Slovenian Veterinary Research 2025 | Vol 62 No 2 | 73 Phenomics Evaluation in Cattle Breeding Key words animal performance; behavior; big data; bioinformatics; cattle breeding, genomics; phenomics; PLF; welfare Afşin Kocakaya1, 2* Bengi Çinar Kul3 1Animal Breeding and Husbandry Department, Faculty of Veterinary Medicine, 2Genetics Department, Graduate School of Health Sciences, 3Genetics Department, Faculty of Veterinary Medicine, Ankara University, 06070, Türkiye *Corresponding author: : akocakaya@edu.tr Abstract: The progress of technology, namely in the realm of computer science, has resulted in the capability to handle extensive sets of information, sometimes referred to as big data, which has substantially influenced the field of genetic science. The dis- cipline of livestock farming has witnessed the emergence of several branches of study, including genomics, proteomics, transcriptomics, and metabolomics. Using phenomics in cattle breeding has gained significant importance in augmenting production and ef- ficiency. Scientists have created livestock phenomics platforms to enhance the welfare and productivity of animals by employing cutting-edge digital technologies and real- time sensors. Incorporating phenomics with genomics has expanded the potential for genetic assessment and breeding initiatives in the livestock sector. Phenomics allows for the assessment of the intricate reactions of animals to environmental stimuli, hence aiding in the advancement of more robust and productive cattle. In summary, of phe- nomics in cattle breeding has significant potential for the future of livestock production, providing prospects for better breeding objectives, promoting animal well-being, and boosting overall output. Received: 4 May 2024 Accepted: 8 October 2024 Slov Vet Res DOI 10.26873/SVR-2047-2024 UDC 636.2:575.112 Pages: 73–82 Review Article Introduction Advancements in technology, particularly computer sci- ence, have facilitated the emergence of big data, creating opportunities for advancements in genetic science. These advancements have brought to light research fields such as genomics, proteomics, transcriptomics, and metabolo- mics (1). The progress in genetics and livestock breeding has led to the development of many application areas that use genomic information for selection (2-5). These applica- tions commonly arise from DNA sequencing and genotyp- ing technologies that have a high efficiency level (5). The emergence of big data has led to the continuing de- velopment of the discipline of OMICS sciences. This field provides the opportunity to perform research and analysis on massive quantities of data that explain the structure and function of a whole biological system at some particular level (1, 6-9). The four major omics sciences, namely ge- nomics, transcriptomics, proteomics, and metabolomics sciences, are organized in a hierarchical order. These sci- ences encompass various subfields such as epigenom- ics, epitranscriptomics, epiprotenomics, interactomics, immunomics, microbiomics, pangenomics, metagenom- ics, nutrigenomics, lipidomics, pharmacogenomics, and phenomics (Figure 1) (1, 7-13). The emergence of various technologies, particularly ge- nomic studies, in the past two decades has greatly facili- tated the development of sustainable livestock systems by enabling efficient breeding of animal populations (2, 15-17). To ensure the long-term viability of livestock farming, en- hancing the development and utilization of species-specific genetic tools and resources is imperative. Genomic selection is a type of marker-assisted selection (MAS) that employs genetic markers throughout the entire genome, ensuring that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker (18, 19). Animal breeding projects have substantially researched the notion of marker-assisted selection (MAS) using marker in- formation(18, 19). Using QTL markers, one can assess an individual's genetic worth. This genetic value may be more 74 | Slovenian Veterinary Research 2025 | Vol 62 No 2 precise than that derived from pedigree and phenotypic data(19). Nevertheless, despite the current state of technological advancement in genomic science, it is seeing a decline in its initial rate of progress (5). Consortia, like the Functional Description of Animal Genomes (FAANG), aim to develop genomic tools specific to each species. Their goal is to uncover fundamental information about how genomes function and to understand the relationship between geno- type and phenotype in livestock animals. They then seek to model this relationship at various levels, including cells, tissues, and the entire animal, to gain insights into the gen- otype-phenotype connection within animal populations (15, 20-22). The measurement and identification of discernible traits in animals are crucial for the efficacy of animal breed- ing (5, 15). The absence of accurate and comprehensive phenotypic data is acknowledged as a substantial obstacle in multiple domains of animal genetics and genomics (5, 15). It is important essential to obtain records of appropri- ate phenotype or phenotypic values (phenotypic data) so that live animal populations can be managed regularly and daily to maximize reproductive strategies, control diseases, ensure animal welfare, and minimize the environmental im- pacts of animal production (23, 24). Phenotypes are record- ed to provide the basis for datasets utilized to monitor to monitor the profitability of growers and develop agricultural policy for the United States of America and other countries (24). The research findings establish that revealing the ac- curacy of expected genetic characteristics in various genet- ic models for evaluating animals with phenotypic records impacts the possibility of genetic advancements (25). Genomic studies have made significant progress in deter- mining animal breeding values. However, the role of epigen- etic factors in determining the breeding value has led to the emergence of phenomic studies. Phenomics is rapidly be- coming a significant new technology in the field of biology, and its development is accelerating. In this review, omics sciences will be presented briefly, and the notion of phe- nomics, which is now in its early stages of development, as well as its various fields of research, will be discussed. What are the omics sciences? The complete analysis of a vast amount of data, some- times known as "big data, " that depicts the entire structure and function of a particular biological system at a specific level is what is meant by the phrase "organic molecular information processing" (OMICS). This strategy has had a significant impact on the approaches that are generally used to investigate biological systems. The combination of the "top-down" strategy, which is frequently utilized in the process of developing the omic, with the "bottom-up" techniques results in the creation of a comprehensive in- strument that enables the effective investigation of biologi- cal systems. The study of complicated illnesses such as cancer has developed from a straightforward differentia- tion between cancerous and healthy cells to a full investi- gation of complex systems in a slow and restricted man- ner. A comprehensive and objective investigation of a large number of alteration layers at the genomic, transcriptomic, proteomic, and metabolic levels is included in this work (7, 26, 27). Since the introduction of the DNA microarray, the first high- throughput device, researchers have rapidly progressed in developing tools for omics research. Omics technologies have been employed following the central dogma to cap- ture several biological changes. These include static ge- nomic alterations, temporal transcriptome perturbations, alternative splicing, as well as spatiotemporal proteome dynamics, and post-translational modifications (PTM). This is in accordance with the core dogma. Furthermore, omics technologies have broadened their range to explore different omics at the epigenetic level, including the epig- enome, epitranscriptome, and epiproteome. These phras- es encompass all alterations of the corresponding omics that extend beyond the data they offer in an individual cell. Moreover, omics technologies let us look at molecular in- teractions at different levels of the interactome, as well as features linked to disease, like the metabolome and immu- nome. Incorporating various omics data has emerged as a prevailing practice in creating a complete and causal re- lationship between the molecular characteristics and phe- notypic manifestations of a particular disease. A single-cell array offers enhanced sensitivity, enabling studies at the level of individual cells. The field of omics, which is quickly progressing and growing, allows us to systematically and precisely investigate the intricate molecular processes that underlie many abnormal characteristics and their observ- able traits. Yet, the complicated characteristics of cellular activity and its decision-making mechanisms consistently stimulate the advancement of novel omics and associated methodologies (7). Figure 1: In systemic biology, the omics cascade chain connects multiple levels of biological data related to a specific phenotype (14) Slovenian Veterinary Research 2025 | Vol 62 No 2 | 75 As our understanding of cellular omics continues to grow, it continuously reshapes our perception of how cells behave, bringing us closer to reality. This poses a challenge in at- taining the objective of complete regulation over the reor- ganization of cellular pathological states. Hence, it is vital to thoroughly assess advancements and continue investiga- tions in omics to forecast future probabilities and expedite the objective (7). The field of omics sciences, which has had significant growth in recent years, is continuously witnessing the emergence of new topics. Phenomics, the central focus of this review, refers to the quantification of how the phe- notype responds to genetic mutations and environmental influences (25, 28-30). What is phenomics? The primary objective of biology is to understand phenotyp- ic characteristics, including aspects related to health, dis- eases, and adaptive fitness (29, 31). In recent years, there has been an explanation for integrated and multivariate biological responses. The primary advantage of this expla- nation is that new omics techniques decrease the element of chance typically associated with research design and the potential for selecting features (31). Nevertheless, the omics field primarily concentrates on the molecular level. Some molecular changes may not directly impact the ob- servable characteristics of an organism, potentially restrict- ing their ecological significance. Therefore, it is imperative to incorporate additional comprehensive and observable methods (32). Phenomics, a term introduced by Dr. Steven A. Garan in 1996, refers to of quantifying how phenotypes react to genetic alterations and environmental factors. The primary goal of phenomics is to thoroughly and expedi- tiously characterize the phenotype, encompassing the organism's physical and biological attributes (25, 28-30). These biological features encompass a range of quantita- tive characteristics, such as individuals' and communities' physical, chemical, and biological attributes. These traits arise from the intricate interplay of genes, epigenetics, mi- crobes, diet, and environmental factors (28). Phenomics is a field of science that covers the methods and tools that enable the application of technologies that allow the col- lection, analysis, and use of large amounts of phenotypic data related to the characteristics of an organism in various dimensions inexpensively and easily (24-31). Phenomics plays a crucial role in ensuring precise and consistent as- sessment of traits or phenotyping, effective management of livestock, and enhancement of genetic qualities (33). In the present day, where climate change is happening rap- idly due to increased global temperatures, it is important to measure the many and constantly changing effects of the environment on the physical characteristics of organisms. The significance of phenomic techniques is growing since they allow for the consideration of the many responses of organisms to the environment, therefore aiding in resolv- ing intricate tasks (32). Although phenomics and genomics share similarities in terms of expression, it is important to note that phenomics does not encompass everything re- lated to the field of phenomics. Currently, it is possible to identify practically the entire genome, but the same level of identification cannot be achieved for phenomics. The disparity arises from the fact that the quantity of data pres- ent in the phenotype surpasses the number of pieces of data present in the genotype (31). The fact that phenotypes differ from cell to cell and moment to moment makes it impossible to define and explain them exactly. Phenomics requires prioritizing the measurement of certain character- istics and achieving a harmonious equilibrium between re- search and explanation (31, 32). An exceptional conceptual framework is crucial for com- prehending the intricate phenomenological realities at dif- ferent organizational levels. Fortunately, this method may utilize established intellectual frameworks for analyzing phenotypic data, including quantitative genetics, evolution- ary biology, epidemiology, and physiology. These disciplines offer approaches for systematically studying and assess- ing the numerous elements that contribute to variation, as well as discerning between causal relationships and simple correlations (31). The production of phenotypic variation results from intri- cate interactions between genotype and environment. In order to thoroughly explore these interactions, it is essential to have enough phenotypic data to rebuild the 'genotype- phenotype' map representations accurately (31). To understand the complex relationship between genetic variety and changes in economically significant features, it is essential to analyze the underlying biological and physi- ological systems. This can be accomplished by creating a precise delineation of phenotypic data. Phenomics-level data are required to understand which genomic variants affect phenotypes, to understand epistasis and pleiotropy, and to provide the raw data needed to decipher the causes of complex biological phenomena (31). The vitality of phenomics in breeding By 2050, the global population is projected to reach 9.1 billion individuals, with the majority residing in developing countries (34, 35). A seventy percent boost in food produc- tion is necessary in the agricultural and livestock systems to adequately feed this population. Meat production from primary food sources is projected to rise by roughly 200 mil- lion tons annually, reaching an estimated 470 million tons by 2050. This increase in demand is driven by population expansion and shifting demographic patterns, particularly in developing countries with increasing incomes (33, 36). Global climate change will make this already challenging process even more difficult for developing countries (36). 76 | Slovenian Veterinary Research 2025 | Vol 62 No 2 Present genetic breeding in livestock depends on genomic data, such as single nucleotide polymorphism genotyping and sequencing, together with accurate recording of essen- tial traits. Integrating pre-existing phenotypes and genomic data has facilitated the identification of economically signif- icant genes and quantitative trait loci (QTL). Furthermore, it has resulted in developing genome-enhanced predicted breeding values that can be effectively utilized in genomic selection (36). Genomic selection has been used extensively for breeding values of animals (19), milk yield (19, 37-41), milk fat(X7), meat yield(42), meat quality(41), carcass characteris- tics(42), feed efficiency(40), fattening performance(43), growth(40, 42), bone development(41), reproductive perfor- mance(44), fertility(40), heat stress(45), adaptation ability to heat and humidity(46), hereditary diseases(47), udder and foot health(48), and BRD resistance(49). In recent years, the area of molecular genetics has made significant progress, leading to a reduction in genotyping costs. The routine genotyping of many young females and almost all young possible bulls is no longer constrained by the genotype's availability. The present obstacle to future assessments of new economically significant characteris- tics is in phenotypes, and precise phenotyping remains a substantial limitation for animal breeding (36, 50). The ab- sence of continuous phenotyping limits the development of production efficiency in livestock, providing an important barrier to breeding improvement (36, 51). There is a need for better definitions of certain traits that are important for the economy to understand better the in- tricate relationships between animals and their biological and physiological systems. Gathering comprehensive phe- notypic and environmental data poses a major challenge in livestock breeding research studies. It is essential to recog- nize and establish consistent new observable traits, rang- ing from gene expression to the attributes of the animal product, and systematically compile them in formats that computers can process. This requires the development of top-notch data collection methods encompassing various research fields at different biological scales. The integra- tion and interpretation of vast amounts of data created by many sources now require vital bioinformatics approaches and modern technologies for data management, process- ing, and analysis. These remarkable advancements will en- able a more comprehensive comprehension of the pheno- type and its role in economically significant traits for cattle production systems (51). The usefulness of phenomics in livestock depends on its ability to produce and show essential results from the large amounts of phenotypic data shared in recent years. This data can be used as a starting point for enhancing the performance and efficiency of livestock systems by utilizing genetic, genomic, and other 'omics' technologies. Furthermore, it may facilitate more precise livestock man- agement and the optimization of feed supplies (33). Phenomics is the scientific investigation of multiple vari- ables in grasslands or shelters with a previously unknown degree of accuracy and precision. It aims to identify eco- nomically significant new traits, such as monitoring feed consumption and determining feed utilization during ani- mal grazing in pastures or within shelters. Implementing this method in phenotyping might enhance the precision of predicting quantitative traits by optimizing the use of nutri- tional and other management measures, hence expediting the rate of genetic improvement for both current and novel qualities (33). Emerging characters in the era of phenomics The definition of the phenotype of an organism can be broad; it generally refers to a set of morphological and physiological characteristics of an organism and also in- cludes behavioral characteristics (14, 52). Characters refer to distinct traits shown by animals that can be objectively quantified and subjected to statistical analysis. Crucial fac- tors in animal breeding are features that possess a sub- stantial genetic profile and yield direct economic, social, and environmental benefits (14). Mike Coffey's often-cited statement, “Dairy cows: pheno- type is king in the age of genotypes, ” highlights the impor- tance of accurately measuring and documenting relevant phenotypes to successfully implement genomic selection (50). In the era of phenomena, phenotypes have received significant attention in research. The significance of com- plex interactions between challenging-to-assess pheno- types and both established and new breeding traits has increased. In the future, the three primary character com- plexes, namely energy efficiency, nutritional and environ- mental resource use, and health and disease resistance traits, as well as animal welfare, are expected to have even greater significance(53). The importance of these charac- ters emphasizes the need to have accurate and thorough knowledge about them(14). Advancements in engineering technologies and the declin- ing cost of electronic technologies enable the creation of sensing solutions that assist in monitoring livestock by automatically gathering data on specific traits, including physiological parameters, production measures, and be- havioral characteristics. One of the present objectives is to measure the activity patterns of animals using sensor- based methods such as pedometers, transponders, bo- luses, and camera systems. By analyzing the unique varia- tions derived from the patterns formed by animal-specific data, one can draw inferences about their health, fertility, or overall well-being. Furthermore, aside from the interplay be- tween animals and their social, and behavioral traits (such Slovenian Veterinary Research 2025 | Vol 62 No 2 | 77 as aggression or calmness), one can also acquire insights about the social networks inside the herd (14, 52). Moreover, mid-RI spectroscopy in dairy farming has been acknowledged as a promising method for gathering data on a large scale for phenotypic and genetic analysis, mak- ing it a subject of ongoing research. Typically, mid-RI spec- troscopy is employed to assess the quality attributes of milk samples. Aside from the conventional properties of milk, such as protein, fat, lactose, and urea content, other milk properties, such as fatty acid composition, protein and min- eral composition, milk coagulation, milk acidity, melamine content, and ketone bodies, can also be estimated and uti- lized to predict factors like body energy status and methane emissions(14). The complexities of new phenotypes Dairy farms and breeders encounter numerous unusual characteristics as they endeavor to attain their breeding objectives. As a result, farmers are faced with multiple new obstacles. Most brand-new characters are challenging to quantify. The complete understanding of these novel fea- tures' biochemical and genetic bases of these novel fea- tures, as well as their correlation to other notable traits, remains incomplete, impeding their effective utilization in breeding programs. Utilizing phenomics science in gather- ing this novel character data and implementing innovative techniques will facilitate the development of an interdisci- plinary and nationwide infrastructure for data collection and study. This will enable the breeding goals to be more effectively aligned with the rapidly evolving and emerging requirements (14). Animal husbandry has been and continues to be an essen- tial factor in increasing the productivity of animals, such as dairy cattle, around the world. The emergence of cost- effective genotyping techniques, such as single nucleotide polymorphisms (SNPs) and sequential genotyping, has made genomic evaluations essential for contemporary dairy cattle breeding methods and programs (2, 14, 54). These evaluations have yielded more significant advance- ments than the successful adoption of artificial insemi- nation (14, 54). Nevertheless, the accuracy of a genomic breeding value estimate depends primarily on the number of animals with recorded phenotypes and the heritability of the observed characteristics (54). The efficacy of animal breeding is predominantly dependent on phenotypic animal observations, and significant advancements are largely at- tributed to accurate feature definitions and comprehensive performance tests (14). Phenomics approaches To cope with the increasing global need for animal pro- tein, the dairy sector must maximize the cow's capacity to transform inedible plant material efficiently. This neces- sitates the optimal utilization of all required agricultural inputs on the farm. To minimize undesirable outputs from livestock, ensure effective management of cows and their surroundings, and provide consumers with confidence in the health and welfare of dairy animals, it is imperative to employ cutting-edge technologies that utilize affordable sensors and advanced data processing tools. Practical co- operation among dairy producers, scientists, and the asso- ciated sector is crucial for converting new technologies into feasible solutions (55). Given the increasing complexity of dairy cattle production systems, measuring all aspects of animal performance throughout an individual's life is critical. The selection con- cept has evolved from focusing solely on traits related to animal productivity to encompassing traits linked to effec- tive resource utilization, enhanced well-being, resilience, and overall welfare. The primary objective of phenome data is to furnish pertinent information to make informed judgments regarding on-farm management and genetic enhancement (55). The recent surge in interest in animal phenotypic data can be attributed to the emergence of advanced elec- tronic sensors capable of gathering precise and frequent measurements of animal performance and environmental conditions in real-time or near-real-time. Figure 2 provides an easy-to-understand summary of the data that can be obtained by selecting these devices (55). To reduce labor requirements, automated data collection and management systems incorporate real-time sensor platform technolo- gies, e-management systems, and predictive characteris- tics (33). Comprehensive and precise phenotyping is critical for breeding programs as well as the genetic study of com- plex characteristics. High-intensity genotyping sequences can recognize an organism's genome, but its "phenotype" is a more intricate concept that is not entirely explicable due to its unpredictability over time and susceptibility to Figure 2: Phenotypes that are gathered on an individual cattle basis with sensor-based devices (55) 78 | Slovenian Veterinary Research 2025 | Vol 62 No 2 environmental influences(16, 31). Despite the decreasing cost of genotyping, there is a need for enhancements to get high-performance phenotypes at a more affordable rate. The number of phenotypes recorded in conventional cattle breeding systems is restricted because of the expen- sive registration process. For instance, farmers typically calculate yearly milk yield by extrapolating from a small set of lactation data. They can now directly evaluate the real- time milk yield for each cow daily. Automated milking ro- bots revolutionize the dairy industry (16). Dairy cow breeders are concerned with body composition and milk yield traits. These measures are not only important for aesthetics, but they also play a critical role in determin- ing an animal's breeding value. An individual's body shape significantly influences their milk production capacity and overall lifespan, affecting the total milk output during their lives. There is a direct relationship between milk production and teat size (16). Dairy farmers are primarily economically affected by leg difficulties or lameness caused by injuries. Claw abnormalities leading to lameness are a significant issue in contemporary global dairy cattle farming, result- ing in pain, discomfort, reduced weight, lower milk yield, decreased reproductive efficiency, higher chances of being removed from the herd, increased treatment expenses, and mortality. This syndrome arises from various causes and predisposing factors, including environmental variables and specific factors in dairy cattle (16, 56). Acquiring com- prehensive body composition-related traits that can im- pact the offspring's success requires significant time and resources. Farmers frequently rely on qualitative assess- ments of morphometric data without quantitative methods (16). Sophisticated machine learning algorithms have brought about significant advances that have profoundly impacted many fields relying on computerized image analysis. These advancements can potentially enhance the results of herd management and animal and plant breeding initiatives. These latest advancements have enabled accurate object detection and classification of many components inside in- tricate images(16). Web-based catalog databases can be automatically ana- lyzed to derive genetic parameters from morphological measures in dairy cattle, turning them into valuable infor- mation. Nye et al.(16) utilized web scraping, deep learning, and statistical methodologies in their study to accomplish their objective. The research suggests combining an unsu- pervised technique for exact automatic picture segmenta- tion with Mask R-CNN, a supervised deep learning method. After removing the backdrop, phenotypic information such as frost color and body composition characteristics can be readily quantified (Figure 3). This method combines genetic and other phenotypic data from dairy cows to reveal the en- tire phenotype of the animal. This work exemplifies the use of cost-effective and non-invasive computer vision meth- ods in dairy cattle breeding operations (16). Precision livestock farming (PLF) is critical to optimizing resource efficiency on production farms by gathering ac- curate measurements of animal characteristics due to the increasing demand for animal products. The efficiency of computer vision (CV) systems depends on the manual col- lection of detailed annotated data, including the x-y coor- dinates of objects in images. Furthermore, the absence of a standardized annotation format for phenomics investiga- tions makes it challenging for scientists to share annotated data for validation and further development (52, 57). Chen et al. aimed to improve video data with explanations and generate attractive training assistance using their software, VTag (Figure 4). These sources are a valuable method for examining behavioral traits as well as conducting complex investigations in animal phenomics (57). Figure 3: Sample inputs and outputs. (A) Initial input image. (B) Mask R-CNN utilized masks.. (C) Raw output from DeepAPS. (D) DeepAPS final result after applying all filters. (E) The last DeepAPS mask that was used on the input image.(F) Sketch the initial input image. (G) Implied physical point positions.(H) Manual color segmentation. Image: Semex (16) Slovenian Veterinary Research 2025 | Vol 62 No 2 | 79 Conclusion Changing climatic conditions have presented challenges for livestock enterprises and farmers in maintaining the sustainability of dairy production systems. The difficulties encompass animal husbandry's environmental and climat- ic effects, the rise in natural resources and feed shortages, biodiversity, animal welfare and health concerns, and an- timicrobial resistance (14, 35, 52, 58). Having strong phe- notypes for these new problems is crucial during the phe- nomics age. In this scenario, technological advancements and access to large amounts of data are critical factors (11, 59, 60). Sophisticated and high-capacity measurement methods determine newer phenotypes. Sensors are be- coming more crucial for many characteristics, like meth- ane emissions, rumen microbiome analysis, mid-infrared spectra of milk samples, and behavioral features (14, 35, 52, 61-63). Modern dairy cow breeding programs strive for high pro- duction efficiency, considering ideal animal health and wel- fare requirements as well as low environmental impact. In the phenomics era, research and practical advancements are concentrated on developing novel phenotypes for ani- mal breeding to address emerging difficulties. Significant Figure 2: Effects of subcutaneous irisin perfusion and/or high-fat diet-induced obesity on A) serum insulin levels and B) serum FNDC5 levels in female rats. Data were expressed as mean ± SEM (one-way ANOVA followed by Tukey’s post-hoc test or Student’s t-test, n = 10 in each group) 80 | Slovenian Veterinary Research 2025 | Vol 62 No 2 gaps remain in comprehending these new characteristics' biological basis and genetic structure. The connection between the genome and phenome is not well known, especially for poorly defined phenotypes that are challenging or costly to measure accurately. Strong interdisciplinary collaboration is essential for developing suitable measuring technologies, study procedures, and assessment methodologies, as well as for analyzing rela- tionships between critical aspects. Some of the investigated qualities may not be suitable for breeding, but they can still be valuable for management purposes. The growing quantity and complexity of breed- ing goal qualities have made the development of balanced breeding goals more complex compared to previous times. Pooling data from different countries with comparable problems and objective directions can accelerate progress by creating a more extensive reference population for over- all selection(14, 33, 35). The development of computer technologies has led to sig- nificant progress in biology, genetics, and plant and animal breeding. This review focuses on the science of phenomics, which is thought to greatly contribute to animal breeding. Phenomics has emerged as a newly developing multidis- ciplinary science in which the data obtained from biology, technology, and climate sciences is searched for ways to cultivate economics. Acknowledgements Conflict of Interest. The authors declared that there is no conflict of interest. References 1. Vailati-Riboni M, Palombo V, Loor JJ. What are omics sciences? In: Ametaj BN, ed. Periparturient diseases of dairy cows. Berlin: Springer, 2017: 1–7. doi: 10.1007/978-3-319-43033-1_1 2. Özbeyaz C, Kocakaya A: Genomic Evaluation in dairy cattle (a review). Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 2011; 51(2): 93–104. 3. Motmain Z, Özdemir M, Ekinci M, Saygılı E, Bilgin E. A meta-analysis of the associations between prolactin (PRL) gene polymorphism and milk production traits in cattle. Kafkas Univ Vet Fak Derg 2022; 28 (5): 627–31, 2022. doi: 10.9775/kvfd.2022.27857 4. Saygili E, Turkyilmaz D, Ekinci K. Associations between MSTN/ HaeIII polymorphism and reproductive and growth characteristics in morkaraman sheep. Kafkas Univ Vet Fak Derg 2022; 28 (6): 717–22. doi: 10.9775/kvfd.2022.27952 5. Pérez-Enciso M, Steibel JP: Phenomes: the current frontier in animal breeding. Genet Sel Evol 2021; 53 (1): 22. doi: 10.1186/ s12711-021-00618-1 6. Houle D. A dispatch from the multivariate frontier. J Evolut Biol 2007; 20 (1): 22–3. doi: 10.1111/j.1420-9101.2006.01237.x 7. Dai X, Shen L. Advances and trends in omics technology development. Front Med 2022; 9: 911861. doi: 10.3389/fmed.2022.911861 8. Anonymous: Omics. Available at: https://en.wikipedia.org/wiki/Omics; Accessed: 15.12.2023 9. Anonymous: The definition of omics. Available at: https://omics.org/ What_is_omics; Accessed: 15.12.2023 10. Subedi P, Moertl S, Azimzadeh O. Omics in radiation biology: surprised but not disappointed. Radiation 2022; 2 (1): 124–9. doi: 10.3390/ radiation2010009 11. Fan T, Bai W, Fu Y, et al. Study on the skeletal muscle transcriptomics of Alxa Gobi camel and Desert camel. Kafkas Univ Vet Fak Derg 2023: 29 (2): 191–9. doi: 10.9775/kvfd.2023.29002 12. Sukhija N, Kanaka KK, Goli RC, et al. The flight of chicken genomics and allied omics-a mini review. Ecol Genet Genom 2023; 29: 100201. doi: 10.1016/j.egg.2023.100201 13. RD-Connect. Omics data. Barcelona: RD-Connect, 2023. https://rd- connect.eu/what-we-do/omics/ (15.12.2023) 14. Seidel A, Krattenmacher N, Thaller G. Dealing with complexity of new phenotypes in modern dairy cattle breeding. Anim Front 2020; 10 (2): 23–8. doi: 10.1093/af/vfaa005 15. Rexroad C, Vallet J, Matukumalli LK. Genome to phenome: improv- ing animal health, production, and well- being—a new USDA blueprint for animal genome research 2018–27. Front Genet 2019; 10: 327. doi: 10.3389/fgene.2019.00327 16. Nye J, Zingaretti LM, Pérez-Enciso M. Estimating conformational traits in dairy cattle with deepaps: a two-step deep learning automated phenotyping and segmentation approach. Front Genet 2020; 11: 513. doi: 10.3389/fgene.2020.00513 17. Wu XL, Ding X, Zhao Y, et al. Editorial: Lactation genomics and phenom- ics in farm animals: Where are we at? Front Genet 2023; 14: 1173595. doi: 10.3389/fgene.2023.1173595 18. Ansari S, Ghavi Hossein-Zadeh N, Shadparvar AA. Genomic predic- tions under different genetic architectures are impacted by mating designs. Vet Anim Sci 2024; 25: 1003730. https://doi.org/10.1016/j. vas.2024.100373 19. Ardicli S, Aldevir Ö, Aksu E, Kucuk K, Gümen A. Associations of bo- vine beta-casein and kappa-casein genotypes with genomic merit in Holstein Friesian cattle. Arch Anim Breed 2024;67(1) 2024: 61–71. doi: 10.5194/aab-67-61-2024 20. Andersson L, Archibald AL, Bottema CD, et al. Coordinated interna- tional action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol 2015; 16 (1): 57. doi: 10.1186/s13059-015-0622-4 21. Giuffra E, Tuggle CK. Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap. Ann Rev Anim Biosci 2019;7(1): 65–88. doi: 10.1146/annurev-animal-020518-114913 22. Anonymous: A coordinated international action to accelerate genome to phenome. Available at: https://www.animalgenome.org/commu- nity/FAANG/; Accessed: 15.12.2023 23. Halachmi I, Guarino M, Bewley J, Pastell M. Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annu Rev Anim Biosci 2019; 7(1): 403–25. doi: 10.1146/ annurev-animal-020518-114851 24. Mrode R, Ekine Dzivenu C, Marshall K, et al. Phenomics and its poten- tial impact on livestock development in low-income countries: inno- vative applications of emerging related digital technology. Anim Front 2020; 10(2): 6–11. doi: 10.1093/af/vfaa002 Slovenian Veterinary Research 2025 | Vol 62 No 2 | 81 25. Houaga I, Mrode R, Opoola O, et al. Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives. Front Genet 2023; 14: 1115973 doi: 10.3389/fgene.2023.1115973 26. Yan S, Nagle DG, Zhou Y, Zhang W. Application of systems biology in the research of tcm formulae. In: Zhang WD, ed. Systems biology and its application in TCM formulas research. London: Elsevier, 2018: 31–67. doi: 10.1016/B978-0-12-812744- 5.00003-5 27. Dalal N, Jalandra R, Sharma M, et al. Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives. Biomed Pharmacother 2020; 131; 110648. doi: 10.1016/j.biopha.2020.110648 28. Jin L. Welcome to the phenomics journal. Phenomics 2021;1(1): 1–2. doi: 10.1007/s43657-020-00009-4 29. McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to comparative developmental physiology. Front Physiol 2023; 14: 1229500. doi: 10.3389/fphys.2023.1229500 30. Anonymous: Phenomics. Available at: https://en.wikipedia.org/wiki/ Phenomics; Accessed: 15.12.2023 31. Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat Rev Genet 2010; 11(12): 855–66. doi: 10.1038/nrg2897 32. Tills O, Holmes LA, Quinn E, Everett T, Truebano M, Spicer JI. Phenomics enables the measurement of complex responses of devel- oping animals to global environmental drivers. Sci Total Environ 2023; 858(2): 159555. doi: 10.1016/j.scitotenv.2022.159555 33. Greenwood PL, Bishop-Hurley GJ, González LA, Ingham A. Development and application of a livestock phenomics platform to en- hance productivity and efficiency at pasture. Anim Product Sci 2016; 56 (8): 1299. doi: 10.1071/AN15400 34. FAO. How to Feed the World in 2050. Rim: Food and Agriculture Organization of the United Nations, 2024. https://www.fao.org/filead- min/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_ in_2050.pdf (10.01.2024) 35. Ardicli S, Samli H, Balci F. Analysis of bovine beta-casein A1 and A2 allele frequency in Holstein-Friesian cows by Real-time PCR with fluo- rescent hybridization probes. Veterinarski arhiv 2023; 93 (3): 279–86. doi: 10.24099/vet.arhiv.1821 36. Ardicli S, Aldevir O, Aksu E, Gumen A. The variation in the beta-ca- sein genotypes and its effect on milk yield and genomic values in Holstein-Friesian cows. Anim Biotechnol 2023; 34(8): 4116–25. doi: 10.1080/10495398.2023.2267614 37. Cobanoglu O, Ardicli S. Genetic variation at the OLR1, ANXA9, MYF5, LTF, IGF1, LGB, CSN3, PIT1, MBL1, CACNA2D1, and ABCG2 loci in turk- ish grey steppe, anatolian black, and eastanatolian red cattle. Turkish J Vet Anim Sci 2022; 46(3): 494–504. doi: 10.55730/1300-0128.4220 38. Sarviaho K, Uimari P, Martikainen K. Signatures of positive selection af- ter the introduction of genomic selection in the Finnish Ayrshire popu- lation. J Dairy Sci 2024; 107(7): 4822–32. doi: 10.3168/jds.2024-24105 39. Hu M, Shi L, Yi W, Li F, Yan S. Identification of genomic diversity and se- lection signatures in Luxi cattle using whole-genome sequencing data. Anim Biosci 2024; 37(3): 461–70. doi: 10.5713/ab.23.0304 40. Ardicli S, Senturk N, Bozkurt B, et al. The impact of genetic variants related to the fatty acid metabolic process pathway on milk produc- tion traits in Jersey cows. Anim Biotechnol 2024; 35(1): 2396421. doi: 10.1080/10495398.2024.2396421 41. Ardicli S, Alpay . The Effects of MC4R and CACNA2D1 gene polymor- phisms on carcass traits and marbling score in turkish native cattle breeds and their crossbreds with the Holstein-Friesians. Genetika 2023; 55(2): 655–72. doi: 10.2298/GENSR2302655A 42. Ardicli S, Dincel D, Samli H, et al. Association of polymorphisms in lipid and energy metabolism-related genes with fattening performance in Simmental cattle. Anim Biotechnol 2023; 34(8): 3428–40. doi: 10.1080/10495398.2022.2152557 43. Aldevir O, Guclu S, Dursun S, et al. The Association Between the STAT1 g.3141C>T polymorphism and reproductive performance in high-yield- ing Holstein-Friesian dairy cows. Large Anim Rev 2023; 29(2): 59–63. 44. Johnson JS, Wen H, Freitas PH, et al. Evaluating phenotypes associat- ed with heat tolerance and identifying moderate and severe heat stress thresholds in lactating sows housed in mechanically or naturally venti- lated barns during the summer under commercial conditions. J Anim Sci 2023; 101: skad129. doi: 10.1093/jas/skad129 45. Shi L, Zhang P, Liu Q, et al. Genome-wide analysis of genetic diversity and selection signatures in zaobei beef cattle. Animals (Basel) 2023; 14(16): 2447. doi: 10.3390/ani14162447 46. Özbeyaz C, Özcan M. Investigation of hereditary cholesterol deficiency (CD) in Holstein Cattle at the state farms in Türkiye. Ankara Univ Vet Fak Derg 2024; 71 (3): 321–8. doi: 10.33988/auvfd.1295330 47. Krupová Z, Kašná E, Zavadilová L, Krupa E, Bauer J, Wolfová M. Udder, claw, and reproductive health in genomic selection of the Czech Holstein. Animals (Basel) 2023; 14(6): 864. doi: 10.3390/ani14060864 48. Hayes BJ, Duff CJ, Hine BC, Mahony TJ. Genomic estimated breed- ing values for bovine respiratory disease resistance in Angus feedlot cattle. J Anim Sci 2024; 102: skae 13. doi: 10.1093/jas/skae113 49. Vieira Ventura R, Fonseca e Silva F, Manuel Yáñez J, Brito LF. Opportunities and challenges of phenomics applied to livestock and aquaculture breeding in South America. Anim Front 2020; 10(2): 45-52. doi: 10.1093/af/vfaa008 50. Visser C, Van Marle-Köster E, Myburgh HC, De Freitas . Phenomics for sustainable production in the South African dairy and beef cattle in- dustry. Anim Front 2020; 10(2): 12–18. doi: 10.1093/af/vfaa003 51. Coffey M. Dairy cows: in the age of the genotype, #phenotypeisking. Anim Front 2020; 1 (2): 19–22. doi: 10.1093/af/vfaa004 52. Baes C, Schenkel F. The future of phenomics. Anim Front 2020; 10(2):4–5. doi: 10.1093/af/vfaa013 53. Chang Y, Brito LF, Alvarenga AB, Wang Y. Incorporating temperament traits in dairy cattle breeding programs: challenges and opportunities in the phenomics era. Anim Front 2020; 10(2):29–36. doi: 10.1093/af/ vfaa006 54. Boichard D, Brochard M. New phenotypes for new breeding goals in dairy cattle. Animal 2012; 6(4):544–50.doi: 10.1017/S1751731112000018 55. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 2008;3(10): e3395. doi: 10.1371/journal.pone.0003395 56. Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE. The future of phenomics in dairy cattle breeding. Anim Front 2020; 10(2): 37–44.doi: 10.1093/af/vfaa007 57. Zlatanović Z, Hristov S, Stanković B, Cincović M, Nakov D, Bojkovski J. Influence of claw disorders on milk production in Simmental dairy cows. Kafkas Univ Vet Fak Derg 2021; 27(1): 103–10. doi: 10.9775/ kvfd.2020.24839 58. Chen CJ, Morota G, Cheng H. 124. VTag: automatic pipeline to an- notate video data for pig phenomics studies. In: Veerkamp RF, eds. Proceedings of 12th World Congress on Genetics Applied to Livestock Production. Wageningen: Wageningen Academic Publishers, 2022: 545–8. doi: 10.3920/978-90-8686-940-4_124 82 | Slovenian Veterinary Research 2025 | Vol 62 No 2 59. Abbasi K, Ali P, Barbour V, et al. Editorial: time to treat the climate and nature crisis as one indivisible global health emergency. Kafkas Univ Vet Fak Derg 2024; 30(1): 1–3. doi: 10.9775/kvfd.2023.editorial 60. Gökdai A, Sakarya E. Determination of goat milk cost and assess- ment of factors affecting the profitability of Saanen goat enterprises in Çanakkale province, Turkey. Ankara Üniv Vet Fak Derg 2022; 69(2): 123–30. doi: 10.33988/auvfd.799114 61. Mat B, Cevger Y. Determination of factors affecting competitiveness through technical and economic analyses of dairy cattle enterprises in Balıkesir province. Ankara Üniv Vet Fak Derg 2022; 69(2): 163–70. doi: 10.33988/auvfd.837725 62. Sucu E, Ak Sonat F. Effects of algae derived pure β–glucan on in vitro rumen fermentation. Ankara Üniv Vet Fak Derg 2023; 70(4): 447–52. doi: 10.33988/ auvfd.1084176. 63. Sızmaz Ö, Köksal BH, Yıldız G. Rumen fermentation characteristics of rams fed supplemental boric acid and humic acid diets. Ankara Üniv Vet Fak Derg 2022; 69(3): 337–40. doi: 10.33988/auvfd.1059346. Vrednotenje fenomike v govedoreji A. Kocakaya, B. Ç. Kul Izvleček: Tehnološki napredek, zlasti na področju računalništva, je omogočil obdelavo obsežnih nizov informacij, ki se včasih imenujejo masovni podatki, kar je bistveno vplivalo na področje genetike. Na področju živinoreje se je po- javilo več raziskovalnih vej, vključno z genomiko, proteomiko, transkriptomiko in metabolomiko. Fenomika je v gove- doreji pridobila velik pomen pri povečanju proizvodnje in učinkovitosti. Znanstveniki so ustvarili platforme za fenomiko v živinoreji, da bi z uporabo najsodobnejših digitalnih tehnologij in senzorjev v realnem času izboljšali dobrobit in produk- tivnost živali. Vključevanje fenomike z genomiko je razširilo možnosti za genetsko ocenjevanje in vzrejne iniciative v živinorejskem sektorju. Fenomika omogoča ocenjevanje zapletenih odzivov živali na okoljske dražljaje, kar pripomore k razvoju močnejšega in produktivnejšega goveda. Če povzamemo, ima fenomika v govedoreji velik potencial za prihod- nost živinoreje, saj zagotavlja možnosti za boljše rejske cilje, spodbuja dobrobit živali in povečuje splošno proizvodnjo. Ključne besede: učinkovitost živali; vedenje; masovni podatki; bioinformatika; govedoreja; genomika; fenomika; PLF; dobrobit živali