ABSTRACT
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.
ABSTRACT
The concentration of livestock production is problematic due to environmental concerns. European regulations are guiding the sector to become increasingly sustainable and, at the same time, maintaining the population in rural areas. The aim was to determine suitable areas in municipalities where livestock is presented as a market option. The methodology applied was based on the combination of multi-criteria methods and geographic information system (GIS) techniques, following three steps: removal of unsuitable zones by sectoral regulations (STEP 1); removal of unsuitable zones due to urban planning, and environmental recommendations (STEP 2); and evaluating the resulting areas depending on the importance of socio-economic, sectoral, and environmental characteristics. This study was based in a Spanish region with ongoing conflicts over land use on the coast but with a high number of rural municipalities at risk of depopulation in the interior. The results showed that 33% of the municipalities of the Valencian Community (VC) had suitable and outranking areas for the development of the swine sector. The 43 municipalities with the highest scores were because of the socio-economic factor and confirmed that suitable livestock development in municipalities with the highest risk of depopulation and low rural tourism activity was a key issue for development.
