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Dieren, data en daredevils

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.

Implications of cellular agriculture for hospitality and tourism management research: sustainable development goal perspective.

Hyper-spectral frequency selection for the classification of vegetation diseases

Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.

Systems Thinking, Mapping and Change in Food and Agriculture

Societal actors across scales and geographies increasingly demand visual applications of systems thinking – the process of understanding and changing the reality of a system by considering its whole set of interdependencies – to address complex problems affecting food and agriculture. Yet, despite the wide offer of systems mapping tools, there is still little guidance for managers, policy-makers, civil society and changemakers in food and agriculture on how to choose, combine and use these tools on the basis of a sufficiently deep understanding of socio-ecological systems. Unfortunately, actors seeking to address complex problems with inadequate understandings of systems often have limited influence on the socio-ecological systems they inhabit, and sometimes even generate unintended negative consequences. Hence, we first review, discuss and exemplify seven key features of systems that should be – but rarely have been – incorporated in strategic decisions in the agri-food sector: interdependency, level-multiplicity, dynamism, path dependency, self-organization, non-linearity and complex causality. Second, on the basis of these features, we propose a collective process to systems mapping that grounds on the notion that the configuration of problems (i.e., how multiple issues entangle with each other) and the configuration of actors (i.e., how multiple actors relate to each other and share resources) represent two sides of the same coin. Third, we provide implications for societal actors - including decision-makers, trainers and facilitators - using systems mapping to trigger or accelerate systems change in five purposive ways: targeting multiple goals; generating ripple effects; mitigating unintended consequences; tackling systemic constraints, and collaborating with unconventional partners.

Precision livestock farming

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.

Using precision farming to improve animal welfare

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.