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Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants

  • Ifeanyichukwu Princewill Ogbuewu EMAIL logo and Christian Anayo Mbajiorgu
Published/Copyright: March 13, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

Milk yield and components in small ruminants fed Saccharomyces cerevisiae (SC) have been investigated, but results were not consistent among investigators. Hence, this trial aimed to explore the efficacy of SC supplementation in improving milk yield and components (i.e., milk proteins, fat, lactose, total solids and ash) in small ruminants. A search performed in Scopus, PubMed and Google Scholar databases yield 1,826 studies, of which 26 met the inclusion criteria. Random-effects model was used to aggregate milk production variables. Meta-regression analysis examined the effect of the following moderators: SC type, ruminant type (sheep or goat), diet type, breed, duration of supplementation and supplementation levels on outcome measures. Subgroup analysis explored the influence of the following moderators: SC type and ruminant type on outcomes measures. SC had positive moderate effect on milk yield (standardised mean difference [SMD] = 0.72; p < 0.001; heterogeneity [I 2] = 73%) and small effect on milk proteins (SMD = 0.46; p = 0.004; I 2 = 83%), milk lactose (SMD = 0.17; p = 0.007; I 2 = 0%) and fat (SMD = 0.28; p = 0.016; I 2 = 70%). Subgroup analysis revealed that SC improved milk yield, lactose and proteins in lactating sheep and milk yield and fat in lactating goats. Our results show that moderators influenced the results of the meta-analysis and explained most of the sources of heterogeneity. In conclusion, SC should be included in small ruminant diets as it had small-to-moderate effects on milk yield and aspects of milk components.

Keywords: yeast; sheep and goats; milk variables; meta-analysis; meta-regression

1 Introduction

Small ruminants (sheep and goats) contribute immensely in improving food security (meat and dairy products) and represent the main source of income for smallholder farmers [1,2]. The demand for dairy products is increasing in developing countries due to rapid population growth, and this is envisaged to continue in the coming years [1]. Goat and sheep milk are vital sources of essential nutrients in human nutrition. Given the increasing consumer demand for high-quality milk and the fact that milk price is determined by milk quality, there is a need to improve milk production and quality using feed additives [3,4].

Saccharomyces cerevisiae (SC), an eco-friendly feed additive has been shown to encourage the proliferation of fibre-digesting microbes in the rumen (paunch), which in turn influence milk production and component yield in ruminants [3,5]. Three different types of SC utilised in ruminant nutrition are active SC (also called live SC), SC culture (also known as fermented SC, SC hydrolysate, scrap SC and SC extract) and inactive SC. Active SC contains pure culture of metabolically active cells, whereas inactive SC has no active cells and is commonly referred to as nutritional yeast. SC culture is a product of SC fermentation. Live SC works by reducing the concentrations of oxygen in the paunch, thereby assisting in the proliferation of bacteria that degrade fibre in the paunch [6,7]. Live yeast also secretes metabolites which bacteria that degrade fibre in the paunch used as nutrient sources [8]. Although SC culture lacks the ability to reduce the concentration of oxygen in the paunch, it produces metabolites that increase rumen microbial activity, alter its fermentation trend, enhance movement of nutrients to the abomasum and stimulate digestion processes [8,9]. SC feed additive promotes healthy rumen environment by inhibiting the growth of lactate-synthesizing microbes and supporting the multiplication of lactate-using microbes [10], which in turn transforms lactate to volatile fatty acids (VFAs) causing a drop in lactate content and a rise in ruminal pH [11,12]. Pinloche et al. [13] reported that SC encourages the growth of trans-11 hydrogenating bacteria in the paunch by inhibiting the number of lactate-producing microbes, which stimulates the production of trans-11 and cis-9, trans-11-conjugated linoleic acid (CLA) in the paunch, thereby aiding milk fat synthesis. Bauman et al. [14] also confirmed that SC modifies ruminal pH by stimulating the activities of ruminal biohydrogenation microbes. The action of SC on milk yield and component characteristics in small ruminants is, however, inconsistent [15,16,17,18,19,20,21,22,23] and may be influenced by factors such as SC type, quantity of SC added to the diets, analytical methods, parity and stage of lactation. In spite of the fact that the mechanisms by which SC increases milk production in small ruminants are not clear. SC may enhance milk yield and quality by increasing the growth of trans-11-hydrogenating bacteria in the rumen via competitive exclusion and antagonism, stabilisation of ruminal pH, improvement in dry matter intake and increase in nutrient digestion and absorption [3,24,25].

The utilisation of meta-analytical approach to resolve divergence in research findings across studies has been encouraged [26,27,28]. This is a statistical technique that employs explicit methods to pool results from published studies with conflicting findings to increase statistical power, detect patterns and identify research gaps that would not be possible in a single study [29,30,31]. However, literature on the meta-analytic effect of SC on performance of lactating small ruminants, to the best of our knowledge, is lacking. Therefore, the purpose of this trial was to perform a meta-analysis asking the research question: what is the effect of diets with and without SC on milk yield and component characteristics of small ruminants?

2 Materials and methods

2.1 Study design and data sources

This investigated adhered to the protocols of the preferred reporting items for systematic review and meta-analyses provided by Moher et al. [32] as shown in Figure 1. Other detailed protocol for conducting this study was not used. Authors methodically searched Scopus, Google Scholar and PubMed databases from February 4 to March 10, 2022, for published trials that examine the impact of SC on production indices in small ruminants. Our search was not constrained by publication year and language. Search terms were sheep, goats, SC, milk yield, milk ash, proteins, lactose, total solids and fat, while search queries were AND and OR. Reference sections of identified studies were also searched for related articles.

Figure 1 Study selection flow chart for the meta-analysis.
Figure 1

Study selection flow chart for the meta-analysis.

  1. Ethical approval: The conducted research is not related to either human or animal use.

2.2 Inclusion criteria

Selection criteria were centred on PICO criteria, where the letters in PICO stand for Population (i.e., small ruminants), Intervention (i.e., SC supplementation), Control group or comparison (i.e., diets with and without SC) and Outcomes (i.e., milk yield, proteins, lactose, fat, ash and total solids). Studies that reported the influence of diets with and without SC supplementation on at least one of the outcome measures in small ruminants were included in this analysis. For an article to be used for the investigation, the experimental diets must be free of antibiotics and other growth-enhancing agents. Study must report the mean, number of goats and sheep and dispersion metric such as standard error (SE), standard deviation (SD) or 95% confidence interval (CI). The initial search result yielded 1,826 articles. Figure 1 shows the overall screening process and number of studies excluded. A total of 1,595 studies were excluded due to duplication based on title and abstract review. One hundred and twenty-seven of the remaining 231 articles were removed after full-text screening due to the articles not being in small ruminant. Out of the 104 studies remaining, 78 were excluded for being review (10), not in measured outcomes (62) and having no extractable data (6). Twenty-six studies were used in the investigation (Figure 1). All identified studies, after exclusion of duplicates, were evaluated and disagreements resolve by consensus.

2.3 Data synthesis

Database of the 26 studies used for the investigation is presented in Table 1. Author’s name, publication year, study country, studied moderators (SC type [active, culture and inactive], ruminant type [sheep versus goat], breed, diet type [forage, concentrate or both], duration of SC supplementation and supplementation level) and measures of variance (SD, SE and 95% CI) were extracted from the 26 publications that were suitable for the study. Information on the mean values of our outcomes of interest for the SC and control groups and the number of animals included in the experimental and control groups were retrieved. SD was estimated from SE following the method of Higgins and Deeks [33] where SE was stated instead of SD. The extracted data were keyed into an excel sheet and transformed to a CSV file which is the only format recognised by the Open Meta-analyst for Ecology and Evolution (OpenMEE) software.

Table 1

Database of studies used for the meta-analysis

References Location Design Studied moderators Milk yield and milk components
Diet type SC type RT Breed DOS (day) Supplementation levels (SL)
Giger-Reverdin et al. [41] France RCBD F + C Live 1 Alpine/Saanen 42 0, 2 MY, MP, MF, ML, MA
Hadjipanayiotou et al. [15] Cyprus — F + C Culture 1, 2 Damascus +/Chios ++ 48 0, 5 MY, MP, MF, MTS, MA
Salama et al. [42] Spain — F + C Culture 1 ** 84 0, 10 MY, MP, MF, MTS
Abd El-Ghani [43] Egypt — F + C Culture 1 Zaraibi 120 0, 3, 6 MY, MP, MF, ML, MTS, MA
Stella et al. [44] Italy — F + C Live 1 Saanen 105 0, 0.2 MY, MP, MF, ML
Ahmed et al. [45] Egypt — F + C Culture 1 Zaraibi 180 0, 1, 2 MY, MP, MF, ML, MTS, MA
Mahrous et al. [46] Egypt — F + C Live 1 Zaraibi 120 0, 3 MY, MP, MF, ML, MTS, MA
Masek et al. [16] Croatia — F + C Live 2 * 140 0, 3, 6 MY, MP, MF, ML, MTS
Baiomy [18] Egypt — F + C Live 2 Ossimi 42 0, 3, 6 MY, MP, MF, ML, MTS
Lima et al. [47] Brazil CRD F + C Culture 1 Saanen 90 102, 229 MY, MP, MF, ML, MTS
Macedo et al. [48] Mexico — F + C Culture 2 Pelibuey ewes 49 0, 14 MP, MF, ML, MTS
Gomes et al. [49] Brazil CRD F + C Inactive 1 Saanen 200 0, 97.2, 234.1 MY
Zaleska et al. [50] Poland — F + C Culture 2 — 28, 70 0, 3, 50 MP, MF, ML
Zicarelli et al. [17] Italy — F + C Culture 1 Cilentana 120 0, 2 MY, MP, MF, ML
Kholif et al. [51] Egypt LSD F + C Live 1 Nubian 22 0, 4 MY, MP, MF, ML, MTS, MA
Ma et al. [52] China — F + C Live 1 Saanen 56 0, 5 MY, MP, MF, ML, MTS
Aazami et al. [19] Italy CRD F + C Culture 1 Saanen 15 0, 3, 5 MY, MP, MF, ML
Elaref et al. [22] Egypt — F + C Live 2 Sohagi ewes 105 0, 5, 10 MY, MP, MF, ML, MTS
Aaliya et al. [53] India CRD F + C Live 1 Boar x local cross 182 0, 2.5, 4 MP, MF, MTS
Abbas et al. [20] SA — F + C Live 1 Beetal 60 0, 5, 10 MY, MP, MF, ML, MTS
Alshanbari et al. [54] SA RCBD F + C Culture 1 Ardi 90 0, 2.5, 3.5, 4.5 MY, MP, MF, ML
Mavrommatis et al. [21] Greece — F + C Live 2 Chios ewes 84 0, 2 MY, MP, MF, ML
Sahoo et al. [55] India — nr Live 1 Beetal 30 0, 2 MY
Khan et al. [23] Pakistan CRD F + C Live 1 Beetal 45 0, 1.5, 3 MY, MP, MF, ML, MTS, MA
Tayeb [56] Iraq CRD C Culture 2 Awassi ewes 84 0, 0.3, 0.6, 0.9 MY, MP, MF, ML
Almallah et al. [57] Iraq CRD C Culture 2 Awassi ewes 17 0, 10 MY, MP, MF, ML

SC – Saccharomyces cerevisiae; SA – Soudi Arabia, 1 – Goat; 2 – Sheep; * Istrian Sheep × East Friesian; ** Murciano-Granadina; RT – ruminant type; C – concentrate; LSD – Latin square design; CRD – complete randomised design; RCBD – randomised complete block design; F – forage; nr – not reported; DOS – duration of supplementation in days; SL – supplementation level, in g/kg feed; MY – milk yield; MP – milk protein; MF – milk fat; ML – milk lactose; MTS – milk total solids; MA – milk ash.

2.4 Statistical analysis

OpenMEE, cross-platform software built by Wallace et al. [34], was used for the analysis. Results were pooled and presented as standardised mean difference (SMD) at 95% CI. SMD was deemed significant when the CI did not overlap [30]. SMD was designated small effect 0.2 ≤ |SMD| < 0.5, moderate effect 0.5 ≤ |SMD| < 0.8 and large effect |SMD| ≥ 0.8 using the classification methods of Cohen [35]. All the forest plots were built with OpenMEE software. Random-effects model (REM) was employed to examine the impact of diets with and without SC on milk characteristics in sheep and goats following the procedures of DerSimonian and Laird [36]. The REM was considered more appropriate instead of the fixed effect model since the 26 studies used for the trial were heterogeneous [37]. Heterogeneity was explored via Q-statistic. Heterogeneity was quantified using I 2-statistic [38] and categorised as small (25%), moderate (50%) and high (≥75%) heterogeneity [39]. The value of 0% suggests the absence of heterogeneity. We used sensitivity analysis (SA) to assess the impact of studies deemed to have an unwarranted influence on the analysed outcomes [40]. It was performed when there is evidence of heterogeneity across studies utilised for the analysis. SA involves completing the same analysis but leaving out one study in each iteration [40].

Subgroup analysis was executed to assess the influence of ruminant type (sheep versus goats) on milk variables. We carried out meta-regression analysis to explore the relationships between SC type, breed, diet type, ruminant type, duration of SC supplementation and supplementation level as moderators and milk production parameters as outcomes. The influence of study design, milking frequency, parity, mode of milking (manual versus machine) and age on milk production and quality in lactating small ruminants fed SC-supplemented diets in this investigation were not tested because of missing data. In addition, we could not consider the effect of the following potential covariates: intake, nutrient digestibility, lactation stage and rumen fermentation on our measured outcomes in lactating small ruminants on dietary SC supplementation due to insufficient data. Attempts made to get the missing information from the corresponding authors failed. Publication bias was determined using Rosenberg’s failsafe number (Nfs) [58] and funnel plot [59]. Funnel graph resembles an inverted symmetrical funnel when there was no publication bias. However, when there is the presence of publication bias the plot would resemble an asymmetrical funnel with spaces in the bottom corner of the funnel [60].

3 Results

3.1 Overview of the meta-analysis

Twenty-six quantitative articles met our selection criteria (Figure 1 and Table 1). The most commonly studied SC type was live SC (13) followed by SC culture (12) and inactive SC (1). The dose level of SC ranged from 0.2 to 234.1 g/kg feed. The studies included in the analysis spanned 25 years (1996–2021). The included articles were performed in 17 countries that cut across five continents, with Egypt having the highest number of studies followed by Italy. Nine studies were from Europe while the remaining 17 were from Africa, Asia, South America and North America.

3.2 Milk yield and proteins

Results showed that SC had positive moderate effect on milk yield (SMD = 0.72; p < 0.001 and I 2 = 73%; Table 2) and milk protein (SMD = 0.46; p = 0.004; I 2 = 83%; Table 2) relative to controls. Subgroup analysis revealed that SC had positive moderate influence on milk yield in goats (SMD = 0.51; p = 0.002; I 2 = 66%; Table 3) and large effect on sheep (SMD = 1.15; p < 0.001; I 2 = 74%; Table 3) compared to controls. Live SC had a positive large effect on milk yield (SMD = 1.38; p < 0.001; Table 4), whereas SC culture had a moderate effect on milk yield (SMD = 0.53; p = 0.002; Table 4) compared to controls. Lactating goats fed diets with and without SC had similar milk protein content (SMD = 0.08; p = 0.460; Table 3). In contrast, SC had a large influence on milk protein content (SMD = 0.89; p = 0.005; I 2 = 93%; Table 3) in lactating sheep compared to controls. Our results also show that SC culture had a moderate influence on milk protein yield (SMD = 0.60; p = 0.037; Table 4) in small ruminants. We found significant heterogeneity across trials that explored the effect of SC on milk yield and proteins as indicated by the I 2 values of 73–83% (Table 2). Results of the relationship between SC supplementation level and milk yield and components are presented in Table 5. Results found no significant relationship between SC supplementation level and milk variables (milk yield and milk proteins). In contrast, duration of supplementation (DOS), ruminant type, SC type and breed were limiting factors in the present investigation and explained most of the variability in milk yield. There were significant associations between milk proteins and studied moderators (DOS, ruminant type, diet type and breed).

Table 2

Effect of SC on milk yield and components in small ruminants

Model results Heterogeneity
Outcomes SMD 95% CI p < 0.05 I 2-test (%) p < 0.05
Milk yield 0.72 0.44 2.00 <0.001 73 <0.001
Milk protein 0.46 0.15 0.77 0.004 83 <0.001
Milk lactose 0.17 0.05 0.30 0.007 0 0.860
Milk fat 0.28 0.05 0.51 0.016 70 <0.001
Milk total solids 0.11 −0.05 0.26 0.173 0 0.975
Milk ash 0.17 −0.08 0.41 0.176 0 0.980

SC – Saccharomyces cerevisiae; SMD – standardised mean difference; CI – confidence interval.

Table 3

Effect of ruminant type on milk yield and components in small ruminants fed SC supplemented diets

Parameters Goats Sheep
SMD 95% CI p < 0.05 SMD 95% CI p < 0.05
Milk yield 0.51 0.19 0.83 0.002 1.15 0.72 1.584 <0.001
Milk protein 0.08 −0.12 0.27 0.460 0.89 0.27 1.512 0.005
Milk lactose 0.13 −0.11 0.38 0.283 0.18 0.01 0.339 0.037
Milk fat 0.30 0.05 0.55 0.019 0.201 −0.19 0.606 0.306
Milk total solids 0.04 −0.19 0.26 0.734 0.17 −0.04 0.387 0.118

SC – Saccharomyces cerevisiae; SMD – standardised mean difference; CI – confidence interval.

Table 4

Subgroup analysis of the effect of SC type on milk yield and components in small ruminants

Parameters Live SC SC culture
SMD 95% CI p < 0.05 SMD 95% CI p < 0.05
Milk yield 1.38 1.03 1.73 <0.001 0.53 0.20 0.86 0.002
Milk protein 0.16 −0.08 0.40 0.198 0.60 0.04 1.15 0.037
Milk lactose 0.12 −0.09 0.33 0.273 0.19 0.02 0.36 0.027
Milk fat 0.28 0.06 0.49 0.012 0.20 −0.18 0.58 0.295
Milk total solids 0.20 0.00 0.41 0.049 −0.03 −0.27 0.21 0.827

SC – Saccharomyces cerevisiae; SMD – standardised mean difference; CI – confidence interval.

Table 5

Relationship between moderators and milk production variables in lactating small ruminant

Measured outcomes Moderators Q M df p < 0.05 R 2 (%)
Milk yield DOS 112 19 0.001 94
SL 22 18 0.234 14
Ruminant type 5.27 1 0.022 17
SC type 32.4 2 9.41 × 10−08 55
Diet type 1.22 1 0.269 1
Breed 58.8 13 8.69 × 10−10 71
Milk proteins DOS 204 22 0.001 96
SL 15.1 16 0.519 12
Ruminant type 6.16 1 0.013 13
SC type 1.33 1 0.248 3
Diet type 4.21 1 0.040 40
Breed 30.3 14 0.007 100
Milk lactose DOS 24.7 18 0.132 0
SL 23.4 16 0.104 100
Ruminant type 0.02 1 0.879 0
SC type 2.00 1 0.658 0
Diet type 0.09 1 0.759 0
Breed 22.8 12 0.030 0
Milk fat DOS 34.7 21 0.031 37
SL 26.6 19 0.114 19
Ruminant type 0.25 1 0.621 0
SC type 0.54 1 0.463 0
Diet type 0.22 1 0.642 0
Breed 52.7 15 4.28 × 10−06 100
Milk total solids DOS 11.3 17 0.841 0
SL 6.27 8 0.617 0
Ruminant type 0.71 1 0.400 0
SC type 2.05 1 0.152 0
Breed 9.20 10 0.513 0

p – probability; Q M – coefficient of moderators; SC – Saccharomyces cerevisiae; df – degree of freedom; R 2 – amount of heterogeneity explained by the moderators; DOS – duration of supplementation; SL – supplementation level.

3.3 Milk lactose, fat, total solids, ash yield and analysis of publication bias

Results demonstrated that SC had small effect on milk lactose (SMD = 0.17; p = 0.007; I 2 = 0%; Table 2) and milk fat (SMD = 0.28, p = 0.016; I 2 = 70%; Table 2) in small ruminants. The effect of ruminant type on milk lactose and fat is described in Table 3. Subgroup analysis suggested that SC treatment had a small impact on milk lactose content in sheep (SMD = 0.18, p = 0.037) and milk fat in does (SMD = 0.30, p = 0.019) compared to controls. In converse, SC did not improve milk lactose in goats and fat yield in sheep. Our results as shown in Table 4 indicate that SC culture (SMD = 0.19; p = 0.027) had a small influence on milk lactose yield in small ruminants. Furthermore, live SC had a small impact on milk fat (SMD = 0.28, p = 0.012; Table 4) and milk total solids (SMD = 0.20, p = 0.049; Table 4). Data on the impact of studied moderators on milk lactose, total solids and fat yield are presented in Table 5. Meta-regression revealed that DOS and breed are predictors of the study effect on milk fat. There was significant relationship between milk lactose and breed. However, there is no relationship between moderators and milk total solids. There is minimal sign of publication bias as displayed in Figures S1–S5. The Rosenberg Nfs for the database is 510, which is 3.6 times higher than the 140 (5 × n + 10) required to proclaim the results of analysis robust in the face of publication bias.

4 Discussion

Results showed that systematic search performed in the three online databases yielded 26 studies published in 17 study countries spanning five continents, with most of the studies published between 2016 and 2021. The continuous yearly increase in SC-based feed additive research in this study could be linked to the growing campaign on the use of biotherapeutic agents as a replacement for antibiotics in animal feed, which have been reported to increase the spread of antibiotic resistant strains of bacteria to the environment and humans via consumption of animal products with antibiotic residues [61].

Our search on available literature revealed that this study could be the first attempt to use meta-analytic method to assess the efficacy of SC administered via feed on milk yield and components in lactating small ruminants. The low-to-medium milk yield and component fractions (milk lactose, protein and fat) found in animals on SC intervention in this study compared to controls suggest that SC-based diets were moderately utilised for milk yield and protein by the lactating small ruminants. The observed moderate effect of SC on milk yield and the small effect on milk composition could be partly explained by SC’s ability to stimulate rumen fermentation, increase the production of VFAs and enhance digestion of fibre in the rumen. Most milk proteins are synthesised within the mammary epithelial cells using the substrates extracted from blood such as free amino acids and peptide-bound amino acids [62]. The mechanism by which SC regulates milk protein yield in lactating ruminants is yet to be properly documented. However, the reported ability of SC to improve the nutrient digestion and assimilation of amino acids may partly explain the moderately higher milk proteins observed in this study when compared to the controls [24]. The result of this study also demonstrated that SC had a small positive effect on milk lactose and fat content, suggesting that SC supplementation had minimal impact on milk lactose and fat. Our results indicate that milk total solids and ash content in lactating small ruminants were not affected by SC supplementation. Few studies [15,34,41,43,46,51] were used to compute the effect of SC on milk ash yield in the present meta-analysis, and the result, therefore, should be interpreted with caution.

The subgroup analysis results revealed that lactating small ruminants that received SC-supplemented diets produced more milk than those on controls. This agrees with Stella et al. [44], who recorded higher milk yield in goats offered SC in comparison with the controls. Contrary to the present finding, Uyeno et al. [25] found that addition of SC at 5 or 10 g/animal/day did not increase milk yield in lactating animals. This discrepancy may be due to differences in species of animal used, lactation stage and SC supplementation level. The significant differences in the milk protein content between goats and sheep might reflect the differences in genetics and physiological abilities to change their milk protein content according to changes in dietary constituents. This finding is consistent with Kanwal et al. [63], who reported higher protein yield in sheep milk than in goat milk.

The result of this meta-analysis suggests that the milk lactose content in lactating goats was not improved by SC additive. In contrast, our result showed that lactating sheep offered SC had significantly higher milk lactose content than controls. The increased milk lactose may be credited to the potential of SC to modulate the rumen environment to favour the production of propionate [64,65], which then enters the tricarboxylic acid cycle to produce oxaloacetate, which in turn produces glucose, a precursor of milk lactose. The observed small positive effect of SC on milk lactose of sheep in this meta-analysis corroborates the findings of Macedo et al. [48], who recorded a 13% increase in milk lactose in lactating Pelibuey sheep offered 14 g SC/head/day compared to those fed diets without SC supplementation.

Milk fat is an essential part of milk that can be easily influenced by nutritional management. Studies have shown that SC increases the concentration of acetate in the rumen [66,67], which according to Hanson and Ballard [68], is the precursor for fat synthesis in the adipose and mammary tissues. The small milk fat yield in lactating goats following SC supplementation in this study may be credited to the low potentiality of SC in increasing acetate level in the fore-stomach. In addition, this could be linked to the low capability of SC to modify rumen biohydrogenation pathway to favour the production of trans-11 and cis-9, trans-11-CLA known to enhance milk fat production.

Live SC had a positive large effect on milk yield, whereas SC culture had a moderate effect on milk yield when compared to controls. The exact mechanism underlying the observed positive large impact of live SC on milk yield in lactating small ruminants in the current study is not clear. However, this could be attributed to the capability of live SC to scavenge excess oxygen in the ruminal fluids, lower the redox potential and enhance the growth of cellulolytic bacteria [6,7], leading to an increase in milk yield. The improved milk protein content in lactating ruminants fed SC culture compared to those fed live SC in this study indicates that SC culture has the potential to produce metabolites that alter the fermentation trend, enhances nutrient digestibility and ammonia uptake and improves microbial protein production [8,9], resulting in higher milk protein content.

There is evidence of large heterogeneity in this meta-analysis as shown by the I 2 values [38]. This problem of heterogeneity was not resolved by subgroup analysis. The present study showed that DOS is a predictor of SC effect on milk yield and some of its constituents and accounted for 94, 96 and 37% of variations in milk yield, proteins and fat, respectively. Our meta-regression found that SC type is a significant predictor of the impact of SC on milk yield and accounted for 55% of the sources of heterogeneity among the studies utilised for the meta-analysis. Meta-regression analysis demonstrated that breed is a limiting factor among the studies included in the meta-analysis that led to the inconsistent results among investigators on the effect of SC supplementation on milk proteins and fats. The effects of factors such as lactation stage and milking frequency in our outcome measures not reviewed in this meta-analysis because of missing data may have accounted for the variations that could not be explained by the studied moderators. In addition, residual heterogeneity can be attributed to variables such as milking frequency, parity, study design, mode of milking, age, SC intake, nutrient digestibility and rumen fermentation, which were not examined in this study due to missing data. Publication bias defined as the tendency of authors or journal editors to publish studies with significant findings is one of the challenges in meta-analytical studies. There is publication bias in this study and the Rosenberg Nfs for the database is 3.6-fold beyond the threshold required to declare the pooled results robust. However, this is not an issue as it would take a relatively large number of unpublished articles to change the significant effects of SC on milk yield and component parameters in lactating small ruminants [69].

5 Future research direction and conclusion

Future research is therefore recommended to determine the effect of the following potential covariates: milking frequency, lactation stage, parity, age, digestibility and rumen fermentation on milk production and component characteristics in small ruminant fed SC supplemented diets that were not reported in this meta-analysis because of insufficient data. In conclusion, our pooled results suggested that SC can be added to the diets of lactating sheep and goats to improve milk yield and protein. This study could be used as a model for future research on the impact of SC on milk production and component yield in small ruminants.

  1. Funding information: The authors state no funding involved.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: All data generated or analysed during this study are included in this published article and its supplementary information files.

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Received: 2022-08-25
Revised: 2023-01-30
Accepted: 2023-01-30
Published Online: 2023-03-13

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0178
Keywords for this article
yeast; sheep and goats; milk variables; meta-analysis; meta-regression
Creative Commons
BY 4.0

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  2. The impact of COVID-19 pandemic on business risks and potato commercial model
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  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
  31. Optimization of extraction using surface response methodology and quantification of cannabinoids in female inflorescences of marijuana (Cannabis sativa L.) at three altitudinal floors of Peru
  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
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