A Meta-Analysis of the Effect of Probiotics Administration on Growth Performance of Suckling Calves in Iran


1 Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Department of Animal Science, Faculty of Agriculture, Malayer University, Malayer, Iran

3 Senior Scientist, Xaniar Ariaie Co., Tehran, Iran


Probiotics have been shown to have beneficial effect on the growth performance of newborn animals. However, the results are inconsistent, especially in Iran. A meta-analysis was carried out to evaluate the effects of probiotics on dry matter intake (DMI), daily weight gain (DWG) and feed efficiency (FE) in suckling calves using data from the studies conducted in Iran. The literature review resulted in 13 documents including 6 and 5 papers written in English and Farsi respectively, as well as 2 papers presented in national conferences. Random effect meta-analysis was performed to evaluate the effect of probiotics on growth performance of the suckling calves using STATA statistical software on 8 papers that were identified as suitable for the objective of this study. Univariate and multivariate meta-regression analyses were also performed to evaluate the effects of experimental period (≤8 weeks vs. >8 weeks), gender of calf (male vs. female), type of probiotic (bacteria vs. a combination of yeast and bacteria), and probiotic administration manner (in milk vs. in starter) on the study outcomes. The results of meta-analysis showed no significant effect of probiotics on DMI. However, a significant difference was identified by subgroup meta-analysis between the experimental period subgroups for the effect of probiotics on DMI. Probiotics administration increased DWG of suckling calves by 47 g/day (p <0.01). The effect of probiotics on feed efficiency was not significant. The results indicated that the probiotic administration may improve DMI and DWG in suckling calves.



Successful calf raising is an investment in the future of dairy herds and is critical to establish an optimal replacement policy. Health and subsequent productivity of suckling calves may be negatively affected by intestinal diseases which are potentially high in intensive rearing systems (Rosmini et al. 2004). Antibiotics have been widely used as feed additives to solving this challenge. However, the use of antibiotics is associated with the development of antibiotic-resistant. Therefore, scientists and practitioners are constantly looking for new alternative feeds or additives that can improve the health, production and profitability of animals (Bryszak et al. 2019; Mravčáková et al. 2019). Probiotics are one of the supplements that are being successfully tested (Zhang et al. 2016; Huang et al. 2019). As an alternative of antibiotics, probiotics are live microbial feed additives that beneficially affect the host animal, generally by improving or restoring the gut flora (Fuller, 1989). Although the beneficial effects of probiotics on calf heath and performance have been wildly investigated, the results are inconsistent. Under the dairy calves rearing condition in Iran, for instance, probiotics administration in suckling calves improved growth rate (Mohamadi-Roodposhti and Dabiri, 2012), feed intake (Seifzadeh et al. 2017) and/or feed conversion ratio (Moslemipur et al. 2014). In contrast, Bakhshi et al. (2006), Bayatkouhsar et al. (2013) and Hosseinabadi et al. (2013) reported no effect of probiotics on growth performance of young calves. This contradiction in the results could be attributed to various factors such as type of probiotic, manner of probiotic delivery to calves and duration of experimental period. On the other hand, there is a regional variation for pre-weaning calf mortality and growth, primarily due to the regional conditions and calf rearing system (Moran, 2011). Therefore, a combination of local study results may be helpful to decide on the use of probiotics as a growth promoter for suckling calves in a certain region, such as Iran with 842000 Holstein cows on commercial dairy farms. Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic to systematically assess previous research studies and derive conclusions about that body of research. Moreover, meta-regression analysis may also be used to investigate the factors contributing to the heterogeneity across studies (Lean et al. 2009). This study was aimed to meta-analysis of the effect of probiotics on growth performance of suckling calves in Iran using data from the local studies.



Literature review, outcome evaluation and data extraction

Literature review was conducted on the basis of a search in Google Scholar, CIVILICA, ScienceDirect, Magiran, Islamic World Science Citation, and Scientific Information Database using combinations of keywords: "probiotic", "calves or calf", "yeast", "protexin" and "Iran" both in English and Farsi. The keywords were also individually searched in the local scientific journals. The search was not restricted to peer-reviewed journals and it included journal articles and conference proceedings. The papers that reported the effect of probiotics feeding on the growth performance of suckling calves were selected for this study. All studies to be included in the meta-analysis were screened using the following standardized criteria: studies should have 1) conducted in Iran, 2) evaluated the effect of probiotics on suckling calves, 3) included at least one control and one treated (probiotic) group, and 4) reported at least one performance parameter e.g. starter dry matter intake (DMI), daily weight gain (DWG) and/or feed efficiency (FE) with a measure of variance (standard error or standard deviation). Because an overall standard error was reported in all of the included studies, no conversion was required to estimate standard error. The mean differences in DMI (kg/day), DWG (kg/day) and FE (DMI:DWG) between treated and control group were used as the outcomes. If the studies reported these outcomes in any other measurement (e.g. g/day), the outcomes were transformed to kg/day. In addition to the outcomes, gender of calves, duration of experiment (≤8 weeks and >8 weeks), type of probiotic (yeast, bacteria, and a composition of yeast and bacteria), and strategy of probiotic delivery (in milk and in starter) data were also extracted from the trials for sub-group analysis.



The extracted data including number of observation, means and standard error for both control and treated groups was subjected as continuous data to random and fixed effects meta-analyses using "metan" command of Stata/SE software (Stata 14.1, Stata Corporation, Col-lege Station, TX, USA) to evaluate the effect of probiotics on DMI, DWG and FE of suckling calves. A random effects meta-analysis considers the variation between studies and assumes that there is a normal distribution for the study effects and the variance of the distribution is estimated from the data (Rabiee et al. 2012). The variance for random effects model was estimated using DerSimonian and Laird invers variance method (Higgins and Thompson, 2002) and the heterogeneity statistic Q was used to determine if there was significant variability between studies (Lean et al. 2009; Rabiee et al. 2012). Because a significant P-value (i.e. <0.05) for the Q statistic was observed, the results from the random-effects model including random effect size (standardized mean difference (SMD)), 95% confidence interval and P-value are reported in this manuscript. Moreover, weighted mean difference (WMD) was measured as an unadjusted difference between control and treated group. The results of sub-group meta-analysis are presented as forest plots.


Meta-regression analysis

Meta-regression analysis was performed on SMD and corresponding standard error values of each compression as dependent variable using "metareg" command of STATA/SE software to explain the sources of heterogeneity that may have influenced study outcomes. In this study, gender of calf, duration of experiment, type of probiotic, and strategy of probiotic delivery were investigated as sources of heterogeneity between the studies. Each source of the heterogeneity was separately subjected to the univariate meta-regression analysis and then multivariate meta-regression analysis were performed for the sources with a P-value of Mirzaei-Alamouti et al. 2015). The heterogeneity source(s) was determined using a step-by-step backward elimination procedure with a significance threshold of 0.05.



A total of 13 documents including 6 and 5 papers written in English and Farsi respectively, as well as 2 papers presented in national conferences were identified. Of the 13 studies identified by literature review, 8 potential studies were eligible for inclusion in this meta-analysis. Out of the 8 studies that met inclusion criteria, 10, 9 and 7 comparisons were usable in the meta-analysis on DMI, DWG and FE, respectively. Details of the studies and comparisons are presented in Table 1. Meta-analyses with too few studies (≤7 studies) are common (Michael et al. 2019). For example, Seide et al. (2019) reported that 31 out of the 40 meta-analyses (~77%) from recent reviews published by the German Institute for Quality and Efficiency in Health Care included only 2 or 3 studies. Turner et al. (2013) concluded that when at least two adequately powered studies are available in meta-analyses, underpowered studies often contribute little information. In the present study, at least two well powered studies were included to the meta-analysis (Bakhshi et al. 2006; Hosseinabadi et al. 2013).


Feed intake

The results of meta-analysis for 10 comparisons from 6 trials showed that the probiotics administration had no significant effect (P=0.085) on DMI of suckling calves (Table 2; Figure 1). However, a significant heterogeneity was observed across studies (P=0.018, I2=58.5%). Based on the univariate meta-regression analysis, calf gender (P=0.182) and duration of experiments (P=0.018) were identified as sources of heterogeneity. Furthermore, P-value of the multivariate meta-regression analysis for these two sources was 0.043 (Table 3). This results suggest that the positive impact of probiotics on DMI of suckling calves is associated to gender of calve as well as duration of experimental period. Gender sub-group meta-analysis showed a numerically higher DMI in male compared to female calves. As shown in Figure 2, DMI was not affected by probiotics administration when feed intake was measured for ≤ 8 weeks. However, the positive effect of probiotics on DMI was more obvious in the studies where feed intake was measured for more than 8 weeks. The solid feed intake in young calves is a function of rumen development as well as many physiological adjustments at the gut, hepatic, and tissue levels (Khan et al. 2011). Probiotics have been shown to improve rumen development in young ruminants (Kmet et al. 1993; Laborde, 2008). The findings of the present meta-analysis, however, suggest that the probiotics have no significant effects on rumen development at pre-weaning stage, but may improve post-weaning solid feed intake.


Table 1 Details of the experiments used in meta-analysis


DMI: dry matter intake; DWG: daily weight gain and FE: feed efficiency.


Table 2 Meta-analysis of the effect of probiotic on dry matter intake (DMI), daily weight gain (DWG) and feed efficiency (FE) of suckling calves


SMD: standardized mean difference; CI: confidence interval; WMD: weighted mean difference and Df: degree of freedom.


Figure 1 Means and forest plot of standardized mean differences (SMD) with their 95% confidence interval (95% CI) for the random effect of probiotic on dry matter intake of suckling calves



Table 3 Meta-regression of factors may have influenced the effect of probiotic on dry matter intake (DMI), daily weight gain (DWG) and feed efficiency (FE) in suckling calves


1 Gender: male vs. female; Duration: ≤ 8 weeks vs. > 8 weeks; Probiotic type: bacteria vs. bacteria and yeast.

CI: confidence interval.


Figure 2 Forest plot of standardized mean differences (SMD) with their confidence interval (95% CI) for the random effect of probiotic on dry matter intake of suckling calves in two experiment period groups (≤8 weeks and >8weeks)



Daily weight gain

Meta-analysis was performed for 9 comparisons from 6 trails to evaluate the effect of probiotics on DWG of suckling calves (Figure 3). Standardized mean difference (95% CI) and P-value were 0.482 (0.137 to 0.819) and 0.006, respectively. Furthermore, a weighted mean difference of 0.047 (-0.003 to 0.098) was also detected between probiotics and control treatments for DWG (Table 2). In a similar meta-analysis, Frizzo et al. (2011) reported a significant positive effect of probiotics administration on weight gain of young calves using 36 comparisons from 21 independent trials. These authors, however, suggested that the probiotics had no significant effect on growth rate of whole milk fed calves or when the duration of the experiment was < 45 days.


Figure 3 Means and forest plot of standardized mean differences (SMD) their confidence interval (95% CI) for the random effect of probiotic on daily weight gain of suckling calves



In the present meta-analysis, all included experiments provided whole milk to the calves and the heterogeneity across studies was non-significant (P=0.352, I2=10.0%) for DWG. Although P-values for both type of probiotic (P=0.261) and duration of experiment (P=0.197) in univariate meta-regression analysis were less than the chosen significance level of 0.3, multivariate meta-regression analysis showed no correlation between these variables and probiotics administration for DWG (P=0.263). These findings are in agreement with heterogeneity test results and suggest that the positive effect of probiotics on DWG of suckling calves is independent of the type of probiotic, duration of experiment, gender of calf, and strategy of probiotic delivery. Furthermore, an average improvement of 47 g in DWG is expected by probiotics administration in suckling dairy calves.


Feed efficiency

As shown in Table 2, a weighted difference mean of -0.037 (-0.096 to 0.021) was detected for FE. Because the efficiency of feed intake in all included experiments was measured as feed conversion ratio (DMI:DWG), the negative value indicated an improvement in feed efficiency by probiotic administration. However, the results of the random effect meta-analysis showed non-significant effect of probiotics on FE of suckling calves (P=0.570). Heterogeneity was not significant across studies (P=0.07). No significant correlation was also observed between heterogeneity sources and probiotic for FE. Although Frizzo et al. (2011) suggested that the probiotics administration improved FE in young calves, these authors, however, noted that probiotics had no significant impact on FE when the calves fed whole milk that is in agreement with our results.



The results of the present meta-analysis indicated that probiotics may improve daily weight gain of suckling calves by 47 g under the rearing conditions in Iran. This effect of probiotics on DWG is, however, independent of calf's gender, probiotic type, experimental period and strategy of probiotic delivery. Moreover, the results suggest a positives impact of probiotics on post-weaning DMI of dairy calves. However, no significant effect of probiotics on feed efficiency was identified by the present meta-analysis.



The authors would like to thank Artiash Gostar Novin Co. and Department of Biotechnology, Vice President of Science and Technology of the Presidency.


One of the goals of cultivating mushrooms is to provide human edible protein and among various edible mushrooms, button mushroom comprises 38% of total mushroom cultivation worldwide (Farsi and Gordan, 2010). Two by-products are obtained from button mushroom farms, one of which is mushroom stem being separated from the main product and discarded during the mushroom crop harvest due to lack of marketability in fresh mushroom crop products. This by-product contains a compound almost similar to that of the mushroom and can be used for ruminant feeding. The second by-product is button mushroom cultivation bed called compost, which is discarded after harvesting. According to a previous study, changes during compost processing and growth of button mushrooms increased dry matter disappearance (DMD) and rice straw protein in the rumen (Kim et al. 2011). Ehtesham and Vakili (2015) replaced wheat straw with button mushroom compost at levels of 15, 25, and 35% in Kurdish lamb ration and reported that this replacement had no negative effects on blood metabolites; they recommended a 25 percent compost replacement instead of wheat straw in the diet. Barati (2014) replaced alfalfa with the silage of white button mushroom compost at 7.2% and 15.9% in the diet of Mehraban lambs, which had no significant negative effects on average daily weight gain, feed conversion efficiency, and average lamb final weight during a 60 d period. They concluded that the silage of button mushroom compost could be a good substitute for a part of dietary forage portion. Addition of mushroom stems (10% and 20%) to the diet of Mehraban male lambs had no negative effects on lamb growth performance, digestibility of dry matter (DM) and other nutrients in the ration. It was found that mushroom stems with high crude protein (CP) content and appropriate amount of energy could replace dietary protein sources (Yousefi, 2016). Therefore, regarding to nutritive value and low costs of these agricultural by-products, it can be used in diets of ruminants. Identification and evaluation of feeds as well as determination of animal nutrition requirements are two important factors in a high-yield and economic production. For this reason, digestive experiments are of particular importance in determining the nutritional value of feeds in animal nutrition. In vitro methods are widely used for prediction of feed digestibility and are a tool for assessing the quality of feeds. A high correlation was also reported between nylon bags and in vitro methods in determining the amounts of dry matter and protein disappearance (Paya et al. 2008). Considering the high correlation between two methods mentioned above, in vitro method can be introduced for estimation of the amount of feed DMD. Regarding to absence of more information about of mushroom wastes, this study was designed and conducted to obtain information about nutritive value, digestion kinetics, gas production parameters and DMD of white button mushroom compost and stems in ruminant nutrition.



The compost was prepared at the end of mushroom harvest period in a field in Ghezeljeh Maidan village, Bostanabad (Eastern Azerbaijan province). The compost samples were selected from different levels of the shelves at different areas of the cultivation room and dried at room temperature to prepare air-dried samples, after which each sample was milled with an appropriate sieve. To prepare the samples of edible white mushroom stem from the same field, edible mushroom stem wastes was prepared after daily mushroom harvesting. The mushroom stem was separated manually from the bed soil and placed at room temperature to prepare air-dried samples without washing. Dry alfalfa was used to compare the nutritive value and chemical composition of these two products with other conventional feeds. Alfalfa was obtained from a dairy farm forage store located in the central part of Tabriz. Approximate analysis of the samples, including DM, CP, ether extract (EE) and crude ash (CA) was performed according to the methods recommended by AOAC (2005). The values of acid detergent fiber (ADF) and neutral detergent fiber (NDF) were determined according to Van Soest et al. (1991). Gas production was measured by the method of Fedorak and Herodi (1983). In this method, fluid displacement in calibrated test tubes, measured by the gas pressure produced in glasses containing rumen fluid and feed samples, represents the amount of produced gas. Rumen fluid was obtained from two of Gezel fistulated, adult one-year-old male rams (50±1 kg), kept separately in individual metabolic cages. To perform this test, 300 mg of each feed, previously milled with a 2 mm diameter pore mill, weighed carefully and poured into 50 mL sterile glass vials. Three replicates were considered for each sample. Two hours after a morning meal, rumen fluid was obtained from two fistulated sheep fed with a concentrated feed and alfalfa for two weeks. The fluid was transferred to the laboratory after being filtered through a four-layer grid inside a flask containing CO2 at 39 ˚C. A mixture (20 mL) of rumen liquid and McDougall (1948) buffer at 1:2 ratio (one portion of rumen fluid and two portions of buffer) was taken from an Erlen already placed on a heater at 39 ˚C under constant CO2 gas flow, and was poured into the vials previously reached the desired temperature to prevent heat shock. The glass vials containing the experiment treatment were anaerobicized by CO2 and riveted tightly with plastic caps and aluminum silica to be impermeable by the air. To correct the gas produced from rumen fluid, five glass jars were considered with no feeds containing only rumen fluid and the buffer. All prepared vials were transferred to a shaker incubator at 120 rpm and 39 ˚C. The amount of produced gas was recorded at 2, 4, 6, 8, 12, 24, 48, 72, 96, and 120 h after incubation (Gallo et al. 2016). The gas production components were determined by the equation:

P= A (1-e-ct)


P: gas production at time t.

A: gas production of soluble and insoluble fractions.

c: gas production rate.

t: fermentation time..

In vitro disappearance method was used to determine DMD, which was similar to the gas production method, except that a hypodermic needle was placed on the rubber cap of the glass vials for the gas exit. Rumen fluid was collected from bovine rumen immediately after slaughter at Tabriz industrial slaughterhouse and then transferred to the laboratory. Three replicates were assigned to each treatment. For each time series, three blank samples were also considered to deduct the amount of rumen fluid during the calculations (Paya et al. 2008). To determine the DM, the samples were removed from the incubator after 4, 8, 12, 24 and 48 h and immediately transferred to a freezer to inhibit the activity of microorganisms. During the experiment and after freezing, the vials were centrifuged at 2500 rpm for 10 minutes, the floating portion/supernatant was separated and the residue was washed with a buffer (sodium hydrogen phosphate, potassium dihydrogen phosphate, NaCl, and distilled water). The vials were re-centrifuged at 2500 rpm for 10 min, the supernatant was separated, and the residue was transferred to an oven. The amounts of DM and DMD were measured after drying samples at 105 ˚C (Paya et al. 2008). DM degradability coefficients of the samples were determined using the exponential equation:

P= a + b(1−e-ct)


P: level of degradation at time t.

a: readily soluble fraction.

b: insoluble fraction but degradable in rumen.

c: rate of degradation of b per hour.

t: time of incubation.

Effective degradability of the samples was calculated using Orskov and MacDonald (1979) equation ED= a + {(b×c) / (c+k)}, taking into account the output rates of 0.02, 0.05, and 0.08 per hour. In this equation, ED is effective degradability and k is the constant of digested leachate outflow rate from the rumen. Other abbreviations are similar to those described in the preceding equation. ME, NEl, and organic matter digestibility (OMD) contents of the samples were calculated using the equations presented by Menke et al. (1979) and Menke and Steingass (1988). The amounts of short-chain fatty acids (SCFA) were calculated based on Getachew et al. (2002).

ME (MJ/Kg DM)= 2.2 + (0.136×GP) + (0.057×CP) + (0.002859×CF2)

NEl (MJ/Kg DM)= (0.101×GP) + (0.051×CP) + (0.11×CF)

OMD (% DM)= 14.88 + (0.8893×GP) + (0.448×CP) + (0.651×ash)

SCFA (mmol/200 mg DM)= (0.0222×GP) – 0.00425

Finally, the data obtained from this experiment were analyzed by SAS (2003) software with ANOVA procedure in a completely randomized design with three treatments of alfalfa, stem, and compost of button mushrooms in five and three replications for evaluating gas production and disappearance of DM, respectively. The statistical model used here was:

Yij= µ + Ti + eij


Yij: volume of produced gas (mL/g DM).

µ: total mean.

Ti: treatment i effect.

eij: test error.



Chemical composition

The chemical composition of the experiment feeds is presented in Table 1. In previous studies, mushroom stem CP levels were reported about 19% (Nasiri et al. 2013) and 24.5% (Marino et al. 2010), which is different from current study. Marino et al. (2010) reported a CP of 24.5% in the mushroom stem, which is in the contrast with that we recorded in this present, due to variance in used coefficient for calculation of CP (8.48 vs. 6.25). However, a coefficient of 4.38 was used in other studies (Nasiri et al. 2013) to calculate the amount of crude protein. In the present study, a crude ash content of 8.6% was estimated for the mushroom stem. Some studies reported different values for crude ash such as 10.2% (Marino et al. 2010), 9.5% (Nasiri et al. 2013), and 7.7% (Yousefi, 2016). The existence of soil in this residue and different sampling methods may be caused different values in above mentioned studies. In our research, the ether extract level (3.48%) of the mushroom stem was in contrast to those of other studies, including 3.7% (Marino et al. 2010) and 2% (Nasiri et al. 2013), which can be due to differences in the test mushroom species. The content of NDF in the mushroom stem was reported from 34.4% (Marino et al. 2010) and 34.9% (Yousefi, 2016). This difference may be due to variance in the mushroom growth stage, experimental mushroom cultivar, and the storage conditions in the mushroom growing room. The contents of mushroom cap and stem are different depending on the growing room conditions such as temperature, air flow rate or slightly delayed harvesting conditions (high growth rate, bulky or thin stems). Any report for NDF of this mushroom stem was not found in the literature reviews. Mushroom compost DM contents were reported from 35.1% (Fazaeli and Shafyee, 2005) and 83% (Ehtesham and Vakili, 2015). Their different DM levels with the present study may be due to variety in type of drying process such as feeding of air-dried compost DM fed to animals, which is not related to compost at the time of evacuation from the mushroom growing room containing high moisture content as one of the important principles of white mushroom cultivation.


Table 1 Chemical composition (% DM) of the experiment feeds (n=3)


DM: dry matter; CP: crude protein; CA: crude ash; EE: ether extract; NDF: neutral detergent fiber and ADF: acid detergent fiber.


Our estimated compost CP (12.8%) is in consistent with that reported by Ehtesham and Vakili (2015), but it is different from those of reported by Fazaeli and Shafyee (2005) and Fazaeli and Talebian (2006), which can be attributed to differences in the sampling methods, mushroom growth rate, number of harvest periods, and differences in the initial compost chemical composition. The content compost ash was achieved about 42.4% in the present study; while, this value was obtained 37%, 35.1% and 35.1% in the studies of Fazaeli and Shafyee (2005), Fazaeli and Talibian (2006), and Ehtesham and Vakili (2015), respectively. The researchers attributed the high compost ash content to the consumption and discharge of straw organic matter by the mushroom (Fazaeli and Talibian, 2006; Ehtesham and Vakili, 2015). They argued that straw is associated with the soil in the white mushroom cultivation system, and the compost top layer is covered with soil for mushroom cultivation. Despite the separation of this layer prior to its use in livestock feeding, they found it impossible to be completely separable. It should be noted that the difference in ash content can be one of the factors affecting different results of other nutrient compounds of compost between the present study and other studies. High ash content in a feed have an adverse relationship with energy content, it would also affect other nutrients being expressed as a percentage of DM. The amount of EE in current study was 3.14%, whereas, a level of 1.26% was reported by Fazaeli and Shafyee (2005). The difference in EE values may be due to differences in the initial composition of the mushroom compost, also the amount of white mushroom residues associated the mycelium growth rate in the grown cultivars. Compost NDF and ADF levels in our study were different from those reported by Ehtesham and Vakili (2015) (28.2% and 9.7%) and Fazaeli and Talebian (2006) (27. 8% and 21%), respectively. The discrepancy between the above levels can be explained by the fact that compost is the main source of mycelium nutrition resulting mushroom production. Moreover, many factors are involved in the mushroom growth at different stages resulting changes in the amount of nutrients in compost.


In vitro gas production

The gas production parameters of experiment feeds are given in Table 2. Among the experiment treatments at the end of 120 h of incubation, the higher and lower amount of gas was produced in the mushroom stem and compost, respectively (P<0.05). This is in contrast to that reported by Yousefi (2016) as higher gas production (281 mL/g DM) was achieved in alfalfa, whereas lower value was obtained in mushroom stem (263 mL/g DM) at the end of a 144 h incubation. The amount of mushroom stem gas production was 228 mL at 120 h of incubation in the present study. Marino et al. (2010) measured gas production up to 96 h after incubation and reported a gas production about 198 ml for mushroom waste (including whole mushroom). The extent of gas production at 24 h (176 mL) in the mushroom stem in our research is inconsistent with that reported by Marino et al. (2010) (138 mL). This can be attributed to the nature of experiment mushroom wastes. Yousefi (2016) reported a 24-h gas production of 193 mL from mushroom stem, which, despite similar experiment feeds, differences can be explained by variance in sampling, mushroom cultivars, the growth stage of the mushroom, and different experiment rumen fluid. In the present study, compost gas production extent was 64 ml at 24 h after incubation. Gas production extent of intact compost silage (with bed soil) with 7.5 and 15% molasses were found about 54 and 65 mL per g DM, respectively. These values in de-soiled compost silage with 7.5 and 15% of molasses were reported about 56.88 and 64.76 mL/g DM, respectively (Kalvandi et al. 2018). The growth rate of mycelium, cultivar type, and harvest stage at sampling time can be explained these different results between studies in over the world. Some of factors affecting the gas production data can be related with harvesting time, amounts of soluble and insoluble carbohydrates, the amount and origin of rumen fluid, rumen fluid donor type, rumen fluid donor diet, and rumen fluid collection time (Taghizadeh et al. 2013).


In vitro DM disappearance

The results of DMD values and degradability coefficients are presented in Table 3 and Table 4, respectively. DMD values were significantly different at all incubation times for experiment feeds.


Table 2 In vitro gas production characteristics (mL/g DM) of feed samples (n=5)


1 A: potential gas production (mL g−1 DM) and c: fractional rate of gas production (h−1).

The means within the same column with at least one common letter, do not have significant difference (P>0.05).

SEM: standard error of the means.


Table 3 In vitro dry matter degradability (% DM; n=3)


The means within the same column with at least one common letter, do not have significant difference (P>0.05).

SEM: standard error of the means.


Table 4 In vitro dry matter degradation characteristics and effective degradation of experiment feeds (% DM)


1, 2, 3 Constants in the equation P= a + b (1-e-ct)


P: level of degradation at time t.

a: readily soluble fraction.

b: insoluble fraction but degradable in rumen.

c: rate of degradation of b per hour.

Effective degradation (k=0.02; 0.05; 0.08)= effective degradability calculated with outflow rates of 2, 5, and 8%.

The means within the same column with at least one common letter, do not have significant difference (P>0.05).

SEM: standard error of the means.


This value for mushroom stem, alfalfa, and compost treatments at 48 h of incubation were 75.3, 49.3, and 44.8, respectively (based on DM %). High correlation was observed between DM and CP disappearance both in vitro and in situ, with alfalfa DMD levels of 50.4 and 48.4 in vitro and in situ, respectively (Paya et al. 2008). Another study reported a DMD value about 48.56 at 48 h for alfalfa using the in situ method (Taghizadeh et al. 2013). In a similar time, a DMD value of 49.33% was observed for alfalfa in the current study. To compare our results with other diet ingredients in livestock diets, in situ evaluation of DMD levels for alfalfa, red clover, and wheat straw led to values of 37, 37.5, and 72.1, respectively, after 48 h of incubation (Paya et al. 2008). A comparison between the degradability of wheat straw (72.5) and mushroom stem (75.3) indicates rather similar DM degradability at 48 h of incubation. However, the degradability process of these two feeds revealed a faster process for the mushroom stem than the wheat straw. It should be noted that results from other studies for DMD of wheat straw and mushroom stem compost are not available. Nonetheless, an in situ study on rice straw compost reported DMD levels of 607, 675, and 689 g/kg at 24, 48, and 72 h of incubation, respectively (Kim et al. 2011). These differences can be due to differences in chemical composition of rice straw compost and its difference with that of wheat straw.


Table 5 Evaluated OMD, ME, NEl and SCFA by in vitro gas production results


OMD: organic matter digestibility (%); ME: metabolizable energy (MJ kg-1 DM); NEl: net energy of lactation (MJ kg-1 DM) and SCFA: short chain fatty acid (mmol 200 mg-1 DM).

The means within the same column with at least one common letter, do not have significant difference (P>0.05).

SEM: standard error of the means.


Due to the low ADF content in the compost, the DM degradability of the compost was expected to be higher than that obtained, but due to the high amount of crude ash in (Table 1), the DM degradability of the compost was lower than the other experiment feeds. The outflow rates of 0.02, 0.05, and 0.08 per hour reduced effective DM degradability with increasing exit rates. Even so, mushroom stem had the highest effective degradability at all passage speeds indicating that mushroom stem can also be used in high-yield livestock diets with high levels of DM intake.


Estimated metabolizable energy, net energy of lactation, digestible organic matter, and short-chain fatty acids

Estimated parameters of gas production for instance ME, NEl, OMD, and SCFAs are shown in Table 5. The compost contained the highest OMD (based on DM %) with no statistical differences despite the numerical differences in this parameter between the compost and mushroom stem. Alfalfa showed the least amount of OMD compared to the other experiment feeds. The calculated ME (MJ/kg DM) was significantly different among the three feeds, with values of 8.01, 7.15, and 4.70 for the mushroom stem, alfalfa, and compost, respectively. ME values of 9.7 and 7.7 MJ/kg DM were found for alfalfa and mushroom stem, respectively (Yousefi, 2016) and 8.00 MJ/kg DM for mushroom residue (Marino et al. 2010). Since some values such as gas production at 24 h plus CP and fat were used in ME prediction formula, a change in each of these elements can be expected differences result in calculated ME. NEl calculated for the stem, alfalfa, and compost contained 4.82, 4.02, and 2.29 MJ/kg DM, respectively. A value of 15.5 MJ obtained for full flowering alfalfa with 15 percent protein in dairy NRC (2001) is higher than that of this research. According to the NRC (2001) report on dairy cows, NDF is on average less digestible compared to non-fibrous carbohydrates; therefore, NDF concentration in feeds or diets is negatively correlated with the energy concentration. The NDF chemical composition (cellulose, hemicellulose, and lignin ratios) affects NDF digestibility. Thus, feeds and diets with similar NDF concentrations do not necessarily have the same NEl concentrations, and some feeds or diets with high NDF may contain higher NEl than those with lower NDF concentrations. The calculated SCFAs (mmol/200 mg DM) were significantly different between the three treatments of mushroom stem (0.78), alfalfa (0.68), and compost (0.28).



Evaluation of nutritional compositions of white mushroom stem, ME and NEl values, DOM levels, and DM and CP digestibility, demonstrates that this byproduct has higher nutritional value than late flowering alfalfa. Hence, it can be used in ruminant diet at a lower price than alfalfa due to its availability in different parts of the country.



The authors gratefully acknowledge the financial support for this work that was provided by University of Tabriz.

Bakhshi N., Ghorbani G.R., Rahmani H.R. and Samie A. (2006). Effect of probiotic and milk feeding frequency on performance of dairy Holstein calves. Int. J. Dairy Sci. 1(2), 113-119.
Bayatkouhsar J., Tahmasebi A.M., Naserian A.A., Mokarram R.R. and Valizadeh R. (2013). Effects of supplementation of lactic acid bacteria on growth performance, blood metabolites and fecal coliform and lactobacilli of young dairy calves. Anim. Feed Sci. Technol. 186, 1-11.
Bryszak M., Szumacher-Strabel M., El-Sherbiny M., Stochmal A., Oleszek W. and Roj E. (2019). Effects of berry seed residues on ruminal fermentation, methane concentration, milk production, and fatty acid proportions in the rumen and milk of dairy cows. J. Dairy Sci. 102(2), 1257-1273.
Frizzo L.S., Zbrun M.V., Soto L.P. and Signorini M.L. (2011). Effects of probiotics on growth performance in young calves: A meta-analysis of randomized controlled trials. Anim. Feed Sci. Technol. 169, 147-156.
Fuller R. (1989). Probiotics in man and animals. A review. J. Appl. Microbiol. 66, 365-378.
Higgins J.P.T. and Thompson S.G. (2002). Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539-1558.
Hossein Abadi M., Dehghan-Banadaky M. and Zali A. (2013). The effect of feeding of bacterial probiotic in milk or starter on growth performance, health, blood and rumen parameters of suckling calves. Res. Anim. Prod. 4, 57-69.
Hosseini P., Abdi-Benemar H., Mirzaei-Aghjehgheshlagh F., Mazaheri J. and Jafari P. (2017). Effect of an Iranian probiotic (Lactofeed) on feed intake and performance of female Holstein calves. Pp. 1-5 in Proc. 1st Int. Conf. Agric. Sci., Mashhad, Iran.
Huang M.L., Huang J.Y., Kao C.Y. and Fang T.J. (2019). Fermented soymilk and soy and cow milk mixture, supplemented with orange peel fiber or Tremella flava fermented powder as prebiotics for high exopolysaccharide-producing Lactobacillus pentosus SLC 13. J. Sci. Food Agric. 99(9), 4373-4382.
Khan M.A., Weary D.M. and von Keyserlingk M.A.G. (2011). Invited review: Effects of milk ration on solid feed intake, weaning and performance in dairy heifers. J. Dairy Sci. 94, 1071-1081.
Kmet V., Flint H.J. and Wallace R.J. (1993). Probiotics and manipulation of rumen development and function. Arch. Anim. Nutr. 44, 1-10.
Laborde J.M. (2008). Effects of probiotics and yeast culture on rumen development and growth of dairy calves. MS Thesis. Louisiana State Univ., Baton Rouge, Louisiana.
Lean I.J., Rabiee A.R., Duffield T.F. and Dohoo I.R. (2009). Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications. J. Dairy Sci. 92, 3545-3565.
Michael H., Thornton S., Xie M. and Tian L. (2019). Exact inference on the random-effects model for meta-analyses with few studies. Biometrics. 75, 485-493.
Mirzaei-Alamouti H., Kazemi-Joujili M., Amanlou H. and Vazirigohar M. (2015). Effect of feeding frequency on digestibility, milk production and composition in lactating cows: A meta-analysis and meta-regression. Iranian J. Anim. Sci. 46, 301-315.
Mohamadi-Roodposhti P. and Dabiri N. (2012). Effects of probiotic and prebiotic on average daily gain, fecal shedding of Escherichia coli, and immune system status in newborn female calves. Asian-Australasian J. Anim. Sci. 25, 1255-1261.
Moran J.B. (2011). Factors affecting high mortality rates of dairy replacement calves and heifers in the tropics and strategies for their reduction. Asian Australasian J. Anim. Sci. 24, 1318-1328.
Moslemipur F., Moslemipur F. and Mostafaloo Y. (2014). Effects of using probiotic and symbiotic in colostrum and milk on passive immunoglobulin transfer rate, growth and health parameters of calf. J. Rumin. Res. 1(4), 19-30.
Mravčáková D., Váradyová Z., Kopčáková A., Čobanová K., Grešáková Ľ. and Kišidayová S. (2019). Natural chemotherapeutic alternatives for controlling of haemonchosis in sheep. BMC Vet. Res. 15(1), 1-13.
Rabiee A.R., Breinhild K., Scott W., Golder H.M., Block E. and Lean I.J. (2012). Effect of fat additions to diets of dairy cattle on milk production and components: A meta-analysis and metaregression. J. Dairy Sci. 95, 3225-3247.
Rosmini M.R., Sequeira G.J., Guerrero-Legarreta I., Martí L.E., Dalla-Santina R., Frizzo L. and Bonazza J.C. (2004). Probiotic production for meat animals: importance of using indigenous intestinal microbiota. Rev. Mex. Ing. Quim. 3, 181-191.
Seide S.E., Röver C. and Friede T. (2019). Likelihood-based random-effects meta-analysis with few studies: Eempirical and simulation studies. BMC Med. Res. Methodol. 19, 16-24.
Seifzadeh S., Mirzaei Aghjehgheshlagh F., Abdi H., Navidshad B. and Ramazani M. (2015). Effect of an herbal medicine and probiotic on feed intake in male Holstein calves. Pp. 1-5 in Proc. 2nd Natl Conf. Appl. Res. Agric. Sci., Tehran, Iran.
Seifzadeh S., Mirzaei Aghjehgheshlagh F., Abdi-benemar H., Seifdavati J. and Navidshad B. (2017). The effects of a medical plant mix and probiotic on performance and health status of suckling Holstein calves. Italian J. Anim. Sci. 16(1), 44-51.
Turner R.M., Bird S.M. and Higgins J.P. (2013). The impact of study size on meta-analyses: Examination of underpowered studies in Cochrane reviews. PLoS One. 8(3), e59202.
Zhang R., Zhou M., Tu Y., Zhang N.F., Deng K.D., Ma T. and Diao Q.Y. (2016). Effect of oral administration of probiotics on growth performance, apparent nutrient digestibility and stress-related indicators in Holstein calves. J. Anim. Physiol. Anim. Nutr. 100(1), 33-38.
Volume 10, Issue 2
June 2020
Pages 213-219
  • Receive Date: 01 July 2019
  • Revise Date: 21 September 2019
  • Accept Date: 15 October 2019
  • First Publish Date: 01 June 2020