Demographic characteristics of the cattle keepers
The results (Table 1) of this study indicated that the majority of the cattle keepers at the Rejaf East Cattle camps were females (82.5%). In the Rejaf East, the cattle camps are occupied by the Dinka ethnic group. According to Dinka culture, milking cows are considered domestic work, and most of the domestic work is done by women. Men drive the cows into the fields during the day and return them to the camp before sunset. The children cleaned the ground of the camp every morning by collecting the dung and burning it at sunset. These findings are in line with those of Tasnier, et al. (2021), who reported that more females go to cattle camps to support children and elderly people, and Adugna et al., 2021, who reported that milking practices are carried out by females rather than males.
Table 1
General information of the respondents
Variable | Highland (n = 80) | Kadoro (n = 80) | Jebel amianin (n = 80) | Overall (n = 240) |
Sex |
Male | 23 (28.7) | 9 (11.3) | 12 (15) | 42 (17.5) |
Female | 57 (71.3) | 71 (88.7) | 68 (85) | 198 (82.5) |
Age of the respondent |
15–20 | 7 (8.8) | 2 (2.5) | 3 (3.5) | 12 (5) |
20–30 | 6 (7.5) | 8 (10.0) | 6 (7.5) | 20 (8.3) |
30–40 | 38 (47.5) | 35 (43.8) | 32 (40) | 105 (43.8) |
40–50 | 22 (27.5) | 27 (33.8) | 24 (30) | 73 (30.4) |
50–60 | 7 (8.8) | 6 (7.5) | 15 (18.8) | 28 (11.7) |
60 + | 0 | 2 (2.5) | 0 | 2 (0.8) |
Education level |
Informal | 35 (43.8) | 37 (46.3) | 43 (53.8) | 115 (47.9) |
Primary | 25 (31.3) | 33 (41.3) | 28 (35.0) | 86 (35.8) |
Secondary | 20 (25.0) | 9 (11.3) | 9 (11.3) | 38 (15.8) |
Tertiary | | 1 (1.3) | | 1 (0.4) |
Family size |
1–5 | 49 (61.3) | 46 (57.5) | 44 (55.0) | 139 (57.9) |
5–10 | 22 (27.5) | 21 (26.3) | 26 (32.5) | 69 (28.8) |
> 10 | 9 (11.3) | 13 (16.3) | 10 (12.5) | 32 (13.3) |
Duration of stay |
1–5 Years | 47 (58.8) | 43 (53.8) | 50 (62.5) | 140 (58.3) |
6–10 Years | 25 (31.3) | 27 (33.8) | 23 (28.7) | 75 (31.3) |
More Than 10 Years | 8 (10.0) | 10 (12.5) | 7 (8.8) | 25 (10. 4) |
The number in brackets is the percentage of respondents from the three locations. n = number of respondents
The majority of the cattle keepers (43.8%) were aged 30–40 years (Table 1). This group of strongly young people can protect cattle from cattle raiders. Cattle raiding has been a traditional practice among pastoral communities in the region, notably among the Nuer, Dinka, and Murle tribes (Marchot, 1983). Many factors have contributed to this and are becoming more intense, involving greater violence, which is occurring on a far larger scale in South Sudan. Cattle raiding is also spurred by rising bride wealth rates, which are usually paid in cattle, without which young men cannot marry (Catley, 2018).
The findings showed that most of the households (47.9%) in the cattle camps had informal education (Table 1). Most of the cattle keepers think that there is no need to go to school when they have a good number of herds. This finding is consistent with that of Gobena et al., 2014), who reported that the majority of the respondents never completed formal education.
Furthermore, the majority of the family members (57.9%) were in the range of 1–5 individuals (Table 1). In cattle camps, most people depend on milk. Due to the seasonality of food production, milk is a critical food at specific times of the year when other foods, e.g., cereals, are not readily available (Catley, 2018). Cows are unable to produce enough milk to satisfy the demand of their family; therefore, some family members stay at home without going to the cattle camps.
The majority of the households (58.3%) on cattle camps had lived for 1–5 years (Table 1). This is because cattle keepers move to higher lands in search of better grazing grounds and remain there for the remainder of the rainy season (Marchot, 1983).
Milk handling and hygienic practices
The results of this study are shown in Table 2. The majority of households in Highland (52.5%), Kadoro (48.8%), and Jebel Amianin (57.5%) cattle camps clean their hearths. This perception shows conscious awareness in regard to proper hygiene practices. Initial contamination starts from an unclean environment that can contaminate the udder surface of the cow. Milk is contaminated by microorganisms when not handled properly.
The findings also indicated that milk producers (100%) at the camps wash their hands before milking cows. Furthermore, after handwashing, the milk producers (100%) never dry their hands with any material such as a towel or a piece of cloth (Table 2). This finding is similar to that of Bekele et al., (2015), who reported that most milk producers in Dangila town in the western Amhara region washed their hands before milking. The washing of hands by all households in the study areas was due to the availability of water and the presence of awareness of milk-handling practices in the locations.
The results of the study revealed that, at the cattle camps (Highland, Kadoro, and Jebel Amianin), there was no washing of the udder or teats of the cows before milking (Table 2). They merely allowed their calves to suckle before milking. It is considered that the calves remove the dirt from the teats and facilitate the let-down of milk. Cleaning and washing the udder of cows before milking is vital for hygienic practices involving milk. The washing of the udder removes the dirty materials from the udder. This is because the udder of a cow has direct contact with dirty materials such as urine and dung and is amenable to feed refusal (Yilma, 2010). When the udders and teats of cows are not washed before milking, pathogenic and non-pathogenic microbes enter the milk during milking, leading to milk contamination and spoilage, which are associated health risks to consumers (Adugna & Eshetu, 2021)
Table 2
Milking and sanitary practices at the cattle camp
Variable | Highland (n = 80) | Kadoro (n = 80) | Jebel Amianin (n = 80) | Overall (n = 240) |
Cleanliness of the milking place |
Very dirty | 1 (1.3) | 2 (2.5) | 1 (1.3) | 4 (1.7) |
Dirt | 5 (6.3) | 3 (3.8) | 5 (6.3) | 13 (5.4) |
Moderate | 32 (40) | 37 (44.9) | 28 (35) | 97 (40.4) |
Clean | 42 (52.5) | 38 (48.8) | 46 (57.5) | 126 (52.5) |
Hand washing before milking a cow |
Yes | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
No | 0 | 0 | 0 | 0 |
Material used to dry hands after washing hand |
Towel | 0 | 0 | 0 | 0 |
Own cloth | 0 | 0 | 0 | 0 |
No drying | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
Washing of udder or teat before milking |
Yes | 0 | 0 | 0 | 0 |
No | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
The number in the bracket is the percentage of the respondents from the three locations. n = number of respondents |
According to Table 3, the findings of the study indicated that milking containers at the three cattle camps, Highland (98.7%), Kadoro (93.7%), and Jebel Amianin (99.2%), were properly cleaned. Hygienic practices related to cleaning milking equipment and the frequency of cleaning are among the major factors affecting the quality of milk and milk products. Milk can easily be contaminated by microorganisms if unhygienically handled.
Most of the households at the cattle camps in Rejaf East (93.4%) used plastic containers for milking and storage of milk, and some households used aluminium (5.8%) and traditional containers (0.8%) (Table 3). These findings are in line with reports from the Ezrha district of the Gurage Zone, where all of the respondents used plastic containers as milking materials (Abebe Bereda, 2012).
Table 3
Milk handling equipment and hygienic at the cattle camp
Variable | Highland (n = 80) | Kadoro (n = 80) | Jebel Amianin (n = 80) | Overall (n = 240) |
Cleanliness of the milking containers |
Very dirty | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
Dirt | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
Moderate | 1 (1.3) | 1 (1.3) | 0 (0) | 2 (0.8) |
Clean | 79 (98.7) | 79 (98.7) | 80 (100) | 238 (99.2) |
Equipment used for milking and storage of milk |
Aluminium | 3 (3.8) | 5 (6.3) | 6 (7.5) | 14 (5.8) |
Plastic | 75 (93.8) | 75 (93.7) | 74 (92.5) | 224 (93.4) |
Tradition utensil (gourd) | 2 (2.5) | 0 (0) | 0 (0) | 2 (0.8) |
Handwashing and milking containers |
Water & soap/detergent | 17 (21.3) | 24 (30) | 28 (35) | 69 (28.8) |
Water | 63 (78.7) | 56 (70) | 52 (65) | 171 (71.2) |
Frequency of cleaning containers |
| Once | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
Twice | 74 (92.5) | 76 (95) | 78 (97.5) | 228 (95) |
Thrice and above | 6 (7.5) | 4 (5) | 2 (2.5) | 12 (5) |
Source of water for washing | | | | |
Tap | 7 (8.8) | 7 (8.8) | 6 (7.5) | 20 (8.3) |
Well | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
Pond | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
River | 28 (35) | 35 (43.8) | 52 (65) | 115 (47.9) |
River and tap | 45 (56.2) | 38 (47.4) | 22 (27.5) | 105 (43.8) |
Consumption of unboiling raw milk |
Yes | 80 (100) | 67 (83.8) | 71 (88.8) | 218 (90.8) |
No | 0 (0) | 13 (16.2) | 9 (11.2) | 22 (9.2) |
Access to milk cooling system |
Yes | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
No | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
Know the means of preserving milk |
Yes | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
No | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
Preservation of milk |
Yes | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
No | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
The number in the bracket is the percentage of the respondents from the three locations. n = number of respondents |
Therefore, cleaning and draining equipment after each milking is important for reducing microbial contamination in milk. Milk producers should pay particular attention to the type and purity of the milking equipment they use for milking. Metal containers are better because they are easy to clean and disinfect (EAS, 2006). Therefore, they should be cleaned after each milking to reduce bacterial contamination before the next milking. This protects consumers’ health from milk-borne diseases and reduces milk spoilage.
However, the majority of the households in the three cattle camps (71.2%) washed their hands and milk containers with water only, and the remaining households (28.8%) used soap/detergent water (Table 3). Washing hands and milking containers with soap and clean water and drying hands with clean pieces of cloth, tissue paper, or clean towel before milking a cow are some of the proper milk handling practices (Yohannis, et al., 2015). Soap, as a detergent, is effective at removing dirt, grime, and bacteria. Therefore, cleaning hands with soap helps to remove bacteria and other microbes from hands. Thus, pathogenic microbes contaminating the hands should be prevented from contaminating the milk during milking. Dirt and wet hands may result in high microbial contamination and spoilage with associated health risks to consumers. Washed milking containers with water only without soap. This may lead to milk contamination, resulting in health risks for consumers.
The results of this study are shown in Table 3. The majority of the households (95%) on the cattle camps clean their milk containers twice a day. The main source of water used at the cattle camps for milking was river (47.9%) and taps (43.8%). The level of water used and the frequency of cleaning reduce contamination and spoilage of milk from milking containers. These results are similar to those of Saba, (2015), who reported that the majority of the respondents in the Ejerie district cleaned milking containers twice a day. When milk is not cleaned and disinfected effectively, it can become wholly contaminated from bacteria in containers (Yohannis, et al., 2015).
The highest percentage (90.8%) of the cattle keepers at the camps consumed unboiled raw milk (Table 3). It is important to note that boiling milk kills pathogenic microorganisms and helps preserve the milk for later use. Therefore, pastoral communities in Rejaf East Payam consuming unboiled raw milk expose them to milk-borne diseases such as brucellosis, campylobacteriosis, cryptosporidiosis, listeriosis, and salmonellosis (Jusupović & Šljivo, 2017).
The findings of the present study also indicated that there was no access to a cooling system (100%) for the three cattle camps. Furthermore, all of the cattle keepers (100%) never knew any means of preserving milk in the cattle camps; therefore, they did not preserve (100%) their milk. Cooling of milk prevents it to stay for long time, and preservation of milk prevents milk contamination and spoilage. When the milk is left at a high temperature for a long time, it can easily go bad; therefore, it is good to keep it cool immediately after being milked (Andreoletti et al., 2009). The rate of growth of spoilage bacteria is reduced by low temperatures (EAS, 2006).
Awareness and training of cattle keepers
The results of this study, shown in Fig. 2, revealed that more than half of the households in the cattle camp in the Highland (70%), Kadoro (77.5%), and Jebel Amianin (67.5%) lack awareness of milk-borne diseases.
In contrast, Weldekidan et al., (2019) reported that half of the respondents described tuberculosis as one of the most common milk-borne diseases among the farmers of the Mendefera Dairy Cooperative Union, Eritrea. Awareness of milk-borne diseases is important in cattle camps because it allows milk producers to adopt safe measures and hygienic practices, including handwashing before milking, washing udder and teats, using clean milking containers, and drinking boiled raw milk (FAO, 2013). This may improve the quality of milk and make it safe for consumption.
The results of this study, shown in Fig. 3, indicated that, in the cattle camps, the common milk-borne diseases known by the cattle keepers with awareness of milk-borne diseases were TB, brucellosis, and leptospirosis. The majority of the cattle keepers at the cattle camps in the Highlands (60%), Kadoro (70%), and Jebel Amianin (66.3%) mentioned brucellosis as one of the most common milkborne diseases in these areas.
The results of this study are shown in Fig. 4. The majority of the households in Highland (90%), Kadoro (75%), and Jebel Amianin (81.2%) cattle camps did not receive awareness or training on milk-borne diseases. The few households in the Highland (10%), Kadoro (25%), and Jebel Amianin (18.8%) cattle camps received awareness and training. These findings agree with other reports from Zimbabwe (Saba, 2015) and Tanzania (Shija, 2013), which also reported a low level of knowledge of awareness of milk handling practices. Awareness and training on milk handling practices are protective measures that should be taken constantly throughout cattle camps. This will improve milk quality and safeguard the health of consumers from milk-borne diseases.
The results of this study, shown in Table 4, indicated that, regarding training at the cattle camp, the majority of the households were trained on milk handling and hygiene (99.2%), and few were trained on milk spoilage (0.8%). Furthermore, the results of the study indicated that, in the three cattle camps, no training was conducted on fodder production versus milk production or fodder enhancement.
Table 4
Training and awareness of cattle keepers
Training item | Highland (n = 80) | Kadoro (n = 80) | Jebel Amianin (n = 80) | Overall (n = 240) |
Fodder vs milk production | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
Milk handling and hygiene | 79 (98.7) | 79 (98.7) | 80 (100) | 238 (99.2) |
Milk spoilage | 1 (1.3) | 1 (1.3) | 0 (0) | 2 (0.8) |
Fodder enhancement | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
The number in the bracket is the percentage of the respondents from the three locations. n = number of respondents |
Milk marketing
The results of this study are shown in Table 5. Almost all the households in the Highland (91.3%), Kadoro (93.8%), and Jebel Amianin (91.3%) cattle camps sell their milk. However, milk sold is not subjected to quality tests before sale, as indicated by 100% of the respondents in the cattle camps. The findings of the present study also indicated that 10% of the households in the study area experienced milk rejection at the sale point due to spoilage. When the milk is spoiled, the households in the cattle camp either pour it out or give it to the dog. The rejection of milk during sale is due to milk contamination, which may be attributed to improper milk handling practices, such as not washing udder, unclean milking containers, or unclean environments (Adugna & Eshetu, 2021). Milk produced under nonhygienic conditions may lead to a high microbial load, which may lead to milk spoilage and thus milk rejection.
Table 5
Variable | Highland (n = 80) | Kadoro (n = 80) | Jebel Amianin (n = 80) | Overall (n = 240) |
Sell of milk |
Yes | 73 (91.3) | 75 (93.8) | 73 (91.3) | 221 (92.1) |
No | 7 (8.7) | 5 (6.2) | 7 (8.7) | 19 (7.9) |
Subjected milk to quality test before selling |
Yes | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
No | 80 (100) | 80 (100) | 80 (100) | 240 (100) |
Experienced milk rejection | | | | |
Yes | 4 (5) | 3 (3.8) | 1 (1.3) | 8 (10) |
No | 76 (95) | 77 (96.2) | 79 (98.7) | 232 (90) |
The number in the bracket is the percentage of the respondents from the three locations. n = number of respondents |
Challenges faced by cattle keepers at cattle camps
The results of this study are shown in Table 6. These findings illustrate the challenges faced by cattle keepers at cattle camps.
Table 6
Challenges faced by cattle keepers
Challenges | Percentages (%) |
Animal diseases (mastitis, East Coast fever, etc.) | 25.4 |
Insufficient veterinary drugs | 18.8 |
Lack of qualified veterinary doctor | 9.6 |
Insecurity in the area | 7.1 |
Cattle raiding (rustling) | 6.3 |
Abduction of children and women | 6.3 |
Conflict between the farmers and cattle keepers | 6.3 |
Lack of supervision from the government | 5 |
Lack of milk cooling facility at the cattle camp | 4.6 |
Poor tradition milking practices | 4.2 |
Children at the cattle camp have no access to education | 4.2 |
Drought | 2.5 |
Physicochemical composition of milk from Rajaf East, South Sudan
Milk fat
The results of this study (Table 7) indicated that the milk fat content varied among the three cattle camps (Highland, Kadoro, and Jebel Amianin). The highest milk fat content was recorded in milk from Jebel Amianin Boma (8.72 ± 1.52%), while the lowest was recorded in milk from the Kadoro cattle camp (6.83 ± 1.23%). One-way ANOVA indicated a significant difference (p = 0.000008) in the fat content of the milk from the three locations. However, the overall mean value of the milk fat in the study area was 7.76 ± 1.47%. Compared to the East African standards, the results showed that the mean milk fat content of milk from the three locations was above the set standard of not less than 3.25%, which indicated that on average, the milk conformed to the standard. Individual sample analysis indicated that all the milk samples (100%) from the Rejaf East Payam conformed to the standard milk fat content (Table 8). Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.150) in the milk fat content of the different cattle breeds in the Rejaf East Payam (Table 7).
Solid non-fat (SNF)
The results of this study indicated that there was no variation in the solid non-fat milk content in the three cattle camps of Rejaf East Payam (Table 7). However, the highest SNF content was recorded in milk from Jebel Amianin Boma (7.73 ± 0.23%), while the lowest was recorded in Highland Boma (7.63 ± 0.36%). One-way ANOVA indicated that there was no significant difference (p = 0.379) in the milk SNF content among the three locations. The overall mean SNF in the study area was 7.68 ± 0.26%. When compared to the East African standards, the results showed that the mean SNF content of milk from the three locations was less than the set standard of no less than 8.5%, which indicated that on average, the SNF content of milk does not conform to the standard. Individual sample analysis indicated that 100% of the milk samples from Rejaf East Payam did not conform to the standard SNF content (Table 8). Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.077) in the SNF content of the milk from different cattle breeds in Rejaf East Payam (Table 7).
Milk density
The results of this study indicated that there was no variation in milk density among the Highland, Kadoro, and Jebel Amianin populations (Table 7). The milk density recorded in the three locations was the same (1.03 ± 0.0 g/ml). One-way ANOVA indicated that there was no significant difference (p = 0.353) in the milk density among the three locations. However, the overall mean value of the milk density in the study area was 1.03 ± 0.00 g/ml. When compared to the East African standards, the results showed that the mean milk density from the three locations was within the set standard range (1.028–1.036 g/ml), which indicated that on average, the milk density conforms to the standard density. Individual sample analysis indicated that only 38.7% of the milk samples from the region of Rejaf East Payam met the standard (Table 8). Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.173) in the milk density among the different cattle breeds in the Rejaf East Payam (Table 7).
Lactose content
The results of this study indicated that there was no variation in the lactose content of milk from the three cattle camps of the Rejaf East Payam (Table 7). The lactose content in the milk from Kadoro and Jebel Amianin was the same (4.21 ± 0.09%), except for that from Highland Boma (4.18 ± 0.20%). One-way ANOVA indicated that there was no significant difference (p = 0.462) in the lactose content of milk from the three locations. However, the overall mean value of the milk lactose content in the study area was 4.21 ± 0.15%. Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.076) in the lactose concentration among the different cattle breeds in the Rejaf East Payam breed (Table 7). Lactose is the main carbohydrate in milk, and it is responsible for the osmotic equilibrium between blood and the alveolar lumen in the mammary gland. The average lactose content of normal cow’s milk is 4.8% (Costa, et al, 2019). The findings of this study indicated that the lactose concentration was less than 4.8%. The lactose content of cow’s milk varies with the breed of cow, individual animals, udder infection (mastitis depresses the secretion of lactose), and stage of lactation (CUDFS, 2011)
Table 7
Variation in the physicochemical quality of milk according to location
Physicochemical parameters | Location | | Breed | Overall mean | EAC Standard |
Highland (n = 25) | Kadoro (n = 25) | Jebel Amianin (n = 25) | | Lughbara (n = 32) | Nilotic (n = 42) |
Fat (%) | 7.72 ± 1.00a | 6.83 ± 1.23b | 8.72 ± 1.52c | | 8.06 ± 1.56a | 7.61 ± 1.42a | 7.76 ± 1.47 | ≥ 3.25 |
SNF (%) | 7.63 ± 0.36a* | 7.68 ± 0.15a* | 7.73 ± 0.23a* | | 7.64 ± 0.28a | 7.70 ± 0.25a | 7.68 ± 0.26 | ≥ 8.50 |
Density (g/ml) | 1.03 ± 0.0a | 1.03 ± 0.00a | 1.03 ± 0.00a | | 1.03 ± 0.00a | 1.03 ± 0.00a | 1.03 ± 0.00 | 1.028–1.036 |
Lactose (%) | 4.18 ± 0.20a | 4.21 ± 0.09a | 4.21 ± 0.09a | | 4.18 ± 0.16a | 4.22 ± 0.14a | 4.21 ± 0.15 | - |
Protein (%) | 2.79 ± 0.12a | 2.80 ± 0.06a | 2.82 ± 0.08a | | 2.79 ± 0.10a | 2.81 ± 0.08a | 2.81 ± 0.09 | - |
Freezing point (0C) | -0.51 ± 0.03a* | -0.51 ± 0.03a* | -0.52 ± 0.02 | | -0.51 ± 0.03a | -0.51 ± 0.03a | -0.51 ± 0.03 | -0.52 to -0.55 |
pH | 6.55 ± 0.21a | 6.61 ± 0.21a | 6.63 ± 0.21a | | 6.59 ± 0.21a | 6.60 ± 0.21a | 6.60 ± 0.21 | 6.6–6.9 |
In the respective rows, the means that share a letter are not significantly different (p > 0.05). Means with* do not conform to the East African Standard n = number of raw milk samples |
Table 8
Conformance to the East African Standard
Physicochemical parameter | Highland (n = 25) | Kadoro (n = 25) | Jebel Amianin (n = 25) | | Overall (%) |
Conform (%) | Not conform (%) | Conform (%) | Not conform (%) | Conform (%) | Not conform (%) | | Conform (%) | Not conform (%) |
Fat (%) | 100 | 0 | 100 | 0 | 100 | 0 | | 100 | 0 |
SNF (%) | 0 | 100 | 0 | 100 | 0 | 100 | | 0 | 100 |
Density (g/ml) | 32 | 68 | 40 | 60 | 44 | 56 | | 38.7 | 61.3 |
Lactose (%) | - | - | - | - | - | - | | - | - |
Protein (%) | - | - | - | - | - | - | | - | - |
Freezing point (0C) | 36 | 64 | 32 | 68 | 60 | 40 | | 36 | 64 |
pH | 52 | 48 | 56 | 44 | 48 | 52 | | 52 | 48 |
n = number of raw milk samples |
Milk protein
The results of this study indicated that there was no variation in milk protein content at different locations (in the Highland, Kadoro, or Jebel Amianin region) in the Rejaf East Payam (Table 7). The milk protein concentrations recorded in the Highland, Kadoro, and Jebel Amianin populations were 2.79 ± 0.12%, 2.80 ± 0.06%, and 2.82 ± 0.08%, respectively. One-way ANOVA indicated that there was no significant difference (p = 0.457) in the protein content of the milk from the three locations. However, the overall mean value of the milk protein in the study area was 2.81 ± 0.09%. Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.245) in the milk protein content from different cattle breeds in the Rejaf East Payam (Table 7).
Freezing point
The results of this study indicated that there was variation in the milk freezing point at three locations (Highland, Kadoro, and Jebel Amianin) in Rejaf East Payam (Table 7). The highest milk freezing point was recorded in the milk samples from Jebel Amianin Boma (-0.52 ± 0.020C), while the lowest freezing points were recorded in those from Highland Boma (-0.51 ± 0.030C) and Kadoro Boma (-0.51 ± 0.030C). One-way ANOVA indicated that there was no significant difference (p = 0.335) in the freezing point of the milk from the three locations. However, the overall mean value of the milk freezing point in the study area was − 0.51 ± 0.030C. Compared to the East African standards, the results showed that the mean freezing point of milk from the three locations in some milk samples was below the set standard range (-0.52 to -0.550C), which indicated that, on average, the freezing point of milk does not conform to the standard. Individual sample analysis indicated that 64% of the milk samples from Rejaf East Payam did not conform to the standard (Table 8). Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.273) in the freezing point of the milk from different cattle breeds in Rejaf East Payam (Table 7). Due to the lack of conformance with the East African Standards, cattle keepers should be trained on animal feeding practices that can improve the solid-non-fat milk content. If the common market is established for East African communities. The milk produced within the country will not be accepted in the common market, resulting in economic loss to the milk producers and traders as well as in the income tax for South Sudan.
Milk pH
The results of this study indicated that there was no variation in the milk pH at three locations (Highland, Kadoro, and Jebel Amianin) in Rejaf East Payam (Table 7). The highest milk pH was recorded for the milk from Jebel Amianin Boma (6.63 ± 0.21), while the lowest was recorded for the milk from Highland Boma (6.55 ± 0.21). One-way ANOVA indicated that there was no significant difference (p = 0.335) in the milk pH among the three locations. However, the overall mean value of the milk pH in the study area was 6.60 ± 0.21. When compared to the East African standards, the results showed that the mean milk pH from the three locations of milk samples fell within the set standard range (6.6–6.9), which indicated that, on average, the pH of the milk samples conformed to the standard. Individual sample analysis indicated that 52% of the milk samples from Rejaf East Payam met these criteria (Table 8). Considering the cattle breeds, the results indicated that there was no significant variation (p = 0.664) in the freezing point of the milk from different cattle breeds in Rejaf East Payam (Table 7).
Variation in milk physicochemical parameters
In this study, principal component analysis (PCA) revealed three locations (Highland, Jebel Amianin, and Kadoro). As the first three PCs generated from this analysis had eigenvalues > 1 and accounted for 81.4% of the total variance in the dataset, these three PCs were retained. These three PCs were then subjected to varimax rotation to bring them into closer alignment with the original variables. The varimax rotated factor loadings, which represent correlations between PCs and the original variables, are shown in Table 9 (varimax rotated PC factor loadings). Loadings with an absolute value greater than 0.500 (shown in bold type) represent a strong influence. PC1 was strongly correlated with the following “raw milk” physicochemical parameters: SNF, lactose, and protein. PC2 was strongly positively correlated with fat. PC3 is positively correlated with pH. The results of the principal component analysis generally indicated that there was no variation in the physicochemical quality of the milk from the three locations, i.e., the Highland, Jebel Amianin, and Kadoro locations, based on the physicochemical parameters. This may be attributed to the fact that Rejaf East Payam has the same breed as well as the same feeding regime.
Table 9
Varimax-rotated principal component factor loadings for raw milk physicochemical parameters
Variable | PC1 | PC2 | PC3 |
Fat (%) | -0.085 | 0.799 | -0.286 |
SNF (%) | 0.529 | -0.033 | 0.052 |
Density (g/ml) | 0.200 | -0.313 | -0.495 |
Lactose (%) | 0.529 | -0.065 | 0.045 |
Protein (%) | 0.503 | -0.001 | 0.095 |
Freezing point (0C) | 0.363 | 0.442 | -0.259 |
pH | 0.090 | 0.251 | 0.770 |
Loadings with an absolute value greater than 0.500 are shown in bold type. | |
Relationships between the physicochemical parameters of raw milk
The results of this study, shown in Table 10, indicated that there was a negative weak correlation between SNF and Fat (-0.182). The correlation between SNF and fat intake was not statistically significant (p = 0.118).
The findings showed that density exhibited a weak negative correlation with fat intake (-0.084), but this correlation was not statistically significant (p = 0.473). Additionally, density exhibited a weakly positive correlation with SNF (0.208), and the correlation was statistically significant (p = 0.009).
As shown in Table 10, lactose was weakly negatively correlated with fat intake (-0.208); however, the correlation was not statistically significant (p = 0.073). The results showed that lactose content was weakly positively correlated with density (0.298). The correlation was statistically significant (p = 0.000). Furthermore, lactose had a strong positive correlation with SNF (0.987), and the correlation was statistically significant (p = 0.009).
The results of this study indicated that protein levels were weakly negatively correlated with fat intake (-0.136) but not significantly correlated with fat intake (p = 0.246). Protein was weakly positively correlated with density (0.233) but was strongly positively correlated with SNF (0.891) and lactose (0.908). This demonstrated that an increase in one of the corresponding parameters led to an increase in the other parameter, and a decrease in the other parameter led to a decrease in the other parameter. This difference may be attributed to the high positive genetic correlation between lactose yield and protein yield (Haile-Mariam & Pryce, 2017). The correlations between protein and density, SNF, and lactose were statistically significant at the respective p values (p = 0.000, p = 0.044, and p = 0.000).
Table 10
Correlations between raw milk physicochemical parameters
| Fat (%) | SNF (%) | Density (g/ml) | Lactose (%) | Protein (%) | Freezing point (0C) |
SNF (%) | -0.182 | | | | | |
Density (g/ml) | -0.084 | 0.298** | | | | |
Lactose (%) | -0.208 | 0.987** | 0.298** | | | |
Protein (%) | -0.136 | 0.891** | 0.233* | 0.908** | | |
Freezing point (0C) | 0.231* | 0.583** | 0.144 | 0.562** | 0.512** | |
pH | 0.029 | 0.157 | -0.111 | 0.129 | 0.170 | 0.010 |
*. Correlation is significant at the 0.05 level (2-tailed). |
**. Correlation is significant at the 0.01 level (2-tailed). |
The results of this study are shown in Table 10. The freezing point was weakly related to fat (0.231) and density (0.144). The correlation between the freezing point and fat density was statistically significant (p = 0.046) but not statistically significant (p = 0.217). The freezing point was moderately positively correlated with SNF (0.583), lactose (0.562), and protein (0.512). Therefore, the correlation was statistically significant (p < 0.05) between the freezing point and the SNF, protein, or lactose concentration.
Table 10 shows that pH was weakly positively correlated with fat (0.029), SNF (0.157), lactose (0.129), protein (0.170), and freezing point (0.010) but weakly negatively correlated with density (-0.111). However, the correlation was not statistically significant (p > 0.05).
Microbial contamination of raw milk at cattle camp
Variation in the microbial quality of milk according to location
These findings indicated that there was no variation in the TVC of the milk samples from the three Bomas in Rejaf East Payam (Table 11). The highest TVC was recorded in milk samples from Highland Boma (5.83 ± 0.92 logCFU/ml), while the lowest was recorded in Kadoro Boma (5.67 ± 0.44 logCFU/ml). One-way ANOVA indicated that there was no significant difference (p = 0.686) in the TVC of milk samples from the three locations. However, the overall mean TVC in the milk samples from the study area was 5.77 ± 0.65 logCFU/ml.
The results of this study showed that there was no variation in the TCC of milk samples from three cattle camps from the Rejaf East Payam (Table 11). The highest TCC was recorded in milk from Kadoro Boma (4.64 ± 0.213 logCFU/ml), while the lowest was recorded in milk from Highland Boma (4.54 ± 0.42 logCFU/ml). One-way ANOVA indicated that there was no significant difference (p = 0.491) in the TCC among the three locations. However, the overall mean TCC of the milk samples from the study area was 4.60 ± 0.27 log CFU/ml.
Table 11
Variation in microbial quality (mean ± SD) of milk according to location
Microbial parameter | Location | Overall |
Highland | Kadoro | Jebel Amianin |
TVC (log CFU/ml) | 5.83 ± 0.92a | 5.67 ± 0.44a | 5.81 ± 0.51a | 5.77 ± 0.65 |
TCC (log CFU/ml) | 4.54 ± 0.42a | 4.64 ± 0.21a | 4.62 ± 0.13a | 4.60 ± 0.27 |
TSC (log CFU/ml) | 2.53 ± 0.31a | 2.502 ± 0.48a | 2.43 ± 0.55a | 2.49 ± 0.44 |
In the respective rows, the means that share a letter are not significantly different (p > 0.05) TVC = Total Viable Count, TCC = Total Coliform Count, and TSC = Total Staphylococcus Count |
The microbial loads recorded in this study are higher than the TVC and TCC values reported by Shija, (2013) in the Lushoto and Handeni districts of Tanzania. The presence of high loads of TVC and TCC in milk samples indicates that the milk has been contaminated as a result of poor hygiene, the use of milking containers that are not properly cleaned, unsanitary milking practices, and the use of contaminated water for cleaning containers (Adugna & Eshetu, 2021). On the other hand, the microbial loads in the milk samples of Rejaf East, South Sudan, were lower than the TVC and TCC reported from Omdurman and Khartoum, Sudan (Nahla et al., 2015).
The results of this study indicated that there was no variation in the TSC of milk samples from different Bomas of the Rejaf East Payam (Table 11). The highest TSC concentration in the milk sample was recorded in milk from Highland Boma (2.53 ± 0.31 logCFU/ml), while the lowest was recorded in Jebel Amianin Boma (2.43 ± 0.55 logCFU/ml). One-way ANOVA indicated that there was no significant difference (p = 0.926) in the TSC among the three locations. However, the overall mean TSC concentration in the study area was 2.43 ± 0.44 log CFU/ml.
The results of this study also indicated the presence of high loads of Staphylococcus aureus (TSC) in the milk samples. The high levels of contamination probably originated from the cows’ udder since all households never washed the udders or teats of cows before milking, and Staphylococcus aureus is found within the environment and is carried by approximately half of the human population. The lack of cooling facilities on cattle camps is another factor that might increase the abundance of Staphylococcus aureus in milk (Andreoletti et al., 2009). When milk is not refrigerated, enterotoxigenic Staphylococcus aureus strains can grow and produce enterotoxin. The high count of Staphylococcus aureus is due to poor personal hygiene practices (Abdalla, 2011). A lack of knowledge about clean milk production is the factor that may have contributed to the poor hygienic quality of milk from the cattle camps of Rejaf East Payam. The presence of Staphylococcus aureus may lead to health risks from milk-borne diseases as well as milk spoilage
Variation in TVC grade, TCC grade, and TSC with location
Milk grade based on the TVC and location
The results of this study, shown in Table 12, revealed that milk samples from Kadoro Boma had the highest percentage (19%) of milk Grade I based on the TVC. The lowest grade I percentage (12.5%) was recorded for Jebel Amianin Boma. Highland Boma had the highest percentage (75%) of Grade II milk, and the lowest percentage (52.3%) of Grade II milk was recorded in Kadoro. Among those with a milk grade III, the highest percentage (28.7%) was recorded in Kadoro Boma. The lowest percentage (10%) of Grade III was recorded for highland Boma.
Milk Grade Based on the TCC and Location
The results of this study, shown in Table 12, indicated that none of the milk samples from Jebel Amianin were very good grade milk according to the TCC. Highland Boma and Kadoro Boma had good-grade milk (5% and 4.9%, respectively). Highland and Jebel Amianin Boma had the highest percentage (75%) of good-grade milk, and the lowest percentage (57.1%) of good-grade milk was recorded in Kadoro. For bad-grade milk, the highest percentage (38%) was recorded in Kadoro Boma, while the lowest percentage (20%) was recorded in Highland Boma.
Table 12
Variations in TVC and TCC grade with location
| Milk grade | Location | | |
Highland | Kadoro | Jebel Amianin | Overall (%) | |
TVC (CFU/ml) | < 200000 | I | 15 | 19 | 12.5 | 15.5 | |
200000–1000000 | II | 75 | 52.3 | 66.7 | 65.7 | |
> 1000000 | III | 10 | 28.7 | 20.8 | 19.8 | |
Total | | | | | | 100 | |
TCC (CFU/ml) | < 1000 | Very good | 5 | 4.9 | 0 | 3.3 | |
1000–50000 | Good | 75 | 57.1 | 75 | 69.0 | |
> 50000 | Bad | 20 | 38 | 25 | 27.7 | |
Total | | | | | | 100 | |
TSC | | Presence | 20 | 28 | 24 | 24 | |
| Absence | 80 | 72 | 76 | 76 | |
Total | | | | | | 100 | |
TVC = Total Viable Count, TCC = Total Coliform Count, and TSC = Total Staphylococcus Count |
Milk-grade TSC according to location
The results of this study are shown in Table 4.12. Kadoro Boma had the highest percentage (28%) of TSC present in the milk samples, followed by Jebel Amianin Boma (24%). The lowest percentage (20%) of TSC was recorded in a milk sample from the Highland Boma.
Effect of handling practices and demographic factors on the microbial quality of milk
The milking handling practices included cleaning milking places, cleaning milking containers, and washing hands with water only or with soap; these practices were assessed at the cattle camps of Rejaf East Payam based on the East African Standards TVC and TCC grades (Figs. 6 and 7). The results showed that, when proper handling practices were observed, milk samples had better TVC and TCC grades, unlike improper milk handling practices, where milk samples had bad TVC and TCC grades. Milk produced under nonhygienic conditions leads to a high microbial load, thus resulting in health risks to consumers.
The study results (Figs. 6 and 7), on the relationship between training and the microbial quality of milk at the three cattle camps of Rejaf East Payam, indicated that milk samples obtained from the households who had received training on milk handling had better TVC and TCC grades than milk samples obtained from the households who had never received awareness and training where milk samples had bad TVC and TCC grades. This showed that awareness and training improved the microbial quality of the milk from the cattle camps.
The results in Fig. 6 on microbial quality versus the source of water used for cleaning the milking container revealed that milk samples in the containers washed with water obtained from the tap had better TVC grades than milk samples in the containers washed with water from river that had bad TVC grades. This showed that river water was most likely contaminated by faecal matter from humans or animals. Therefore, this calls for the provision of safe and clean water to pastoral communities of Rejaf East Payam.
Figure 7 shows that, in the milking container used, the milk samples from the aluminium containers had better microbial quality (TCC grade). On the other hand, the milk samples in the plastic containers had bad microbial quality (TCC grade). Metal containers are better because they are easy to clean and disinfect (EAS, 2006)