Measure Of Feed Efficiency Research Articles

PSLBII-5 Impacts of residual feed intake measured as a heifer in drylot on mature cows and calves grazing native pasture

Abstract Selection for feed efficiency is crucial for improving economic and environmental sustainability in beef cattle production. Residual feed intake (RFIfat) is a commonly used measure of feed efficiency and is determined in a drylot setting by calculating the difference between observed and expected dry matter intake (DMI), where expected feed intake is adjusted for metabolic body weight (BW), average daily gain (ADG) and body fatness. Lower RFIfat cattle are more efficient animals as they eat less than predicted for the same growth, body size and body fatness. Limited studies have been conducted evaluating the role of RFIfat on the subsequent performance of cow-calf pairs on native range. Kinsella Composite cows and calves (n = 120) grazed native pasture from July 11 to Sept 12 (summer) and from Sept 13 to Nov 2 (fall) of 2023 were included in this study. All dams had previously been evaluated for RFIfat as a heifer (8 to 12 mo of age) using the GrowSafe Feed Intake System (Vytelle, Canada). Cow and calf ADG were subjected to an analysis of covariance using lm function from stats R core package (R Core Team, 2024), with cow age (2 to 8 yr of age) as the fixed effect and RFIfat as the covariate, analyzing separately by season. An unbalanced Tukey HDS post-hoc were applied for age and p-values < 0.05 were considered significant. Weight gain was assessed on cows with an initial BW on pasture of 598.4 ± 68.4 kg, and 2 to 8 yr of age, and on calves having an initial on pasture BW of 106 ± 21.5 kg, and 65 ± 14 d of age. Cow age affected (P < 0.01) cow ADG during summer, with the youngest dams (2 yr old) gaining more weight than older animals (7 yr old; Figure 1A). No effect of RFIfat was found on cow weight gain during summer grazing. However, during fall grazing, RFIfat affected (P < 0.01) cow ADG (Figure 1B). Low-RFIfat cows (efficient) had increased weight gain during fall grazing as compared with high-RFIfat cows (inefficient: Figure 1B). Calf ADG was not affected (P > 0.1) by cow age, cow RFIfat and cow ADG during summer and fall. We conclude that cows with low RFIfat exhibited superior body weight gain, during fall grazing on native range when forage nutrient quality and quantity become limiting.

PSVII-9 Improvement of feed efficiency in Korean Hanwoo cattle: investigation of feed intake and analysis of residual feed intake (RFI)

Abstract Feed efficiency is an important economic trait in beef cattle production, and improving feed efficiency can reduce production costs and environmental impact. Residual feed intake (RFI) is a useful measure of individual feed efficiency, representing the difference between the actual feed intake of an animal and its predicted feed intake based on its body weight (BW) and growth. This study aimed to investigate feed intake and analyze RFI in Hanwoo cattle to improve feed efficiency. Data were collected from 120 Hanwoo steers (9 to 11 mo old) from six different test stations across the country. Individual daily feed intake of concentrates and forage was recorded over 70 d, and BW was measured at the beginning and end of the feeding trial. Daily feed intake was calculated using the TDN values of the concentrates and forage. Expected feed intake was estimated using a linear regression model based on metabolic BW and daily gain. RFI was calculated as the difference between expected and actual feed intake. Average daily gain ranged from 1.05 to 1.71 kg/d, and average daily feed intake ranged from 5.10 to 7.72 kg/d across the test stations. RFI values ranged from -0.84 to 0.95 kg/d, indicating significant variation in feed efficiency among the animals. This study provides valuable information on feed intake and RFI in Hanwoo cattle. Further research with a larger dataset is needed to develop a genetic evaluation model for RFI and to select animals with superior feed efficiency for improving Hanwoo cattle production.

62 Relationship between feed efficiency and reproductive measures in beef heifers

Abstract The objective of this study was to determine the influence of residual feed intake (RFI) classification on reproductive measures and pregnancy rates in beef heifers. During an 8-yr period, 1,090 heifers were tested for feed efficiency in 70-d trials using a GrowSafe system. Heifers had ad libitum access to water and diets designed to meet NRC requirements for 0.90 kg/d gain. Diets, fed as a total mixed ration, were 85% forage and 15% concentrate which included vitamin and mineral supplements. Heifers were weighed at weaning, the beginning of the RFI trial, at yearling, end of the RFI trial and at breeding. Residual feed intake values were calculated, and heifers were grouped into RFI categories of Average (AVG), Efficient (EFF) or Inefficient (INE). At 13 mo of age, reproductive tract scores (RTS) and pelvic areas were measured. In 5 of the years, heifers (n = 487) also had antral follicle counts (AFC) determined via transrectal ultrasonography. Heifers were estrous synchronized and artificially inseminated (AI) with AI bulls balanced across efficiency categories. Estrus response to synchronization was recorded at fixed-time AI. Pregnancy status and fetal age were determined via transrectal ultrasonography at 60 d and 100 d after AI. Records from 1,078 heifers were used in the analysis (AVG = 428; EFF = 317; INE = 333). Dry matter intake (DMI), body weights, average daily gain (ADG), RTS, and AFC were analyzed using Mixed procedures in SAS. Estrus response and pregnancy rates were evaluated using Logistic procedures. Differences among RFI categories (P < 0.0001) were a result of differences in DMI (P < 0.001) averaging 12.5, 11.5, and 10.2 kg/d (SE = 0.44 kg/d) for INE, AVG and EFF heifers, respectively. In addition, ADG was greater (P < 0.05) for AVG compared with INE heifers. Prior to RFI classification, birth weight was less (P < 0.03) in EFF compared with AVG heifers, and weaning weight (P < 0.01) greater in EFF compared with AVG or INE heifers. However, yearling weight and breeding weight were similar (P = 0.60) across RFI category. Indicators of reproductive development, RTS (P = 0.24) and AFC (P = 0.79) were not different among RFI category. Estrus response to synchronization increased (P < 0.04) for INE (77.3%) compared with AVG (69.1%) or EFF (70.1%) heifers. However, fixed-time AI and final pregnancy rates were similar (P > 0.69) among RFI categories and averaged 54.7%, 54.5%, 52.7% and 89.7%, 88.8%, 87.8% for AVG, EFF and INE heifers, respectively. In conclusion, differences in feed efficiency as indicated by RFI had limited influence on measures of reproductive development in beef heifers and did not alter pregnancy rates. Therefore, placing emphasis on feed efficiency in developing beef heifers should not alter reproductive performance.

Validation of the Residual Feed Intake Model in Brangus Heifers: Determination of the Optimal Days on Feed Interval to Estimate Dry Matter Intake and Average Daily Gain.

Brangus cattle are gaining popularity in the Southeast U.S. due to the desirable heat tolerance from their Brahman influence combined with the superior carcass merit aspects of Angus genetics. However, little is known about the optimal evaluation conditions for this hybrid breed when placed on test for Residual Feed Intake (RFI), a heritable measure of feed efficiency that allows improvement in performance without altering carcass traits. To address this, dry matter intake (DMI) was measured on Brangus heifers for 70-d to determine the optimal days on feed required to estimate feed intake and ADG and assess if inclusion of ultrasound measures of carcass merit into the model impact RFI rankings for this breed. The 56-d test period had a regression coefficient of 0.96 (p < 0.0001), R2 = 0.94, rp = 0.97 (p < 0.0001), and rs = 0.97 (p < 0.0001), indicating little change in rank of cattle for DMI compared to a 70-d test. ADG was the limiting factor in determining test duration. Based upon examining only heifers that calved, ultrasound backfat measures should be included in the RFI model to normalize for differences in heifer maturity. Results from this study indicate that a test duration of 56-d is sufficient to accurately estimate DMI in this population. This data indicates on-test duration can be shortened, enhancing the rate of genetic change by reducing cost and increasing the number of animals that can be tested annually.

Integrated multi-omics reveals the relationship between growth performance, rumen microbes and metabolic status of Hu sheep with different residual feed intakes

Residual feed intake (RFI) is a metric that provides a more accurate measure of feed efficiency. The lower the RFI, the higher the feed efficiency. The changes in the host microbiome and metabolome contribute to the greater feed efficiency of low RFI (LRFI) animals. The aim of this study was to explore the differences in rumen microorganisms, rumen metabolites and plasma metabolites of Hu sheep with differing RFI through the microbiome and metabolome. A total of 80 Hu sheep were used. The experiment consisted of a 15-d pretrial period and a 128-d experimental period. The RFI in the experimental period was calculated for all sheep, and the sheep were screened into high RFI (HRFI, n = 8) and LRFI (n = 8) groups. The HRFI and LRFI sheep did not differ in their initial and final body weights, average daily gain and body measurements, but the dry matter intake of LRFI sheep was significantly decreased (28.4%, P < 0.001). The sheep with LRFI had higher digestibility of crude protein (P = 0.010) and ether extract (P = 0.010) compared to HRFI group. The concentrations of acetate (P = 0.036), propionate (P = 0.010), valerate (P = 0.027) and total volatile fatty acids (P = 0.048) in rumen of LRFI group were higher compared to HRFI group. The results of 16S rDNA sequencing indicated that the sheep with LRFI had higher proportions of Prevotella genus in rumen liquid (P = 0.031). The rumen metabolome and plasma metabolome results showed that the citrate cycle, pyruvate metabolism and alanine, aspartate and glutamate metabolism processes were more active for sheep in LRFI group, which provided more energy substrate such as malic acid, oxoglutaric acid and citric acid. In conclusion, sheep with LRFI can utilize feed more efficiently, and the more active energy metabolism pathway and the production of energy substances may account for the higher feed efficiency.

Compilations and updates on residual feed intake in sheep.

The increasing global demand for food and the strong effect of climate change have forced animal science to advance regarding new methods of selection in search of more efficient animals in production systems. Feed consumption represents more than 70% of the costs of sheep farms, and more efficient animals can increase the farmers' profitability. One of the main measures of feed efficiency is estimated residual feed intake (RFI), created in 1963 by Robert Koch for estimation in cattle and later adapted for sheep. Animals with negative RFI values (RFI-) are more efficient than animals with positive values (RFI+), with influence on the variables of performance, carcass quality and production of enteric gases. The RFI is the most common and accepted metric of the feed efficiency trait for genetic selection, since it is independent of growth traits, unlike the feed conversion ratio. The purpose of this review article was to present updated literature information on the relationship of RFI estimates with performance measures, molecular markers, greenhouse gas production and feed efficiency, the technical aspects and physiological basis of metabolic in sheep.

1H-NMR-Based Plasma Metabolomic Profiling of Crossbred Beef Cattle with Divergent RFI Phenotype

This study focused on exploring the metabolomic profiles of crossbred beef cattle with varying levels of residual feed intake (RFI), a measure of feed efficiency in beef cattle. Sixty-seven crossbred growing beef steers (BW = 277 ± 29.7 kg) were subjected to a high-forage total mixed ration for 64 days to determine their RFI phenotypes. At the end of the 64d feeding trial, beef steers were divided into two groups based on their RFI values: low (or negative)-RFI beef steers (n = 28; RFI = −1.08 ± 0.88 kg/d) and high (or positive)-RFI beef steers (n = 39; RFI = 1.21 ± 0.92 kg/d). Blood samples were collected, and plasma samples were analyzed using Nuclear Magnetic Resonance spectroscopy, resulting in the identification of 50 metabolites. The study found a distinct metabolomic signature associated with RFI status. Eight metabolites, including amino acids (tyrosine, glycine, valine, leucine, and methionine) and other compounds (dimethyl sulfone, 3-hydroxy isovaleric acid, citric acid, creatine, and L-carnitine), showed differential abundance between low- and high-RFI groups. Specifically, tyrosine, glycine, and dimethyl sulfone exhibited significant specificity and sensitivity, which produced a discriminatory model with an area under the receiver operating characteristic (ROC) curve of 0.7, making them potential markers for RFI. A logistic regression model incorporating these biomarkers effectively distinguished between high- and low-RFI steers, with a threshold cutoff point of 0.48, highlighting a distinctive metabolite profile associated with efficient nutrient utilization in low-RFI cattle. The logistic regression model, incorporating these biomarkers, holds promise for accurately categorizing RFI values, providing insights into the metabolic basis of feed efficiency in beef cattle.

119 Plasma metabolomics and feed behavior patterns in feed efficient or inefficient beef bulls

Abstract Residual feed intake (RFI) is a measure of feed efficiency (FE). Differences in plasma metabolites concentration found in animals with contrasting RFI are potential biomarkers of FE. Furthermore, feed behavior patterns could also be used to identify animals with contrasting RFI. We hypothesize that the integration of plasma metabolomics data and feed behavior can predict the future animal performance. We used 302 bulls subjected to an ad libitum diet for 56-d while allocated in pens equipped with GrowSafe System to evaluate RFI. Blood samples were collected for metabolomics analysis at the beginning (d 0) and at the end (d 56) of the trial. Feeding behavior metrics including feeding bout (FB), and eating time were estimated. Subsequently, the bulls were classified as low RFI (LRFI) and high RFI (HRFI). The performance test showed no differences between LRFI and HRFI animals in the initial body weight (LRFI: 408.62 ± 47.43 Kg; HRFI: 407.64 ± 60.22; P = 0.41), final body weight (LRFI: 510.33 ± 58.28; HRFI: 500.82 ± 72.23; P = 0.46), and average daily gain (LRFI: 1.67 ± 0.28; HRFI: 1.54 ± 0.36; P = 0.42). But the dry matter intake was less for LRFI (6.45 ± 1.78) in comparison with HRFI animals (8.81 ± 2.63; P &amp;lt; 0.001). Metabolomics analysis performed on the top and bottom 30 bulls based on RFI, reveled increased concentrations of 18 metabolites in LRFI bulls on d 0, and 12 different metabolites on d 56. Among these compounds, Cer(d18:1/23:0) level was statistically greater on both d 0 (LRFI: 0.233 ± 0.016 µM; HRFI: 0.187 ± 0.014 µM, P = 0.039) and d 56 (LRFI: 0.267 ± 0.016 µM; HRFI: 0.205 ± 0.013 µM, P = 0.02), emerging as a potential biomarker distinguished by the multivariate analysis, capable of discerning between LRFI and HRFI animals. On the other hand, FB (LRFI: 13 events/d; HRFI: 15 events/d; P = 0.019) and eating time LRFI: 119 min/d; HRFI: 153 min/d; P &amp;lt; 0.001) were different between the two groups. The more efficient animals (LRFI) were observed to visit the feed bunk less frequently and spend less time consuming feed compared with the less efficient animals (HRFI). In conclusion, further exploration of the metabolites’ pathways and their roles and recognition of feeding behavior patterns could provide valuable insights into the mechanisms contributing to FE.

Feed efficiency of dairy cattle as genetic trait

This review article is devoted to the use of feed efficiency traits in dairy cattle breeding. An efficient cow is defined as the one that produces the same amount of milk and milk solids while consuming less feed and remaining healthy and fertile; thus, allowing to reduce costs without decrease in production. Improving feed efficiency is economically important due to the increasing price of fodder. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. Nowadays genetic evaluation of dairy cattle for feed efficiency is routinely conducted in several countries, including Australia, USA, Canada, Netherlands, Denmark, Sweden, Finland, Norway and United Kingdom. Different countries use different measures of feed efficiency of dairy cows. The main feed efficiency traits are dry matter intake, gross feed efficiency, residual feed intake, energy balance and feed saved. Genome-wide association studies demonstrated that feed efficiency in polygenic trait. Nevertheless, several genes with large effects on feed efficiency were identified. Estimates of heritability of these traits vary from 0.07 to 0.49 and show the presence of considerable genetic variation of these traits and therefore, the possibility of their genetic improvement under the conditions of inclusion in breeding programs. Changes in diet and rumen microbiome substantially impact feed efficiency of dairy cows. Feed efficiency is related to methane emissions and excess nitrogen excretion. Genetic improvement of feed efficiency requires recording of individual data on feed intake in cows. Such data are limited. Two options exist to solve this problem: use of indirect predictors and genomic prediction. Accuracy of genomic prediction varies from 0.21 to 0.61 across countries. International cooperative pro­jects such as Efficient Dairy Genome Project in Canada were launched to establish large databases and to increase accuracy of feed efficiency traits genomic prediction. Future directions of research are the use of novel technologies: mid-infrared spectroscopy, artificial intelligence, holo-omics.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Performance, feed efficiency, feeding behavior, and cortisol concentration of lambs ranked for Residual Feed Intake and Residual Intake and Gain.

This study aimed to evaluate the performance, feed efficiency, feeding behavior, and cortisol concentrations of lambs ranked into different groups of Residual Feed Intake (RFI) and Residual Intake and Gain (RIG). Forty Santa Inês lambs with a mean initial age of 120 ± 30 days and mean initial (IBW) and final body weight (FBW) of 28.9 ± 3.2 and 43.7 ± 3.9 kg, respectively, were used. The animals were fed at an automatic feed and water station (Intergado®) for 60 days and classified at the end of the experiment as low-RFI/RIG, medium-RFI/RIG, and high-RFI/RIG according to RFI and RIG. Performance, feed efficiency measures, ingestive behavior, temperament, and cortisol concentrations were measured, and Pearson's correlation analysis was performed to evaluate the relationship between the variables studied. The variables that differed most between RFI groups were dry matter intake, feed conversion, and feed efficiency, with better values in efficient animals (RFI-). Regarding the RGI groups, the average daily gain of animals classified as RIG- was 19.37% lower than that of animals classified as RIG± and 39.28% lower than that of animals classified as RIG+. Only the hourly feeding rate per day (p = 0.04) differed significantly between RFI and RIG classes, while no differences were observed for the ingestive behavior variables or cortisol. The number of visits to the feed bunk (total, with or without intake) showed no correlation with any performance or efficiency measure. Identifying animals ranked into different RFI and RIG classes shows that more efficient animals have a lower feed intake for the same weight gain and spend less time at the feed bunk.

- Invited Review - Ruminal ciliates as modulators of the rumen microbiome.

Ruminal ciliates are a fundamental constituent within the rumen microbiome of ruminant animals. The complex interactions between ruminal ciliates and other microbial guilds within the rumen ecosystems are of paramount importance for facilitating the digestion and fermentation processes of ingested feed components. This review underscores the significance of ruminal ciliates by exploring their impact on key factors, such as methane production, nitrogen utilization efficiency, feed efficiency, and other animal performance measurements. Various methods are employed in the study of ruminal ciliates including culture techniques and molecular approaches. This review highlights the pressing need for further investigations to discern the distinct roles of various ciliate species, particularly relating to methane mitigation and the enhancement of nitrogen utilization efficiency. The promotion of establishing robust reference databases tailored specifically to ruminal ciliates is encouraged, alongside the utilization of genomics and transcriptomics that can highlight their functional contributions to the rumen microbiome. Collectively, the progressive advancement in knowledge concerning ruminal ciliates and their inherent biological significance will be helpful in the pursuit of optimizing rumen functionality and refining animal production outcomes.

Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs

BackgroundFeed efficiency is a research priority to support a sustainable meat production. It is recognized as a complex trait that integrates multiple biological pathways orchestrated in and by various tissues. This study aims to determine networks between biological entities to explain inter-individual variation of feed efficiency in growing pigs.ResultsThe feed conversion ratio (FCR), a measure of feed efficiency, and its two component traits, average daily gain and average daily feed intake, were obtained from 47 growing pigs from a divergent selection for residual feed intake and fed high-starch or high-fat high-fiber diets during 58 days. Datasets of transcriptomics (60 k porcine microarray) in the whole blood and metabolomics (1H-NMR analysis and target gas chromatography) in plasma were available for all pigs at the end of the trial. A weighted gene co-expression network was built from the transcriptomics dataset, resulting in 33 modules of co-expressed molecular probes. The eigengenes of eight of these modules were significantly (P le 0.05) or tended to be (0.05 < P le 0.10) correlated to FCR. Great homogeneity in the enriched biological pathways was observed in these modules, suggesting co-expressed and co-regulated constitutive genes. They were mainly enriched in genes participating to immune and defense-related processes, and to a lesser extent, to translation, cell development or learning. They were also generally associated with growth rate and percentage of lean mass. In the whole network, only one module composed of genes participating to the response to substances, was significantly associated with daily feed intake and body adiposity. The plasma profiles in circulating metabolites and in fatty acids were summarized by weighted linear combinations using a dimensionality reduction method. Close association was thus found between a module composed of co-expressed genes participating to T cell receptor signaling and cell development process in the whole blood and related to FCR, and the circulating concentrations of polyunsaturated fatty acids in plasma.ConclusionThese systemic approaches have highlighted networks of entities driving key biological processes involved in the phenotypic difference in feed efficiency between animals. Connecting transcriptomics and metabolic levels together had some additional benefits.

Characterization of feed efficiency-related key signatures molecular in different cattle breeds.

Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle and is independent of economic traits such as body weight and average daily gain. However, key traits with commonality or specificity among beef cattle breeds at the same level of RFI have not been reported. Accordingly, the present study hypothesized that signatures associated with feed efficiency would have commonality or specificity in the liver of cattle breeds at the same RFI level. By comparing and integrating liver transcriptome data, we investigated the critical signatures closely associated with RFI in beef cattle using weighted co-expression network analysis, consensus module analysis, functional enrichment analysis and protein network interaction analysis. The results showed that the consensus modules in Angus and Charolais cattle were negatively correlated, and four (turquoise, red, tan, yellow) were significantly positively correlated in Angus liver, while (turquoise, red) were significantly negatively correlated in Charolais liver. These consensus modules were found to be primarily involved in biological processes such as substance metabolism, energy metabolism and gene transcription, which may be one of the possible explanations for the difference in feed efficiency between the two beef breeds. This research also identified five key candidate genes, PLA2G12B, LCAT, MTTP, LCAT, ABCA1 and FADS1, which are closely associated with hepatic lipid metabolism. The present study has identified some modules, genes and pathways that may be the major contributors to the variation in feed efficiency among different cattle breeds, providing a new perspective on the molecular mechanisms of feed efficiency in beef cattle and a research basis for investigating molecular markers associated with feed efficiency in beef cattle.

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Machine intelligence applied to sustainability: A systematic methodological proposal to identify sustainable animals

We consider sustainable animals those which are well-adapted to the environment, productive, feed efficient in converting foods into animal products (milk, meat, wool and honey), clinically healthy and presenting a low carbon and water footprints, having their animal welfare assured. Methodologies to identify these animals are necessary, though non-existent. Here, we develop a systematic methodological approach using several machine learning techniques to identify sustainable animals through phenotypic biomarkers for sustainable livestock production systems. Thermoregulatory, morphological, haematological, serum biochemical, hormonal and productive responses, carcass traits and feed efficiency measures of 62 bulls were collected and analyzed for this study. The database contains 40 variables with 10,354 pieces of information. We identified, through machine learning, a typology for sustainable animals, which were bulls with sustainable resilience and adaptive capacity to the environment, productive sustainability with heavy carcasses and high yield and feed sustainability with negative RFI. The main phenotypic biomarkers to identify sustainable animals are rectal temperature, mean corpuscular volume and white blood cells – sustainable resilience and adaptive capacity; precocity, birth weight and carcass yield – productive sustainability; and residual feed intake – feed sustainability. We recommend using this methodological proposal to identify sustainable animals, in which they consume less food, are adapted to the environment and are more productive, increasing the farm's income and possibly reducing the water and carbon footprints.

Impact of parity differences on residual feed intake estimation in Holstein cows

Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. In lactating dairy cattle, RFI is typically obtained as the difference between dry matter intake observations and predictions from regression on known energy sinks, and effects of parity, days in milk, and cohort. The impact of parity (lactation number) on the estimation of RFI is not well understood, so the objectives of this study were to (1) evaluate alternative RFI models in which the energy sinks (metabolic body weight, body weight change, and secreted milk energy) were nested or not nested within parity, and (2) estimate variance components and genetic correlations for RFI across parities. Data consisted of 72,474 weekly RFI records of 5,813 lactating Holstein cows collected from 2007 to 2022 in 5 research stations across the United States. Estimates of heritability, repeatability, and genetic correlations between weekly RFI for parities 1, 2, and 3 were obtained using bivariate repeatability animal models. The nested RFI model showed better goodness of fit than the nonnested model, and some partial regression coefficients of dry matter intake on energy sinks were heterogeneous between parities. However, the Spearman's rank correlation between RFI values calculated from nested and nonnested models was equal to 0.99. Similarly, Spearman's rank correlation between the RFI breeding values from these 2 models was equal to 0.98. Heritability estimates for RFI were equal to 0.16 for parity 1, 0.19 for parity 2, and 0.22 for parity 3. Repeatability estimates for RFI across weeks within parities were high, ranging from 0.51 to 0.57. Spearman's rank correlations of sires' breeding values were 0.99 between parities 1 and 2, 0.91 between parities 1 and 3, and 0.92 between parities 2 and 3. We conclude that nesting energy sinks within parity when computing RFI improves model goodness of fit, but the impact on the estimated breading values appears to be minimal.

Phenotypic evaluation of feed efficiency, growth and carcass traits in native turkeys

Improving feed efficiency decreases feed intake, total cost, and the environmental emission of poultry production. This study aimed to investigate different feed efficiency, growth and carcass traits between high and low feed efficiency birds in Iranian native turkeys. Growth and carcass characteristics of native turkeys were recorded. Four different feed efficiency traits, including feed conversion ratio (FCR), residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) were calculated. The phenotypic correlations were calculated among feed efficiency measurements and different growth traits. High and low feed efficiency birds based on FCR were compared for growth and carcass traits. The phenotypic correlation between FCR and RFI was 0.5 and FCR was strongly correlated with RG and RIG. Breast muscle weight of high feed efficiency birds based of FCR was significantly higher than low feed efficiency birds. The results showed that phenotypic selection based on each of the feed efficiency traits will automatically progress the others, however, using FCR can be more straightforward in local farms and results in producing more beneficial turkeys with better growth and carcass features.

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.

PSVIII-B-17 Supplemental Organic Trace Minerals and a Yeast Culture Product in Newly Weaned Steer Calves: Effects of Use and Delivery Method on Growth Performance, Efficiency, and Hepatic Trace Minerals

Abstract The objective was to determine if delivery method of organic trace minerals and Saccharomyces cerevisiae yeast culture product influence growth performance, feed efficiency, and hepatic trace mineral measures in newly-weaned steers. Crossbred steers (n = 192; 256 ± 14 kg) were used in a 49-d receiving experiment. Within 36h of weaning, steers were weighed, allotted to 24 pens (n = 8 steers/pen; 8 pens/treatment) and randomly assigned to treatments: traditional receiving diet (Con); traditional receiving diet plus the “stress-pack” directly in the diet (Force); traditional receiving diet plus a low-moisture, cooked molasses block fortified with the “stress-pack” (Tub). “Stress-pack” was offered the first 28 d of the 49-d. Biopsy samples were collected from a subsample of steers (n = 14 steers) on the day of weaning and subsequent samples were collected from the same steer (n = 1 steer/pen) on d 14, 28 and 49 for hepatic trace mineral concentration determination. A treatment × day interaction (P≤0.01) for hepatic Cu concentration was noted. Force had greater hepatic Cu (P≤0.05) compared to Tub and Con for the entire period. Tub had greater hepatic Cu compared to Con on d 14 and 28 (P &amp;lt; 0.05), but was similar to Con on d 49 (P&amp;gt;0.10). Force tended (P=0.08) to have greater DMI compared to Tub from d 1 to 14. From d 15 to 28, steers offered “stress-pack” had greater DMI (P=0.01) and tended (P=0.07) to have greater ADG compared to Con by 12.5%. From d 29 to 49, “stress-pack” steers had greater DMI (P=0.01) and Force consumed 6.9% more DM compared to Tub (P=0.01). Cumulative DMI (P=0.01) and ADG (P=0.05) was greater for Force compared to Tub by 5.4% and 9.4%, respectively. Application of “stress-pack” in diets offered to newly-weaned cattle enhanced production responses, but delivery method influences DMI and daily gain.