Search Results (17)

This study evaluated the productivity, nutritional composition, amino acid profile, fatty acid profile and presence of Salmonella spp. of housefly larva meal reared on domestic animal manure. A study was conducted to produce larva on three types of manure in a controlled environment located at 3200 mASL. Adult flies used as brood stock were reared in advance to avoid contamination with pathogenic germs and were fed sugar syrup and pasteurized milk to promote oviposition. Data were analyzed by ANOVA, the Kruskal–Wallis test and descriptive statistics, using confidence intervals. The results indicate that the type of organic substrate had an effect on the time of development, weight, size and percentage mortality of larva, being higher in a mixture of swine manure and poultry manure. Regarding nutritional composition, it was determined that larva meals contain 56.5% crude protein, 13.07% fat, 12.03% carbohydrates, 10.93% ash and 6.77% crude fiber. The most abundant fatty acids are palmitic acid with 29.34%, palmitoleic acid with 21.65% and oleic acid with 26.53%. An adequate balance of amino acids was determined, highlighting among them the content of arginine and threonine within the essential amino acids. House fly larva meals contain an adequate balance of nutrients and can be used as an ingredient for animal feed formulation. However, their use in animals should be further evaluated in future studies to assess their viability, absorption, bioavailability, and potential allergic reactions. Full article

Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment. Full article

Rooster behavior and activity are critical for egg fertility and hatchability in broiler and layer breeder houses. Desirable roosters are expected to have good leg health, reach sexual maturity, be productive, and show less aggression toward females during mating. However, not all roosters are desirable, and low-productive roosters should be removed and replaced. The objectives of this study were to apply an object detection model based on deep learning to identify hens and roosters based on phenotypic characteristics, such as comb size and body size, in a cage-free (CF) environment, and to compare the performance metrics among the applied models. Six roosters were mixed with 200 Lohmann LSL Lite hens during the pre-peak phase in a CF research facility and were marked with different identifications. Deep learning methods, such as You Only Look Once (YOLO) models, were innovated and trained (based on a comb size of up to 2500 images) for the identification of male and female chickens based on comb size and body features. The performance matrices of the YOLOv5u and YOLOv11 models, including precision, recall, mean average precision (mAP), and F1 score, were statistically compared for hen and rooster detection using a one-way ANOVA test at a significance level of p < 0.05. For rooster detection based on comb size, YOLOv5lu, and YOLOv11x variants performed the best among the five variants of each model, with YOLOv5lu achieving a precision of 87.7%, recall of 56.3%, and mAP@0.50 of 60.1%, while YOLOv11x achieved a precision of 86.7%, recall of 65.3%, and mAP@0.50 of 61%. For rooster detection based on body size, YOLOv5xu, and YOLOv11m outperformed other variants, with YOLOv5xu achieving a precision of 88.9%, recall of 77.7%, and mAP@0.50 of 82.3%, while YOLOv11m achieved a precision of 89.0%, recall of 78.8%, and mAP@0.50 of 82.6%. This study provides a reference for automatic rooster monitoring based on comb and body size and offers further opportunities for tracking the activities of roosters in a poultry breeder farm for performance evaluation and genetic selection in the future. Full article

This study aims to evaluate environmental performance indicators and eco-controls in the poultry production chain in Goiás, with a focus on forest management, waste generation, water resources, energy use, emissions, and environmental accounting. A mixed-methods approach was used, combining qualitative and quantitative data from 13 agro-industrial companies, 230 farms, and 816 broiler houses. The results highlight the role of environmental management accounting (EMA) in monitoring and improving environmental practices, supporting continuous performance assessment. Econometric analysis revealed a positive link between sustainability practices and economic growth, as measured by GDP per capita. However, productive capacity and energy efficiency showed no significant impact at the 5% level. The study limitations include the focus on a single region and industry, which may limit the generalizability of the findings. Future research should expand to other chains and regions to assess broader applicability and explore the public policy impacts on environmental sustainability, as well as the impact of public policies on environmental sustainability within the sector. Full article

Background: Pekin ducks are well-known meat-type ducks, whereas Shaoxing ducks are bred for their egg-laying abilities. Growth and development of poultry species is well studied; however, very little is known regarding differences in intestinal gene expression between Pekin and Shaoxing ducks. Methods: To investigate intestinal differences between Pekin and Shaoxing ducks, we conducted transcriptome analysis on ileal and cecal tissues from five 42-day-old ducks per breed, raised under identical housing and feeding conditions to minimize environmental influences. Results: The results showed that a total of 379 differentially expressed genes (DEGs) with p < 0.05 and |log₂FoldChange| > 1 were identified in the ileum when Pekin ducks were compared to Shaoxing ducks, among which 158 were upregulated and 221 were downregulated. Compared to Shaoxing ducks, a total of 367 DEGs with p < 0.05 and |log₂FoldChange| > 1 were identified in the ceca of Pekin ducks, among which 204 were upregulated and 163 were downregulated. Among these DEGs, nine genes were reported to be associated with growth and metabolism, namely, P2RX6, KCNJ6, CASQ2, EHHADH, ACSBG1, ELOVL4, AIF1L, VILL, and FABP1. Functional enrichment analyses using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases indicated that the DEGs were significantly involved in pathways such as calcium signaling, unsaturated fatty acid biosynthesis, fatty acid degradation, and tryptophan metabolism. Conclusions: In conclusion, our study identified transcriptome differences in the intestines of meat-type and laying-type ducks, offering insights into the genetic basis of their growth and metabolic differences. Future studies should validate key genes and explore environmental influences on gene expression. Full article

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 tons in 2020 and the agricultural source (manure management sector) contributes the highest proportion of the ammonia inventory. However, the methodology for the study of ammonia emissions in Korea has some limitations regarding the representativeness of the sites selected and the reliability of the measurement method. In this study, we aimed to recalculate the ammonia emissions from the livestock industry in Korea using the UK’s estimation method, which uses the life cycle assessment of livestock manure. Three major animal types, i.e., cattle (beef cattle and dairy cows), pigs and chickens, and three major processes based on the manure flow, i.e., housing, manure storage and treatment and land application processes, were considered. The total ammonia emissions were estimated to be approximately 33% higher than the official ammonia emissions stated by the CAPSS. For the manure flow, the ammonia emissions were the highest from land application processes. The ammonia emissions from dairy cow and poultry manure were much higher than those stated by the CAPSS, while the emissions from beef cattle and pig manure showed similar levels. The methodology used in this study can offer an alternative approach to the ammonia emission estimation of the manure management sector in the agriculture industry of Korea. Korean emission factors based on the manure flow should be developed and applied in the future. Full article

Egg production is amongst the most rapidly expanding livestock sectors worldwide. A large share of non-renewable energy use in egg production is due to the operation of heating, ventilation, and air conditioning (HVAC) systems. Reducing energy use, therefore, is essential to decreasing the environmental impacts of intensive egg production. This review identifies market-ready alternatives (such as heat pumps and earth–air heat exchangers) to traditional HVAC systems that could be applied in the industrial egg sector, specifically focusing on their use in temperate and continental climates. For this analysis, energy simulations were run to estimate the typical thermal loads of caged and free-run poultry housing systems in various Canadian locations, which were used as examples of temperate and continental climates. These estimations were then used to evaluate alternative HVAC systems for (1) their capability to meet the energy demands of egg production facilities, (2) their environmental impact mitigation potential, and (3) their relative affordability by considering the insights from a systematic review of 225 relevant papers. The results highlighted that future research should prioritize earth–air heat exchangers as a complementary system and ground source heat pumps as a stand-alone system to reduce the impacts associated with conventional HVAC system operation in egg production. Full article

In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature–humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored. Full article

In recent years, interest has grown among poultry nutritionists in using alternative protein sources, such as insect meal, to meet the protein requirements of poultry due to sustainability concerns surrounding traditional protein sources such as soybean and fish meal. Insect meal can be produced from different insects, including black soldier fly, mealworms, and house crickets, and its nutrient composition varies depending on the insect species, the substrate they are reared on, and the production method. This review article provides an updated overview of insect meal as a new form of protein concentrate in poultry diets, including its nutritional value, advantages, challenges, and future prospects. Insect meal has been shown to be a rich source of protein, amino acids (lysine, methionine), and minerals (calcium, phosphorus, zinc), with a high digestibility rate, making it a valuable feed ingredient for poultry production. Additionally, using insect meal in poultry feed could reduce the cost of production and the environmental impact on the industry. Furthermore, the use of insect meal has the potential to improve the growth performance and meat quality of poultry species. However, several challenges related to large-scale insect production, legal regulatory frameworks, and consumer acceptance need to be addressed. Future research and development could help overcome these challenges and increase the adoption of insects as a potential source of protein in poultry feed. This review provides an updated and comprehensive overview of insects as a potential source of protein for poultry nutrition and highlights the possible perspectives of insect meal to contribute to a more sustainable and efficient poultry production system. While challenges remain, the utilization of insect meal in poultry feed has the capability to enhance the sustainability and efficiency in the poultry industry. Hence, insect meal emerges as a highly encouraging protein alternative, offering sustainable prospects for its utilization within the poultry sector. However, advancements in insect production technology and efficiency have the potential to raise the production scale while lowering prices, making insect meals more affordable compared to conventional protein sources. Based on the comprehensive analysis, it is recommended to further explore the practical implementation of insect meal as a reliable and efficient means of supplying protein in poultry nutrition. Full article

The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months’ laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. Full article

Despite projected global rises in chicken consumption, growing environmental and welfare challenges threaten the future of commercial poultry production. Though some of these challenges, such as biosecurity, sourcing, pollution, and waste, have been thoroughly researched, the open-ended, complex, and interrelated nature of the sector means that it is difficult for poultry producers to know how to change. Design may offer a new way to analyse and reframe these challenges, to speculate on a range of different solutions for these complex systems of production. This paper reflects on the research-by-design methods applied to reimagine environmentally sustainable, high-welfare poultry housing futures. The paper is based on an eighteen-month long, multidisciplinary research project with a large U.K.-based poultry farming integrator, a poultry house ventilation and equipment supplier, and academic partners with expertise in research-by-design and bird welfare. After contextualising challenges faced by the poultry sector, the paper outlines a three-step, iterative approach within which design methods were applied, beginning with (1) a baseline analysis of farm inputs, outputs, actors, and networks, and then (2) a consolidation of themes and scenarios, leading to the development of (3) a compendium of ideas for the future of poultry farming. The Results section presents three design propositions, each imagining different futures by recreating the farm as a system of “closed-loop” flows, reframing the “chicken as client” and challenging current centralised models of production to connect consumers to food provenance and impact. These propositions function as vehicles to test design methods, such as designing for resource flows challenging actor hierarchies and hacking stakeholder networks. While some interesting ideas are presented, the paper highlights the complexity of the challenge and reflects on the value of design to reframe these challenges to collaboratively foster new perspectives and mindsets. Full article

Globally to date, established international standards for animal welfare, a priority of sustainable agriculture, have primarily focused on large-scale producers. However, across Latin America, including in Ecuador’s Galápagos Islands, smallholder farms play a critical role in food safety and security. We assessed five basic animal welfare measures (feed and water access, shelter availability and housing systems, animal health management, animal behavior, and timely euthanasia) for poultry, pigs, and cattle on Floreana Island, Galápagos. Utilizing assessment standards from multiple US sources and international standards, we developed a questionnaire and used it to conduct in-depth interviews during 4–5 July 2022 with eight participating producers, representing 75% of animal agriculture on Floreana. While we identified opportunities to enhance competencies in animal health management and timely euthanasia, farms performed well in the other assessed measures. Future work should promote knowledge transfer and in-country capacity building in farm biosecurity, access to veterinary care, antimicrobial resistance surveillance, and euthanasia methods. Efforts to positively impact smallholder farm livelihoods in Galápagos—one of the most biodiverse and protected ecosystems on the planet—will sustainably support human health through the interconnected realms of animal health and welfare, wildlife and environmental health, and food safety and security. Full article

Indoor environmental control is usually applied in poultry farming to ensure optimum growth conditions for birds. However, these control methods represent a considerable share of total energy consumption, and the trend of applying new equipment in the future for precision livestock farming would further increase energy demand, resulting in an increase in greenhouse gas emissions and management costs. Therefore, to ensure optimum efficiency of both energy use and livestock productivity, a customized hourly model was developed in the present study to interpret and analyze the electronically collected data. The modules for estimating indoor gas concentrations were incorporated into the present model, as this has not been properly considered in previous studies. A validation test was performed in a manure-belt layer house using sensors and meters to measure the indoor environmental parameters and energy consumption. The predicted results, including indoor temperature, relative humidity, carbon dioxide and ammonia concentrations, showed good agreement with the measured data, indicating a similar overall trend with acceptable discrepancies. Moreover, the corresponding differences between the measured and simulated energy consumption for heating, tunnel ventilation and base ventilation were 13.7, 7.5, and 0.1%, respectively. The total energy demand estimated by the model showed a limited discrepancy of approximately 10.6% compared with that measured in reality. Although human factors, including inspection, cleaning, vaccination, etc., were not included in the model, the validation results still suggested that the customized model was able to accurately predict the indoor environment and overall energy consumption during poultry farming. The validated model provides a tool for poultry producers to optimize production planning and management strategies, increase the production rate of unit energy consumption and achieve precision livestock farming from an energy consumption standpoint. Full article

Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens’ behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8–16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals’ behaviors and welfare status in commercial cage-free houses. Full article

The presence of keel bone fractures (KBF) in laying hens has been documented and discussed by several authors, nevertheless the causative factors behind KBF remain uncertain. High prevalence of KBF have been reported in all commercial egg production systems, in different genetic lines and at different ages. Several of the proposed causal mechanisms behind KBF are linked to selection for efficient production. It is, therefore, of interest to explore whether less selected breeds have a lower occurrence of keel bone fractures compared to reports from highly selected, modern laying hen breeds. Thus, the aim of the current study was to investigate keel bones of hens from four non-commercial layer breeds. Birds were housed in furnished cages and keel bones examined at 30 and 63 weeks of age, using a portable X-ray equipment. The results from this descriptive study indicate a low prevalence of KBF at both ages in all four breeds, with only five KBF detected in 213 X-ray pictures taken from 126 birds. Of these, four of the KBF were observed in the most genetically selected breed, with an early onset of lay. None of the roosters examined exhibited KBF. The overall low numbers of KBF found indicate that genetic factors may be involved in KBF and, thus that selective breeding may help to reduce the susceptibility to KBF. Finally, this study highlights the importance of poultry conservation to secure genetic diversity, which may be an important resource in future selection schemes. Full article