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Escherichia coli (E. coli) is a commensal bacteria found in the gastrointestinal tract of poultry; however, some strains are pathogenic and can cause a wide range of diseases. In addition, some strains of pathogenic E. coli can survive in the litter between flocks, making litter management critical for reducing E. coli-associated infections. Biochar (BC) is a porous, carbonaceous material that may be a beneficial litter amendment to reduce moisture and microbial loads. The objectives of this study were to evaluate the effects of pine BC, miscanthus BC, and Poultry Litter Treatment (PLT) on E. coli, total aerobic bacteria populations, and bacterial communities when added to used broiler litter. Pine and miscanthus BC were mixed into poultry litter at inclusion rates of 5%, 10%, 20%, 25%, and 30% w/w. PLT was surface applied at a rate of 0.73 kg/m2. Baseline E. coli and aerobics were measured after a 48-hr litter incubation period and just prior to adding litter treatments. Escherichia coli and aerobics were enumerated 2 and 7 days after adding treatments. Overall, pine BC at 30% had the lowest E. coli and aerobic counts (5.98 and 6.44 log 10 colony-forming units [CFU]/g, respectively); however, they were not significantly different from the control (P ≤ 0.05). At day 2, 30% pine BC inclusion rate treatment resulted in a significant reduction in E. coli and aerobic bacteria counts compared to the control. Miscanthus BC application did not result in significant reductions in E. coli or aerobic bacteria at days 2 or 7. PLT had the highest E. coli (7.07 log 10 CFU/g) and aerobic counts (7.21 log 10 CFU/g) overall. Bacterial community analysis revealed that the alpha and beta diversity between pine BC- and PLT-treated litter were significantly different. However, neither BC type significantly impacted bacterial diversity when compared to the control. Differences in E. coli and aerobic counts between BC types may be attributed to variations in feedstock physiochemical properties. Evaluación de los efectos del biocarbón de pino y de miscanto sobre Escherichia coli, bacterias aerobias totales y comunidades bacterianas en la cama comercial de pollos de engorde. Escherichia coli (E. coli) es una bacteria comensal que se encuentra en el tracto gastrointestinal de las aves comerciales; sin embargo, algunas cepas son patógenas y pueden causar una amplia variedad de enfermedades. Además, algunas cepas de E. coli patógena pueden sobrevivir en la cama entre parvadas, lo que hace que el manejo de la cama sea fundamental para reducir las infecciones asociadas con E. coli. El biocarbón (BC) es un material carbonoso poroso que puede ser un aditivo beneficioso en la cama para reducir la humedad y las cargas microbianas. Los objetivos de este estudio fueron evaluar los efectos del biocarbón de pino, de miscanthus y de un producto comercial para el tratamiento de la cama en avicultura (PLT) sobre E. coli, sobre poblaciones de bacterias aeróbicas totales y comunidades bacterianas cuando se agregan a la cama de pollos de engorde usada. Se mezclaron biocarbón de pino y miscanthus en la cama de aves de corral con tasas de inclusión por peso del 5 %, 10 %, 20 %, 25 % y 30 %. Se aplicó el producto PLT en la superficie a razón de 0.73 kg/m2. La E. coli y los aeróbicos de referencia se midieron después de un período de incubación de la cama de 48 horas y justo antes de agregar los tratamientos de la cama. Se enumeraron Escherichia coli y bacterias aeróbicas 2 y 7 días después de agregar los tratamientos. En general, el biocarbón de pino al 30 % tuvo los recuentos más bajos de E. coli y aeróbicos (5.98 y 6.44 log10 unidades formadoras de colonias [UFC]/g, respectivamente); sin embargo, no fueron significativamente diferentes del control (P ≤ 0.05). En el día 2, el tratamiento con una tasa de inclusión de biocarbón de pino al 30 % dio como resultado una reducción significativa en los recuentos de bacterias aeróbicas y E. coli en comparación con el control. La aplicación de biocarbón de miscanto no resultó en reducciones significativas de E. coli o bacterias aeróbicas en los días 2 o 7. El producto PLT tuvo los recuentos más altos de E. coli (7.07 log10 CFU/g) y aeróbicos en general (7.21 log10 CFU/g). El análisis de la comunidad bacteriana reveló que la diversidad alfa y beta entre la cama tratada con biocarbón de pino y producto PLT era significativamente diferente. Sin embargo, ninguno de los tipos de biocarbón afectó significativamente la diversidad bacteriana en comparación con el control. Las diferencias en los recuentos de E. coli y de bacterias aeróbicas entre los tipos de biocarbón pueden atribuirse a variaciones en las propiedades fisicoquímicas de la materia prima. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals. [ABSTRACT FROM AUTHOR]