Beef Research Articles

Archeozoological materials of the settlement of the golden horde time Podymalovo–1

The article presents the results of an archaeozoological study of osteological materials collected during 2022 excavations at the Podymalovo-1 settlement. The settlement dates back to the Golden Horde period and lays in Ufimsky District, the Republic of Bashkortostan. The analysis of the animal species range, the anatomical spectrum of their bones, age and size of slaughtered animals, traces of pathological changes in their bones suggests a reconstruction of the main economic activities of the settlement's inhabitants. It is found that domestic ungulates at Podymalovo-1 were used by farms in multiple ways. The population kept cattle, small ruminants and horses. Cattle breeding was associated with dairy and meat production, while horses were mainly used for farm work. Livestock was kept until mature. It can be explained through their lifetime use to obtain milk, wool, or help. The monument shows traces of leather production and bone carving. Animal bones were used as raw materials for bone items and tools. Beef and horse meat dominated in the settlement residents' diet. Probably, as they consumed horse meat mostly, and never had pork as their meat, they acquired associ- ated ethnic and cultural traditions and religious prohibitions. Determining the season when domestic ungulates were slaughtered showed that people lived there all year round, while livestock was slaughtered more often in warm seasons than in cold ones. The settlement’s life was the most intense in summer and autumn presumably because Podymalovo-1 residents actively interacted with passing trade caravans.

Insertion of agronomy and animal science course students in bovine slaughterhouses, in mato grosso do sul, directs learning: a rural extension perspective

Rural extension is a tool that has become essential for the efficient and rapid implementation of good agricultural and livestock practices, seeking the development of rural properties. Carrying out an internship provides the student with professional and personal growth, as having the opportunity to follow the routine of their future profession allows them to improve their learning. In this context, the article explored the insertion of students from Agronomy and Zootechnics courses in beef production, on properties and in beef slaughterhouses. Describing the targeted experience and learning of students, it can contribute to carcass yield and meat quality, highlighting the importance of the partnership between the University, producers and agribusinesses. During the months of November 2022 to January 2023, students from the Agronomy and Zootechnics courses followed the activities of a company in the state of Mato Grosso do Sul, which carries out technical monitoring of slaughter, and provides consultancy to improve the quality of beef. Given the aspects reported, we can say that carrying out this internship contributed to providing new experiences for these students, especially in rural areas and meat quality. Making it possible to improve and consolidate knowledge about cattle farming, animal nutrition, health management, slaughter and meat quality. By carrying out activities during the internship, it served to influence professional training. The activities proved to be extremely relevant in the construction of theoretical-practical knowledge among those involved, industry, company, students and producers and the suggestions given to producers provided differentiated training as it covered different areas of the beef cattle production chain.

Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m.

This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less overfitting. Confusion matrices indicated similar patterns of prediction errors for both versions, but YOLOv10 showed better consistency. This study concludes that while both YOLOv8 and YOLOv10 are effective in detecting cattle feeding behaviors, YOLOv10 exhibited superior average performance, learning rate, and speed, making it more suitable for practical field applications.

Relative prices and relative supplies in the UK beef meat industry: A wavelet cross-correlation analysis

The objective of the paper is to assess the contemporaneous and temporal links between the relative beef prices and the composition of beef supply (slaughters) in the UK, using weekly data and wavelet cross-correlation analysis. For female bovine animals (heifers and adult cows) and for most of the frequencies considered, the price ratio has a negative contemporaneous association with the supply ratio while for male bovine animals (steers and young bulls) it has a positive one. The difference in the sign of the contemporaneous link is in line with the argument in the Animal Economics literature that, while bovines (female or male) are both consumption and capital goods, the value of females as capital tends to be greater than that of males. Moreover, the contemporaneous links are asymmetric (i.e., their absolute magnitude depends on the frequency they are calculated). The temporal links are strong. For female bovines and for small frequencies, the relative prices are likely to lead relative supplies; for male bovines, it holds the opposite.

Analysis Of The Profitability Of The Beef Cattle Business In Lolong Guba District, Buru Regency

Analysis Of The Profitability Of The Beef Cattle Business In Lolong Guba District, Buru Regency

Meat Quality Comparison in Bali, Wagyu, and Their Cross-Breed Cattle Using Ultrasound Imaging

This study aimed to compare the meat quality of different beef cattle breeds using ultrasound imaging. A total of 28 cattle, aged 1-2.5 years, from three breeds were analyzed: Wagyu (n=7), Bali (n=10), and Wagyu×Bali cross-breeds (n=11). Meat quality traits, including longissimus dorsi thickness (LDT), backfat thickness (BFT), intramuscular fat (IMF), and marbling score (MS), were assessed using ultrasound imaging. The association between breed and meat quality was analyzed using a completely randomized design (CRD) followed by Tukey's test. Additionally, principal component analysis (PCA) was employed to identify clusters of meat quality potential among the different breeds. The results indicated that Wagyu×Bali cross-breeds exhibited the highest LDT (46.380±4.770 mm), though the difference was not statistically significant (P≥0.05) compared to either Bali or Wagyu cattle. However, significant differences (P≤0.05) were observed between Bali and Wagyu cattle. For BFT, MS, and IMF, Wagyu cattle outperformed both Bali cattle and Wagyu×Bali cross-breeds, with values of 5.490±0.806 mm, 6.010±0.998, and 49.05±8.140%, respectively. The PCA revealed two primary clusters: the first cluster, comprising Wagyu cattle, accounted for 75.6% of the diversity and was characterized by BFT, IMF, and MS as key variables. The second cluster included Bali cattle and Wagyu×Bali cross-breeds, representing 21.5% of the diversity, without any specific meat quality variable as a defining marker. Ultrasound imaging effectively estimated meat quality in Bali cattle and their cross-breeds, demonstrating its potential as a tool for meat quality assessment across different breeds.

The Rumen and Gastrointestinal Microbial Environment and Its Association with Feed Efficiency and Pregnancy in Female Beef Cattle

The Rumen and Gastrointestinal Microbial Environment and Its Association with Feed Efficiency and Pregnancy in Female Beef Cattle

Research on the Behavior Recognition of Beef Cattle Based on the Improved Lightweight CBR-YOLO Model Based on YOLOv8 in Multi-Scene Weather.

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

Exogenous Cellulase Enzyme Supplementation in Complete Feed Based On Fermentation of Banana Stems for Nutritional Consumption of Beef Cattle

Exogenous Cellulase Enzyme Supplementation in Complete Feed Based On Fermentation of Banana Stems for Nutritional Consumption of Beef Cattle

Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering

Abstract Body size parameters of beef cattle are crucial for assessing growth status and breeding value. In actual farming environments, the various postures of beef cattle and complex backgrounds can affect the accuracy and stability of non-contact body measurement methods. Therefore, this paper proposes a novel method called the cattle body measurement method (CBMM), which combines keypoint detection with local point cloud clustering. First, a keypoint detection model based on YOLOv8-SimBiFPN is constructed. This model enhances the feature extraction and fusion capabilities of YOLOv8-pose by introducing SimAM and BiFPN into the backbone and neck networks, respectively, and realizes 2D keypoint detection for beef cattle in various postures. Second, a 3D keypoint-locating algorithm based on Density-based spatial clustering of applications with noise (DBSCAN) is proposed. This algorithm utilizes 2D keypoints, depth maps and camera parameters to generate local point clouds, which are then clustered using DBSCAN to segment cattle body point clouds, thereby relocating the 3D keypoints based on their positional features. Finally, body size parameters are calculated based on the 3D keypoints and distance formulae. In our experiment, the mean average precision (mAP@0.5) of YOLOv8-SimBiFPN reached 99.1% on an Angus beef cattle keypoint detection dataset. The mean absolute percentage errors for measuring beef cattle withers height, hip height, body depth, body length, and oblique body length using the CBMM were 4.37%, 4.96%, 6.47%, 4.84%, and 4.14%, respectively. In summary, our method can achieve non-contact body measurement for beef cattle in a free-moving state with high accuracy and stability.

Green Chromatographic Method for The Determination of Synthetic Azo Dyes in Processed Beef and Chicken Meat Products

Green Chromatographic Method for The Determination of Synthetic Azo Dyes in Processed Beef and Chicken Meat Products

Carbon emission measurement and regional decomposition analysis of China’s beef cattle farming industry

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.

Evaluating genotype by environment interaction for growth traits in Limousine cattle

Environmental conditions affect the growth and health of animals, making it crucial to quantify heat stress and the genetic basis of heat tolerance in animal breeding. The main objective of this study was to evaluate heat stress on beef cattle growth and investigate the genetic background of tolerance to harsh environmental conditions in the Italian Limousine beef cattle. Three growth traits were analysed: average daily gain (ADG), weaning weight (WW), and yearling weight (YW). Data were collected from animals raised between 1991 and 2022 and combined with 14 environmental covariates. Records for ADG, WW, and YW encompassed 108 205, 100 058, and 24 939 individuals, respectively, with 4 617, 4 670, and 2 048 genotyped individuals. Climatic variables were compared for inclusion in a linear mixed model using the Deviance Information Criterion. Multiple-trait models and genomic information incorporated environmental conditions with the largest impact on the studied traits Genotype by environment interaction (G × E) was detected in all the studied traits, showing substantial heterogeneity of the variance components across the different environments (Env). Heritability for WW remains constant among Env; instead, for ADG and YW decreased under uncomfortable environmental conditions. YW showed the lowest genetic correlation (0.28) between divergent conditions (Env 2 and Env 5,) for ADG and WW correlations dropped below 0.50 among Env. The values of genetic correlations indicate that growth traits are moderately to strongly affected by G × E. Eigenvalue decomposition of the additive genetic (co)variance matrix for ADG, WW, and YW indicated that three components accounted for over 0.80 of the proportion of the variance explained, suggesting different animal performances across Env. Spearman rank correlations showed potential re-ranking of genotyped sires, because ADG, WW, and YW showed correlations between Env below 0.80. The accuracy of single-step genomic EBV was higher compared to EBV for al traits. Overall, the result confirms the existence of G × E for growth traits in the Italian Limousine population. Including G × E in the model allows for more environment-aware predictions, helping breeders understand how different genetic bases respond to varying conditions. Genomic predictions incorporating G × E could accelerate genetic gains and improve response to selection for heat tolerance.

Peningkatan pengetahuan untuk pencegahan stunting melalui sosialisasi dan demo pembuatan makanan balita berbahan dasar protein hewani bagi kelompok istri peternak sapi pedaging rumahan di Desa Lando, Lombok Timur, Nusa Tenggara Barat [Increasing knowledge for stunting prevention through socialization and demonstration for creating animal protein-based toddler food for a group of wives of home-based beef cattle farmers in Lando Village, East

Stunting is a condition of failure to thrive in children under five (toddlers) which can hinder the child's physical and mental development. Data from the 2023 Indonesian Health Survey (SKI) shows that the number of stunted toddlers in West Nusa Tenggara (NTB) Province is still quite high, namely 24.6%. Stunting cannot be cured but can be prevented. One of the prevention efforts is to increase the knowledge of mothers or prospective mothers regarding stunting and preventing it by providing food with high nutrition. This Community Service Activity (PKM) was carried out with the aim of educating mothers and wives of home-based beef cattle farmers in Lando Village, East Lombok, NTB about preventing stunting by providing highly nutritious supplementary food (PMT) and stunting detection. The methods used were socialization and demonstration of making toddler food. The results of this PKM activity were that there was a significant increase in participants' knowledge regarding the material being socialized (p0.0001). The conclusion that can be drawn is that There had been an increase in knowledge among PKM participants regarding stunting and preventing stunting through providing food with high protein content.

Nitrogen fertiliser application rate to temperate mid-season grass: Digestion, nitrogen balance and rumen microbiota in beef cattle, and rumen fermentation and methane production in vitro

Nitrogen (N) excretion by cattle, particularly urinary N, can have detrimental environmental impacts on air and water quality, and contributes to greenhouse gas emissions. The effects of increasing the application rate of inorganic N fertiliser from 15 (N15) to 80 (N80) kg/ha per cut to Lolium perenne dominant swards in summer, on intake, rumen fermentation, rumen microbial populations, apparent total-tract digestibility and N-balance in beef cattle, and in vitro fermentation and methane output, were studied. Sixteen suckler-bred Charolais steers, used in a randomised block design experiment, were offered fresh grass mechanically harvested 21-d after N fertiliser application. Similar grass was incubated in an eight-vessel in vitro RUSITEC system. Grass crude protein concentration was 50 g/kg dry matter (DM) higher for N80 compared to N15. There was no difference in grass DM intake between treatments. Rumen fermentation variables did not differ between treatments, except for the molar proportion of propionate, which was greater for N80 than N15. Gross microbial community structure in the rumen was not significantly altered by inorganic N fertiliser application rate. The relative abundance of individual genera Lachnospiraceae_NK3A320_group and Ruminococcaceae_NK4A214_group, and Acetitomaculum were significantly lower at the higher N application rate. Mean plasma urea concentration was greater for N80 compared to N15. In vivo digestibility of DM, organic matter, neutral detergent fibre and acid detergent fibre was unaffected by fertiliser N application rate. Nitrogen intake was 75 g/d greater, and urinary and faecal N excretion were 20 and 5 g/d greater, respectively, for N80 than N15. The quantity of N retained and N use efficiency was greater for N80 compared to N15. In vitro NH3 concentration was greater for N80 than N15, whereas other rumen fermentation variables, and in vitro methane and total gas output, did not differ between treatments. Reducing the inorganic N fertiliser rate applied to mid-season temperate grass reduced N excretion from beef cattle, which is environmentally beneficial, with no effect on in vitro methane production.

Relationship between the rumen microbiome and liver transcriptome in beef cattle divergent for feed efficiency

BackgroundFeed costs account for a high proportion of the variable cost of beef production, ultimately impacting overall profitability. Thus, improving feed efficiency of beef cattle, by way of determining the underlying genomic control and selecting for feed efficient cattle provides a method through which feed input costs may be reduced whilst also contributing to the environmental sustainability of beef production. The rumen microbiome dictates the feed degradation capacity and consequent nutrient supply in ruminants, thus potentially impacted by feed efficiency phenotype. Equally, liver tissue has been shown to be responsive to feed efficiency phenotype as well as dietary intake. However, although both the rumen microbiome and liver transcriptome have been shown to be impacted by host feed efficiency phenotype, knowledge of the interaction between the rumen microbiome and other peripheral tissues within the body, including the liver is lacking. Thus, the objective of this study was to compare two contrasting breed types (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI) over contrasting dietary phases (zero-grazed grass and high-concentrate), based on gene co-expression network analysis of liver transcriptome data and microbe co-abundance network of rumen microbiome data. Traits including RFI, dry matter intake (DMI) and growth rate (ADG), as well as rumen concentrations of volatile fatty acids were also included within the network analysis.ResultsOverall, DMI had the greatest number of connections followed by RFI, with ADG displaying the fewest number of significant connections. Hepatic genes related to lipid metabolism were correlated to both RFI and DMI phenotypes, whilst genes related to immune response were correlated to DMI. Despite the known relationship between RFI and DMI, the same microbes were not directly connected to these phenotypes, the Succiniclasticum genus was however, negatively connected to both RFI and ADG. Additionally, a stepwise regression analysis revealed significant roles for both Succiniclasticum genus and Roseburia.faecis sp. in predicting RFI, DMI and ADG.ConclusionsResults from this study highlight the interactive relationships between rumen microbiome and hepatic transcriptome data of cattle divergent for RFI, whilst also increasing our understanding of the underlying biology of both DMI and ADG in beef cattle.

Bacillary hemoglobinuria in beef cattle infected with Fascioloides magna in Missouri

Bacillary hemoglobinuria in beef cattle infected with <i>Fascioloides magna</i> in Missouri

Analysis of Factors Affecting Beef Cattle Business Income (Case in Cipari Village, Cigugur District, Kuningan Regency)

The purpose of this study is to find out the factors that affect the income of beef cattle business in Cipari Village, Cigugur District, Kuningan Regency. This research was carried out at the Beef Cattle Farm Owned by Cipari Village, Kalibaru Village, Cigugur District, Kuningan Regency, West Java Province. The time of this research will be carried out in May - July 2024. The method in this study uses a quantitative descriptive method with a survey approach. The sampling technique in this study is to use saturated sampling (census). The sample in this study is all cattle breeders in Cipari Village consisting of 17 farmers with the ownership of 2 to 4 beef cattle. The Data Analysis Techniques carried out in this study include descriptive analysis quantitative, i.e. Multiple linear regression analysis. The results of the study show that (1) The number of beef cattle, the selling price of livestock, the labor cost factor, the price of beef cattle, the feed price factor, and the price of medicines, simultaneously have a significant effect on the income of the beef cattle business in Cipari Village, Cigugur District, Kuningan Regency; (2) The most influential factor on the income of the beef cattle business is the Number of Beef Cow Factor, because the number of beef cattle has the most significant influence on income.

Maternal effects and its importance in the genetic evaluations of preweaning live weight traits of beef cattle. A review.

Maternal effects in cattle genetics are defined as the causal influence of the phenotype or maternal genotype on the offspring's phenotype by effects occurring when the genetic and environmental characteristics of the mother influence the phenotype of the offspring beyond the direct inheritance of genes. Its relevance has been strongly described in genetic models focused on the genetic improvement of preweaning traits in cow-calf beef cattle production systems. Here, basic concepts and the importance of maternal effects when using linear and animal model procedures for genetic evaluations of growth and live-weight traits in beef cattle are reviewed and discussed. A brief history of estimation methods from classical studies to recent studies used for the development of animal models for studying maternal effects is also provided. Some important biometric concepts for maternal effect estimation are described, and the antagonism between direct genetic effects and maternal effects, its biological basis, and sources of error in the estimation of direct genetic and maternal covariance are discussed. Finally, some genomic perspectives are presented.

A comparative evaluation of maize silage quality under diverse pre-ensiling strategies.

Maize silage serves as a significant source of energy and fibre for the diets of dairy and beef cattle. However, the quality of maize silage is contingent upon several crucial considerations, including dry matter loss, fermentative profile, pH level, ammonia content, and aerobic stability. These aspects are influenced by a multitude of factors and their interactions, with seasonality playing a crucial role in shaping silage quality. In this study an open-source database was utilised to assess the impact of various pre-ensiling circumstances, including the diversity of the chemical composition of the freshly harvested maize, on the silage quality. The findings revealed that seasonality exerts a profound influence on maize silage quality. Predictive models derived from the composition of freshly harvested maize demonstrated that metrics were only appropriate for screening purposes when utilizing in-field sensor technology. Moreover, this study suggests that a more comprehensive approach, incorporating additional factors and variability, is necessary to better elucidate the determinants of maize silage quality. To address this, combining data from diverse databases is highly recommended to enable the application of more robust algorithms, such as those from machine learning or deep learning, which benefit from large data sets.