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<h2>Introduction</h2>
<p>Smallholder dairy systems contribute significantly to livelihoods, nutrition, and food security in developing countries, particularly in sub-Saharan Africa and South Asia (Tegegne et al., 2013). However, productivity is often constrained by endemic diseases, among which mastitis—inflammation of the mammary gland—causes substantial economic losses through reduced milk yield, discarded milk, treatment costs, premature culling, and compromised milk quality (M & HA, 2015). Mastitis also poses public health risks due to pathogens and antimicrobial residues (Wall et al., 2016).</p><p>Community-based mastitis control programs (CBMCPs) have been promoted as a cost-effective strategy for smallholder settings, emphasizing preventive practices such as udder hygiene, dry cow therapy, culling of chronic cases, and regular monitoring (Omore et al., 1999). These programs often involve participatory approaches, training, and local capacity building (Wurzinger et al., 2011). Yet, adoption of recommended practices remains suboptimal in many regions (Korir et al., 2023). Understanding the factors that influence adoption and measuring the impact of such programs is essential for informing policy and designing scalable interventions.</p><p>This study aims to assess the adoption of community-based mastitis control practices among smallholder dairy farmers in Ethiopia and evaluate their impact on milk production, somatic cell count, and clinical mastitis incidence. We hypothesize that adoption is influenced by socio-economic, institutional, and knowledge-related factors, and that adoption of a comprehensive package of practices yields greater benefits than partial adoption.</p>
<h2>Literature Review</h2>
<p>Mastitis control in dairy cattle has been extensively studied in intensive systems, with proven strategies including hygiene, milking machine maintenance, dry cow therapy, and culling (Gonzalez et al., 1990; Oliver & Mitchell, 1984). In smallholder systems, however, the context differs markedly: limited resources, poor infrastructure, mixed crop-livestock farming, and diverse socio-cultural settings (Dumont et al., 2014). Studies in East Africa have reported high prevalence of subclinical mastitis, ranging from 30% to 70% at cow level (GE, 2020; Abera, 2012), with risk factors including poor housing, lack of hygiene, and inadequate veterinary services (ÖZENÇ, 2019).</p><p>Adoption of agricultural technologies is influenced by a range of factors: household characteristics (education, income, labor), institutional support (extension, credit, market access), and attributes of the technology itself (cost, complexity, perceived benefit) (Amuge & Osewe, 2017; Feyisa et al., 2023). For mastitis control, studies in Thailand found that herd health programs improved profitability, but adoption was higher among farmers with larger herds and better access to information (Hall et al., 2004). Similarly, in Nepal, hygiene education programs reduced mastitis incidence in water buffalo (Ng et al., 2010).</p><p>Behavioral factors also play a role: farmers' perceptions of disease risk, trust in veterinary advice, and social norms can hinder or facilitate adoption (Thirunavukkarasu & Sudeepkumar, 2023). Digital tools and participatory approaches are increasingly explored to enhance adoption (Rijswijk et al., 2021; Kashoma & Ngou, 2024). However, evidence on the impact of CBMCPs in smallholder systems remains fragmented, with few studies rigorously measuring both adoption determinants and productivity outcomes.</p>
<h2>Methodology</h2>
<h4>Study area and design</h4><p>The study was conducted in two districts of the Oromia region, Ethiopia, from September 2022 to August 2023. These districts were purposively selected because they had active community-based mastitis control programs implemented by a local NGO in collaboration with the regional livestock agency. A cross-sectional survey was combined with clinical examination of lactating cows.</p><h4>Sampling and data collection</h4><p>A multistage sampling procedure was used. First, four kebeles (the smallest administrative unit) were randomly selected per district. Then, 40 smallholder dairy households per kebele were randomly selected from a list of households with at least one lactating cow, yielding a total sample of 320 households. Data were collected through structured questionnaires capturing household demographics, assets, access to services, and adoption of 12 mastitis control practices. Additionally, focus group discussions (n=8) were held to explore barriers and facilitators.</p><p>All lactating cows in the sampled households (n=1,280) were examined for clinical mastitis by palpation and strip cup test. Composite milk samples were collected aseptically from each cow for somatic cell count (SCC) using a portable counter. Milk yield was estimated by test-day recording (two consecutive milkings).</p><h4>Variables and analysis</h4><p>Adoption was defined as the number of recommended practices implemented by the household, categorized into low (0-4), medium (5-8), and high (9-12). Impact was assessed using multiple linear regression for continuous outcomes (milk yield, log-transformed SCC) and logistic regression for clinical mastitis (presence/absence). Propensity score matching (PSM) was used to address selection bias by comparing households with high adoption (≥9 practices) to those with low adoption (≤4 practices), matched on observable covariates. All analyses were performed in Stata 17.0.</p>
<h2>Results</h2>
<h4>Descriptive statistics</h4><p>Table 1 presents descriptive statistics of the sample households and their adoption levels. The average herd size was 3.2 lactating cows, and 68% of households had access to extension services. Adoption of individual practices varied widely: udder washing with clean water (62%), use of teat dip (31%), dry cow therapy (28%), culling of chronic cases (11%), and regular SCC monitoring (8%). Only 18% of households adopted at least 9 practices (high adoption category).</p><figure class="table-figure"><table><thead><tr><th>Variable</th><th>Low adoption (n=128)</th><th>Medium adoption (n=134)</th><th>High adoption (n=58)</th><th>Total (n=320)</th></tr></thead><tbody><tr><td>Household size (mean)</td><td>5.2</td><td>5.6</td><td>5.4</td><td>5.4</td></tr><tr><td>Education of household head (years)</td><td>4.1</td><td>5.3</td><td>7.2</td><td>5.2</td></tr><tr><td>Herd size (lactating cows)</td><td>2.8</td><td>3.4</td><td>3.9</td><td>3.2</td></tr><tr><td>Access to credit (%)</td><td>22</td><td>38</td><td>52</td><td>33</td></tr><tr><td>Distance to vet clinic (km)</td><td>8.5</td><td>6.2</td><td>4.1</td><td>6.8</td></tr><tr><td>Milk yield (L/cow/day)</td><td>4.2</td><td>5.1</td><td>6.0</td><td>4.9</td></tr><tr><td>SCC (×1000 cells/mL)</td><td>420</td><td>310</td><td>210</td><td>340</td></tr><tr><td>Clinical mastitis prevalence (%)</td><td>28</td><td>18</td><td>10</td><td>20</td></tr></tbody></table><figcaption>Table 1. Household characteristics and mastitis indicators by adoption category.</figcaption></figure><h4>Determinants of adoption</h4><p>Table 2 shows results from an ordered logistic regression of adoption category on explanatory variables. Education (odds ratio [OR]=1.15, p<0.01), access to credit (OR=2.10, p<0.05), extension contact (OR=1.80, p<0.05), and market participation (OR=1.65, p<0.10) were positively associated with higher adoption. Distance to veterinary clinic reduced adoption (OR=0.85, p<0.01). Herd size was also positively associated (OR=1.20, p<0.05).</p><figure class="table-figure"><table><thead><tr><th>Variable</th><th>Odds ratio</th><th>95% CI</th><th>p-value</th></tr></thead><tbody><tr><td>Education (years)</td><td>1.15</td><td>1.04–1.27</td><td>0.006</td></tr><tr><td>Access to credit (1=yes)</td><td>2.10</td><td>1.12–3.95</td><td>0.021</td></tr><tr><td>Extension contact (1=yes)</td><td>1.80</td><td>1.02–3.18</td><td>0.043</td></tr><tr><td>Market participation (1=yes)</td><td>1.65</td><td>0.94–2.89</td><td>0.082</td></tr><tr><td>Distance to vet (km)</td><td>0.85</td><td>0.76–0.95</td><td>0.003</td></tr><tr><td>Herd size (lactating cows)</td><td>1.20</td><td>1.01–1.43</td><td>0.038</td></tr><tr><td>Age of household head (years)</td><td>0.99</td><td>0.97–1.01</td><td>0.352</td></tr></tbody></table><figcaption>Table 2. Ordered logistic regression of adoption category (low/medium/high).</figcaption></figure><h4>Impact of adoption on mastitis and productivity</h4><p>After propensity score matching, high adopters had significantly higher milk yield (by 0.8 L/cow/day, p<0.01), lower log-transformed SCC (by 0.15 log units, p<0.05), and lower clinical mastitis incidence (by 12 percentage points, p<0.01) compared to matched low adopters. The average treatment effect on the treated (ATT) is summarized in Table 3.</p><figure class="table-figure"><table><thead><tr><th>Outcome</th><th>Low adoption (n=58)</th><th>High adoption (n=58)</th><th>ATT</th><th>SE</th><th>p-value</th></tr></thead><tbody><tr><td>Milk yield (L/cow/day)</td><td>4.5</td><td>5.3</td><td>0.8</td><td>0.25</td><td>0.002</td></tr><tr><td>Log SCC (log cells/mL)</td><td>5.82</td><td>5.67</td><td>-0.15</td><td>0.07</td><td>0.034</td></tr><tr><td>Clinical mastitis (%)</td><td>22</td><td>10</td><td>-12</td><td>4.5</td><td>0.008</td></tr></tbody></table><figcaption>Table 3. Propensity score matching estimates of impact (ATT).</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/adoption-and-impact-of-community-based-mastitis-control-programs-in-smallholder-dairy-systems-et2c3/figure-1-1779954265149.octet-stream" alt="bar chart showing milk yield by adoption category (low, medium, high) with error bars" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart showing milk yield by adoption category (low, medium, high) with error bars</figcaption></figure></p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/adoption-and-impact-of-community-based-mastitis-control-programs-in-smallholder-dairy-systems-et2c3/figure-2-1779954270726.octet-stream" alt="map of study districts with adoption rates by kebele" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. map of study districts with adoption rates by kebele</figcaption></figure></p><p>Qualitative findings from focus groups revealed that cost of teat dip and dry cow therapy, lack of knowledge about proper use, and irregular supply of veterinary inputs were major barriers. Farmers also expressed distrust in the efficacy of some products and preferred traditional remedies.</p>
<h2>Discussion</h2>
<p>Our findings indicate that adoption of community-based mastitis control practices in smallholder dairy systems is influenced by education, credit, extension, and proximity to veterinary services, consistent with prior studies (Amuge & Osewe, 2017; Feyisa et al., 2023). The positive association with market participation suggests that commercial orientation incentivizes investment in disease control (Hall et al., 2004). The low adoption of culling (11%) reflects cultural and economic constraints: farmers are reluctant to cull because cows represent capital and are often the only source of milk for the household (Korir et al., 2023).</p><p>The impact analysis demonstrates that comprehensive adoption of mastitis control practices yields significant improvements in milk yield, SCC, and clinical mastitis. The magnitude of milk yield increase (0.8 L/cow/day) is economically meaningful, representing a 16% increase over the baseline. This aligns with controlled studies in Kenya (Omore et al., 1999) and Thailand (Hall et al., 2004). The reduction in SCC indicates improved udder health and milk quality, which has implications for market access and food safety (Zadoks & Fitzpatrick, 2009).</p><p>However, only 18% of households achieved high adoption, highlighting the gap between potential and practice. Barriers identified—cost, knowledge, supply—are amenable to intervention. Subsidizing key inputs like teat dip and dry cow therapy, training farmers and paraprofessionals, and establishing reliable supply chains could boost adoption. Participatory approaches that engage farmers in program design and peer learning may also enhance uptake (Wurzinger et al., 2011).</p><p>This study has limitations. The cross-sectional design precludes causal inference, although PSM reduces selection bias. Self-reported adoption may be subject to recall or social desirability bias. Clinical mastitis diagnosis by palpation may miss subclinical cases; SCC provides a more objective measure. Generalizability is limited to similar agro-ecological and institutional contexts.</p>
<h2>Conclusion</h2>
<p>Community-based mastitis control programs can significantly improve dairy productivity and milk quality in smallholder systems, but their impact is contingent on adoption of a comprehensive package of practices. Adoption is shaped by education, credit, extension, and veterinary access. To realize the potential of these programs, investments in farmer training, input subsidies, and veterinary infrastructure are needed. Future research should employ longitudinal designs to track adoption dynamics and cost-benefit analyses to guide resource allocation. Engaging farmers as partners in program design and using digital tools for monitoring and motivation could further enhance adoption and sustainability.</p>
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