Spectral entropy of early-life distress calls as an iceberg indicator of chicken welfare

Classifying vocal responses of broilers to environmental stressors via artificial neural network

Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transformer artificial neural network (ANN). Two consecutive trials were conducted with varying amounts of collected data, and three ANN models with the same architecture but different parameters were examined. The impacts of adding broiler age data as an input attribute and varying input audio waveform lengths on model performance were assessed. Model performance improved with the inclusion of broiler age and longer audio waveforms when trained on smaller datasets. Additionally, the study evaluated the impact of majority vote decision-making across the three ANN model sizes, showing improvement in mean average precision (mAP), particularly for models with shorter audio inputs. Overall, the largest ANN model achieved the highest mAP score of 0.97 for the larger dataset, with small variations among different model sizes. These findings highlight the potential of using a single model to accurately classify multiple types of broiler stress calls. By enhancing the timing of human intervention during critical growth stages, the proposed method may significantly improve broiler welfare and farm management efficiency.

Chicks produce consonant, sometimes jazzy, sounds

Several animal species prefer consonant over dissonant sounds, a building block of musical scales and harmony. Could consonance and dissonance be linked, beyond music, to the emotional valence of vocalizations? We extracted the fundamental frequency from calls of young chickens with either positive or negative emotional valence, i.e. contact, brood and food calls. For each call, we calculated the frequency ratio between the maximum and the minimum values of the fundamental frequency, and we investigated which frequency ratios occurred with higher probability. We found that, for all call types, the most frequent ratios matched perfect consonance, like an arpeggio in pop music. These music-like intervals, based on the auditory frequency resolution of chicks, cannot be miscategorized into contiguous dissonant intervals. When we analysed frequency ratio distributions at a finer-grained level, we found some dissonant ratios in the contact calls produced during distress only, thus sounding a bit jazzy. Complementing the empirical data, our computational simulations suggest that physiological constraints can only partly explain both consonances and dissonances in chicks' phonation. Our data add to the mounting evidence that the building blocks of human musical traits can be found in several species, even phylogenetically distant from us.

Exposure to calls before hatching affects the post-hatching behaviour of domestic chickens

The soundscape experienced by animals early in life can affect their behaviour later in life. For birds, sounds experienced in the egg can influence how individuals learn to respond to specific calls post-hatching. However, how early acoustic experiences affect subsequent social behaviour remains unknown. Here, we investigate how exposure to maternal 'cluck' calls pre-hatching affects the behaviour of domestic chickens (Gallus gallus domesticus) at 3-5 days and 17-21 days old. We incubated eggs and played cluck calls to half of them. After hatching, we raised chicks in small groups occupying different enclosures. At 3-5 days old, we tested chicks' responses to three stimuli: (i) background sound, (ii) chick calls and (iii) cluck calls. We found that the pre-hatching experience of cluck calls reduced the likelihood of moving in response to all three stimuli. At 17-21 days old, some chicks explored beyond their own enclosure and 'visited' other groups. Chicks exposed to cluck calls before hatching were three times more likely to enter another group's enclosure than control chicks, and this was unaffected by the chicks' social connectedness. Our results indicate age- and context-dependent responses of chicks to pre-hatching cluck-call playbacks, with potential long-term effects on individual social behaviour.

Review: Environmental enrichment builds functional capacity and improves resilience as an aspect of positive welfare in production animals

The success of the animal in coping with challenges, and in harnessing opportunities to thrive, is central to its welfare. Functional capacity describes the capacity of molecules, cells, organs, body systems, the whole animal, and its community to buffer against the impacts of environmental perturbations. This buffering capacity determines the ability of the animal to maintain or regain functions in the face of environmental perturbations, which is recognised as resilience. The accuracy of physiological regulation and the maintenance of homeostatic balance underwrite the dynamic stability of outcomes such as biorhythms, feed intake, growth, milk yield, and egg production justifying their assessment as indicators of resilience. This narrative review examines the influence of environmental enrichments, especially during developmental stages in young animals, in building functional capacity and in its subsequent expression as resilience. Experience of enriched environments can build skills and competencies across multiple functional domains including but not limited to behaviour, immunity, and metabolism thereby increasing functional capacity and facilitating resilience within the context of challenges such as husbandry practices, social change, and infection. A quantitative method for measuring the distributed property of functional capacity may improve its assessment. Methods for analysing embedded energy (emergy) in ecosystems may have utility for this goal. We suggest functional capacity provides the common thread that links environmental enrichments with an ability to express resilience and may provide a novel and useful framework for measuring and reporting resilience. We conclude that the development of functional capacity and its subsequent expression as resilience is an aspect of positive animal welfare. The emergence of resilience from system dynamics highlights a need to shift from the study of physical and mental states to the study of physical and mental dynamics to describe the positive dimension of animal welfare.

Calls of the little auk (Alle alle) chicks reflect their behavioural contexts

Animal vocalisations can often inform conspecifics about the behavioural context of production and the underlying affective states, hence revealing whether a situation should be approached or avoided. While this is particularly important for socially complex species, little is known about affective expression in wild colonial animals, and even less to about their young. We studied vocalisations of the little auk (Alle alle) chicks in the Hornsund breeding colony, Svalbard. Little auks are highly colonial seabirds, and adults convey complex behavioural contexts through their calls. We recorded chick calls during two contexts of opposite affective valence: handing by a human, and while they interact with their parents inside the nest. Using permuted discriminant function analysis and a series of linear mixed models, we examined the effect of the production context/associated affective valence on the acoustic parameters of those calls. Calls were reliably classified to their context, with over 97% accuracy. Calls uttered during handling had higher mean entropy, fundamental frequency, as well as lower spectral centre of gravity and a less steep spectral slope compared to calls produced during interactions with a parent inside the nest. The individuality of handling calls, assessed by information content, was lower than the individuality of calls uttered in the nest. These findings suggest that seabird chicks can effectively communicate behavioural/affective contexts through calls, conveying socially important messages early in development. Our results are mostly in line with emotional expression patterns observed across taxa, supporting their evolutionary continuity.

Active walking in broiler chickens: a flagship for good welfare, a goal for smart farming and a practical starting point for automated welfare recognition

Automated assessment of broiler chicken welfare poses particular problems due to the large numbers of birds involved and the variety of different welfare measures that have been proposed. Active (sustained, defect-free) walking is both a universally agreed measure of bird health and a behavior that can be recognized by existing technology. This makes active walking an ideal starting point for automated assessment of chicken welfare at both individual and flock level.

New Behavioral Handling Test Reveals Temperament Differences in Native Japanese Chickens

It is well known in the poultry industry that fear and stress experienced during the handling of day-old chicks in commercial hatcheries can have long-lasting effects on their behavior later in life. These hatchery-related stresses are more intense and complex than those encountered in traditional behavioral tests. Consequently, a single behavioral test may not be sufficient to measure hatchery stresses and chicken temperament. In this study, we developed a new behavioral handling test for day-old chickens, which incorporated concepts from established behavioral tests used with both young and adult birds. The new test assessed 10 behavioral traits, including vocalization frequency and responses to human interaction. It was conducted on 96 two-day-old chicks from seven breeds of native Japanese and Western chickens. The results of the principal component analysis classified chicken temperaments into three distinct categories: bustle, aggression, and timidity. Using these categories, the seven breeds were classified into five groups, each with distinct temperaments. This study highlights the reliability and value of the new handling test in characterizing the temperaments of various chicken breeds and provides insights into the complex behaviors of chickens.

Welfare of broilers on farm

This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under- and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm.

Energy Assessment from Broiler Chicks' Vocalization Might Help Improve Welfare and Production

Vocalization seems to be a viable source of signal for assessing broiler welfare. However, it may require an understanding of the birds' signals, both quantitatively and qualitatively. The delivery of calls with a specific set of acoustic features must be understood to assess the broiler's well-being. The present study aimed to analyze broiler chick vocalization through the sounds emitted during social isolation and understand what would be the flock size where the chicks present the smallest energy loss in vocalizing. The experiments were carried out during the first 3 days of growth, and during the trial, chicks received feed and water ad libitum. A total of 30 1-day-old chicks Cobb® breed were acquired at a commercial hatching unit. The birds were tested from 1 to 3 days old. A semi-anechoic chamber was used to record the vocalization with a unidirectional microphone connected to a digital recorder. We placed a group of 15 randomly chosen chicks inside the chamber and recorded the peeping sound, and the assessment was conducted four times with randomly chosen birds. We recorded the vocalization for 2 min and removed the birds sequentially stepwise until only one bird was left inside the semi-anechoic chamber. Each audio signal recorded during the 40 s was chosen randomly for signal extraction and analysis. Fast Fourier transform (FFT) was used to extract the acoustic features and the energy emitted during the vocalization. Using data mining, we compared three classification models to predict the rearing condition (classes distress and normal). The results show that birds' vocalization differed when isolated and in a group. Results also indicate that the energy spent in vocalizing varies depending on the size of the flock. When isolated, the chicks emit a high-intensity sound, "alarm call", which uses high energy. In contrast, they spent less energy when flocked in a group, indicating good well-being when the flock was 15 chicks. The weight of birds influenced the amount of signal energy. We also found that the most effective classifier model was the Random Forest, with an accuracy of 85.71%, kappa of 0.73, and cross-entropy of 0.2.

Sex Detection of Chicks Based on Audio Technology and Deep Learning Methods

The sex detection of chicks is an important work in poultry breeding. Separating chicks of different sexes early can effectively improve production efficiency and commercial benefits. In this paper, based on the difference in calls among one-day-old chicks of different sexes, a sex detection method based on chick calls is designed. Deep learning methods were used to classify the calls of chicks and detect their sex. This experiment studies three different varieties of chicks. The short-time zero-crossing rate was used to automatically detect the endpoints of chick calls in audio. Three kinds of audio features were compared: Spectrogram, Cepstrogram and MFCC+Logfbank. The features were used as the input in neural networks, and there were five kinds of neural networks: CNN, GRU, CRNN, TwoStream and ResNet-50. After the cross-comparison experiment of different varieties of chicks, audio features and neural networks, the ResNet-50 neural network trained with the MFCC+Logfbank audio features of three yellow chick calls had the highest test accuracy of 83% when testing Three-yellow chicks' calls. The GRU neural network trained with the Spectrogram audio features of native chick calls had the highest test accuracy of 76.8% when testing Native chicks' calls. The ResNet-50 neural network trained with Spectrogram audio features of flaxen-yellow chick calls had the highest test accuracy of 66.56%when testing flaxen-yellow chick calls. Multiple calls of each chick were detected, and the majority voting method was used to detect the sex of the chicks. The ResNet-50 neural network trained with the Spectrogram of three yellow chick calls had the highest sex detection accuracy of 95% when detecting the three yellow chicks' sex. The GRU neural network trained with the Spectrogram and cepstrogram of native chick calls and the CRNN network trained with the Spectrogram of native chick calls had the highest sex detection accuracy of 90% when detecting the native chicks' sex. The Twostream neural network trained with MFCC+Logfbank of flaxen-yellow chick calls and the ResNet-50 network trained with the Spectrogram of flaxen-yellow chick calls had the highest sex detection accuracy of 80% when detecting the flaxen-yellow chicks' sex. The results of the cross-comparison experiment show that there is a large diversity between the sex differences in chick calls of different breeds. The method is more applicable to chick sex detection in three yellow chicks and less so in native chicks and flaxen-yellow chicks. Additionally, when detecting the sex of chicks of a similar breed to the training chicks, the method obtained better results, while detecting the sex of chicks of other breeds, the detection accuracy was significantly reduced. This paper provides further perspectives on the sex detection method of chicks based on their calls and help and guidance for future research.

Welfare of domestic birds and rabbits transported in containers

This opinion, produced upon a request from the European Commission, focuses on transport of domestic birds and rabbits in containers (e.g. any crate, box, receptacle or other rigid structure used for the transport of animals, but not the means of transport itself). It describes and assesses current transport practices in the EU, based on data from literature, Member States and expert opinion. The species and categories of domestic birds assessed were mainly chickens for meat (broilers), end-of-lay hens and day-old chicks. They included to a lesser extent pullets, turkeys, ducks, geese, quails and game birds, due to limited scientific evidence. The opinion focuses on road transport to slaughterhouses or to production sites. For day-old chicks, air transport is also addressed. The relevant stages of transport considered are preparation, loading, journey, arrival and uncrating. Welfare consequences associated with current transport practices were identified for each stage. For loading and uncrating, the highly relevant welfare consequences identified are handling stress, injuries, restriction of movement and sensory overstimulation. For the journey and arrival, injuries, restriction of movement, sensory overstimulation, motion stress, heat stress, cold stress, prolonged hunger and prolonged thirst are identified as highly relevant. For each welfare consequence, animal-based measures (ABMs) and hazards were identified and assessed, and both preventive and corrective or mitigative measures proposed. Recommendations on quantitative criteria to prevent or mitigate welfare consequences are provided for microclimatic conditions, space allowances and journey times for all categories of animals, where scientific evidence and expert opinion support such outcomes.

Social behavior in farm animals: Applying fundamental theory to improve animal welfare

A fundamental understanding of behavior is essential to improving the welfare of billions of farm animals around the world. Despite living in an environment managed by humans, farm animals are still capable of making important behavioral decisions that influence welfare. In this review, we focus on social interactions as perhaps the most dynamic and challenging aspects of the lives of farm animals. Social stress is a leading welfare concern in livestock, and substantial variation in social behavior is seen at the individual and group level. Here, we consider how a fundamental understanding of social behavior can be used to: (i) understand agonistic and affiliative interactions in farm animals; (ii) identify how artificial environments influence social behavior and impact welfare; and (iii) provide insights into the mechanisms and development of social behavior. We conclude by highlighting opportunities to build on previous work and suggest potential fundamental hypotheses of applied relevance. Key areas for further research could include identifying the welfare benefits of socio–positive interactions, the potential impacts of disrupting important social bonds, and the role of skill in allowing farm animals to navigate competitive and positive social interactions. Such studies should provide insights to improve the welfare of farm animals, while also being applicable to other contexts, such as zoos and laboratories.

Automated identification of chicken distress vocalizations using deep learning models

The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

Bird Welfare in Zoos and Aquariums: General Insights across Industries

Animal welfare is a priority across accredited zoological institutions; however, historically, research has been prioritized for mammals. Bird-focused studies accounted for less than 10% of welfare research in zoos and aquariums over the last ten years. Due to the lack of scientific publications on bird welfare, zoo scientists and animal practitioners can look to other industries such as agriculture, laboratories, and companion animal research for insight. This qualitative review highlights findings across industries to inform animal care staff and scientists on the welfare needs of birds within zoos and aquariums. Specifically, the review includes an overview of research on different topics and a summary of key findings across nine resources that affect bird welfare. We also highlight areas where additional research is necessary. Future welfare research in zoos and aquariums should prioritize studies that consider a diversity of bird species across topics and work to identify animal-based measures with empirical evidence. Moving forward, research from other industries can help develop innovative research on bird welfare within zoos and aquariums.

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.

The Animal Welfare Science of Working Dogs: Current Perspectives on Recent Advances and Future Directions

Working dogs are prevalent throughout our societies, assisting people in diverse contexts, from explosives detection and livestock herding, to therapy partners. Our scientific exploration and understanding of animal welfare have grown dramatically over the last decade. As community attitudes toward the use of animals continue to change, applying this new knowledge of welfare to improve the everyday lives of working dogs will underpin the sustainability of working with dogs in these roles. The aim of this report was to consider the scientific studies of working dogs from the last decade (2011–2021) in relation to modern ethics, human interaction, and the five domains of animal welfare: nutrition, environment, behavioral interaction, physical health, and mental state. Using this framework, we were able to analyze the concept and contribution of working dog welfare science. Noting some key advances across the full working dog life cycle, we identify future directions and opportunities for interdisciplinary research to optimize dog welfare. Prioritizing animal welfare in research and practice will be critical to assure the ongoing relationship between dogs and people as co-workers.

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

The Use of Artificial Intelligence in Assessing Affective States in Livestock

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

Measuring Farm Animal Emotions-Sensor-Based Approaches

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no 'benchmarks' or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal's emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals' compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.

Acoustic Description of the Soundscape of a Real-Life Intensive Farm and Its Impact on Animal Welfare: A Preliminary Analysis of Farm Sounds and Bird Vocalisations

Poultry meat is the world's primary source of animal protein due to low cost and is widely eaten at a global level. However, intensive production is required to supply the demand although it generates stress to animals and welfare problems, which have to be reduced or eradicated for the better health of birds. In this study, bird welfare is measured by certain indicators: CO2, temperature, humidity, weight, deaths, food, and water intake. Additionally, we approach an acoustic analysis of bird vocalisations as a possible metric to add to the aforementioned parameters. For this purpose, an acoustic recording and analysis of an entire production cycle of an intensive broiler Ross 308 poultry farm in the Mediterranean area was performed. The acoustic dataset generated was processed to obtain the Equivalent Level (Leq), the mean Peak Frequency (PF), and the PF variation, every 30 min. This acoustical analysis aims to evaluate the relation between traditional indicators (death, weight, and CO2) as well as acoustical metrics (equivalent level impact (Leq) and Peak Frequency) of a complete intensive production cycle. As a result, relation between CO2 and humidity versus Leq was found, as well as decreases in vocalisation when the intake of food and water was large.