Evaluating broiler welfare and behavior as affected by growth rate and stocking density.

This study evaluated the welfare and behaviors of Cobb 700 broilers as affected by growth rate (GR) and stocking density (SD). Slower-growth (weight gain < 50 g/d) and medium-growth (weight gain = 50-60 g/d) broilers were produced by providing 57.1% and 78.6% of the feed intake listed in the Cobb 700 production manual for standard (fed ad libitum) broilers (weight gain > 60 g/d). Broilers at all 3 GRs were reared at 2 SDs of 30 and 40 kg/m2. Broiler welfare indicators, including gait score, tibia strength, feather coverage, and footpad condition were evaluated when birds reached 1, 2, and 3 kg of body weight. The activity index was determined by overhead cameras and image processing, and the time spent at feeders was recorded using the radio-frequency identification (RFID) systems. The results show that it took 45 d for standard, 52 d for medium-growth, and 62 d for slower-growth broilers to reach a 3 kg market body weight. Feed conversion ratios (FCR, kg/kg) were 1.57 for standard, 1.67 for medium-growth, and 1.80 for slower-growth broilers. Growth rate and SD had an interaction effect on feather cleanliness (P = 0.03), and belly feather coverage (P = 0.02). Slower-growth broilers were more active and had better feather coverage and gait scores than medium-growth and standard broilers (all P < 0.01) but may feel hungry and depressed, medium-growth broilers spent the most time at the feeder among the 3 growth groups (P = 0.02), and standard broilers showed the best production performance. Broilers at 30 kg/m2 showed better bone strength (P = 0.04), and footpad condition (P < 0.01) compared to those at 40 kg/m2. In conclusion, reducing GR and SD may slightly improve broiler leg health at the high expense of compromised production performance and prolonged production cycles.

Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm.

Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Effects of a Sprinkler and Cool Cell Combined System on Cooling Water Usage, Litter Moisture, and Indoor Environment of Broiler Houses.

Climate change is a serious challenge to food production around the world. Sustainability and water efficiency are critical to a poultry industry faced with global production concerns including increased demands for high-quality, affordable animal protein and greater environmental pressures resulting from rising global temperatures, flock heat stress, and limits on water availability. To address these concerns, a commercial sprinkler system used in combination with a cool cell system was evaluated against a cool cell system alone for two summer flocks of heavy broilers at Mississippi State University to determine effects of sprinkler technology on cooling water usage, litter moisture, and in-house environments. Environmental data were calculated and recorded throughout the flocks. The combination house exhibited a 2.2 °C (4 °F) increase in daily maximum temperature, lower coincident relative humidity, and a 64% (62,039 L/flock) reduction in average cooling water usage over the cool cell-only house. Litter moisture for the combination house tended to be numerically lower but showed no significant difference at several time points between and across flocks. A combined sprinkler/cool cell system reduced cooling water use by 64% over two flocks compared to a cool cell alone system and decreased in-house relative humidity levels.

High throughput genomic sequencing of bioaerosols in broiler chicken production facilities.

Chronic inhalation exposure to agricultural dust promotes the development of chronic respiratory diseases among poultry workers. Poultry dust is composed of dander, chicken feed, litter bedding and microbes. However, the microbial composition and abundance has not been fully elucidated. Genomic DNA was extracted from settled dust and personal inhalable dust collected while performing litter sampling or mortality collection tasks. DNA libraries were sequenced using a paired-end sequencing-by-synthesis approach on an Illumina HiSeq 2500. Sequencing data showed that poultry dust is predominantly composed of bacteria (64-67%) with a small quantity of avian, human and feed DNA (< 2% of total reads). Staphylococcus sp. AL1, Salinicoccus carnicancri and Lactobacillus crispatus were the most abundant bacterial species in personal exposure samples of inhalable dust. Settled dust had a moderate relative abundance of these species as well as Staphylococcus lentus and Lactobacillus salivarius. There was a statistical difference between the microbial composition of aerosolized and settled dust. Unlike settled dust composition, aerosolized dust composition had little variance between samples. These data provide an extensive analysis of the microbial composition and relative abundance in personal inhalable poultry dust and settled poultry dust.