Dairy constraint analysis in Eritrea, with special emphasis on Asmara and surrounding dairy farms.

Abstract

The overall objective of the study reported in this thesis was to assess the existing situation of the dairy sub-sector in Eritrea with special emphasis on the Asmara and surrounding dairy farms. The specific objective was analyse historical data and carry out a survey to identify the constraints of the dairy sub-sector in Eritrea. Historically, commercial dairy farming in Eritrea was started by Italian settlers during the 19th century when Eritrea was under Italian colonization. The growing demand for milk and milk products, especially in the urban centres stimulated the development of dairy farms by Italian settlers who owned large areas of fertile land mainly in the highlands, in which they established modern dairy farms using high merit dairy breeds such as the Holstein-Friesian. The introduction of artificial insemination (AI) in 1938 by the Italians for research purpose at the Veterinary Institute also contributed to dairy development. Based on the efficiency of A.I. services over time, four phases of development were identified. During 1972-1976 good achievement per inseminator and high effective inseminations were recorded. In 1978 -1980 the achievements declined but with increased number of calves and high effective inseminations as compared to phase one. From 1982-1989 the activity increased with effective inseminations ranging from 48-75.3%. During 1992-2000 effective inseminations ranged from as high as 58.8% to as low as 31.3%. Effective inseminations increased with the increased number of A.I. technicians. The highest achievements were recorded during the years 1993, 1998 and 1999. A survey was conducted on urban and peri-urban dairy farmers located in and around Asmara. Thirty dairy farmers were randomly selected and interviewed. Data on herd composition and breed, feed type and source, type of mating, dairy housing, health, farmers' status, milk production and marketing, problems and constraints faced by dairy farmers were collected. Interviewed farmers were characterised based on location, herd size, health service, and farmers' status. Dairy farmers varied in their access to forage. High proportions of peri-urban farmers have access to cultivated forage while high proportions of urban farmers have access to purchased forage. Usage of cultivated and purchased forage were related to location (p<0.05). Natural, AI and combined systems of mating were used by 86.7%,3.3% and 10% of

farmers, respectively. Both urban and peri-urban farmers used the in-door system of housing. Foot and mouth disease (FMD), lumpy skin disease, nutritional disorders, mastitis, abortion, tuberculosis and digital problems were the most prevalent diseases. Dairying was considered by a majority (73%) of farmers as a primary means of income but by 27% as a part time means of earning an additional income. A large amount (67%) of milk produced was marketed through the formal channel while the rest (23%) was sold directly to consumers. Secondary data were collected from different institutions and libraries such as MoA, ASMDFCA and Asmara Pavoni social centre, through personal contacts with various individuals. A data set of 563 dairy farmers that included infonnation on farm identification number, location, breed, herd structure (cows, calves, heifers, bulls), milk yield/farm/day and milk yield /cow/day was subjected to cluster analysis. The main objective of clustering was to classify farmers according to their characteristic features. Three clusters, namely clusters 1, 2 and 3, were identified and had, respectively 47.04%, 45.06% and 7.91% urban (n=253) and 37.13%, 61.56% and 1.3% peri-urban (n=307) farmers. Clusters 1, 2, and 3, had 10±0.6, 5±0.5, 23±1.82 cows, and produced 49.7±3.45kg, 24.3±3.02kg and 117±10.73 kg of milk/farm/day, respectively. A group survey was conducted to identify the major constraints impeding dairy production. Pairwise ranking of the identified problems was done using a matrix. The constraints subjected to pairwise ranking were: feed shortage, lack of co-ordination, lack of skilled manpower, disease and shortage of veterinary drugs, lack of extension package, shortage of land, lack of milk cooling and transport facilities, lack of know-how and inconsistent AI service. Shortage of land, feed shortage and lack of co-ordination were identified as the major constraints facing dairy farmers. Dairy herd constraint analysis was also done on Elabered estate dairy. It is the only dairy farm that keeps dairy records. Data on lactation number, breed, last calving date, A.I. date and due date, body weight and height, milk yield per cow, condition score, number of AI's and pregnancy status of cows, were analysed using "Gwion dairy analysis programme by dairy man". Mean milk yield, condition score and average live body weight of the herd were 11.6±2 kg/day, 2±0.56 and 497.63±89 kg, respectively. Days in milk of first calvers and older cows fell between the ranges of 41-327 and 201-245 days, respectively. First and second calvers were below target average mature body weights. Mean age by stage of lactation of cows was 3.3±1.8 and mean age at first calving of the herd was between the range of 39-43 months. Mean calving interval (Cl), calving interval spread index, voluntary waiting period, average services per pregnant cows (SPC) and maximum services per conception were 435 ±88days, 55, 52days, 1.8±1.1 and 7, respectively. Poor heifer growth and poor fertility status were major constraints identified. The suspected causes were poor data management, poor heat detection and poor nutrition. Generally, the dairy sub-sector in Eritrea comprises urban and peri-urban dairy farms of different sizes operating in and around urban centres. The sustainability of the systems will be determined by their location towards the centre. With the expansion of towns and residential areas some dairies may discontinue. Social, environmental and economical aspects should be given due consideration for sustainable dairy production systems. Therefore, identifying the target group should help to intervene and solve constraints.