Energy use and indoor climate in livestock buildings for pigs

Swedish pig farming is facing two climatic challenges: minimizing greenhouse gases and adapting to warmer climates in confined livestock buildings. Climate change leads to more heat waves, causing pigs in confined buildings to endure heat stress more often and for longer periods. Heat stress not only affects the animals' welfare and health negatively, but it also implies a risk of economic losses for farmers, as heat stress can result in slower growth, impacts on reproduction and increased mortality. Pigs are particularly sensitive to heat because they do have few sweat glands. This introductory paper on the subject “Improving energy-efficiency and indoor climate of livestock buildings for pigs through passive and active adaptation measures” highlightsthe need for adaptation measures due to climate change. The main aim of this introductory paper is to provide a summary of current research and knowledge on the energy efficiency and indoor climate of livestock buildings for pigs, as well as the need for further research on pig buildings in Sweden. Many studies have evaluated potential adaptation measures to lower indoor temperatures and reduce heat stress in warmer climates. The most commonly implemented measures for cooling are increased airflow and air velocity, as well as evaporative cooling. The reviewed articles also indicated that insulation and mechanical ventilation are required in warmer climates to maintain an acceptable indoor climate.The main conclusions are that:(1) Heat stress for pigs will increase due to global warming, necessitating adaptation measures toreduce indoor temperatures in warmer climates.(2) Technical solutions are available to reduce indoor temperatures in warmer climates. However,studies on the investment costs and energy use of these solutions are lacking.(3) To reduce the environmental impact of livestock buildings intended for pigs, it is necessary todevelop energy-saving solutions, improve management practices, and use non-fossil energy sources.(4) Computer simulations can be used as a tool to predict thermal climate and energy use in livestockbuildings.(5) It is recommended to develop a common framework and use standardised functional units toenable comparison and simplify evaluation of results from different studies

Understanding and predicting pen fouling, tail biting, and diarrhoea in farmed pigs.

This PhD research investigated precision animal farming, specifically emphasising commercially reared pigs and their welfare, addressing concerns like pen fouling, tail-biting, and diarrhoea. While animal welfare in pig farming is critical, there is a lack of comprehensive predictive models that integrate various factors affecting pig behaviours. The primary objective was to create advanced algorithms and predictive models that combine mechanistic modelling and machine learning to better understand and predict pig behavioural dynamics related to welfare issues. Various methods were employed, including transfer function models to link water consumption with temperature differences, analysing spatial positioning in relation to fouling events, and employing neural network architectures for time series data. Bayesian networks were utilised for simulating intervention scenarios. Several significant discoveries were made during the research. Anomalies in pigs' water consumption that were linked to temperature variations were effectively identified by the transfer function model, giving valuable insights into pen fouling and tail-biting incidents. It was also discovered that a crucial role in influencing fouling events in pigs is played by spatial positioning and temperature differences between different activity areas within pig pens. Superior predictive capabilities for events such as fouling, tail-biting, and diarrhoea were demonstrated by the innovative application of a neural network approach to predict these events. Furthermore, an early warning system that utilised hierarchical clustering and principal component analysis was introduced, which showed strong predictive potential. Finally, this research also demonstrated that Bayesian Network simulations can be used as a non-invasive method to test for potential strategies to mitigate welfare issues in farmed pigs while also providing practical insights for better farm management. This research offers vital tools and insights for advancing precision pig farming, fostering a more sustainable and ethical approach. The developed algorithms not only contribute to better pig welfare but also enhance monitoring, potentially leading to increased farm profitability. While the models are promising, further refinement and research into the various factors affecting pig behaviour are recommended

Effect of Fans’ Placement on the Indoor Thermal Environment of Typical Tunnel-Ventilated Multi-Floor Pig Buildings Using Numerical Simulation

An increasing number of large pig farms are being built in multi-floor pig buildings (MFPBs) in China. Currently, the ventilation system of MFPB varies greatly and lacks common standards. This work aims to compare the ventilation performance of three popular MFPB types with different placement of fans using the Computational Fluid Dynamics (CFD) technique. After being validated with field-measured data, the CFD models were extended to simulate the air velocity, air temperature, humidity, and effective temperature of the three MFPBs. The simulation results showed that the ventilation rate of the building with outflowing openings in the endwall and fans installed on the top of the shaft was approximately 25% less than the two buildings with fans installed on each floor. The ventilation rate of each floor increased from the first to the top floor for both buildings with a shaft, while no significant difference was observed in the building without a shaft. Increasing the shaft’s width could mitigate the variation in the ventilation rate of each floor. The effective temperature distribution at the animal level was consistent with the air velocity distribution. Therefore, in terms of the indoor environmental condition, the fans were recommended to be installed separately on each floor

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Methods to counteract heat stress in growing-finishing pigs

Grisar är högproducerande djur som är känsliga för värmestress. Klimatförändringarna leder till ökade perioder av värme och det är därför viktigt att finna applicerbara metoder som erbjuder grisarna kyla. Värmestress påverkar grisarnas välfärd och leder till en rad olika förändringar i det naturliga beteendet. Värmen påverkar inte bara grisen negativt utan orsakar också en ökad arbetsbelastning för lantbrukaren samt resulterar i sämre betalt för slutprodukten. Det finns ett antal metoder för att kyla grisarna som studerats i olika delar av världen. Grisens värmeavgivning kan ökas genom att erbjuda ett vätande kylsystem såsom dusch och bad. Även kylning i golvet och ökad lufthastighet används för att öka värmeavgivningen. Lufttemperaturen i stallarna kan sänkas med evaporativ kylning samt dimmspridning och intresset ökar också för att utveckla energisnåla system som markvärmeväxlare. Studien sammanställer de olika kylmetoderna och utvärderar huruvida de kan appliceras i ett stall för slaktgrisproduktion i Sverige. System som dusch, bad, kylt golv, markvärmeväxlare, ökad lufthastighet samt dimmspridning är lämpliga metoder i svenska grisstallar. Evaporativ kylning är en mindre lämplig metod med tanke på Sveriges klimat.Pigs are high producing animals that are sensible for heat stress. Climate change is leading to increased periods of heat and it is important to find methods that offers the pigs´ cooling. Heat stress affects the pigs´ welfare and leads to multiple changes in their natural behaviour. The heat not only affects the pig negatively, but also causes an increased workload for the farmer and less payment for the product. There are several cooling methods studied around the world. The pigs´ heat loss can be increased by wetting their skin using shower or bath. Floor cooling and high-velocity air stream also increases the pigs´ heat loss. Air temperature in the stable can be decreased using evaporative cooling or high-pressure nozzles and there is an increasing interest in using energy-efficient systems such as earth-air heat exchangers. The study aims to complie the different cooling methods and evaluate whether they can be applied in a stable for finishing pigs in Sweden. Systems like shower, bath, floor cooling, earth-air exchanger, high-velocity air stream and high-pressure nozzles are suitable in Swedish pig stables. Evaporative cooling are less suitable considering the Swedish climate

Housing Environment and Farm Animals' Well-Being

This reprint contains articles from the Special Issue of Animals “Housing Environment and Farm Animals' Well-Being”, including original research, review, and communication related to livestock and poultry environmental management, air quality control, emissions mitigation, and assessment of animal health and well-being

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced