A systematic review of the effects of silvopastoral system on thermal environment and dairy cows' behavioral and physiological responses.

Does the silvopastoral system (SPS) promote a satisfactory thermal environment for dairy cows to perform their natural behaviors and perform a suitable thermoregulatory function? To answer this, peer-reviewed articles, written in English and evaluating the effects of silvopastoral systems on thermal environment, dairy cows' behavior, and physiology were used in this systematic review; additionally, a bibliometric approach was performed. Web of Science and Google Scholar were used to compile the literature. The resulting articles (1448) underwent a 4-step appraisal process and resulted in 19 articles that fitted our inclusion criteria. Microclimate variables and thermal comfort indicators were the most researched topics (discussed in 89% of studies); 47% of studies addressed cattle behavior and 36% physiological responses. Our review highlights different benefits of silvopastoral systems for grazing dairy cows. For example, the SPS provides a more comfortable thermal environment than treeless pasture, which increases feeding behaviors; furthermore, dairy cows in SPS show lower drinking events, surface temperature, and respiratory rate than cows raised in treeless pasture. However, for nine of the variables related to cows' behavior (e.g., resting, rumination) and physiology responses (e.g., internal temperature), the results of the studies were unclear. Furthermore, behaviors associated with lying down (e.g., idling and rumination) and milk production in SPS were explored only in six and two studies, respectively. These findings provide consistent evidence that the silvopastoral systems are beneficial to thermal comfort of dairy cows; nonetheless, the effect on cows' behavioral and physiological responses is still scarce and unclear.

Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic.

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers' age (21-35 days, 25-39 days, and 28-42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers' age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers' age, relative humidity, and the relative cooling efficiency of the facilities.

Factors Associated With the Development and Prevalence of Abnormal Behaviors in Horses: Systematic Review With Meta-Analysis.

Researchers believe that the development and prevalence of abnormal behaviors in horses may be caused by several environmental and biological factors, and the literature offers numerous reports that discuss the causes and effects of stereotypies in these animals. In this light, this study aimed to conduct a systematic review and meta-analysis of the scientific literature, summarizing the main risk factors associated with the development of abnormal behaviors in horses. The searches were conducted over the course of four years in Portuguese, Spanish, and English. The publications reviewed were full text research thesis or articles that addressed issues within the following criteria: (1) presentation of epidemiological information on the studied population; (2) prevalence of abnormal behaviors in equine populations; (3) factors associated with the development or prevalence of stereotypies. Data were extracted from each study and inserted into an Excel spreadsheet to be analyzed through descriptive statistics. In addition, the Mann-Whitney U test was used to verify the existence of significant differences between the methodologies (direct observation/questionnaires). The dataset was also analyzed through data mining to identify the main factors that influence the prevalence of abnormal behaviors in the studied population. The results showed a great variation in the mean prevalence of abnormal behaviors, with no significant difference between research conducted through questionnaires or direct observation, and the data mining technique identified that incorrect nutritional management may be the main factor influencing the development and prevalence of abnormal behaviors in horses.

Classification of environmental factors potentially motivating for dairy cows to access shade.

The aim of this Research Communication was to apply the data mining technique to classify which environmental factors have the potential to motivate dairy cows to access natural shade. We defined two different areas at the silvopastoral system: shaded and sunny. Environmental factors and the frequency that dairy cows used each area were measured during four days, for 8 h each day. The shaded areas were the most used by dairy cows and presented the lowest mean values of all environmental factors. Solar radiation was the environmental factor with most potential to classify the dairy cow's decision to access shaded areas. Data mining is a machine learning technique with great potential to characterize the influence of the thermal environment in the cows' decision at the pasture.

Influence of microclimate on dairy cows' behavior in three pasture systems during the winter in south Brazil.

The aim of this study was to evaluate the influence of microclimate on thermal comfort and dairy cows' behavior in different pasture systems during the winter of a subtropical climate. The experiment was carried out in June and August 2020, covering the winter season in the southern hemisphere. Local microclimatic variables and cows' behaviors were compared in two conditions (shaded and sunny), in three pasture systems: silvopastoral system with disperse trees (SPSdisp), silvopastoral system with trees along the border fences (SPSfen), and treeless pasture (TLP). The black globe-humidity index (BGHI) was calculated to measure thermal comfort. During the afternoon the BGHI values were above animal thermal comfort at the sunny condition in the three pasture systems. The lowest average values (p < 0.05) of BGHI and soil surface temperature were recorded in the SPSdisp and SPSfen when compared to TLP. The highest value of soil surface temperature observed in the three systems were: in the sunny condition of the SPSdisp (24.7 °C) and SPSfen (23.1 °C), both between 13:00-14:55; in the TLP (28.5 °C) between 12:00-12:55. The time cows spent on each behavior was different (p < 0.05) among the pasture systems and conditions (shaded and sunny): in both SPSs, cows spent more time lying (resting and rumination) at the sunny condition between 12:00-12:50, and more time standing (resting and ruminating) at the shaded condition between 13:00-13:50. The frequency of drinking water was approximately 33% greater in the TLP than in the SPSs when the BGHI exceeded the comfort threshold (12:00-12:50). In the silvopastoral systems the cows had the option to compensate the heat loss caused by low temperatures staying at the sun; they could also use shade when motivated to do so, either to seek protection from the sun in the middle of the day or to rest.

Efficiency of cooling systems in broiler houses during hot days / Eficiência dos sistemas de resfriamento em aviários durante dias quentes

ABSTRACT: This study evaluated: (1) the vulnerability of broiler houses with different cooling systems, and (2) the spatial distribution of environmental variables during hot days. Four potentially vulnerable commercial broiler houses in southern Brazil were selected according to the following parameters: absence or presence of different cooling systems, broilers older than 28 days, and outside air dry-bulb temperature over 30°C. Broiler house vulnerability was classified according to the cooling and mechanical ventilation system: cellulose pad cooling (CPC), sprinkling (SPK), fogging (FOG), and mechanical ventilation without evaporative cooling system (VTL). The air dry-bulb temperature (Tdb, °C) and relative humidity (RH, %) were recorded every 10 min. For each broiler house, we evaluated: (1) relative cooling efficiency (RCE) and (2) inside spatial distribution of microclimate variables using a geostatistical technique. The CPC and SPK did not differ (P<0.05) in RCE (81.6% and 80.7%, respectively), but both differed from FOG (23.8%) and VLT (1.87%) systems. The highest variations in indoor Tdb were recorded in the FOG (7 °C), followed by the SPK (4 °C) and CPC (3 °C). In the CPC, there was an increase in RH from the middle to the end of the broiler house near the exhaust fans. In conclusion, the relative cooling efficiency and the inside spatial distributions of environmental variables in the broiler houses were influenced by the existing cooling system.

RESUMO: Os objetivos deste estudo foram (1) comparar a vulnerabilidade de aviários com diferentes sistemas de resfriamento, e (2) avaliar a distribuição espacial interna das variáveis ambientais durante dias quentes. Quatro aviários comerciais potencialmente vulneráveis no sul do Brasil foram selecionados de acordo com os seguintes parâmetros de elegibilidade: ausência ou presença de diferentes sistemas de resfriamento; frangos de corte com mais de 28 dias de idade; e temperatura de bulbo seco do ar externa acima de 30 °C. A vulnerabilidade do aviário foi classificada de acordo com o sistema de resfriamento e ventilação mecânica, sendo: resfriamento por pad cooling (CPC), aspersão (SPK), nebulização (FOG) e ventilação mecânica sem sistema de resfriamento evaporativo (VTL). A temperatura de bulbo seco do ar (Tbs, °C) e a umidade relativa do ar (UR, %) foram coletadas por registradores de dados autônomos a cada 10 min. Para cada aviário foram avaliados: (1) eficiência relativa de resfriamento (RCE) e (2) distribuição espacial interna das variáveis ambientais por técnica de geoestatística. O CPC e SPK não diferiram (P>0,05) na RCE (81,6% e 80,7% respectivamente), mas ambos diferiram do FOG (23,8%) e VLT (1,87%). As maiores variações na Tbs do ar interno foram registradas no FOG (7 °C), seguido pelo SPK (4 °C) e CPC (3 °C). No CPC, houve um aumento da umidade relativa do meio para o final do aviário, próximo aos exaustores. Em conclusão, a eficiência relativa de resfriamento, assim como a distribuição espacial das variáveis ambientais internas foram influenciadas pelo sistema de resfriamento de cada aviário.

Classificação de características produtivas fenotípicas de diferentes raças de poedeiras através da mineração de dados / Phenotypic production characteristics from different breeds of laying hens through data mining

O estudo foi realizado com o banco de dados do Programa de Melhoramento Genético de Poedeiras de Ovos Marrons do Laboratório de Avicultura (LAVIC) do Departamento de Zootecnia da Universidade Federal de Santa Maria (UFSM), onde são utilizadas aves das raças Plymouth Rock White (PRW), Plymouth Rock Barred (PRB) e Rhode Island Red (RIR). Os dados utilizados foram referentes a duas gerações das três raças durante o primeiro ciclo de produção, dos anos de 2009 e 2010. Os parâmetros avaliados foram: taxa de postura semanal, peso dos ovos e peso corporal. O estudo foi dividido em duas abordagens de análise, a primeira análise foi realizada com os dados produtivos com o objetivo de identificar pontos críticos de seleção fenotípica, relacionados à caracterização zootécnica, e a segunda abordagem consistiu na associação dos mesmos parâmetros produtivos, com os dados climáticos de Santa Maria - RS. Os dados foram analisados pela técnica de Mineração de Dados, sendo utilizada a tarefa de classificação com a construção de árvore de decisão pelo algoritmo J48. A mineração dos dados indicou a taxa de postura média na 25ª semana de idade e posteriormente o peso médio dos ovos na 33ª semana de idade como as principais variáveis relacionadas com a diferenciação das raças estudadas, (precisão de 0,833). Na segunda abordagem, as árvores de classificação obtidas apresentaram precisão entre 0,88 e 0,91, sugerindo uma forte associação entre as variáveis ambientais e o desempenho produtivo das aves. Poedeiras PRW são mais sensíveis ao calor que as RIR e PRB e apresentam, na fase de pré-pico, uma maior taxa de postura. A Mineração de Dados permite classificar a produtividade das raças de poedeiras de ovos marrons, podendo indicar as influências ambientais sobre o fenótipo das diferentes raças estudadas.

The study was conducted with the database obtained from the Breeding Program of Brown Egg Laying Hens of the Poultry Laboratory (LAVIC) of the Department of Animal Science of the Federal University of Santa Maria (UFSM), where it is used poultry breeds such as White Plymouth Rock (PRW), Barred Plymouth Rock (PRB) e Red Rhode Island (RIR). The data used referred to two generations of these three breeds during the first production cycle of 2009 and 2010. The parameters evaluated were: weekly rate of egg laying, egg weight and body weight. The research was divided into two approaches of analysis, the first being performed with the production data, aiming to identify critical points of phenotypic selections related to zootechnical characterization, and the second approach consisted in the association of the same production parameters together with the weather data of Santa Maria - RS. The data was analyzed using the Data Mining technique, using the classification task by building the decision tree with the J48 algorithm. The data mining indicated the average rate of egg laying at 25 weeks of age and the average weight of eggs at 33 weeks of age, and pointed these as the main variables related to the differentiation of breeds studied, (accuracy of 83.3%). On the second approach, the decision trees obtained showed accuracy between 88% and 91%, suggesting a strong association between the environmental variables and the production performance of the birds. The PRW breed is more sensitive to heat than the RIR and the PRB breed and they showed a higher rate of production during the pre-peak phase. The data mining allowed to classify the productivity of the brown egg laying hens, which may indicate environmental influences on the phenotype of the different breeds studied.