Statistical modeling of ruminal pH parameters from dairy cows based on a meta-analysis

Changes in Parameters Registered by Innovative Technologies in Cows with Subclinical Acidosis

The hypothesis of this study was that there were changes in biomarkers registered by innovative technologies in cows with subclinical acidosis. The aim of this study was to identify changes in the in-line milk fat-to-protein ratio and cow feeding behaviors such as reticulorumen pH, reticulorumen temperature, cow activity, and water intake with subclinical acidosis. From a total of 98 cows, 59 cows were selected to meet the following criteria (2 or more lactations, with 31 days in milk (DIM)). The selected animals were separated into two groups based on general clinical examination and reticulorumen pH: the subclinical acidosis group (SCA, n = 23) and the healthy group (HC, n = 36). During the diagnosis of subclinical acidosis and following the clinical examination of the healthy group using the BROLIS HerdLine system, the daily averages of milk yield (kg/day), milk fat (%), milk protein (%), and the milk fat-to-protein ratio were recorded. Simultaneously, by using Smaxtec technology, reticulorumen parameters and cow activity, including pH, temperature (°C), rumination time (minutes/day), and water intake (hours/day), were registered. Changes in parameters measured using innovative technologies were able to identify cows with subclinical acidosis. Cows with subclinical acidosis had a lower reticulorumen pH by 18.8% (p < 0.0001), a decreased milk yield by 10.49% (p < 0.001), a lower milk fat-to-protein ratio by 11.88% (p < 0.01), and a decreased rumination time by 6.59% (p < 0.01). However, the activity of these cows was higher by 57.19% (p < 0.001) compared to healthy cows. From a practical point of view, we suggest that veterinarians and farmers track parameters such as reticulorumen pH, milk yield, milk fat-to-protein ratio, rumination time, and activity for the identification of subclinical acidosis.

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = -0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Metagenomics analysis reveals differences in rumen microbiota in cows with low and high milk protein percentage

Variation exists in milk protein concentration of dairy cows of the same breed that are fed and managed in the same environment, and little information was available on this variation which might be attributed to differences in rumen microbial composition as well as their fermentation metabolites. This study is aimed at investigating the difference in the composition and functions of rumen microbiota as well as fermentation metabolites in Holstein cows with high and low milk protein concentrations. In this study, 20 lactating Holstein cows on the same diet were divided into two groups (10 cows each), high degree of milk protein group (HD), and low degree of milk protein (LD) concentrations based on previous milk composition history. Rumen content samples were obtained to explore the rumen fermentation parameters and rumen microbial composition. Shotgun metagenomics sequencing was employed to investigate the rumen microbial composition and sequences were assembled via the metagenomics binning technique. Metagenomics revealed that 6 Archaea genera, 5 Bacteria genera, 7 Eukaryota genera, and 7 virus genera differed significantly between the HD and LD group. The analysis of metagenome-assembled genomes (MAGs) showed that 2 genera (g__Eubacterium_H and g__Dialister) were significantly enriched (P < 0.05, linear discriminant analysis (LDA) > 2) in the HD group. However, the LD group recorded an increased abundance (P < 0.05, LDA > 2) of 8 genera (g__CAG-603, g__UBA2922, g__Ga6A1, g__RUG13091, g__Bradyrhizobium, g__Sediminibacterium, g__UBA6382, and g__Succinivibrio) when compared to the HD group. Furthermore, investigation of the KEGG genes revealed an upregulation in a higher number of genes associated with nitrogen metabolism and lysine biosynthesis pathways in the HD group as compared to the LD group. Therefore, the high milk protein concentration in the HD group could be explained by an increased ammonia synthesis by ruminal microbes which were converted to microbial amino acids and microbial protein (MCP) in presence of an increased energy source made possible by higher activities of carbohydrate-active enzymes (CAZymes). This MCP gets absorbed in the small intestine as amino acids and might be utilized for the synthesis of milk protein. KEY POINTS: • Rumen microbiota and their functions differed between cows with high milk protein % and those with low milk protein %. • The rumen microbiome of cows with high milk protein recorded a higher number of enriched genes linked to the nitrogen metabolism pathway and lysine biosynthesis pathway. • The activities of carbohydrate-active enzymes were found to be higher in the rumen of cows with high milk protein %.

On-farm evaluation of multiparametric models to predict subacute ruminal acidosis in dairy cows

This research aimed: (i) to evaluate on-farm (FARM data) multiparametric models developed under controlled experiment (INRAE data) and based on non-invasive indicators to detect subacute ruminal acidosis (SARA) in dairy cows. We also aimed to recover high discrimination capacity, if needed, by (ii) building new models with combined INRAE and FARM data; and (iii) enriching the models increasing from 2 to 5 indicators per model. For model enrichment, we focused on indicators determinable on-farm by quick and inexpensive routine analysis. Fifteen commercial dairy farms were selected to cover a wide range of SARA risk. In each farm, four Holstein early-lactating healthy primiparous cows were selected based on their last on-farm recording of milk yield and somatic cell count analysis. Cows were equipped with a reticulo-rumen pH sensor. The pH kinetics were analysed over a subsequent 7-day period. Relative pH indicators were used to classify cows with or without SARA. Milk, blood, faeces, and urine were collected for analysis of the indicators included in the models developed by Villot et al. (2020) on INRAE data that were externally evaluated using FARM data. Then, new models based on the same indicators were developed combining INRAE and FARM data to test whether a possible loss in performance was due to a limited validity domain of model by Villot et al (2020). Finally, the models developed combining INRAE and FARM data were adapted to the on-farm application and enriched by increasing indicators from 2 to 5 per model using linear discriminant analysis and leave-one-out cross-validation. The sensitivities (true-positive rate) in external evaluation on FARM data were substantially lower than those from cross-validation by Villot et al. (2020) (range: 0.1-0.75 vs 0.79-0.96, respectively), and the specificities (true-negative rate) showed a larger range with lower minimum values (range: 0.18-1.0 vs 0.62-0.97, respectively). The sensitivities of new models developed combining INRAE and FARM data ranged from 0.63 to 0.77. Models involving blood cholesterol, β-hydroxybutyrate, haptoglobin, milk and blood urea, and models involving milk fat/protein ratio, dietary starch proportion, and milk fatty acids had the highest performances, whereas models including sieved faecal residues and urine pH had the lowest. Enriching models to three indicators per model improved sensitivity and specificity, but the inclusion of more indicators was less or not effective. Larger field trials are required to validate our results and to increase variability and validity domain of models.

Relationship between reticular pH parameters and potential on-farm indicators in the early lactation of dairy cows

Subacute ruminal acidosis (SARA) is an important nutritional disorder affecting animal welfare and economy of milk production. Definitions rely on ruminal pH but due to limitations of its measurement, indicators reflecting low pH are highly desirable. The aim of this study was to investigate the relationship between reticular pH and 18 on-farm indicators in milk, blood, faeces, urine and chewing behaviour in early lactating dairy cows. Ten farms were visited for 3 weeks and in total samples of 100 cows (10 per farm) were taken. The statistics and graphical visualization were performed using Pearson correlation and linear regression models on an animal individual level as well as with linear mixed models. Eight indicators (milk fat, fat-to-protein ratio, rumination time, feed intake time, rumination frequency, rumination boluses, lying time and faecal pH) were statistically significant associated with the daily animal individual reticular pH average. However, none of the models including the potential explanatory variables explained more than 5% of the pH variations. The study confirms the necessity of pH measurement to detect SARA risk animals in early lactation dairy cows.

Comprehensive Review on the Interactions of Clay Minerals With Animal Physiology and Production

Clay minerals are naturally occurring rock and soil materials primarily composed of fine-grained aluminosilicate minerals, characterized by high hygroscopicity. In animal production, clays are often mixed with feed and, due to their high binding capacity towards organic molecules, used to limit animal absorption of feed contaminants, such as mycotoxins and other toxicants. Binding capacity of clays is not specific and these minerals can form complexes with different compounds, such as nutrients and pharmaceuticals, thus possibly affecting the intestinal absorption of important substances. Indeed, clays cannot be considered a completely inert feed additive, as they can interfere with gastro-intestinal (GI) metabolism, with possible consequences on animal physiology. Moreover, clays may contain impurities, constituted of inorganic micronutrients and/or toxic trace elements, and their ingestion can affect animal health. Furthermore, clays may also have effects on the GI mucosa, possibly modifying nutrient digestibility and animal microbiome. Finally, clays may directly interact with GI cells and, depending on their mineral grain size, shape, superficial charge and hydrophilicity, can elicit an inflammatory response. As in the near future due to climate change the presence of mycotoxins in feedstuffs will probably become a major problem, the use of clays in feedstuff, given their physico-chemical properties, low cost, apparent low toxicity and eco-compatibility, is expected to increase. The present review focuses on the characteristics and properties of clays as feed additives, evidencing pros and cons. Aims of future studies are suggested, evidencing that, in particular, possible interferences of these minerals with animal microbiome, nutrient absorption and drug delivery should be assessed. Finally, the fate of clay particles during their transit within the GI system and their long-term administration/accumulation should be clarified.

Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows

A routine monitoring for subacute ruminal acidosis (SARA) on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short- and medium-chain FA, higher levels of C17:0, odd- and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.

The Milk Fat-to-Protein Ratio as Indicator for Ruminal pH Parameters in Dairy Cows: A Meta-Analysis

Subacute ruminal acidosis (SARA) represents one of the most important nutritional disorders in high-producing dairy farms. The determination of ruminal pH is a key factor for the diagnosis of SARA. However, measuring ruminal pH in the field is not practicable. Therefore, indicators that reflect the ruminal pH are in demand. The main objective of this study was to examine the relationship between the milk fat-to-protein ratio (FPR) and ruminal pH parameters (daily mean pH, daily time with pH < 5.8, and pH range) on a meta-analytical level including 47 studies with 189 treatment means. Besides the FPR, it was examined how a stepwise extension of further indicators (milk yield, rumination time, and dietary starch and structure effectiveness) can improve the prediction of ruminal pH parameters. Significant associations between milk FPR and ruminal pH parameters were found. The inclusion of further on-farm indicators improved the prediction of daily mean ruminal pH up to Rm2 = 0.46 and time with pH < 5.8 up to Rm2=0.58. Still, a considerable part of variability was explained by the random factor study. Additional information (dietary PUFA content) may improve the models in further investigations.

Modeling reticular and ventral ruminal pH of lactating dairy cows using ingestion and rumination behavior

The prevention and control of metabolic and digestive diseases is an enormous challenge in dairy farming. Subacute ruminal acidosis (SARA) is assumed to be the most severe feed-related disorder and it impairs both animal health and economic efficiency. Currently, ruminal pH as well as variables derived from the daily pH curve are the main indicators for SARA. The objective of this study was to explain the daily pH course in the ventral rumen and reticulum of dairy cows using ingestion pattern and rumination behavior data gathered by automated data recording systems. The data of 13 ruminally fistulated lactating cows were collected at the experimental station of the Friedrich-Loeffler-Institut (Brunswick, Germany). The data included continuous pH measurements, which were recorded simultaneously in the reticulum by pH-measuring boluses and in the ventral rumen by a separate data logger. In addition, rumination behavior was measured using jaw movement sensors, and feed and water intakes were recorded by transponder-assisted systems. Milk yield and body weight were determined during and after each milking, respectively. For statistical evaluation, the data were analyzed using time-series modeling with multiple linear mixed regressions. Before applying the developed mathematical statistical modeling, we performed a plausibility assessment to ensure data quality. The major part of the mathematical statistical modeling consisted of data preparation, where all variables were transformed into a uniform 1-min resolution. Signal transformations were used to model individual feed and water intakes as well as rumination behavior events over time. Our results indicated that diurnal pH curves of both the reticulum and ventral rumen could be predicted by the transformed feed and water intake rates. Rumination events were associated with a marginal temporal increase in pH. We observed that the pH of the ventral rumen was delayed by approximately 37 min compared with that of the reticulum, which was therefore considered in the modeling. With the models developed in this study, 67.0% of the variance of the reticular pH curves and 37.8% of the variance of the ruminal pH curves could be explained by fixed effects. We deduced that the diurnal pH course is, to a large extent, associated with the animal's individual feed intake and rumination behavior.