Search Results (32)

Genomic selection (GS) is a new landmark method in modern animal breeding programs, and it has become a tool for routine genetic evaluation regarding dual-purpose cattle breeding. In this study, we employed data on milk-production, reproduction, and growth measurements of dual-purpose Simmental cows during the period 1987–2022 from two large-scale farms in Northwest China. For this purpose, we used a single-trait model based on the A-array PBLUP and H-array ssGBLUP to perform genetic evaluation of milk-production, reproduction, and growth traits by applying the restricted maximum likelihood (REML) methods. The results revealed that the heritability based on the additive genetic correlation matrix was approximately 0.09–0.31 for milk-production traits, 0.03–0.43 for reproduction traits, and 0.13–0.43 for growth traits. In addition, the heritability based on the genome–pedigree association matrix was similarly 0.09–0.32 for milk-production traits, 0.04–0.44 for reproductive traits, and 0.14–0.43 for growth traits. In the entire population, the reliability of genomic estimated breeding values (GEBVs) increased by 0.6–3.2%, 0.2–2.4%, and 0.5–1.5% for milk-production, reproductive traits, and growth traits, respectively. In the genotyped population, the reliability of GEBV for milk-production and reproduction traits increased by 1.6–4.0% and 0.4–3.6%, respectively, whereas the reliability of GEBV for growth traits decreased by 12.0–17.0%. These results suggest that the construction of an H-matrix with ssGBLUP could improve the heritability and reliability of breeding values for milk-production and reproduction traits. However, the advantage was not evident for growth traits in smaller populations. The present results thus provide a basis for future application of genomic genetic evaluation of dual-purpose Simmental cattle, providing data support for the selection and marketing of excellent breeding bulls, thereby helping to establish a basis for their independently bred breeding bull. Full article

In the socio-institutional framework of the Minangkabau society in West Sumatra, Indonesia—where women are typically assumed to have full power over land due to the matrilineal system of land ownership—this study asks: To what extent do women actually exercise power over land ownership and decision-making, and what factors influence this power? Comprising 212 households, a methodical household survey carried out in 2024 across the regencies of Lima Puluh Kota and Padang Pariaman employed quantitative approaches and comparative analysis across rural and peri-urban areas. The survey results confirm the initial hypothesis, showing high rates of land ownership among women in West Sumatra, largely attributed to the matrilineal system. Land ownership by itself, though, does not significantly increase women’s influence in households. Rather, women’s decision-making in Lima Puluh Kota is strongly influenced by other assets such as ownership of cattle, poultry, and electronic items; in Padang Pariaman, time allocated to farming and social events has more influence. These findings underline the complex reality behind nominal land rights and practical empowerment, thereby stressing the need to consider broader socioeconomic factors. The report advises more research on how religious interpretations and modernization are altering West Sumatra’s customary matrilineal customs and women’s empowerment. Full article

Deep learning-based individual recognition of beef cattle has improved the efficiency and effectiveness of individual recognition, providing technical support for modern large-scale farms. However, issues such as over-reliance on back patterns, similar patterns of adjacent cattle leading to low recognition accuracy, and difficulties in deploying models on edge devices exist in the process of group cattle recognition. In this study, we proposed a model based on improved YOLO v5. Specifically, a Simple, Parameter-Free (SimAM) attention module is connected with the residual network and Multidimensional Collaborative Attention mechanism (MCA) to obtain the MCA-SimAM-Resnet (MRS-ATT) module, enhancing the model’s feature extraction and expression capabilities. Then, the $L_{MPDIoU}$ loss function is used to improve the localization accuracy of bounding boxes during target detection. Finally, structural pruning is applied to the model to achieve a lightweight version of the improved YOLO v5. Using 211 test images, the improved YOLO v5 model achieved an individual recognition precision (P) of 93.2%, recall (R) of 94.6%, mean Average Precision (mAP) of 94.5%, FLOPs of 7.84, 13.22 M parameters, and an average inference speed of 0.0746 s. The improved YOLO v5 model can accurately and quickly identify individuals within groups of cattle, with fewer parameters, making it easy to deploy on edge devices, thereby accelerating the development of intelligent cattle farming. Full article

All agricultural sectors are under the influence of environmental factors, which act alongside the flow of activities. In the context of efforts to adapt to the effects of climate change, the purpose of this work is to evaluate the level of endowment of cattle farms with equipment and facilities involved in ensuring an adequate microclimate, in the efficient management and administration of feed and water for animals. This research is based on the processing of data from 83 cattle farms in Romania, of different sizes and located in different landforms, collected through a quantitative survey, through a questionnaire. This paper indicates that the existing level of these types of facilities is insufficient and highlights the importance of investments in equipment necessary to adapt to the effects of climate change, especially for smaller farms, but also for large farms. These types of investment refer to technologies for air cooling, microclimate control, feed management, and automation. This paper highlights the need to increase the technological level in Romanian cattle farms, to adapt to climate change challenges. The promotion of appropriate technologies must be included in an integrated strategy for the equipping and modernization of cattle farms, for an effective diminution of climate risks. This means adopting a systemic approach that includes investments in infrastructure, innovation, and support for farmers. Full article

Weight is the most basic and important indicator in cattle management, and automation of its measurement serves as a fundamental step toward modern smart livestock farming. Automated weighing systems (AWS) capable of continuously measuring cattle weight, even during movement, have been explored as key monitoring components in smart livestock farming. However, owing to the high measurement variability caused by environmental factors, the accuracy of AWSs has been questioned. These factors include real-time fluctuations due to animal activities (e.g., feeding and locomotion), as well as measurement errors caused by residual feed or excreta within the AWS. Therefore, this study aimed to develop an algorithm to enhance the reliability of steer weight measurements using an AWS, ensuring close alignment with actual cattle body weight. Accordingly, daily weight data from 36 Hanwoo steers were processed using a three-stage approach consisting of outlier detection and removal, weight estimation, and post-processing for weight adjustment. The best-performing algorithm that combined Tukey’s fences for outlier detection, mean-based estimation, and post-processing based on daily weight gain recommended by the National Institute of Animal Science achieved a root mean square error of 12.35 kg, along with an error margin of less than 10% for individual steers. Overall, the study concluded that the AWS measured steer weight with high reliability through the developed algorithm, thereby contributing to data-driven intelligent precision feeding. Full article

This article presents the findings of a scientific study investigating the efficacy of various assessment techniques used to evaluate the adaptability and productive qualities of Aberdeen Angus cattle on three prominent farms in the northern region of the Republic of Kazakhstan. A comprehensive analysis of the haematological and biochemical parameters of experimental groups of cattle with different genotypes (American, Canadian, and Estonian selection) was conducted. The studies revealed notable variability in haematological and biochemical indicators, contingent on the origin. Concurrently, the dynamics of the aforementioned indicators did not exceed the physiological norms. The modern allelofund was characterised with the help of microsatellite markers, and the level of genetic diversity of Aberdeen Angus cattle of different genotypes was estimated. The research uncovered the genealogical structure of the populations, the purity of the populations, the provenance, the polymorphism level, the heterozygosity indices, and the Wright fixation index (Fis). The genotyping of cattle of the Aberdeen Angus breed on 15 microsatellite markers yielded the establishment of 80 alleles in the Kolos-firm LLP, 77 alleles in the Vishnevskoe LLP, and 92 alleles in the Sever-Agro N LLP. The total expected heterozygosity was He = 0.673, while the observed heterozygosity was Ho = 0.710. Full article

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management. Full article

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. Full article

Dairy production on farms is based on properly selected technologies implemented in various areas of the barn and outside the livestock buildings. These technologies are subject to assessment, for example, to determine the possibilities of their further improvement in the given production conditions of the farm. When assessing dairy production technology on a farm, human interests are taken into account, including workload, time and access to modern tools supporting the control of production processes. The aim of this review is to identify and discuss factors in dairy production technologies that may affect the welfare of dairy cattle. The considerations indicate that in the technologies of cow feeding, watering and housing, the priority is to improve the technology in terms of ensuring the comfort of animals using feed, water and a place to rest. However, in the case of the assessment of milking automation, the key importance of increasing human comfort was indicated, taking into account the comfort of cows, which is an additional factor justifying the implementation of technical progress in milking. The assessment of various dairy production technologies on farms is an excellent opportunity to develop discussions on the place of dairy cattle welfare in the sustainable development of farms and the priorities set for improving dairy production. Full article

Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming. Full article

With the increase in the scale of breeding at modern pastures, the management of dairy cows has become much more challenging, and individual recognition is the key to the implementation of precision farming. Based on the need for low-cost and accurate herd management and for non-stressful and non-invasive individual recognition, we propose a vision-based automatic recognition method for dairy cow ear tags. Firstly, for the detection of cow ear tags, the lightweight Small-YOLOV5s is proposed, and then a differentiable binarization network (DBNet) combined with a convolutional recurrent neural network (CRNN) is used to achieve the recognition of the numbers on ear tags. The experimental results demonstrated notable improvements: Compared to those of YOLOV5s, Small-YOLOV5s enhanced recall by 1.5%, increased the mean average precision by 0.9%, reduced the number of model parameters by 5,447,802, and enhanced the average prediction speed for a single image by 0.5 ms. The final accuracy of the ear tag number recognition was an impressive 92.1%. Moreover, this study introduces two standardized experimental datasets specifically designed for the ear tag detection and recognition of dairy cows. These datasets will be made freely available to researchers in the global dairy cattle community with the intention of fostering intelligent advancements in the breeding industry. Full article

Lameness is a crucial welfare issue in the modern dairy cattle industry, that if not identified and treated early causes losses in milk production and leads to early culling of animals. At present, the most common methods used for lameness detection and assessment are various visual locomotion scoring systems, which are labour-intensive, and the results may be subjective. The purpose of this project is to develop an integrated system for early detection of lameness in cattle, using force plate gait analysis and pattern recognition techniques to identify changes in gait which indicate the onset of lameness. The system will be tested on the natural onset of lameness in an organised farm environment. Full article

To ensure a supply of dairy products, modern dairy farming has assumed an intensive nature, characterized by production in collective facilities with the presence of thermal conditioning, some automation level, and high-use inputs. Among the systems used for dairy cattle confinement, Compost-Bedded Pack Barns (CBPs) have been gaining importance and increasingly have been used in recent decades. CBPs must be designed and managed to ensure the best thermal comfort conditions throughout the year and, consequently, improve productivity, milk quality, and the health of the dairy herd. In this context, modeling via Computational Fluid Dynamics (CFD) emerges as a tool with huge potential for studying the thermal environmental conditions in the beds of CBPs, making it possible to improve projects and/or management practices in this kind of facility. This document is organized as a review, and its objective is to present the state of the art of the applicability of the CFD technique in the study of heat and mass transfer in CBP systems. So far, only four studies have used CFD for modeling CBP systems and have shown that the use of this tool helps to better understand the phenomena of heat and mass transfer in this kind of facility. Therefore, it is important that more studies using this technique in CBP systems be conducted, including additional considerations on constructive elements, animals, and the presence of beds in composting. Full article

Every week, 1–2 breeds of farm animals, including local cattle, disappear in the world. As the keepers of rare allelic variants, native breeds potentially expand the range of genetic solutions to possible problems of the future, which means that the study of the genetic structure of these breeds is an urgent task. Providing nomadic herders with valuable resources necessary for life, domestic yaks have also become an important object of study. In order to determine the population genetic characteristics, and clarify the phylogenetic relationships of modern representatives of 155 cattle populations from different regions of the world, we collected a large set of STR data (10,250 individuals), including unique native cattle, 12 yak populations from Russia, Mongolia and Kyrgyzstan, as well as zebu breeds. Estimation of main population genetic parameters, phylogenetic analysis, principal component analysis and Bayesian cluster analysis allowed us to refine genetic structure and provided insights in relationships of native populations, transboundary breeds and populations of domestic yak. Our results can find practical application in conservation programs of endangered breeds, as well as become the basis for future fundamental research. Full article

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock. Full article