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Accurate measurements of concentration and ruminal degradability of corn silage starch is necessary for formulation of diets that meet the energy requirements of dairy cows. Five corn silage hybrids ensiled for 0 (unfermented), 30, 60, 120, and 150 d were used to determine the effects of ensiling time on starch degradability of corn silage. In addition, the effects of grind size of silage samples on 7-h in vitro starch degradability and the relationship between in vitro, in situ and near-infrared reflectance spectroscopy (NIRS) starch degradability were studied. In situ disappearance of corn silage starch increased from 0 to 150 d of ensiling, primarily as a result of an increase in the washout or rapidly degraded fraction of starch, particularly during the first 60 d of ensiling. When analyzed in vitro and by NIRS, ensiling time increased corn silage starch degradability either linearly or to a greater extent during the first 2 mo of ensiling. Differences in in situ starch disappearance among corn silage hybrids were apparent during the first 2 mo of ensiling but were attenuated as silages aged. No differences among hybrids were detected using a 7-h in vitro starch digestibility approach. Results from the in vitro subexperiment indicate that 7-h in vitro starch degradability was increased by reducing grind size of corn silage from 4 to 1 mm, regardless of ensiling duration. Fine grinding corn silages samples (i.e., 1-mm sieve) allowed distinguishing low- from medium- and high-starch degradability rated hybrids. Correlations among in situ, in vitro and NIRS measurements for starch degradability were medium to high (r ≥0.57); however, agreement among methods was low (concordance correlation coefficient ≤0.15). In conclusion, ensiling time linearly increased degradation rate of corn silage resulting in greater in situ starch disappearance after 150 d of ensiling. Reductions in grind size from 4 to 1 mm resulted in greater in vitro starch degradability, regardless of e

The volatile organic compounds (VOC) are the fermentation products and used to assess the quality of ensiled forages. The study aimed to monitor the VOC concentrations in silage and haylage by GC/FID system and to reveal the relationships among these different end-fermentation products by carrying out a multivariate analysis. Forty-six compounds were quantified by GC/FID system in a single chromatographic run. The average repeatability and reproducibility coefficients of variation obtained on standard solutions, composed by 0.1 mL/L of each VOC (solution A) or volatile fatty acids (VFA, solution B), were 0.028 and 0.017 (absolute value), respectively. The average repeatability and reproducibility coefficients of variation obtained on silage samples were 0.026 and 0.027, respectively. Mean values of recovery obtained on the standard solutions at different concentration levels of compounds were in the range 0.95–1.04 (absolute values), and no effect of analyte concentration was observed on recovery. When multivariate analysis was carried out on whole database, 7 factors were retained and they explained 0.50 of the total variability. The factors were interpreted for their biological meanings, based on specific VOC loading on each extracted latent structure. Alfalfa haylage and ryegrass silage were characterized by higher contents of valeric and butyric acids, methyl and ethyl butyrate, 2,3-butaneldiol, 1-butanol, and 2-butanol compared with the other fermented products. Except for mixture crop silage, all the fermented products containing legume crops had higher levels of 2-methyl-1-propanol, 2-methyl-1-butanol, 3-methyl-1-butanol, 2-phenyl ethanol, and methanol and lower contents of ethanol, ethyl lactate, and ethyl acetate compared with the other fermented products. Silage containing grass crops had higher levels of acetic and lactic acids, and 1,2-propanediol respect to the other investigated products. The most part of the investigated fermented products were charac

Parmigiano Reggiano is a hard cheese with a Protected Designation of Origin (PDO) certification that also applies to the grated product. The percentage of rind in grated Parmigiano Reggiano is regulated by the PDO production Specification and must not exceed the limit of 18% (w/w). The present study evaluates the potential of an untargeted foodomics approach to detect anomalous inclusions of rind in grated Parmigiano Reggiano cheese. In particular, a combined metabolomics and peptidomics approach was used to detect potential markers of counterfeits (rind > 18%). In the framework of realistic food integrity purposes, non-Parmigiano Reggiano grated samples and different ripening times were also considered. Untargeted metabolomics allowed detecting 347 compounds, with a prevalence of amino acids and peptide derivatives, followed by fatty acyls and other compounds (such as lactones, ketones, and aldehydes) typically related to proteolysis and lipolysis events. Overall, the unsupervised multivariate statistics showed that the ripening time plays a hierarchically higher impact than rind inclusion in determining the main differences in the chemical profiles detected. Interestingly, supervised statistics highlighted distinctive markers for ripening time and rind inclusion, with only 16 common discriminant compounds being shared between the two conditions. The best markers of rind inclusion > 18% were 2-hydroxyadenine (VIP score = 1.937; AUC value = 0.83) and the amino acid derivatives argininic acid (VIP score = 1.462; AUC value = 0.75) and 5-hydroxyindole acetaldehyde (VIP score = 1.710; AUC value = 0.86). Interestingly, the medium-chain aldehyde 4-hydroperoxy-2-nonenal was a common marker of both ripening time and anomalous rind inclusion (>18%), likely arising from the lipid oxidation processes. Finally, among potential marker peptides of rind inclusion, the alpha-S1 casein proteolytic product (F)FVAPFPEVFGK(E) could be identified.

In this work, milk samples collected in a cohort of intensive dairy farms of the Po Valley (Italy) were screened for their (poly)-phenolic profile to check the occurrence of phenolic metabolites of biological interest. The selected dairy farms were previously classified on the basis of their cow feeding system, considering the utilization of corn silage as the main ingredient of the rations. Overall, ultra-high-pressure liquid chromatography coupled with mass spectrometry using an Orbitrap analyzer, followed by unsupervised and supervised statistics, allowed identifying clear different phenolic distributions in the milk samples. Accordingly, a great variability in the phenolic profiles of the different milk samples was observed, with two main phenolic clusters outlined by the unsupervised hierarchical clustering approach and not fully correlated to the nutritional strategy considered. The variables’ importance in the projection approach allowed selecting the most important metabolites, resulting in samples’ discrimination. Among the most discriminative compounds, we found phenolic metabolites (such as hippuric acid and 4-hydroxyhippuric acid), followed by lignans (such as enterolactone) and isoflavonoids (such as equol and O-desmethylangolensin). Taken together, our findings suggested that both the feeding systems and the ability of dairy cows to process parent phenolic compounds were the main factors providing the final (poly)-phenolic profile of the milk samples. Future targeted and ad hoc studies appear of great interest to evaluate the potential biological effects of these compounds on cow health

Sixty-six dairy farms (as average, 167.0 ± 106.5 milking cows and milk yield of 31.4 ± 5.1) were visited to identify different nutritional approaches adopted in dairy cows diet formulation. Forage, TMR, faecal, and milk samples from lactating herds were collected and diet composition, in vitro methane production, in vivo nutrient digestibility, faecal fermentation profile and milk yield and milk quality were characterised. A hierarchical cluster analysis was applied and six nutritional approaches in diet formulation corresponding with six different clusters (CL) were identified. The CL discriminated the farm feeding choices in the following nutritional strategies, based on high use of: (i) high moisture corn (HMC) and legume silage, (ii) compound feed, (iii) corn and soy meals, (iv) HMC and soy meal, (v) corn meal and protein compound feeds, or (vi) HMC and protein compound feed strategies, respectively for CL1 to CL6. The milk yield tended (p =.061) to differ among CL. The greatest (>1.60) feed efficiency was calculated for CL1 and 5, whereas the lowest (<1.45) for CL 2. The CL2 and 5 had the highest feed costs (i.e. 0.24 or 0.22 €/kg of milk, respectively). Interestingly, the high use of HMC or steam flaked corn resulted associated to higher farm performances. Particularly, HMC and legume silage (CL1), HCM and soy meal (CL4) and HCM and protein compound feed (CL6) nutritional strategies resulted more efficient and profitable, having the greatest feed efficiency, protein and starch degradability as well as income over feed costs. Differentiate carbohydrates in dairy cow diets seems highly convenient.HIGHLIGHTS Six different nutritional strategies were identified based on use of single ingredients and a great variability in both economic and feed efficiency was observed for dairy farms clustering into different groups The use of high moisture ear corn in substitution of corn meal increased dry matter intake, feed efficiency, milk yield and lactose co

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as “economic efficiency,” “energy utilization,” “break-even point,” “milk-to-feed price,” and “bargaining power of the farm,” in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the p

The chemical composition of milk can be significantly affected by different factors across the dairy supply chain, including primary production practices. Among the latter, the feeding system could drive the nutritional value and technological properties of milk and dairy products. Therefore, in this work, a combined foodomics approach based on both untargeted metabolomics and metagenomics was used to shed light onto the impact of feeding systems (i.e., hay vs. a mixed ration based on hay and fresh forage) on the chemical profile of raw milk for the production of hard cheese. In particular, ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) was used to investigate the chemical profile of raw milk (n = 46) collected from dairy herds located in the Po River Valley (Italy) and considering different feeding systems. Overall, a total of 3320 molecular features were putatively annotated across samples, corresponding to 734 unique compound structures, with significant differences (p < 0.05) between the two feeding regimens under investigation. Additionally, supervised multivariate statistics following metabolomics-based analysis allowed us to clearly discriminate raw milk samples according to the feeding systems, also extrapolating the most discriminant metabolites. Interestingly, 10 compounds were able to strongly explain the differences as imposed by the addition of forage in the cows’ diet, being mainly glycerophospholipids (i.e., lysophosphatidylethanolamines, lysophosphatidylcholines, and phosphatidylcholines), followed by 5-(30,40-Dihydroxyphenyl)-gamma-valerolactone-40-O-glucuronide, 5a-androstan-3a,17b-diol disulfuric acid, and N-stearoyl glycine. The markers identified included both feed-derived (such as phenolic metabolites) and animal-derived compounds (such as lipids and derivatives). Finally, although characterized by a lower prediction ability, the metagenomic profile was found to be significantly correlat

In this study, an untargeted metabolomics approach based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) was used for investigating changes in chemical profiles of cow milk considering diets based on mycotoxins-contaminated corn silages. For this purpose, 45 milk samples were classified into five clusters according to the corn silage contamination profile, namely (1) low levels of Aspergillus-and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites, and subsequently analyzed by UHPLC-HRMS followed by a multivariate statistical analysis (both unsupervised and supervised statistical approaches). Overall, the milk metabolomic profile highlighted potential correlations between the quality of contaminated corn silages (as part of the total mixed ration) and milk composition. Metabolomics allowed to identify 628 significant milk metabolites as affected by the five levels of corn silage contamination considered, with amino acids and peptides showing the highest metabolite set enrichment (134 compounds). Additionally, 78 metabolites were selected as the best discriminant of the prediction model built, possessing a variable importance in projection score >1.2. The average Log Fold-Change variations of the discriminant metabolites provided evidence that sphingolipids, together with purine and pyrimidine-derived metabolites were the most affected chemical classes. Also, metabolomics revealed a significant accumulation of oxidized glutathione in milk samples belonging to the silage cluster contaminated by emerging Aspergillus toxins, likely involved in the oxidative imbalance. These preliminary findings provide new insights into the potential role of milk metabolomics to provide chemical indicators of mycotoxins-contaminated corn silage fe

A linear programming model that selects the optimal cropping plan and feeds allocation for diets to minimize the whole dairy farm feed costs was developed. The model was virtually applied on 29 high-yielding Holstein-Friesian herds, confined, total mixed ration dairy farms. The average herd size was 313.2 ± 144.1 lactating cows and the average land size was 152.2 ± 92.5 ha. Farm characteristics such as herd structure, nutritional grouping strategies, feed consumption, cropping plan, intrinsic farm limitations (e.g., silage and hay storage availability, water for irrigation, manure storage) and on farm produced forage costs of production were collected from each farm for the year 2017. Actual feeding strategies, land availability, herd structure, crop production costs and yields, and milk and feed market prices for the year 2017 were used as model inputs. Through optimization, the feeding system was kept equal to the actual farm practice. The linear program formulated diets for each animal group to respect actual herd dry matter intake and fulfill actual consumption of crude protein, rumen-degradable and rumen-undegradable fractions of crude protein, net energy for lactation, neutral detergent fiber, acid detergent fiber, forage neutral detergent fiber, and nonfiber carbohydrate. Production levels and herd composition were considered to remain constant as the nutritional requirement would remain unchanged. The objective function was set to minimize the whole-farm feed costs including cash crop sales as income, and crop production costs and purchased feed costs as expenses. Optimization improved income over feed costs by reducing herd feed costs by 7.8 ± 6.4%, from baseline to optimized scenario, the improved was explained by lower feed costs per kilogram of milk produced due to a higher feed self-sufficiency and higher income from cash crop. In particular, the model suggested to maximize, starting from baseline to optimized scenario, the net energy for lactation (+8.

The feeding system represents one of the main factors driving raw milk composition, thus determining differences in nutritional value and technological properties. In this preliminary study, untargeted metabolomics with ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) coupled with both unsupervised and supervised multivariate statistics was used to investigate the chemical profile of bulk milk collected from dairy cows (n = 103) following different feeding regimens, being corn silage (MS-FS, n = 51), hay (H-FS, n = 35) and a mixed ration consisted in fresh forage and hay (MR-FS, n = 17). Overall, a total of 1686 metabolites was identified by means of UHPLC-QTOF, with significant differences (p < 0.05) between the three feeding regimens under investigation. The metabolites detected mainly belonged to lipids (mainly glycerophospholipids and triglycerides), followed by oligopeptides, steroid derivatives, and secondary metabolites (such as phenolic compounds and terpenoids). Interestingly, multivariate statistics applied to the metabolomics data revealed intriguing differences in the discriminant markers detected. The markers identified included both feed-derived (such as phenolic metabolites) but also animal-derived compounds (such as fatty acids). Therefore, our results provide comprehensive insights into the metabolomics profile of different bulk milk samples, suggesting also an indirect influence of feeding regimens on its chemical signature.

The study was performed on forages (n = 8), nonforage fibrous feeds (n = 10), and crop residues (n = 2). Samples were characterized for in situ NDF degradability (NDFD) at 6, 12, 18, 24, 30, 36, 48, 72, 96, 120, and 240 h of ruminal incubation. Then, samples were characterized for enzymatic NDFD by adopting a multistep enzymatic method consisting of a preincubation (PreInc) phase followed by enzymatic incubation (EnzInc) steps. In the PreInc phase, samples were incubated in a NaOH solution for 0, 30, 60, or 90 min. Then, in the EnzInc phase, samples were first incubated in a buffered enzymatic solution containing hemicellulase, cellulase, and Viscozyme L enzymes. Then, samples were incubated in a xylanase-buffered enzymatic solution. These 2-step EnzInc lasted for a total of 16 (8 h for the first enzymatic step + 8 h for the second enzymatic step), 32 (16 + 16 h), or 48 h (24 + 24 h). The enzymatic NDFD coefficients were increased by increasing both PreInc and EnzInc incubation times, and no PreInc x EnzInc interaction was observed, except for ryegrass hay. On average, enzymatic NDFD increased (P < 0.05) by 0.35, 0.54, or 0.68, respectively, for 30-, 60-, or 90-min PreInc compared with 0-min PreInc. The enzymatic NDFD increased (P < 0.05), on average, by 0.11 in 32-h EnzInc or 0.16 in 48-h EnzInc with respect to 16-h EnzInc. Enzymatic NDFD were used to predict in situ NDFD coefficients by adopting single regression equations. High coefficients of regression (R-2 > 0.80, P < 0.05) and low errors of prediction were measured when specific enzymatic conditions were performed to predict in situ NDFD at intermediate (from 24 to 48 h) ruminal incubation. Generally, worse regression performances were obtained when enzymatic NDFD were used to predict in situ NDFD evaluated after shorter or longer incubation times. The direct prediction of the rate of NDF degradation was not possible using enzymatic NDFD coefficients. Even if the proposed multistep enzymatic method appeared p

A survey regarding crop enterprise management, forages cost of production, dairy cattle management including reproductive management, housing, heat abatement, body condition scoring, nutrition, grouping strategies, and income over feed cost performance, was carried out from December 2016 to January 2018 on 50 dairy farms by the Department of Animal Science, Food and Nutrition of Università Cattolica del Sacro Cuore (Piacenza, Italy). A total of 41 herds (82%) completed the survey. Average herd size was 327 ± 162 lactating cows with the average land size of 160 ± 94 ha per farm. Herds were located in the provinces of Cremona (17), Brescia (8), Mantova (7), Piacenza (5), Cuneo (4), Bergamo (3), Lodi (3), Torino (2), and Venezia (1). These farms sold 32.8 ± 2.01 kg of milk/day per cow, had an annual culling rate of 34.0 ± 4.00%, a calving interval of 14.16 ± 0.58 months., and a 21-days pregnancy rate of 17.05 ± 2.58%. Implementing effective management strategies to contrast the damage caused by Ostrinia nubilalis, Diabrotica spp. and Myocastor coypus were identified as the main crop enterprise challenges. Main forages cultivated were alfalfa and corn silage second seeding with a total cost of production of (€/ha) 1968 ± 362 and 2,581 ± 221, with an average yield of 9.61 ± 1.24 and 17.22 ± 2.46 ton of DM per hectare, respectively. Results of this study can provide useful benchmark or reference for dairy management practices, crops and dairy performances, forages production costs on very well-managed North Italian dairy farms at the present time.Highlights benchmarks for dairy farms management practices, economic and reproductive performance cost of production of forages in northern Italy.

The purpose of this study was to optimize the conditions of a previously proposed enzymatic method used to estimate in situ undigested neutral detergent fiber (uNDF). We used a multi-step enzymatic approach, in which samples were first solubilized in NaOH solutions as a preincubation (PreInc) phase. After rinsing, samples were incubated (24 h at 39°C) in a buffered solution (pH 6) containing hemicellulase, cellulase, and Viscozyme L enzymes (Sigma-Aldrich s.r.l., Milan, Italy), followed by incubation (24 h at 39°C) in a buffered solution (pH 5) containing xylanase. Two sets of experiments were performed: a calibration trial (that tested different PreInc conditions on 9 selected forages) and a validation trial (that verified the results by testing multiple samples of 6 different forage types and a group of fibrous by-products). In the calibration trial, samples (300 mg in Ankom F57 filter bags; Ankom Technology Corp., Fairport, NY) were preincubated at 39°C in a 0.1 M NaOH solution for 90, 180, or 240 min, or in 0.2, 0.5, 1.0, or 2.0 M NaOH solution for 90 min. The results indicated that the best PreInc method, in terms of intra-laboratory repeatability and estimation of reference in situ values, was 90 min in a 0.2 M NaOH solution. Thus, we used this PreInc condition to determine enzymatic uNDF of 257 samples in the validation trial. Although the selected method generally had good accuracy in predicting in situ uNDF, inconsistencies were noted for certain forage types. Overall, when enzymatic uNDF was used to predict the in situ uNDF of all samples, the regression was satisfactory (intercept = 7.098, slope = 0.920, R2 = 0.73). The regression models developed for alfalfa hays, corn silages, and small grain silages had also acceptable regression performances and mean square error of prediction (MSEP) values, and the main sources of MSEP variation were error due to incomplete (co)variation and random error. Even when R2 values were >0.70, the MSEP value of the regre

The study was performed on forages (n = 8), nonforage fibrous feeds (n = 10), and crop residues (n = 2). Samples were characterized for in situ NDF degradability (NDFD) at 6, 12, 18, 24, 30, 36, 48, 72, 96, 120, and 240 h of ruminal incubation. Then, samples were characterized for enzymatic NDFD by adopting a multistep enzymatic method consisting of a preincubation (PreInc) phase followed by enzymatic incubation (EnzInc) steps. In the PreInc phase, samples were incubated in a NaOH solution for 0, 30, 60, or 90 min. Then, in the EnzInc phase, samples were first incubated in a buffered enzymatic solution containing hemicellulase, cellulase, and Viscozyme L enzymes. Then, samples were incubated in a xylanase-buffered enzymatic solution. These 2-step EnzInc lasted for a total of 16 (8 h for the first enzymatic step + 8 h for the second enzymatic step), 32 (16 + 16 h), or 48 h (24 + 24 h). The enzymatic NDFD coefficients were increased by increasing both PreInc and EnzInc incubation times, and no PreInc × EnzInc interaction was observed, except for ryegrass hay. On average, enzymatic NDFD increased (P < 0.05) by 0.35, 0.54, or 0.68, respectively, for 30-, 60-, or 90-min PreInc compared with 0-min PreInc. The enzymatic NDFD increased (P < 0.05), on average, by 0.11 in 32-h EnzInc or 0.16 in 48-h EnzInc with respect to 16-h EnzInc. Enzymatic NDFD were used to predict in situ NDFD coefficients by adopting single regression equations. High coefficients of regression (R2> 0.80, P < 0.05) and low errors of prediction were measured when specific enzymatic conditions were performed to predict in situ NDFD at intermediate (from 24 to 48 h) ruminal incubation. Generally, worse regression performances were obtained when enzymatic NDFD were used to predict in situ NDFD evaluated after shorter or longer incubation times. The direct prediction of the rate of NDF degradation was not possible using enzymatic NDFD coefficients. Even if the proposed multistep enzymatic metho