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

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors

Simple Summary Smart farming is a concept of agricultural innovation that combines technological, social, economic and institutional changes. It employs novel practices of technologies and farm management at various levels (specifically with a focus on the system perspective) and scales of agricultural production, helping the industry meet the challenges stemming from immense food production demands, environmental impact mitigation and reductions in the workforce. Precision Livestock Farming (PLF) systems will help the industry meet consumer expectations for more environmentally and welfare-friendly production. However, the overwhelming majority of these new technologies originate from outside the farm sector. The adoption of new technologies is affected by the development, dissemination and application of new methodologies, technologies and regulations at the farm level, as well as quantified business models. Subsequently, the utilization of PLF in the pig and especially the poultry sectors should be advocated (the latter due to the foreseen increase in meat production). Therefore, more significant research efforts than those that currently exist are mainly required in the poultry industry. The investigation of farmers' attitudes and concerns about the acceptance of technological solutions in the livestock sector should be integrally incorporated into any technological development.Abstract More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development

Participatory modelling for holistic understanding of catchment health and human health in Andean rural microcatchments :the case of Calabazas

PhD ThesisIn rural catchments of developing countries, land use change, inadequate access to education, health care, water and sanitation, and lack of institutional support are common problems which affect poor people. Integrated Water Resource Management (IWRM) which advocates for the coordinated management of water, land and related resources, and EcoHealth which holds that human health and wellbeing are outcomes of effective ecosystem management, promote catchments as tangible contexts to fulfil overlapping objectives across fields. This research links IWRM and EcoHealth using System Dynamics (SD) as a tool to increase the level of shared understanding of the socioeconomic and environmental factors influencing environmental health and human health and wellbeing in an Andean rural microcatchment in Colombia. Stakeholders´ knowledge was elicited through semi-structured interviews and documents. A Causal Loop Diagram was prepared to organize this knowledge and to identify the model structure. Information on socioeconomic and environmental variables was collected through three surveys: i) household; ii) stream water, and iii) drinking water. The household survey captured relevant social determinants of health. The stream water survey investigated stream health in relation to point and non-point pollution sources. The drinking water survey identified risks to water quality. Using SD principles and the Stella software, a series of focus groups enabled stakeholders to develop a semi-quantitative model. The resultant model comprised six interrelated sectors: population, economic, land use, stream health, human health, and management. The modelling process increased stakeholders´ understanding of their system, and helped them to identify interactions of distal and proximal factors to produce outcomes on catchment and human health. The model was a strategy for integration and a communication tool. The process allowed the incorporation of knowledge, concerns and perceptions from the different actors, disciplines, institutions and sectors involved. The process facilitated identification of limitations and benefits of existing policies and the need for policies to address neglected problems. The research contributed to methodology development in the field of IWRM – EcoHealth, testing System Dynamics Modelling as a strategy to elucidate complex social, economic and environmental linkages at the catchment scale that could be applicable to similar rural mountainous contexts.COLCIENCIAS: IFS

Abstracts of Papers, 84th Annual Meeting of the Virginia Academy of Science

Full abstracts of papers for the 84th Annual Meeting of the Virginia Academy of Science, May 25-26, 2006, Virginia Polytechnic Institute and State University, Blacksburg, V