Silage

Fourteen Belgian Blue calves (1–2 years old) naturally infested with P. ovis mites were included in the animal study. Skin scrapings were collected from each animal for mite counts and mite identification on day-7. The CI was determined for each animal by recording the skin lesions (on both sides of the animal) on a silhouette [22 (link)]. Animals were randomly assigned to treatment and control group using CI as stratification factor.
On day 0, all animals in the treatment group were weighed and injected intramuscularly with dexamethasone (MSD Animal Health, Belgium) at a dose of 0.06 mg/kg body weight. Control animals were injected with the same volume of physiological saline (0.9%). On day 7 and day 14, the treatment was repeated.
All animals were followed for 4 weeks, whereby the CI was determined weekly for each animal as described above. Punch biopsies were taken on day 0, day 7 and day 28 from the edge of active lesions, following the administration of a local anaesthetic (3–4 mL 4% procaine hydrochloride and 0.0036% adrenaline tartrate, KELA, S.C.-epidural, Belgium). The 4 mm biopsy was immediately fixed in 4% formaldehyde and paraffin-embedded for histology and immunohistochemistry. At day-7 and day 28 post treatment, P. ovis mites in the active lesions were counted, as described above.
All animals were housed together in a pen on straw bedding, and were provided with corn silage, grass silage and water ad libitum and a daily ration of 1.5–2.0 kg concentrates per animal. All animals were checked weekly for any adverse reactions to the dexamethasone treatment by clinical examination [24 ] and by ultrasonography (Tringa Linear Vet, Esaote, the Netherlands) to detect (sub)clinical pneumonia. At the end of the animal study all animals were treated topically with amitraz with 2 weeks interval as described above.

The experiment started individually for each cow with the expected day 42 ante partum (ap) and ended at day 110 pp. A total of 59 pluriparous German Holstein dairy cows, including eight rumen- and duodenum-cannulated cows, were assigned to two groups, a control (CON, n = 30) and an L-carnitine group (CAR, n = 29), balanced for numbers of lactation (2–5 lactations), body weight (568–1008 kg), body condition score (2.5–4.75) and fat-corrected milk yield of previous lactation. To circumvent ruminal degradation, the cows in CAR received 125 g of a rumen-protected L-carnitine product (Carneon 20 Rumin-Pro, Kaesler Nutrition GmbH, Cuxhaven, Germany) per cow and day, which was included in the concentrate feed. This amount corresponded to a daily L-carnitine intake of 25 g per cow and day. To balance the fat content of the L-carnitine product, CON obtained an equivalent fat product (BergaFat F-100 HP, Berg+Schmidt GmbH & Co. KG, Hamburg, Germany) as used for the rumen protection of the L-carnitine. The cows were kept in a free-stall barn with slatted floors and cubicles with rubber pads and were rehoused for calving in the calving pen, where a maximum of two cows were kept in one straw bedding box.
Both groups were fed with a partial mixed ration (PMR). Whereas the composition of roughage remained unchanged during the whole trial (70% maize silage and 30% grass silage), the proportion of roughage to concentrate was variable in accordance with the recommendation of nutrient and energy supply of the Society of Nutrition Physiology (GfE). Initially, up to day 1 ap, diets of 80% roughage and 20% concentrate were fed. The amount of concentrate was increased from 30% to 50% up to 14 days pp and from then on, 50% concentrate was constantly fed up to day 110 pp. The PMR was offered by feed-weigh troughs (Roughage Intake Control, System Insentec B.V., Marknesse, The Netherlands) and the supplementary, restricted, pelleted concentrate was provided via concentrate feeding stations (Insentec B.V., Marknesse, The Netherlands). Water was offered for ad libitum intake. The components and the chemical composition of roughages and concentrate feed are shown in Table 1.

In this study, the database was built by collecting the datasets from previously published articles. In detail, literature was obtained through a number of steps, i.e., identification and screening, and then valid articles were inserted into an excel spreadsheet. During the identification process, the search engines, namely Google, Scopus, and Google Scholar, were used for searching the datasets of the previously published articles. The keywords used were lactic acid bacteria, silage quality, bacterial diversity, and fermentation.
The identification process was carried out based on the titles of the collected articles. In this stage, we put general criteria in the article that would be involved in the database. These criteria are as follows: (1) Article must be written in the English language; (2) Only published articles; (3) Collected article must contain a control treatment with at least one experiment of LAB addition among their treatments; and (4) The collected articles must contain at least one parameter on silage microbiome or at least one parameter on silage quality. Here, in this stage, we obtained 181 articles.
The process was continued by scanning the entire abstract of each of the collected articles to ensure that the article is valid to be used in this stage. At this stage, 54 articles were obtained. The screening process was done by reading carefully the entire content of each collected article to determine which one of the collected articles is valid to be inserted into the database. The literature obtained at this stage was 37 articles with 185 studies and 485 datasets. All valid articles were inserted into an excel spreadsheet. Information about articles used in the database is presented in Table 1.
While creating the excel spreadsheet, datasets were divided into main categories which are main and branched cells. In the main cells, we included information on authors, year of publication, treatments, studies, doses, and substrates used as raw materials of the experience. Information on observed parameters was inserted in the branched cells of the created excel spreadsheet. The observed parameters include the chemical composition of silage material (DM, dry matter content; OM, organic matter; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; WSC, water-soluble carbohydrates; EE, ether extract; ASH, the ash content in the fresh material; DM recovery, cellulose and hemicellulose; and ADL, acid detergent lignin), silage quality (pH value; LA, lactic acid; AA, acetic acid; PA, propionic acid; BA, butyric acid; AS, aerobic stability; LAB, lactic acid bacteria; AB, aerobic bacteria; yeasts, yeasts and molds, molds, and the starch content), silage microbiome, and information on sequencing. All the data of the targeted parameters were inserted into the created excel sheet to be ready for evaluation.

The articles were selected following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Moher et al., 2009 (link); Mikolajewicz and Komarova, 2019 (link)), and data analysis were carried out using mixed model methodology as described by Abdelbagi et al. (2021 (link)) and Irawan et al. (2021 (link)). In this methodology, doses and LAB types included in the experiments were treated as a fixed factor, while the studies were treated as a random factor. Different values and means were accepted to be significant if the p-value is <0.05. As shown in Table 1, there were many types of substrates used for ensiling. In this study, to investigate the influence of the substrate, we calculated the interaction effects between the substrate and LAB types as well as doses of LAB, as presented in Tables 2, 3. The dataset presentation in figures was carried out using Microsoft excel 2013. Data of the silage microbiome were extracted from the figures using GetData digitizer version 2.26.0.20 software (http://getdata-graph-digitizer.com/). Before analyzing the relationships among response parameters and treatments, silage quality and silage microbiome were transformed into relative changes in treatment and control. The relationships between parameters and treatments were analyzed using hierarchical cluster analysis and were visualized using the heatmap.2 function from the gplots package in the R Console Version 4.2.1 (R Core Team, 2022 ).