Behavioral observation. Animal behavior refers to the actions, reactions, and activities exhibited by animals in response to internal or external stimulation (Broom & Johnson, 1993); (Kokocińska & Kaleta, 2016); (Malott & Kohler, 2021). The EFSA guidance on animal welfare risk assessment defines “response of an animal or an effect on an animal” as an animal-based measure. It may be taken directly or indirectly from the animal and includes animal records. However, evaluation of some behaviors may vary according to available resources, and behaviors should be assessed together (Panel et al., 2023)

Understanding animal behavior is crucial in assessing their welfare. Behavioral observations can provide insight into animals’ physical and mental states by identifying patterns and deviations of natural behavior (Broom, 2010)(Harikrishnan, 2021)(Pisula, 1999). The natural behavior that animals exhibit is a result of their developed cognitive and emotional systems that enable them to interact with the environment, including performing certain pleasurable behaviors and promoting biological functioning (Hemsworth & Edwards, 2020); (Khullar & Jena, 2021)

Animals experiencing positive welfare are more likely to exhibit natural behavior. Deprivation of natural behaviors can lead to physiological distress, reduced production, and increased mortality. Consequently, behavioral observations are crucial in identifying animal stress and discomfort, allowing timely intervention to enhance animal welfare and reduce stress levels. The classical methods used to identify and measure behaviors can be complex in a large group of animals, but their importance cannot be overstated. It is urgent that we address animal stress and discomfort, and behavioral observations are a key tool in this endeavor.

In the case of laying hens, multiple housing systems have been developed accommodating large groups of hens that exceed 25,000 birds, making it unfeasible to observe individual animals through conventional methods (Siegford et al., 2016). Therefore, the use of new technologies in observing the behavior of laying hens is crucial due to the limitations of traditional methods (Yang et al., 2023); (Yang et al., 2024). New technologies have been reported to improve behavioral assessment through continuous automated monitoring, offering more accurate and objective insight (Daigle, 2013); (Leroy et al., 2006); (Watters et al., 2021). For example, studies conducted in laying hens demonstrated that thermal imaging cameras could accurately detect plumage damage with differences between body regions (Pichová & Bilčík, 2017); (Schreiter & Freick, 2022). Wearable sensors have been successfully utilized to monitor laying hen behaviors, providing real-time data on behavior and physiological responses (Fujinami et al., 2023). The sensors can be attached to hens and recognize various hen behaviors, categorizing them into different intensity levels for optimal management of modern poultry systems (Shahbazi et al., 2023). For instance, wearable inertia sensor technology and a machine learning model (ML) can analyze laying-hen behaviors with an accuracy of 90%, allowing early detection of stress or distress by identifying and analyzing changes in vocalization patterns, such as frequency, duration, and intensity (Derakhshani et al., 2022); (Heuvel et al., 2022). Currently, different technologies have been developed to evaluate changes in laying hen´s behavior as listed in Table 1.

In summary, knowledge concerning animal behavior is essential for improving animal production because it allows the design of production systems that meet animals’ needs and promote positive welfare, which can lead to reducing stress and improving animal health in general, generating greater productivity and profitability for farmers (Madzingira, 2018); (Orihuela, 2021). However, behavior observation technologies have limitations, in the case of traditional technologies, subjectivity, difficulty in quantifying behaviors, influence of environmental factors, sampling bias, and lack of standardization (Bateson & Martin, 2021); (Dawkins, 2004); (Decina et al., 2019); (Fraser & Matthews, 1997); (Jones, 1996); (Weeks & Nicol, 2006). In the case of new technologies, despite the generation of objective data without disturbing the animals, they have limitations in terms of implementation at a commercial scale (Ben Sassi et al., 2016).

Physiological measures. Physiological measures refer to quantitative assessments of biological processes within an organism, providing insights into its internal state and functioning (Serra et al., 2018). Physiological markers offer advantages, such as objectivity, comparability between species, and the ability to reflect past well-being states with different temporal resolutions, allowing a dynamic view of well-being over time (Beaulieu, 2024); (Replication in field ecology: Identifying challenges and proposing solutions, 2021). Some examples of physiological markers include endocrine and hormonal parameters, metabolic and biochemical indicators, oxidative stress markers, cardiovascular and respiratory indicators, behavioral and physical health observations, immune function, and body temperature (Guevara et al., 2022); (James et al., 2023).

Endocrine biomarkers. Endocrine indicators refer to hormones that regulate various body functions (Hiller-Sturmhöfel & Bartke, 1998). For example, hormone levels in biological samples such as blood, plasma, feathers, eggs, droppings, or urine provide insight into the physiological state of the individual (Carbajal et al., 2014); (Downing & Bryden, 2008); (Häffelin et al., 2020); (Rettenbacher et al., 2004); (Steckl & Ray, 2018). Thus, several hormonal stress biomarkers in birds have been describedTable 2.

Methodologies used to measure hormone biomarkers are listed in Table 3 and 4. This includes Enzyme-Linked Immunosorbent Assay (ELISA), radioimmunoassay (RIA), Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography- Tandem Mass Spectrometry (LC-MS/MS), and high- performance liquid chromatography (HPLC) (Tian et al., 2018). Also, among these methods, immunoassays such as RIA and ELISA are the most used for quantifying hormones in biological samples (Nouri et al., 2020). Both methods use similar principles for quantifying hormones and bioanalytical methods, in which the

reaction of an antigen (analyte) and an antibody is employed to detect and quantify the analyte (Aydin, 2015)(Darwish, 2006). However, detecting the antibody- antigen complex differs: ELISA uses enzymes, whereas RIA uses radioisotopes (Hackney, 2018)(Klee, 2003). Therefore, because of radioactive isotopes, RIA has been replaced by ELISA kits that allow the quantification of hormones without radioactivity (Kinn Rød et al., 2017). For example, RIA has been used to measure corticosterone in hens housed in cages, floor, and organic systems (Pia Franciosini et al., 2005), and used ELISA to measure the effect of chronic exposure to high temperatures and ammonia concentrations on reproductive hormones in birds (Li et al., 2020).

However, these types of immunoassays have disadvantages, such as providing data for only one hormone per run and substantial cross-reactivity (Abdel-Khalik et al., 2013). Freeze-thaw cycles of samples can significantly decrease corticosterone concentrations from their initial values (Häffelin et al., 2020), and differences in sensitivity between kits and techniques can alter the results (Bekhbat et al., 2018).

Nevertheless, other methods could be more accurate in measuring hormones. Chromatography is a method characterized by the separation of different molecules in a mixture by the distribution between two phases, called a stationary phase (SP) and a mobile phase (MP) (Coskun, 2016). Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and high-performance liquid chromatography (HPLC) provide good separation, sensitivity, and limit of detection for hormones superior to immunoassays (Chafi & Ballesteros, 2022); (McDonald et al., 2011). The HPLC separates analytes according to their distribution between a mobile liquid phase and a stationary solid phase (Hell et al., 2014).

Gas chromatography (GC) coupled with mass spec- trometry (MS) is commonly used for the identification of potential steroids and metabolites because of its high chromatographic resolution capacity and reproduc- ible ionization efficiency (Niessen, 2001)(Stan, 2005). Although GC/MS has better chromatographic resolution than LC-MS/MS, it must overcome problems related to derivatization (Bowden et al., 2009). Derivatization is the process of chemically altering an analyte or analytes. Chromatography has been used to determine stress- related hormone levels in broilers, hens, and ducks from serum, feather, egg albumen, and yolk samples under dif- ferent conditions (Afrouziyeh & Zuidhof, 2022); (Caulfield & Padula, 2020); (Oluwagbenga et al., 2022). The results of LC-MS/MS and ELISA methods for measuring stress-re- lated hormone (corticosterone) concentrations in plasma were highly correlated in broiler breeders (Afrouziyeh & Zuidhof, 2022). Also, GC-MS has been used to detect ste- roid hormones in eggs despite being involved in a tedious derivatization process (Fritsche et al., 1999); (Hartmann et al., 1998). In contrast, without derivatization, LC-MS/MS has been employed to assess synthetic steroid hormones in egg samples derived from eight standard commercial poultry layer breeds (Li et al., 2019). Therefore, despite its capacity for high throughput and potential, LC-MS/MS exhibits several constraints, including sensitivity, specific- ity, and performance (Adaway et al., 2015); (Grebe & Singh, 2011); (McDonald et al., 2011).

Physiological parameters. On the other hand, heart rate has been used to indicate animal welfare to allow understanding of some responses to the environment and challenges in their environment. It is of interest for research on social behavior, animal cognition, and individual differences (Wascher, 2021). Heart rate and heart rate variability (HR/HRV) are non-invasive techniques that can assess welfare, with potential applications in real-time monitoring of welfare (Kim et al., 2021); (Borell et al., 2007); (Wascher, 2021). Wearable bioelectric recording systems have been used successfully to monitor the heart rate and its variability through electrocardiography signals in chickens. The backpack electrocardiography system used in this study may be best suited for application in freely moving poultry (Ahmmed et al., 2023), but heart rate is strongly affected by social interactions in a wide range of species and used to mark and quantify individual levels of stress in response to anthropogenic disturbances or environmental challenge (Wascher, 2021) which could generate individual variations in the measurements.

Likewise, respiratory rate has been used to indicate avian stress and health status. However, it is pivotal to detect respiratory rates that are contactless and stress-free in poultry to avoid alterations due to manipulation or external factors. Moreover, with many birds in production systems in commercial conditions, it is unfeasible to detect a reliable respiratory rate truth evaluation with manual measures. (Wang et al., 2022) compared respiratory rate estimation techniques without the video magnification algorithm (RR-D) and with the video magnification algorithm (RR-D-EVMGS) to improve the detection accuracy of the broiler respiration rates. This technique and the algorithm require further optimization, but it is a promising prospect to bring support for respiratory diseases and stress monitoring.

Animal body temperature, such as respiratory and heart rates, is closely related to the physiological, metabolic, emotional, and welfare status (Giloh et al., 2012). Body temperatures respond to external and internal factors and may reflect responses to the environment or some internal challenge of the animals. Therefore, it is an essential indicator for measuring the state of the animal. The body temperature of laying hens can be monitored using different technologies, among them thermal imaging, as the non-invasive method is capable of evaluating the temperature through the energy emitted by the animal’s skin surface captured by an image visible to the human eye (Morgado et al., 2022). (Giloh et al., 2012) used infrared thermographic measurement by infrared thermal imaging of skin surface temperature in monitoring the thermal status of chickens in a commercial flock. They concluded that this methodology requires the selection of specific surface sites and correlating their body temperature under various environmental conditions, and found that facial surface temperature is strongly correlated with body temperature, which can provide valuable information regarding their thermal comfort and potential heat stress (Morgado et al., 2022). In addition, infrared measurements have shown acclimation to persistent high temperatures, and acclimated birds did not display high concentrations of corticosterone, which highlights their lower stress level (Giloh et al., 2012). Assessing welfare is difficult with a single parameter; doing so only by measuring physiological parameters is challenging. It is challenging due to the absence of well-defined physiological standards for each condition. Different rearing conditions, feeds, environments, breeds, densities, genetic lines, and immunity status can interact and cause response variations depending on the conditions.

Environmental parameters. Environmental conditions significantly impact animal welfare, providing the necessary conditions for animals to exhibit their natural behaviors in their natural habitat (Koknaroglu & Akunal, 2013). Critical environmental factors that ensure animal welfare include temperature, relative humidity, air quality, illumination, and noise (González-Salcedo et al., 2020); (Li et al., 2023). High temperature and humidity generate heat stress in laying hens, affecting their reproductive performance, eggshell quality, and immune function (Effect of heat stress on production parameters and immune responses of commercial laying hens, 2004); (Nardone et al., 2010).

Thermographic imaging through infrared thermography (IR) can indirectly assess physiological activity that occurs when animals react to different environmental situations and emotional stimuli by measuring the surface temperatures of specific regions (comb, beak, eye, and head) that are influenced by blood perfusion, tissue thermal conductivity, and metabolic heat generation (Tattersall, 2016); (Travain & Valsecchi, 2021); (Heuvel et al., 2022).

In addition, the detrimental effects of poor air quality, regarding dust and ammonia, on laying hen welfare have been reported (David et al., 2015). Equipment to measure multiple parameters of air quality has been created. The portable monitoring unit (PMU) allows the measurement of ammonia (NH3) and carbon dioxide (CO2). The iPMU (Intelligent Portable Monitoring Unit) was created and has undergone significant upgrades, including a new data acquisition and control system, wireless data transfer capability, and a new commercial NH3 electrochemical sensor (Ji et al., 2016).

Other potential environmental stressors that cause stress and distress should be routinely monitored. This includes ambient light and noise levels (Council, 2008). Exposing laying hens to levels of continuous noise measured as 80 dBA and 100 dBA caused reductions in their egg-laying rates and caused changes in the rates of abnormal eggs. Continuous noise, increased stress hormone cortisol (Lee et al., 2003), and 75 dB sound stimulus caused stress and fear in laying hens. Noise negatively influences their fearfulness, showing increments in the tonic immobility duration (Campo et al., 2005). In broilers, noise stimuli of both 80 dB and 100 dB intensities for 10 min significantly elevated plasma corticosterone levels (Chloupek et al., 2009). Some noise- related technologies include sound meters and loggers to measure and record decibel levels in hen housing. Then, sensor technologies can obtain objective, continuous, and contactless measures of animal behavioral and physiological welfare indicators.

Health status. Animal health assessment is non-invasive and can be performed through cage-side or pen-side visual observation and/or physical examination of an animal (Cohen & Ho, 2023). Nonetheless, it is crucial to recognize that animal health encompasses more than merely the absence of illnesses and injuries. The Swiss Animal Welfare Act not only focuses on the health of animals but also seeks to safeguard their dignity and overall well-being (Thomann et al., 2023). Assessing the health of laying hens for welfare purposes involves the evaluation of health indicators, such as infectious and parasitic diseases, production diseases, physical damage, and mortality (Erensoy et al., 2021). New technology, such as computer vision or deep learning models, allows monitoring of the spatial distribution of cage-free hens and some behaviors to indicate a flock’s health and welfare (Yang et al., 2023); (Yang et al., 2023).

Radio Frequency Identification (RFID) is a technology employed to monitor the movement behavior of hens and predict individual health status, such as infections (Welch et al., 2023). Likewise, respiratory diseases can be detected by changes in vocalizations and ground throat vocalizations or sneeze detection (Banakar et al., 2016); (Carpentier et al., 2019); (Mahdavian et al., 2021). The onset of specific viral diseases like Newcastle (Cuan et al., 2022), avian influenza (Astill et al., 2018); (Cuan et al., 2020), and infectious bronchitis can be detected by vocalizations. The reactions to vaccines in hens can also be differentiated by acoustic technology (Ginovart-Panisello et al., 2024).

Physical damage, such as plumage condition, feather pecking, cannibalism, and injuries, is the most critical factor affecting feather conditions in laying hens (Erensoy et al., 2021). Using machine vision (RGB and RGB-D cameras), (Lamping et al., 2022) assessed plumage conditions on commercial white-laying hen farms from a deep convolutional neural network called ChickenNet (Lamping et al., 2022). This system provides a holistic assessment of the plumage by computing a plumage condition score for each hen detected. The best result obtained among all tested configurations was a mean average precision of 98.02% for hen detection. In comparison, 91.83% of the plumage condition scores provide a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.

Another technology widely used to measure feather cover quality is IR. This useful tool is not biased by the subjective component and provides higher precision than feather damage scoring (Pichová & Bilčík, 2017). The IR is a tool that can evaluate the changes in the surface temperature, derived from an inflammatory process that would make it possible to objectively determine the depth of the damage to the dermis (Zhang et al., 2023) demonstrated that the proposed RGB-D-T model based in the deep learning was more efficient than the other three traditional stereo matching algorithms in the detect the region of feather damage and assess the depth of feather damage (Zhang et al., 2023). In addition, automated image processing and statistical analysis using optical flows and Markov models for predicting feather damage in laying hens allow the identification of flocks with the probable prevalence of damage and injury later in the lay (Lee et al., 2011). Some behaviors of birds reveal health problems in the flock, which are related to diseases such as lameness (Alencar Nääs et al., 2021). (Yang et al., 2024) used multiple chicken trackers developed using six convolutional neural networks to monitor activity in cage-free chickens, and the results indicate that the average accuracy is between 80% and 94% (Yang et al., 2024). This tracker can detect piling and smothering behaviors and footpad problems in cage-free chicken environments. It can be a valuable tool for detecting early problems in the flock in real-time and a handy tool for evaluating multiple welfare parameters.

Finally, evaluating mortality rates and pathological changes in laying hens has been widely used to assess welfare in flocks (Erensoy et al., 2021). The flock’s health status can be assessed using management-based measures, which are based on records. In this case, the total mortality of the flock is a pivotal indicator at the end of production and allows the welfare of the farm and the production system to be assessed (Panel et al., 2023).

Preference tests. Preference tests have been used as a tool in the study of animal welfare by establishing animals’ preferences for shared resources and enrichments (Fraser & Matthews, 1997) A behavioral preference indicates the outcome when a bird chooses, e.g., between different foraging, nesting, or dustbathing substrates or for perches of different characteristics (Panel et al., 2023) under different situations and used as a welfare indicator. Several studies have evaluated animal preferences through choice tests that involve repeated measurements of stimulus choices, such as food items, to understand captive animals’ preferences (Lewis et al., 2022); (Turner et al., 2023). In laying hens, preference tests remain a valuable tool in welfare assessments, establishing preferences for resources and enrichment environments (Nicol, 2023). However, obtaining a feasible measure of these tests without making inferences about what animals prefer is complex. Moreover, early chick environments, such as the provision of litter and perches, can predict laying hen welfare. In the study conducted by (Skånberg et al., 2021), Leghorn classic chicks were presented with six different types of litter (crushed straw pellets, hemp shavings, peat, sand, straw, wood shavings) and six different types of perches (narrow or wide forms of rope, flat or round wood) (Skånberg et al., 2021). The study showed that different litter types were preferred for different chicks’ behaviors. Dust bathing occurred on sand and peat, but chicks foraged more on wood shavings, hemp shavings, and sand than peat and pellets. The study also found that perch width and shape affected perch use and balance, measured as the likelihood of successful or problematic landings, and suggested that presenting several litter types could better fulfill laying hens’ chicks’ behavioral needs. Additionally, other preference studies showed a hen’s preferences for sunlight-filtering shade cloth shelters about different sunlight wavelengths on the range of commercial free- range laying hens. They showed hens prefer shelters that block more sunlight, especially with high sunlight intensity (Rana et al., 2022). Therefore, preference studies are essential to determine birds’ comfort based on their

perception and to adjust situations or infrastructure that improve the flock’s welfare.

Utilizing omics methodologies provides a comprehensive strategy for thoroughly examining biological systems by analyzing and assessing enormous amounts of data representing a specific biological system’s composition and operational mechanisms within a particular context or level (Dai & Shen, 2022). In animal welfare, omics technologies have the potential to provide novel insights into the general biological understanding of the interactions between various physiological systems that participate in stress resilience, behavior, and production (Kasper et al., 2020). Among the emerging technologies for animal welfare assessment, genomics, epigenomics, transcriptomics, proteomics, and metabolomics have been included because of their ability to comprehensively study biological systems (Suravajhala et al., 2016).

Genomics involves the study of whole genomes, including coding and non-coding components (Nguyen, 2024). Approaches used for genomic research include whole-genome sequencing, whole-exome sequencing, and targeted sequencing to acquire detailed data, as well as the use of bioinformatics tools for genome assembly, annotation, detection of structural variations, and comparative analysis between species (Cammen et al., 2016); (Satam et al., 2023). This offers the potential to understand the host genetic factors that influence susceptibility, resistance, and immune responses to infectious diseases, creating an excellent opportunity to enhance the genetic well-being of animals by improving the precision of breeding values for the selection of candidates or related individuals, even in the absence of additional stressors (Brito et al., 2020); (Nguyen, 2024).

Genomics has the potential to address a variety of welfare concerns by improving the fitness of the animal for the given environment, which might lead to increased contentment and decreased stress of birds in those production environments (Muir et al., 2014). Genomic selection is an emerging tool that can be used for effective and rapid selection under different environmental conditions (Budhlakoti et al., 2022). In breeding programs for layers, genomic selection can increase the efficiency of breeding programs regarding genetic progress and economic gain by enhancing selection accuracy or shortening the generation interval (Sitzenstock et al., 2013). Similarly, (Alemu et al., 2016) indicated that genomic selection for socially affected traits is a promising tool for improving survival time in laying hens with intact beaks (Bahrndorff et al., 2016).

Genome-wide association studies (GWAS) are an approach used in genomics that allows the identification of genomic regions associated with groups of individuals with a particular phenotype (e.g., diseases, traits, behavioral outcomes) across a population to understand the genetic architecture of the phenotype better (Sitzenstock et al., 2013); (Uffelmann et al., 2021). By GWAS, animal breeding programs can improve animal welfare by contributing to better health care and management by identifying genetic markers associated with desirable traits such as disease resistance, temperament, and physical characteristics, thus allowing selective breeding programs to improve (Baker et al., 2019). (Lutz et al., 2017) used GWAS to identify genetic factors associated with feather pecking and aggressive pecking, discovering that numerous genes with minor effects were responsible for controlling these behaviors; however, no single nucleotide polymorphism (SNP) had a significant impact that justified its use in marker-assisted selection (Lutz et al., 2017).

Epigenetics is the study of changes in gene function that are mitotically and/or meiotically heritable, yet potentially reversible, molecular modifications to DNA and chromatin without altering the underlying DNA sequence (Wu & Morris, 2001). Epigenetic mechanisms include but are not limited to DNA methylation/ demethylation and hydroxymethylation, histone acetylation/deacetylation, histone phosphorylation/ dephosphorylation, noncoding RNA, microRNAs, and transcriptome actions, which play essential roles in modulating genomic function and stability (Ibeagha-Awemu & Yu, 2021); (Steiger & Thaler, 2016). These mechanisms function as intermediates between the genome and the environment, regulating various cellular processes and expressing the phenotype (Ibeagha-Awemu & Yu, 2021).

Among the techniques employed in the examination of epigenomics are DNA methylation profiling, chromatin accessibility mapping, histone modification analysis, chromatin conformation analysis, and the merging of DNA methylation profiles with RNA-seq data (Satam et al., 2023). Research is being conducted in epigenetics to identify epigenetic markers of long-term stress in production animals (Silva et al., 2021). Epigenetic biomarkers are particularly promising for analyzing animal welfare and other attributes of interest in the animal agriculture industry because they integrate multidimensional context-dependent information. They could be applied to animal health and environmental exposure monitoring, two critical aspects of animal welfare assessments (Whelan et al., 2023).

Several studies showed epigenetic changes in hens in different conditions. For example, (Pértille et al., 2020) used a study of the epigenome through methylome (Pértille et al., 2020). They identified stress-associated DNA methylation profiles from male White Leghorn chickens subjected to social isolation compared to controls across different biomes to detect whether a standard stress-related epigenetic profile is a potential, and obtained some candidate genes for stress diagnosis across layer populations of chickens reared in different conditions. Likewise, (Guerrero-Bosagna et al., 2020) concluded that relative DNA methylation differences in the nidopallium are responsible for the non-genetic factors involved in the emergence of differential behavioral patterns in hens (Guerrero-Bosagna et al., 2020).

Transcriptomics refers to the study of the structure, function, and evolution of the ‘transcriptome,’ i.e., the complete set of all the ribonucleic acid (RNA) molecules (called transcripts) expressed in some given entity, such as a cell, tissue, or organism (Skerrett-Byrne Anthony et al., 2023). Some of the goals of transcriptomics include cataloging the entirety of transcriptome components, such as mRNAs, ncRNAs, and small RNAs (excluding rRNAs), investigating post-transcriptional modifications, and quantifying fluctuations in transcript expression during developmental stages and diverse conditions (Skerrett-Byrne Anthony et al., 2023).

Currently, transcriptome research studies have become a popular methodology due to technological advances and high sensitivity, throughput, and accuracy techniques used to quantify mRNA (Long, 2020); (Rodríguez-Hernández et al., 2021). Transcriptome research has been studying stress and stress factors due to their capacity to elucidate stress mechanisms and their influence on the production based on a genetic level (Herrera-Sánchez et al., 2023); (Li et al., 2011), which could help to improve animal welfare evaluation (Wang & Ma, 2019). The production systems and stress factors during poultry production can evoke changes in gene transcription related to productivity and metabolism, among others (Chen et al., 2021); (Herrera-Sánchez et al., 2024), and could affect protein synthesis, producing changes in internal and external egg quality (Rodríguez-Hernández et al., 2024).

For example, through transcriptome analysis of heat-treated and control layers, it is possible to identify the differentially expressed genes (DEGs) related to the layer’s response to stressors and may serve as targets for genetic selection to improve heat tolerance in layers (Wang et al., 2021). Another example of transcriptome use includes using the brain transcriptome study using RNA-seq to identify genes and biological pathways responsible for feather pecking (Falker-Gieske et al., 2020). In our studies, we have evaluated the transcriptome of caged and cage-free hens, finding statistically significant differences in the hypothalamus (138 DEGs), in liver tissues (209 DEGs) and spleen (19 DEGs), between hens from both egg production systems, in the liver transcriptome of hens housed in the conventional cage versus cage free production system, genes such as TENM2, GRIN2C, ACACB, and SH3RF2 were identified, which can modulate fat synthesis in the liver, indicating that the production system would produce changes in triglyceride production in birds, demonstrates the influence of the production system and production conditions on genetic regulation under these conditions (Herrera-Sánchez et al., 2025).

Proteomics refers to the study of the proteome, i.e., the entire complement of proteins, including different posttranslational modifications (PTMs) expressed by cells or homogeneous tissues at a specific time (Conti & Alessio, 2015). The main proteomic approaches encompass the study of a specific proteome in a cell type or tissue, including information on protein abundance, their variations, and modifications, together with the analysis of protein-protein interactions with partners and networks to understand gene function (Liang et al., 2002). Proteomics using biomarkers is a very suitable method in animal breeding for understanding physiological processes and adaptation to environmental conditions, including stress and welfare (Adnane et al., 2024).

Some studies have used proteomics to measure the influence of feed components, additives, or environmental or microbial challenges in hens. (Ding et al., 2020) identified by proteomics analysis the effects of tea polyphenol supplementation on the mechanism of albumen quality by regulating the antioxidant activity of proteins that affect egg weight, Haugh Units, albumen height, strength, hardness, gumminess, and chewiness of albumen. Likewise, (Liang et al., 2024) found that HSP90, XDH, and POSTN proteins in chicken serum may be optimal biomarkers for detecting heat stress levels in chickens. Also, (Shen et al., 2021) identified critical proteins in chicken serum that may play a role in follicle development during reproductive phase transitions.

In addition, (Kang & Shim, 2020) carried out a proteomic analysis of chronic and early heat exposure in one-day-old chicks. They found that acute heat stress caused significant changes in the expression of 97 filtered proteins compared with the control. Early exposure to heat improved the expression of 62 proteins after chickens were subjected to acute heat stress. (Zheng et al., 2021) challenged broiler chickens with Escherichia coli lipopolysaccharide (LPS) and determined that 111 proteins were differentially expressed in the liver of broiler chickens, which triggered alterations in their hepatic proteome. This study provided new insights into the mechanisms by which immune challenge impairs bird growth or productivity.

Metabolomics has emerged as a powerful tool to elucidate biochemical processes and principles in organisms. This technique studies all low molecular weight molecules (metabolites) within a biological sample (cell/tissue/organelle) following a specific cellular process (Færgestad et al., 2009); (Lee et al., 2024). Unlike other “omics” technologies, metabolomics serves as a direct biomarker of biological systems by investigating the changes of metabolites over time after stimulation or perturbation of biological systems, such as mutation of a particular gene or environmental change (Patti et al., 2012). Metabolomics, a relatively new field that emerged in response to genetics and proteomics, can illustrate the physiological state of an organism by monitoring changes in endogenous metabolites (Huang et al., 2022).

Techniques used in metabolomics, such as Nuclear Magnetic Resonance spectroscopy (NMR), Fourier transform-infrared spectroscopy (FT-IR), and MS coupled with liquid chromatographic separation techniques, including GC-MS, LC-MS, FT-MS, and UPLC-MS, can be used for large-scale metabolomics analysis (Tolani et al., 2021). Among the analytical platforms in metabolomics, GC–MS and LC-MS techniques are the most used (Sun & Xia, 2023). Metabolomics studies have been used to measure health status and hen welfare. Metabolomics identifies metabolic changes in hosts in response to disease. (Lee et al., 2024) employed a metabolomics approach to explore differentially expressed amino acids and rewired metabolic networks under multiple Eimeria species challenges in laying hens.

It has also been shown that restrictive and non- restrictive production systems can affect the metabolism of birds. (Yang et al., 2022) showed that restrictive and non-restrictive production systems can affect the metabolism of birds using Jianghan hens reared in caged and cage-free groups, resulting in differences in glycolipid and lipid metabolism and altered levels of intramuscular fat content and other flavor precursors. Pathways such as glycerolipid metabolism, adipocytokine signaling, and metabonomic pathways such as linoleic acid, glycerophospholipid, arginine, proline, and β-alanine metabolism may be responsible for the meat quality and flavor change, and the cage-free system showed a positive effect on the improvement of chicken- muscle-eating quality.

Likewise, animal husbandry can be improved by identifying how metabolic pathways change due to diet, environmental stress, health, and mental state. This will define management strategies to improve animal welfare in food-producing animals (Fabrile et al., 2023). (Lee et al., 2022) investigated the effect of an animal-friendly raising environment on chicken thighs’ quality, storage stability, and metabolomic profiles in two different environment-raising systems. They resulted in the differential regulation of metabolic pathways and physicochemical quality, especially in Glycolysis-related products. The results indicated that the animal welfare environment could influence the metabolomic properties of breast thigh meat in broilers, which may affect the sensory quality of meat. Another example is the use of metabolomics to examine the influence of rearing methods (floor and cage) on bone quality parameters in chickens using metabolomics analysis using LC-MS/MS. (Li et al., 2023) identified 257 differential metabolites and 15 metabolic pathways responsible for bone quality parameters; these results suggest that the cage-rearing system deteriorates bone quality parameters.

In laying hens, for example, (Huang et al., 2022) combined analysis of transcriptomics and metabolomics to identify differential metabolites and genes potentially regulating egg production with correlations and integrated gene-metabolite between two groups of laying hens with high and low egg production, the Ninghai indigenous chicken and Wuliangshan black-boned chicken. Analyses of metabolomics and transcriptomics found the genes that potentially regulate egg production processes, including P2RX1, INHBB, VIPR2, and FABP3, as well as the essential ovarian metabolites 17α-hydroxyprogesterone, iloprost, spermidine, and adenosine. They identified two essential metabolite pairs through gene and metabolite association analysis, namely, VIPR2–Spermidine and P2RX1–Spermidine during egg production.

The use of omic sciences allows for a closer approach to biological processes in birds. New studies that involve correlations between transcriptomic and metabolomic data are valuable data that, together with productive parameters and other indicators of welfare, can give a more holistic view of poultry welfare status.

Currently, omics methods are routinely used to identify genes involved in host-pathogen interactions, assess environmental resistance and fitness traits, and pinpoint animals with disease resistance. However, several challenges remain in implementing these technologies, particularly in developing countries, including limited access to advanced tools, high costs of laboratory tests, and the need for continued research on animal welfare in specific contexts to discover new biomarkers. Moreover, research using these technologies requires a holistic and interdisciplinary approach integrating ethology, neuroscience, data analysis, and evolutionary biology to enable a more comprehensive evaluation of animal welfare (Choudhary et al., 2024); (Neethirajan, 2025).