Leoni Ferreira Martins

Current rations are formulated on the requirements of a representative cow plus a correction factor. This is not precision feeding because in practice cows are fed as a pen, and within a pen, dry matter intake (DMI) among cows is not uniform and has skew or kurtosis. Since ration formulation depends on DMI as a binding constraint, rations formulated to the pen DMI could be more precise if the distribution of DMI is better described. The objectives of this study were to: 1. Develop an algorithm, the distribution shape model (DSM), to predict pen DMI based on the distribution of cow measures which could easily be estimated on a commercial dairy farm; and 2. Compare predictions of pen DMI from the DSM to predictions of pen DMI based on a representative cow using the National Academies of Sciences, Engineering, and Medicine (NASEM) equation model (NSM). Individual cow DMI from previously published studies was used to train an Extreme Gradient Boosting (XGB) algorithm to predict the distribution shape of pen DMI (Beta or Generalized Normal) from parity, week in milk, and descriptive statistics of milk yield. The NSM predicted DMI for a representative cow from pen-average inputs, and this prediction was multiplied by the number of cows, simulating the scenario where DMI is assumed to be uniform across a pen. Both models were tested by comparing predicted with observed total pen DMI using an independent testing dataset of 229,909 pens generated from previously collected cow observations. For pens classified as Beta, the mean squared error of prediction (MSEP) of pen DMI from the DSM and NSM was 5249 and 9736 kg², respectively (root mean squared error of prediction, RMSEP, 72.5 and 98.7 kg/d), with 3.0% and 80.2% due to mean bias, 5.4% and 8.3% due to regression slope not equal to 1, and 91.6% and 11.5% due to random variation. For pens classified as Generalized Normal, MSEP was 2086 and 6431 kg² (RMSEP 45.7 and 80.2 kg/d), with 0.8% and 73.2% due to mean bias, 7.6% and 4.7% due to slope not equal to 1, and 91.6% and 22.1% due to random variation. The NSM consistently overpredicted pen DMI, contributing to its higher error. Including the distribution shape in the prediction of DMI improved precision and accuracy of pen DMI prediction and should be considered when estimating the total daily DMI fed to a pen.

Improving nitrogen (N) efficiency in dairy production systems is important due to environmental concerns and the economic cost of dietary protein. A substantial proportion of dietary N consumed by lactating dairy cows is not converted into milk protein and is instead excreted in urine and consequently manure. Advances in protein nutrition have shifted feeding strategies from crude protein (CP)–based approaches toward metabolizable protein and, more recently, amino acid (AA)–based systems designed to better match absorbed AA supply with animal needs. This review synthesizes current knowledge on AA nutrition in lactating dairy cows, with emphasis on the biological regulation of milk protein synthesis, identification of limiting AA, and opportunities to improve N utilization efficiency through more precise diet formulation. Absorbed AA originate primarily from microbial protein synthesized in the rumen and rumen-undegraded feed protein digested postruminally, and the relative contribution of these sources influences both the quantity and profile of AA available for metabolism. Milk protein synthesis is regulated not only by AA supply but also by energy availability, endocrine signaling, and tissue nutrient partitioning. Among individual AA, methionine, lysine, and histidine are most consistently associated with limitations to milk protein synthesis in dairy cows under common feeding conditions. Evidence indicates that balancing diets for these AA can increase milk protein yield, improve feed N conversion into milk N, and allows reductions in dietary CP without compromising production. However, practical implementation of AA-based feeding strategies remains constrained by limitations in predicting microbial protein synthesis and postruminal AA supply. Nutritional models provide useful tools for evaluating AA adequacy, but prediction errors and biological variability require that model outputs be interpreted alongside production responses and metabolic context. Overall, improved alignment of AA supply with metabolic demand represents a key strategy for enhancing productivity while reducing environmental N losses in dairy production systems.

The dairy industry in the United States has undergone significant intensification over the past several decades, resulting in improved nutrient use efficiency and economic gains, largely driven by genetic selection, improved nutrition, and better management practices. However, environmental concerns such as GHG emissions and N excretion by dairy farms remain. Concurrently, the industry faces structural changes marked by farm consolidation, particularly affecting small farms, leading industry professionals to seek strategies aimed at increasing nutrient use efficiency and farm profitability. These are all timely issues where nutrition can have a positive effect. In the present review, we discuss historical and current feeding strategies and explore opportunities for precision nutrition in dairy systems. We investigate how feed and feeding management strategies may be refined for modern dairy farming using advanced knowledge, equipment, and technology. Traditional TMR feeding, although effective at reducing digestive issues and standardizing nutrient intake, often fails to account for individual cow variation in nutrient demands and use efficiency. Nutritional grouping has emerged as a practical step toward more precise feeding, improving both nutrient delivery and income-over-feed costs. Furthermore, in a hypothetical scenario, precision nutrition could be achieved through individualized feeding, particularly in systems using robotic milking or out-of-parlor feeders, as well as component feeding approaches. Whereas individualized concentrate feeding has demonstrated potential to reduce nutrient waste and environmental effect (e.g., 10% reduction in enteric CH₄ emissions and up to 40% decrease in urinary N excretion), evidence for consistent improvements in overall lactational performance remains limited. Moreover, its adoption is often constrained by practical and economic challenges, including changes of cow feeding behavior and investments in equipment and technology. The future of precision feeding will depend on improved real-time data collection, automated systems, refined models for predicting individual cow response to nutrients, and real-time data integration-innovations that are emerging but not yet fully realized. Looking ahead, advances in dairy farming efficiency are likely to come from integrating individualized feeding strategies with broader understanding of biological variability in nutrient use efficiency among cows, thereby linking precision nutrition to both environmental sustainability and profitability in dairy production.

The objective was to evaluate the fit statistics for the predictions of ruminal outflow of protein fractions and EAA by the NRC, National Academies of Sciences, Engineering, and Medicine (NASEM), Cornell Net Carbohydrate and Protein System (CNCPS), and NittanyCow (NC) models in lactating dairy cows. Sixty-seven studies published in English between 1984 and 2020 with 251 treatment means were used for analysis. Lactational performance (DMI, DIM, milk yield, milk fat and true protein, and BW data), dietary nutrient composition (CP, NDF, ether extract, and ash), and ruminal outflow of total AA, microbial N, nonammonia nonmicrobial N, and individual EAA determined by omasal or duodenal digesta-sampling techniques were extracted from publications and used in the analysis when available. Production and dietary nutrient composition data reported in the studies were used as inputs to predict ruminal outflows of those same variables using NRC, NASEM, CNCPS, and NC models. For all nutritional models, dietary concentration (% DM) of CP was identical or allowed to vary by 0.5 percentage units maximum to that reported in the studies. Fit statistics and performances of nutritional models were assessed using Lin's concordance correlation coefficient (CCC), root mean squared error (RMSE, g/d and % of observed), as well as mean and linear biases. Models performed similarly when predicting ruminal total AA outflow, with CCC ranging from 49% to 57% and RMSE ranging from 19% to 30% of observed. Predictions of ruminal microbial CP (MicP) and nonammonia nonmicrobial CP (NANMCP) outflows had CCC ranging from 43% to 60% and RMSE ranging from 24% to 34% of observed. Predictions of Lys, Met, and His had CCC ranging from 38% to 62% and RMSE ranging from 23% to 40% observed. Overall, model-fit statistics indicated superior performance of NRC, NASEM, and NC in predicting all individual EAA outflows compared with CNCPS (except for Leu in NC). NittanyCow and NASEM, respectively, demonstrated superior performance in predicting total AA compared with NCR and CNCPS. Additionally, NC was superior to NRC and NASEM, which in turn were superior to CNCPS, in predicting ruminal MicP outflow. Both NRC and NC were superior to NASEM and CNCPS in predicting ruminal NANMCP outflow. The NASEM model was outperformed by NRC, CNCPS, and NC in predicting ruminal nonammonia N outflow. Mean and observed biases of concern (i.e., >5.0% of observed) were identified across all models, and these biases should be considered by nutritionists when balancing and evaluating rations targeting individual EAA in high-producing dairy cows.

The objective of this study was to evaluate the effects of individual AA on ruminal fermentation and N utilization in vitro. Four, 24-h batch-culture incubations were conducted with rumen fluid from 3 (2 per incubation run) donor lactating dairy cows as inoculum and a standard TMR (16.5% CP, 30.9% NDF, and 25.5% starch of DM) as the basal substrate. Nitrogen-15 labeled ammonium sulfate (165 mg/L; 15 mg ¹⁵N/L) was used as a tracer to determine microbial ammonia-N incorporation in all treatments. Amino acids were supplemented individually (at 155 mg/L) as follows: His, Leu, Lys, Met, Phe, Trp, Val (EAA1; incubations 1 and 2), and Arg, Asp, Asn, Cys, Glu, Gln, Gly, Ile, Pro, Ser, Thr, and Tyr (EAA2 and NEAA; incubations 3 and 4). Each incubation included treatments with ammonium sulfate (AS; 101 mg/L) as a negative control and hydrolyzed CN (Amicase; 177 mg/L) as a positive control. Incubation medium samples were collected at 24 h and analyzed for VFA and ammonia-N concentrations, as well as NDF degradability. A subset of samples was processed for determination of ¹⁵N enrichment (i.e., atom percent excess) of 3 rumen N pools: solids, bacterial, and ammonia pools, respectively. For statistical analysis, the individual AA treatments were grouped and analyzed by set of incubations: EAA1 (incubations 1 and 2), EAA2 and NEAA (incubations 3 and 4) versus Amicase and AS. Additionally, responses to Amicase and individual AA treatments were compared expressed as the percent difference from AS (negative control). In incubations 3 and 4, compared with AS and EAA2 treatment groups, Amicase and NEAA had a greater total gas production. Total VFA was greatest for Amicase, while the molar proportion of acetate and acetate-to-propionate ratio were lowest and isovalerate was greatest for the EAA1 group. On an individual AA basis and relative to AS (negative control) acetate-to-propionate ratio decreased for Cys, Asp, Tyr, Gln, Met, Asn, Ile, and Thr by 4.5% to 12.5%. Furthermore, the isobutyrate molar proportion increased by 98.2% with Val, whereas Pro increased valerate by 84.1%, and Ile and Leu increased isovalerate by 71.1% and 61.1%, respectively. Nitrogen-15 enrichment of the bacterial-N pool in incubations 1 and 2 was greater for the EAA1 group compared with AS and Amicase, and the incorporation of ammonia-N into bacterial-N was greater for the EAA1 group, and tended to be greater for Amicase, versus AS. Incorporation of ammonia-N into bacterial-N was greater for Leu, Tyr, Met, Ile, Val, Trp, Gly, Phe, His, Pro, Cys, and Arg, ranging from 9.9% to 23%, relative to AS, but the proportion of bacterial-N in solids-N (i.e., a proxy for microbial protein synthesis) was not affected. Overall, supplemental AA (supplied at a dose of 155 mg/L) had minor effects on in vitro fermentation variables and fiber degradability, but supplementation of branched-chain AA and Pro increased molar proportions of their respective branched-chain VFA. Data demonstrated that 9 EAA and 3 NEAA supplemented individually improved the incorporation of ammonia-N into bacterial-N, indicating that some AA can enhance N utilization in the rumen.

A meta-regression was conducted to determine the production effects of 3-nitrooxypropanol (3-NOP) and investigate their associations with dose, dietary nutrient composition, and supplementation length in dairy cows. Forty treatment and control mean comparisons extracted from 21 studies conducted or published between 2014 to 2024 were used in the meta-regression. Response variables were DMI, milk yield (MY), ECM yield, ECM feed efficiency, BW, BW change, and concentrations of milk fat, true protein, lactose, and MUN. Treatment and control mean differences (MD) as well as standardized MD were calculated and used for data presentation and statistical analysis, respectively. Dose (± SD; 77.0 ± 33.17 mg 3-NOP/kg DMI), forage-to-concentrate ratio (FC; 58.9 ± 8.83%, expressed as average ± SD% of forage in the diet), dietary concentrations of CP, ether extract, NDF, and starch (16.3 ± 1.84%, 4.3 ± 1.03%, 33.7 ± 4.40%, and 20.9 ± 3.97%, as average ± SD% of DM, respectively), supplementation length (days), and DIM and BW of the cows at the beginning of the study were used as continuous explanatory variables. Mixed-effects models were fitted using the robust variance estimation method. Full models containing uncorrelated explanatory variables (Pearson correlation 3 concentration. Concentrations of total VFA, acetate, and the acetate-to-propionate ratio were decreased, whereas the concentration of propionate was increased, and that of butyrate was numerically increased by 3-NOP supplementation. Overall, 3-NOP supplementation of dairy cow diets decreased DMI and MY but did not affect ECM because of increased milk fat and true protein contents. Increased milk fat and MUN responses can be explained by shifted ruminal fermentation, and the increased milk true protein response may be explained by increased rumen propionate molar proportion by 3-NOP supplementation.

A meta-analysis was performed to investigate the effects of capsaicin (CAP) on lactational performance and blood chemistry and cell counts in dairy cattle. The database comprised 11 peer-reviewed studies published between 2011 and 2024. The overall effect of CAP, challenge (e.g., ketosis, glucose tolerance, LPS), and dose were considered explanatory variables in the analysis. Lactation performance response variables included DMI, milk yield (MY), ECM yield, feed efficiency (FE), milk components, and BW. Hematological response variables included red and white blood cell counts and blood BHB, total fatty acids, insulin, and glucose concentrations. Data were analyzed using random- and mixed-effect models in the "robumeta" package in RStudio. Milk yield was increased by 2.9% by CAP when compared with control. Capsaicin supplementation increased FE by 3.4% compared with control. Milk fat concentration and yield were also increased by CAP compared with control by 2.6% and 4.0%, respectively. Blood glucose concentrations were decreased 2.5% by CAP supplementation, whereas insulin levels were unaffected. Cows fed CAP during a challenge had higher MY and FE and tended to have lower blood glucose than their control counterparts. Overall, this analysis suggests that CAP supplementation may be directly affecting host physiology by altering glucose metabolism, but further research to define the mechanism is warranted.

Yeast culture and phytonutrients are dietary supplements with distinct modes of action, and they may have additive effects on the performance of dairy cattle. The objective of this study was to investigate the effects of a preparation of phytonutrients and a yeast culture from Saccharomyces cerevisiae on lactational performance, total-tract digestibility of nutrients, urinary nitrogen losses, energy metabolism markers, and blood cells in dairy cows. Thirty-six mid-lactation Holstein cows (10 primiparous and 26 multiparous) were used in an 8-wk randomized complete block design experiment with a 2-wk covariate period, 2 wk for adaptation to the diets, and a 4-wk experimental period for data and samples collection. Following a 2-wk covariate period, cows were blocked by days in milk, parity, and milk yield and randomly assigned to 1 of 3 treatments (12 cows per treatment): basal diet supplemented with 14 g/cow per day yeast culture (YC; S. cerevisiae), basal diet supplemented with 1.0 g/cow per day phytonutrients (PN; 5.5% cinnamaldehyde, 9.5% eugenol, and 3.5% capsicum oleoresin), or basal diet supplemented with a combination of YC and PN (YCPN). Treatments were top-dressed once daily on the total mixed ration at time of feeding. Dry matter intake, milk yield, and feed efficiency were not affected by treatments. Milk composition and energy-corrected milk yield were also not affected by supplementation of YC, PN, and YCPN. There were no differences in intake or total-tract digestibility of dietary nutrients among treatments. Compared with YC, the PN and YCPN treatments tended to decrease the proportion of short-chain fatty acids in milk fat. There was an additive effect of YC and PN supplementation on urinary urea nitrogen (UUN) excretion relative to total nitrogen intake. Cows fed a diet supplemented with YCPN had lower UUN excretion than cows in YC and tended to have lower UUN excretion compared with PN. Blood monocytes count and percentage were decreased in cows fed PN and YCPN diets compared with YC. Treatments did not affect concentrations of blood β-hydroxybutyrate and total fatty acids. Overall, lactational performance, digestibility of nutrients, energy metabolism markers, and blood cells were not affected by YC, PN, or YCPN supplementation. A combination of PN and YC had an additive effect on nitrogen excretion in dairy cows.

Botanical extracts have a potential to modify ruminal fermentation while enhancing metabolism and immunity in dairy cows. The objective of this study was to investigate the effects of a combination of Capsicum oleoresin and clove essential oil (botanicals; BTC) on lactational performance, nutrient utilization, enteric methane (CH₄) emissions, and blood parameters in dairy cows. Twenty Holstein cows (12 multiparous and 8 primiparous) averaging (±SD) 77 ± 28 d in milk in the beginning of the study were used in a replicated 4 × 4 Latin square design experiment with 4 periods of 28 d each. Cows were grouped into squares based on parity, milk yield and days in milk, and assigned to 1 of 4 treatments: control (CON), 150, 300, or 600 mg/cow per day of BTC. Cows received the same basal diet and BTC were top-dressed on the total mixed ration once daily. Dry matter intake, milk production, and milk composition were not affected by BTC supplementation, except for milk fat content that tended to be increased in BTC, compared with CON. Daily CH₄ emission (measured using the GreenFeed system) was linearly decreased by up to 7.5% with increasing doses of BTC. Treatment decreased CH₄ yield (kg of CH₄ ÷ kg of DMI) and tended to decrease CH₄ intensity (kg of CH₄ ÷ kg of milk or energy-corrected milk yields) by 5% in BTC, compared with CON. Supplementation of BTC resulted in a quadratic decrease of serum β-hydroxybutyrate in all cows, and a linear decrease of serum insulin concentration in primiparous but not in multiparous cows. Nutrient utilization and other blood parameters (e.g., blood cells count) were not affected by BTC in the current study. The reduction of enteric CH₄ emission demonstrates a moderate mitigation effect on carbon footprint of milk by BTC supplementation. These results must be further investigated and confirmed in longer-term experiments.

Increasing production efficiency of dairy cows through improved health is not possible without adequate nutrition. Hippocrates (460–370 BC), the father of modern medicine, apparently claimed that all diseases begin in the gut, and this might be appropriately applied to dairy cows. The transition period, defined as 3 wk before and 3 wk after parturition, is characterized by extensive metabolic and physiologic changes mediated by homeostatic and homeorhetic processes ( Figure 1; Bauman and Currie, 1980), and the use of nutritional and management strategies during this period can provide long-lasting effects for dairy cows. For instance, failing to manage the calcium status of the animal during the transition period can be detrimental to milk production in the subsequent lactation. Multiparous cows with delayed or persistent hypocalcemia measured at days 1 and 4 postpartum produced less milk (up to 7.2 kg/d in the first 6 wk of lactation) than cows with transient or no hypocalcemia ( Seely et al., 2021).