The Power of Social Capital: How Cooperative Membership and Social Network Shapes Climate Smart Agriculture Practices in Northwest Ethiopia

The Power of Social Capital: How Cooperative Membership and Social Network Shapes Climate Smart Agriculture Practices in Northwest Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Power of Social Capital: How Cooperative Membership and Social Network Shapes Climate Smart Agriculture Practices in Northwest Ethiopia Mezgebu Aynalem, Zemen Ayalew, Aemro Tazeze Terefe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract Vegetable production and productivity in Ethiopia are threatened by climate change and climate variability. Adopting Climate Smart Agriculture (CSA) practices reduces the negative impacts of climate related shocks on production. This paper investigates the impact of cooperative membership and social networks (SN) on CSA practices among vegetable farmers in Northwest Ethiopia. The analysis used primary data from 550 farmers and analyzed them by jointly applying a Poisson Endogenous Treatment model and mediation analysis. Results showed that cooperative membership and SNs significantly influence the adoption intensity of CSA. Other household level factors sex, education, family size, landholding, distance to market, and experience of climate shock influence the adoption intensity of CSA. The Average Treatment Effects show that SNs increase the adoption intensity of onion, potato, and tomato CSA practices by 1.829, 1.756, and 1.318 practices, respectively. Cooperative membership also resulted in the adoption of 1.652, 1.402, and 1.202 more CSA practices than non members for the respective crops. The mediation analysis reveals that SN and cooperative membership increase CSA practice intensity both directly and indirectly through improved access to CSA related information, input supply, and extension services. The direct effects of SNs range from 0.287 to 0.359 and that of cooperative membership ranges between 0.237 and 0.322. These findings underscore the need to strengthen farmer to farmer SNs for rapid dissemination of CSA related information and empowering agricultural cooperatives to serve as institutional hubs for inputs supply and extension services, given their proven mediating role in increasing adoption intensity. Vegetable farming Poisson endogenous treatment Mediation effect Social capital Northwest Ethiopia Figures Figure 1 Figure 2 1. Introduction Agriculture is increasingly vulnerable to the impacts of climate change and variability, which are taking the forms of rising temperatures, irregular rainfall, and frequent floods and droughts (Praveen & Sharma, 2019 ; Sivakumar, 2020 ; Vijai et al., 2023 ). Climate stresses pose an imminent threat to crop production and productivity, particularly in low income, agriculture based countries like Ethiopia, where the majority of the population depends on farming for their livelihood (Gezie, 2019 ; Tesema et al., 2025 ). In Ethiopia, agriculture accounts for 34.6% of the GDP, serving as a major source of livelihoods for 80% of the population and generating 90% of export revenue, thus the risk is high, especially among smallholder farmers who have limited adaptive ability (FAO, 2023 ) Production of vegetables is among the most climate sensitive and resource intensive agricultural practices (Degefu & Kifle, 2024 ; Laxman et al., 2024 ). Vegetable production makes a vital contribution to rural livelihoods and employment in Ethiopia (Hunde, 2017 ). Nevertheless, vegetable productivity, particularly for onion, tomato, and potato, has increasing barriers due to climate change and climate variability (Getahun, 2020 ). Climate Smart Agriculture (CSA) practice have been promoted as a holistic approach to boosting productivity, resilience, and environmental integrity (Abhilash et al., 2021; Azadi et al., 2021 ). Higher CSA practices relevant to vegetable production include increased irrigation, crop diversification, organic fertilizer use, and integrated pest management (Bayu, 2020 ). However, despite the availability of these practices, their adoption among smallholder farmers remains limited, largely due to inadequate access to reliable information and training, high costs, and uncertainty about their benefits (Mosha & Ngulube, 2025 ). By addressing the problem of information asymmetry and the cost associated with it and enabling collective learning and behavior change, it acts as an important enabler in this regard (Granovetter, 1985 ; Ostrom, 1990 ). Accessing cooperatives and social networks in rural agrarian settings are essentially the two most important avenues through which farmers gain access to information and develop adequate trust in CSA practices in agriculture (Bandiera & Rasul, 2006 ; Krishnan & Patnam, 2014 ). These pathways are particularly influential for CSA practices because the advantages in terms of soil fertility management, water management, improved seeds, and integrated resource management take some time to develop and require learning to address the associated risks and concerns related to adoption in this regard (Abate et al., 2019 ; Fanta et al., 2024 ; Ma & Rahut, 2024 ). Empirical evidence reinforces the importance of social capital for climate adaptation. Belay and Fekadu ( 2021 ) and Mataraci and Buyukdagli ( 2024 ) found that trust, information sharing, and group membership significantly increased the likelihood of adopting climate adaptation strategies. Ogunleye et al. ( 2021 ) and Ogunnaike et al. ( 2021 ) showed that dense interpersonal networks enhance access to climate information and enable faster diffusion of CSA practices. This studies similarly demonstrate that interactive social networks support innovation processes by shaping collaborative learning and reinforcing pro innovation norms (Afranaa Kwapong & Ankrah, 2023 ). Although few studies explicitly examine social capital mechanisms, the CSA adoption literature has predominantly focused on socio economic and agro ecological determinants such as farm size, market access, and input availability (Abdulai & Huffman, 2014 ; Esfandiari et al., 2020 ). Moreover, strong social networks are more likely to access timely information, mobilize resources, and adopt climate resilient practices (Cishahayo et al., 2024 ). A recent meta analysis by Wang et al. ( 2024 ) provides strong global evidence that social networks substantially increase smallholders’ likelihood of adopting CSA technologies, although the magnitude varies with network strength, trust, and community structure. Much of the evidence on CSA focuses on binary adoption adopt versus non adopter by neglecting adoption intensity, or the number, depth, and cumulative benefits of CSA practices (Kassie et al., 2015 ; Wossen et al., 2015 ). Adoption intensity is important because resilience and productivity gains increase with cumulative practice use (Ma & Rahut, 2024 ), and decisions on intensity are even more sensitive to information flows, trust, peer effects, and social reinforcement than initial adoption (Bandiera & Rasul, 2006 ; Krishnan & Patnam, 2014 ). Theoretically, cooperative membership and social networks may enable different causal mechanisms to drive the adoption decision. Social networks help spread information quickly, provide emotional support, and apply normative pressure to adopt (Granovetter, 1985 ; Rogers, 2003 ), while cooperatives offer better access to inputs, formal training, and collective action promoting innovation (Ostrom, 1990 ). The literature on innovation diffusion suggests that network tie strength, diversity, and centrality are crucial in conditioning learning and adoption behavior (Conley & Udry, 2010 ; Kline & Moretti, 2014 ). This implies that not all social capital is equal or functions similarly. A systematic comparison of cooperative versus network based social capital is thus necessary. In Northwest Ethiopia, where climate variability is high, vegetable production is expanding, and cooperative structures are deeply embedded in rural community life. Most of the studies on social capital and adaptation, however, focus on either general climate strategies (Belay & Fekadu, 2021 ) or specific institutional arrangements (Kahsay & Endalew, 2025 ) without explicitly investigating CSA adoption intensity of cooperatives and social networks. Moreover, previous research has largely focused on technology adoption in general, with limited attention to how social capital enables the transformation of existing knowledge, traditional practices, and local resources into scalable, climate-resilient agricultural strategies. No existing study integrates these dimensions into a unified framework tailored to the specific social and cooperative context. However, despite the growing body of evidence, this study adds to the literature by providing robust empirical evidence on how different dimensions of social capital particularly cooperative membership and social networks influence the intensity of CSA adoption through specific mediating mechanisms in the context of vegetable based farming systems in Northwest Ethiopia. The study goes beyond the methodological limitation of focusing solely on direct effects by explicitly identifying and testing the mediating mechanisms through which social capital operates namely, access to CSA related information, access to input supply, and access to extension thereby offering a more nuanced account of exactly how social capital translates into higher adoption intensity. 2. Theoretical framework This research is informed by a unified paradigm of economic and social theories that cumulatively explain the role of social capital, conceptualized in this research as cooperative membership and social networks, in shaping the intensity of CSA adoption. Each of these theoretical approaches contributes uniquely to an understanding of how social structure and institutions matter for knowledge diffusion, motivations, transaction costs, collective action, and adaptability. Collective action theory describes how social groups overcome the problem of freeriding to make joint investments (Olson Jr, 1971 ). In farming, cooperatives represent institutional approaches that bring farmers together, monitor them, and pool risks to make investments that are costly to individual members. The use of joint provision of inputs, training, and risk sharing, membership in cooperatives reduces adoption costs of individual members and hence adoption intensity of CSA (Bernard & Spielman, 2009 ; Kahsay & Endalew, 2025 ). According to diffusion theory, technology adoption is affected by social interactions, information, and learning (Rogers, 2003 ). Social networks are important conduits that enable farmers to learn by observation, share experiences, and build less uncertain attitudes towards new practices. More frequent interactions are associated with learning and experimentation, indicating that farmers who are connected by strong social networks are most likely to adopt a wider set of CSA practices, resulting in higher adoption intensity (Bandiera & Rasul, 2006 ; Conley & Udry, 2010 ). Resource based theory defines social capital as a productive asset that can provide access to information, credit, labor, and social support (Coleman, 1988 ). Cooperative membership represents institutionalized social capital that leverages collective assets, while social networks provide relational social capital through the exchange of information and social support. Both of these sources of social capital can supplement other sources of capital, such as physical and human capital, and can also mitigate limitations that hamper farmers' ability to adopt a combination of several interrelated CSA practices (Belay & Fekadu, 2021 ). Transaction Cost Economics argues that institutions develop to lower the search, negotiation, and enforcement costs of agreements (Williamson, 1989 ). Being part of an association lowers these costs in that it organizes input acquisition, extension, and association services needed for implementing CSA. Low transaction costs promote and lower the adoption of various practices of CSA, while high transaction costs limit adoption intensity (Bernard et al., 2008 ). Resilience theory is the function of social systems in absorbing the shock of change and learning in the face of uncertainty (Folke et al., 2004 ). Social capital can be seen to increase the level of resilience by facilitating learning together in the face of climate change risk. The increased level of adoption intensity of CSA can be seen to represent not only adoption but also an adaptive path. Together, these theories imply a multi mechanism causal model where cooperative membership and social networks shape CSA adoption intensity via distinct mechanisms: cooperative membership is seen to reduce transaction costs, whereas social networks are seen to aid information spread and learning. Social capital of both types plays a role in resilience via its ability to enable synergistic adoption of CSA bundles. 3. Materials and Methods 3.1 Description of the Study Area The study area is in the Amhara region of Northwest Ethiopia, specifically in Mecha District of North Gojjam Zone, Ayehu Guagusa of Awi Zone, and Fogera District of South Gondar Zone (Fig. 1 ). The Amhara region is known to be a region of varied agro ecological zones with high agricultural potential. It is known to be a source of income for a high proportion of the population. Agricultural production is mainly based on crop production, specifically cereals and vegetable production, in addition to livestock production (Amhara Regional State Meteorological Agency, 2023 ). North Mecha District is among the irrigable districts of the North Gojjam Zone. Onion, potato, and tomato production are increasing in the district, making it an ideal location for CSA practices (Amhara Regional State Meteorological Agency, 2023 ). Ayehu Guagusa District, in the Awi Zone, falls in the mid to high altitude agro ecological zones of the region. Vegetable production is an important livelihood strategy, besides cereal and livestock production (Ayew Guagusa District Agricultural Office, 2024 ). Fogera District, in the South Gondar Zone, surrounding the shores of Lake Tana, is a flood prone region in the study area. Although the region has been widely recognized as a major vegetable producer, water stagnation and recurrent floods significantly threaten agricultural productivity (Fogera District Administration Office, 2024 ). Social network and cooperative systems in the region play an important social aspect, and they operate in conjunction with the indigenous institutions of Idir (Eder) and Equb (Iqub), which enhance mutual trust, reciprocity, and interaction in the region. 3.2 Data Type, Source and Methods of Data Collection The research utilized a mixed research design that incorporated both qualitative and quantitative research methods. The primary research was conducted using semi structured household surveys to identify demographic and socioeconomic aspects as well as adoption of CSA practices for vegetable production. The secondary data was conducted from district agricultural offices, Amhara Regional Bureau of Agriculture, as well as the Ethiopian Statistical Service. A multi stage sampling design was followed. In the first stage, the zones were selected based on their contribution to vegetable production and their vulnerability to climate change. The zones selected were North Gojjam, Awi, and South Gondar. In the second stage, a vegetable producing district was selected from each of the zones. In the North Gojjam zone, the selected district is Mecha. In the Awi zone, the selected district is Ayehu Guagusa. In South Gondar zone, the selected district is Fogera. The districts were selected based on their contribution to vegetable production. They are also highly sensitive to climate change. For stage three, households that grew vegetables were chosen using probability proportional to size (PPS), based on the number of vegetable producers in each district. A total of 550 households were chosen, consisting of 221 in Mecha, 152 in Ayehu Guagusa, and 177 in Fogera. Overall, 550 vegetable farmers (481 onion, 495 potato, and 429 tomato farmers) were interviewed. It is important to note that many farmers were involved in the production of multiple crop types, so the crop specific sample sizes are not mutually exclusive. 3.3 Method of Data Analysis Both descriptive and econometric methods were employed. The descriptive method involved the use of frequencies, percentages, means, and standard deviations, of the sampled households in the different social capital participation levels (social networks and cooperative membership). Along with the processing of the quantitative results, the results of the Focus Group Discussion (FGDs) and the Key Informat Interviews (KIIs) conducted were analyzed. For the econometric analysis, to strengthen causal inference and enhance robustness, this study integrates mediation analysis with the Poisson Endogenous Treatment (PET) framework. One of the most important econometric challenges in determining the impact of social capital on the level of CSA practice intensity is the issue of missing counterfactual outcomes. This is due to the fact that a farmer can be seen in a single treatment state at a time (for instance, being in social capital networks or not), and as a consequence, it is impossible to directly view the other case or counterfactual (Wooldridge, 2003 ). Conventional regression methods may yield biased and inconsistent estimates in the presence of endogenous treatment assignment and selection bias, because farmers self select into social capital networks based on both observable and unobservable characteristics (Heckman, 1979 ; Imbens & Wooldridge, 2009 ; Wooldridge, 2010 ). To address these concerns, similar studies employed two common econometric methods: propensity score methods and instrumental variables (IV) methods (Kassie et al., 2011 ; Danso-Abbeam & Baiyegunhi, 2019 ). While propensity score based methods such as matching, regression adjustment, and inverse probability weighting account for observable heterogeneity, they are limited in addressing unobserved variables that may influence both treatment and outcome simultaneously (Heckman, 1979 ; Imbens & Wooldridge, 2009 ; Wooldridge, 2010 ). The PET approach was used to estimate the causal effect of social capital on CSA adoption intensity while accounting for selection bias and endogeneity due to observed and unobserved variables. The mediation analysis is a supplement to the previous approach in understanding how social capital affects CSA adoption intensity. In particular, mediation analysis highlights access to CSA related information, input supply, and extension services as channels of transmission between social networks/cooperative membership and CSA adoption intensity. PET has many advantages in handling endogeneity in count outcome variables and yields reliable results for the Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATET), and Average Treatment Effect on the Untreated (ATU); but it does not explicitly show how the adoption intensity of CSA is influenced by social capital through certain pathways. On the other hand, the mediation analysis framework done through the generalized structural equation modeling approach in GSEM is appropriate for handling nonlinear relationships among variables where there are binary mediators and count outcomes (Baron & Kenny, 1986 ; Holmbeck, 1997 ; Rucker et al., 2011 ; Duguma & Bai, 2025 ). By jointly applying PET and mediation analysis, this study combines the strengths of both approaches: PET provides reliable results on causal parameters, and mediation analysis allows for a clear decomposition of the total effect. The consistency in results from both techniques strengthens the findings and offers a deeper understanding on both the direction and the mechanisms through which the adoption intensity of CSA is influenced by social capital. The PET model is specified as follow: Treatment equation for modeling the endogeneity of social capital participation: $${T}_{k=}^{*}{Z}_{k\sigma}^{{\prime}}+{\mu}_{k}\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots.\dots\dots...1$$ With \({T}_{k}=1if{(T}_{k}^{*}>0);\) Z k represents observed covariates predicting SC participation, and µ k is the error term. Outcome equation, specified as a Poisson distribution for count data: \({P(W}_{k}\left|{G}_{k,}{T}_{k,}{\epsilon}\text{k}\right)\) μ k ∼Poisson(μi​), μ i ​= exp \({(G}_{k}^{{\prime}}{\beta}+\sigma{T}_{k}+{\epsilon}_{k}\) ) \(\dots\dots\dots...2\) Where \({\epsilon}_{k}\) ​ is an unobserved disturbance entering multiplicatively via the log-link. Joint distribution of unobservable, Endogeneity arises because µ k and ε k may be correlated. The PET model assumes: Cov(μk, εk) = \(⌈\begin{array}{c}{\sigma}^{2}\sigma\rho\\\sigma\rho1\end{array}⌉\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots..3\) Potential outcomes and treatment effects Define the potential counts: $${{\mu}}_{i}\left(1\right)=\text{e}\text{x}\text{p}{(G}_{k}^{{\prime}}{\beta}+{\alpha}.1+{\epsilon}_{k}),{{\mu}}_{i}(0)=\text{e}\text{x}\text{p}{(G}_{k}^{{\prime}}{\beta}+{\alpha}.0+{\epsilon}_{k})\dots\dots\dots\dots\dots...4$$ The corresponding expected counts (integrating over the joint distribution implied by the model) yield the standard causal estimands: ATE (Average Treatment Effect): ATE = E [ \({W}_{1k}\) - \({W}_{0k}\) ] = E [ \(E{(W}_{1k}-{W}_{0k}\) | \({Z}_{k}\) , \({G}_{k})\) ] \(\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots..\dots\dots....5\) ATET (Average Treatment Effect on the Treated): ATET = E [ \({W}_{1k}\) - \({W}_{0k}\) | \({T}_{k}\) = 1] = E [ \(E{(W}_{1k}-{W}_{0k}\) | \({Z}_{k}\) , \({G}_{k}),{T}_{k}\) = 1] \(\dots\dots\dots.\dots\dots...6\) ATU (Average Treatment Effect on the Untreated): ATU = E [ \({W}_{1k}\) - \({W}_{0k}\) | \({T}_{k}\) = 0] = E [ \(E{(W}_{1k}-{W}_{0k}\) | \({Z}_{k}\) , \({G}_{k}),{T}_{k}\) = 0] \(\dots\dots\dots\dots\dots.....7\) In practice, these are computed from the fitted PET model as contrasts of predicted means with T set to 1 vs. 0 (while keeping the appropriate conditioning overall for ATE, conditional on T = 1 for ATET, and on T = 0 for ATU). Standard errors are obtained by the delta method. In addition to the above, the mediation structure is specified as a system of equations: $${{MED}^{*}}_{i}={{\gamma}}_{0}+{{\gamma}}_{1}{SC}_{i}+\sum Controls+{{\epsilon}}_{i}\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots..\dots..\dots\dots.....8$$ $${CSA\_\text{I}\text{n}\text{t}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}}_{i}={{\beta}}_{0}+{{\beta}}_{1}{MED}_{i}+\sum Controls+{{\epsilon}}_{i}\dots\dots\dots\dots\dots\dots\dots\dots\dots\dots..\dots\dots..9$$ $${CSA\_\text{I}\text{n}\text{t}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}}_{i}={{\alpha}}_{0}+{{\alpha}}_{1}{SC}_{i}+{{\beta}}_{2}{MED}_{i}+\sum Controls+{{\epsilon}}_{i}\dots\dots\dots\dots\dots\dots\dots\dots\dots..10$$ Where SC i ​ denotes social capital (either social networks or cooperative membership), and MED i ​ represents the mediating variables. The mediators are binary, while CSA adoption intensity is a count variable. Separate mediation models are estimated for social networks and cooperative membership to capture the distinct pathways through which social capital influence CSA adoption intensity. Indirect effects are computed as the product of the estimated coefficients linking social capital to each mediator and the coefficients linking the mediator to CSA adoption intensity. To ensure valid statistical inference in this non linear setting, bootstrapped standard errors are employed. 3.4 Measurement of variables The variables used in this study were measured based on established empirical literature on CSA, and social capital. The operational definitions are provided below. 3.4.1 Climate Smart Agriculture Adoption Intensity CSA adoption intensity refers to the number of CSA practices implemented by each household. Consistent with prior studies that conceptualize CSA intensity as a count of multiple interrelated practices (Teklewold et al., 2013 ; Asfaw et al., 2016 ; Wainaina et al., 2016 ). This study measures intensity by summing twelve practices. These practices are terracing/bunds (X₁), reduced tillage (X₂), crop residue use (X₃), crop rotation (X₄), agroforestry/shade nets (X₅), rescheduling planting (X₆), intercropping (X₇), compost/manure application (X₈), improved seed varieties (X₉), integrated pest management (X₁₀), furrow irrigation (X₁₁) and rainwater harvesting (X₁₂). Each practice is associated with increased productivity enhancement, risk reduction, and enhancement of adaptive capacities (Lipper et al., 2014 ). 3.4.2 Social Capital Social capital can be defined widely as the structural components and processes that enable collective action, information flow, and cooperation (Coleman, 1988 ; Putnam, 2015 ). This study uses two indicators for social capital measurement: (i) the involvement of farmers in agricultural cooperatives and (ii) farmers' involvement in information sharing social network structures with their neighboring farmers. These two components cover the structural and relational aspects of social capital with established positive impacts on agricultural information access, uncertainty reduction, trust development, and learning processes for agricultural practices adoption (Ogunleye et al., 2021 ). Social networks are more specific about the social relations of farmer interactions with other farmers that offer access to information exchange, observation, and learning. From existing literature on the matter, it can be ascertained that indirect interaction and observation between farmers of surrounding areas do contribute immensely to the development and spread of agricultural innovations because they mitigate the risks for the farmers associated with the introduction of the innovation (Bandiera & Rasul, 2006 ; Conley & Udry, 2010 ; Krishnan & Patnam, 2014 ). For the purpose of measurement a binary variable would be established that would assign a value of 1 if the farmer regularly communicates with the neighboring farmers on issues concerning the implementation of CSA principles and a value of 0 otherwise. This measurement would establish uniformity based on the existing researcher (Belay & Fekadu, 2021 ; Ogunleye et al., 2021 ; Wang et al., 2024 ). Cooperative membership is a variable that indicates a farmer’s involvement with a formal agricultural cooperative as a means of having collective access to resources, markets, inputs, credit, as well as extension services. Cooperatives have been found to be fundamental for improving farmers' access to information and decreasing transaction costs; hence affecting the adoption of climate smart innovations (Kassie et al., 2015 ; Kahsay & Endalew, 2025 ). 3.4.3 Control Variables A set of control variables is included based on prior empirical literature and expert judgment on factors affecting CSA practice uptake. Different studies show that sex of household, access to credit, drought stress, pest and disease, experienced flooding, soil fertility status, family size, age of the household, education level, distance to road, distance to market, TLU, land size of vegetables, districts and Log_Nonfarm inc affect farmers’ innovation decisions. 3.4.4 Mediator Variables To unpack the pathways through which social capital (operationalized as cooperative membership and social networks) affects the intensity of CSA adoption, this study examines three mediators: access to CSA related information, access to inputs supply, and extension access (Fig. 2 ). Each mediator represents a plausible mechanism grounded in theory and empirical work showing how interpersonal ties and collective institutions translate social relationships into observable changes in CSA practices uptake. Information transmission and peer learning are central mechanisms in diffusion and social capital theories: farmers embedded in rich social networks or cooperatives are more likely to receive timely, relevant, and credible information about CSA practices, demonstration outcomes, and locally appropriate implementation strategies (Rogers, 2003 ). Empirically, information exposure has been shown to increase both the probability and breadth of adoption because it reduces uncertainty and facilitates learning by observation and learning by doing (Conley & Udry, 2010 ; Krishnan & Patnam, 2014 ). In the present study, access to CSA related information is measured as a binary variable equal to 1 if the farmer received CSA related information during the production season, through peers, cooperatives, or local institutions (Bandiera & Rasul, 2006 ; Conley & Udry, 2010 ; Belay & Fekadu, 2021 ). The expected indirect effect is positive cooperatives and denser social networks raise information exposure, which in turn increases CSA adoption intensity. Many CSA practices require material inputs (improved seed, fertilizer, composting materials, irrigation equipment, or water harvesting supplies) or small investments to implement at scale. Cooperatives commonly act as intermediaries that aggregate demand, obtain inputs at lower transaction costs, and facilitate credit or in kind supply schemes; social networks can also support informal input sharing or collective purchasing intensity (Bernard et al., 2008 ). Access to inputs therefore functions as a resource facilitation mediator: when social capital eases access to affordable, timely inputs, farmers are better able to adopt multiple, complementary CSA practices simultaneously. This mediator is measured as a binary variable equal to 1 if the farmer accessed inputs through cooperatives, networks, or local institutions during the production year (Bernard et al., 2008 ; Kahsay & Endalew, 2025 ). The hypothesized indirect effect is positive social capital increases input access, which increases CSA adoption intensity. Formal agricultural extension complement social channels by providing technical guidance, demonstration plots, and follow up support services that cooperatives frequently help coordinate and that social networks help disseminate (Kassie et al., 2015 ; Kahsay & Endalew, 2025 ). Extension access encompasses both the frequency and the quality of contact with extension agents or structured training events focused on CSA. This mediator is measured as a binary indicator equal to 1 if the household received CSA related extension contact within the production year. The mediation logic holds that cooperative membership and active social networks increase the likelihood of extension exposure and in turn extension exposure increases farmers’ capacity and confidence to adopt CSA practices (Kassie et al., 2015 ; Belay & Fekadu, 2021 ; Kahsay & Endalew, 2025 ). The expected indirect effect through extension access is therefore positive. 4. Result and Discussion 4.1 Demographic and socio economic characteristics of the sampled farmers The tables below present summary statistics for the explanatory variables of onion, potato, and tomato farmers, together with chi-square and t-test results comparing farmers who participate in cooperative membership with those who do not, as well as farmers who engage in social networks and those who are not engaged. Table 1 Summary statistics of discrete explanatory variables of vegetables Onion Potato Tomato Variables Category % Coop memb (χ²) Social network (χ²) % Coop memb (χ²) Social network (χ²) % Coop membe (χ²) Social network (χ²) Sex of household Male 55.72 0.71 2.92*** 58.59 0.59 2.05 50.12 0.53 0.12 Female 44.28 41.41 49.58 Access to Credit Yes 38.67 3.86* 19.72*** 35.56 9.42*** 12.34*** 32.63 2.76* 17.18*** No 61.33 64.44 67.37 Drought stress Yes 55.09 6.93*** 3.86* 68.89 15.25*** 27.54*** 69.23 2.70* 23.17*** No 44.91 31.11 30.77 Pest and disease Yes 68.61 4.54** 6.73*** 72.53 0.53 3.2* 68.30 0.4 7.22*** No 31.39 27.47 31.70 Experience flooding Yes 68.19 15.39*** 8.15*** 69.49 0.90 5.07** 70.16 6.91*** 11.49*** No 31.81 30.51 29.84 Soil fertility status Not fertile 16.84 2.16 0.23 17.78 1.88 4.22 16.08 1.99 1.522 Medium 76.51 75.76 77.39 Fertile 6.65 6.46 6.53 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sources: Own survey result, 2025 Tables 1 present the demographic and socio economic characteristics of onion, potato, and tomato farmers in Northwest Ethiopia. The sample shows a relatively balanced gender distribution, although male headed households were slightly dominant across all vegetable types: 55.7% for onion, 58.6% for potato, and 50.1% for tomato. The chi-square results indicate no statistically significant differences in cooperative membership by gender across the three crops. However, social network participation differs significantly by gender for onion farmers, suggesting that male and female farmers vary in their engagement in peer to peer communication regarding CSA practices. Access to credit services was low, with 38.7% of onion, 35.6% of potato, and 32.6% of tomato producers reporting access. The chi-square tests reveal strong and statistically significant differences in both cooperative membership and social network participation by credit access status across all crops. Among climate risks, very high percentages of farmers had been affected by drought stress (55.1% onions, 68.9% potatoes, 69.2% tomatoes), pest and disease attacks (68.6%, 72.5%, 68.3%), and floods (68.0%, 69.5%, 70.2%). The chi-square statistics show highly significant differences in cooperative membership and social network participation by climate risks variables. Exposure at this high level is an indication of the susceptibility of vegetable production systems to climate fluctuation and biotic stress. These shocks will tend to affect farmers' risk perception and can be determinants for CSA adoption as farmers seek adaptation measures (Cishahayo et al., 2023 ). Perception of soil fertility also indicates that most farmers classified their land as medium fertile (around 76–77%), while a very small percentage of around 6–7% said their soil was fertile. The chi-square tests show no statistically significant differences in cooperative membership or social network participation across soil fertility categories, suggesting that social capital participation is driven more by institutional and risk related factors than by perceived soil quality. Table 2 Summary statistics of continuous explanatory variables of vegetables Variables Onion Potato Tomato Mean Coop memb (t-test) Social network (t-test) Mean Coop memb (t-test) Social network (t-test) Mean Coop memb (t-test) Social network (t-test) Family size 5.75 -0.82 -8.81*** 6.12 -4.06*** -5.17*** 5.48 -8.13*** -11.22*** Age of household 44.7 0.76 9.39*** 46.03 0.64 0.09 43.15 6.95 11.73*** Education level 2.37 5.42*** 13.07*** 2.56 -0.09 1.29* 1.55 7.62*** 10.22*** Distance to road 36.11 5.86*** 13.61*** 37.59 1.58* 1.14 44.88 5.07*** 9.64*** Distance to Mkt 74.03 5.50 13.22*** 74.03 1.99** 1.73* 72.35 7.36*** 10.94*** TLU 5.08 0.30 8.36*** 5.38 4.05*** 5.30*** 4.64 8.45*** 12.40*** Land size of vegetables 0.28 2.19** 5.03*** 0.16 2.88*** 3.68*** 0.14 4.54*** 9.82*** Log Nonfarm inc 9.86 2.82*** 0.35 9.89 0.18 0.61 9.88 1.90* -1.23 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sources: Own survey result, 2025 Proceeding to continuous characters in (Table 2 ), an average family size ranged from 5.48 (tomato) to 6.12 (potato). The t-test results reveal highly significant differences in family size across social network participation for all three crops, and across cooperative membership for potato and tomato farmers. This indicates that households with larger family sizes are more likely to participate in social capital structures. The mean age for the respondents was approximately mid 40s per crop, meaning that most of the farmers are at working age. The t-test results show statistically significant differences in age across social network participation for onion and tomato farmers, with older farmers more likely to engage in social networks. In contrast, cooperative membership does not differ significantly by age for all product producers. Education levels were quite low at 2.4 years for onion producers, 2.6 for potato, and 1.6 for tomato. The t-test results indicate strong and statistically significant differences in both cooperative membership and social network participation by education level. Distances to roads and market centers were important, averaging approximately 36–45 minutes to the nearest road and over 70 minutes to markets. The t-test results show significant differences in cooperative membership and social network participation across distance to road and distance to market. Livestock ownership, measured in Tropical Livestock Units (TLU), averages around five units across crops. The t-test results show strong and statistically significant differences in social network participation for all crops, and in cooperative membership for potato and tomato farmers. The properties of vegetable land were overall small, i.e., 0.28 ha for onion, 0.16 ha for potato and 0.14 ha for tomato. The t-test results reveal consistent and statistically significant differences across both cooperative membership and social network participation, with farmers cultivating larger vegetable plots more likely to participate in social capital structures. Finally, log non farm income was around 9.8 for all groups, which means that farmers diversify livelihood sources outside agriculture. Nonfarm income provides liquidity for financing technology adoption but, depending on returns, it may also draw labor away from farming activities (Cishahayo et al., 2023 ). 4.2 Adoption intensity of CSA practices on social capital Table 3 Adoption intensity of CSA practice on cooperative membership Adoption intensity Onion Potato Tomato Total Non-Participants Participants Total Non-Participants Participants Total Non-Participants Participants Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent 0 54 11.23 50 23.70 4 1.48 20 4.04 18 9.52 2 0.65 31 7.23 26 17.33 5 1.79 2 6 1.25 4 1.90 2 0.74 29 5.86 14 7.41 15 4.90 32 7.46 27 18.00 5 1.79 3 47 9.77 16 7.58 31 11.48 88 17.78 54 28.57 34 11.11 71 16.55 42 28.00 29 10.39 4 102 21.21 40 18.96 62 22.96 69 13.94 33 17.46 36 11.76 94 21.91 29 19.33 65 23.30 5 102 21.21 37 17.54 65 24.07 126 25.45 45 23.81 81 26.47 81 18.88 12 8.00 69 24.73 6 42 8.73 12 5.69 30 11.11 51 10.30 10 5.29 41 13.40 34 7.93 5 3.33 29 10.39 7 33 6.86 14 6.64 19 7.04 47 9.49 10 5.29 37 12.09 17 3.96 2 1.33 15 5.38 8 23 4.78 9 4.27 14 5.19 30 6.06 3 1.59 27 8.82 28 6.53 2 1.33 26 9.32 9 52 10.81 25 11.85 27 10.00 13 2.63 1 0.53 12 3.92 20 4.66 2 1.33 18 6.45 10 20 4.16 4 1.90 16 5.93 22 4.44 1 0.53 21 6.86 21 4.90 3 2.00 18 6.45 Sources: Own survey result, 2025 Adoption intensity of CSA practices in the study area varied considerably, ranging from zero (non adopters) to ten practices out of the 12 identified CSA options. Tables 3 and 4 present the distribution of adoption intensity across onion, potato, and tomato producers, disaggregated by social network and cooperative membership participation, respectively. In (Table 3 ) above shows that cooperative membership had a strong influence on CSA practice adoption intensity. Among onion farmers, 23.7% of non members were in the non adopting category, compared to just 1.5% of members. Also, members were more at high to medium adoption categories, such as five practices (24.1%) and six practices (11.1%), compared to non members, who were predominantly at the zero and low adoption categories. This suggests that cooperative membership consolidates commitment towards adopting multiple practices. Whereas in potato farmers, 9.5% of the non members did not adopt any CSA practice, nearly all cooperator members adopted at least two practices. The largest proportion of members (26.5%) adopted five practices, but merely 23.8% of non members adopted five practices. Cooperative farmers were also more likely to adopt greater quantities (seven or more) than non members, meaning that cooperatives increase both the width and intensity of adoption. In tomato production, cooperative membership has a stronger effect. About 17.3% of the non members did not use any practice, while only 1.8% of the participants completely shunned CSA. Cooperative members were extremely grouped in four (23.3%) and five (24.7%) practices, while non members were grouped in zero and lower practices. This illustrates that cooperative membership reduces total exclusion from CSA and raises intensification of adoption. Table 4 Adoption intensity of CSA practice on social networks Adoption intensity Onion Potato Tomato Total Non-Participants Participants Total Non-Participants Participants Total Non-Participants Participants Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent 0 54 11.23 49 29.34 5 1.59 20 4.04 18 11.11 2 0.65 31 7.23 26 18.84 5 1.72 2 6 1.25 2 1.2 4 1.27 29 5.86 12 6.67 17 5.40 32 7.46 26 18.84 6 2.06 3 47 9.77 16 9.58 31 9.87 88 17.78 50 27.78 38 12.06 71 16.55 37 26.81 34 11.68 4 102 21.21 32 31.37 70 68.63 69 13.94 29 16.11 40 12.70 94 21.91 23 16.67 71 24.40 5 102 21.21 18 10.78 84 26.75 126 25.45 44 24.44 82 26.03 81 18.88 12 8.70 69 23.71 6 42 8.73 13 7.78 29 9.24 51 10.30 10 5.56 41 13.02 34 7.93 5 3.62 29 9.97 7 33 6.86 10 5.99 23 7.32 47 9.49 10 5.56 37 11.75 17 3.96 2 1.45 15 5.15 8 23 4.78 5 2.99 18 5.73 30 6.06 3 1.67 27 8.57 28 6.53 2 1.45 26 8.93 9 52 10.81 17 10.18 35 11.15 13 2.63 1 0.56 12 3.81 20 4.66 2 1.45 18 6.19 10 20 4.16 5 2.99 15 4.78 22 4.44 1 0.56 21 6.67 21 4.90 3 2.17 18 6.19 Sources: Own survey result, 2025 Table 4 highlights the social network impact on the number of CSA practices adopted. Among onion producers, 29.3% of non networked farm households were not adopting, but just 1.6% of networked farmers were non adopters. However, within social networks, adoption tended to cluster at moderate to higher intensity levels, with a substantial proportion of farmers collectively adopting multiple practices specifically four (22.6%), five (26.8%), and six (9.2%) CSA practices. For potato growers, the intensity of adoption was also quite high. Roughly 11.1% of the non networked group adopted neither of the CSAs, while all the networked adopters adopted two or more. Socially networked farmers bunched at five (26.0%) and six (13.0%) practices, well above non networked farmers. This indicates that social networks facilitate information diffusion, uncertainty reduction, and moral support for adopting technology (Cheng, 2022 ). Tomato producers are also highly variable. Network households were overrepresented among adopters (18.8%) and very low adopters, while network members were found in four (24.4%), five (23.7%), and six (10.0%) practices. Additionally, network membership reduced by almost half the probability of adopting zero of the CSA practices in total. This indicates that social networks play a crucial role in learning and shared exposure to new practices. These findings are in line with social capital theory (Coleman, 1988 ) it emphasizes that trust and reciprocity based networks minimize adoption risk through facilitating the exchange of information and collective action. 4.3 Impact of social capital on adoption intensity of CSA practice Table 5 Impact of social networks and cooperative membership on the adoption intensity of CSA practice VARIABLES Social network Cooperative membership Onion Potato Tomato Onion Potato Tomato Intensity of CSAP Social network Intensity of CSAP Social network Intensity of CSAP Social network Intensity of CSAP Cooperative membership Intensity of CSAP Cooperative membership Intensity of CSAP Cooperative membership Social network 0.382** 0.307*** 0.146* 0.326** 0.290*** 0.101* (0.174) (0.100) (0.154) (0.197) (0.107) (0.177) AGE -0.017 0.084 0.008** 0.003 0.098** -0.013 -0.004 -0.027 -0.007 -0.013 0.099** 0.066** (0.021) (0.055) (0.014) (0.045) (0.041) (0.099) (0.020) (0.057) (0.014) (0.045) (0.041) (0.093) Sex of household -0.001 0.272* 0.001 0.391*** 0.065 0.027 0.003 0.286* 0.007 0.331** 0.064 0.384** (0.058) (0.148) (0.049) (0.150) (0.055) (0.159) (0.058) (0.153) (0.049) (0.149) (0.060) (0.156) Education level 0.115* 0.308* 0.042** -0.038 0.028** 0.745*** 0.081** -0.049 0.039** -0.009 0.016 -0.189 (0.060) (0.167) (0.030) (0.098) (0.075) (0.177) (0.059) (0.167) (0.030) (0.098) (0.064) (0.177) Family size -0.070 -0.277 0.021 0.623*** 0.299*** 0.090 -0.057 -0.312 0.010 0.499** 0.300*** 0.210 (0.077) (0.204) (0.061) (0.213) (0.096) (0.283) (0.079) (0.205) (0.061) (0.209) (0.097) (0.276) TLU 0.133 0.419 0.059** 0.905*** -0.163 -1.010** 0.091 0.701*** 0.073 0.767*** -0.152 0.241 (0.098) (0.261) (0.064) (0.220) (0.199) (0.457) (0.107) (0.262) (0.063) (0.216) (0.193) (0.427) Land size of vegetables -0.770 3.237** 1.566** 4.086** 0.573 3.967 0.854* 2.503** 1.655*** 3.312* 0.507 -1.606 (0.518) (1.270) (0.629) (1.867) (0.940) (2.897) (0.516) (1.272) (0.627) (1.879) (0.917) (2.965) Distance to road -0.012*** -0.022* -0.001 -0.016* -0.029*** -0.006 -0.008** -0.017 -0.001 -0.023** -0.029*** -0.005 (0.004) (0.012) (0.003) (0.009) (0.007) (0.016) (0.004) (0.011) (0.003) (0.009) (0.007) (0.016) Distance to the market 0.001 0.013 0.001 -0.008 -0.018*** -0.006 0.002 0.003 0.001 -0.014** -0.018*** 0.008 (0.003) (0.008) (0.002) (0.006) (0.004) (0.009) (0.003) (0.008) (0.002) (0.006) (0.004) (0.009) Log Nonfarm inc 0.073*** 0.009 0.047** 0.067 -0.014 0.196*** 0.063*** 0.127** 0.046** 0.078 -0.011 -0.037 (0.022) (0.057) (0.020) (0.064) (0.025) (0.067) (0.023) (0.058) (0.020) (0.064) (0.023) (0.064) Credit access 0.126 0.382* 0.179* 0.391 0.135 -0.278 0.125 0.378* 0.181 0.406 0.139 -0.174 (0.080) (0.228) (0.282) (0.797) (0.129) (0.387) (0.082) (0.217) (0.282) (0.783) (0.128) (0.387) Drought stress -0.197* 0.654** 0.049 0.962*** 0.341** 0.287 0.153 -0.456 0.053** 0.997*** 0.336** -0.131 (0.110) (0.285) (0.074) (0.187) (0.162) (0.411) (0.106) (0.299) (0.075) (0.189) (0.161) (0.367) Pest and disease 0.076* 0.072 -0.038 0.281 0.045 -0.447 0.036 0.436*** -0.033 0.232 0.053 0.184** (0.059) (0.158) (0.288) (0.811) (0.166) (0.421) (0.066) (0.159) (0.288) (0.798) (0.164) (0.379) Experienced flooding 0.046 -0.085 -0.020 0.403*** 0.106 -0.022 -0.014 0.470*** -0.013 0.337** 0.106 -0.010 (0.055) (0.149) (0.053) (0.154) (0.083) (0.227) (0.064) (0.147) (0.052) (0.155) (0.083) (0.216) Soil fertility (not fertile as reference) -medium 0.234*** 0.051 0.004 -0.074 0.019 0.420** 0.216*** 0.245 0.001 -0.014 0.013** 0.104 (0.066) (0.165) (0.054) (0.173) (0.066) (0.185) (0.068) (0.165) (0.054) (0.173) (0.063) (0.177) -fertile 0.116 -0.012 -0.030 0.577* -0.051 -0.078 0.068 0.466 -0.038 -0.509* -0.049 0.061* (0.108) (0.284) (0.100) (0.295) (0.110) (0.325) (0.113) (0.287) (0.100) (0.294) (0.110) (0.297) District (Ayehu as reference) - Mecha -0.023 -0.220 -0.027** -0.428** 0.040** 0.107 -0.021 -0.261 -0.027** -0.444** 0.041** -0.114 (0.060) (0.164) (0.051) (0.173) (0.064) (0.198) (0.062) (0.168) (0.052) (0.173) (0.065) (0.186) - Fogera 0.098** 0.331** -0.083 -0.507*** -0.041 0.413* 0.111** 0.483*** -0.081* -0.555*** -0.046 0.205 (0.064) (0.166) (0.057) (0.176) (0.081) (0.229) (0.069) (0.172) (0.057) (0.177) (0.079) (0.225) Constant 0.949 -3.765* 0.884 -2.479 -0.969 5.601* 0.602 -0.955 0.832 -1.900 -1.091 -4.922 (0.735) (1.974) (0.722) (2.316) (1.262) (3.087) (0.730) (2.001) (0.720) (2.312) (1.219) (3.024) athrho -0.146 1.430 1.511 -0.042 1.665 1.495 (0.504) (5.995) (2.754) (0.547) (3.856) (5.015) lnsigma -1.593*** -5.563 -4.228 -1.566*** -4.871 -4.866 (0.211) (14.361) (6.436) (0.197) (7.613) (1.452) rho -0.145 0.892 0.907 -0.042 0. 931 0.904 (0.493) (1.229) (0.487) (0.546) (0.514) (0.914) sigma 0.203 0.004 0.015 0.209 0.008 0.008 (0.043) (0.055) (0.094) (0.041) (0.058) (0.112) Log-likelihood -1422.2938 -1296.0765 -1151.0789 -1411.1456 -1300.7558 -1166.0643 Wald Chi-square chi2(18) = 71.25 Prob > chi2 = 0.0000 Wald chi2(18) = 77.52 Prob > chi2 = 0.0000 Wald chi2(18) = 165.99 Prob > chi2 = 0.0000 Wald chi2(18) = 68.81 Prob > chi2 = 0.0000 Wald chi2(18) = 75.45 Prob > chi2 = 0.0000 Wald chi2(18) = 163.94 Prob > chi2 = 0.0000 Wald test of independent equations (rho = 0): chi2(1) = 0.08 Prob > chi2 = 0.077 (rho = 0): chi2(1) = 0.06 Prob > chi2 = 0.081 (rho = 0): chi2(1) = 0.30 Prob > chi2 = 0.058 (rho = 0): chi2(1) = 0.01 Prob > chi2 = 0.094 (rho = 0): chi2(1) = 0.19 Prob > chi2 = 0.066 (rho = 0): chi2(1) = 0.09 Prob > chi2 = 0.076 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sources: Own survey result, 2025 In (Tables 5 ) the diagnostic statistics of the PET model further validate the robustness of the estimation. In both tables, the correlation coefficient (ρ) between the error terms of the treatment and outcome equations was statistically significant in the models, and the Wald test of independent equations (p < 0.01) strongly rejected the null hypothesis of ρ = 0. This establishes the presence of endogeneity, implying that unobservable characteristics simultaneously affect social capital engagement (through networks or cooperative membership) and the intensity of CSA practice adoption. Social networks and cooperative membership positively and significantly influenced CSA adoption intensity in onion, potato, and tomato production. Social network impacts were consistent and strong, significantly and positively affecting CSA adoption intensity in onion (β = 0.382, p < 0.05), potato (β = 0.307, p < 0.01), and tomato (β = 0.146, p < 0.10). These mean that farmers with larger and stronger networks embrace a greater intensity of CSA practices compared to those with weaker ties. These findings are consistent with the study by Saptutyningsih et al. ( 2020 ). Social networks offer informal communication channels for the exchange of information, peer learning, and knowledge transfer that can alleviate risk perceptions related to new technology. Within the Ethiopian smallholder setting where the availability of extension services can be limited, farmer-to-farmer contact is seen to be playing a critical complementary role in the transfer of practice related to the adoption of CSA as well as building trust in the associated benefits. The result is consistent with the findings from the literature emphasizing the importance of social capital (Wossen et al., 2015 ; Felek & Yayeh, 2025 ). Similarly, cooperative membership positively and significantly influenced the intensity of CSA adoption in all vegetables: onion (β = 0.326, p < 0.05), potato (β = 0.290, p < 0.01), and tomato (β = 0.101, p < 0.10). Being a member of a cooperative improves a farmer’s ability to obtain agricultural inputs, extension, and credit, in addition to enabling farmer cooperation and participation in training. Thus, these institutional improvements minimize information asymmetry and transaction costs, enabling farmers to maximize the potential of different CSA practices. Empirical evidence confirms these institutional improvements, suggesting that cooperatives improve members’ participation in innovation by improving their bargaining power (Bernard & Spielman, 2009 ; Fischer & Qaim, 2012 ; Mojo et al., 2017 ). Several household level variables were also significant determinants of CSA intensity. Age positively influenced adoption in potato suggesting that older farmers with accumulated farming experience are likely to intensify adoption. Level of education had positive effects on CSA adoption intensity for onion and potato, consistent with the proposition that education increases capacity to absorb information and appreciate long term benefits of CSA (Cishahayo et al., 2023 ). Sex of household head had a positive effect through in social networks and cooperative membership, particular for onion and tomato, reflecting the role of male headed households in accessing networks and cooperatives more effectively. However, this also reflects gender inequality in access to social institutions, which aligns with findings that women farmers face structural barriers to cooperative membership and information channels (Meinzen-Dick et al., 2014 ). Family size positively influenced CSA intensity in the instance of potato and tomato, suggesting household labor availability as an important determinant of the adoption of labor intensive CSA practices such as mulching, soil conservation, or organic fertilizer use. This finding is consistent with labor endowment theories of technology adoption (Feder et al., 1985 ). Resource related characteristics also influenced adoption intensity. Farm size allocated to vegetables significantly increased CSA adoption intensity for potato farmers and positively influenced social capital participation, suggesting scale advantages in adopting multiple practices. Distance to roads and markets consistently reduced CSA intensity across crops, indicating geographic isolation as a limitation to information, input availability, and market participation. This agrees with the evidence by (Cishahayo et al., 2023 ). Nonfarm income also positively influenced onion and potato adoption intensity, suggesting that liquidity from off farm sources reduces financial constraints. This idea is supported by (Cishahayo et al., 2023 ). Exposure to drought stress magnified CSA significantly for tomato and onion, and also increased farmers' reliance on social capital. This concurs with the induced innovation hypothesis (Thirtle, 1985 ), according to which resource shortage or shocks stimulate adaptive responses. Also, pest and disease incidence and flooding experience also influenced social capital participation in several cases, reinforcing the role of social institutions as coping mechanisms in risk prone environments. This drought and climate change related variables had a significant aligned with (Aryal et al., 2020 ; Cishahayo et al., 2023 ). Districts play a substantial role in participating in social capital as well as the intensity of adoption of the CSA. Compared to the Ayehu district, the Mecha district had a negative and significant impact on the intensity of adoption of the CSA among potato growers in the social capital model as well as the cooperative membership model. This indicates that for potato growers, the Mecha district records fewer adopters of the CSA compared to the Ayehu district. Furthermore, the Fogera district was found to have significantly lower levels of participation in social capital as well as membership of the cooperative for potato growers. This is evident in the negative and significant coefficients of the treatment variables. On the other hand, the Fogera district was found to have a significantly positive impact on the intensity of adoption of the CSA for onion growers in the two models. This indicates that the onion growers in the Fogera district adopt more of the CSA compared to the growers in the Ayehu district. Additionally, the Fogera district was found to have a significantly positive impact on social network membership. Concerning the production of tomatoes, the district level variables were generally statistically significant for Mecha districts for the adoption intensity of CSA in the social network and cooperative membership models, which suggests that Mecha districts have a significant influence on adoption intensity. Additionally, Fogera districts have a positive and significant effect on social network participation. Comparative analysis indicates that formal and informal dimensions of social capital play complementary roles. Farmers are influenced by social networks primarily through informal trust relations and learning from peers (Conley & Udry, 2010 ), while cooperatives operate on the basis of structured institutional arrangements that facilitate input access, knowledge transfer, and market access (Bernard & Spielman, 2009 ; Fischer & Qaim, 2012 ). Surprisingly, the coefficient size shows that social networks affected onion farmer’s slightly more than cooperative membership, perhaps because of the higher market orientation of onion farming, where learning from and observing peers would be more effective. Cooperatives were highly important for every crop, suggesting their institutional role in CSA adoption (Liu et al., 2024 ). Table 6 Treatment effects of vegetables Treatment effects Social network Cooperative membership Onion Potato Tomato Onion Potato Tomato ATE 1.791** 1.435*** 1. 212** 1.595* 1.36*** 1.102*** (0.781) (0.448) (0.710) (0.958) (0.483) (0.821) ATET 1.829** 1.576*** 1. 318*** 1.652** 1.402*** 1.202** (0.628) (0.420) (0.747) (0.371) (0.455) (0.889) ATU 1.77** 1.365*** 1. 207*** 1.55** 1.287*** 1.002** (0.672) (0.156) (0.062) (0.800) (0.149) (0.01) Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sources: Own survey result, 2025 According to the result of (Table 6 ), the ATE shows the average effect of being a participant in social capital (either the network or cooperative) on CSA adoption intensity for all the farmers included in the sample. Results confirm that social network membership significantly increases adoption intensity by 1.791 for onion practices, 1.435 for potato, and 1.212 for tomato. Membership in a cooperative also increases CSA intensity by 1.652, 1.402, and 1.202 for onion, potato, and tomato, respectively. Strong and significant (p < 0.05 or p < 0.01) results indicate that social capital has a system wide effect in scaling CSA practice in all farmer categories. This finding emphasizes the primary facilitative function performed by social institutions in lowering information asymmetry, lowering technology adoption expenses, and making the mobilization of resources easier. It is theoretically consistent with the diffusion of innovations theory (Rogers et al., 2014 ), which argues that personal and group communication makes adoption of innovation easier, and with social capital theory (Coleman, 1988 ), which emphasizes the need for trust, norms, cooperatives, and networks as channels through which collective action takes place. The ATET estimates the impact of social capital on treated farmers who are already part of networks or cooperatives. Results indicate that the intensity of adoption rises by 1.829 (onion), 1.573 (potato), and 1.318 (tomato) CSA practices among those in the network. Additionally, cooperative members adopt 1.652 (onion), 1.402 (potato), and 1.202 (tomato) practices more than they would without being a member of a cooperative. This stronger impact for participants compared to the general public suggests that socially embedded actors benefit disproportionately from group oriented training possibilities, peer education, and shared resources. This is in line with Felek and Yayeh ( 2025 ) and Mojo et al. ( 2017 ). The ATU implies the gains if the non members (farmers with no networks or cooperative membership) become members. The effect is strong and positive, with membership in networks having the potential to increase the intensity of adoption by 1.77 (onion), 1.365 (potato), and 1.207 (tomato) practices and cooperative membership by 1.55, 1.287, and 1.002 practices, respectively. These counterfactual results show the untapped potential among non members. If integrated into social institutions, such farmers would significantly raise the adoption of CSA practices. This implies that enhancing access to cooperatives and strengthening local farmer networks can yield enormous aggregate climate adaptation and resilience benefits. Together, the ATE, ATET, and ATU results confirm that both social networks and cooperative membership causally raise the intensity of CSA adoption, with similar effects for onion, potato, and tomato. Of particular interest is the fact that ATET values higher than ATE or ATU show that program participants are already realizing considerable benefits, but if participation hurdles are crossed, there exists considerable potential for expanding coverage among non participants. 4.4 Mediating effect analysis Table 7 Result of mediating effect Effect of social network Effect of cooperative membership Path Onion Potato Tomato Path Onion Potato Tomato SN→ INF 0.950*** 0.728*** 0.602*** COP→ INF 0.433** 0.868*** 0.843*** (0.197) (0.190) (0.206) (0.185) (0.193) (0.225) SN→ EXT 0.258* 0.935*** 0.296* COP→ EXT 0.037* 0.749*** 0.098* (0.202) (0.205) (0.194) (0.199) (0.207) (0.223) SN→ SUP 0.751*** 0.187* 0.943*** COP→ SUP 0.059* 0.034 0.656*** (0.198) (0.194) (0.208) (0.178) (0.190) (0.222) INF → CSAI 0.113** 0.072* 0.101* INF → CSAI 0.142*** 0.064 0.103* (0.054) (0.042) (0.052) (0.052) (0.042) (0.053) EXT → CSAI 0.052** 0.055* 0.005* EXT → CSAI 0.063* 0.074* 0.023* (0.048) (0.045) (0.046) (0.048) (0.046) (0.049) SUP→CSAI 0.130*** 0.061 0.116** SUP→CSAI 0.169*** 0.073* 0.156*** (0.047) (0.038) (0.055) (0.047) (0.039) (0.055) SN→INF→CSAI 0.107** 0.054* 0.061 COP→INF→CSAI 0.061** 0.055* 0. .087* (0.056) (0.034) (0.038) (0.035) (0.039) (0.051) SN→EXT→CSAI 0.013* 0. 052** 0. 002* COP→EXT→CSAI 0.002* 0. 056** 0. 002* (0.016) (0.044) (0.014) (0.012) (0.038) (0.007) SN→ SUP→CSAI 0.097** 0. 011* 0. 110* COP→ SUP→CSAI 0.01* 0. 002* 0. 102** (0.044) (0.014) (0.058) (0.030) (0.014) (0.051) Direct effect (SN→CSAI) 0.287*** 0.359*** 0.324*** Direct effect (COP→CSAI) 0.237*** 0.322*** 0.286*** (0.065) (0.047) (0.052) (0.054) (0.042) (0.070) Total effect 0.505*** 0.476*** 0.497*** Total effect 0.328*** 0.435*** 0.477*** (0.082) (0.061) (0.074) (0.074) (0.059) (0.087) Notes: SN (social network), EXT (extension access), SUP (access to inputs supply), INF (access to CSA related information), CSAI (climate smart agriculture intensity), and COP (cooperative membership) Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 In the mediation analysis, the total effect of social capital is further disaggregated into the direct effect and indirect effects that work through the following three binary mediators: information about CSA practices, input supply, and extension facilitation. The results are summarized in (Table 7 ) indicate that social capital positively and significantly affects information about CSA practices for all crops, with the strongest positive association estimated in the case of onions (β = 0.950, < 0.01), followed by potatoes (β = 0.728, < 0.01) and tomatoes (β = 0.602, < 0.01). This highlights the key role that person to person communication can play in diffusing information about CSA practices in regions that lack institutional means of information dissemination. Membership in a cooperative organization also positively influences information about CSA practices in line with the idea that farmer organizations with high quality social capital can improve the ability of their members to access, understand, and communicate agricultural information (Liu et al., 2024 ). Access to CSA information, further, positively and significantly influences CSA adoption intensity for most crops, thereby validating the importance of information as a factor for adopting different CSA methods as farmers need to be informed about both short term management needs and long term gains. The indirect pathways (SN → INF → CSAI and COP → INF → CSAI) are significant, showing information access as an important mediation channel between social capital and a rise in adoption intensity, which is consistent with a study by Hailu et al. ( 2025 ). This analysis also shows that social networks positively influence extension access, indicating that farmers who are socially connected are more visible or can collectively create demands for services from extension agents. Membership in cooperatives is also positively and significantly related to extension access, indicating that formal institutions support farmers in accessing public extension services. This is consistent with findings presented by Okori et al. ( 2022 ). Extension access has a statistically significant effect on the adoption intensity of CSA practices for all crops. However, the magnitude of the coefficients is less compared to information and input supply. A similar trend is observed, meaning that extension services have a non driving role in the adoption intensification process but can be viewed as a reinforcement process. The results show that the indirect effects, represented by the paths SN → EXT → CSAI and COP → EXT → CSAI, are statistically significant but small. These results are consistent with Wang et al. ( 2020 ). Access to CSA related input supply is found to be an extremely substantial mediator. Social networks are found to have made a considerable difference in regard to input sources such as climate resilient seeds, organic fertilizers, and water harvesting methods. Being members of cooperatives is also found to have substantially impacted input accessibility, which aligns with the rationale of cooperatives to have been established for input distribution. Access to input supply has a strong and positive relation with CSA adoption intensity, verifying that even very informed farmers will not be able to enhance CSA intensity without access to input supplies. The indirect effects on input supply (SN → SUP → CSAI and COP → SUP → CSAI) are all significant in most crop equations, with coefficients relatively large compared to extension access. The implication here is that input availability is a crucial constraint on enhancing CSAI, following Liu et al. ( 2024 ). However, despite the influence of the mediating variables, the direct effects of social networks and cooperative membership on the adoption intensity of CSA are highly significant and positively affect all crops. This indicates the partial mediating effect and the fact that the influence of social capital on CSA adoption occurs in two different dimensions: first in the visible domain of information and inputs and second in a more subtle domain. Finally, the total effects show that social networks have a relatively stronger overall impact on CSA adoption intensity than cooperative membership for onion and tomato farmers, while for potato farmers, cooperative membership has a relatively larger overall impact. This crop specific variation reflects differences in production risk, market orientation, and dependence on collective input and knowledge systems. 5. Conclusion and Recommendation The evidence suggests that social networks and cooperative membership are significant and positive determinants of the intensity of adoption for each vegetable type. Social networks have more pronounced effects because they enable person to person learning, and membership of cooperatives impacts adoption by improving input/output, extension, and institutional support. The results of the treatment effect indicate large unrealized adoption potentials among the non adoptees, suggesting that improved adoption of CSA could be realized by ensuring increased participation of farmers in social institutions. The mediation analysis reveals that access to CSA related information, input supply, and extension services constitute the principal mechanisms through which social capital enhances CSA adoption intensity. Build and stimulate farmer based social networks as a core CSA diffusion mechanism. Agricultural development projects ought to formalize farmer based learning interfaces or platforms. Improve the overall functionality of agricultural cooperatives to facilitate CSA intensification. Cooperatives can extend beyond the supply of agricultural inputs to offer integrated CSA related services, which may involve input packages, training, and collective marketing of climate resilient agricultural products. The key mediating constraints should be addressed by improving access to information and inputs related to CSA. Information and input supply are the most mediating factors. Therefore, improving access to knowledge related to CSA and access to inputs should be prioritized. Extension systems should collaborate more closely with cooperatives and social networks to improve outreach in the study areas. The negative effects of distance to roads and markets highlight the need for targeted investments in rural infrastructure and decentralized service delivery. Mobile extension services and satellite input distribution points will reduce location constraints and improve adoption levels of distant farmers. Finally, ensure location and crop targeted strategies for CSA scale up. Findings of district level disparities in adoption levels indicate that a one size fits all approach may be inappropriate, thus location considerate strategies based on differential crop risks are required to optimize CSA interventions. Despite its contributions, this study has some limitations. Due to data constraints, social capital is measured using only two dimensions social networks and agricultural cooperatives membership. Other important aspects of social capital, such as trust, training, norms of reciprocity, leadership, and collective action, could not be incorporated. Future research should focus on this shortcoming by including more social capital variables in their study, developing a composite social capital indicator, and employing a unified measurement method in order to capture more accurately its complexity and effects on CSA adoption intensity. Declarations Competing interests : The authors declare no competing interests Ethics approval : This study was approved by the Institutional Research Ethical Review Committee (IRERC) of Debre Markos University, Burie Campus with Getnet Haimanot (Assistant Professor), Yilkal Messelu (Assistant Professor), and Wbalem Gobie (Assistant Professor) as the approving authorities. All procedures were carried out in accordance with relevant guidelines and regulations. A formal ethics approval document has been issued by IRERC of Debre Markos University, Burie Campus. The reference number for the approval is RCSTTD/1382/01/17 . Consent to participants The study did not involve the use of human or animal tissues. All participants were adults (≥ 18 years of age). Before data collection, participants were provided with detailed information about the study’s objectives, procedures, and benefits. For this study informed consent was obtained from all participants prior to their participation. Participation was entirely voluntary, and respondents were assured of confidentiality and anonymity throughout the study. Clinical trial Not applicable Consent for publication: Not applicable. Funding: The authors did not receive support from any organization for the submitted work. Author Contribution The authors spent their time on writing a proposal, collect primary data, search secondary data, data management, analyze and write up the final manuscript. **Mezgebu Aynalem** contribution: Formal analysis, Investigation, Software, Visualization, Writing original draft Project administration, Writing review & final editing. **Zemen Ayalew and Aemro Tazeze** contribution: Conceptualization, Data management, Resources, Methodology, Supervision & Writing review & final editing. Acknowledgements: We would like to thank the study area district of agricultural experts and all respondents as sources of this valuable information to investigate the existing situations. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8999225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609281768,"identity":"aa54ee69-4270-44ff-a5fe-8ff4092fb775","order_by":0,"name":"Mezgebu Aynalem","email":"data:image/png;base64,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","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Mezgebu","middleName":"","lastName":"Aynalem","suffix":""},{"id":609281769,"identity":"f732240c-cb43-49d3-8780-4789777c8c6a","order_by":1,"name":"Zemen Ayalew","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Zemen","middleName":"","lastName":"Ayalew","suffix":""},{"id":609281770,"identity":"4175437d-b04c-4d48-a9bb-99a1892d637a","order_by":2,"name":"Aemro Tazeze Terefe","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Aemro","middleName":"Tazeze","lastName":"Terefe","suffix":""}],"badges":[],"createdAt":"2026-03-01 04:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105202831,"identity":"77e69c5a-cd23-4c59-a916-403f143c4d5e","added_by":"auto","created_at":"2026-03-23 11:58:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37884,"visible":true,"origin":"","legend":"\u003cp\u003eOwn map of the study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999225/v1/39131a7cf4ecb35c3b1552e6.jpg"},{"id":105202830,"identity":"94c8f6ff-79bd-44a9-ab14-08606d9864c0","added_by":"auto","created_at":"2026-03-23 11:58:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31776,"visible":true,"origin":"","legend":"\u003cp\u003eThe mechanism of social capital on the vegetable CSA adoption intensity\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8999225/v1/47848d18c2473066f6fbe452.png"},{"id":105202839,"identity":"aa67d784-af12-465b-bbf4-ac2eaa7d87b7","added_by":"auto","created_at":"2026-03-23 11:58:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2324620,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999225/v1/6d1e92e8-0389-48a2-bb40-b44b94b3ca9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Power of Social Capital: How Cooperative Membership and Social Network Shapes Climate Smart Agriculture Practices in Northwest Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgriculture is increasingly vulnerable to the impacts of climate change and variability, which are taking the forms of rising temperatures, irregular rainfall, and frequent floods and droughts (Praveen \u0026amp; Sharma, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sivakumar, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vijai et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Climate stresses pose an imminent threat to crop production and productivity, particularly in low income, agriculture based countries like Ethiopia, where the majority of the population depends on farming for their livelihood (Gezie, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tesema et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In Ethiopia, agriculture accounts for 34.6% of the GDP, serving as a major source of livelihoods for 80% of the population and generating 90% of export revenue, thus the risk is high, especially among smallholder farmers who have limited adaptive ability (FAO, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eProduction of vegetables is among the most climate sensitive and resource intensive agricultural practices (Degefu \u0026amp; Kifle, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Laxman et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Vegetable production makes a vital contribution to rural livelihoods and employment in Ethiopia (Hunde, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, vegetable productivity, particularly for onion, tomato, and potato, has increasing barriers due to climate change and climate variability (Getahun, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate Smart Agriculture (CSA) practice have been promoted as a holistic approach to boosting productivity, resilience, and environmental integrity (Abhilash et al., 2021; Azadi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher CSA practices relevant to vegetable production include increased irrigation, crop diversification, organic fertilizer use, and integrated pest management (Bayu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, despite the availability of these practices, their adoption among smallholder farmers remains limited, largely due to inadequate access to reliable information and training, high costs, and uncertainty about their benefits (Mosha \u0026amp; Ngulube, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By addressing the problem of information asymmetry and the cost associated with it and enabling collective learning and behavior change, it acts as an important enabler in this regard (Granovetter, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Ostrom, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Accessing cooperatives and social networks in rural agrarian settings are essentially the two most important avenues through which farmers gain access to information and develop adequate trust in CSA practices in agriculture (Bandiera \u0026amp; Rasul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krishnan \u0026amp; Patnam, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These pathways are particularly influential for CSA practices because the advantages in terms of soil fertility management, water management, improved seeds, and integrated resource management take some time to develop and require learning to address the associated risks and concerns related to adoption in this regard (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fanta et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma \u0026amp; Rahut, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical evidence reinforces the importance of social capital for climate adaptation. Belay and Fekadu (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Mataraci and Buyukdagli (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that trust, information sharing, and group membership significantly increased the likelihood of adopting climate adaptation strategies. Ogunleye et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Ogunnaike et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that dense interpersonal networks enhance access to climate information and enable faster diffusion of CSA practices. This studies similarly demonstrate that interactive social networks support innovation processes by shaping collaborative learning and reinforcing pro innovation norms (Afranaa Kwapong \u0026amp; Ankrah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although few studies explicitly examine social capital mechanisms, the CSA adoption literature has predominantly focused on socio economic and agro ecological determinants such as farm size, market access, and input availability (Abdulai \u0026amp; Huffman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Esfandiari et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, strong social networks are more likely to access timely information, mobilize resources, and adopt climate resilient practices (Cishahayo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent meta analysis by Wang et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provides strong global evidence that social networks substantially increase smallholders\u0026rsquo; likelihood of adopting CSA technologies, although the magnitude varies with network strength, trust, and community structure.\u003c/p\u003e \u003cp\u003eMuch of the evidence on CSA focuses on binary adoption adopt versus non adopter by neglecting adoption intensity, or the number, depth, and cumulative benefits of CSA practices (Kassie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wossen et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Adoption intensity is important because resilience and productivity gains increase with cumulative practice use (Ma \u0026amp; Rahut, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and decisions on intensity are even more sensitive to information flows, trust, peer effects, and social reinforcement than initial adoption (Bandiera \u0026amp; Rasul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krishnan \u0026amp; Patnam, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTheoretically, cooperative membership and social networks may enable different causal mechanisms to drive the adoption decision. Social networks help spread information quickly, provide emotional support, and apply normative pressure to adopt (Granovetter, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Rogers, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), while cooperatives offer better access to inputs, formal training, and collective action promoting innovation (Ostrom, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The literature on innovation diffusion suggests that network tie strength, diversity, and centrality are crucial in conditioning learning and adoption behavior (Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kline \u0026amp; Moretti, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This implies that not all social capital is equal or functions similarly. A systematic comparison of cooperative versus network based social capital is thus necessary.\u003c/p\u003e \u003cp\u003eIn Northwest Ethiopia, where climate variability is high, vegetable production is expanding, and cooperative structures are deeply embedded in rural community life. Most of the studies on social capital and adaptation, however, focus on either general climate strategies (Belay \u0026amp; Fekadu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or specific institutional arrangements (Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) without explicitly investigating CSA adoption intensity of cooperatives and social networks. Moreover, previous research has largely focused on technology adoption in general, with limited attention to how social capital enables the transformation of existing knowledge, traditional practices, and local resources into scalable, climate-resilient agricultural strategies. No existing study integrates these dimensions into a unified framework tailored to the specific social and cooperative context.\u003c/p\u003e \u003cp\u003eHowever, despite the growing body of evidence, this study adds to the literature by providing robust empirical evidence on how different dimensions of social capital particularly cooperative membership and social networks influence the intensity of CSA adoption through specific mediating mechanisms in the context of vegetable based farming systems in Northwest Ethiopia. The study goes beyond the methodological limitation of focusing solely on direct effects by explicitly identifying and testing the mediating mechanisms through which social capital operates namely, access to CSA related information, access to input supply, and access to extension thereby offering a more nuanced account of exactly how social capital translates into higher adoption intensity.\u003c/p\u003e"},{"header":"2. Theoretical framework","content":"\u003cp\u003eThis research is informed by a unified paradigm of economic and social theories that cumulatively explain the role of social capital, conceptualized in this research as cooperative membership and social networks, in shaping the intensity of CSA adoption. Each of these theoretical approaches contributes uniquely to an understanding of how social structure and institutions matter for knowledge diffusion, motivations, transaction costs, collective action, and adaptability.\u003c/p\u003e \u003cp\u003eCollective action theory describes how social groups overcome the problem of freeriding to make joint investments (Olson Jr, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). In farming, cooperatives represent institutional approaches that bring farmers together, monitor them, and pool risks to make investments that are costly to individual members. The use of joint provision of inputs, training, and risk sharing, membership in cooperatives reduces adoption costs of individual members and hence adoption intensity of CSA (Bernard \u0026amp; Spielman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to diffusion theory, technology adoption is affected by social interactions, information, and learning (Rogers, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Social networks are important conduits that enable farmers to learn by observation, share experiences, and build less uncertain attitudes towards new practices. More frequent interactions are associated with learning and experimentation, indicating that farmers who are connected by strong social networks are most likely to adopt a wider set of CSA practices, resulting in higher adoption intensity (Bandiera \u0026amp; Rasul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResource based theory defines social capital as a productive asset that can provide access to information, credit, labor, and social support (Coleman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Cooperative membership represents institutionalized social capital that leverages collective assets, while social networks provide relational social capital through the exchange of information and social support. Both of these sources of social capital can supplement other sources of capital, such as physical and human capital, and can also mitigate limitations that hamper farmers' ability to adopt a combination of several interrelated CSA practices (Belay \u0026amp; Fekadu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTransaction Cost Economics argues that institutions develop to lower the search, negotiation, and enforcement costs of agreements (Williamson, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Being part of an association lowers these costs in that it organizes input acquisition, extension, and association services needed for implementing CSA. Low transaction costs promote and lower the adoption of various practices of CSA, while high transaction costs limit adoption intensity (Bernard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResilience theory is the function of social systems in absorbing the shock of change and learning in the face of uncertainty (Folke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Social capital can be seen to increase the level of resilience by facilitating learning together in the face of climate change risk. The increased level of adoption intensity of CSA can be seen to represent not only adoption but also an adaptive path.\u003c/p\u003e \u003cp\u003eTogether, these theories imply a multi mechanism causal model where cooperative membership and social networks shape CSA adoption intensity via distinct mechanisms: cooperative membership is seen to reduce transaction costs, whereas social networks are seen to aid information spread and learning. Social capital of both types plays a role in resilience via its ability to enable synergistic adoption of CSA bundles.\u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Description of the Study Area\u003c/h2\u003e \u003cp\u003eThe study area is in the Amhara region of Northwest Ethiopia, specifically in Mecha District of North Gojjam Zone, Ayehu Guagusa of Awi Zone, and Fogera District of South Gondar Zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Amhara region is known to be a region of varied agro ecological zones with high agricultural potential. It is known to be a source of income for a high proportion of the population. Agricultural production is mainly based on crop production, specifically cereals and vegetable production, in addition to livestock production (Amhara Regional State Meteorological Agency, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNorth Mecha District is among the irrigable districts of the North Gojjam Zone. Onion, potato, and tomato production are increasing in the district, making it an ideal location for CSA practices (Amhara Regional State Meteorological Agency, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ayehu Guagusa District, in the Awi Zone, falls in the mid to high altitude agro ecological zones of the region. Vegetable production is an important livelihood strategy, besides cereal and livestock production (Ayew Guagusa District Agricultural Office, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fogera District, in the South Gondar Zone, surrounding the shores of Lake Tana, is a flood prone region in the study area. Although the region has been widely recognized as a major vegetable producer, water stagnation and recurrent floods significantly threaten agricultural productivity (Fogera District Administration Office, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Social network and cooperative systems in the region play an important social aspect, and they operate in conjunction with the indigenous institutions of Idir (Eder) and Equb (Iqub), which enhance mutual trust, reciprocity, and interaction in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Type, Source and Methods of Data Collection\u003c/h2\u003e \u003cp\u003eThe research utilized a mixed research design that incorporated both qualitative and quantitative research methods. The primary research was conducted using semi structured household surveys to identify demographic and socioeconomic aspects as well as adoption of CSA practices for vegetable production. The secondary data was conducted from district agricultural offices, Amhara Regional Bureau of Agriculture, as well as the Ethiopian Statistical Service.\u003c/p\u003e \u003cp\u003e A multi stage sampling design was followed. In the first stage, the zones were selected based on their contribution to vegetable production and their vulnerability to climate change. The zones selected were North Gojjam, Awi, and South Gondar. In the second stage, a vegetable producing district was selected from each of the zones. In the North Gojjam zone, the selected district is Mecha. In the Awi zone, the selected district is Ayehu Guagusa. In South Gondar zone, the selected district is Fogera. The districts were selected based on their contribution to vegetable production. They are also highly sensitive to climate change. For stage three, households that grew vegetables were chosen using probability proportional to size (PPS), based on the number of vegetable producers in each district. A total of 550 households were chosen, consisting of 221 in Mecha, 152 in Ayehu Guagusa, and 177 in Fogera. Overall, 550 vegetable farmers (481 onion, 495 potato, and 429 tomato farmers) were interviewed. It is important to note that many farmers were involved in the production of multiple crop types, so the crop specific sample sizes are not mutually exclusive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Method of Data Analysis\u003c/h2\u003e \u003cp\u003eBoth descriptive and econometric methods were employed. The descriptive method involved the use of frequencies, percentages, means, and standard deviations, of the sampled households in the different social capital participation levels (social networks and cooperative membership). Along with the processing of the quantitative results, the results of the Focus Group Discussion (FGDs) and the Key Informat Interviews (KIIs) conducted were analyzed.\u003c/p\u003e \u003cp\u003eFor the econometric analysis, to strengthen causal inference and enhance robustness, this study integrates mediation analysis with the Poisson Endogenous Treatment (PET) framework. One of the most important econometric challenges in determining the impact of social capital on the level of CSA practice intensity is the issue of missing counterfactual outcomes. This is due to the fact that a farmer can be seen in a single treatment state at a time (for instance, being in social capital networks or not), and as a consequence, it is impossible to directly view the other case or counterfactual (Wooldridge, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Conventional regression methods may yield biased and inconsistent estimates in the presence of endogenous treatment assignment and selection bias, because farmers self select into social capital networks based on both observable and unobservable characteristics (Heckman, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Imbens \u0026amp; Wooldridge, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wooldridge, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these concerns, similar studies employed two common econometric methods: propensity score methods and instrumental variables (IV) methods (Kassie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Danso-Abbeam \u0026amp; Baiyegunhi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While propensity score based methods such as matching, regression adjustment, and inverse probability weighting account for observable heterogeneity, they are limited in addressing unobserved variables that may influence both treatment and outcome simultaneously (Heckman, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Imbens \u0026amp; Wooldridge, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wooldridge, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PET approach was used to estimate the causal effect of social capital on CSA adoption intensity while accounting for selection bias and endogeneity due to observed and unobserved variables. The mediation analysis is a supplement to the previous approach in understanding how social capital affects CSA adoption intensity. In particular, mediation analysis highlights access to CSA related information, input supply, and extension services as channels of transmission between social networks/cooperative membership and CSA adoption intensity.\u003c/p\u003e \u003cp\u003ePET has many advantages in handling endogeneity in count outcome variables and yields reliable results for the Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATET), and Average Treatment Effect on the Untreated (ATU); but it does not explicitly show how the adoption intensity of CSA is influenced by social capital through certain pathways. On the other hand, the mediation analysis framework done through the generalized structural equation modeling approach in GSEM is appropriate for handling nonlinear relationships among variables where there are binary mediators and count outcomes (Baron \u0026amp; Kenny, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Holmbeck, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Rucker et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Duguma \u0026amp; Bai, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By jointly applying PET and mediation analysis, this study combines the strengths of both approaches: PET provides reliable results on causal parameters, and mediation analysis allows for a clear decomposition of the total effect. The consistency in results from both techniques strengthens the findings and offers a deeper understanding on both the direction and the mechanisms through which the adoption intensity of CSA is influenced by social capital. The PET model is specified as follow:\u003c/p\u003e \u003cp\u003eTreatment equation for modeling the endogeneity of social capital participation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${T}_{k=}^{*}{Z}_{k\\sigma}^{{\\prime}}+{\\mu}_{k}\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots.\\dots\\dots...1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWith \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{k}=1if{(T}_{k}^{*}\u0026gt;0);\\)\u003c/span\u003e\u003c/span\u003e Z\u003csub\u003ek\u003c/sub\u003e represents observed covariates predicting SC participation, and \u0026micro;\u003csub\u003ek\u003c/sub\u003e is the error term.\u003c/p\u003e \u003cp\u003eOutcome equation, specified as a Poisson distribution for count data:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P(W}_{k}\\left|{G}_{k,}{T}_{k,}{\\epsilon}\\text{k}\\right)\\)\u003c/span\u003e \u003c/span\u003eμ\u003csub\u003ek\u003c/sub\u003e \u0026sim;Poisson(μi​), μ\u003csub\u003ei\u003c/sub\u003e​= exp \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(G}_{k}^{{\\prime}}{\\beta}+\\sigma{T}_{k}+{\\epsilon}_{k}\\)\u003c/span\u003e\u003c/span\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dots\\dots\\dots...2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon}_{k}\\)\u003c/span\u003e\u003c/span\u003e​ is an unobserved disturbance entering multiplicatively via the log-link.\u003c/p\u003e \u003cp\u003eJoint distribution of unobservable, Endogeneity arises because \u0026micro;\u003csub\u003ek\u003c/sub\u003e and ε\u003csub\u003ek\u003c/sub\u003e may be correlated. The PET model assumes:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCov(μk, εk) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lceil;\\begin{array}{c}{\\sigma}^{2}\\sigma\\rho\\\\\\sigma\\rho1\\end{array}\u0026rceil;\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots..3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePotential outcomes and treatment effects\u003c/p\u003e \u003cp\u003eDefine the potential counts:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${{\\mu}}_{i}\\left(1\\right)=\\text{e}\\text{x}\\text{p}{(G}_{k}^{{\\prime}}{\\beta}+{\\alpha}.1+{\\epsilon}_{k}),{{\\mu}}_{i}(0)=\\text{e}\\text{x}\\text{p}{(G}_{k}^{{\\prime}}{\\beta}+{\\alpha}.0+{\\epsilon}_{k})\\dots\\dots\\dots\\dots\\dots...4$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe corresponding expected counts (integrating over the joint distribution implied by the model) yield the standard causal estimands:\u003c/p\u003e \u003cp\u003eATE (Average Treatment Effect):\u003c/p\u003e \u003cp\u003eATE\u0026thinsp;=\u0026thinsp;E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{1k}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E{(W}_{1k}-{W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e | \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{k})\\)\u003c/span\u003e\u003c/span\u003e]\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots..\\dots\\dots....5\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eATET (Average Treatment Effect on the Treated):\u003c/p\u003e \u003cp\u003eATET\u0026thinsp;=\u0026thinsp;E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{1k}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e | \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{k}\\)\u003c/span\u003e\u003c/span\u003e = 1] = E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E{(W}_{1k}-{W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e | \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{k}),{T}_{k}\\)\u003c/span\u003e\u003c/span\u003e= 1]\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dots\\dots\\dots.\\dots\\dots...6\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eATU (Average Treatment Effect on the Untreated):\u003c/p\u003e \u003cp\u003eATU\u0026thinsp;=\u0026thinsp;E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{1k}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e | \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{k}\\)\u003c/span\u003e\u003c/span\u003e = 0] = E [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E{(W}_{1k}-{W}_{0k}\\)\u003c/span\u003e\u003c/span\u003e | \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{k}),{T}_{k}\\)\u003c/span\u003e\u003c/span\u003e= 0]\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dots\\dots\\dots\\dots\\dots.....7\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn practice, these are computed from the fitted PET model as contrasts of predicted means with T set to 1 vs. 0 (while keeping the appropriate conditioning overall for ATE, conditional on T\u0026thinsp;=\u0026thinsp;1 for ATET, and on T\u0026thinsp;=\u0026thinsp;0 for ATU). Standard errors are obtained by the delta method.\u003c/p\u003e \u003cp\u003eIn addition to the above, the mediation structure is specified as a system of equations:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${{MED}^{*}}_{i}={{\\gamma}}_{0}+{{\\gamma}}_{1}{SC}_{i}+\\sum Controls+{{\\epsilon}}_{i}\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots..\\dots..\\dots\\dots.....8$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${CSA\\_\\text{I}\\text{n}\\text{t}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}}_{i}={{\\beta}}_{0}+{{\\beta}}_{1}{MED}_{i}+\\sum Controls+{{\\epsilon}}_{i}\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots..\\dots\\dots..9$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${CSA\\_\\text{I}\\text{n}\\text{t}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}}_{i}={{\\alpha}}_{0}+{{\\alpha}}_{1}{SC}_{i}+{{\\beta}}_{2}{MED}_{i}+\\sum Controls+{{\\epsilon}}_{i}\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots\\dots..10$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere SC\u003csub\u003ei\u003c/sub\u003e​ denotes social capital (either social networks or cooperative membership), and MED\u003csub\u003ei\u003c/sub\u003e​ represents the mediating variables. The mediators are binary, while CSA adoption intensity is a count variable.\u003c/p\u003e \u003cp\u003eSeparate mediation models are estimated for social networks and cooperative membership to capture the distinct pathways through which social capital influence CSA adoption intensity. Indirect effects are computed as the product of the estimated coefficients linking social capital to each mediator and the coefficients linking the mediator to CSA adoption intensity. To ensure valid statistical inference in this non linear setting, bootstrapped standard errors are employed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Measurement of variables\u003c/h2\u003e \u003cp\u003eThe variables used in this study were measured based on established empirical literature on CSA, and social capital. The operational definitions are provided below.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Climate Smart Agriculture Adoption Intensity\u003c/h2\u003e \u003cp\u003eCSA adoption intensity refers to the number of CSA practices implemented by each household. Consistent with prior studies that conceptualize CSA intensity as a count of multiple interrelated practices (Teklewold et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Asfaw et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wainaina et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study measures intensity by summing twelve practices. These practices are terracing/bunds (X₁), reduced tillage (X₂), crop residue use (X₃), crop rotation (X₄), agroforestry/shade nets (X₅), rescheduling planting (X₆), intercropping (X₇), compost/manure application (X₈), improved seed varieties (X₉), integrated pest management (X₁₀), furrow irrigation (X₁₁) and rainwater harvesting (X₁₂). Each practice is associated with increased productivity enhancement, risk reduction, and enhancement of adaptive capacities (Lipper et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Social Capital\u003c/h2\u003e \u003cp\u003eSocial capital can be defined widely as the structural components and processes that enable collective action, information flow, and cooperation (Coleman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Putnam, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This study uses two indicators for social capital measurement: (i) the involvement of farmers in agricultural cooperatives and (ii) farmers' involvement in information sharing social network structures with their neighboring farmers. These two components cover the structural and relational aspects of social capital with established positive impacts on agricultural information access, uncertainty reduction, trust development, and learning processes for agricultural practices adoption (Ogunleye et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial networks are more specific about the social relations of farmer interactions with other farmers that offer access to information exchange, observation, and learning. From existing literature on the matter, it can be ascertained that indirect interaction and observation between farmers of surrounding areas do contribute immensely to the development and spread of agricultural innovations because they mitigate the risks for the farmers associated with the introduction of the innovation (Bandiera \u0026amp; Rasul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Krishnan \u0026amp; Patnam, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For the purpose of measurement a binary variable would be established that would assign a value of 1 if the farmer regularly communicates with the neighboring farmers on issues concerning the implementation of CSA principles and a value of 0 otherwise. This measurement would establish uniformity based on the existing researcher (Belay \u0026amp; Fekadu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ogunleye et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCooperative membership is a variable that indicates a farmer\u0026rsquo;s involvement with a formal agricultural cooperative as a means of having collective access to resources, markets, inputs, credit, as well as extension services. Cooperatives have been found to be fundamental for improving farmers' access to information and decreasing transaction costs; hence affecting the adoption of climate smart innovations (Kassie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Control Variables\u003c/h2\u003e \u003cp\u003eA set of control variables is included based on prior empirical literature and expert judgment on factors affecting CSA practice uptake. Different studies show that sex of household, access to credit, drought stress, pest and disease, experienced flooding, soil fertility status, family size, age of the household, education level, distance to road, distance to market, TLU, land size of vegetables, districts and Log_Nonfarm inc affect farmers\u0026rsquo; innovation decisions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4 Mediator Variables\u003c/h2\u003e \u003cp\u003eTo unpack the pathways through which social capital (operationalized as cooperative membership and social networks) affects the intensity of CSA adoption, this study examines three mediators: access to CSA related information, access to inputs supply, and extension access (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each mediator represents a plausible mechanism grounded in theory and empirical work showing how interpersonal ties and collective institutions translate social relationships into observable changes in CSA practices uptake.\u003c/p\u003e \u003cp\u003eInformation transmission and peer learning are central mechanisms in diffusion and social capital theories: farmers embedded in rich social networks or cooperatives are more likely to receive timely, relevant, and credible information about CSA practices, demonstration outcomes, and locally appropriate implementation strategies (Rogers, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Empirically, information exposure has been shown to increase both the probability and breadth of adoption because it reduces uncertainty and facilitates learning by observation and learning by doing (Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Krishnan \u0026amp; Patnam, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the present study, access to CSA related information is measured as a binary variable equal to 1 if the farmer received CSA related information during the production season, through peers, cooperatives, or local institutions (Bandiera \u0026amp; Rasul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Belay \u0026amp; Fekadu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The expected indirect effect is positive cooperatives and denser social networks raise information exposure, which in turn increases CSA adoption intensity.\u003c/p\u003e \u003cp\u003eMany CSA practices require material inputs (improved seed, fertilizer, composting materials, irrigation equipment, or water harvesting supplies) or small investments to implement at scale. Cooperatives commonly act as intermediaries that aggregate demand, obtain inputs at lower transaction costs, and facilitate credit or in kind supply schemes; social networks can also support informal input sharing or collective purchasing intensity (Bernard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Access to inputs therefore functions as a resource facilitation mediator: when social capital eases access to affordable, timely inputs, farmers are better able to adopt multiple, complementary CSA practices simultaneously. This mediator is measured as a binary variable equal to 1 if the farmer accessed inputs through cooperatives, networks, or local institutions during the production year (Bernard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The hypothesized indirect effect is positive social capital increases input access, which increases CSA adoption intensity.\u003c/p\u003e \u003cp\u003eFormal agricultural extension complement social channels by providing technical guidance, demonstration plots, and follow up support services that cooperatives frequently help coordinate and that social networks help disseminate (Kassie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Extension access encompasses both the frequency and the quality of contact with extension agents or structured training events focused on CSA. This mediator is measured as a binary indicator equal to 1 if the household received CSA related extension contact within the production year. The mediation logic holds that cooperative membership and active social networks increase the likelihood of extension exposure and in turn extension exposure increases farmers\u0026rsquo; capacity and confidence to adopt CSA practices (Kassie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Belay \u0026amp; Fekadu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kahsay \u0026amp; Endalew, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The expected indirect effect through extension access is therefore positive.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Demographic and socio economic characteristics of the sampled farmers\u003c/h2\u003e \u003cp\u003eThe tables below present summary statistics for the explanatory variables of onion, potato, and tomato farmers, together with chi-square and t-test results comparing farmers who participate in cooperative membership with those who do not, as well as farmers who engage in social networks and those who are not engaged.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of discrete explanatory variables of vegetables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoop memb (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial network (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoop memb (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSocial network (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCoop membe (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSocial network (χ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.92***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccess to Credit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.86*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e19.72***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17.18***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrought stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.93***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.86*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e27.54***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.70*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e23.17***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePest and disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.22***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperience flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.91***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.49***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSoil fertility status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot fertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard errors in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eSources: Own survey result, 2025\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the demographic and socio economic characteristics of onion, potato, and tomato farmers in Northwest Ethiopia. The sample shows a relatively balanced gender distribution, although male headed households were slightly dominant across all vegetable types: 55.7% for onion, 58.6% for potato, and 50.1% for tomato. The chi-square results indicate no statistically significant differences in cooperative membership by gender across the three crops. However, social network participation differs significantly by gender for onion farmers, suggesting that male and female farmers vary in their engagement in peer to peer communication regarding CSA practices.\u003c/p\u003e \u003cp\u003eAccess to credit services was low, with 38.7% of onion, 35.6% of potato, and 32.6% of tomato producers reporting access. The chi-square tests reveal strong and statistically significant differences in both cooperative membership and social network participation by credit access status across all crops. Among climate risks, very high percentages of farmers had been affected by drought stress (55.1% onions, 68.9% potatoes, 69.2% tomatoes), pest and disease attacks (68.6%, 72.5%, 68.3%), and floods (68.0%, 69.5%, 70.2%). The chi-square statistics show highly significant differences in cooperative membership and social network participation by climate risks variables. Exposure at this high level is an indication of the susceptibility of vegetable production systems to climate fluctuation and biotic stress. These shocks will tend to affect farmers' risk perception and can be determinants for CSA adoption as farmers seek adaptation measures (Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Perception of soil fertility also indicates that most farmers classified their land as medium fertile (around 76\u0026ndash;77%), while a very small percentage of around 6\u0026ndash;7% said their soil was fertile. The chi-square tests show no statistically significant differences in cooperative membership or social network participation across soil fertility categories, suggesting that social capital participation is driven more by institutional and risk related factors than by perceived soil quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of continuous explanatory variables of vegetables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoop memb (t-test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial network (t-test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoop memb (t-test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSocial network (t-test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCoop memb (t-test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSocial network (t-test)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.81***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-8.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-11.22***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11.73***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.62***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.22***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.58*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.64***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Mkt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.73*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.94***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.40***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size of vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.54***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.82***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Nonfarm inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.82***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.90*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard errors in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eSources: Own survey result, 2025\u003c/p\u003e \u003cp\u003eProceeding to continuous characters in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), an average family size ranged from 5.48 (tomato) to 6.12 (potato). The t-test results reveal highly significant differences in family size across social network participation for all three crops, and across cooperative membership for potato and tomato farmers. This indicates that households with larger family sizes are more likely to participate in social capital structures. The mean age for the respondents was approximately mid 40s per crop, meaning that most of the farmers are at working age. The t-test results show statistically significant differences in age across social network participation for onion and tomato farmers, with older farmers more likely to engage in social networks. In contrast, cooperative membership does not differ significantly by age for all product producers.\u003c/p\u003e \u003cp\u003eEducation levels were quite low at 2.4 years for onion producers, 2.6 for potato, and 1.6 for tomato. The t-test results indicate strong and statistically significant differences in both cooperative membership and social network participation by education level. Distances to roads and market centers were important, averaging approximately 36\u0026ndash;45 minutes to the nearest road and over 70 minutes to markets. The t-test results show significant differences in cooperative membership and social network participation across distance to road and distance to market.\u003c/p\u003e \u003cp\u003eLivestock ownership, measured in Tropical Livestock Units (TLU), averages around five units across crops. The t-test results show strong and statistically significant differences in social network participation for all crops, and in cooperative membership for potato and tomato farmers. The properties of vegetable land were overall small, i.e., 0.28 ha for onion, 0.16 ha for potato and 0.14 ha for tomato. The t-test results reveal consistent and statistically significant differences across both cooperative membership and social network participation, with farmers cultivating larger vegetable plots more likely to participate in social capital structures. Finally, log non farm income was around 9.8 for all groups, which means that farmers diversify livelihood sources outside agriculture. Nonfarm income provides liquidity for financing technology adoption but, depending on returns, it may also draw labor away from farming activities (Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Adoption intensity of CSA practices on social capital\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdoption intensity of CSA practice on cooperative membership\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdoption intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e21.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e26.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e24.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e13.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eSources: Own survey result, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdoption intensity of CSA practices in the study area varied considerably, ranging from zero (non adopters) to ten practices out of the 12 identified CSA options. Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e present the distribution of adoption intensity across onion, potato, and tomato producers, disaggregated by social network and cooperative membership participation, respectively.\u003c/p\u003e \u003cp\u003eIn (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) above shows that cooperative membership had a strong influence on CSA practice adoption intensity. Among onion farmers, 23.7% of non members were in the non adopting category, compared to just 1.5% of members. Also, members were more at high to medium adoption categories, such as five practices (24.1%) and six practices (11.1%), compared to non members, who were predominantly at the zero and low adoption categories. This suggests that cooperative membership consolidates commitment towards adopting multiple practices.\u003c/p\u003e \u003cp\u003eWhereas in potato farmers, 9.5% of the non members did not adopt any CSA practice, nearly all cooperator members adopted at least two practices. The largest proportion of members (26.5%) adopted five practices, but merely 23.8% of non members adopted five practices. Cooperative farmers were also more likely to adopt greater quantities (seven or more) than non members, meaning that cooperatives increase both the width and intensity of adoption.\u003c/p\u003e \u003cp\u003eIn tomato production, cooperative membership has a stronger effect. About 17.3% of the non members did not use any practice, while only 1.8% of the participants completely shunned CSA. Cooperative members were extremely grouped in four (23.3%) and five (24.7%) practices, while non members were grouped in zero and lower practices. This illustrates that cooperative membership reduces total exclusion from CSA and raises intensification of adoption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdoption intensity of CSA practice on social networks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdoption intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eNon-Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e26.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e21.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e24.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e26.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e23.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eSources: Own survey result, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e highlights the social network impact on the number of CSA practices adopted. Among onion producers, 29.3% of non networked farm households were not adopting, but just 1.6% of networked farmers were non adopters. However, within social networks, adoption tended to cluster at moderate to higher intensity levels, with a substantial proportion of farmers collectively adopting multiple practices specifically four (22.6%), five (26.8%), and six (9.2%) CSA practices.\u003c/p\u003e \u003cp\u003eFor potato growers, the intensity of adoption was also quite high. Roughly 11.1% of the non networked group adopted neither of the CSAs, while all the networked adopters adopted two or more. Socially networked farmers bunched at five (26.0%) and six (13.0%) practices, well above non networked farmers. This indicates that social networks facilitate information diffusion, uncertainty reduction, and moral support for adopting technology (Cheng, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTomato producers are also highly variable. Network households were overrepresented among adopters (18.8%) and very low adopters, while network members were found in four (24.4%), five (23.7%), and six (10.0%) practices. Additionally, network membership reduced by almost half the probability of adopting zero of the CSA practices in total. This indicates that social networks play a crucial role in learning and shared exposure to new practices. These findings are in line with social capital theory (Coleman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) it emphasizes that trust and reciprocity based networks minimize adoption risk through facilitating the exchange of information and collective action.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Impact of social capital on adoption intensity of CSA practice\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of social networks and cooperative membership on the adoption intensity of CSA practice\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eCooperative membership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCooperative membership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCooperative membership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eIntensity of CSAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCooperative membership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.382**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.307***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.326**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.290***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.101*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e 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align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.099**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.066**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.286*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.331**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.384**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.115*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.308*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.745***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.081**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.499**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.300***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.276)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.010**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.701***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.767***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.427)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size of vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.237**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.566**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.086**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.854*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.503**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.655***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.312*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(2.965)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.023**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Nonfarm inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.073***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.127**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredit access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.382*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.378*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.197*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.654**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.962***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.341**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.997***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.336**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.367)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePest and disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.184**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.379)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.470***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.337**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.216)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eSoil fertility (not fertile as reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.420**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.216***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.013**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-fertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.577*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.509*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.061*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.297)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eDistrict (Ayehu as reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Mecha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.428**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.444**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.041**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Fogera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.507***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.413*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.111**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.483***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.081*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.765*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.601*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(2.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(2.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(1.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(3.024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eathrho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(3.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(5.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnsigma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.593***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.566***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(14.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(7.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(1.452)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0. 931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.914)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esigma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1422.2938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-1296.0765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1151.0789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1411.1456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-1300.7558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-1166.0643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald Chi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003echi2(18) = 71.25\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWald chi2(18)\u0026thinsp;=\u0026thinsp;77.52 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eWald chi2(18)\u0026thinsp;=\u0026thinsp;165.99 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eWald chi2(18) = 68.81\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eWald chi2(18) = 75.45\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eWald chi2(18) = 163.94\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of independent\u003c/p\u003e \u003cp\u003eequations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.06 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.30 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.01 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e(rho\u0026thinsp;=\u0026thinsp;0): chi2(1)\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard errors in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eSources: Own survey result, 2025\u003c/p\u003e \u003cp\u003eIn (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) the diagnostic statistics of the PET model further validate the robustness of the estimation. In both tables, the correlation coefficient (ρ) between the error terms of the treatment and outcome equations was statistically significant in the models, and the Wald test of independent equations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) strongly rejected the null hypothesis of ρ\u0026thinsp;=\u0026thinsp;0. This establishes the presence of endogeneity, implying that unobservable characteristics simultaneously affect social capital engagement (through networks or cooperative membership) and the intensity of CSA practice adoption.\u003c/p\u003e \u003cp\u003eSocial networks and cooperative membership positively and significantly influenced CSA adoption intensity in onion, potato, and tomato production. Social network impacts were consistent and strong, significantly and positively affecting CSA adoption intensity in onion (β\u0026thinsp;=\u0026thinsp;0.382, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), potato (β\u0026thinsp;=\u0026thinsp;0.307, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and tomato (β\u0026thinsp;=\u0026thinsp;0.146, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). These mean that farmers with larger and stronger networks embrace a greater intensity of CSA practices compared to those with weaker ties. These findings are consistent with the study by Saptutyningsih et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Social networks offer informal communication channels for the exchange of information, peer learning, and knowledge transfer that can alleviate risk perceptions related to new technology. Within the Ethiopian smallholder setting where the availability of extension services can be limited, farmer-to-farmer contact is seen to be playing a critical complementary role in the transfer of practice related to the adoption of CSA as well as building trust in the associated benefits. The result is consistent with the findings from the literature emphasizing the importance of social capital (Wossen et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Felek \u0026amp; Yayeh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, cooperative membership positively and significantly influenced the intensity of CSA adoption in all vegetables: onion (β\u0026thinsp;=\u0026thinsp;0.326, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), potato (β\u0026thinsp;=\u0026thinsp;0.290, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and tomato (β\u0026thinsp;=\u0026thinsp;0.101, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Being a member of a cooperative improves a farmer\u0026rsquo;s ability to obtain agricultural inputs, extension, and credit, in addition to enabling farmer cooperation and participation in training. Thus, these institutional improvements minimize information asymmetry and transaction costs, enabling farmers to maximize the potential of different CSA practices. Empirical evidence confirms these institutional improvements, suggesting that cooperatives improve members\u0026rsquo; participation in innovation by improving their bargaining power (Bernard \u0026amp; Spielman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fischer \u0026amp; Qaim, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mojo et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral household level variables were also significant determinants of CSA intensity. Age positively influenced adoption in potato suggesting that older farmers with accumulated farming experience are likely to intensify adoption. Level of education had positive effects on CSA adoption intensity for onion and potato, consistent with the proposition that education increases capacity to absorb information and appreciate long term benefits of CSA (Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sex of household head had a positive effect through in social networks and cooperative membership, particular for onion and tomato, reflecting the role of male headed households in accessing networks and cooperatives more effectively. However, this also reflects gender inequality in access to social institutions, which aligns with findings that women farmers face structural barriers to cooperative membership and information channels (Meinzen-Dick et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Family size positively influenced CSA intensity in the instance of potato and tomato, suggesting household labor availability as an important determinant of the adoption of labor intensive CSA practices such as mulching, soil conservation, or organic fertilizer use. This finding is consistent with labor endowment theories of technology adoption (Feder et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResource related characteristics also influenced adoption intensity. Farm size allocated to vegetables significantly increased CSA adoption intensity for potato farmers and positively influenced social capital participation, suggesting scale advantages in adopting multiple practices. Distance to roads and markets consistently reduced CSA intensity across crops, indicating geographic isolation as a limitation to information, input availability, and market participation. This agrees with the evidence by (Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nonfarm income also positively influenced onion and potato adoption intensity, suggesting that liquidity from off farm sources reduces financial constraints. This idea is supported by (Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExposure to drought stress magnified CSA significantly for tomato and onion, and also increased farmers' reliance on social capital. This concurs with the induced innovation hypothesis (Thirtle, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), according to which resource shortage or shocks stimulate adaptive responses. Also, pest and disease incidence and flooding experience also influenced social capital participation in several cases, reinforcing the role of social institutions as coping mechanisms in risk prone environments. This drought and climate change related variables had a significant aligned with (Aryal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cishahayo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDistricts play a substantial role in participating in social capital as well as the intensity of adoption of the CSA. Compared to the Ayehu district, the Mecha district had a negative and significant impact on the intensity of adoption of the CSA among potato growers in the social capital model as well as the cooperative membership model. This indicates that for potato growers, the Mecha district records fewer adopters of the CSA compared to the Ayehu district. Furthermore, the Fogera district was found to have significantly lower levels of participation in social capital as well as membership of the cooperative for potato growers. This is evident in the negative and significant coefficients of the treatment variables. On the other hand, the Fogera district was found to have a significantly positive impact on the intensity of adoption of the CSA for onion growers in the two models. This indicates that the onion growers in the Fogera district adopt more of the CSA compared to the growers in the Ayehu district. Additionally, the Fogera district was found to have a significantly positive impact on social network membership.\u003c/p\u003e \u003cp\u003eConcerning the production of tomatoes, the district level variables were generally statistically significant for Mecha districts for the adoption intensity of CSA in the social network and cooperative membership models, which suggests that Mecha districts have a significant influence on adoption intensity. Additionally, Fogera districts have a positive and significant effect on social network participation.\u003c/p\u003e \u003cp\u003eComparative analysis indicates that formal and informal dimensions of social capital play complementary roles. Farmers are influenced by social networks primarily through informal trust relations and learning from peers (Conley \u0026amp; Udry, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while cooperatives operate on the basis of structured institutional arrangements that facilitate input access, knowledge transfer, and market access (Bernard \u0026amp; Spielman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fischer \u0026amp; Qaim, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Surprisingly, the coefficient size shows that social networks affected onion farmer\u0026rsquo;s slightly more than cooperative membership, perhaps because of the higher market orientation of onion farming, where learning from and observing peers would be more effective. Cooperatives were highly important for every crop, suggesting their institutional role in CSA adoption (Liu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment effects of vegetables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSocial network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCooperative membership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.791**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.435***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 212**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.595*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.102***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.821)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.829**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.576***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.652**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.402***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.202**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.889)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.365***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 207***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard errors in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eSources: Own survey result, 2025\u003c/p\u003e \u003cp\u003eAccording to the result of (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the ATE shows the average effect of being a participant in social capital (either the network or cooperative) on CSA adoption intensity for all the farmers included in the sample. Results confirm that social network membership significantly increases adoption intensity by 1.791 for onion practices, 1.435 for potato, and 1.212 for tomato. Membership in a cooperative also increases CSA intensity by 1.652, 1.402, and 1.202 for onion, potato, and tomato, respectively. Strong and significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) results indicate that social capital has a system wide effect in scaling CSA practice in all farmer categories. This finding emphasizes the primary facilitative function performed by social institutions in lowering information asymmetry, lowering technology adoption expenses, and making the mobilization of resources easier. It is theoretically consistent with the diffusion of innovations theory (Rogers et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which argues that personal and group communication makes adoption of innovation easier, and with social capital theory (Coleman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which emphasizes the need for trust, norms, cooperatives, and networks as channels through which collective action takes place.\u003c/p\u003e \u003cp\u003eThe ATET estimates the impact of social capital on treated farmers who are already part of networks or cooperatives. Results indicate that the intensity of adoption rises by 1.829 (onion), 1.573 (potato), and 1.318 (tomato) CSA practices among those in the network. Additionally, cooperative members adopt 1.652 (onion), 1.402 (potato), and 1.202 (tomato) practices more than they would without being a member of a cooperative. This stronger impact for participants compared to the general public suggests that socially embedded actors benefit disproportionately from group oriented training possibilities, peer education, and shared resources. This is in line with Felek and Yayeh (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Mojo et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ATU implies the gains if the non members (farmers with no networks or cooperative membership) become members. The effect is strong and positive, with membership in networks having the potential to increase the intensity of adoption by 1.77 (onion), 1.365 (potato), and 1.207 (tomato) practices and cooperative membership by 1.55, 1.287, and 1.002 practices, respectively. These counterfactual results show the untapped potential among non members. If integrated into social institutions, such farmers would significantly raise the adoption of CSA practices. This implies that enhancing access to cooperatives and strengthening local farmer networks can yield enormous aggregate climate adaptation and resilience benefits.\u003c/p\u003e \u003cp\u003eTogether, the ATE, ATET, and ATU results confirm that both social networks and cooperative membership causally raise the intensity of CSA adoption, with similar effects for onion, potato, and tomato. Of particular interest is the fact that ATET values higher than ATE or ATU show that program participants are already realizing considerable benefits, but if participation hurdles are crossed, there exists considerable potential for expanding coverage among non participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Mediating effect analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of mediating effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eEffect of social network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eEffect of cooperative membership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr; INF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.950***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.728***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.602***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr; INF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.433**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.868***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.843***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr; EXT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.258*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.296*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr; EXT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.749***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.098*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr; SUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.751***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.943***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr; SUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.656***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.222)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF \u0026rarr; CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINF \u0026rarr; CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.103*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXT \u0026rarr; CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEXT \u0026rarr; CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUP\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSUP\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.073*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr;INF\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr;INF\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0. .087*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr;EXT\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 052**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr;EXT\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 056**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0. 002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u0026rarr; SUP\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 110*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOP\u0026rarr; SUP\u0026rarr;CSAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0. 102**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect (SN\u0026rarr;CSAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect effect (COP\u0026rarr;CSAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.237***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.322***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.286***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.505***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.476***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.497***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.328***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.435***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.477***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: SN (social network), EXT (extension access), SUP (access to inputs supply), INF (access to CSA related information), CSAI (climate smart agriculture intensity), and COP (cooperative membership)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard errors in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eIn the mediation analysis, the total effect of social capital is further disaggregated into the direct effect and indirect effects that work through the following three binary mediators: information about CSA practices, input supply, and extension facilitation. The results are summarized in (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) indicate that social capital positively and significantly affects information about CSA practices for all crops, with the strongest positive association estimated in the case of onions (β\u0026thinsp;=\u0026thinsp;0.950, \u0026lt; 0.01), followed by potatoes (β\u0026thinsp;=\u0026thinsp;0.728, \u0026lt; 0.01) and tomatoes (β\u0026thinsp;=\u0026thinsp;0.602, \u0026lt; 0.01). This highlights the key role that person to person communication can play in diffusing information about CSA practices in regions that lack institutional means of information dissemination.\u003c/p\u003e \u003cp\u003eMembership in a cooperative organization also positively influences information about CSA practices in line with the idea that farmer organizations with high quality social capital can improve the ability of their members to access, understand, and communicate agricultural information (Liu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Access to CSA information, further, positively and significantly influences CSA adoption intensity for most crops, thereby validating the importance of information as a factor for adopting different CSA methods as farmers need to be informed about both short term management needs and long term gains. The indirect pathways (SN \u0026rarr; INF \u0026rarr; CSAI and COP \u0026rarr; INF \u0026rarr; CSAI) are significant, showing information access as an important mediation channel between social capital and a rise in adoption intensity, which is consistent with a study by Hailu et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis analysis also shows that social networks positively influence extension access, indicating that farmers who are socially connected are more visible or can collectively create demands for services from extension agents. Membership in cooperatives is also positively and significantly related to extension access, indicating that formal institutions support farmers in accessing public extension services. This is consistent with findings presented by Okori et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExtension access has a statistically significant effect on the adoption intensity of CSA practices for all crops. However, the magnitude of the coefficients is less compared to information and input supply. A similar trend is observed, meaning that extension services have a non driving role in the adoption intensification process but can be viewed as a reinforcement process. The results show that the indirect effects, represented by the paths SN \u0026rarr; EXT \u0026rarr; CSAI and COP \u0026rarr; EXT \u0026rarr; CSAI, are statistically significant but small. These results are consistent with Wang et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccess to CSA related input supply is found to be an extremely substantial mediator. Social networks are found to have made a considerable difference in regard to input sources such as climate resilient seeds, organic fertilizers, and water harvesting methods. Being members of cooperatives is also found to have substantially impacted input accessibility, which aligns with the rationale of cooperatives to have been established for input distribution.\u003c/p\u003e \u003cp\u003eAccess to input supply has a strong and positive relation with CSA adoption intensity, verifying that even very informed farmers will not be able to enhance CSA intensity without access to input supplies. The indirect effects on input supply (SN \u0026rarr; SUP \u0026rarr; CSAI and COP \u0026rarr; SUP \u0026rarr; CSAI) are all significant in most crop equations, with coefficients relatively large compared to extension access. The implication here is that input availability is a crucial constraint on enhancing CSAI, following Liu et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, despite the influence of the mediating variables, the direct effects of social networks and cooperative membership on the adoption intensity of CSA are highly significant and positively affect all crops. This indicates the partial mediating effect and the fact that the influence of social capital on CSA adoption occurs in two different dimensions: first in the visible domain of information and inputs and second in a more subtle domain.\u003c/p\u003e \u003cp\u003eFinally, the total effects show that social networks have a relatively stronger overall impact on CSA adoption intensity than cooperative membership for onion and tomato farmers, while for potato farmers, cooperative membership has a relatively larger overall impact. This crop specific variation reflects differences in production risk, market orientation, and dependence on collective input and knowledge systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Recommendation","content":"\u003cp\u003eThe evidence suggests that social networks and cooperative membership are significant and positive determinants of the intensity of adoption for each vegetable type. Social networks have more pronounced effects because they enable person to person learning, and membership of cooperatives impacts adoption by improving input/output, extension, and institutional support. The results of the treatment effect indicate large unrealized adoption potentials among the non adoptees, suggesting that improved adoption of CSA could be realized by ensuring increased participation of farmers in social institutions. The mediation analysis reveals that access to CSA related information, input supply, and extension services constitute the principal mechanisms through which social capital enhances CSA adoption intensity.\u003c/p\u003e \u003cp\u003eBuild and stimulate farmer based social networks as a core CSA diffusion mechanism. Agricultural development projects ought to formalize farmer based learning interfaces or platforms. Improve the overall functionality of agricultural cooperatives to facilitate CSA intensification. Cooperatives can extend beyond the supply of agricultural inputs to offer integrated CSA related services, which may involve input packages, training, and collective marketing of climate resilient agricultural products.\u003c/p\u003e \u003cp\u003eThe key mediating constraints should be addressed by improving access to information and inputs related to CSA. Information and input supply are the most mediating factors. Therefore, improving access to knowledge related to CSA and access to inputs should be prioritized. Extension systems should collaborate more closely with cooperatives and social networks to improve outreach in the study areas.\u003c/p\u003e \u003cp\u003eThe negative effects of distance to roads and markets highlight the need for targeted investments in rural infrastructure and decentralized service delivery. Mobile extension services and satellite input distribution points will reduce location constraints and improve adoption levels of distant farmers. Finally, ensure location and crop targeted strategies for CSA scale up. Findings of district level disparities in adoption levels indicate that a one size fits all approach may be inappropriate, thus location considerate strategies based on differential crop risks are required to optimize CSA interventions.\u003c/p\u003e \u003cp\u003eDespite its contributions, this study has some limitations. Due to data constraints, social capital is measured using only two dimensions social networks and agricultural cooperatives membership. Other important aspects of social capital, such as trust, training, norms of reciprocity, leadership, and collective action, could not be incorporated. Future research should focus on this shortcoming by including more social capital variables in their study, developing a composite social capital indicator, and employing a unified measurement method in order to capture more accurately its complexity and effects on CSA adoption intensity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Research Ethical Review Committee (IRERC) of Debre Markos University, Burie Campus with Getnet Haimanot (Assistant Professor), Yilkal Messelu (Assistant Professor), and Wbalem Gobie (Assistant Professor) as the approving authorities. All procedures were carried out in accordance with relevant guidelines and regulations. A formal ethics approval document has been issued by IRERC of Debre Markos University, Burie Campus. The reference number for the approval is \u003cstrong\u003eRCSTTD/1382/01/17\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not involve the use of human or animal tissues. All participants were adults (\u0026ge;\u0026thinsp;18 years of age). Before data collection, participants were provided with detailed information about the study\u0026rsquo;s objectives, procedures, and benefits. For this study informed consent was obtained from all participants prior to their participation. Participation was entirely voluntary, and respondents were assured of confidentiality and anonymity throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThe authors spent their time on writing a proposal, collect primary data, search secondary data, data management, analyze and write up the final manuscript. **Mezgebu Aynalem** contribution: Formal analysis, Investigation, Software, Visualization, Writing original draft Project administration, Writing review \u0026amp;amp; final editing. **Zemen Ayalew and Aemro Tazeze** contribution: Conceptualization, Data management, Resources, Methodology, Supervision \u0026amp;amp; Writing review \u0026amp;amp; final editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the study area district of agricultural experts and all respondents as sources of this valuable information to investigate the existing situations.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used for this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbate TM, Dessie AB, Mekie TM. 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Reasoning about rational agents. MIT Press; 2003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWossen T, Berger T, Di Falco S. Social capital, risk preference and adoption of improved farm land management practices in Ethiopia. Agric Econ. 2015;46(1):81\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/agec.12142\u003c/span\u003e\u003cspan address=\"10.1111/agec.12142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vegetable farming, Poisson endogenous treatment, Mediation effect, Social capital, Northwest Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8999225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVegetable production and productivity in Ethiopia are threatened by climate change and climate variability. Adopting Climate Smart Agriculture (CSA) practices reduces the negative impacts of climate related shocks on production. This paper investigates the impact of cooperative membership and social networks (SN) on CSA practices among vegetable farmers in Northwest Ethiopia. The analysis used primary data from 550 farmers and analyzed them by jointly applying a Poisson Endogenous Treatment model and mediation analysis. Results showed that cooperative membership and SNs significantly influence the adoption intensity of CSA. Other household level factors sex, education, family size, landholding, distance to market, and experience of climate shock influence the adoption intensity of CSA. The Average Treatment Effects show that SNs increase the adoption intensity of onion, potato, and tomato CSA practices by 1.829, 1.756, and 1.318 practices, respectively. Cooperative membership also resulted in the adoption of 1.652, 1.402, and 1.202 more CSA practices than non members for the respective crops. The mediation analysis reveals that SN and cooperative membership increase CSA practice intensity both directly and indirectly through improved access to CSA related information, input supply, and extension services. The direct effects of SNs range from 0.287 to 0.359 and that of cooperative membership ranges between 0.237 and 0.322. These findings underscore the need to strengthen farmer to farmer SNs for rapid dissemination of CSA related information and empowering agricultural cooperatives to serve as institutional hubs for inputs supply and extension services, given their proven mediating role in increasing adoption intensity.\u003c/p\u003e","manuscriptTitle":"The Power of Social Capital: How Cooperative Membership and Social Network Shapes Climate Smart Agriculture Practices in Northwest Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 11:55:19","doi":"10.21203/rs.3.rs-8999225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T13:31:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T18:00:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T01:45:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T16:37:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T15:49:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T10:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303922463156551385258360538752372643366","date":"2026-04-16T07:18:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180690636973177573620630958709084006995","date":"2026-04-15T18:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206833699903121369787560619438393790300","date":"2026-04-15T09:08:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205176794175303187465610512686699048438","date":"2026-04-13T21:28:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39644108455226338468120751639355180562","date":"2026-04-13T09:15:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200902026578795137486364210920991311718","date":"2026-04-08T04:27:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15425817446413896788452618583891095593","date":"2026-04-08T01:38:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T12:40:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271089968530327814307456123042219469373","date":"2026-03-28T12:00:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T02:01:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T13:56:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T10:35:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T10:31:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-01T04:36:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ace74fad-59c8-47ce-a6b1-4c664b9bf524","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T06:55:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 11:55:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8999225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8999225","identity":"rs-8999225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}