Automatic detection of bumblefoot in cage-free hens using computer vision technologies.

Cage-free (CF) housing systems are expected to be the dominant egg production system in North America and European Union countries by 2030. Within these systems, bumblefoot (a common bacterial infection and chronic inflammatory reaction) is mostly observed in hens reared on litter floors. It causes pain and stress in hens and is detrimental to their welfare. For instance, hens with bumblefoot have difficulty moving freely, thus hindering access to feeders and drinkers. However, it is technically challenging to detect hens with bumblefoot, and no automatic methods have been applied for hens' bumblefoot detection (BFD), especially in its early stages. This study aimed to develop and test artificial intelligence methods (i.e., deep learning models) to detect hens' bumblefoot condition in a CF environment under various settings such as epochs (number of times the entire dataset passes through the network during training), batch size (number of data samples processed per iteration during training), and camera height. The performance of 3 newly developed deep learning models (i.e., YOLOv5s-BFD, YOLOv5m-BFD, & YOLOv5x-BFD) were compared in detecting hens with bumblefoot of hens in CF environments. The result shows that the YOLOv5m-BFD model had the highest precision (93.7%), recall (84.6%), mAP@0.50 (90.9%), mAP@0.50:0.95 (51.8%), and F1-score (89.0%) compared with other models. The observed YOLOv5m-BFD model trained at 400 epochs and batch size 16 is recommended for bumblefoot detection in laying hens. This study provides a basis for developing an automatic bumblefoot detection system in commercial CF houses. This model will be modified and trained to detect the occurrence of broilers with bumblefoot in the future.

Deep Learning Methods for Tracking the Locomotion of Individual Chickens.

Poultry locomotion is an important indicator of animal health, welfare, and productivity. Traditional methodologies such as manual observation or the use of wearable devices encounter significant challenges, including potential stress induction and behavioral alteration in animals. This research introduced an innovative approach that employs an enhanced track anything model (TAM) to track chickens in various experimental settings for locomotion analysis. Utilizing a dataset comprising both dyed and undyed broilers and layers, the TAM model was adapted and rigorously evaluated for its capability in non-intrusively tracking and analyzing poultry movement by intersection over union (mIoU) and the root mean square error (RMSE). The findings underscore TAM's superior segmentation and tracking capabilities, particularly its exemplary performance against other state-of-the-art models, such as YOLO (you only look once) models of YOLOv5 and YOLOv8, and its high mIoU values (93.12%) across diverse chicken categories. Moreover, the model demonstrated notable accuracy in speed detection, as evidenced by an RMSE value of 0.02 m/s, offering a technologically advanced, consistent, and non-intrusive method for tracking and estimating the locomotion speed of chickens. This research not only substantiates TAM as a potent tool for detailed poultry behavior analysis and monitoring but also illuminates its potential applicability in broader livestock monitoring scenarios, thereby contributing to the enhancement of animal welfare and management in poultry farming through automated, non-intrusive monitoring and analysis.

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.

Osteoimmunology: A Link between Gastrointestinal Diseases and Skeletal Health in Chickens.

Bone serves as a multifunctional organ in avian species, giving structural integrity to the body, aiding locomotion and flight, regulating mineral homeostasis, and supplementing calcium for eggshell formation. Furthermore, immune cells originate and reside in the bone marrow, sharing a milieu with bone cells, indicating a potential interaction in functions. In avian species, the prevalence of gastrointestinal diseases can alter the growth and the immune response, which costs a great fortune to the poultry industry. Previous studies have shown that coccidiosis and necrotic enteritis can dramatically reduce bone quality as well. However, possible mechanisms on how bone quality is influenced by these disease conditions have not yet been completely understood, other than the reduced feed intake. On the other hand, several mediators of the immune response, such as chemokines and cytokines, play a vital role in the differentiation and activation of osteoclasts responsible for bone resorption and osteoblasts for bone formation. In the case of Eimeria spp./Clostridium perfringens coinfection, these mediators are upregulated. One possible mechanism for accelerated bone loss after gastrointestinal illnesses might be immune-mediated osteoclastogenesis via cytokines-RANKL-mediated pathways. This review article thus focuses on osteoimmunological pathways and the interaction between host immune responses and bone biology in gastrointestinal diseases like coccidiosis and necrotic enteritis affecting skeletal health.

Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques.

Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird-1 (3-10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.

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.

Tracking floor eggs with machine vision in cage-free hen houses.

Some of the major restaurants and grocery chains in the United States have pledged to buy cage-free (CF) eggs only by 2025 or 2030. While CF house allows hens to perform more natural behaviors (e.g., dust bathing, perching, and foraging on the litter floor), a particular challenge is floor eggs (i.e., mislaid eggs on litter floor). Floor eggs have high chances of contamination. The manual collection of eggs is laborious and time-consuming. Therefore, precision poultry farming technology is necessary to detect floor eggs. In this study, 3 new deep learning models, that is, YOLOv5s-egg, YOLOv5x-egg, and YOLOv7-egg networks, were developed, trained, and compared in tracking floor eggs in 4 research cage-free laying hen facilities. Models were verified to detect eggs by using images collected in 2 different commercial houses. Results indicate that the YOLOv5s-egg model detected floor eggs with a precision of 87.9%, recall of 86.8%, and mean average precision (mAP) of 90.9%; the YOLOv5x-egg model detected the floor eggs with a precision of 90%, recall of 87.9%, and mAP of 92.1%; and the YOLOv7-egg model detected the eggs with a precision of 89.5%, recall of 85.4%, and mAP of 88%. All models performed with over 85% detection precision; however, model performance is affected by the stocking density, varying light intensity, and images occluded by equipment like drinking lines, perches, and feeders. The YOLOv5x-egg model detected floor eggs with higher accuracy, precision, mAP, and recall than YOLOv5s-egg and YOLOv7-egg. This study provides a reference for cage-free producers that floor eggs can be monitored automatically. Future studies are guaranteed to test the system in commercial houses.

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

Confined animal feeding operations (CAFOs) are the main sources of air pollutants such as ammonia (NH3) and greenhouse gases. Among air pollutants, NH3 is one of the most concerned gasses in terms of air quality, environmental impacts, and manure nutrient losses. It is recommended that NH3 concentrations in the poultry house should be controlled below 25 ppm. Otherwise, the poor air quality will impair the health and welfare of animals and their caretakers. After releasing from poultry houses, NH3 contributes to the form of fine particulate matters in the air and acidify soil and water bodies after deposition. Therefore, understanding the emission influential factors and impacts is critical for developing mitigation strategies to protect animals' welfare and health, environment, and ecosystems. This review paper summarized the primary NH3 emission influential factors, such as how poultry housing systems, seasonal changes, feed management, bedding materials, animal densities, and animals' activities can impact indoor air quality and emissions. A higher level of NH3 (e.g., >25 ppm) results in lower production efficiency and poor welfare and health, e.g., respiratory disorder, less feed intake, lower growth rates or egg production, poor feed use efficiency, increased susceptibility to infectious diseases, and mortality. In addition, the egg quality (e.g., albumen height, pH, and condensation) was reduced after laying hens chronically exposed to high NH3 levels. High NH3 levels have detrimental effects on farm workers' health as it is a corrosive substance to eyes, skin, and respiratory tract, and thus may cause blindness, irritation (throat, nose, eyes), and lung illness. For controlling poultry house NH3 levels and emissions, we analyzed various mitigation strategies such as litter additives, biofiltration, acid scrubber, dietary manipulation, and bedding materials. Litter additives were tested with 50% efficiency in broiler houses and 80-90% mitigation efficiency for cage-free hen litter at a higher application rate (0.9 kg m-2). Filtration systems such as multi-stage acid scrubbers have up to 95% efficiency on NH3 mitigation. However, cautions should be paid as mitigation strategies could be cost prohibitive for farmers, which needs assistances or subsidies from governments.

Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning.

Animal behavior monitoring allows the gathering of animal health information and living habits and is an important technical means in precision animal farming. To quickly and accurately identify the behavior of broilers at different days, we adopted different deep learning behavior recognition models. Firstly, the top-view images of broilers at 2, 9, 16 and 23 days were obtained. In each stage, 300 images of each of the four broilers behaviors (i.e., feeding, drinking, standing, and resting) were segmented, totaling 4800 images. After image augmentation processing, 10,200 images were generated for each day including 8000 training sets, 2000 validation sets, and 200 testing sets. Finally, the performance of different convolutional neural network models (CNN) in broiler behavior recognition at different days was analyzed. The results show that the overall performance of the DenseNet-264 network was the best, with the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 days old, respectively. In addition, the efficient channel attention was introduced into the DenseNet-264 network (ECA-DenseNet-264), and the results (accuracy rates: 85%, 95%, 92%, 89.5%) confirmed that the DenseNet-264 network was still the best overall. The research results demonstrate that it is feasible to apply deep learning technology to monitor the behavior of broilers at different days.

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor.

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.

Different Non-cage Housing Systems Alter Duodenal and Cecal Microbiota Composition in Shendan Chickens.

Housing systems are among the most important non-genetic factors affecting hen production performance and intestinal microbes. With increased interest in animal welfare, cage-free laying hen housing systems have become common, providing behavioral freedom and health benefits. The present study aimed to compare the effects of plastic net housing system (NRS) and floor litter housing system (LRS) on the composition and function of the duodenal and cecal microbiota in Shendan chicken, one of the most popular laying hen strains in China. The associations between the differential microbiota abundance and production traits and intestinal morphological parameters were determined. Compared with the LRS, the NRS improved the laying rate (p < 0.05) and increased the villus height (VH) of the duodenum (p < 0.05) and the VH-to-crypt depth ratio (VCR) of the cecum (p < 0.05). Alpha diversity analysis showed that LRS chickens had a significantly higher diversity and richness than NRS chickens. Beta diversity analysis demonstrated differences in the microbiota composition based on housing systems. Within the cecum, Proteobacteria and Kiritimatiellaeota were significantly more abundant in the LRS than in the NRS (p < 0.05), while Bacteroidetes were significantly less abundant in the LRS (p < 0.05). Phascolarctobacterium and Ruminococcaceae_UCG-005 were significantly less abundant in the LRS (p < 0.05) compare to the NRS. Within the duodenum, Lactobacillus was significantly less abundant in the LRS (p < 0.05) than in the NRS, while Pseudomonas was significantly more abundant in the LRS (p < 0.05). Cecal Phascolarctobacterium and Ruminococcaceae_UCG-005 were significantly positively correlated with eggshell strength (R = 0.608, p < 0.01) and egg weight (R = 0.526, p < 0.05), respectively. Duodenal Lactobacillus was significantly positively correlated with VH and VCR (R = 0.548 and 0.565, p < 0.05), while Pseudomonas was significantly negatively correlated with the Haugh unit (R = -0.550, p < 0.05). In conclusion, there are differences in the cecal and duodenal microbiota compositions of Shendan laying hens reared in different non-cage housing systems, and the NRS was superior to the LRS in improving the laying performance and intestinal morphology and microecological environment.

Determination of bacterial abundance and communities in the nipple drinking system of cascading cage layer houses.

Water quality is critical for egg production and animal health in commercial layer housing systems. To investigate microbial contamination in nipple drinking system in layer houses, the bacterial abundance and communities in water pipes and V-troughs on different tiers (e.g., 1st, 3rd, 5th, and 7th tiers) of a layer house with 8 overlapping cage tiers were determined using qRT-PCR and 16S rRNA sequencing. The water bacterial abundance (i.e., genome 16S rDNA copy number, WBCN) in water pipes and V-troughs did not significantly differ among tiers, but they were 46.77 to 1905.46 times higher in V-troughs than that in water pipes (P < 0.05) for each tier. Illumina sequencing obtained 1,746,303 effective reads from 24 water samples in V-troughs of 4 tiers (six samples from each tier). Taxonomic analysis indicated that the 1st and 5th tiers were predominated by Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes, while the 3rd and 7th tiers were predominated by Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria. The top four genera were Acinetobacter, Streptococcus, Rothia and Comamonas among measured tiers. The high bacterial abundance and bacterial OTUs of water in the V-troughs reflect poor water quality, which may adversely affect growth and health of laying hens. Therefore, it is suggested that water quality in the V-tough should be checked more frequently in commercial layer houses.

Horizontal Gene Transfer Is the Main Driver of Antimicrobial Resistance in Broiler Chicks Infected with Salmonella enterica Serovar Heidelberg.

The overuse and misuse of antibiotics in clinical settings and in food production have been linked to the increased prevalence and spread of antimicrobial resistance (AR). Consequently, public health and consumer concerns have resulted in a remarkable reduction in antibiotics used for food animal production. However, there are no data on the effectiveness of antibiotic removal in reducing AR shared through horizontal gene transfer (HGT). In this study, we used neonatal broiler chicks and Salmonella enterica serovar Heidelberg, a model food pathogen, to test if chicks raised antibiotic free harbor transferable AR. We challenged chicks with an antibiotic-susceptible S. Heidelberg strain using various routes of inoculation and determined if S. Heidelberg isolates recovered carried plasmids conferring AR. We used antimicrobial susceptibility testing and whole-genome sequencing (WGS) to show that chicks grown without antibiotics harbored an antimicrobial resistant S. Heidelberg population at 14 days after challenge and chicks challenged orally acquired AR at a higher rate than chicks inoculated via the cloaca. Using 16S rRNA gene sequencing, we found that S. Heidelberg infection perturbed the microbiota of broiler chicks, and we used metagenomics and WGS to confirm that a commensal Escherichia coli population was the main reservoir of an IncI1 plasmid acquired by S. Heidelberg. The carriage of this IncI1 plasmid posed no fitness cost to S. Heidelberg but increased its fitness when exposed to acidic pH in vitro. These results suggest that HGT of plasmids carrying AR shaped the evolution of S. Heidelberg and that antibiotic use reduction alone is insufficient to limit antibiotic resistance transfer from commensal bacteria to Salmonella enterica. IMPORTANCE The reported increase in antibiotic-resistant bacteria in humans has resulted in a major shift away from antibiotic use in food animal production. This shift has been driven by the assumption that removing antibiotics will select for antibiotic susceptible bacterial taxa, which in turn will allow the currently available antibiotic arsenal to be more effective. This change in practice has highlighted new questions that need to be answered to assess the effectiveness of antibiotic removal in reducing the spread of antibiotic resistance bacteria. This research demonstrates that antibiotic-susceptible Salmonella enterica serovar Heidelberg strains can acquire multidrug resistance from commensal bacteria present in the gut of neonatal broiler chicks, even in the absence of antibiotic selection. We demonstrate that exposure to acidic pH drove the horizontal transfer of antimicrobial resistance plasmids and suggest that simply removing antibiotics from food animal production might not be sufficient to limit the spread of antimicrobial resistance.

Succession patterns of the bacterial community in poultry litter after bird removal and sodium bisulfate application.

Sulfate-based acid amendments are used for treating litter between broiler chicken flocks and during grow-out for in-house ammonia abatement. These amendments reduce litter pH and inhibit ammonia volatilization by converting ammonia to nonvolatile ammonium. Research on the effects of acid amendments on litter microbiota is limited and usually done in microcosms, which do not replicate natural environments. In this study, we determined the changes in bacterial populations present in litter during downtime (the period after a flock was removed and before new broiler chicks were placed) and 24 h before and after the application of a sodium bisulfate (NaHSO4 )-based amendment. We used DNA sequencing technologies to characterize the litter microbiota, elucidating microbial shifts in litter samples with respect to downtime, litter depth, and NaHSO4 application. During downtime (∼18 d), the litter microbiota was dominated by Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. Sodium bisulfate affected the microbiota in the top layer (3 cm) of reused litter topdressed with fresh pine shavings and resulted in an increase in Escherichia spp. and Faecalibacterium spp. and a decrease in members of the phylum Acidobacteria. Furthermore, culturable Escherichia coli decreased by 1.5 log units during downtime, but an increase was observed for topdressed litter after NaHSO4 was applied. Although the effect of acidifiers on ammonia reduction, bird performance, and litter performance are well documented, their effect on litter bacteria is not well understood. Our results suggest that acidifiers may perturb litter bacteria when topdressed with fresh pine shavings and that further research is required.

Effect of the Pellet and Mash Feed Forms on the Productive Performance, Egg Quality, Nutrient Metabolism, and Intestinal Morphology of Two Laying Hen Breeds.

One of the most important factors that determine feed utilization by chickens is the feed form. Although it is generally believed that pellet diets have a positive effect on chicken growth, there are some studies that have indicated no difference between pellet and mash on chickens performance. This study was conducted to assess the effects of feed form on production performance, egg quality, nutrient metabolism and intestinal morphology in two breed laying hens. Two hundred and sixteen 25-week-old Hy-Line brown (n = 108) and Hy-Line grey (n = 108) hens were selected. Each breed was randomly allocated into two treatments with 6 replications (9 birds in each replication), which were fed mash and pellet diets, respectively. Production performances were recorded daily and egg quality traits were measured every two weeks. At 42 weeks of age, one bird per replication from each experimental group was selected for metabolism determination and intestine morphology observation. Compared with mash diets, pellet diets improved laying rate (p < 0.05), ADFI (average daily feed intake, p < 0.05), egg weight, shell strength, yolk proportion and Haugh unit (p < 0.05) in both breeds and reduced the FCR (feed conversion ratio, p < 0.05) in Hy-Line grey. The apparent digestibility of DM% (dry matter) and CP% (crude protein) were significantly higher (p < 0.05) in both breed laying hens fed pellet than those fed mash. The apparent digestibility of P% (phosphorus) and Ca% (calcium) was higher in Hy-Line grey fed pellet and was higher in Hy-Line brown fed mash. Compared to mash diets, pellet diets increased the VH (villus height), CD (crypt depth) and VCR (ratio of villus height to crypt depth) of the small intestine of Hy-Line grey, and increased the VH and CD of duodenum and ileum of Hy-Line brown. Overall, pellet diets improved production performance and nutrition metabolism through positive changes in the laying rate, feed intake, egg albumen quality and apparent digestibility of laying hens. The current findings provided support for the advantages of feeding pellets during the peak egg laying period for the two popular laying hen strains, Hy-Line brown and Hy-Line grey.

COVID-19 in Farm Animals: Host Susceptibility and Prevention Strategies.

COVID-19 is caused by the virus SARS-CoV-2 that belongings to the family of Coronaviridae, which has affected multiple species and demonstrated zoonotic potential. The COVID-19 infections have been reported on farm animals (e.g., minks) and pets, which were discussed and summarized in this study. Although the damage of COVID-19 has not been reported as serious as highly pathogenic avian influenza (HPAI) for poultry and African Swine Fever (ASF) for pigs on commercial farms so far, the transmission mechanism of COVID-19 among group animals/farms and its long-term impacts are still not clear. Prior to the marketing of efficient vaccines for livestock and animals, on-farm biosecurity measures (e.g., conventional disinfection strategies and innovated technologies) need to be considered or innovated in preventing the direct contact spread or the airborne transmission of COVID-19.

A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment.

The presence equipment (e.g., water pipes, feed buckets, and other presence equipment, etc.) in the poultry house can occlude the areas of broiler chickens taken via top view. This can affect the analysis of chicken behaviors through a vision-based machine learning imaging method. In our previous study, we developed a machine vision-based method for monitoring the broiler chicken floor distribution, and here we processed and restored the areas of broiler chickens which were occluded by presence equipment. To verify the performance of the developed restoration method, a top-view video of broiler chickens was recorded in two research broiler houses (240 birds equally raised in 12 pens per house). First, a target detection algorithm was used to initially detect the target areas in each image, and then Hough transform and color features were used to remove the occlusion equipment in the detection result further. In poultry images, the broiler chicken occluded by equipment has either two areas (TA) or one area (OA). To reconstruct the occluded area of broiler chickens, the linear restoration method and the elliptical fitting restoration method were developed and tested. Three evaluation indices of the overlap rate (OR), false-positive rate (FPR), and false-negative rate (FNR) were used to evaluate the restoration method. From images collected on d2, d9, d16, and d23, about 100-sample images were selected for testing the proposed method. And then, around 80 high-quality broiler areas detected were further evaluated for occlusion restoration. According to the results, the average value of OR, FPR, and FNR for TA was 0.8150, 0.0032, and 0.1850, respectively. For OA, the average values of OR, FPR, and FNR were 0.8788, 0.2227, and 0.1212, respectively. The study provides a new method for restoring occluded chicken areas that can hamper the success of vision-based machine predictions.