Goats play an important role in the socio-economic and cultural aspects of the rural population [1,2]. In comparison with other livestock species such as cattle, goats are renowned for their high ability to survive harsh environmental conditions [3,4] and their lower feed and capital requirements [5,6,7]. More so, goat milk fetches higher prices per litre compared to cow milk since the former has nutritionally desirable traits, such as lower concentrations of C12:0, C14:0, C16:0 and Na: K ratios, and higher concentrations of cis-polyunsaturated fatty acids (PUFA) compared to the latter [8]. Regarding meat from the goats, chevon contains less fat, fewer calories and higher levels of protein and iron in comparison with beef, pork, mutton and broiler meat [9,10]. South Africa has approximately 2 million goats of which 29.6% are in the Eastern Cape Province (Statistics South Africa (Stats SA), 2016). If proper attention is accorded to the farming of this livestock species, goats could become a vehicle for poverty alleviation and wealth creation [11]. Farmers in the communal areas of South Africa can accomplish this objective through devising strategies aimed at efficiently and effectively raising goats. Such strategies should, however, be grounded on the determination of the nutritional status of livestock. Knowledge of the nutritional status of livestock enables owners to neither underfeed their goats resulting in sub-optimal production nor overfeed them culminating in unnecessary costs that then translate into reduced profits. Body weights (BWs), body condition scoring (BCS) and blood metabolite profiling have been used to evaluate the nutritional status of livestock under communal breeding systems [12-14]. Although some farmers might use body weight as a determinant of nutrient status, the index can be misleading if not properly interpreted. In case of female animals, it is important to have information on which animals will be pregnant as this physiological status might exaggerate the actual weight of the animal. Even so, the situation is complicated by the possibility of single, twin or triplet foetuses in pregnant animals which might not give the true reflection of the weight of the animal. Another challenge associated with using the BW index is lack of access to weighing facilities in the communal areas [15]. On the other hand, body condition scoring (BCS) has been reported by various authors as the main indicator of nutritional status. This technique, however, has its disadvantages in that it only assesses subcutaneous fat and bias between the evaluators may influence the results, as such, it is subjective. It is under such scenarios that blood metabolites become relevant and are strongly recommended. Blood metabolite concentrations constitute an integrated index of the adequacy of nutrient availability in relation to nutrient utilization which involves the physiological state and lead to the known status of nutrients [16-18]. Knowledge of the metabolite profile is crucial in predicting, diagnosing, and preventing various nutritional, pathological, and metabolic disorders before adverse and irreparable conditions occur [19]. Among other factor, stress, age, sex, genetics, management, type of housing, physiological status and other environmental factors have been reported to have profound effects on BW, BCS and biochemical profiles in small ruminants [17,20,21]. It is crucial, therefore, to specify the individual values of metabolic profile and acid-base status for each goat breed. It is documented that there is a breed-specific close relationship between BCS and particular blood metabolites [22]. In our knowledge a few studies have been conducted on use of blood metabolites as indicator of nutritional statuses of goats raised under communal goat breeding systems. This investigation, therefore, was conducted to determine if a relationship exists among BWs, BCS and blood metabolites of non-descript goats reared in Santombe village of the Eastern Cape Province. The study was conducted in Santombe village that falls under Umzimvubu Local Municipality of the Alfred Nzo district. The village is situated 3km North of Mount Ayliff town at 30°49'25"S and 27°21'93"E. Its elevation is 1 330 m above the sea level and most of the land is gently undulating. The mean annual rainfall is 780 mm whilst the mean annual temperatures that ranges from 13°C to 29°C. The village covers an area of 719 ha of which a large (>80%) area is rangeland. The rangelands at Santombe are shared by members of the community and grazed continuously with no restrictions on stocking rates. The vegetation surrounding Santombe is a mixture of East Griqualand Grassland and Drakensberg Foothill Moist Grassland [23] with browsing capacity of 1218 goats per annum. The main economic activities in the study area are livestock rearing and sand mining. The estimated livestock population is 1800, of which 64% are goats, followed by sheep (27%) and cattle (9%).Thirty-three (27 females with kids, 6 males) non-descript 6 and 8 tooth goats were randomly selected from one household (Table 1) using a convenient sampling technique. The description by [24] that healthy goats be given BCS of 2.3 to 4.0 was utilized. Therefore, the selected goats were considered clinically health animals during data collection as their BCS ranged from 3.0 to 4.0. The goats were kraaled at night and browsed on rangelands from 0800 hrs to 1500 hrs with ad libitum availability of water. The main reason for the goats to be kraaled at 1500 hrs was to enable suckling kids that remained in the kraal, to have access to their mothers early. Multi-sire breeding system was the dominant practice employed in the village, where bucks were running with does throughout the year. Plastic ear tags were used to monitor and facilitate accurate recording. The main source of feed was grass species and browse plants found in the rangelands. The major vegetation of the area composed of herbaceous plant species that included Alloteropsis appendiculatus, Aristida congesta, Cynodon dactylon, Cymbopogon plurinodis, Elionorus muticus, Eragrostischloromelas, Eragrostis curvula, Eragrostis lehmanniana, Eragrostis plana, Heteropogon contortus, Hyperhenia hirta, Paspalam dilatatum and Sporobulus africanus. The predominant woody plants species that goats had at their disposal during the experiment were Aloe ferox, Artemisia afra, Coddia rudis, Diospyrosis lysiodes, Ehretia rigida, Euclea undulata, Grewia occidentalis, Maytenus polycantha, Olea europaea, Rhamnus prinoides, Sersia lucida, Scutia myrtina, Vachellia karoo and Vachellia mearnsii. Body weights, Body Condition Scores and blood samples were collected once from each of the goats during the spring season, in September 2020 from 0700hrs to 0900hrs. The body weights were recorded using a digital scale as indicated by [25].The BCS were scored on a scale of 1 to 5, with a score of 1 indicating a thin and emaciated goat whilst a condition of 5 indicated an obese goat as explained by [24]. The BCS was assigned not only through visual appraisal of an animal but through palpating the lumbar area, sternum and the ribs [26] as well. From each goat, a 10ml blood sample was collected from the jugular vein into tubes that contained no anticoagulants. The blood samples were transported to the laboratory in an ice pack for analysis of total protein (TP), urea, cholesterol, calcium, and magnesium and phosphorous. Total protein was analysed spectrophotometrically as described by [27]. The Colorimetric method was used to analyse calcium (Ca) phosphorus (P) and magnesium (Mg) [28]. For the analysis of urea and cholesterol (Chol), the enzymatic method was used. Data collected was subjected to the Statistical Analysis System version 9.1 (SAS, 2003). The effects of age and sex on BCS, BW and biochemical parameters (using a serum) were determined using the generalised linear model procedures (SAS, 2003). Correlation coefficients for BCS, BW and biochemical parameters were determined using PROC CORR procedures of SAS (2003). Mean separation of the least square means were performed using the PDIFF procedure. Statistical significance was tested at a 95 % level with all results with P<0.05 considered to be statistically significant. There were significant differences (P<0.001) in BW and BCS among sex and age groups as shown in (Table 2). With reference to each age group, the body weights of males were higher than for females. For BCS, significantly higher scores were observed in the male goats compared with the female counterparts in both age groups (Table 2). Values with different superscript within a row differ significantly (P<0.001) Body condition score (BCS); Body weight (BW); Female (F); Male (M) The serum concentration of TP and P were higher than the normal reference values but no significant differences were found either between the two age groups or between the sexes (Table 3). Urea, magnesium and calcium levels were within the reference range for healthy goats under field conditions with neither age nor sex effects on the levels of these metabolites as indicated in (Table 3). The results also revealed that cholesterol was significantly lower (P<0.01) in the female 6 tooth goats compared to the other groups. Values with different superscript within a column differ significantly (P<0.001).

Table 1: Age-groups of bucks and does sampled in the study area.

Table 2: Effect of age and sex on BW and BCS.

Table 3:  Blood metabolites and macro-mineral profile for non-descript goats of different sex and age groups.

Table 4: Correlation for BW, BCS, blood metabolites and macro-minerals among non-descript goats.

Reference Values: RV, Minimum: Min, Maximum: Max.

Correlation coefficients among BW, BCS and blood metabolites are shown in Table 4. Body weight was positively correlated to BCS (r=0.677; P<0.01). There, however, were no correlations (P>0.05) between BWs and several blood metabolites such as urea, Chol, Mg, P and Ca. Total protein was only positively correlated with BW (r=0.400; P<0.05) with no correlation between the TP and other blood parameters and BCS. Urea had no correlation with the other studied blood metabolites, BW and BCS. Cholesterol was positively correlated to BCS (r=0.402; P<0.05) and there was no correlation with the blood parameters and BW. As shown in (Table 4), BCS were not (P>0.05) correlated with TP, urea, Mg and calcium. Amongst the given parameters, magnesium was positively correlated with TP only. No correlation existed between phosphorus and the given parameters. The same observation was also detected regarding calcium the finding, from the current study, that males were heavier than female goats might be attributed to hormonal differences and environmental factors. Similar findings were reported by [29] from a research with the indigenous Maefur goats in Ethiopia, where males were found to be heavier than female goats (42.8 ± 6.6 kg versus 37.2 ± 4.1 kg) [30] also had similar findings from a research with the South African indigenous goat where 8 tooth male goats weighed 37.7 ± 7.17 kg whilst females of the same age weighed 31.4 ± 5.87 kg. There are challenges in communal farming set up; one of these is unavailability of weighing scales which assist in marketing live animals as well as selection for breeding purpose. The current study focused on investigation of relationship between BW, BCS and biochemical parameters in non-descriptive goats. Most blood metabolites showed results within the reference values reported by [31]. However, the values of TP, Chol and P were exceptions. The TP, Chol and P values were observed to be higher than the reference values. High TP levels might be due to dominance of acceptable browse species such as Diospyros and Cordia species which have a crude protein (CP) content above 8% [32] which is good for feed intake and optimum rumen function in goats. Alternatively, the elevation might be due to breed trait as reported by [33] among other indigenous small ruminant’ breeds. Research studies [34,35] have, however, revealed that total protein (TP) is known to increase due to some adverse scenarios such as dehydration, chronic inflammation and para- proteinaemia and access to feed containing excess protein. The observed positive correlation between TP and BW might indicate a desirable high level of TP. However, such elevated TP levels warrant investigation so that if they are due to an anomaly then such a condition can be corrected. The high P concentrations obtained in the current study might be due to the availability of browse plants such as Vachellia karoo which have calcium and phosphorus contents of around 1.73 and 0.13%, respectively [36]. The high levels might also be attributed to the fact that the experimental goats (30 to 36 months of age) were still growing [37] reported that the level of P decreases as the goats grow older since there is reduced capacity to assimilate P from the diet as the goats grow. Lack of an age effect on P with reference to goats under the current study could be due to the fact that the two age groups were close to each other. The findings that urea, magnesium and calcium were within the normal range could probably indicate that the goats had access to browse and forage that had enough of the minerals during the time the blood was sampled. There, however, is need to investigate if there will not be deficiencies in other seasons. Cholesterol plays an important role as an antioxidant and metabolism of cell-soluble hormones, hormone production,cortisone, bile formation, and fat soluble vitamins [3]. Cholesterol is of clinical importance when present in abnormal concentrations, like many other essential components of the body. Our current finding of the high Chol levels that are above the reference values, usually exhibits abnormalities in the synthesis, degradation and transport of related lipoprotein particles [38]. The high Chol levels, however, might emanate from the absence of excess dietary energy intake which results in the animal mobilizing body fat reserves [39,40]. The higher cholesterol levels in adult goats compared to values from young female goats can be attributed to the fact that the metabolite is a precursor to all sex steroid hormones and is, therefore, high in concentrations in adult livestock compared to young animals [41]. The significant correlation between BW and BCS indicates that body weight can be predicted based on body condition score. Similar results were observed by [12] on indigenous Nguni goats of South Africa. This indicates that the combination of BW and BCS would be a good indicator of live weight in goats in fields with no access to weighting scale. Contrary to this finding, [42] in Senegalese goats and [43] in indigenous Tswane goats reported a negative correlation between BW and BCS. Sufficient supply of protein is a very crucial factor for proper growth. The findings from this study shows a positive correlation (r=0.400; P<0.05) between TP and BW. This differs from the findings of [24] who reported that change in protein levels had no effect on body weight of Badali kids. Cholesterol is an energy source in all living organisms. Body energy reserves, mainly represented by body fat and muscle content, are important determinants of reproductive performance. Based on our results Chol had a positive correlation with body condition score. Similar results were found in previous studies [44,12]. Observed a negative correlation between BCS and Chol on indigenous Nguni goats. Non-genetic factors such as sex and age had a significant effect on body weight, body condition and some biochemical parameters of non-descriptive goats. There were strong positive relationships between BW, BCS, total protein, cholesterol and magnesium [45-49]. Therefore, BW, BCS and some blood metabolites could be good indicators of  nutritional status of goats in Santombe village. Further studies covering all the seasons, with larger sample sizes, are required to give better understanding of the effect of the studied parameters on the goats studied. Follow up investigation might include enzyme activities and parameters of the acid–base status to provide more in-depth information so as to explain situations that could not be explained by the current study.

Acknowledgement

The authors would like to acknowledge the farmers who willingly participated in this study and the Eastern Cape Department Agriculture Rural Development and Agrarian Reform for providing logistical support.

Authors Contributions

SM, NN, FRG, SW, TLT, MY and MSJ participated in designing the study. SM, NN, MSJ, ST, TK, SG, TT, LQ, BG, MM and BBM were involved in data collection. SM, FRG and MY did data analysis and interpretation of results. SM wrote the manuscript. FRG and NN revised the manuscript. All authors read and approved the final manuscript.

Conflict of Interest Declaration

No potential conflict of interest was reported by the authors.

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