Ammonia emissions, impacts, and mitigation strategies for poultry production: A critical review

Identification and functional analysis of circular RNA expression profiles associated with ammonia exposure in chicken lungs

Ammonia acts as a detrimental atmospheric pollutant, posing a sever threat to respiratory tract health and causing lung injury in humans and animals. Circular RNAs (circRNAs) are a distinctive class of non-coding RNA generated by back-splicing of linear RNA, implicated in various biological processes. However, their role in the immune response of chicken lungs to ammonia exposure remains unclear. In this study, we examined the expression profiles of circRNAs in chicken lungs under ammonia stimulation. In total, 61 differentially expressed (DE) circRNAs were identified between the ammonia exposure and control groups, including 17 up-regulated and 44 down-regulated circRNAs. The source genes of these DE circRNAs were predominantly enriched in Influenza A, SNARE interactions in vesicular transport, and Notch signaling pathway. Notably, nine DE circRNAs (circNBAS, circMTIF2, circXPO1, circSNX24, circRAB11A, circARID3B, circUSP54, circPPARA, and circERG) were selected for validation the reliability and authenticity of RNA-seq data. Results showed the back-splicing circular structure, as well as the reliability and accuracy of RNA-seq data in quantifying circRNA expression, as the RT-qPCR results were in agreement with the RNA-seq data. Moreover, we constructed the circRNA-miRNA-mRNA regulatory networks and identified several regulatory networks in chicken lungs under ammonia stimulation, including circRAB11A-gga-miR-191b-3p-BRD2 and circARID3B-gga-miR-1696-CKS2. Taken together, our study delineates the circRNA expression profile and their potential roles in the immune response of chicken lungs to ammonia exposure. These findings offer insights into molecular mechanisms that may mitigate diseases associated with ammonia induced respiratory tract pollution in humans and animals.

An ultra-sensitive ammonia sensor based on a quartz crystal microbalance using nanofibers overlaid with carboxylic group-functionalized MWCNTs

Detecting ammonia at low concentrations is crucial in various fields, including environmental monitoring, industrial processes, and healthcare. This study explores the development and performance of an ultra-sensitive ammonia sensor using carboxylic group-functionalized multi-walled carbon nanotubes (f-MWCNTs) overlaid on polyvinyl acetate nanofibers coated on a quartz crystal microbalance (QCM). The sensor demonstrates high responsiveness, with a frequency shift response of over 120 Hz when exposed to 1.5 ppm ammonia, a sensitivity of 23.3 Hz ppm-1 over a concentration range of 1.5-7.5 ppm, and a detection limit of 50 ppb. Additionally, the sensor exhibits a rapid response time of only 14 s, excellent selectivity against other gases, such as acetic acid, formaldehyde, methanol, ethanol, propanol, benzene, toluene, and xylene, and good stability in daily use. These characteristics make the sensor a promising tool for real-time ammonia detection in diverse applications.

Impact of adding zeolite to broilers' diet and litter on growth, blood parameters, immunity, and ammonia emission

This work was designed to assess the impact of varying zeolite concentrations in diet and litter to enhance broiler's growth performance, immunity, and litter quality. A complete random arrangement was used for distributing 525 unsexed "Cobb 500" broiler chicks into seven treatments (75 chick / treatment), each treatment divided into 3 replicates (25 chicks / replicate). The 1st group (control one) received the recommended basal diet. Zeolite has been introduced to the basal diet (ZD) of the second, third, and fourth groups at concentrations of 5, 10, and 15 g/kg, respectively. The 5th, 6th and 7th groups used zeolite mixed with litter (ZL) at 0.5, 1, and 1.5 kg/m2 of litter, respectively. Due to the obtained results, adding zeolite with levels 15 g/kg of diet and 1.5 kg/1 m2 of litter, a significant improvement occurred in live body weight (LBW), body weight gain (BWG), feed intake (FI), feed conversion ratio (FCR) and European production efficiency factor (EPEF). Also, transaminase enzymes (ALT and AST), creatinine, white blood cells (WBCs) and different Immunoglobulins were significantly increased with different zeolite levels, except urea concentrations which showed reduced due to different zeolite treatments. In addition, spleen relative weight hasn't been affected by zeolite treatments, even though thymus and bursa relative weights had been affected significantly. Moreover, the antibodies' production to Newcastle disease virus (NDV) and Avian influenza virus (AIV) had increased significantly with adding zeolite with levels 10 g/kg of diet and 1.5 kg/1m2 of litter. Litter quality traits (NH3 concentration, pH values, and Moisture content) were improved with zeolite addition. So, zeolite could be employed in both feed and litter of broilers to maximize their production, immunity and improve farm's climate.

Advanced Materials for NH3 Capture: Interaction Sites and Transport Pathways

Ammonia (NH3) is a carbon-free, hydrogen-rich chemical related to global food safety, clean energy, and environmental protection. As an essential technology for meeting the requirements raised by such issues, NH3 capture has been intensively explored by researchers in both fundamental and applied fields. The four typical methods used are (1) solvent absorption by ionic liquids and their derivatives, (2) adsorption by porous solids, (3) ab-adsorption by porous liquids, and (4) membrane separation. Rooted in the development of advanced materials for NH3 capture, we conducted a coherent review of the design of different materials, mainly in the past 5 years, their interactions with NH3 molecules and construction of transport pathways, as well as the structure-property relationship, with specific examples discussed. Finally, the challenges in current research and future worthwhile directions for NH3 capture materials are proposed.

In-house ammonia induced lung impairment and oxidative stress of ducks

Ammonia (NH3) is a toxic gas that in intensive poultry houses, damages the poultry health and induces various diseases. This study investigated the effects of NH3 exposure (0, 15, 30, and 45 ppm) on growth performance, serum biochemical indexes, antioxidative indicators, tracheal and lung impairments in Pekin ducks. A total of 288 one-day-old Pekin male ducks were randomly allocated to 4 groups with 6 replicates and slaughtered after the 21-d test period. Our results showed that 45 ppm NH3 significantly reduced the average daily feed intake (ADFI) of Pekin ducks. Ammonia exposure significantly reduced liver, lung, kidney, and heart indexes, and lowered the relative weight of the ileum. With the increasing of in-house NH3, serum NH3 and uric acid (UA) concentrations of ducks were significantly increased, as well as liver malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPX-Px) contents. High NH3 also induced trachea and lung injury, thereby increasing levels of tumor necrosis factor-α (TNF-α) and interleukin-4 (IL-4) in the lung, and decreasing the mRNA expressions of zonula occludens 1 (ZO-1) and claudin 3 (CLDN3) in the lung. In conclusion, in-house NH3 decrease the growth performance in ducks, induce trachea and lung injuries and meanwhile increase the compensatory antioxidant activity for host protection.

Effects of dietary supplementation of postbiotic derived from Bacillus subtilis ACCC 11025 on growth performance, meat yield, meat quality, excreta bacteria, and excreta ammonia emission of broiler chicks

The primary aim of this study was to explore the impact of dietary supplementation with a postbiotic derived from Bacillus subtilis ACCC 11025 on growth performance, meat yield, meat quality, excreta bacterial populations, and excreta ammonia emissions of broiler chicks. A total of 480 day-old Arbor Acre broiler chicks, initially weighing 52.83 ± 1.38 g, were randomly allocated into 4 distinct groups. Each group was housed in 6 separate cages, each containing 20 birds. The experimental phase spanned 42 d, divided into 2 periods (d 1-21 and d 22-42). Dietary interventions were based on a basal diet, with postbiotic supplementation at levels of 0.000, 0.015, 0.030, or 0.045%. Our findings indicate that dietary supplementation with postbiotic had a positive influence on body weight gain (BWG) and feed efficiency. The most substantial improvements in BWG and feed efficiency were observed in the group of broiler chicks fed a diet containing 0.015% postbiotic. Furthermore, the inclusion of postbiotic in the diet led to an increase in the yield of breast and leg muscles, with a significant difference in meat yields observed between the control group and the group receiving 0.015% postbiotic supplementation. It's noteworthy that dietary manipulation did not exert any discernible impact on the quality of breast and leg muscle samples. Concurrently, we observed an elevation in serum albumin and total protein contents corresponding to the increasing postbiotic dosage in the diet. Additionally, dietary supplementation with postbiotic effectively controlled the emission of ammonia from excreta and reduced the abundance of Salmonella in excreta while enhancing the presence of Lactobacillus bacteria. The group receiving 0.015% postbiotic supplementation displayed the lowest levels of ammonia emission and the highest counts of Lactobacillus bacteria in excreta. In light of these results, we conclude that dietary supplementation with 0.015% postbiotic represents an efficacious strategy for increasing BWG and meat yield of broiler chicks by enhancing feed efficiency as well as mitigating ammonia emissions from excreta by modulating the composition of excreta bacterial communities.

The potential of pumice as a litter material and its influence on growth performance, carcass parameters, litter quality traits, behavior, and welfare in broiler chickens

This study evaluated the possibilities of pumice (light stones) as litter material in broiler production. Experimental treatments included wood shavings (WS), acidic pumice (AP), and basic pumice (BP) alone, and in combination; wood shaving + acidic pumice (WSAP) and wood shaving + basic pumice (WSBP) in a ratio of 1:1. Two trials were performed, one in summer, and the other in winter. Each trial involved 750 mixed-sex Ross (308) broilers. Also, there were 15 replicate pens with 50 broilers and a stocking density of 12.5 birds/m2 for each pen at the beginning of each trial. Performance, litter quality, carcass parameters, body and leg abnormalities, body temperature, fear and stress responses, proportional asymmetry, and some behavior expressions were investigated. The litter treatment influenced the final live body weight, litter moisture, ammonia concentration, footpad dermatitis, hock burn, breast blister, hot carcass yield, heart, liver, spleen, abdominal fat, wing and neck ratio, breast and back cleanliness, and the expression of dust bathing and foraging behaviors (P < 0.01; P < 0.05). Furthermore, there was a seasonal effect on live body weight, feed conversion ratio, livability, litter pH, 42-day litter moisture, hot carcass yield, back cleanliness, footpad dermatitis, hock burn, footpad temperature, heterophil-to-lymphocyte ratio, and expression of pecking behavior (P < 0.01; P < 0.05). It is suggested that acidic pumice stone alone or in a mixture with wood shavings could be used as a reliable litter material, alternative to wood shavings.

Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities

The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH3) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption. Four identical CF rooms were utilized, each accommodating 175 hens of 77 wk of age (WOA). A Latin Square Design method was employed, with 4 treatment lengths: T1 = control (0 m), T2 = 12 ft (3.7 m), T3 = 24 ft (7.3 m), and T4 = 36 ft (11.0 m), where room and WOA are considered as blocking factors. Daily PM concentrations, temperature, and humidity measurements were conducted over 24 h, while NH3 levels, litter moisture content (LMC), and ventilation were measured twice a week in each treatment room. Statistical analysis involved ANOVA, and mean comparisons were performed using the Tukey HSD method with a significance level of P ≤ 0.05. This study found that the EPI system with longer wires reduced PM2.5 concentrations (P ≤ 0.01). Treatment T2, T3, and T4 led to reductions in PM2.5 by 12.1%, 19.3%, and 31.7%, respectively, and in small particle concentrations (particle size >0.5 μm) by 18.0%, 21.1%, and 32.4%, respectively. However, no significant differences were observed for PM10 and large particles (particle size >2.5 μm) (P < 0.10), though the data suggests potential reductions in PM10 (32.7%) and large particles (33.3%) by the T4 treatment. Similarly, there was no significant impact of treatment on NH3 reduction (P = 0.712), possibly due to low NH3 concentration (<2 ppm) and low LMC (<13%) among treatment rooms. Electricity consumption was significantly related to the length of the EPI system (P ≤ 0.01), with longer lengths leading to higher consumption rates. Overall, a longer-length EPI corona pipe is recommended for better air pollutant reduction in CF housing. Further research should focus on enhancing EPI technology, assessing cost-effectiveness, and exploring combinations with other PM reduction strategies.

Pollutant Gases to Algal Animal Feed: Impacts of Poultry House Exhaust Air on Amino Acid Profile of Algae

Algae provide a rich source of proteins, lipids, vitamins, and minerals, making them valuable feed ingredients in animal nutrition. Beyond their nutritional benefits, algae have been recognized for their potential to mitigate the negative environmental impacts of poultry production. Poultry production is crucial for the global food supply but contributes to environmental concerns, particularly in terms of ammonia and carbon dioxide gas emissions. This study emphasizes the importance of reducing greenhouse gas and ammonia production in poultry operations by utilizing algae species suitable for animal consumption, highlighting the need for sustainable feed sources. This study investigated the effects of poultry exhaust air and culture conditions on the amino acid profiles of three microalgae species, namely, Scenedesmus sp. (AQUAMEB-60), Ankistrodesmus sp. (AQUAMEB-33), and Synechococcaceae (AQUAMEB 32). The experiments were conducted in a commercial broiler farm in Bursa, Turkey, focusing on reducing pollutant gas emissions and utilizing poultry exhaust air in algae cultivation. The highest protein content of 50.4% was observed in the biomass of Synechococcaceae with BBM and DI water. Scenedesmus sp. had the highest carbohydrate content of 33.4% cultivated with DI water. The algae biomass produced from Synechococcaceae growth with DI water was found to have the highest content of essential and nonessential amino acids, except for glutamic acid and glycine. The arsenic, cadmium, and mercury content showed variations within the following respective ranges: 1.076-3.500 mg/kg, 0.0127-0.1210 mg/kg, and 0.1330-0.0124 mg/kg. The overall operating costs for producing 1.0 g L-1 d-1 of dry algal biomass with the existing PBR system were $0.12-0.35 L-1 d-1, $0.10-0.26 L-1 d-1, and $0.11-0.24 L-1 d-1 for Scenedesmus sp., Ankistrodesmus sp., and Synechococcaceae, respectively. The operating cost of producing 1.0 g L-1 d-1 of protein was in the range of $0.25-0.88 L-1 d-1 for the three algae species. The results provide insights into the potential of algae as a sustainable feed ingredient in animal diets, emphasizing both environmental and economic considerations. The results demonstrated a considerable reduction in the production costs of dry biomass and protein when utilizing poultry house exhaust air, highlighting the economic viability and nutritional benefits of this cultivation method.

The improved bioavailability of zein/soybean protein isolate by puerarin in vitro

As the largest emitter of greenhouse gases, the livestock and poultry industry is facing the challenge of increasing production to meet global demand while reducing environmental impacts. Improving feed digestibility by optimizing feed structure (e.g., exogenous additive) is one of the green breeding measures to alleviate carbon pressure. In this study, the interaction mechanism and in vitro digestibility properties of puerarin (PUE) with feed proteins (zein and soy protein isolate (SPI)) to form Zein-PUE and SPI-PUE complexes were investigated mainly by multispectral and molecular docking techniques. Results indicated that the addition of PUE improved the physicochemical properties of proteins (e.g., solubility and disulfide bond contents). Then, the spectral results showed that the binding processes were spontaneous, and the protein structure tended to loose and disordered after binding, and more hydrophobic residues were exposed to the hydrophilic microenvironment. Moreover, on the basis of molecular docking revealed that PUE bound to zein by hydrogen bond, electrostatic and hydrophobic interactions, while with SPI by hydrogen bond and hydrophobic interaction. Finally, in vitro digestion experiments demonstrated that the bioavailability of Zein-PUE and SPI-PUE complexes increased by 1.15 % and 2.11 %, respectively. Overall, PUE is a promising feed additive beneficial for enhancing protein digestibility and bioavailability.

Algorithm for Autonomous Management of a Poultry Farm by a Cyber-Physical System

The article presents a Cyber-Physical System (CPS) for intelligent management of a poultry farm for broiler meat production, with a fully autonomous microclimate control. Innovative concepts have been introduced for automated management and changing parameters according to pre-set conditions and schedules, with the possibility that the parameters of the algorithm can be further adjusted by the operator. The proposed CPS provides for high productivity with minimal production waste, at optimized costs and with minimization of human errors. The CPS is built on the basis of cost-oriented components. A Raspberry Pi 4 8 GB is used as the server, and the free open-source software OpenHAB 3.0 is used to optimize the cost of building the system as much as possible.

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

Chinese Herbal Extracts Mitigate Ammonia Generation in the Cecum of Laying Hens: An In Vitro Study

The objectives of the study were to screen one or several Chinese herbal extracts with good ammonia emission reduction effects using an in vitro gas production study. The study consisted of a control (without Chinese herbal extract), and 11 experimental groups with added cinnamon extract (CE), Osmanthus extract (OE), tangerine peel extract (TPE), dandelion extract (DE), Coptis chinensis extract (CCE), honeysuckle extract (HE), Pulsatilla root extract (PRE), yucca extract (YE), licorice extract (LE), Ginkgo biloba extract (GBE), or astragalus extract (AE). The results showed that HE, PRE, YE, LE, GBE, and AE significantly reduced ammonia production (p ≤ 0.05). The most significant ammonia inhibition was achieved via AE, resulting in a 26.76% reduction. In all treatments, Chinese herbal extracts had no significant effect on pH, conductivity, or uric acid, urea, and nitrate-nitrogen concentrations (p > 0.05). However, AE significantly reduced urease activity and the relative activity of uricase (p ≤ 0.05). AE significantly increased the relative abundance of Bacteroides and decreased the relative abundance of Clostridium, Desulfovibrio, and Prevotell (p ≤ 0.05). Astragalus extract inhibited ammonia emission from laying hens by changing the gut microbial community structure, reducing the relative abundance of ammonia-producing bacteria, and reducing microorganisms' uricase and urease activities.

On effect of poultry manure treatment with Effective Microorganisms with or without zeolite

The decomposition process of poultry manure is generally mediated by microorganisms, whose degradation activity has beneficial effects on soil fertility but, on the other hand, leads to the generation of malodour gas. Indeed, a relevant problem of poultry farms is represented by the release of bad smells, which are mainly a consequence of decomposition process of chicken feces, chicken bedding, plumes, dropped feed, and dust. Furthermore, the unpleasant odour, associated with poultry manure degradation, not only limits its use in agriculture but also negatively affects the housing communities located near the farms. This study aimed at evaluating the effects in vitro of different doses of Effective Microorganisms (EM), mainly consisting of live communities of lactic acid bacteria, photosynthetic bacteria, and yeasts, on poultry manure alone or with zeolite, a porous mineral with absorbent and ion-exchange properties, belonging to the family of aluminosilicates. The obtained results demonstrated that these treatments were able to reduce the poultry manure malodours, associated mainly with a decrease in the ammonia (NH3) levels with respect to controls. The pH tended to increase, the nitrogen to go down, and the phosphorus to go up. Thus, all the effects described above were evident, testifying to a slower degradation of proteins, both with EM alone or in combination with zeolite. The presence of a pool of pesticides (65 components) was evaluated, and no variation was observed in the different experimental conditions versus control, as well as for REEs and metals. In conclusion, these preliminary results demonstrated that the use of EM with or without the addition of zeolite is a valid tool to eliminate the bad smell of manure and to make it a useful product as a fertilizer.

A Computer Vision-Based Automatic System for Egg Grading and Defect Detection

Defective eggs diminish the value of laying hen production, particularly in cage-free systems with a higher incidence of floor eggs. To enhance quality, machine vision and image processing have facilitated the development of automated grading and defect detection systems. Additionally, egg measurement systems utilize weight-sorting for optimal market value. However, few studies have integrated deep learning and machine vision techniques for combined egg classification and weighting. To address this gap, a two-stage model was developed based on real-time multitask detection (RTMDet) and random forest networks to predict egg category and weight. The model uses convolutional neural network (CNN) and regression techniques were used to perform joint egg classification and weighing. RTMDet was used to sort and extract egg features for classification, and a Random Forest algorithm was used to predict egg weight based on the extracted features (major axis and minor axis). The results of the study showed that the best achieved accuracy was 94.8% and best R2 was 96.0%. In addition, the model can be used to automatically exclude non-standard-size eggs and eggs with exterior issues (e.g., calcium deposit, stains, and cracks). This detector is among the first models that perform the joint function of egg-sorting and weighing eggs, and is capable of classifying them into five categories (intact, crack, bloody, floor, and non-standard) and measuring them up to jumbo size. By implementing the findings of this study, the poultry industry can reduce costs and increase productivity, ultimately leading to better-quality products for consumers.

Automatic detection of brown hens in cage-free houses with deep learning methods

Computer vision technologies have been tested to monitor animals' behaviors and performance. High stocking density and small body size of chickens such as broiler and cage-free layers make effective automated monitoring quite challenging. Therefore, it is critical to improve the accuracy and robustness of laying hens clustering detection. In this study, we established a laying hens detection model YOLOv5-C3CBAM-BiFPN, and tested its performance in detecting birds on open litter. The model consists of 3 parts: 1) the basic YOLOv5 model for feature extraction and target detection of laying hens; 2) the convolution block attention module integrated with C3 module (C3CBAM) to improve the detection effect of targets and occluded targets; and 3) bidirectional feature pyramid network (BiFPN), which is used to enhance the transmission of feature information between different network layers and improve the accuracy of the algorithm. In order to better evaluate the effectiveness of the new model, a total of 720 images containing different numbers of laying hens were selected to construct complex datasets with different occlusion degrees and densities. In addition, this paper also compared the proposed model with a YOLOv5 model that combined other attention mechanisms. The test results show that the improved model YOLOv5-C3CBAM-BiFPN achieved a precision of 98.2%, a recall of 92.9%, a mAP (IoU = 0.5) of 96.7%, a classification rate 156.3 f/s (frames per second), and a F1 (F1 score) of 95.4%. In other words, the laying hen detection method based on deep learning proposed in the present study has excellent performance, can identify the target accurately and quickly, and can be applied to real-time detection of laying hens in real-world production environment.

Mislaying behavior detection in cage-free hens with deep learning technologies

Floor egg-laying behavior (FELB) is one of the most concerning issues in commercial cage-free (CF) houses because floor eggs (i.e., mislaid eggs on the floor) result in high labor costs and food safety concerns. Farms with poor management may have up to 10% of daily floor eggs. Therefore, it is critical to improving floor eggs management. Detecting FELB timely and identifying the reason behind its cause may address the issue. The primary objectives of this research were to develop and test a new deep-learning model to detect FELB and evaluate the model's performance in 4 identical research CF houses (200 Hy-Line W-36 hens per house), where perches and litter floor were provided to mimic commercial tiered aviary system. Five different YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were trained and compared. According to a dataset of 5400 images (i.e., 3780 for training, 1080 for validation, and 540 for testing), YOLOv5m-FELB and YOLOv5x-FELB models were tested with higher precision (99.9%), recall (99.2%), mAP@0.50 (99.6%), and F1-score (99.6%) than others. However, the YOLOv5m-NFELB model has lower recall than other YOLOv5-NFELB models, although it was tested with higher precision. Similarly, the speed of data processing (4%-45% FPS), and training time (3%-148%) were higher in the YOLOv5s model while requiring less GPU (1.8-4.8 times) than in other models. Furthermore, the camera height of 0.5 m and clean camera outperform compared to 3 m height and dusty camera. Thus, the newly developed and trained YOLOv5s model will be further innovated. Future studies will be conducted to verify the performance of the model in commercial CF houses to detect FELB.

Potential for Ammonia Generation and Emission in Broiler Production Facilities in Brazil

Air quality is one of the main factors that must be guaranteed in animal production. However, the measurement of pollutants is still a problem in several countries because the available methods are costly and do not always apply to the reality of the constructive typology adopted, as in countries with a hot climate, which adopt predominantly open facilities. Thus, the objective of the present study was to develop predictive models for the potential generation and emission of ammonia in the production of broiler chickens with different types of litter, different reuse cycles and under different climatic conditions. Samples of poultry litter from thirty commercial aviaries submitted to different air temperatures were analyzed. The experiment was conducted and analyzed in a completely randomized design, following a factorial scheme. Models were developed to predict the potential for generation and emission of ammonia, which can be applied in facilities with ambient conditions of air temperature between 25 and 40 °C and with wood shaving bed with up to four reuse cycles and coffee husks bed with up to six reuse cycles. The developed and validated models showed high accuracy indicating that they can be used to estimate the potential for ammonia generation and emission.

Volatile organic compound emissions in free-range chicken production: Impacts on environment, welfare and sustainability

<abstract> <p>The increasing demand for free-range poultry products has led to a surge in their availability in the market, prompting a potential decline in premium prices associated with these products. This shift places considerable pressure on upstream costs in chicken production. A comprehensive under-standing of its impact on the environment is essential to ensure the success of commercial and industrial free-range chicken production. However, there exists a significant knowledge gap concerning the emission and concentrations of volatile organic compounds (VOCs) from organic-free range chicken, and their environmental implications have yet to be understood. We aim to address this critical knowledge gap by elucidating the role of VOC emissions in chicken production and assessing their impact on human and animal health, as well as environmental challenges. Understanding the implications of VOC emissions is essential for promoting sustainable and responsible free-range chicken farming practices. By identifying the sources of VOC emissions and their impacts, stakeholders can implement appropriate measures to optimize air quality and enhance the well-being of chickens and workers. Ultimately, this review highlights the role of VOCs in animal production, providing valuable insights for improving the efficiency, environmental sustainability and welfare aspects of free-range chicken farming.</p> </abstract>