Alexandra Ferreira da Silva Cordeiro

Field evaluation of broiler gait score using different sampling methods

Brazil is today the worlds largest broiler meat exporter; however, in order to keep this position, it must comply with welfare regulations while maintaining low production costs. Locomotion problems restrain bird movements, limiting their access to drinking and feeding equipment, and therefore their survival and productivity. The objective of this study was to evaluate locomotion deficiency in broiler chickens reared under stressful temperature conditions using three different sampling methods of birds from three different ages. The experiment consisted in determining the gait score of 28, 35, 42 and 49-day-old broilers using three different known gait scoring methods: M1, birds were randomly selected, enclosed in a circle, and then stimulated to walk out of the circle; M2, ten birds were randomly selected and gait scored; and M3, birds were randomly selected, enclosed in a circle, and then observed while walking away from the circle without stimulus to walking. Environmental temperature, relative humidity, and light intensity inside the poultry houses were recorded. No evidence of interaction between scoring method and age was found however, both method and age influenced gait score. Gait score was found to be lower at 28 days of age. The evaluation using the ten randomly selected birds within the house was the method that presented the less reliable results. Gait score results when birds were stimulated to walk were lower than when they were not simulated, independently of age. The gait scores obtained with the three tested methods and ages were higher than those considered acceptable. The highest frequency of normal gait score (0) represented 50% of the flock. These results may be related to heat stress during rearing. Average gait score incresead with average ambient temperature, relative humidity, and light intensity. The evaluation of gait score to detect locomotion problems of broilers under rearing conditions seems subjective and difficult to be properly performed.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Entre os desafios da suinocultura no atual mercado competitivo, destaca-se a rastreabilidade do produto, que garante, entre muitos pontos, a questao do bem-estar animal. A vocalizacao e uma ferramenta util para identificar situacoes de estresse em suinos e pode ser usada em registros de bem-estar, em processos de rastreabilidade. Este trabalho teve o objetivo de identificar estresse em leitoes atraves da vocalizacao, classificando esse estresse em tres niveis: sem estresse, estresse moderado e estresse agudo. Foi realizado um experimento em granja comercial da cidade de Holambra-SP, onde se gravou a vocalizacao de vinte leitoes durante o procedimento de castracao, separados em dois grupos: sem anestesia e com anestesia local a base de Lidocaina. Para a captura dos sinais acusticos, foi utilizado um microfone unidirecional conectado a um gravador digital, em que os sinais foram digitalizados a uma frequencia de 44.100 Hz. Para analises dos sinais sonoros, foi usado o software Praat®, e diferentes algoritmos de mineracao dos dados foram aplicados no software Weka®. A selecao de atributos melhorou a acuracia do modelo, sendo que o melhor metodo de selecao de atributos usado foi o Wrapper, enquanto os melhores algoritmos de classificacao foram o k- NN e o Naive Bayes. De acordo com os resultados, foi possivel classificar o nivel de estresse em suinos atraves de sua vocalizacao.

Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets

Simple Summary This research aimed to analyze the possibility of assessing piglets’ welfare using the records of their vocalization. The trial was done in a pig commercial farm, and we recorded the vocal signals from piglets in several stressful exposure situations. Data mining techniques were applied to the processed signals in order to obtain a stress classification using the recorded data. We found that, using the piglets’ vocalization, it was possible to identify the most frequent stressful conditions at the farrowing phase, namely: pain, cold and hunger. Abstract Assessing pigs’ welfare is one of the most challenging subjects in intensive pig farming. Animal vocalization analysis is a noninvasive procedure and may be used as a tool for assessing animal welfare status. The objective of this research was to identify stress conditions in piglets reared in farrowing pens through their vocalization. Vocal signals were collected from 40 animals under the following situations: normal (baseline), feeling cold, in pain, and feeling hunger. A unidirectional microphone positioned about 15 cm from the animals’ mouth was used for recording the acoustic signals. The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency. The collected sounds were edited and analyzed. The J48 decision tree algorithm available at the Weka® data mining software was used for stress classification. It was possible to categorize diverse conditions from the piglets’ vocalization during the farrowing phase (pain, cold and hunger), with an accuracy rate of 81.12%. Results indicated that vocalization might be an effective welfare indicator, and it could be applied for assessing distress from pain, cold and hunger in farrowing piglets.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Problemas locomotores impedem a ave de se movimentar livremente, prejudicando o bem-estar e a produtividade, alem de gerarem lesoes nas pernas dos frangos. O objetivo deste trabalho foi avaliar a influencia da idade, do uso de vitamina D, da assimetria de membros e do gait score, no grau de lesoes de perna em frangos de corte, utilizando mineracao de dados. A analise foi realizada em um conjunto de dados obtidos de um experimento de campo, em que foram utilizados dois grupos de aves com 30 aves cada, sendo um grupo-controle e outro tratado com vitamina D. Foram avaliados o gait score, a assimetria entre os dedos dos pes direito e esquerdo, e o grau de lesoes de perna. O software Weka® foi utilizado na mineracao de dados. Em particular, o algoritmo C4.5 (tambem conhecido como J48 no ambiente Weka) foi utilizado para a geracao de uma arvore de decisao. Os resultados mostraram que a idade e o fator que mais influencia o grau de lesoes de perna e que os dados provenientes das avaliacoes de gait score nao se mostraram confiaveis para estimar problemas locomotores em frangos de corte.

The Use of Thermal Images for Identifying Stress Condition in Piglets

One of the biggest challenges of the meat industry is the provision of animal welfare. Under stress, several physiological changes in the animal organism as increased metabolism might occur, and that can cause change in skin temperature. Thermography allows measuring the surface temperature in a noninvasive manner, without any contact with the animal. The objective of this study was to evaluate whether it is possible to identify conditions of stress in pigs from thermography. Forty piglets in nursery phase photographed separately with the infrared camera of the 880 brands tested in five different situations: normal or stress-free, hungry, cold, thirst and pain. The pictures were analyzed in the Testo ® software. We obtained the maximum temperature, minimum and average area covering most of the back of the animal. ANOVA with Tukeys test (a <0.05) were used for comparison of means, and data were processed using the Minitab ® software.

Energy expenditure in vocalizations of pigs under stress

A suinocultura moderna enfrenta muitos desafios, dentre os quais o crescimento da produtividade, a melhoria da qualidade de carne, a reducao de danos ambientais e a reducao de custo de producao. Suinos gastam energia na vocalizacao, especialmente quando submetidos a determinado estresse. O desperdicio de energia pode aumentar o custo de producao e conduzir a um maior dano ambiental. O objetivo deste estudo foi estimar a energia gasta pelos leitoes sob o estresse da castracao e seu efeito sobre o ganho de peso dos animais. Dois grupos de dez animais cada foram castrados, sendo um grupo com anestesia local e outro sem anestesia. As vocalizacoes dos leitoes foram gravadas durante as diferentes fases do processo de castracao, sendo objeto de uma estimativa da quantidade de energia emitida durante cada vocalizacao. Posteriormente, esta energia foi associada com o ganho de peso dos animais. Nao houve diferenca no gasto total da energia em ambos os grupos, uma vez que a energia utilizada durante a aplicacao da anestesia foi semelhante a energia gasta durante a castracao dos animais sem anestesia. Tambem nao houve correlacao entre a energia gasta e o ganho de peso dos animais. Foi possivel estimar a quantidade de energia emitida atraves de vocalizacao. Esta energia gasta por leitoes com dor foi maior do que em outras situacoes de manejo (contencao, pesagem e soltura).

Using decision tree for classifying farrowing piglets stress conditions using vocalization.

The objective of this study was to classify different situations of stress from the vocalization of pigs in the phase of motherhood. Vocalization of 40 pigs was recorded in different stress situations in the farrowing phase (without stress, heat stress, pain, restrained from nursing) . To capture the acoustic signals unidirectional microphone was used at approximately 15 cm from the mouth of the piglets. This microphone was connected to a digital recorder, and the signals were digitized at a frequency of 44,100 Hz. Sound signals were edited and analyzed using the Praat ® software. Each collected signal was divided into three samples, and from each sample, it was extracted the maximum and minimum amplitudes, the frequency of Pith; and the frequency of formants 1, 2, 3 and 4, was obtained using that attributes within the range of amplitude (the difference of the amplitudes maximum and minimum), and duration of the signal. The algorithm J48 of Weka ® software was used for determining the stress condition. It was possible to classify four levels of stress: no-stress, pain, cold and hunger from the vocalization of pigs to the farrowing phase, with 77.95% accuracy.