The role of power relationships, trust and social networks in shaping milk quality in Kenya

The objective of this study was to examine social networks in dairy value chains (DVCs) in Kenya and understand how DVC actors' power relationships and trust influence their behaviour regarding milk quality. We conducted a stakeholder analysis using the Net-Map tool in Laikipia, Nakuru and Nyandarua counties in Kenya. VisuaLyzer software was used to analyse the social networks. Thematic content analysis of the discussions, recorded during the mapping exercise, was undertaken using ATLAS.ti. Formal DVC had more actors and dense social networks characterised by vertical and horizontal integration, high levels of power asymmetries between actors, limited trust and short-term contractual arrangements. Informal DVC was characterised by fewer actors and less dense social networks, low levels of power asymmetries between actors and a high level of trust due to the existence of reciprocal personal relationships. Milk was perceived to be of higher quality in the formal value chain reflecting top-down enforcement of milk standards, bottom-up collective action, power asymmetries and contractual relationships. Poor milk quality management in the informal DVC underscores the need for powerful actors, e.g. regulatory agencies, and buyers such as processors, to influence other DVC actors' behavioural change. Understanding and leveraging DVC social networks and actors' power and addressing power asymmetries and enhancing trust between actors will increase compliance with milk quality standards. There is an urgent imperative to design policies and interventions which empower DVC actors, by providing economic incentives, enhancing their skills and knowledge and their access to infrastructure which facilitates milk quality improvement

Adoption of dairy technologies in smallholder dairy farms in Ethiopia

The adoption of modern agricultural technologies in Ethiopia’s dairy production system remains underutilized and under-researched yet it is a promising sector to aid in reducing poverty, improving the food security situation and the welfare of rural households, and in ensuring environmental sustainability. This paper uses the Negative Binomial regression model to examine determinants of multiple agricultural technology adoption in the Addis Ababa and Oromia regions of Ethiopia. Data was collected from 159 smallholder dairy farms in Ethiopia's Addis Ababa and Oromia regions exploring 19 technologies used by the farmers during the study period. The findings show that farm location and herd size impact adoption decisions. Increasing herd size is associated with increased uptake of multiple technologies. Further, as farmer education level increases the more likely farmers are to adopt multiple technologies. The increase in the number of female workers is positively associated with the adoption of multiple dairy technologies. In terms of farmers'/workers’ years of experience, those with no years of work experience are less likely to have adopted multiple technologies than those with more than 5 years of experience. However, this could be due to a number of factors where experience stands as a proxy value. Trust in information from government agencies was associated with a higher propensity to adopt multiple dairy technology as was farmer perception of fellow farmers as peers compared to those who perceive them as competitors. This is an important finding as it may help policymakers or institutions explore knowledge exchange and diffusion of innovation strategies tailored to specific farming and community situations. Studies have shown that farmers within a social group learn from each other more fully about the benefits and usage of new technology. These findings are of value in future technology adoption studies, particularly which factors influence the intensity of adoption of multiple technologies by smallscale producers

Intra-annual variation in feed and milk composition in smallholder dairy farms in Kenya

This longitudinal study explored intra-annual variation in feed availability and the chemical composition of milk and feed resources at smallholder dairy farms in Nakuru county, Kenya. Feed and milk samples were collected for a full year, every last week of the month, from 43 purposively selected farms. Feed and milk samples were analysed for nutritional composition using near infrared spectroscopy (NIRS) and Ekomilk milk analyser, respectively. The main basal feeds were indigenous grasses, Napier grass, maize and bean stover and maize silage, which farmers supplemented with purchased commercial concentrates and/or purchased or homemade total mixed rations (TMR). Commercial concentrates had the highest crude protein (CP) content (17.4 ± 3.9)% dry matter (DM), while maize stover had the lowest (8.7 ± 3.3% DM). All the feeds had low metabolisable energy (ME) that ranged from 7.0 ± 0.8 (MJ/kg DM) megajoules per kilogram of dry matter (MJ/kg DM) for maize stover to 8.9 ± 0.8 for dairy meal. Only grasses showed significant seasonal variation in CP and NDF (P > 0.00). Milk physicochemical composition was within the range stipulated by the Kenya Bureau of Standards (KEBS). Milk physicochemical composition showed negligible seasonal variations to significantly affect milk processing, which suggests that farmers can cope with feed scarcity. Nevertheless, seasonal feed availability is a persistent challenge in smallholder dairy farms. There is a need to ensure sufficient feed availability throughout the year in smallholder dairy farms through feed conservation, feeding management and ration preparation to enable consistent milk production and physicochemical composition

The role of power relationships, trust and social networks in shaping milk quality in Kenya

The objective of this study was to examine social networks in dairy value chains (DVCs) in Kenya and understand how DVC actors' power relationships and trust influence their behaviour regarding milk quality. We conducted a stakeholder analysis using the Net-Map tool in Laikipia, Nakuru and Nyandarua counties in Kenya. VisuaLyzer software was used to analyse the social networks. Thematic content analysis of the discussions, recorded during the mapping exercise, was undertaken using ATLAS.ti. Formal DVC had more actors and dense social networks characterised by vertical and horizontal integration, high levels of power asymmetries between actors, limited trust and short-term contractual arrangements. Informal DVC was characterised by fewer actors and less dense social networks, low levels of power asymmetries between actors and a high level of trust due to the existence of reciprocal personal relationships. Milk was perceived to be of higher quality in the formal value chain reflecting top-down enforcement of milk standards, bottom-up collective action, power asymmetries and contractual relationships. Poor milk quality management in the informal DVC underscores the need for powerful actors, e.g. regulatory agencies, and buyers such as processors, to influence other DVC actors' behavioural change. Understanding and leveraging DVC social networks and actors' power and addressing power asymmetries and enhancing trust between actors will increase compliance with milk quality standards. There is an urgent imperative to design policies and interventions which empower DVC actors, by providing economic incentives, enhancing their skills and knowledge and their access to infrastructure which facilitates milk quality improvement

Adoption of dairy technologies in smallholder dairy farms in Ethiopia

The adoption of modern agricultural technologies in Ethiopia’s dairy production system remains underutilized and under-researched yet it is a promising sector to aid in reducing poverty, improving the food security situation and the welfare of rural households, and in ensuring environmental sustainability. This paper uses the Negative Binomial regression model to examine determinants of multiple agricultural technology adoption in the Addis Ababa and Oromia regions of Ethiopia. Data was collected from 159 smallholder dairy farms in Ethiopia's Addis Ababa and Oromia regions exploring 19 technologies used by the farmers during the study period. The findings show that farm location and herd size impact adoption decisions. Increasing herd size is associated with increased uptake of multiple technologies. Further, as farmer education level increases the more likely farmers are to adopt multiple technologies. The increase in the number of female workers is positively associated with the adoption of multiple dairy technologies. In terms of farmers'/workers’ years of experience, those with no years of work experience are less likely to have adopted multiple technologies than those with more than 5 years of experience. However, this could be due to a number of factors where experience stands as a proxy value. Trust in information from government agencies was associated with a higher propensity to adopt multiple dairy technology as was farmer perception of fellow farmers as peers compared to those who perceive them as competitors. This is an important finding as it may help policymakers or institutions explore knowledge exchange and diffusion of innovation strategies tailored to specific farming and community situations. Studies have shown that farmers within a social group learn from each other more fully about the benefits and usage of new technology. These findings are of value in future technology adoption studies, particularly which factors influence the intensity of adoption of multiple technologies by smallscale producers.</p

Adoption of dairy technologies in smallholder dairy farms in Ethiopia

The adoption of modern agricultural technologies in Ethiopia’s dairy production system remains underutilized and under-researched yet it is a promising sector to aid in reducing poverty, improving the food security situation and the welfare of rural households, and in ensuring environmental sustainability. This paper uses the Negative Binomial regression model to examine determinants of multiple agricultural technology adoption in the Addis Ababa and Oromia regions of Ethiopia. Data was collected from 159 smallholder dairy farms in Ethiopia's Addis Ababa and Oromia regions exploring 19 technologies used by the farmers during the study period. The findings show that farm location and herd size impact adoption decisions. Increasing herd size is associated with increased uptake of multiple technologies. Further, as farmer education level increases the more likely farmers are to adopt multiple technologies. The increase in the number of female workers is positively associated with the adoption of multiple dairy technologies. In terms of farmers'/workers’ years of experience, those with no years of work experience are less likely to have adopted multiple technologies than those with more than 5 years of experience. However, this could be due to a number of factors where experience stands as a proxy value. Trust in information from government agencies was associated with a higher propensity to adopt multiple dairy technology as was farmer perception of fellow farmers as peers compared to those who perceive them as competitors. This is an important finding as it may help policymakers or institutions explore knowledge exchange and diffusion of innovation strategies tailored to specific farming and community situations. Studies have shown that farmers within a social group learn from each other more fully about the benefits and usage of new technology. These findings are of value in future technology adoption studies, particularly which factors influence the intensity of adoption of multiple technologies by smallscale producers.</p

Exploring the adoption of food safety measures in smallholder dairy systems in Ethiopia: implications for food safety and public health

Abstract Milk is highly perishable and can be a conduit for the transmission of zoonotic foodborne pathogens. This cross-sectional survey involving 159 farming households and 18 participant observations in participating farms was undertaken in Addis Ababa and surrounding areas in Oromia, Ethiopia to assess the adoption of food safety measures in smallholder farms. Adoption of food safety measures at the farm level influences milk quality and safety across the entire milk value chain, from “grass to glass”. This study considered the adoption of 36 different food safety measures (FSM) including animal health, milking hygiene, hygienic milk storage, and hygienic milking premises. A weighted food safety index (FSI, ranging from 0 to 100) was calculated for each household based on FSM adopted. Ordinary Least Squares linear regression was used to quantify the factors of FSM adoption by smallholder farmers. The overall food safety index ranged between 59.97—60.75. A majority of farmers may be classified as moderate adopters of FSM (index ranging between 30–70%). Farm and farmers’ characteristics such as herd size, farmer’s education level, farmer’s expertise in dairying, and participation of the farm in the formal milk value- chain, were shown to positively influence the level of adoption of FSM. Low farm-level adoption of FSM has food safety and public health implications as it can lead to milk contamination and, therefore, expose consumers to foodborne diseases. There is an imperative for policymakers to design and implement policies and intervention strategies that lead to increased farmer training related to livestock production and awareness of the important role that FSM adoption can play in improving food safety and public health. </p