Ethiopia has huge livestock resources of varied and diversified genetic pool with specific adaptations to wide range of production environments, production systems and ethnic communities (Duguma, 2010; Melesse et al., 2013). Sheep production is one of the integral components of livestock production activities under small holders. The country has about 31.3 million heads of sheep (CSA, 2017/2018) and 14 traditional populations (Gizaw et al., 2007). The majority of sheep are reared in the highlands and mid-altitude of Ethiopia where mixed crop-livestock production dominates (Berhanu and Haile, 2009; Tibbo, 2006). For the smallholder farmers, sheep have multipurpose roles as generating income, meat, skin, wool, manure, and risk alleviation during crop failures and social function (Nigussie et al., 2015). Increasing population, urbanization and income level, increases the demand for animal source food; export and domestic market for mutton and live animal has created more opportunity for sheep production in Ethiopia (Mohammed et al., 2015).

Even though multipurpose role and large size of Ethiopian sheep population, productivity is relatively low (Getachew et al., 2010). Furthermore, the production system is constrained by several factors such as feed scarcity, lack of technical capacity of the farmer, disease and parasite prevalence, low genetic potential for functional traits of the animal, lack of appropriate breeding strategies and limited understanding of the production systems (Gizaw et al., 2013a; Gatew et al., 2017). The severity of constraints varies with different production system and agro-ecological zones. Therefore, there is a need to design sheep breeding improvement scheme that consider the existing smallholder production system. Characterizing sheep genetic resources, assessment of productivity level of sheep population in its existing production environment, and identification and prioritization of the constraints of production are important to design sustainable genetic improvement programs and to develop effective intervention strategies which are compatible with the production system (Kosgey and Okeyo, 2007).

Sheep populations in Ethiopia as identified by Gizaw et al. (2007) are highly linked with ethnic groups and agro-ecology. Most traditional sheep types are reared by and named after specific communities. Some communities related special cultural values to their sheep and exclude the use of breeding stock from other populations resulting in cultural barrier to gene flow (Melesse et al., 2013). Studies also indicated that morphological diversity follows ecological patterns and production systems (Wagari et al., 2020). Sheep production in the mixed crop-livestock production systems of the highland areas has a vital role in contributing to the food security and generating cash income in Ethiopia in general, in Gurage zone in particular. There is high integration between enset and livestock production in enset-cattle based mixed production system of Gurage zone which has a strong bond in determining the livelihoods and food security of the rural farming families (Wonchesa  et  al.,  2019).  Sheep  production has versatile roles in areas across hill sides of Zebidar Mountain of central highland which is not accessible for crop production. Indigenous sheep can efficiently utilize marginal land and able to produce under adverse climatic conditions. So that given the adaptive potentials of the existing sheep, improving sheep productivity can be used for poverty reduction and improving the livelihoods of smallholders in such very cool areas of Gurage zone.

Information on husbandry practices and phenotypic characterization of indigenous sheep types can serve as basis for sustainable improvement and determining productive performance variation between and within breeds (Gizaw et al., 2013b). Husbandry practices such as management, production constraints, feeding and breeding aspects are among the factors which can affect the phenotypic characteristics of animals (Dagnew et al., 2017; Gatew et al., 2017). In addition, information on body weight with other body measurements is important to monitor the growth potential of the sheep population and to estimate correlations between body weight and morphometrical measurements (Asefa et al., 2017). Prediction of body weight using linear body measurement is also important to determine medication dosage during treatment and the amount of feed required for the animal without weighing scale, and simplicity of measurement under field condition (Abera et al., 2014).

However, information on phenotypic traits of indigenous sheep types and their husbandry practices in Gurage Zone is limited. Furthermore, updating of the previous findings is important since genetic resources and husbandry practices are dynamic (Solkner et al., 1998). Thus more detailed phenotypic characteristics and understanding husbandry practices of indigenous sheep in Gurage zone are important. Therefore, the study was conducted to generate information on husbandry practices, phenotypic characteristics and determination of live body weight using morphometrical measurements of indigenous sheep types in Gurage zone of Southern Ethiopia, so that the information generated can be used to design sustainable genetic improvement programs and to develop effective intervention strategies which are compatible with the production system.

Descriptions of the study area

The study was conducted from September 2019 to April 2020 in Gurage zone, which is found in the Southern Nations, Nationalities and Peoples’ Region of Ethiopia. It is located between 37° 28' and 38° 38' East longitude and 7° 28' and 8° 27' North latitude with altitudinal ranges from 1600 to 3650 m above sea level. The annual rainfall of the area ranges between 800 and 1400 mm. The average annual temperature is about 18°C. The agricultural practice in these areas is Enset based crop-livestock production system in which livestock play important role for crop production and the livelihood of the farmers (Wonchesa et al., 2019). According to CSA (2016a), Gurage zone livestock population constituting 1,678,455 cattle, 776,900 sheep, 260,420 goats, 1,020,000 chickens, 128,532 horses and 9,464 mules and donkeys.

Sampling methods

First, discussions were made with Gurage zone livestock and fishery resource development bureau officers to select districts. Ejha and Gumer districts were purposively selected based on sheep population distribution, flock size, their contribution to farmers and availability of transportation. Within the selected districts, a rapid field survey and discussions were made with respective zonal and district livestock experts, leaders and elders to locate appropriate peasant associations for the baseline data collection. Accordingly, 12 peasant associations (6 from Gumer and 6 from Ejha district) were selected. A total of 156 households head (13 households from each peasant association) who own at least three sheep were randomly selected and interviewed using pre-tested semi-structured questionnaire; adopting a questionnaire developed by International Livestock Research Institute and Oromia Agricultural Development Bureau for survey of livestock breeds in Oromia (Workneh et al., 2004).

Data for the assessment of quantitative and qualitative traits were collected from 402 sheep (288 ewes and 114 rams) that were drawn from sampled households. Two ewes from each household were randomly selected while all adult male sheep were recorded due to their limited number. Age of the sheep was estimated from dentition class following the procedure described by Wilson and Durkin (1984). The sheep were categorized by age group as 0PPI, 1PPI, 2PPI, and 3PPI which represents no pair of permanent incisor (6-12 months), one pair of permanent incisors (1-1.5 years), two pairs of permanent incisors (1.5-2 years), and more than two pairs of permanent incisors (>2 years), respectively.

Data collection procedure

Information on husbandry practices like flock structure, production objectives, selection criteria, production constraints, production performance and other related issues were collected from 156 household head using questionnaires. Field measurements and observations were implemented based on the description of FAO (2012). Qualitative and morphometrical measurements like: coat color pattern, color type, hair coat type, horn presence or absence, horn shape, horn orientation, facial (head) profile, wattle (presence or absence), and  ruff (presence  or  absence)  were  observed  and recorded using a format developed for the purpose. The quantitative traits measured were body weight (BW), body length (BL), height at wither (HW), chest girth (CG), pelvic width (PW), canon length (CL), canon circumference (CC) and tail length (TL). Linear body measurements were made using measuring tape while live body weight were taken using suspended spring balance having 50 kg capacity.

Data management and statistical analysis

All the data obtained from field observations and measurements were coded and recorded into Microsoft EXCEL softwares. The survey data was analyzed and presented in the form of descriptive summaries (mean, standard deviation, frequency and percentage). Indices  for  ranking  sheep  production  constraints,   feed  sources, sheep production objectives, and ram and ewe selection criteria by the smallholder farmers were computed for the first three ranks following the formula: Index = sum of (3 for rank 1 + 2 for rank 2 + 1 for rank 3) given for an individual attribute divided by the sum of (3 for rank 1 + 2 for rank 2 + 1for rank 3) for overall attributes. Qualitative data was analyzed following the frequency procedures of statistical Analysis System (SAS release 9.1, 2008). The General Linear Model (GLM) procedure of SAS was employed to analyze quantitative variables. When analysis of variance declares significance, least square means was separated using adjusted  Tukey-Kramer test. The model employed for analyses of body weight and other linear body measurements used were:

Y_ijk = m + S_i + A_j + (SA_{) ij}+ D_k + e_ijk

where Y_ijk = observed variable of i^th sex, j^th age and k^th district, m = overall mean, Si = effecet of the i^th sex (i= 1, 2), Aj= effect of the j^th age group (j = 1, 2, 3, 4), (AS)_ij  = the interaction effect of the i^th sex by the j^th age, D_k= effect of the k^th district (k=1, 2), and e_ijk = random residual error.

Pearson's correlation coefficients were estimated between live body weight and other body measurements within sex and age group. Within each age and sex group, stepwise regression procedure (SAS, release 9.1, 2008) was used to determine the best-fitted regression equation for the prediction of body weight from body measurements; which were selected based on the values of coefficient of determination (R²). The following models were used for the analysis of multiple linear regressions.

Y_j= β0 +β₁X₁ +β₂X₂ +β₃X₃ + β₄X₄ + β₅X₅ +β₆X₆ +β₇X₇ + e_j,

where Y_j = the dependent variable bodyweight, β0 = the intercept; X_1, X2, _…, X₇ are the independent variables of body length, height at wither chest girth, pelvic width, canon bone length, and canon bone circumference  and   tail  length,  respectively.   β₁, β₂ ... β7  are  the regression coefficient of the variables X₁, X₂ …X7, respectively. e_j = the residual random error.

Livestock composition and holding pattern

The major livestock species observed in the study area were sheep, cattle, goats, horse, mule and donkey (Table 1).  Sheep were the predominant species in both areas accounting for 5.19±0.27 and 4.41±0.1 in Gumer and Ejha districts, respectively. The least square mean (±SE) sheep flock size per household in Gumer was significantly (p<0.05) higher than in Ejha district. The overall mean number of sheep holding per household found in this study (4.80±0.1) was comparable with the previous study of Kocho (2007) in the Alaba area (reported five sheep). On the other hand, the overall mean number of sheep per household in the current study area was relatively higher than those reported by Shenkute (2009)  and  Ferew  (2008)  who  reported  four and two sheep per household for south west Ethiopia. The present study revealed that, households in both areas keep sheep as the primary animal probably because of their ability to survive in a poor management and production environment.

Sheep flock structure

From the flock inventory, 41% of ewes and 7% males were above one year of age. The percentage of young female and male lambs which are less than six months of age were 14 and 15%, respectively; while 13% female and 6% male were within six to twelve months of age. Castrates constitute only 3%. The higher proportion of ewes is in good agreement with the findings of Kocho (2007) and Shenkute (2009). According to respondents, only few males are required for breeding and the remaining males should be sold at early age because of feed shortage and the need to maximize their breeding female animals in the flock. The remaining rams were castrated and finished for markets. In most cases, male animals are castrated at about 2.5 to 3 years of age for fattening purpose. Farmers sell fattened males at about 2 to 3 years of age. The proportion of intact male (ram) to ewe ratio was 1:22, which is in the range of the recommended breeding male to female ratio (1:25) for sheep under traditional production system (Wilson and Durkin, 1984).

Purpose of keeping sheep

According to respondents in all study area mentioned that cash income was the primary reason of keeping sheep, followed by saving, meat  consumption and  manure  with index values of 0.47, 0.36, 0.11 and 0.05, respectively (Table 2). The purpose of sheep keeping identified in this study is in line with that of previous studies (Legesse, 2008; Zelealem et al., 2012). Gizaw et al. (2013b) also reported that, male lambs are mainly kept for income generation and risk mitigation while females are reared for reproduction/breeding. However, during money shortage and unavailability of male lambs, ewes can also be sold for income generation to purchase agricultural inputs. These imply that indigenous sheep can play important roles in poverty reduction at the smallholder level, if adequate efforts are made to improve their productivity.

Feed resources

The major feed resources during wet and dry seasons of the year in the study area indicated in Table 3. Natural pasture was the main feed resource during the wet season followed by crop thinning and improved forage with the corresponding index values of 0.55, 0.36 and 0.09, respectively. However, during dry season, crop residues, natural pasture, improved forage, conserved hay and concentrated feeds were the main feed resources recorded with index value of 0.37, 0.34, 0.07 and 0.04, respectively. Improved grass species and forage trees, especially Tree Lucerne species planted on around   homesteads,   boarders,   hedge   and  sides   of cropping land. In the highlands of Ethiopia, the major basal feed resources are natural pasture, crop residues and stubble grazing, and their contribution varies from area to area based on cropping intensity and population pressure (Tolera et al., 2012). The author also indicated that in the mixed cereal livestock production systems of Ethiopia, crop residues provide on average about 50% of the total feed source for ruminant livestock during the dry period. 

Selection criteria and breeding practices

Preference for breeding ewes and rams by sheep producers are summarized in Table 4. For sheep keepers in Ejha district, the  most  important  selection  criteria  for breeding ewes were mothering ability, body size, coat color and horn with the index values of 0.62, 0.28, 0.08 and 0.02, respectively. Likewise, mothering ability, body size, color and horn were important selection criteria by farmers in Gumer district with index values of 0.61, 0.34, 0.04 and 0.01, respectively. This result is in agreement with Duguma et al. (2011) who reported a high choice preference for good mothering ability of ewes in four indigenous sheep breeds (Horro, Menz, Afar and Bonga) of Ethiopia.

Body size followed by growth rate and coat color were found as the most important selection criteria of breeding ram in both study districts. The preference of big body size and fast growth rate as the preferred attributes, since the main purpose of keeping sheep was for cash source. The animals with  big size are highly demanded in market and fetch good local market prices (Abebe et al., 2020; Abera et al., 2014). For ram selection, farmers target was not only for breeding purpose but also they take into account the traits that affect the market value. For instance, rams with either completely black color, or abnormal legs, or without horn or small size are not selected as such rams showing these features do not attract buyers and obtained low price (Nigussie et al., 2015). Sheep producers further reported that, rams with any defects or visible injuries on testis was not selected for breeding.

The mean number of breeding ram per flock within the interviewed households was 0.34 head.  Sheep producers who do not have breeding ram stated that they tend to borrow ram from neighbor or mating took place at random with rams present in the flocks in adjacent grazing land and watering point. Uncontrolled natural mating was common in the study districts.

Major constraints associated with sheep production

The major constraints associated with sheep production in the study areas are shown in Table 5.  Feed shortage, disease and market price fluctuation were the first, second and third major constraints of sheep production with the corresponding index values of 0.43, 0 26 and 0.19 for Ejha; and 0.45, 0.27 and 0.14 for Gumer respondents, respectively. The study revealed feed scarcity was the main challenges of sheep production and productivity. This is mainly due to shortage of land for grazing and fodder production as the result of expansion of crop agriculture in the expense of grazing land (Gizaw et al., 2010a; Tibbo, 2006).

According to respondents, all species of animals graze together in grazing lands and fallow lands. In this type of herding, lambs usually are more vulnerable to the effects of feed shortage and malnutrition. Mortalities due to feed scarcity and malnutrition are common especially during the late dry seasons (March to June). Disease is the second most important sheep production constraints as reported by  sheep  owners  of  both  study  districts.  The most prevalence diseases of sheep in the study areas include: Ovine pasteurollosis, Orf, internal and external parasites.  Mortality rate before weaning reaches about 20 to 24%. Similar studies also reported mortality rates greater than 20% for Horro and Menz sheep breeds (Gizaw et al., 2008a; Kocho, 2007) under smallholder management condition.

Reproductive performances

Age at first service (AFS)

The least squares mean (LSM ± SE) age at first service of male and female sheep at both districts is shown in Table 6. The mean value of age at first service for male sheep were 7.63±0.1 and 8.1±0.2 months at Gumer and Ejha districts, respectively, while for female sheep 7.80±0.6 and 8.42±0.8 months at Gumer and Ejha study areas, respectively. At Gumer district, both male and female sheep indicated significantly (P<0.5) shorter age at first service than at Ejha district. The location difference may be attributed to management practices of farmers in the respective areas to attain earlier puberty.

Age at first lambing (AFL)

The estimated age at first lambing (AFL) at Gumer and Ejha study area were 13.5±0.2 and 14.0±0.2 months, respectively (Table 6). The shorter age at first lambing significantly (p<0.5) observed at Gumer than Ejha district ewes. The overall mean value of age at first lambing found in the current study is comparable with those of previous reports in Alaba area (Kocho, 2007) 13 months, Bonga sheep (Shenkute, 2009) 12.9 months in western Ethiopia and in eastern Ethiopia (Nigussie et al., 2015) 13.8 months. Age at first lambing is a function of puberty, age at first breeding, conception and completeness of pregnancy. These reproductive traits influenced by genotype of an individual, environment, nutrition and season of  birth. So  that  shorter  age  at  first lambing  at Gumer could be due to management and environmental effects.

Lambing interval (LI)

The estimated mean lambing interval of indigenous sheep at Gumer and Ejha districts were 8.2±0.22 and 8.4±0.12 months, respectively (Table 6). The finding of lambing interval under the current study confirms with previous studies of Marufa et al. (2017) reported 8.2 months for Abera sheep; Nigussie et al, (2015) 8.6 months for eastern Ethiopia sheep, Shenkute (2009) reported 8 months for Bonga sheep, Abegaz (2007) 8 months for Gumuz sheep, Kocho (2007) 7.8 months for Arsi-Bale sheep. Longer lambing interval (15 months) was reported for Washera (Taye et al., 2010). The variations observed between the different findings could be differences in management, breed and production system. Early weaning and proper feeding was reported as the most important measure in improving conception rate and shortening of lambing interval (Adane and Girma, 2008). Improving management and early weaning practice can help to achieve shorter lambing interval.

Litter size

Mean litter size of indigenous sheep type in the current study area was 1.51±0.07 and 1.47±0.12 for Gumer and Ejha districts, respectively (Table 6). Higher litter size (1.51±0.07) was recalled by Gumer than Ejha respondents. The overall mean value (1.49±1.15) of this report is comparable with that of previous studies on the other breeds in different parts of Ethiopia. For example, an average litter size of 1.5 for Adillo sheep was reported by Legesse (2008) and Shenkute (2009) reported 1.4 for Bonga sheep; Similarly, Taye et al. (2010) reported litter size of 1.34 for Horro Sheep  and  Abegaz  (2007) 1.3  for Gumuz sheep. Gizaw et al. (2008a) reported 1.17 for Arsi-Bale sheep in Alaba district. Litter size for tropical breeds varies between 1.08 and 1.75 with an average of 1.38 lambs per parturition per ewe (Kosgey and Okeyo, 2007). The study indicated that the sheep in the current study area have acceptable litter size.

Qualitative characteristics

The overall observed phenotypes of coat color and hair type of indigenous sheep types in the study area are presented in Table 7. The sheep in the study area have predominantly patchy coat color patterns (52.0%) followed by plain (31%) and spotted (17%). The result is consistent with Arsi Bale sheep reported by Gizaw et al. (2007). The most commonly observed coat color types were brown dominant (brown with black and/or white) (35.8%), followed by black dominant (black with brown and/or white) (20.4%), white dominant (white with brown and/or black) (19.2%), brown (14.2%), white (8.2%), and black (2.2%). About 75% of the sheep possess the combinations of brown and/or black and/or white color (Figure 1). The possible explanations could be the presence of strong selection pressure by sheep keepers as they believe that sheep with this coat color type (which is locally called ^“Gerebet^”) are prolific and productive. However, almost all sheep keepers dislike sheep with solid black coat color as the sheep usually sold with cheap prices than those with other colors. This is probably related to their cultural beliefs. Gizaw et al. (2008c) reported that coat color is among the qualitative characteristics which are used by the local communities to select breeding rams and ewes. The majority of the sheep had long and coarse hair (55.7%) whereas short and coarse hair accounted to 44.3%. Gizaw et al. (2008c) reported long coarse wool hair coat type for fat tailed sheep in central high land areas. Such hair type is frequently  seen  across  the  cool  to very cool mountains and plateau of the central highlands of Ethiopia. Almost  all   sampled    sheep    were    exclusively characterized by long fat tail type and the dominant tail  shape  is cylindrical with straight tail (93.3%) (Table 8). Most of the female sheep (91.3%)  showed  straight  facial  profile  while  the rest had small proportion of slightly convex head (8%). Most of the male sheep (84.2%) were characterized by having straight facial followed by slightly concave (12.3%) and slightly convex (3.5%) shape. Almost all male sheep (95.6%) and the majority of female (84.4%) possessed horn. The dominant horn shape in the male sheep was curving downward (51%) followed by horn shape curving upward (21.1%), and straight (18.4%) and rudimentary (10%).

Live body weight and linear body measurements

The means (±SE) for effects of sex, age, district and sex by age on the body weight (kg) and linear measurements (cm) of sheep are shown in Table 9. The main sources of variation on live body weight and linear body measurements were sex, age and the interaction between them. The overall mean live body weight of male and female sheep was 28.3 and 23.4 kg, respectively. Sex  of the sheep in the current study area exerted significant differences (p<0.05) on live body weight and other linear body measurements, except for tail length. The finding of the current report  is in line with the reports of Taye et al. (2010) who reported a significant effect of sex on body weight, body length, height at wither and chest girth in Washara sheep. The mean value of body weight (kg) at age group 0PPI, 1PPI, 2PPI and >2PPI was recorded as 19.7±0.6, 24.0±0.0, 31.7±0.7 and 37.8±0.7 kg, respectively for male sheep. The mean body weight for female sheep for the respective age categories was 17.2±0.5, 22.7±0.4, 25.6±0.4 and 28.2±0.3 kg.  As the age of the animal increased, the live body weight and other linear body measurements of sheep increased significantly (p<0.05). The report of the current finding is in line with previous studies by Melesse et al. (2013) who reported that age had a significant (p<0.05) effect on body weight and other morphometrical measurements of indigenous sheep in Southern Ethiopia. Similar finding was reported by Abera  et  al. (2014) who noted that body weight and morphometrical measurements increased with increasing age of sheep in Debrelibanos and Wuchale districts. Moreover, similar reports were revealed by Tilahun et al. (2014) for Elle sheep breed and Gebreyowhens et al. (2016) for Tigray sheep.

The report of the current study also indicated that, the interaction effect of sex and age group was significant (p<0.05) for all quantitative traits considered except for tail length, but the effect of sex by age was prominent in male sheep as a result of hormonal difference between males and females (Çilek, 2014). In the current study, area location had no effect on body weight but significantly affected the linear body measurements. Accordingly, the Gumer sheep population had significantly larger linear body measurements (P<0.05) than Ejha sheep population.

A strong correlation of body weight was recorded with chest girth; body length and height at wither in which the r values were 0.95, 0.91, and 0.90 for rams and 0.91, 0.86 and 0.84 for ewes, respectively. The coefficient of correlation was relatively higher in rams sheep (r = 0.95) than in ewes (r = 0.91) between body weight and chest girth.

The higher correlation coefficient observed in rams indicated that, the live bodyweight could be predicted with higher precision in rams as compared to their counterpart ewes. The highest correlation of chest girth with body weight than other body measurements was in line with previous studies of Gizaw et al. (2008c), Zewdu (2009), Melesse et al. (2013) and Mohamed et al. (2015).

Prediction of body weight using body measurements

Stepwise multiple linear regression analysis for prediction

of live body weight using morphometrical measurements was carried out within each sex and age group (Tables 11 and 12). The prediction results were best assessed by including chest girth, body length and pelvic width values of morphometrical traits from overall rams and ewes. As presented in Table 11, the body weight of rams 0PPI (6-12 months of age) is best predicted by fitting measurements of chest girth, pelvic width and body length. The best predictor for male 1PPI (1-1.5 year old) was found to be chest girth alone, while the body weight of rams above 2 years of age is best predicted by fitting measurements of chest girth and body length. Similarly, the best predictors of ewes at different age group are shown in Table 12. The best predictor for ewes aged 0PPI (6-12 months of age) was found to be chest girth alone. The body weight of ewes above 1PPI (1-1.5 year of age) can be assessed by chest girth, body length and canon circumference. However, the body weight for ewes with 2PPI (1.5-2 years old) can be predicted by chest girth only. The study also indicated that for determination of body weight of ewes over 2 years old was found to be best by fitting measurements of chest girth and body length. The report of the current finding is consistent with previous studies by Melesse et al. (2013) for indigenous sheep types in southern region (Kambata, Hadya, Sidama, Geddeo, Wolita, Silte and Gurage).

The regression study indicated that chest girth was the most variable to estimate reliable body weight. Hence, chest girth alone or in combination with body length and pelvic width could be used as reliable predictors of live weight for all age groups of both sexes in the study area. However, better correlation coefficient was obtained as more variables were included in the prediction equation. These findings are in line with those reported by Tadesse and Gebremariam (2010) for Northern Tigray sheep and Melesse et al. (2013) for sheep in South Ethiopia.

The  prediction of overall live body weight of sheep best assessed by including CG and BL values for both ramsand ewes:

(1) Fitted equation for prediction of ram’s body weight: BW =  -37.79 + 0.90×CG; considering CG alone or BW= -39.73+0.60×CG+0.40×BL

(2) Fitted equation for prediction of ewes body weight: BW = -24.05 + 0.70*CG; considering CG alone or BW=-29.68+0.51×CG+0.33×BL

Indigenous sheep production has multifunctional roles and contributing a lot to the livelihoods of the small holder farmers. However, feed scarcity, diseases and parasite prevalence, marketing and poor infrastructure were the major constraints hampering sheep productivity in the study areas. The majority of sheep were characterized by patchy color pattern and the combinations of brown and/or black and/or white color type which is locally called it “Gerebet”. The study indicated variations within the studied sheep population in qualitative and quantitative trait, so that there is a great possibility for genetic improvement through selection of rams within population. In the current study, location had no effect on body weight but significantly affected the linear body measurements. Accordingly, the sheep in Gumer district had significantly (p<0.05) higher linear body measurements (P<0.05) than Ejha district. The regression study indicated that chest girth was the most variable to estimate reliable live body weight. Hence, heart girth alone or in combination with body length could be used as the best predictor of live weight for all age groups of both sexes where sensitive weighing scales are not readily available. Furthermore, well organized on-farm monitoring and genetic breed characterization is very important to fully characterize sheep breed types found in the study area. Finally, implementation of planned genetic improvement strategy through community based breeding program that considers multifunctional roles of sheep and the existing production system.

The author has not declared any conflict of interests.