The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken’s floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.

The success of poultry litter reuse in U.S. poultry production can be attributed to the efficient treatment methods used by producers during downtimes (the time lapse between consecutive flocks, during which the broiler house is empty). During this period, reused litter may be decaked, tilled/windrowed, or treated with acid‐based amendments to reduce ammonia and bacteria levels. Competitive exclusion, pH, and temperature are proposed factors that influence the level of pathogens and the overall litter microbiome during downtimes. We previously reported on the bacterial genetic factors associated with the fitness of two strains of Salmonella enterica serovar Heidelberg (SH) incubated for 14 d in reused litter. Here, we investigated the physicochemical parameters and the microbiome of the litter correlating with SH abundance during this period. We used 16S ribosomal RNA gene sequencing to determine the litter microbiome and whole genome sequencing to characterize bacteria with competitive exclusion potential against SH. The β diversity of the litter microbiome was significantly affected by the duration of incubation, microcosm, and microcosm plus Heidelberg strain combinations. In addition, β diversity was significantly affected by litter parameters, including NH₄, pH, moisture, water activity, and aluminum. The major phyla observed in the reused litter throughout the 14‐d incubation experiment were Firmicutes and Actinobacteria, although their abundance differed by microcosm and time. Amplicon‐specific variants homologous to the members of the genera Nocardiopsis and Lentibacillus and the family Bacillaceae_2 were found to significantly correlate with the abundance of Salmonella. A consortium of Bacillus subtilis strains isolated from the litter microcosms reduced the growth of SH in vitro.

S.Heidelberg is a clinically important serovar, linked to foodborne illness and among the top 5 serovars isolated from poultry in the United States and Canada. Acquisition of new genetic material from the microbial flora in the gastrointestinal tract of food animals, including broilers, may contribute to increased fitness of pathogens likeS.Heidelberg and may increase their level of antibiotic tolerance. Therefore, it is critical to gain a better understanding of the interactions that occur between important pathogens and the commensals present in the animal gut and other agroecosystems. In this report, we show that the native flora in broiler ceca were capable of transferring mobile genetic elements carrying the AmpC β-lactamase (bla_CMY-2) gene to an important foodborne pathogen,S. Heidelberg. The potential role for bacteriophage transduction is also discussed.

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.

Salmonella enterica subsp. enterica serovar Heidelberg (S. Heidelberg) is a clinically-important serovar linked to food-borne illness, and commonly isolated from poultry. Investigations of a large, multistate outbreak in the USA in 2013 identified poultry litter (PL) as an important extra-intestinal environment that may have selected for specific S. Heidelberg strains. Poultry litter is a mixture of bedding materials and chicken excreta that contains chicken gastrointestinal (GI) bacteria, undigested feed, feathers, and other materials of chicken origin. In this study, we performed a series of controlled laboratory experiments which assessed the microevolution of two S. Heidelberg strains (SH-2813 and SH-116) in PL previously used to raise 3 flocks of broiler chickens. The strains are closely related at the chromosome level, differing from the reference genome by 109 and 89 single nucleotide polymorphisms/InDels, respectively. Whole genome sequencing was performed on 86 isolates recovered after 0, 1, 7 and 14 days of microevolution in PL. Only strains carrying an IncX1 (37kb), 2 ColE1 (4 and 6kb) and 1 ColpVC (2kb) plasmids survived more than 7 days in PL. Competition experiments showed that carriage of these plasmids was associated with increased fitness. This increased fitness was associated with an increased copy number of IncX1 and ColE1 plasmids. Further, all Col plasmid-bearing strains had hotspot mutations in 37 loci on the chromosome and in 3 loci on the IncX1 plasmid. Additionally, we observed a decrease in susceptibility to tobramycin, kanamycin, gentamicin, neomycin and fosfomycin for Col plasmid-bearing strains. Our study demonstrates how positive selection from poultry litter can change the evolutionary path of S. Heidelberg.

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.