Impact of dairy husbandry training on milk production and income: Essay Example, 1812 words GradesFixer

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Impact of dairy husbandry training on milk production and income

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Introduction Promoting sustainable agriculture and rural development plays a crucial role to satisfy the ever increasing demand of growing population in developing countries including Ethiopia (United Nations, 2015). This creates an opportunity for smallholder farmers to benefit from the growing demand for dairy products through income and employment generation (Dessisa et al., 2015). However, the dairy sector in Ethiopia lacks the required technological, organizational as well as institutional capacities (Yilma et al., 2011). Among the major factors that affect dairy farmers’ access to and benefits from extension service are low economic statuses of the majority of the farmers to afford improved dairy practices, land size, education and access to credit (Anandajayasekeram et al. 2008).

In addition to this, agricultural extension services in developing countries are affected by lack of practical skills of the extension workers, poor in-service training facilities, multiple role of extension workers, inadequate communication and partnership among actors for uptake and scaling up, inadequate knowledge and inadequate budgets for promotion of services and ineffectiveness in the extension system (Workneh and Ponnusamy, 2015). The benefits that farmers obtain from training and the resulting impact of the services depend, to a great extent, on their level of participation, which in turn is influenced by their direct and indirect access to the services (Muluken and Sassi, 2014). Studies in many developing countries indicated that training on dairy farming had positive and highly significant relationship with the adoption of improved dairy husbandry practices (Dehinenet et al., 2014; Lemma et al., 2012; Luyombya, 2014; Kazanga, 2012; Quddus, 2013; Samuel et al., 2016), increase in yield (Kazanga, 2012) and technical efficiency (Ayele et al., 2006; Thangata and Mequaninte, 2011). Sharma et al., 2014 reported that training programs has a significant impact in uptake of new technologies, help in achieving sustainable production and in turn will increase the income and employment in the rural areas. On the other hand, a study by Tripp et al. (2005) confirms the importance of training, which can contribute to enhancement of farmers’ skills in farming works. Most of the studies conducted addressed the impact of agricultural training on intermediate outcomes such as adoption rate of diary technologies, agricultural productivity and technical efficiency but they lack sufficient information on the overall economic impact of training on milk income at smallholder dairy farmers’ condition. In addition to this, the contents of training provided, their aim, the type of services available and breed used were quite variable. It has been also reported that many foreign aid agencies fund large-scale agricultural training for farmers in developing countries, but little rigorous research has been conducted on whether these programs are effective (Waddington et al., 2010).

Even though dairy production is an essential component of rural livelihood, there is no clear information available that indicate the exact economic impact of dairy husbandry training on milk income. The current study attempts to answer the following question: What would happen to milk yield and milk income of small scale dairy farmers had they not been trained? We hypothesized that dairy husbandry training components positively influence household income. Therefore, the primary objective of this study was to analyze the impact of dairy husbandry training on milk productivity and income of smallholder dairy farmers in two districts of west Shewa zone in Oromia regional state of Ethiopia. 2. Training on dairy husbandry practice and technology dissemination in Ethiopia Governmental, non-governmental, private and international organizations have been engaged in promoting and disseminating dairy production technologies to smallholder farmers through various channels of extension such as technology verification and demonstrations, training and farmer-to-farmer information exchange mechanisms (Samuel et al., 2016).

Limited access to training is one of the major constraints for smallholder dairy farmers that affect technology adoption, dairy productivity and income. Lack of awareness of best practices in livestock production and deficiencies in livestock management skills among the rural community has hampered livestock development in to other African countries, livestock extension coverage and technology adoption are very low and it is biased against livestock sector. Previous study by Kasahun and Jeilu, 2012 concluded that adoption of dairy technology is a significant determinant for the increase in the household income of dairy farmers. In Ethiopia technology is generated by research centers and universities. After verification at farmers’ field, the technology is multiplied and transferred to smallholder farmers through extension sector of the ministry of Agriculture. The development agents in each locality are responsible for technology flow to smallholder farmers. Figure 1 illustrates how technology flows in the dairy sector. In the present study, a multi stage random sampling method was used in selecting participant smallholder dairy farmers from the two districts. Sixty of the selected participant smallholder dairy farmers were trained intensively for two days on dairy husbandry practices by researchers at Holeta agricultural research center in May 2016. Three development agents from each district attended the training for the purpose of assisting the trained dairy farmers on the application of the information at household level and for further follow up of their progress. The contents of the training are indicated in Table 1. 3. Materials and Methods 3.1 Study area, sampling method and data collection This study was conducted in Adaberga and Cheliya district of west Shewa zone, Ethiopia. The two districts are characterized by crop-livestock mixed farming system where livestock in general and dairy production, in particular, contributes significantly to livelihoods of the smallholder farmers. Adaberga district is located 64 km west of Addis Ababa, capital city of Ethiopia. It is situated at an altitude ranging from 1,166 to 3,238 m above sea level and with an estimated area of 131.12 km square. The area receives on average an annual rainfall ranging from about 887 to 1,194 mm. The average annual daily temperature of the area ranges from 11 to 21oc. The population of Adaberga district is 120,654 based on the information from district agricultural office. Livestock production is an essential part of the farming system as nearly all land preparation is done with ox-drawn plows.

They also provide farmers with transport, manure, and fuel. They are an important insurance during hardship times. The district consists of 46,541 cattle, 57,511 sheep and 43,574 goats. Cheliya district is also located in west Shewa zone of Oromia state in Ethiopia. The area is located at 175 km west of Addis Ababa. It is situated at an altitudinal range of 1,700 to 3,060 m above sea level. The average annual daily temperature of the area ranges from 10 to 25oc. The population of this district is 182,262 (CSA, 2012). According to Cheliya district agricultural office (2012), the district possesses livestock population consisting of 124,713 cattle, 22,220 goats, 11,578 horses, 8,294 mule, 1,331 donkeys, 34,348 sheep, and 53,930 poultry. The two districts were purposively selected based on access to training in dairy husbandry practice and density of livestock population. A cross-sectional survey was conducted and the data was collected from a total of 180 smallholder dairy farmers (90 from each district). Sixty of the participant smallholder dairy farmers (30 from each district) were trained on dairy husbandry practices. The remaining 120 smallholder dairy farmers, the control group (60 from each district), were randomly selected based on ownership of lactating dairy cows from nearby villages that were not included in the training to avoid possible spillover effects likely to occur between farmers within the same village. A semi-structured questionnaire was prepared and pre-tested to ensure necessary adjustments before the actual data were collected. A face to face interview was employed to collect the primary data from the selected participant dairy farmers. Both qualitative and quantitative data were collected.

The information collected from the participants includes demographic (age of the household head, sex of the household head, educational status, family size) and socio-economic characteristics (experience in dairying, extension and veterinary service obtained, area of land allocated to forage production, access to credit, access to feed, access to market, milk (sold, consumed, processed, yield and income) and price of milk and milk products). The survey was conducted in March 2017, which was 10 months after the training provided. Both continuous and dummy variables and outcome indicators included in the model are defined in Table 2. 3.2 Empirical model Propensity score matching technique (Dehejia and Wahba, 2002; Heckman et al., 1997; Rosenbaum and Rubin, 1985) was used to evaluate participation in dairy husbandry training on milk production and income of smallholder dairy farmers. In the case of the non-experimental method the presence of selection bias which arises due to differences in observable characteristics can be avoided by the use of PSM model. In this study, participant dairy farmers both trained (treated) and non-trained (control) groups were matched based on their observable characteristics and the impact of training on the mean values of the outcome variables were calculated.

The PSM technique is therefore used to control selection bias since it accounts between the outcomes of the treatment and control groups (Fancesconi and Heerink, 2010). This provides an unbiased estimate by controlling observable factors and reduces matching problems (Becker and Ichino, 2002). Prior to the estimation of PSM, all the explanatory covariates included in the model were checked for the existence of multicollinearity and heteroscedasticity problems using Variation Inflation Factor (VIF) and Breusch-pagan/Cook-Weisberg test respectively. The estimation process for dairy husbandry training on milk production and income was done using psmatch2 in STATA ® 13.1 (Leuven and Sianesi, 2003). The following 12 explanatory variables were selected (Age, sex, education, family size, experience, extension service, crossbred cow, forage land, credit service, market distance, cooperative membership, veterinary service). The propensity score for each observation was calculated using a logit model and the predicted value indicates the likelihood of the dairy household being included in the training. In the present study, we focus on the following specific variables as outcome indicator: (1) average annual milk income from milk and milk products; (2) average milk production; (3) average milk processed; (4) average milk sold; (5) and average milk consumed. The ATT is then calculated as the mean difference in outcomes across the trained and non-trained farmers. The validity of PSM depends on two conditions. The first one is conditional independence (unobserved factors do not affect participation) and the second one is common support or overlap in propensity scores across the participants and non-participant samples (Khandker et al., 2010). The assumption in the first condition is that treatment needs to fulfill the criterion of being exogenous, implying that any difference in outcome between the trained and non-trained farmers with the same value of characteristics can be attributed only due to the dairy husbandry training. This assumption can be denoted as Y1, Y0 ⊥on characteristics X, Y1 and Y0 are the outcomes for the trained and non-trained farmers, respectively. The second assumption, common support, ensures that individuals/groups with the same values for characteristics X have a positive probability of being both trained and non-trained farmers (Heckman et al., 1999).

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