Leonardo V. S. Barbosa

The study aimed to forecast ammonia exposure risk in broiler chicken production, correlating it with health injuries using machine learning. Two chicken breeds, fast-growing (Ross^®) and slow-growing (Hubbard^®), were compared at different densities. Slow-growing birds had a constant density of 32 kg m⁻², while fast-growing birds had low (16 kg m⁻²) and high (32 kg m⁻²) densities. Initial feeding was uniform, but nutritional demands led to varied diets later. Environmental data underwent selection, pre-processing, transformation, mining, analysis, and interpretation. Classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were employed for predicting ammonia risk (10–14 pmm, Moderate risk). Cross-validation was used for model parameterization. The Spearman correlation coefficient assessed the link between predicted ammonia risk and health injuries, such as pododermatitis, vision/affected, and mucosal injuries. These injuries encompassed trachea, bronchi, lungs, eyes, paws, and other issues. The Multilayer Perceptron model emerged as the best predictor, exceeding 98% accuracy in forecasting injuries caused by ammonia. The correlation coefficient demonstrated a strong association between elevated ammonia risks and chicken injuries. Birds exposed to higher ammonia concentrations exhibited a more robust correlation. In conclusion, the study effectively used machine learning to predict ammonia exposure risk and correlated it with health injuries in broiler chickens. The Multilayer Perceptron model demonstrated superior accuracy in forecasting injuries related to ammonia (10–14 pmm, Moderate risk). The findings underscored the significant association between increased ammonia exposure risks and the incidence of health injuries in broiler chicken production, shedding light on the importance of managing ammonia levels for bird welfare.

The objective of the study was to predict the risk of exposure to ammonia concentration in the production of broiler breeds of slow and fast-growing with low and high production density and correlate with the incidence of health injuries in broiler chickens using a learning machine. Two commercial lines of broiler chicken were used, one with fast-growing (Ross®, slaughter age 42 days) and another with slow-growing (Hubbard®, slaughter age 63 days). All slow-growing birds were housed at a density of 32 kg/m2. Fast-growing birds were housed in two different housing densities: low housing density with a final housing density of 16 kg/m2 and high density with a final housing density of 32 kg/m2. A total of 1250 birds were used in this experiment, 450 of which were fast-growing birds and 800 were slow-growing birds. In each room, 306 birds were randomly distributed in 18 boxes (6 boxes for each treatment and 17 birds per box), each with 3 nipple drinkers and a manual feeder. The dimensions of the high-density boxes were 1 x 1.3 m2 , while the low-density boxes had the same number of animals housed in a larger area, with dimensions of 2 x 1.3 m2 . The remaining 319 birds were housed randomly throughout the room and outside the pens to simulate a commercial production system condition (stock density). All birds were fed with an initial commercial feed from day 1 to day 18 of the experiment, and from day 18 until the end, each breed was fed with different feeds according to their nutritional demands. The following data analysis steps were performed: data selection, pre-processing, transformation, mining, analysis, and, interpretation of results. The classification algorithms, decision tree (J48), SMO (Sequential Minimal Optimization), Naive Bayes, and Multilayer Perceptron were applied to the training and test data sets to build a rule model for predicting ammonia risk levels in broiler chickens. The cross-validation technique was used to parameterize the analysis in all models. From the database of the first phase of analysis, with the classifier to predict the risk condition of ammonia concentration, the Spearman correlation coefficient (ρ, rho), considering the presence of pododermatitis, vision/affected and mucosal injury, which includes assessments of the trachea, bronchi, lungs, eyes, paw injuries, and other injuries. A non-parametric correlation measure was applied to injury incidence data as a function of ammonia risk level (1 and 10 ppm) to correlate injury incidence and ammonia level in the conditions studied. The best predictive model capable of evaluating and obtaining better performance was the Multilayer Perceptron when we considered greater accuracy by the level of risk of exposure to ammonia in the broiler chicken production process, including fast and slow-growing. Birds exposed to higher levels of ammonia concentration have a higher correlation coefficient when the relationship between the variables is strong. The Spearman correlation coefficient shows a stronger association between increased risks of ammonia exposure and the incidence of chicken injuries.

Ammonia is an important pollutant emitted by broiler litter that can accumulate inside farms, impairing animal health and welfare productivity. An experiment was designed to evaluate of precision husbandry practices such as the effect of ventilation, animal density and growth rate as management options to reduce the adverse effects of ammonia exposure on productive parameters in broiler houses. Two identical experimental rooms were used in this study. They were programmed to differ in ammonia concentration from day 32 of the growing period (10 and 20 ppm in Room 1 and Room 2, respectively). Three treatments were tested in each room: slow growth in high stocking density (SHD), fast growth in low density (FLD) and fast growth in high density (FHD). Animal weight, feed intake and feed conversion ratio were determined weekly. In addition, the immune status of animals was assessed by weighing the organs related to immune response as stress indicators. Increasing ventilation was effective to control ammonia concentrations. Exposure to ammonia caused no significant effect on productive parameters. However, lowering stocking density improved response to higher ammonia concentrations by lowering the feed conversion ratio. No other relevant effects of differential exposure to ammonia were found in fast-growing animals, either at high or low stocking density. The use of slow-growing breeds had no effect on production parameters. Despite having a slower growth rate, their feed conversion ratio was not different from that of fast-growing breeds. The productive performance of slow-growing animals was not affected by the differential exposure to ammonia, but the reduced spleen size would suggest an impairment of the immune system.