future research directions to enhance farming sustainability?

2 Methodology

2.1 Data collection

In this review, data collection was based on the Preferred Reporting Items of the systematic review and meta-analysis (PRISMA 2020) guideline, and the review protocol (research questions, eligibility criteria, search strategy, and screening procedure) was set a priori and designed by the research team thoroughly before starting data extraction. Since this review synthesizes bibliometric and thematic data on Climate-Smart Agriculture (CSA) rather than appraising a clinical or agronomic intervention, the standard PICOS framework (Population, Intervention, Comparator, Outcome, Study design) could not be applied directly. The review therefore adapted the PICOS elements to its bibliometric-synthesis design, as detailed in Table 1 below. This adaptation reduces the risk of bias, improves the transparency of study selection, and provides a stable evidence base for analysis.

Table 1. Adapted PICOS framework used to define the scope and eligibility criteria.

2.1.1 Search strategy

Scopus and Web of Science were used to retrieve literature because these databases cover peer-reviewed publications in various fields and they both offer export formats that can be analyzed using bibliometric analysis software. Both databases were searched on 15 March 2026 and all records indexed until that date were searched, but no lower publication date limit was put in place to ensure the search captured all the indexed literature on the topic. To be consistent and reproducible, the title, abstract and keywords fields of both databases were searched using the same Boolean search string. The search query was, (“Climate Smart Agriculture” OR “CSA” OR “Climate-smart farming” OR “Climate-resilient agriculture” OR “climate-resilient farming” OR “Climate mitigation agriculture” OR “Climate mitigation farming”) AND (“Carbon sequestration” OR “Soil carbon sequestration” OR “Carbon storage in soils” OR “Soil organic carbon” OR “low-carbon” OR “carbon-smart” OR “carbon capture”) AND (“Sustainability” OR “Sustainable development” OR “Economic” OR “social” OR “environmental*”). This search strategy was designed to comprehensively capture the literature examining the intersections of Climate-Smart Agriculture, carbon sequestration, and sustainability across their environmental, economic, and social dimensions.

2.1.2 Search criteria

The selection criteria have been developed based on the PRISMA framework. The PRISMA flow chart (Figure 1) shows the number of records found, filtered, excluded, and included in every step. After duplicate removal, the remaining records were screened against the pre-defined inclusion and exclusion criteria, combining title/abstract relevance screening with full-text eligibility and quality assessment. The inclusion criteria included peer-reviewed journal articles published in English and in open access. The use of English-language and open-access restriction was motivated by two factors (i) all six co-authors are qualified to review English-language literature and (ii) the open-access restriction is due to a resource constraint because the review team did not have institutional access to some subscription-based journals and that, open-access status is unrelated to the quality of the methodology used in a particular paper. We recognize that both limitations may cause selection bias through under-representation of subscription-only and non-English studies.

2.1.3 Data extraction

A total of 314 papers were obtained from Scopus and 197 papers from Web of Science during data collection. The inclusion criteria were open-access articles, peer-reviewed journals and written in English, and the exclusion criteria were non-open-access articles, reviews, articles in other languages and book chapters. With such criteria, 221 articles of Scopus and 131 articles of Web of Science were filtered out during the first screening. Subsequently, Scopus and Web of Science articles were combined in RStudio and 53 duplicate records were eliminated by matching with DOI and metadata. The title and abstract of each remaining 106 articles were then independently screened by two reviewers and studies were filtered out of the articles that were not related to CSA, carbon sequestration and sustainability, and the risk of bias in the remaining full-text articles was appraised using the quality-assessment procedure.

2.1.4 Quality assessment

The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool (MMAT) (Hong et al., 2018). The MMAT is particularly suitable for reviews to evaluate the studies against five core criteria namely, clarity of research questions/objectives, suitability of study design and data sources, adequacy of the sampling or bibliometric procedure, transparency of analysis, and clarity of reported findings. A structured checklist based on MMAT was used to carry out a formal quality assessment of the short-listed full texts. Each criterion was rated as “Yes” (criterion met), “No” (criterion not met), or “Can’t tell” (insufficient information). All 73 studies met the initial screening criteria, indicating clear research questions and adequate data to address them. The qualitative, quantitative descriptive and mixed methods studies included in this review exhibited moderate to high methodological quality. The most common limitations were related to insufficient reporting of sampling procedures, non-response bias, data collection methods, and the integration of mixed methods components, resulting in several “Can’t tell” ratings. As no critical methodological flaws were identified, all eligible studies were retained for the final synthesis.

2.2 Data analysis

RStudio was used to merge information from various databases because of its flexibility to manage bibliometric analysis and data visualization. It also has an advanced duplicate removal function that checks the details of the paper rather than only title ensuring the accurate removal of duplicate records. It also produces bibliometric outputs such as network maps, thematic clusters and concepts with an option to adjust for specific statistical needs. To organize the discussion of selected studies, TCM framework was applied. This method begins with identifying theories, examining their context. Variables such as dependent, independent moderating and mediating factors are grouped. This also includes aspects like methodology, sample size, analytical techniques, software and tools used to provide a complete overview of the research designs applied.

The study further employed the Theories-Contexts-Methods (TCM) framework and the Antecedent-Decision-Outcome (ADO) framework (Paul et al., 2017; Paul and Benito, 2018). TCM allows for a comprehensive evaluation of the theoretical foundations, research contexts, and methodological approaches used in a particular domain. Meanwhile, ADO enables a structured categorization of the key drivers (antecedents), decisions (dependent variables), and outcomes in a given field of study (Lim et al., 2021). As shown in Figure 2, this combination provides a clear understanding of the research and avoids the flaws of an individual framework approach (Lim et al., 2021; Paul et al., 2021; Paul and Benito, 2018). This thorough review helped us identify research gaps and suggest lines of future research. In this study, the software of RStudio, Bibliography and VOSviewer was used for quantitative bibliometric analysis and visualization of the results.

3 Results and discussions

3.1 Bibliometric analysis

In general, the Scopus and Web of Science dataset, covering the period of time between 2017 and 2026, demonstrates a significant improvement in publication output during the review period, with the impact metrics correspondingly rising (Figure 3). The main concentration of output is over the past seven years. The average age of the documents is 2.96 years and the most part of the literature that has been retrieved was published recently; the mean age is a descriptive measure of the research activity that increased in the past since it may be due to lag in citing older records. The sample consists of 73 documents by 56 different sources meaning that in journals, the publications are spread among many publications, not many publications in one publication. The trend in this particular dataset can be characterized by the annual growth rate of 22.03 percent, which must be interpreted as descriptive but not predictive over a short period of observation (nine years) and due to the indexing lag effects of the past few years. Authorship pattern shows that there is co-research practice, with 391 instances of authors throughout the dataset and on average almost 6 authors per document, which is a relatively high co-authorship rate in this literature. The cross-border co-authorship also stands out, where 26.03% of the publications involve cross-national teamwork. The mean number of citations per document is 27.25, and the number of unique keywords is 305, which indicates a thematically diverse literature that includes a number of inter-related sub-topics. Combined, these bibliometric measures characterize a research niche that is still emerging and is attracting increased scholarly interest; we observe that bibliometric expansion and citation measures are indicators of indexing and publication pattern, and cannot, in isolation, be interpreted as a measure of the scientific maturity of the field, or future publication trends, which would require observational and qualitative support.

3.1.1 Annual scientific production

As shown in Figure 4, annual output was low in the earliest years studied (2017-2020), consistent with an emerging research area. A notable upswing around 2020 is associated with a further integration of CSA into the global sustainability discourse, but this time-related correlation is merely descriptive and does not establish a causal link. The level of output has been on the increase since 2022, and output levels are projected to exceed 12 articles annually by 2024, which coincides with the growing attention to carbon neutrality commitments and the development of voluntary carbon markets in agricultural systems, a contextual explanation, not a tested causal mechanism. The apparent decline in 2025 output could probably be due to 2025 publications not being fully indexed at the time of the search instead of a drop in research interest, but this cannot be told with the current data and must be interpreted as a rough estimate. Collectively, the data can be interpreted as a shift in the occasional early research contributions to a more active and sustained publication stream; but the time series (nine years) is relatively short, and the delay in citation and indexing is notorious, so we should not over-extrapolate this trend to come up with predictions about future publication patterns.

3.1.2 Countries scientific collaboration

Figure 5 maps the international co-authorship networks underlying research on Climate Smart Agriculture. As shown on the world map, key countries like the U.S., China, and a number of European countries are already well linked with other countries around the globe, with existing research infrastructures and policy frameworks. This is due to their concentration of research funds, institutions of carbon-market and their long-term academic infrastructures. These nations are key carbon market architects. They also are major sponsors of climate studies. Their centrality is thus more about resource control than research output. Meanwhile, African countries, such as Kenya, Ethiopia and Zimbabwe, are peripheral, building links with their regional and international partners. The visibility of Ethiopia is probably the combination of numerous factors: its early engagement in the carbon-related initiatives, such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation) and Green Legacy Initiative, its strong exposure to climate variability and land degradation, and the long-standing presence of donor agencies, including USAID (United States Agency for International Development), and World Bank-based programs. The location is associated with its research base, such as Center for International Forestry Research (CIFOR) and the International Centre for Research in Agroforestry (ICRAF), and its status as a pilot in carbon-offset. The connections of Zimbabwe are probably related to the academic connections with Europe in the colonial times and the post-colonial period. This logic of donor and infrastructural exchange, instead of organics, is reinforced by the bilateral patterns in the tables. The Kenya, Germany and Tanzania, United Kingdom (UK) links are a reflection of long-standing development cooperation. The UK, United States of America (USA) axis extending to Europe and Africa, Asia and Latin America indicate their cumulative contribution to global climate finance. It produces two different logics: a continued North-South setup, in which African institutions largely become data points or implementation allies, and a new South-South setup, like Kenya-Tanzania-Ethiopia connections, and indicates new regional capacity without Northern brokers. This difference is significant. In the places where North-South relationships prevail, the research agenda is more apt to be determined by donor priorities and international market standards rather than local needs. The development of South-South cooperation, despite its meager sizes, indicates changing the ownership of research. It should be expanded as a priority, in order to make carbon credit science answer to African circumstances, instead of international verification standards only. The distribution highlights the significance of global collaboration for enhancing the research of agricultural carbon credits. Leadership in Africa in terms of involvement is rising, with developed economies offering expertise and institutional support. These partnerships are vital to building inclusivity, methodological variety and making sure that carbon credit programs are tailored to the world and local context.

3.1.3 Three field plots

From the three-field plot (Figure 6) it is evident that the research landscape is well grounded with Ethiopia as the predominant node tying authors to the themes of climate and agriculture. The central position of Ethiopia is indicated by the high concentration of scholarly output, and its strong links with the keywords CSA, climate change, food security and adaptation. The fact that “Ethiopia” itself is being used as an adjective indicates that the researchers are not just dealing with global problems of climate and agriculture, but are investigating global reality, precisely in Ethiopia. There are a number of prominent authors with close connections to Ethiopia who contribute much to this research emphasis. There is another small but identifiable Nigerian author group that also touches on similar themes, indicating activity outside of Ethiopia. Despite Ethiopia’s prominence, the network also features the involvement of countries around the globe, including the USA, South Africa, the UK, China, Canada, India, Nigeria and Australia, which suggests that research on climate and agriculture is not geographically siloed. CSA is the most prevalent theme in the dataset, with robust connections to climate change and food security, as well as adaptation. Such a grouping indicates a holistic strategy to resilience building in agricultural systems. Emerging directions include ‘regenerative agriculture’ and ‘sustainability,’ which represents a shift in research interests beyond adaptation toward restorative and lasting solutions. The fact that Ethiopia is so prominent is reflective of its role as a scientific epicenter, one that is motivated by the pragmatic necessity of being a climate vulnerable country with pressing food security issues. There is a high relevance of the themes to national needs, which reflects the applied and solution-oriented nature of the scholarship. Simultaneously, the participation of both developed and developing nations highlights the need for global cooperation, sharing of expertise, and collaborative efforts to tackle challenges. The overall impression of the visualization is an interdisciplinary, policy-relevant and global context of food systems with a shared objective of making them resilient to climate stress. The central role of Ethiopia, the predominance of CSA, and the growing themes of sustainability point to both the present agendas and the future course of climate-agriculture research.

3.1.4 Thematic analysis

Thematic mapping (Figure 7) is based on four quadrants, each representing a research theme in relation to its density and its centrality. The motor themes, such as Smallholder farming, Ethiopia, Agricultural technology, Agricultural policy, Crop production, and Livelihood are well developed and centrally driving the research agenda of the field. Themes of smallholder farming and livelihood prevail since majority of the interventions of carbon credit and climate-smart agricultural in the Global South center around systems of smallholder farming, as most agricultural producers in the target areas are smallholders. The emergence of Ethiopia as a motor theme, as opposed to a peripheral case study, is an indicator of the long-term mass and methodological maturity of Ethiopian research, in line with its dominance indicates that Ethiopia is less a case among many, and more a de facto reference case that determines how the field defines and studies carbon credit interventions.

The niche topics are China, emission control, agricultural emissions, empirical analysis, soil carbon and adaptation, which are quite developed yet specialized, falling outside the mainstream of the field. This is probably an indication of a difference in traditions of research. The carbon studies done in China are more inclined to domestic policy goals and massive emissions accounting, which adheres to another methodological rationale than the smallholder- and livelihood-oriented research that has dominated the motor themes. The soil carbon and emission control, as well, are more technically niche subfields, needing biophysical measures skills that are less widely incorporated into the more socioeconomic and policy-driven literature on agricultural carbon credits, making them methodologically rich but thematically insulated.

The basic themes of agricultural production, climate change, food security, farming systems, adaptive management and technology adoption are well researched but less methodologically developed. Their density and centrality are high implying that they are foundational concepts that the field relies upon in the background as framing instead of locations of methodological innovation. This trend is typical of newly interdisciplinary areas, in which overall, well-established ideas serve as the foundation of the literature and are not themselves the target of new empirical growth.

The emerging or declining themes, such as the agriculture, Malawi, and economic development draw the lowest-density, lowest-centrality quadrant. Their location may suggest topics that are still in early stages and are yet to be solidified into the main branch of the field, like Malawi as a more recent geographic example than Ethiopia and Kenya, or themes that are no longer popular as the field itself becomes more specific to target policy and technical issues. It is difficult to draw a clear line between these two opportunities based on bibliometric positioning only, and it would be well supplemented by the cross-reference of the years of publication within this quadrant to showing whether these themes are being talked up or down.

Combined, this thematic structure suggests that the agricultural carbon credit research field is now held together by policy- and livelihood-driven thematic concerns, focusing on particular high-visibility nations, like Ethiopia, whereas more technical and measurement-driven thematic concerns are still relatively siloed. To seal this gap, soil carbon and emissions measurement research can be incorporated with the smallholder and policy literature could be a methodological improvement in the field.

3.1.5 Factorial analysis

The factorial map obtained shows the conceptual structure of the literature along two very different, but interconnected, research trends (Figure 8). The studies related to innovation and adaptation are located in Dimension 1, whereas the studies related to policy analysis, system-oriented and production-oriented are located in Dimension 2, suggesting a distinction between exploratory context-specific studies and more systems-oriented and production-oriented studies. Themes related to sustainability are further distinguished in Dimension 2, namely those related to soil carbon and farming systems, as opposed to those related to behavior and decision making, such as farmer attitudes and adoption processes. There are three predominant thematic domains. First, a cluster of policy and adoption production activities imply a well-delineated and developed body of work that connects the institutional context, farmer response, and productivity outcomes. Second, a central cluster that brings together the smallholder system, livelihoods, climate change and food security underscores the importance of the smallholder sector as a key link between environmental change and socio-economic resilience. Third, a more decentralized innovation–adaptation cluster focuses on mitigation measures, adaptive management and the empirical, with experiential and local dimensions. Interestingly, policy and adoption domains are still weakly linked to sustainability-related concepts, meaning that the application of environmental goals is not well translated into the framework of agricultural systems. Overall, the factorial structure highlights the need for greater biophysical, socio-economic, and policy coherence and scalability to achieve agricultural systems solutions in the face of climate change.

3.1.6 Life cycle analysis

The analysis of the number of publications per year and the total number of publications per year, with a logistic growth model, shows a distinguishable life cycle of the research field under study (Figure 9). The overall growth pattern of this bibliographic collection is classic logistic (S-shaped) for cumulative publications and has a bell-shaped curve of annual publications. The “Cumulative Growth Curve” is an S-curve which shows a slow take-off in paper accumulation, followed by a rapid “take-off” and then a deceleration toward saturation. Life Cycle - Annual Publications plot indicates that publications have been low in the beginning, peaked and are predicted to drop quickly. The inflection point, where cumulative growth rate is maximal and annual publications peak around 2024–2025 with an observed peak of 13–14 publications. The logistic model is good fit for the observed data. The R² value of 0.883 indicates that 88.3% of the variance is explained by the model. The annual publications also show a good level of predictive accuracy as the RMSE is around 1.48 publications in comparison to the actual counts. There are minor deviations in early years (e.g. 2017), observed counts are close to the fitted curve, and near the peak, the peak is slightly lower than the observed counts. It can be observed that data from 2026 indicate a slight decrease, which is consistent with the model’s prediction. The cumulative plot is very close to the observed data and so there is increased confidence in the model. The logistic model parameters yield quantitative information: the carrying capacity, K = 118.03 publications, is the expected saturation level; the intrinsic growth rate, r = 0.40, is the growth rate in the exponential phase; and the inflection point, t₀ = early 2025, is the year that corresponds to the peak annual output. The timeline of publications has been segmented into three phases: Emergence Phase (showing slow growth in publications of 1–5 per year) between 2016 and 2019, Expansion Phase (showing rapid growth in publications of 10-0) between 2020 and 2025, Maturity/Saturation Phase (forecasting a decline in publications of 0–5 per year) after 2025 onwards. Concurrently, the cumulative growth curve will start to flatten out, gradually approaching its carrying capacity (K = 118 publications). Projected curve indicates that the 90% saturation point is expected to be reached in the next 5–10 years. This stage is when the field is approaching a stage where new discoveries or fundamental contributions will become less frequent. The implication of these patterns is critical to an understanding of the maturity of research field and its future. Research is expected to be reduced considerably in the number of new publications while continuing. This does not mean it is a dead industry, it just means there’s an end to the period of explosive growth. The future course of the field will probably be in terms of the application of present knowledge to real-world problems as well as a further search for more fine-tuned and consolidated areas of research. Further connections with other specialties and further specialization in specific fields is also anticipated. Accordingly, the field will experience fewer fundamental advances, and more emphasis will be placed on incremental advances and interdisciplinary research.

3.1.7 Network analysis

The visualization of the network shows a dense and interconnected structure, suggesting a well-established and integrated research landscape focused on climate change and climate-smart agriculture (Figure 10). The overall density seems quite high, and there are many overlapping links, indicating a high degree of overlap among concepts. This means it is cohesive in terms of theme and able to be inter-disciplinary. The network is moderately centralized with some dominant nodes as the intellectual anchors and still distributed connected to clusters. The centrality of ‘climate change’ is the most prominent node in the core of the network in all centrality measures.

It has a high degree centrality because it has direct links to almost all the thematic areas, and high betweenness centrality because it is a critical node that establishes links between different subfields. Its high closeness centrality as it is likely to be very close to other nodes indicates that it has a great degree of reachability. Likewise, the term “climate-smart agriculture” serves as an important integrative node that links the themes of adaptation, mitigation, and agricultural productivity, having the potential for a high eigenvector centrality given its connections with other central nodes. “Food security” and “agricultural production” are prominent within the green and blue clusters, respectively, indicating their importance in sustainability and productivity discourses. Some nodes like “adaptation,” “mitigation,” and “adoption” have intermediary functions and are characterized by high betweenness centrality, connecting technical, policy, and practice-oriented domains. At the same time, nodes such as “smallholder”, “Ethiopia” and “innovation” emerge as context specific but well-connected nodes, indicating an emphasis of applied research.

The network shows clearly the four distinct main clusters, Table 2. The green cluster deals with sustainability, food systems and ecological practices. Productivity, adaptation strategies and analytical approaches are highlighted in the blue cluster. The red cluster focuses on socio-economic aspects such as smallholders, resilience, and technological innovations. The yellow cluster brings together emissions, policy and adaptive management. Such communities represent thematic specialization and cross-cutting integration, both at the level of thematic research streams and at the level of scientific and technology integration. The strength of the edges within clusters seems high, suggesting high intra-community cohesion, and there are plenty of inter-cluster connections, suggesting disciplinary exchange. Remarkably, “climate-smart agriculture” and adaptation” are nodes that serve as bridges linking knowledge flows between the clusters. The isolated nodes are few, indicating that there is little fragmentation, and integration of the knowledge domain. The network provides a very policy-relevant and interconnected research landscape. Clusters are complementary perspectives (ecological, technical and socio-economic) that drive the structure of the system, with central nodes such as climate change and climate-smart agriculture. The results indicate the need to develop the field by bolstering bridging concepts and the link of smallholder systems to global systems.

Table 2. Thematic clusters identified through keyword co-occurrence network analysis.

3.2 TCM - ADO framework

Figure 11 shows a comprehensive synthesis of literature on CSA-based on the TCM and ADO frameworks. It provides a clear conceptual framework for why current studies exist, where they are used, and how, and identifies method approaches, as well as mapping out key drivers, processes and outcomes. The integrative view aids in the understanding of the intellectual landscape of the discipline and points to future research.

3.2.1 Theory

The theoretical analysis underpinning this review draws upon a diverse set of frameworks, as listed in Table 3. Evidently, the theory of diffusion of innovations is the most used theory among the articles. The theory of diffusion of innovations (DOI) assumes that innovations are not static but are constantly evolving and growing over time. Specifically, innovations are most likely to be adopted by members of the same social group or class who are already using similar technologies (Aziz et al., 2024). The DOI describes how agricultural technologies are adopted over time through communication, information, and knowledge. The DOI theory has five characteristics that determine the rate of adoption of agricultural technologies: relative advantage, compatibility, complexity, trialability, and observability. In addition, the theory states that the decision to adopt an innovation is viewed as a cognitive process that follows stages of knowledge about the technology, persuasion, decision, implementation, and confirmation (Teklu et al., 2023b).

Table 3. Prominent theories in the CSA literature.

According to expected utility theory assumptions, farmers that make a utility maximization decision to adopt new agricultural technologies, such as CSA practices, based on the risks and potential uncertainties are also subject to farm input constraints (Belay et al., 2024). Within the framework of random utility theory, farmers are assumed to be risk neutral, making their decisions regarding the adoption of CSA contingent upon expected net yield outcomes. Consequently, the choice between CSA adoption and non-adoption is guided by their perception of whether CSA practices will enhance net yields (Amadu et al., 2020). Grounded theory is embraced to inductively derive context-specific understanding of regenerative agriculture embedded in the lived experiences of farmers as they actively develop, implement, and advocate regenerative practices within their agricultural systems (Cusworth et al., 2024).

Agricultural technology adoption theory is a multidisciplinary field that attempts to explain why some farmers accept new technologies while others do not, by combining elements of decision theory and the diffusion of innovation theory. For example, smallholder farmers expect benefits or utilities from adopting CSA practices that maximize their income and food security (Belay et al., 2024). The theory of climate change was utilized to study the perceptions of participants regarding climate change and other threats to sustainable farming in mountain regions, revealing considerable variations (Mathews et al., 2018).

The farmers in the mountainous areas had mixed opinions with regard to climate change. Some doubted, saying it wasn’t reality, it was just the perception. Others noted changes such as changes in the timing of precipitation in the summer and predicted that farming methods should change accordingly, for instance, by practicing minimum or no-till or by conserving soil moisture. Several participants pointed out the uncertainty of the past few years and that they no longer knew what to expect. Dry spells now happened in succession, as in the 1980s, but this was not what most farmers compared today’s situation to, one of whom described the situation as a drought. Some acknowledged they weren’t completely sure about the term “climate change”, again indicative of the range of understanding and interpretation among the group. Existing studies have predominantly employed innovation and diffusion theory to compensate for the limited empirical evidence available to assess farmers’ awareness of CSA practices. In addition, other theoretical frameworks, including intersectionality, decision theory, the unified theory of acceptance and use of technology, economic constraint theory, adopter perception theory, the sustainable livelihood framework, affordance theory, feminist standpoint theory, punctuated equilibrium theory, friction or traction theory, and the theory of planned behavior, have been applied only sporadically, typically appearing in the literature once.

3.2.2 Context

The Context in TCM analysis gives the regions across which the studies have been conducted. It is evident that the studies are geographically diverse because they cover over 29 countries across six continents (Figure 11). Carbon farming has become an emerging topic that has been addressed in various parts of the world, and research has been conducted at diverse geographical locations and settings, considering its relevance to climate change and various changes occurring in the environment. The context focuses on the geographical context; a systematic review has revealed that a large portion of the research has been conducted in developing (China, Kenya, Ghana, and Malawi) and underdeveloped nations (Ethiopia, Mali), with a significant portion of the population having agricultural land. Majority of the studies in the developing and underdeveloped countries has been done because women face gender-specific constraints related to customary laws, which are in favor of men, and lack of equal opportunities and limited access to productive resources that contribute to the reduction of their agricultural productivity (Tesfaye et al., 2023), challenges like low returns from the agricultural sector as well as issues in the eco-system such as pests and weed control, nutrient cycling, nitrogen fixation, soil regeneration as well as food security for the population as a whole but in particular for small-scale farmers (Teklu et al., 2022), also studies were sought to ascertain the current state of CSA practices among Kakamega County’s smallholder farmers to identify the main drivers of CSA adoption as Most of Kenya’s population’s livelihoods and agri-food systems rely on rain-fed agriculture making them vulnerable to climate change (Ndung’u et al., 2023), also studies discusses the use of soil health indicators to measure the effectiveness of implemented CSA practices including Conservation Agriculture in maize-based systems and Soil and Water Conservation in Highland African systems as Soil health is key to building more climate-resilient agricultural systems and can be improved through CSA practices that also enhance soil carbon storage (Dougill et al., 2021). Combined, these results underline that the research into carbon farming is geographically imbalanced, with a focus on the developing and underdeveloped world, and bring to the fore the need to investigate more into the developed world. This will not only reinforce the generalizability of theory, but also improve the global significance of carbon farming as an important solution to climate change.

3.2.3 Methods

As mentioned in the introduction, the methodological overview in Table 4 outlines the diversity of approaches that were used, ranging from quantitative to qualitative, to mixed methods. The majority of studies used quantitative analysis or experimental designs, then used mixed methods to discover and establish the factors and dimensions. A few studies used qualitative methods. Structured questionnaires, semi-structured interviews, and surveys were used as quantitative data collection methods and qualitative data collection methods, respectively. The models used were common analytical techniques such as the Multinomial Endogenous Switching Regression (MESR) model, Ordinary Least Squares (OLS) model, and the Multinomial Endogenous Treatment Effect (METE) model. The software used were most commonly the Statistical Package for Social Sciences (SPSS) and multiple versions of STATA. Table 4 shows the software tools used that were World Weather Online, GPS, SSIS, NVivo 12, MS Excel, Ken-Q software, Open Data Kit, MS MaxQDA Version 2011, Atlas. ti v7.0. Empirical studies on adoption decision-making factors should, therefore, consider different CSA practices and associated effects on crop productivity. Panel data techniques are suggested to overcome the problem of unobserved heterogeneity and time-invariance. Furthermore, future research should assess the extent to which social-psychological factors influence adoption behavior to recommend for integrated and context-specific recommendations to promote behavioral change among farmers in adopting CSA.

Table 4. Research methodologies used across the reviewed CSA literature.

3.2.4 Antecedents

The review identified five categories of antecedents that influenced the adoption of CSA. This explains several factors and the implications thereof, and relates the most common antecedents and their interconnections.

3.2.4.1 Socio-demographic factors

Farmers’ socio-demographic factors significantly affect their adoption behavior. Risk attitudes are influenced by age, where younger farmers are more innovative compared to older farmers (Djido et al., 2021; Msweli et al., 2024; Muriithi et al., 2021; Page and Witt, 2022; Setshedi and Modirwa, 2020; Teklu et al., 2022). Gender plays a critical role in access to resources and decision-making power, often limiting women’s ability to adopt CSA technologies (Belay et al., 2024; Masha et al., 2024; Obi and Maya, 2021; Pandeya et al., 2024). Education and farming experience enhance farmers’ capacity to process information and implement improved practices (Erekalo and Yadda, 2023; Martey et al., 2020; Ndung’u et al., 2023; Otara et al., 2023; Pandeya et al., 2024; Shahzad et al., 2021; Sisay et al., 2023). Household characteristics such as household size and labor availability determine the ability to undertake labor-intensive CSA interventions (Abegunde et al., 2022; Agarwal et al., 2022; Kangogo et al., 2021; Martey et al., 2020; Muriithi et al., 2021; Obi and Maya, 2021). Additionally, marital status (Djido et al., 2021; Msweli et al., 2024; Setshedi and Modirwa, 2020), household headship (Djido et al., 2021), and employment status (Masha et al., 2024) influence financial stability and decision autonomy. Behavioral traits such as risk-taking, innovativeness, and proactiveness are also important, as CSA adoption often involves uncertainty and delayed returns (Kangogo et al., 2021). Access to mobile phones and ICT devices further strengthens adoption by enabling access to information, extension services, and financial tools (Djido et al., 2021; Teklu et al., 2023b; Tetteh Quarshie et al., 2023).

3.2.4.2 Resource endowment factors

Resource endowments determine the feasibility of adopting CSA practices. Land size, ownership, and tenure security are critical, as secure and larger landholdings encourage long-term investments in sustainable practices (Amadu et al., 2020; Clay and Zimmerer, 2020; Imran et al., 2019; Karlsson et al., 2018; Norse, 2012; Tadesse et al., 2021; Tankha et al., 2020; van Veelen, 2021). Livestock and asset ownership reflect household wealth and provide resilience against climate shocks (Djido et al., 2021; Sisay et al., 2023; Yitbarek and Tesfaye, 2022). Access to credit and broader wealth indicators influence farmers’ financial capacity to invest in CSA technologies (Obi and Maya, 2021; Pandeya et al., 2024; Setshedi and Modirwa, 2020; Sisay et al., 2023). Furthermore, operational and market costs including input costs and transaction costs can either facilitate or constrain adoption, particularly for resource-poor farmers (Norse, 2012; Tetteh Quarshie et al., 2023).

3.2.4.3 Information and institutional factors

Institutional support and information access are key enablers of CSA adoption. Extension services play a vital role in knowledge dissemination and capacity building (Ha and Bac, 2021; Obi and Maya, 2021; Shahzad et al., 2021; Yitbarek and Tesfaye, 2022). Climate information services such as weather forecasts and training programs, support farmers’ adaptive decision making (Setshedi and Modirwa, 2020; Sisay et al., 2023). With the assistance of NGOs and development programs, there is better access to resources and training (Ifeanyi-Obi et al., 2022; Wilson et al., 2022). Irrigation schemes increase the water use efficiency and foster water-smart farming (Ighodaro et al., 2020; Tsige, 2019; Tsige et al., 2024). In addition, digital finance and ICT-based information sources are also growing in significance and provide better access to credit facilities, markets, and advisory services (Li et al., 2024; Tetteh Quarshie et al., 2023).

3.2.4.4 Environmental factors

The need for and type of CSA practices adopted are influenced by environmental conditions. The suitability of interventions is determined by the soil fertility, the extent of land and slope (Erekalo and Yadda, 2023; Sisay et al., 2023; Tadesse et al., 2021). Rainfall variability (Ogada et al., 2020; Teklu et al., 2023b; Tione et al., 2022), temperature changes (Memarbashi et al., 2022; Ogada et al., 2020; Teklu et al., 2023b) and exposure to climate shocks (Amadu et al., 2020; Tione et al., 2022) constitute the key influencers of adoption. The adoption of water efficient technologies (Imran et al., 2019; Ndung’u et al., 2023) is influenced by water-related factors such as water availability, access to irrigation water, and water quality. Environmental constraints also help to inform land use decisions, which, in turn, inform the selection of CSA practices (Clay and Zimmerer, 2020; Traore et al., 2021).

3.2.4.5 Technological factors

In addition to external environment, the nature of CSAs is important in shaping decisions to adopt. CSA is not a single technology but rather a cluster of technologies comprising of conservation agriculture, agroforestry, improved crop varieties, water management methods, and integrated soil fertility management (ISFM) (Djido et al., 2021; Andati et al., 2023). The perceived attributes of these practices (relative advantage, compatibility to ongoing practices, complexity, trialability, and observability) significantly influence adoption behavior (Teklu et al., 2023b). Practices with obvious economic advantages and those that are simple and do not require a change in farmers’ knowledge systems are more likely to be embraced. Other factors that influence adoption decisions include labor needs, cost and time for returns (Murray et al., 2016). For example, expensive or postponed benefits of a practice might deter smallholder farmers (Norse, 2012; Tetteh Quarshie et al., 2023). Likewise, parts of the world that are vulnerable to climate change are more appealing for risk-reducing technologies (RDTs, such as drought-resistant crops). There are also the complementarities and trade-offs between CSA practices, which is also important (Quarshie et al., 2023; Schaafsma et al., 2018; Tilahun et al., 2023). Farmers do not generally implement individual practices, but rather packages of practices, depending on their resources and environmental contexts. Thus, local contexts and dynamics of farming systems must be taken into account in the design and promotion of CSA technology sets.

3.2.5 Decisions

Decisions (D) in the context of climate-smart agriculture (CSA) represent the observable behavior of farmers in terms of implementing or not, and how and to a certain degree, they implement CSA.

3.2.5.1 Adoption decisions

In CSA, adoption decisions are essentially those of farmers’ decisions to accept or reject climate-smart options, which may involve a willingness to give up traditional farming practices for more sustainable and climate-resilient alternatives (Erekalo and Yadda, 2023; Ha and Bac, 2021; Meshesha et al., 2022; Sisay et al., 2023; Teklu et al., 2023b; Tione et al., 2022; Tran et al., 2020). This decision also includes the extent to which farmers invest in CSA-related inputs such as improved seeds (Andati et al., 2023; Kangogo et al., 2021; Martey et al., 2020), organic fertilizers (Ha and Bac, 2021; Tabe-Ojong et al., 2023), irrigation systems (Imran et al., 2019; Tankha et al., 2020), and soil management technologies (Erekalo and Yadda, 2023; Sisay et al., 2023), indicating both initial acceptance and commitment to long-term implementation.

3.2.5.2 Technology choice

Technology choice reflects farmers’ decisions for a specific one or more selected practices from a wide array of possible practices such as intercropping, integrated farming, minimum tillage, mulching, and composting (Aziz et al., 2024; Murray et al., 2016). Given that these choices are affected by socio-economic and agro-ecological factors, including resources availability (Imran et al., 2019; van Veelen, 2021), perceived benefits (Hou and Hou, 2019; Msweli et al., 2024; Quarshie et al., 2023; Teklu et al., 2023b), and local environmental conditions (Maka et al., 2019; Traore et al., 2021), farmers will choose practices that are best suited for their socio-economic and agro-ecological settings.

3.2.5.3 Intensity of adoption

Intensity of adoption is characterized by the extent of the adoption of the CSA practices which may be quantified by the number of technologies adopted, the percentage of the land used for these practices, and the amount of investments put into them. Farmers might choose one or more practices, and the implementation intensity might suggest levels of integration into farming systems and the degree of involvement in sustainable agriculture (Martey et al., 2020).

3.2.5.4 Adaptation and risk management decisions

Adaptation and risk management decisions also include a wider range of coping strategies adopted by farmers such as crop diversification (Erekalo and Yadda, 2023; Sisay et al., 2023), introduction of drought resistant varieties (Erekalo and Yadda, 2023; Schaafsma et al., 2019; Teklu et al., 2023a), changing planting dates (Meshesha et al., 2022; Tadesse et al., 2021; Teklu et al., 2022), and adoption of soil and water conservation measures (Dougill et al., 2021; Memarbashi et al., 2022; Meshesha et al., 2022; Tadesse et al., 2021; Teklu et al., 2022, 2023b). These decisions are tied closely with the adoption of CSA and are proactive steps toward building resilience, decreasing vulnerability, and maintaining productivity in agriculture in a changing climate (Mathews et al., 2018; Pandeya et al., 2024; Quarshie et al., 2023).

3.2.6 Outcomes

In the ADO context, Outcomes (O) are the results of farmers’ decisions on adoption and implementation of CSA practices and are measured in both economic and livelihood terms. One of the most commonly reported primary outcomes in the literature is agricultural productivity gains, frequently in the form of enhanced yield due to improved soil management, water use efficiency and water-resistant crops (Amadu et al., 2020; Bazzana et al., 2022; Imran et al., 2019; Martey et al., 2020). The productivity improvement improves household food availability and access and helps to drive food security (Masha et al., 2024; Quarshie et al., 2023; Tabe-Ojong et al., 2023). Beyond production outcomes, there are also economic impacts, including enhanced farm income, increased household income, and in certain instances, boosted off-farm income through diversification options which lessen the vulnerability to climate-sensitive agriculture (Abegunde et al., 2022; Imran et al., 2019; Meshesha et al., 2022; Msweli et al., 2024; Sarvina et al., 2021; Sisay et al., 2023; Tsige, 2019; Tsige et al., 2024). Importantly, CSA practices also help farmers have a more stable income as they manage risk caused by climate variability and help reduce yield fluctuations over time (Hellin and Fisher, 2018; Teklu et al., 2023a; Tran et al., 2020; Yitbarek and Tesfaye, 2022). Beyond economic outcomes, CSA adoption enhances resilience to climate change by strengthening farmers’ capacity to withstand and recover from climate shocks such as droughts and floods (Martey et al., 2020; Schaafsma et al., 2019). Furthermore, long-term sustainability outcomes emerge through improved soil health, efficient resource use, and reduced environmental degradation (Pandeya et al., 2024; Wilson et al., 2022). Overall, these outcomes demonstrate that CSA adoption not only improves immediate productivity and income but also supports broader goals of resilience, food security, and sustainable agricultural development. Owing to climate change and extreme weather conditions that affect agricultural productivity, studies examining the relationship between CSA adoption, yields, and food security find a positive association between the adoption of CSA practices and yields, with practices such as climate-smart groundnut varieties, cereal–groundnut intercropping, and the use of organic fertilizers leading to greater yields and food security gains (Tabe-Ojong et al., 2023). As agriculture affects both carbon emissions and carbon sinks, ecological low-carbon agriculture emphasizes intensifying agricultural resource utilization, reducing excessive agricultural inputs, and lowering greenhouse gas emissions (Norse, 2012; Teklu et al., 2022, 2023b; Tetteh Quarshie et al., 2023). However, findings indicate regional development imbalances, low willingness of farmers to participate, insufficient technological reserves, and challenges in realizing the value of ecological products, while also providing insights for advancing sustainable agricultural development (Guo and Guo, 2024). CSA has also enhanced the viability and sustainability of smallholder farming, as farmers respond to climate change with support from government institutions and extension agencies facilitating the adoption of climate-smart farming technologies (Phiri et al., 2021).

3.2.7 Critical synthesis and strength of evidence

While the preceding subsections provide the summary of the theories, contexts, methods, antecedents, decisions, and outcomes as reported in the 73 studies reviewed, the evidence base is not uniform and several tensions warrant explicit discussion rather than remaining embedded within descriptive categories. There is significant variation in the magnitude of CSA productivity and emission benefits that are reported. There is evidence of yield increase of 15-30% and GHG-reduction of up to 25 percent (e.g., Begna and Wakweya, 2026) in some studies, but in others, the effect after accounting for farm heterogeneity and adoption intensity is weak or non-significant. Such variations seem to be related to study design (cross-sectional and longitudinal), the outcome of adoption (binary and intensity-based) and agro-ecological context as opposed to CSA practices per se. Smallholder, rain-fed farming systems in Sub-Saharan Africa and South/Southeast Asia are also strongly focused on the evidence of adoption drivers. The scarcity of similar empirical research in developed and mechanized agricultural economies limits the generalizability of the identified antecedent-outcome associations to other areas. Also, the majority of the literature reviewed is based on cross-sectional household survey data and self-reported adoption that is vulnerable to recall and social-desirability bias and cannot make causal conclusions about the adoption decisions and their purported consequences. The proportion of studies utilizing experimental, quasi-experimental, or panel research designs that could be used to derive stronger causal interpretations is small. There is also a lack of consistency in outcome measurement across the literature, where carbon sequestration is proxied in different ways using soil organic carbon content or practice adoption reported by the farmer or modeled carbon emissions reduction, and so direct comparisons of effect sizes between studies is not possible. Together the evidence of the advantages of CSA to carbon and productivity would best be described as intermediate and situation-dependent as opposed to consistently strong, and the same must be said about the interpretation of the policy and future research ramifications.

The results indicate that future CSA studies need to go beyond the description of determinants of adoption to come up with more stringent causal evidence of long-term environmental and socioeconomic consequences. An increased utilization of longitudinal, panel, and quasi-experimental research designs would help the investigators to differentiate between short-term adoption impacts and long-term effects on carbon sequestration, productivity, and resilience. Furthermore, it would be prudent to extend empirical studies to more advanced and highly mechanized agricultural systems to enhance the external validity of existing evidence and enable the meaningful comparison between local and global contexts. Such comparative evidence would aid in such comparative practices of CSA that are universally applicable and those that need to be adjusted to a particular institutional and agro-ecological environment.

4 Implications

4.1 Sustainability implications

The Sustainable Development Goals (SDGs) provide a global (Eta et al., 2023) framework for addressing interconnected challenges of poverty, hunger, inequality, and environmental sustainability. Within this context, CSA have been widely examined as integrative approaches that advance multiple SDGs simultaneously (Table 5). There is evidence supporting the potential of CSA in enhancing gender empowerment and promoting gender equality. CSA promotes gender empowerment and equality by ensuring that both men and women benefit from CSA practices (Tesfaye et al., 2023). CSA have important implications for some of the sustainable development targets like reducing hunger and ensuring healthy diets for all. It also has implications for meeting the twin goals of increasing agricultural productivity while maintaining environmental sustainability (Tabe-Ojong et al., 2023). Fentie and Beyene (2019), found that adopting CSA positively impacts household yield and crop income. The findings indicate that farmers could increase crop income and reduce food insecurity by implementing CSA practices and technology. Farmers who use CSA practices consume more diverse food and yield higher income than nonadopters do. Moreover, the positive effects of CSA adoption can improve farmers’ adaptive capacity to climate risks (Belay et al., 2024). Furthermore, reorientation on the part of local drivers responsible for driving the CSA for sustainable food systems is essential to draw attention to the need to select climate adaptation and food production practices that adapt well to changing climate to grow the food they eat without undermining the economic, social and environmental bases that generate food security and nutrition for future generations (Quarshie et al., 2023). CSA practices can also help in reducing the adverse impacts of climate change on crop productivity and should, therefore, be promoted across the country (Shahzad et al., 2021). All these evidence underscores their potential to transform agricultural systems while promoting social equity and environmental resilience. Policymakers and practitioners seeking to mainstream CSA into development strategies, can reinforcing global efforts to achieve the SDGs in an integrated and inclusive manner. Special focus must be given to women, resource-poor farmers, and other socially marginalized populations, whose involvement is frequently limited due to lack of access to land, finance, and extension services, and decision-making opportunities. Accordingly, sustainability outcomes must be assessed not just on the basis of productivity and environmental performance but also in the light of indicators of social inclusion, equity and long-term livelihood resilience.

Table 5. CSA literature linked to sustainable development goals (SDGs).

4.2 Policy implications

The CSA policies should shift the resilience narratives to quantifiable carbon sequestration results integrated within the farming systems. Findings of this review indicate that conservation agriculture, agroforestry, and soil management (CSA practices) improve soil organic carbon (SOC), microbial activity, and long-term ecosystem functioning, but their incorporation into policy and adoption frameworks is weak. Consequently, policy regimes must consider carbon-focused incentives, including results-based payment schemes, carbon credit schemes, and soil carbon monitoring schemes to explicitly reward farmers on the basis of sequestration results as opposed to only productivity increases.

Social protection should be targeted, and it must be coupled with climate mitigation co-benefits. In extremely poor and female-headed households, support mechanisms (cash transfers, insurance, and labor programs) should be conditional or tied with the adoption of CSA, especially practices with high carbon sequestration potential agroforestry and cover cropping (Hellin and Fisher, 2018). This makes sure that the social equity goals are not considered as separate policy objectives, but instead as the ones that are aligned with the low-carbon agricultural transitions.

The policies of capacity-building should no longer focus on general awareness creation but rather on technical training on carbon-enhancing practices and measurement. The findings emphasize that the central aspects of CSA effectiveness are soil health and carbon storage, but farmers do not always have the technical skills to implement and monitor these processes. Training on the dynamics of carbon in soil, regenerative practices, and participating in the carbon market should therefore be included in the strengthening of extension systems, supported by universities and agricultural institutions (Setshedi and Modirwa, 2020). This too covers the reported disparity between the knowledge of biophysical sustainability and the system of actual adoption.

One of the critical policies disconnects that have been identified in the current study is poor integration between sustainability goals and adoption/decision frameworks. Although CSA has a close association with productivity and food security, its benefits in terms of carbon sequestration are not effectively translated into actionable farm-level incentives. Policy should therefore encourage the adoption of bundled CSA adoption strategies, whereby farmers are encouraged to adopt complementary practices (e.g., minimum tillage + crop rotation + organic inputs) that optimize carbon storage as opposed to isolated technologies. This involves the design of integrated programs in relation to resource constraints and decision-making behavior of farmers.

Carbon-oriented agricultural transitions need to be explicitly supported by the policies of digital and financial inclusion. Digital finance and ICT access may enable farmers to engage in new carbon markets, access climate information, and lower transaction costs related to certification and monitoring (Li et al., 2024). Green financial products, co-developed by governments and financial institutions, should be tailored to CSA bundles, especially when smallholder cooperatives are involved, to allow aggregation of carbon benefits at scale.

Place-based and context-specific policy design is also needed to scale CSA to carbon sequestration. The results highlight the fact that the effectiveness of CSA is strongly determined by local agro-ecological conditions, soil properties, and climate variability. As a policy, it should not be one-size-fits-all but rather a promotion of localized CSA portfolios which should be supported by decentralized extension systems and regional research collaborations (Djido et al., 2021). This is especially critical in climate-sensitive areas where carbon restoration of soils can be both mitigating and adaptive.

The focus on structural inequalities should not be sidelined; however, the policies must specifically address the fact that gender and resource constraints restrict the ability to participate in carbon-oriented agriculture. Women must be provided with equitable access to land, credit, and training in carbon-smart practices, so that they can participate fairly in CSA-driven carbon markets (Tsige et al., 2024).

Lastly, governments ought to establish working roadmaps that connect CSA adoption to national climate ambitions, such as Nationally Determined Contributions (NDCs). This involves setting up standardized monitoring, reporting, and verification (MRV) systems of soil carbon, integrating CSA into national climate policies, and enhancing coordination between research, extension, and policy institutions (Ifeanyi-Obi et al., 2022). It is necessary to align CSA so as to transform CSA into a scalable, measurable, and policy-driven strategy to carbon sequestration. These policy directions have shown that CSA needs more than individual practices to be adopted in order to promote it. Governments need to implement unified policy frameworks that are a combination of financial incentives, carbon markets, extension services, digital technologies, and institutional support to lower the barriers to adoption and reward quantifiable environmental results. These coordinated strategies would further enhance the role of CSA in national climate commitments and at the same time, productivity, resilience, and carbon sequestration goals are met concurrently.

5 Future research directions

The future of climate-smart agriculture (CSA) should not be limited to a continuation of general adoption studies but rather an enhanced integration of carbon sequestration dynamics into the process of farmer decision-making and policy frameworks. This review reveals a critical gap between biophysical processes of sustainability, especially accretion of soil organic carbon (SOC), and the socio-economic and policy processes that drive adoption, indicating the need to adopt interdisciplinary and systems-oriented research designs.

One of the priorities is the creation of longitudinal and experimental studies that directly quantify the results of carbon sequestration over time. Although the current literature indicates positive correlations between CSA adoption and productivity or resilience, there is a lack of empirical data that can capture dynamic changes in the soil carbon stock, microbial activity, and long-term ecosystem functions. The future study ought to, therefore, utilize panel data, field experiments, and quasi-experimental designs to provide causal relationships between CSA practices and carbon sequestration outcomes (Tabe-Ojong et al., 2023).

In addition, the development of joint analytical frameworks that jointly model adoption choices, the intensity of use, and the environmental impact is required. Existing methods tend to assume that adoption is a binary event, and that the bundling and complementation of CSA practices is not a decisive factor in maximizing carbon storage. Future research needs to factor in the multi-method approaches (e.g., SEM, system dynamics, and causal inference models) to capture the complex interdependencies among adoption behavior, resource constraints, and carbon-related outcomes (Jones et al., 2023).

Farmer-based and participatory research is still necessary but needs to be expanded to co-design carbon-smart interventions and monitoring systems. Providing farmers with empowerment at the design stage can improve the contextual relevance of CSA practices and make them more scalable, as well as provide bottom-up validation of regenerative and carbon-sequestering practices (Dougill et al., 2021). This is particularly important given that grounded evidence shows regenerative agriculture practices are often locally developed and adapted by farmers themselves.

The heterogeneity of CSA effects across agro-ecological and socio-economic settings should also be a subject of future research. Carbon sequestration practices widely differ in terms of soil type, weather, and agriculture. Thus, studies that are context-specific and location-based are required to determine the best CSA portfolios and prevent generalized policy prescriptions (Shahzad et al., 2021).

The other important research gap is the application of CSA to the new carbon markets and financial systems. Empirical studies that analyze the ways in which smallholder farmers can be involved in carbon credit systems, how transaction costs and verification challenges influence participation, and how the benefits are distributed across value chains are limited. The future research to consider should be institutional models, aggregation mechanisms, and digital MRV (Monitoring, Reporting, Verification) systems to make carbon farming systems scalable and inclusive.

The dimensions of gender and social inclusion are to be analyzed more thoroughly, in connection with access to carbon-related benefits and power of making decisions. Future studies should not focus on identifying gender gaps but on how structural inequalities contribute to participation in carbon-sequestering activities and climate finance systems and how CSA interventions can be designed to yield equitable results (Tsige et al., 2024).

Also, in future research, one should examine how social-psychological and behavioral variables contribute to the adoption of carbon-oriented CSA practices. In addition to economic incentives, risk perception, institutional trust, and climate change attitudes are also important factors affecting adoption decisions. By applying these dimensions to empirical models, more behaviorally informed and context-sensitive policy recommendations can be given (Erekalo and Yadda, 2023).

Lastly, future studies must reinforce the association between CSA adoption, market integration, and value chain development especially within the framework of sustainable and low-carbon food systems. The way in which improved access to markets, financial services, and social networks affects both the level of adoption and carbon performance is critical to designing scalable interventions (Bazzana et al., 2022). It involves the analysis of the ways CSA practices can be entrenched in more extensive agri-food system changes, aligning productivity, resilience, and climate mitigation goals.

6 Limitations of the study

Although this review represents a synthesis of the available literature on Climate-Smart Agriculture and carbon sequestration, a few methodological aspects must be taken into account when interpreting the results. The search was limited to Scopus and Web of Science, potentially missing studies that were only indexed in other databases (e.g., CAB Abstracts, AGRIS, or regional repositories). Limiting eligibility to English-language and open-access publications could have excluded potentially relevant evidence and caused selection bias. Similar to any literature-based review, there is a risk of publication bias inflating the perceived strength of CSA evidence. The omission of grey literature, such as working papers, government and NGO reports, theses, and conference proceedings, could have missed out on emerging or practice-based evidence. Also, both the bibliometric and TCM-ADO analyses are descriptive, indicating reflection of the research trends, but not necessarily the causal efficacy of specific research. Further databases, grey literature, non-English sources, and meta-analytic synthesis should be included in future reviews in the cases of adequate similar studies.

7 Conclusion

This review is a synthesis of CSA in a more detailed manner and its use in carbon sequestration and transitions to sustainable farming practices. The results indicate that CSA practices, especially conservation agriculture, agroforestry, and integrated soil management, have a significant positive impact on soil organic carbon (SOC) accretion, the functionality of soil microbiomes, and greenhouse gas emissions, and at the same time, CSA practices enhance productivity and resilience. Nevertheless, as the results also point to a critical structural gap: whereas CSA is closely related to food security and adaptation outcomes, its potential as a carbon sequester remains largely incompletely integrated into the process of decision-making and policy making of the farmers. There are weak correlations between socio-economic, institutional, and behavioral drivers of adoption and environmental outcomes, and this restricts the full implementation of CSA as a climate mitigation strategy.

The major strength of this study is a combination of bibliometric and theory-driven research that integrates TCM and ADO frameworks to map the relationships between antecedents, decisions and outcomes in a systematic manner. This methodology helps reveal a disjointed research space, in which biophysical processes (e.g., soil carbon dynamics) are not adequately related to adoption behavior and policy mechanisms. The factorial and network analysis further emphasize the prevalence of the productivity and adaptation themes, and the limited translation of sustainability goals into actionable agricultural systems. By filling the gaps in these dimensions, this review introduces a systems level explanation of the way carbon, adoption, and policy interact.

The scientific and practical implications of these findings can be considered both scientific and practical. To make CSA a scalable low-carbon agricultural route, further efforts should entail incorporating carbon measurement, incentive schemes, and collective adoption of practices into the research and the policy-making process. It is important to strengthen the connection between the outcomes of soil carbon, the incentive of farmers, and the institutional frameworks to shift the conceptual sustainability goals into the measurable climate mitigation outcomes. Finally, this research paper emphasizes that to attain sustainable transformation in agriculture, it is not only necessary to promote the adoption of CSA but also to restructure agricultural systems in a manner that expressly values and operationalizes the concept of carbon sequestration as a fundamental product of farming activities.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcosc.2026.1916821/full#supplementary-material

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Footnotes

Edited by: Petros Chavula, Kobe University, Japan

Reviewed by: Shivani Garg, Kurukshetra University, India

Mulala Jimaima, University of Zambia, Zambia