Hasil untuk "Dairy processing. Dairy products"

Swati Sethi, S. Tyagi, R. Anurag

P. Schönfeld, L. Wojtczak

Short- and medium-chain fatty acids (SCFAs and MCFAs), independently of their cellular signaling functions, are important substrates of the energy metabolism and anabolic processes in mammals. SCFAs are mostly generated by colonic bacteria and are predominantly metabolized by enterocytes and liver, whereas MCFAs arise mostly from dietary triglycerides, among them milk and dairy products. A common feature of SCFAs and MCFAs is their carnitine-independent uptake and intramitochondrial activation to acyl-CoA thioesters. Contrary to long-chain fatty acids, the cellular metabolism of SCFAs and MCFAs depends to a lesser extent on fatty acid-binding proteins. SCFAs and MCFAs modulate tissue metabolism of carbohydrates and lipids, as manifested by a mostly inhibitory effect on glycolysis and stimulation of lipogenesis or gluconeogenesis. SCFAs and MCFAs exert no or only weak protonophoric and lytic activities in mitochondria and do not significantly impair the electron transport in the respiratory chain. SCFAs and MCFAs modulate mitochondrial energy production by two mechanisms: they provide reducing equivalents to the respiratory chain and partly decrease efficacy of oxidative ATP synthesis.

A. Lichtenstein, L. Appel, Maya K. Vadiveloo et al.

Poor diet quality is strongly associated with elevated risk of cardiovascular disease morbidity and mortality. This scientific statement emphasizes the importance of dietary patterns beyond individual foods or nutrients, underscores the critical role of nutrition early in life, presents elements of heart-healthy dietary patterns, and highlights structural challenges that impede adherence to heart-healthy dietary patterns. Evidence-based dietary pattern guidance to promote cardiometabolic health includes the following: (1) adjust energy intake and expenditure to achieve and maintain a healthy body weight; (2) eat plenty and a variety of fruits and vegetables; (3) choose whole grain foods and products; (4) choose healthy sources of protein (mostly plants; regular intake of fish and seafood; low-fat or fat-free dairy products; and if meat or poultry is desired, choose lean cuts and unprocessed forms); (5) use liquid plant oils rather than tropical oils and partially hydrogenated fats; (6) choose minimally processed foods instead of ultra-processed foods; (7) minimize the intake of beverages and foods with added sugars; (8) choose and prepare foods with little or no salt; (9) if you do not drink alcohol, do not start; if you choose to drink alcohol, limit intake; and (10) adhere to this guidance regardless of where food is prepared or consumed. Challenges that impede adherence to heart-healthy dietary patterns include targeted marketing of unhealthy foods, neighborhood segregation, food and nutrition insecurity, and structural racism. Creating an environment that facilitates, rather than impedes, adherence to heart-healthy dietary patterns among all individuals is a public health imperative.

K. Helrich

Ye Li, Mei-Rong Lv, Yan-Jin Wei et al.

Shannon D Rezac, Car Reen Kok, Melanie L. Heermann et al.

The popularity of fermented foods and beverages is due to their enhanced shelf-life, safety, functionality, sensory, and nutritional properties. The latter includes the presence of bioactive molecules, vitamins, and other constituents with increased availability due to the process of fermentation. Many fermented foods also contain live microorganisms that may improve gastrointestinal health and provide other health benefits, including lowering the risk of type two diabetes and cardiovascular diseases. The number of organisms in fermented foods can vary significantly, depending on how products were manufactured and processed, as well as conditions and duration of storage. In this review, we surveyed published studies in which lactic acid and other relevant bacteria were enumerated from the most commonly consumed fermented foods, including cultured dairy products, cheese, fermented sausage, fermented vegetables, soy-fermented foods, and fermented cereal products. Most of the reported data were based on retail food samples, rather than experimentally produced products made on a laboratory scale. Results indicated that many of these fermented foods contained 105−7 lactic acid bacteria per mL or gram, although there was considerable variation based on geographical region and sampling time. In general, cultured dairy products consistently contained higher levels, up to 109/mL or g. Although few specific recommendations and claim legislations for what constitutes a relevant dose exist, the findings from this survey revealed that many fermented foods are a good source of live lactic acid bacteria, including species that reportedly provide human health benefits.

A. Lisuzzo, M.C. Alterisio, S. Esposito et al.

ABSTRACT: Mastitis is an udder inflammation and infection causing several economic losses in dairy cows. The milk metabolomic changes associated with clinical or subclinical mastitis have been investigated. However, little is known on milk metabolome associated with intramammary infection. The aim of this study was to investigate the quarter-milk metabolomic profile affected by intramammary infection, subclinical mastitis, and clinical mastitis in dairy cows. A total of 80 quarter-milk samples of multiparous Holstein-Friesian dairy cows were used in this cross-sectional design study. Samples were equally divided into 4 groups: healthy (H; no clinical signs of mastitis, SCC <200,000 cells/mL, negative at bacteriological analysis); intramammary infection (IMI group; no clinical signs of mastitis, SCC <200,000 cells/mL, positive at bacteriological analysis); subclinical mastitis (SCM; no clinical signs of mastitis, SCC ≥200,000 cells/mL, positive at bacteriological analysis); and clinical mastitis (CM; clinical signs of mastitis, SCC ≥200,000 cells/mL, positive at bacteriological analysis). Statistical analysis was conducted by fitting a linear mixed model with the group as the fixed effect, quarter nested within animal as random effect, and DIM as covariate. Analysis identified 45 metabolites, and among them 34 were significantly different among groups. Among these, 18 metabolites (2-aminoadipate, Ala, creatine-phosphate, dimethylamine, N-acetyl-Gly, O-phosphocholine, glucose, lactose, maltose, cis-aconitate, carnitine, fumarate, lactate, phenylacetate, 2-ketobutyrate, acetoacetate, citicoline, and orotate) progressively changed from the H to the CM stage, and other 12 metabolites (Leu, taurine, Val, arabinose, galactose, ribose, acetate, formate, pyruvate, 5-dodecenoic acid, 3-hydroxybutyrate, and ascorbate) differed only in the CM group. These metabolites were related to blood-milk barrier damage, inflammation, oxidative stress, cell proliferation, energy and lipid metabolisms, the citrate (TCA) cycle, systemic energy status, and microorganism metabolism. These results suggest that metabolomic alterations in milk begin to occur when the mammary gland is infected with somatic cells within normal limits and progressively worsen.

Xiuli Li, Xiaowen Wang, Le Sun et al.

ABSTRACT: Arginine is one of the most versatile among the AA because of its important role in multiple functions of the body. However, the effect of Arg supplementation on lactational performance in dairy cows, and the associated influencing factors, have not been well characterized. Weighted mean differences (WMD) of continuous variables from 14 articles published by March 31, 2025, were pooled by random-effects models using the Stata version 17 to explore the details regarding Arg application in dairy cows. Results showed that Arg supplementation increased milk yield (WMD = 1.29 kg/d, [95% CI: 0.58, 1.99]), milk protein yield (WMD = 0.04 kg/d, [95% CI: 0.00, 0.07]), and milk fat yield (WMD = 0.06 kg/d, [95% CI: 0.02, 0.10]) with significant heterogeneities (Varlevel1 < 75%), but had no overall effects on DMI, milk protein concentration, milk fat concentration, lactose yield, and lactose concentration. Moderator analysis revealed that the positive effects of Arg supplementation on milk yield, milk protein yield, and milk fat yield were more prominent in dairy cows in early lactation or being fed an MP-adequate diet or corn silage–based diet. Compared with feeding rumen-protected Arg, Arg supplementation through infusion increased the milk yield (WMD = 1.21 kg/d, [95% CI: 0.48, 1.95]) and had a tendency to increase the milk protein yield (WMD = 0.04 kg/d, [95% CI: 0.00, 0.08]). Regression analysis showed that graded increases of total digestible Arg supply had linear effects on milk yield and milk fat yield. These findings indicated the beneficial effects of supplemental Arg on lactational performance in dairy cows.

A. Mouhanna, L. Rey-Cadilhac, M. Berton et al.

ABSTRACT: Milk fatty acid (FA) composition is an indicator of both farm management and the nutritional quality of dairy products. Few studies have linked diverse, multicountry observational farm data to milk FA variation through a validated machine learning workflow. We surveyed 75 European farms representing a broad gradient of production intensity, analyzed seasonally pooled bulk milk samples for 12 FA traits, and examined 29 management practices. A 2-stage workflow combined optimized random forests (RF) to predict FA and rank practices, with conditional inference trees to visualize management synergies and trade-offs. RF models achieved high predictive accuracy (R2 ≥ 0.50) for 8 traits: α-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid, CLA, n-6:n-3 PUFA ratio, linoleic acid, vaccenic acid (VA), and branched-chain fatty acids (BCFA). Conditional inference trees models had comparable accuracy (R2 ≥ 0.50) for all these traits except VA and BCFA. Across models, fresh grass intake, maize silage and concentrate use, stocking rates, herd size, milk yield, and mineral fertilizer were dominant drivers, together explaining most variance in the models. Farms adopting low-input, pasture-based strategies were consistently associated with lower n-6:n-3 PUFA ratios and higher n-3 PUFA, CLA, and BCFA in milk, highlighting synergies alongside trade-offs between production intensity and nutritional quality. Although this profile is associated with favorable health outcomes and contributes to meeting dietary recommendations, further targeted validation is needed to confirm generalizability and adaptability across dairy production contexts.

Shuhan Zhang, Deepak Aryal, Yi-Zhuang You

We realize a broad class of code constructions, including Kramers-Wannier duality, tensor product, and check product, as quantum processes consisting of ancilla initialization, local unitaries, and projective measurements. Using ZX-calculus, we represent these transformations diagrammatically and provide a systematic algorithm for extracting quantum circuits. Central to our framework is the observation that the physical content of a classical LDPC code is captured by the operator algebra associated with its Tanner graph, and that code transformations correspond to maps between such algebras. Kramers-Wannier duality then admits a natural interpretation as gauging, while tensor and check products correspond to coupled-layer constructions in which interlayer coupling and projection implement a quotient on stacked operator algebras. Together, these results establish a unified framework connecting code transformations, quantum circuits, and mappings between distinct quantum phases of matter.

Yanan Zhao, Xingchao Jian, Feng Ji et al.

We present an uncertainty principle for graph signals in the vertex-time domain, unifying the classical time-frequency and graph uncertainty principles within a single framework. By defining vertex-time and spectral-frequency spreads, we quantify signal localization across these domains. Our framework identifies a class of signals that achieve maximum concentration in both the spatial and temporal domains. These signals serve as fundamental atoms for a new vertex-time dictionary, enhancing signal reconstruction under practical constraints, such as intermittent data commonly encountered in sensor and social networks. Furthermore, we introduce a novel graph topology inference method leveraging the uncertainty principle. Numerical experiments on synthetic and real datasets validate the effectiveness of our approach, demonstrating improved reconstruction accuracy, greater robustness to noise, and enhanced graph learning performance compared to existing methods.

Chloé Desmousseaux, Morgan Guilbaud, Gwenaëlle Jard et al.

ABSTRACT: Raw milk is known to harbor a complex microbial community, including microorganisms of technological and human health interest. However, it can also be a source of pathogenic and spoilage bacteria, such as spore-forming bacteria and Pseudomonas spp. Despite cleaning and disinfection procedures, biofilms in milking machines are difficult to remove and represent a major source of milk contamination. This work aims to describe biofilms in milking machines at both the laboratory and farm scales. Encouraging studies on the microbiota of milking machine biofilms, the parameters influencing changes in biofilm composition, and the methods used to characterize them are essential for managing the formation and composition of these biofilms. Enhancing such knowledge will help improve the understanding of milking machine biofilms and their impact on the quality of milk and dairy products.

M.C. Barry, M.B. Hall

ABSTRACT: Sizes and rates of potentially digestible (B) and undegradable (C) pools of amylase-treated neutral detergent fiber (aNDF) are used to predict ruminal aNDF digestibility (raNDFD%) in widely used dairy cattle diet formulation programs. An exponential 3-pool (3P) model has been suggested for estimating digestion kinetics parameters for this purpose; however, the approach has not been compared with using a simpler exponential 2-pool (2P) model, nor with using commercial laboratory data on which applications would rely, nor on model effect on predictions of raNDFD%, which is the aim of their application. Our objective was to determine whether the 2P or 3P model most accurately and efficiently characterizes aNDF digestion kinetics and whether the models differ in predicted raNDFD%. Dry forages and silages (6 alfalfas, 6 species of grasses) were analyzed by 2 commercial laboratories that each performed 2 in vitro incubation runs with mixed ruminal microbes, with samples and blanks in duplicate at each of 11 time points; residual aNDF (Ut) was measured at each time point. Sampling hours (t) were 0, 3, 6, 12, 18, 24, 30, 48, 72, 120, and 240 h. Outlier Ut values were removed. Pools as proportions of aNDF were B in 2P, B1 rapid and B2 slow in 3P, and C in both; B pools have digestion rates (kd, h−1; denoted as kd“Bpool”) and lag (h). Models were fit to data for each forage in each incubation with equations 2P: Ut = B × e(−kdB × z) + C and 3P: Ut = B1 × e(−kdB1 × z) + B2 × e(−kdB2 × z) + C, where z = [−(lag − t − |t − lag|)/2]. There were 48 curves for each model. Parameters were estimated with the optim function in base R. The Akaike information criterion (AIC) was used to select the model with the best fit for each forage in each incubation: 16 3P and 32 2P curves were selected. Expressed as (difference between runs)/mean, average deviations between runs for laboratories 1 and 2, respectively, were as follows: for 3P, B1 = 0.50, 0.17; B2 = 0.26, 0.33; C = 0.50, 0.06; kdB1 = 0.81, 0.32; and kdB2 = 0.93, 0.54; for 2P, B = 0.04, 0.01; C = 0.07, 0.01; and kdB = 0.17, 0.08. Estimates of raNDFD% for 2P and 3P were calculated with no lag at passage rates (kp) reported for forages of 0.02 through 0.07 h−1. T-tests determined whether differences were ≠ 0 for 2P − 3P for raNDFD% at each kp for each feed evaluated. With 2P minus 3P differences in raNDFD% listed sequentially by 0.01 h−1 from kp = 0.02 to 0.07 h−1, for 16 AIC-selected 3P curves, differences were −0.29, −0.45, −0.65, −0.87, −1.04, and −1.20%, and for 32 AIC-selected 2P curves, values were −0.15, −0.13, −0.14, −0.17, −0.19, and −0.22%. Some differences were significant, but all were quite small. With little difference between models, use of the more complex 3P conferred no advantage over 2P for prediction of raNDFD% in this dataset.

Hongzhu Zhang, Huimin Shi, Shendong Zhou et al.

ABSTRACT: Subacute ruminal acidosis can cause liver injury in ruminants. Ferroptosis, an iron-dependent cell death, is involved in many liver diseases. This study aimed to investigate ferroptosis in SARA-induced liver injury and explore the changes in hepatic iron metabolism. Twelve ruminally cannulated, lactating Hu sheep (parity: 2 or 3; BW: 50.6 ± 4.0 kg; 18.8 ± 3.6 DIM; MY: 0.52 ± 0.08 kg/d; mean ± SD) were divided into 2 groups (n = 6/group) and fed a low-grain diet (LG; grain/forage ratio = 3:7, 24.89% starch and 40.66% NDF) or a high-grain diet (HG; grain/forage ratio = 7:3, 38.64% starch and 24.41% NDF) for 8 wk. Rumen pH was measured weekly 10 min before feeding and 1, 2, 3, 4, 5, 6, and 8 h after feeding. On d 57, all sheep were slaughtered after collecting hepatic vein blood, and liver tissue was collected. The HG diet significantly decreased rumen pH compared with the LG diet; the rumen pH on d 56 in the HG group was <5.6 at 1, 2, 3, and 4 h after feeding. Plasma concentrations of LPS, malondialdehyde (MDA), IL-1β, and IL-6 at 4 h after feeding increased, whereas glutathione (GSH) and glutathione peroxidase 4 (GPX4) decreased. Moreover, lipid reactive oxygen species, ferrous ion, and MDA were elevated, whereas GSH was decreased in the liver of the HG group. For ferroptosis-related proteins, feeding a high-grain diet led to increased acyl-CoA synthetase long chain family member 4 (ACSL4) and arachidonate 15-lipoxygenase (ALOX15) and decreased GPX4 and solute carrier family 7 member 11 (SLC7A11). For ferritinophagy-related proteins, feeding a high-grain diet decreased ferritin heavy chain 1 (FTH1) and increased nuclear receptor coactivator 4 (NCOA4) and microtubule-associated protein 1 light chain 3 II (MAP1LC3-II). Regarding iron metabolism, increased protein expression of nuclear mothers against decapentaplegic homolog1/5/8 (SMAD1/5/8) and hepcidin, decreased protein expression of ferroportin, and iron deposits were observed in the liver of the HG group. Furthermore, feeding high-grain diets also increased inflammatory signaling-related proteins IL-6 and phospho-signal transducer and activator of transcription 3 (p-STAT3). Taken together, this study suggests that SARA induced liver injury and ferroptosis. Enhanced ferritinophagy, disordered iron metabolism, and elevated inflammatory response may mediate ferroptosis in the livers of sheep fed a high-grain diet.

Dewi Endah Kharismawati, Toni Kazic

Accurate maize stand counts are essential for crop management and research, informing yield prediction, planting density optimization, and early detection of germination issues. Manual counting is labor-intensive, slow, and error-prone, especially across large or variable fields. We present MaizeStandCounting (MaSC), a robust algorithm for automated maize seedling stand counting from RGB imagery captured by low-cost UAVs and processed on affordable hardware. MaSC operates in two modes: (1) mosaic images divided into patches, and (2) raw video frames aligned using homography matrices. Both modes use a lightweight YOLOv9 model trained to detect maize seedlings from V2-V10 growth stages. MaSC distinguishes maize from weeds and other vegetation, then performs row and range segmentation based on the spatial distribution of detections to produce precise row-wise stand counts. Evaluation against in-field manual counts from our 2024 summer nursery showed strong agreement with ground truth (R^2= 0.616 for mosaics, R^2 = 0.906 for raw frames). MaSC processed 83 full-resolution frames in 60.63 s, including inference and post-processing, highlighting its potential for real-time operation. These results demonstrate MaSC's effectiveness as a scalable, low-cost, and accurate tool for automated maize stand counting in both research and production environments.

Giovanni Bologni, Richard Heusdens, Richard C. Hendriks

Acoustic beamforming models typically assume wide-sense stationarity of speech signals within short time frames. However, voiced speech is better modeled as a cyclostationary (CS) process, a random process whose mean and autocorrelation are $T_1$-periodic, where $α_1=1/T_1$ corresponds to the fundamental frequency of vowels. Higher harmonic frequencies are found at integer multiples of the fundamental. This work introduces a cyclic multichannel Wiener filter (cMWF) for speech enhancement derived from a cyclostationary model. This beamformer exploits spectral correlation across the harmonic frequencies of the signal to further reduce the mean-squared error (MSE) between the target and the processed input. The proposed cMWF is optimal in the MSE sense and reduces to the MWF when the target is wide-sense stationary. Experiments on simulated data demonstrate considerable improvements in scale-invariant signal-to-distortion ratio (SI-SDR) on synthetic data but also indicate high sensitivity to the accuracy of the estimated fundamental frequency $α_1$, which limits effectiveness on real data.

Zihan Yin, Subhradip Chakraborty, Ankur Singh et al.

Near-tissue computing requires sensor-level processing of high-resolution images, essential for real-time biomedical diagnostics and surgical guidance. To address this need, we introduce a novel Capacitive Transimpedance Amplifier-based In-Pixel Computing (CTIA-IPC) architecture. Our design leverages CTIA pixels that are widely used for biomedical imaging owing to the inherent advantages of excellent linearity, low noise, and robust operation under low-light conditions. We augment CTIA pixels with IPC to enable precise deep learning computations including multi-channel, multi-bit convolution operations along with integrated batch normalization (BN) and Rectified Linear Unit (ReLU) functionalities in the peripheral ADC (Analog to Digital Converters). This design improves the linearity of Multiply and Accumulate (MAC) operations while enhancing computational efficiency. Leveraging 3D integration to embed pixel circuitry and weight storage, CTIA-IPC maintains pixel density comparable to standard CTIA designs. Moreover, our algorithm-circuit co-design approach enables efficient real-time diagnostics and AI-driven medical analysis. Evaluated on the EndoVis tissu dataset (1280x1024), CTIA-IPC achieves approximately 12x reduction in data bandwidth, yielding segmentation IoUs of 75.91% (parts), and 28.58% (instrument)-a minimal accuracy reduction (1.3%-2.5%) compared to baseline methods. Achieving 1.98 GOPS throughput and 3.39 GOPS/W efficiency, our CTIA-IPC architecture offers a promising computational framework tailored specifically for biomedical near-tissue computing.

Zheting Zhang, Kexin Jiang, Aolin Yang et al.

The flavor of dairy products crucially affects consumer purchase preference. Although the flavor and sensory perception of milk can be influenced by heat treatment during processing, the exact mechanism remains unclear. Therefore, this study analyzed the whey protein content and structural changes of milk heated at different time and temperature combinations and evaluated the flavor compounds and sensory characteristics of milk. The results showed that higher temperatures changed the secondary milk whey protein structures and gradually increased α-lactalbumin, β-lactoglobulin, and lactoferrin denaturation in the milk. There were differences in sensory characteristics of milk heated at different time and temperature combinations. The correlation analysis indicated that whey protein denaturation was negatively correlated with 1-octen-3-ol (<i>p</i> < 0.05) and positively associated with hexanal, isovaleric acid, <i>γ</i>-nonalactone, methyl palmitate, and phenol (<i>p</i> < 0.01). The changes in the content and secondary structure of whey proteins affected the interaction between flavor compounds and whey protein, which affected the release of flavor compounds. Consequently, the sensory characteristics of milk were influenced. This study provides a theoretical basis for exploring the interaction between whey proteins and flavor compounds.

Sushil K. Jain, Jeffrey Justin Margret, Rajesh Parsanathan et al.

ABSTRACT: Dairy products, such as whey proteins, have been effectively used to enhance the effectiveness of vitamin D (VD) fortification and optimize circulating 25-hydroxyvitamin D [25(OH)VD] levels. Whey protein is rich in l-cysteine (LC) which is the precursor of hydrogen sulfide (H2S), enhances glutathione (GSH) biosynthesis, and promotes positive nitrogen balance. Zucker diabetic fatty (ZDF) rats were used as a model in this study to examine the hypothesis that LC supplementation enhances blood levels of H2S and nitrite (NO2) and reduces inflammation biomarkers. Rats were gavaged daily (orally) with either saline placebo or LC along with a high-calorie diet starting at 6 wk of age. Fasting blood levels showed LC supplementation significantly increased circulatory levels of H2S and NO2 compared with placebo rats. LC supplementation increased plasma concentration of 25(OH)VD and vitamin C and lowered leptin and BW gain in ZDF rats. Furthermore, to assess the effect of H2S and NO2 in raising 25(OH)VD levels, the in vitro effect of H2S/NO2 on vitamin D metabolism genes was examined using THP-1 monocytes. The exogenous H2S and NO2 treatment upregulated the relative expression of CYP2R1 and CYP27B1 genes in cultured monocytes. This study suggests a potential mechanism for the observed increase in circulating 25(OH)VD levels following LC supplementation.

G.I. Zanton, M.Z. Toledo

Balancing dairy cow diets for AA is an effective strategy to reduce dietary CP concentration, maintain levels of productivity, and increase nitrogen use efficiency. Most studies evaluating supplemental rumen-protected Met (sRPMet) focus on cows in established lactation; however, there is an increasing body of evidence suggesting that initiating sRPMet feeding to transition dairy cows is beneficial to production, reproduction, and health. Therefore, the objective of this study was to evaluate the effects of feeding sRPMet before and after calving through meta-analysis on pre- and postpartum performance and selected metabolic parameter responses. A literature search was conducted for published papers reporting on the effects of feeding sRPMet starting before parturition and continuing through early lactation, resulting in 21 publications with 40 treatment comparisons. Studies provided sRPMet both before (average of 8.20 [±2.94 SD] g of metabolizable sRPMet/d, which began at 19.3 [±4.23 SD] d before calving) and after calving (10.53 [±3.30 SD] g of metabolizable sRPMet/d for an average of 85.9 [±38.36 SD] DIM). Prepartum DMI and pre- and postpartum BW and BCS were unaffected by sRPMet. In contrast, postpartum DMI, milk yield, milk fat and true protein yield, and milk fat and true protein concentration were increased by sRPMet. Most production responses to sRPMet declined as lactation progressed where the predicted response in milk fat and true protein yield was 118 and 92 g/d at 21 DIM, respectively. Postpartum circulating metabolites were unaffected by sRPMet; however, the sample sizes for these analyses were much lower than for production responses. This meta-analysis indicates that feeding sRPMet before and after calving results in increased productivity beyond that which would be expected by providing sRPMet in established lactation alone.