Agriculture in Sub Saharan Africa (SSA) is increasingly vulnerable to climate variability, with smallholder farmers disproportionately exposed to erratic rainfall, prolonged droughts, and other environmental stressors that directly undermine food production and rural livelihoods (Omotoso et al., 2023; Bedeke, 2023; Dougill et al., 2021; Muthoka et al., 2024). Meanwhile, similar challenges confront farming systems globally across Africa, Asia and Latin America, where climate variability threatens to reverse development gains and deepen food insecurity among smallholder farmers. In Uganda’s central cattle corridor particularly Luwero and Nakasongola districts, these risks are more acute due to the region’s semi-arid conditions, characterized by recurrent rainfall failure, pest and disease outbreaks and cyclical livestock losses (Mubiru et al., 2018). These challenges have positioned CSA as a central pillar of global adaptation and resilience-building agenda, thus aligning with SDGs 1, 2, and 13 and international frameworks such as the Paris Agreement and Global Alliance for Climate-Smart Agriculture (Alexander, 2019).

Climate-Smart Agriculture (CSA) has been promoted as an integrated strategy to mitigate such challenges through practices that enhance productivity, increase climate resilience and reduce emissions (Joosten and Grey, 2017; Jirata et al., 2016). Interventions such as water harvesting, drought tolerant crops, agroforestry, and improved livestock breeds have been advanced under programs such as the Global Climate Change Alliance (GCCA+) and the National Adaptation Program of Action (NAPA), with the aim of supporting smallholder adaptation across agro-ecological zones (Miola et al., 2015; Rizzo, 2018). The promoted CSA practices collectively contribute to agro-ecological intensification and ecosystem services that support climate resilience across smallholder systems. Water-logged practices such as roof-top and surface-water harvesting enhance on-farm water regulation, reduce dependence on erratic rainfall, and buffer crops against prolonged dry spells (Mubiru et al., 2018; Thornton et al., 2024). Crop support technologies such as staking stabilize climbing crops (e.g., tomatoes), reduce lodging and improve light distribution, thereby supporting yield reliability under weather stress (Wubetie and Wubetu, 2025; Falodun and Bakare, 2023). Soil-cover and soil-fertility practices including mulching, manuring/composting, land fallowing and legume rotation enhance soil organic matter, nutrient recycling, biological activity and water retention, promoting both adaptation and mitigation through carbon sequestration (Lal, 2020; Tittonell et al., 2020). Tree-based practices such as shade trees in coffee or bananas and broader silivopastoral systems regulate microclimates, reduce heat and wind stress, stabilize soils and increase on-farm biodiversity, strengthening ecological buffering capacity (Mbow et al., 2019; Zeleke et al., 2024). Improved crop varieties (beans, maize, bananas) and improved livestock breeds enhance genetic resilience to drought, pests and disease, thereby enhancing farm-level stability under climatic variability (FAO, IFAD, UNICEF, WFP and WHO, 2021; Erick et al., 2025). Livestock-focused interventions such as zero-grazing and grazing-land management reduce rangeland pressure, promote pasture regeneration, and improve manure capture for nutrient cycling (Gaspard et al., 2022). Hay production ensures dry-season feed reserves, reducing overgrazing and stabilizing livest3ock productivity during droughts (Thornton and Herrero, 2015; Zeleke et al., 2024). Acaricide application, when integrated within responsible animal-health systems supports livestock resilience by controlling climate-sensitive tick infestations and sustaining manure-based nutrient flows (Bekele et al., 2021; Kivaria et al., 2025). Spatial planting practices such as line planting and correct spacing improve light interception, airflow, and water-use efficiency thus reducing disease pressure and enhancing resource-use optimization (Njogu et al., 2024). Early planting aligns cropping cycles with shifting rainfall patterns, increasing moisture-use efficiency and reducing exposure to mid-season droughts (Thornton et al., 2021). Biogas integration contributes to both adaptation and mitigation by transforming animal waste into renewable energy and reducing methane emissions from open manure decomposition (FAO, IFAD, UNICEF, WFP and WHO, 2021). Collectively, these practices restore and enhance ecosystem services including water regulation, soil fertility, biodiversity support, microclimate stabilization, and nutrient cycling, all of which directly strengthen the climate resilience of smallholder farming systems.

Despite widespread promotion of CSA, it’s continued in Uganda remains uneven (Nakabugo et al., 2019). Like elsewhere, many households adopt CSA practices during climate crises or in response to project-driven extension campaigns but later discontinue them when institutional support diminishes or economic constraints arise (Murray et al., 2016; Lipper et al., 2017). This discontinuity highlights an important knowledge gap, i.e., understanding why some households continue using CSA practices over time, while others revert to conventional practices. Continued use is not simply a function of awareness or availability but it reflects how different households evaluate costs, risks and long-term benefits, shaped by their access to land, capital, labor availability, and institutional services (Kuivanen et al., 2016; Mthethwa et al., 2022).

The central Uganda cattle corridor presents a particularly useful context for such an inquiry. While many CSA interventions have been piloted here, few studies have empirically examined how the diversity of farming households affects their ability and willingness to sustain specific practices beyond project lifecycles. Households widely vary in landholding size, income sources, gender roles, and institutional access which are factors that influence not only initial adoption but also the capacity to maintain labor-or capital-intensive innovations (Kpadonou et al., 2017; Antwi-Agyei et al., 2021). For example, wealthier or more connected households may be able to sustain high-input practices such as improved seed, improved livestock breeds, or pasture improvement, while poorer households often retain low-cost, labor-based practices such as mulching or manuring (Makate et al., 2018a; Makate et al., 2018b). These differences determine whether CSA adoption translates into durable transformation or remains temporary (Mnukwa et al., 2025).

Recent studies across African emphasize the importance of household typologies in explaining such heterogeneity. Studies in Zimbabwe (Makate et al., 2018a; Makate et al., 2018b); Ghana (Antwi-Agyei et al., 2021), Mali (Ölkers et al., 2025) and Kenya (Kihoro et al., 2021) demonstrate that classifying households according to resource endowments and institutional linkages provides deeper insights into CSA persistence than aggregated adoption models, as it captures the interplay of land, labor, and credit access. In contrast, non-typology studies (such as Omotoso et al., 2023; Mthethwa et al., 2022) often treat smallholders as a uniform category, overlooking the structural and institutional inequalities that shape sustained practice use and inadvertently masking patterns of exclusion. Globally, this reflects a broader gap in CSA literature where much emphasis has been placed on adoption metrics rather than behavioral persistence and resource adaptation over time (Mensah et al., 2024). Understanding continuity therefore requires frameworks that can account for both resource heterogeneity and institutional context.

Gender dynamics further complicate these relationships. For instance, across Uganda and neighboring countries, women encounter persistent barriers in land ownership, decision-making autonomy, and access to inputs, markets and extension services which are factors that directly constraint the ability of these women in sustaining CSA practices (Beuchelt and Badstue, 2013; Farnworth et al., 2017). Therefore, integrating gender perspectives within typology analysis enhances the explanatory power of the CSA practices research by shedding light on how social norms and resource inequality interact to influence continuity and household outcomes (Watson et al., 2018; Nhim et al., 2019).

To address these dynamics, this study adopts a typology-based approach that systematically classifies households into distinct groups based on shared socio-economic, institutional, and agro-ecological characteristics. Typologies reveal how combinations of resources such as land, labor, education and social capital shape adaptive behavior and long-term capabilities (Bidogeza et al., 2009; Tittonell et al., 2010). This approach provides a detailed lens for interpreting CSA continuity, moving beyond generalized averages to expose structural and relational heterogeneity among farming households.

This study is anchored in the Resource-Based Theory (RBT), which posits that the performance and adaptation depend not only on access to resources but also on how these resources are configured, coordinated and renewed over time (Barney, 1991). From this perspective, household typologies represent bundles of these tangible (e.g., land, livestock) and intangible (e.g., knowledge, institutional trust) assets/resources, that shape the capacity to sustain beneficial innovations. Innovations. Applying RBT to CSA therefore offers a theoretical bridge between household resource endowments and the persistence of adaptive agricultural practices.

Therefore, this study investigates how smallholder household typologies shape continued use of CSA practices in the central Uganda cattle corridor. It specifically focuses on four analytical dimensions (i) determining the proportion of households that have adopted and continued to use CSA practices; (ii) identifying and characterizing household typologies based on socio-economic and institutional characteristics; (iii) examining the CSA practices most continued within each typology; and (iv) assessing how typological differences explain variations in CSA continuity. Through these objectives, the study provides empirical evidence on the post-adoption dynamics of CSA use and informs the design of typology-responsive, equity-sensitive interventions tailored to the realities of smallholder farming systems in climate-vulnerable areas.

The study fills a gap in CSA research by examining the continued use through a typology-based lens that explains variations among smallholder households in Uganda’s central cattle corridor, a drought-prone region highly exposed to climate shocks. The findings directly address Uganda’s urgent challenges of food insecurity (affecting almost 40% of the households), land degradation and recurrent climate hazards, thus providing evidence for cost-effective, typology-specific CSA interventions. The results inform climate adaptation financing, extension reforms, and implement of Uganda’s fourth National Development Plan (NDP IV-2024/24–2029/30) as well as Vision 2040, therefore advancing national goals of climate-resilient agricultural transformation.

Beyond Uganda, the study contributes to the global discourse on climate-resilient agri-food systems by providing insights into post-adoption dynamics of CSA which is one of the underexplored dimensions in sustainability science. Its typology-based framework offers transferable lessons for semi-arid regions across African and other regions where smallholder heterogeneity and limited adaptive capacity constrain long-term sustainability. The evidence therefore enriches international understanding of how household-level resource configurations influence the persistence of CSA and thus align with global targets under SDG 2 (Zero hunger), SDG 13 (Climate action), and SDG 1 (No poverty) as well as SDG indictor 2.4.1 on sustainable agriculture. Beyond regional relevance, the study supports Africa’s Agenda 2063 Goals 1 and 5 by offering scalable, context-responsive CSA models for semi-arid regions. Globally, the findings will provide comparative insight that can inform policy dialogues under the United Nations Framework Convention on Climate Change (UNFCCC), Intergovernmental Panel on Climate Change (IPCC) among others thus fostering cross-country learning as well as South–South collaboration on typology-responsive programming.

This study applies the RBT at the household level to explain the continuity of CSA practices among smallholder farmers. Following Wernerfelt (1984), Barney (1991), and Grant (1991), household endowments are viewed as capability bundles whose properties (i.e., VRIN) determine persistence rather than mere uptake of CSA innovations. In operational terms, valuable resources (such as fertile land, diversified income, and access to extension knowledge) are those that directly enhance agronomic performance or reduce production risk. Rare resources (including affordable credit, reliable market access, and secure land tenure) are unevenly distributed and confer advantage to those who possess them. Inimitable resources (e.g., accumulated experience, regular extension contact, and strong farmer-group ties) are difficult to replicate because they are path-dependent or socially embedded. Whereas non-substitutable resources, such as perennial water sources or cohesive producer groups, have no affordable or functional equivalents within local production systems. Together these VRIN attributes form tangible, intangible and human resource configurations that justify household typologies, which in turn mediate the relationship between resource endowments and continued CSA use which in this study is captured as VRIN-Typology-Continuity pathway.

In this study, tangible resources include farmland size, livestock holdings, asset index, and household incomes which shape household ability to invest in and sustain CSA practices such as drought-tolerant crops or zero grazing (Ogada et al., 2020). Intangible resources encompass access to extension services, market linkages, credit facilities and information via telecommunication tools such as radios and phones which are essential for continued practice use through enhanced knowledge, peer-learning and institutional support (Morales-Zamorano et al., 2020; Tereshina et al., 2020; Nguyen-Anh et al., 2022; Molloy et al., 2011). Human capital, including education level of the household head, farming experience of the household, and availability of household labor, determines the capacity of farmers to interpret information, make informed decisions, and manage practices sustainably over the long term (Saffu et al., 2008; Mugera, 2012).

This RBT framework posits that the continued use of CSA practices is not solely a function of initial adoption, but rather the result of dynamics interactions between household resource endowments, socio-economic conditions, institutional access, and the perceive utility of those practices. The inter-related factors are expected to influence both the selection and sustained use of CSA practices over time. The choice of the RBT over behavioral or diffusion-based theories such as Technology Acceptance Model (TAM) or the Innovation Diffusion Theory (IDT) is justified because RBT explains continuity rather than initial adoption. Diffusion models emphasize awareness and perception, whereas RBT focuses on the internal capacities and the strategic use of resources that make sustained innovation possible. This makes RBT more suitable for understanding continued CSA use and household resilience in semi-arid smallholder systems. Key household level socio-economic attributes such as household size, dependency ratio (i.e., number of dependents against number of working people in the household), labor availability, and education level of the household head shape eventual decision-making, risk tolerance, and implementation capacity. Figure 1 below has these details.

Institutional dimensions such as household participation in CSA-related programs, membership in local producers or savings groups, and proximity to extension services or input/output markets affect access to critical support mechanisms that facilitate practice. On the other hand, contextual and perceptual variables such as exposure to climate-related shocks (e.g., prolonged droughts) and perceived land productivity or soil fertility also influence household-level adaptation strategies. To capture heterogeneity among farming households, the framework introduces an intervening variable called household typology which is constructed using cluster analysis based on shared socio-economic, resource-based, and institutional characteristics. This typology serves to differentiate households in terms of their capability to sustain CSA practices and offers a lens for analyzing variation in continuity outcomes.

These typologies represent homogenous categories of farm households, each with a unique profile of capacities and constraints that mediate the relationship between resources and sustained use of CSA practices. Figure 1 below shows the relationship between these variables.

The study collected comprehensive data on household-level characteristics, resource endowments, access to services, and the uptake and continuity of CSA practices. Household socioeconomic attributes included the education level of the household head, household size, marital status, years of agricultural engagement by the household, and availability of household labor. Economic variables captured included total household income from both farm and non-farm sources, landholding size, access to credit, membership in community-based groups, and receipt of remittances. Information on access to agricultural extension services and community tools (e.g., radio, mobile phones) was recorded, along with the household’s proximity to essential infrastructure such as schools, health centers, water sources, extension offices, and all-weather roads.

To assess vulnerability and resilience capacity, data on climate-related experiences were collected, including household-level exposure to climatic shocks, perceived soil fertility, total livestock ownership (measured in Tropical Livestock Units) and asset-based wealth index. CSA adoption was defined as the household’s first-time uptake of a given practice, coded as 1 (adopted) or 0 (not adopted). Continuity of use was assessed only among adopting households. Based on the number of years a practice had been maintained since initial uptake, duration of use was grouped into three categories, i.e., 1–5 years (low), 6–10 years (moderate) and over 10 years (high), following the classification approach used by Mutombo and Musarandega (2023) to distinguish short-term experimentation from sustained behavioral change. This allows for standardized comparison of persistence across households regardless of initial adoption levels (Adego et al., 2018). For crop-related practices, the area under application (in acres) was recorded at household level; for livestock-related practices, the household herd size was documented. This multi-dimensional data structure allowed for robust analysis of both the extent and durability of CSA practice use within climate-vulnerable farming households.

Data were collected using structured household survey questionnaires and semi-structured interview guides for key informants and focus group discussions. The household questionnaire captured quantitative data on household demographics, resource endowments, institutional access, adoption and continuity of CSA practices. Qualitative tools were used to explore perception and benefits, as well as context-specific factors influencing the sustained use of these practices. To ensure construct validity, the questionnaire items were grounded in established CSA adoption and resilience frameworks while at the same time including indicators that had been used in empirical studies in Sub Saharan Africa such as (Antwi-Agyeri et al., 2023; Kangogo, 2022; Gebre et al., 2021). The questions were aligned with the theoretical constructs derived from the RBT, which informed the study’s conceptual design. Content validity was ensured through expert reviews and pre-testing but at the same time, academic supervisors reviewed the tools to assess the appropriateness and completeness of the items. The tools were then pre-tested with 30 households in non-sampled parishes (i.e., Butuntumula sub county which is also found in Luwero district) to evaluate clarity, relevance, and sequencing of the questions. Feedback from the pre-test informed final revisions to eliminate ambiguity and ensure comprehensive coverage of all relevant domains.

Based on the PCA analysis, all components with eigenvalues greater than 1 were retained. A total of 29 CSA practices were included in the analysis, and all were retained for interpretation. From Table 3 below, all factor loadings equal to or greater than ±0.30 were used to identify defining variables, while practices that cross-loaded on multiple components were excluded from the naming logical. The PCA produced 10 distinct components that together explain 64.08% of the total variance and offered insights into the dominant patterns of CSA uptake and bundling among farming households in Uganda’s central cattle corridor. Although the cumulative variance explained (64.08%) falls slightly below the conventional 70–80% benchmark, it was deemed adequate given the multidimensional nature of the household-level behavioral and institutional data, where higher-order noise is expected (Hair et al., 2019; Jolliffe and Cadima, 2016). Components were retained using Kaiser’s criterion (eigenvalues >1), and the cutoff point was further validated through visual inspection of the scree plot (Figure 3), which showed a clear inflection after the tenth component. This approach ensured that the retained components captured the most interpretable and theoretically meaningful dimensions of CSA practice continuity.

Component 1 explains 7.92% of the total variance and is positively associated crossbreeding, spraying accaricide, silivopastoral technologies, and land fallowing. These practices reflect household efforts to enhance livestock productivity and resilience through improved breeds, vector control, pasture regeneration, and controlled grazing. Thus, component 1 is defined as livestock and land-based resilience practices. Component 2 explains 7.80% of the total variance and is positively associated with planting shed trees and trench digging. These practices are focused on reducing runoff, increasing infiltration, and promoting landscape greening, especially in erosion-prone areas. Thus component 2 is defined as tree-based soil and water conservation practices. Component 3 explains 7.68% of the total variance and is positively associated with planting in lines, minimum tillage, and cover-cropping. These practices are central to conservation agriculture and aim to reduce soil disturbance, improve soil structure, and increase moisture retention. Thus component 3 is defined as conservation tillage and soil protection practices. Component 4 explains 7.10% of the total variance and is positively associated with rooftop water harvesting, early planting, and planting crops on tree land. These practices are used to adapt to early-season rainfall uncertainty and optimize crop establishment through improved water access. Thus, component 4 is defined as early-season water harvesting planting strategies. Component 5 explains 6.99% of the total variance and is positively associated with drought-tolerant varieties, correct spacing, and harvesting water. These practices aim to improve plan survival and yield under low-moisture conditions through water-efficient cropping systems. Thus component 5 is defined as drought-adaptive seed and spacing technologies.

Component 6 explains 6.01% of the total variance and is positively associated with zero grazing, pasture growing, and improvement of cattle. These practices enhance livestock productivity and reduce land degradation through confined feeding systems and deliberate fodder production. Thus component 6 is defined as intensified livestock and fodder systems. Component 7 explains 5.92% of the total variance and is positively associated with boundary planting, manuring, application of chemicals, and harvesting water. These practices are aimed at enhancing field protection, nutrient use, and water availability, especially in intensively cultivated soils/plots. Therefore, component 7 is defined as integrated nutrient, input and field protection practices. Component 8 explains 5.08% of the total variance and is positively associated with application of herbicides and staking. These practices are typical in labor-saving horticultural systems and support plant health and structure. Thus, Component 8 is defined as input-supported horticultural practices.

Component 9 explains 5.07% of the total variance and is positively associated with mulching and improvement of cattle. These practices enhance soil fertility and moisture retention while increasing the supply of organic inputs through livestock. Thus, component 9 is defined as organic soil and livestock improvement practices. Finally, component 10 explains 4.50% of the total variance and is positively associated with use of improved crop varieties and application of manure. These practices combine genetic and organic approaches to boost crop productivity. Thus, component 10 is defined as genetic and organic yield enhancement practices.

To ensure conceptual clarity, practices exhibiting significant cross-loadings (0.30 on more than one component) were excluded from interpretation. CSA practices such as zero grazing, herbicide application and improved cattle breeds loaded simultaneously on both crop and livestock-related factors. These exclusions improved discriminant validity across the ten retained components. In Table 3, roof-top water harvesting refers to specifically household level rainwater capture systems from roofs while water harvesting denoted field-based run-off collection techniques such as digging pits or trenches lined with heavy duty polyethene bags. Each component label was based on functional or thematic coherence of practices with the highest loadings thus ensuring alignment with CSA domains such as soil fertility management, water conservations and livestock resilience.

To examine heterogeneity among the farming households, standardized PCA scores were subjected to hierarchical cluster analysis using Ward’s linkage method. As shown in Figure 4, the dendrogram initially indicated five household clusters based on Euclidean distance. Clusters joined at shorter vertical distances (G1 and G2) share closer socioeconomic and institutional attributes while G4 and G3/G55 appear more distinct. Owing to their near-identical characteristics, clusters III and V were merged resulting in four final typologies used for subsequent analysis. These clear branch separations in the dendrogram validate the robustness of this classification and align with RBT’s proposition that households differ in their ability to combine tangible and intangible resources for continued CSA engagement. A detailed description of each cluster is given in the section below with further details in Table 4.

Cluster 1: Moderately Resourced CSA-Engaged Households (8.5%): These households are characterized by relatively long farming experience (28.6%), moderate landholding size (6.2 acres) and complete reliance on farming as a primary source of livelihood. They enjoy access to communication tools such as radios (100%) and mobile phones (9.7%) and they live close to key services such as extension offices (3.2 km) and health centers (2.3 km). Despite limited access to credit (6%) and extension services (3%), these households report high levels of continued use of diverse CSA practices such as soil fertility-enhancing practices like manuring (100%), planting shed trees (97%), planting in lines (94%) and legumes (100%). Their use of drought-tolerant varieties and water conservation practices is limited. This group exemplifies households with moderate resources who are behaviorally committed to CSA, relying largely on their farming experience and intrinsic motivation.

Cluster II: Mainstream households with selective CSA uptake (77.1%): Representing most of the households, this category has average landholdings (5.7 acres), moderate farming experience (21 years), and limited formal education (4.4 years). most members rely fully on farming (88%) and maintain moderate access to mobile phones (70%) and radios (72%). Their access to extension (2%) and credit (24%) is average, while distances to services such as extension office (7.8 km) are longer. They show moderate but selective CSA practice continuity, favoring herbicide application (40%), mulching (35%), manure (41%), legumes (50%) and shed tree planting (59%). These households appear to adopt CSA practices based on practical fit and resource availability, but their engagement remains partial which is probably due to constrained access to services and limited resources.

Cluster III (Merged): Resource-Constrained and livestock-orientated households (12.4%). The merged cluster integrates the formerly distinct resource-constrained (Cluster III) and the Youth-driven, livestock-oriented (Cluster V). The merger was statistically guaranteed due to the extremely small size of Cluster V (0.28%), which contained a single household, rendering it unsuitable for inferential analysis. Conceptually, both clusters shared key features of limited landholdings (averaging 2–3 acres), weak access to credit (≤20%) and extension services (≤14%), and low engagement with capital-intensive crop-based CSA practices. However, within this broader resource-constrained group, a subset of younger households demonstrates a focused orientation towards livestock-based CSA strategies (including zero grazing, pasture improvement and acaricide use) driven by livelihood adaptation to land scarcity. Their selective specialization suggests persistence rather than broad adoption. Therefore, the merged cluster represents a continuum of adaptive behavior and the depth of CSA engagement.

Cluster IV: High-Resource, Institutionally Supported CSA-Intensive Households (1.93%). Though small, this cluster includes households with the most extensive landholdings (19.7 acres), highest education levels (7.7 years), longest farming experience (34 years), and best institutional support whereby 86% report access to extension and 57% to credit. These households live closest to basic services including schools (0.43 km), roads (0.29 km) and water points (0.29 km). Their CSA engagement is comprehensive, with high continuity in advanced practices such as cover cropping (86%) drought-tolerant varieties (86%), improved crops (86%), manure application (100%) and livestock-oriented strategies such as spraying (71%). These households are structurally well-positioned to adopt, integrate, and sustain complex CSA practices and demonstrate the highest adaptive capacity.

To validate the statistical distinctiveness of household typologies, one-way ANOVA and logistical region tests were conducted on key differentiating variables as shown in Table 5. Significant variation was observed in farmland size across clusters [F(3, 343) = 4.96, p = 0.002], with Tukey pairwise comparison confirming that high-resource CSA-intensive households possessed significantly larger landholdings than other clusters (p < 0.001). Differences in annual farm income were not statistically significant [F(3, 343) = 1.51, p = 0.212], suggesting that income disparities may be moderated by uniform market conditions across typologies. However, access to extension varied strongly among groups [χ²(3) = 20.29, p = 0.0001], with Bonferroni post-hoc tests showing that high-resource CSA-intensive households received significantly more institutional support than the rest (p < 0.01).

Although grounded in Uganda’s central cattle corridor, the study’s findings resonate with broader global audience in post-adoption dynamics of CSA. The mechanisms identified (i.e., capability-demand fit, resource complementarity and institutional density) mirror patterns reported across Africa, Asia and Latin America, where CSA continuity improves when the resource intensity of practices aligns with households’ replenishable asset base and local service ecosystems (Makate et al., 2018a; Makate et al., 2018b; Mahajan and Gupta, 2023; Zeleke et al., 2024). The observed persisitence of integrated bundles such as improved seed, organic manures and water management highlights global evidence that bundling lowers risk and transaction costs, fostering self-reinforcing learning loops that sustain use. Moreover, the study’s emphasis on typology-responsive targets aligns with internal policy agenda such as SDG 2.4.1; African Agenda 2063 as well as Global Alliance for CSA all of which advocate for context-specific and equity-aware resilience pathways.

This study offers new empirical evidence on how continued use of CSA practices among farming households in Uganda’s central cattle corridor resonate with typologies. Guided by the Resource-Based Theory, the study demonstrated that CSA continuity is not a uniform outcome but a function of how households mobilize and combine their tangible, human and institutional resources. By integrating PCA and Cluster Analysis (CA), the study identified distinct household typologies with differentiated resource portfolios that strongly influence CSA adoption and continued use. The findings confirm that continuity is highest among households with strategically aligned resource configurations, particularly those with adequate land, education, extension access and stable income streams. These households are not necessarily the wealthiest but are resource coherent. Conversely, households with fragmented or constrained resource bases often adopt practices temporarily or discontinue them due to unmet resource demands.

The study validates the practical value of household typologies as operational expressions of the Resource-Based Theory. These typologies thus offer a scalable approach to target CSA interventions more effectively by moving beyond generic demographic segmentation towards resource-informed programming. Therefore, understanding the relational dynamics between assets, knowledge and institutional interfaces allows for more tailored and sustainable CSA scaling within the farming communities. Continued (sustained) use of these CSAs emerges not from exposure alone but from enabling environments that reinforce the deployment and integration of resources.

First, policy makers are encouraged to institutionalize typology-based planning within CSA programs by using household typologies derived from resource endowments rather than broad demographic categories. Program design could begin with detailed profiling of farming households based on their combinations of landholding size, labor availability, education and access to infrastructure or institutions. Once mapped, differentiated CSA packages should be assigned based on capabilities, for instance high-capacity households should receive multi-practice, market-oriented CSA bundles; while resource-constrained households may benefit from simplified, labor-efficient practices tailored to their realities. This typology-aligned targeting can be embedded within existing frameworks such as the Parish Development Model (PDM) or the Operation Wealth Creation (OWC) to enable equitable allocation of inputs, training and subsidies. The approach is practical and scalable given its reliance on routinely collected socio-economic data and its compatibility within digital farmer registries and e-voucher systems.

Second, government and development partners are encouraged to continue strengthening institutional access by decentralizing CSA service delivery through localized, multi-service platforms that combine inputs, extension, credit and market support. The study highlights that continued CSA use depends on proximity to these services. Establishing agro-service centers at parish or sub county levels (as one-stop points for extension advice, inputs, financial services and market linkages) could further advance the on-going decentralization agenda. These centers may be co-managed through public-private partnerships (PPPs) with agro-dealers, cooperatives and microfinance institutions. In areas with limited infrastructure, mobile extension units or community-based paraprofessionals (e.g., Village-Based Agents -VBAs) can help ensure last-mile delivery. Empowering farmers to monitor performance will enhance accountability and inclusion within local governance frameworks.

Third, CSA programming could increasingly promote integrated practice bundles that align with farmers’ existing systems and resource configurations rather than focusing on isolates technologies. The findings show that sustainability is greatest where CSA practices reinforce/complement each other as seen in combinations of manure use, mulching and improved seed. Policy incentives and extension support can therefore encourage bundled adoption pathways that fit household typologies, thereby enhancing continuity and resilience within Uganda’s agricultural sector.