Inaugural lecture as Lector Precision Livestock Farming at HAS University of Applied Sciences on October 14, 2016. PLF, Precision Livestock Farming, uses technologies to continuously monitor animal behaviour, animal health, production and environmental impact.
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Animal welfare is a multidimensional phenomenon and currently its on-farm assessment requires complex, multidimensional frameworks involving farm audits which are time-consuming, infrequent and expensive. The core principle of precision agriculture is to use sensor technologies to improve the efficiency of resource use by targeting resources to where they give a benefit. Precision livestock farming (PLF) enables farm animal management to move away from the group level to monitoring and managing individual animals. A range of precision livestock monitoring and control technologies have been developed, primarily to improve livestock production efficiency. Examples include using camera systems monitoring the movement of housed broiler chickens to detect problems with feeding systems or disease and leg-mounted accelerometers enabling the detection of the early stages of lameness in dairy cows. These systems are already improving farm animal welfare by, for example, improving the detection of health issues enabling more rapid treatment, or the detection of problems with feeding systems helping to reduce the risk of hunger. Environmental monitoring and control in buildings can improve animal comfort, and automatic milking systems facilitate animal choice and improve human-animal interactions. Although these precision livestock technologies monitor some parameters relevant to farm animal welfare (e.g. feeding, health), none of the systems yet provide the broad, multidimensional integration that is required to give a complete assessment of an animal’s welfare. However, data from PLF sensors could potentially be integrated into automated animal welfare assessment systems, although further research is needed to define and validate this approach.
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Lector Precision Livestock Farming, Lenny van Erp, neemt je in deze rondleiding mee langs een aantal onderzoeken die het lectoraat in studiejaar 2019/2020 heeft uitgevoerd met onze afstuderende studenten. Je wandelt digitaal langs onder meer de onderzoekslijnen melkvee, pluimvee, varkens en gezelschapsdieren en paarden. De onderzoeken gaan over nieuwe sensoren, nieuwe technologieën en data om meer te kunnen zeggen over gedrag, gezondheid en welzijn van de dieren.
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Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm,feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods(supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.
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The ‘precision’ in Precision Livestock Farming is not the same as that of precision engineering in high-tech industry. It’s not about micro- and nanometers, but about the precise control of farming management with the aid of sensing and data processing. However, there is overlap in the technological domain, for instance regarding sensors and robots. So, both worlds can learn from each other. That’s why in Den Bosch, mechanical engineering students pursuing a minor in ‘Machines in Motion’ are working on a farming application. Mikroniek offers a sneak preview.
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Inaugurele rede als Lector Precision Livestock Farming bij HAS hogeschool op 14 oktober 2016. PLF, in het Nederlands Precisielandbouw in de veehouderij, maakt gebruik van technologieën om diergedrag, diergezondheid, productie en milieubelasting continu te monitoren.
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The objective of this book ‘An introduction to Smart Dairy Farming’ is to provide insight in the development of the Smart Dairy Farming (SDF) concept and advise as to how to apply this knowledge in the field of activities of students from universities of applied science. The information in this book includes background information and comprehensive insight in the concept of SDF.
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Vanuit het lectoraat Dairy wil de lector ‘Herd Management en Smart Dairy Farming’ door middel van het gebruik van moderne (sensor)technieken en beslissingsondersteunende managementsystemen het veemanagement op het primaire melk-veebedrijf verbeteren, om zo een beter rendement te behalen. Zowel financieel als op het gebied van duurzaamheid, diergezondheidszorg en dierenwelzijn. Daarbij sluit het naadloos aan op het werkgebied van de lector ‘Duurzame Melkveehouderij’, die zich richt op ontwikkeling en overdracht van kennis op het gebied van verduurzaming van de melkveehouderij. Daarbij staat de praktische uitrol van de afspraken in de Duurzame Zuivelketen centraal. Oftewel: hoe kunnen we, in een internationaal concurrerende markt, een rendabele en duurzame melkveehouderij creëren die maatschappelijk geaccepteerd wordt?
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In order to receive a licence to produce, poultry farmers have to take into account societal demands, among others: animal welfare, healthy working conditions for the workers and landscape quality. A way to reach a combination of these goals is to create a design for the poultry house and outdoor run. We propose a methodology based on five steps, which enables us to create a design thattakes into consideration societal demands and that can be tested on its effects. These five steps are: 1. Giving a theoretical background on the societal demands (hen ethology, farm management and landscape quality) and based on this; 2. Giving a set of design criteria. 3. Describing the current state of the farm, in order to know its current qualities, 4. Making a design of the farm using the sets of criteria as guiding principle. 5. Reflecting on the design, to show whether the different criteria can be combined and where compromises are needed. A case study on an organic farm in the centre of the Netherlands showed that hen welfare, farm management and landscape quality can be improved together, although some measures do not add to all design criteria. Especially the effect on landscape quality and farm management is variable: the latter is also depending on the personal motivation of the farmer.
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