This study examined the daily feeding and drinking patterns of 18,800 broiler flocks, specifically the feeding and lighting programs. The feeding and lighting programs were a) ad libitum feeding (free access to feed) with a 23-hr light and 1-hr dark (23L:1D) photoperiod; b) meal-time feeding of 2-hr on and 2-hr off, six meals per day with a 23L:1D photoperiod; and c) meal-time feeding with continuous daytime light but intermittent nighttime light, which coincided with meal hours. Drinking water was continuously available. The ad libitum-fed boilers had more feeding activities in the morning, whereas the meal-fed broilers had more feeding activities during both morning and evening periods. Hourly feed use was much more uniform for the ad libitum-fed broilers than for the meal-fed broilers. The meal-fed broilers generally completed 70 to 75% of their meal in the first hour of the two-hour meal period, with the second hour feed use being similar to that in ad libitum feeding. Thus, the meal-fed broilers had higher peak feed demand than did the ad libitum-fed broilers. However, under intermittent nighttime lighting, the meal-fed broilers had uniform hourly feed use for the night and early morning meals. The drinking patterns of the broilers were influenced greatly by lighting programs, but were rather independent of the feeding schemes.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

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.

Lighting intensity (LI) affects broilers’ behavior, circadian rhythms, physiology, and welfare, making it essential in commercial broiler management. This study evaluated the effects of three LIs (50, 20, and 5 lux) on Ross 708 and Cobb 700 broilers from day 8 to 56 in separate trials conducted under identical conditions. Growth performance (body weight (BW), feed intake, feed conversion ratio (FCR)) and welfare indicators (gait, feather condition, temperature, and footpad dermatitis were assessed biweekly. Ross broilers at 50 lux showed reduced BW on day 42 (p = 0.03), with no BW differences observed on day 56 (p = 0.14). FCR was unaffected by LI for Ross broilers. For Cobb broilers, BW was not affected by LI, though birds under 5 lux exhibited approximately 2.6% lower FCR on day 28 (p = 0.04) and 7.8% lower FCR on day 42 (p = 0.01). LI of 50 lux was associated with increased back temperatures, while bird at 5 lux showed better feather coverage and lower belly temperature (p < 0.01). Ross under 20 lux showed poorer feather cleanliness (p < 0.01), while those under 50 lux had better gait scores on day 56 (p < 0.01). These results demonstrate that production and welfare responded differently to LI, and that these responses also varied between strains. These findings contribute to sustainable poultry production by balancing welfare, efficiency, and growth.

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.

Broiler gait score (GS) evaluation relies on manual assessments by experts, which can be laborious, hindering timely welfare management. Deep learning (DL) models, conversely, may serve as a cost-effective solution in evaluating GS via automated detection of broiler mobility. This study aimed to develop a vision-based YOLOv8 model to detect the locations of individual broilers, allowing for continuous tracking of birds within a pen and determining bird walking distances, speeds, idleness and movement ratios, and time at the feeder and drinker ratios. Then, Machine Learning (ML) models were developed to estimate the GS level from the mobility indicators in a lab setting. Ten broilers were color-coded and recorded via a top-view camera for 41 days. Their GS were assessed manually twice per week. The YOLOv8 model was trained, validated, and tested with 600, 150, and 50 images, respectively, and subsequently applied to the dataset yielding each broiler’s mobility indicators. The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. The YOLOv8 model was developed with 91% training, 89% testing, and 87% validation mean average precision (mAP) accuracies in identifying color-coded broilers. After tracking, the model estimated an average of 472.26 ± 234.18 cm hourly distance traveled and 0.13 ± 0.07 cm/s speed by a broiler. It was found that with deteriorated GS levels (i.e., worse walking ability), broilers walked shorter distances (p = 0.001), had lower speeds (p = 0.001), were increasingly idle and less mobile, and were increasingly stationed near or around the feeder. The movement ratio, average hourly walking distance, hourly average speed, and age variables were found to be the most significant variables (p < 0.005) in predicting GS levels. These variables were further reduced to one, the average hourly walking distance, because of high collinearity and were used to predict the GS with ML models. The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy.