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Can use of online measurements in EVOP designs reduce the time to locate production optimum?

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

The purpose of this project is to investigate if application of online sensor measurements can improve the use of Evolutionary operation (EVOP) tools in livestock farms by reducing manual recording and by observing intermediate problem measures at an early stage. An example with reduction of tail-biting is used as a case. Traditionally, the frequency of the "tail-biting" in a pen is used to indicate if a treatment reduces the occurrence of tail-biting or not. Power calculations have shown that to detect a treatment difference of 10% vs 20%, 6 batches with 16 pens per section are needed to yield a power of 80%. It is generally accepted that there will be behavioural signs before an outbreak of tail-biting, such as increased activity (restlessness) and likely changes in the circadian rhythm. Using those indicators, based on online measurements of water consumption, it is our hypothesis that we can reduce the number of batches or pens needed and still get the same power. This is based on the assumption that early indicators are more sensitive than the event "tail-biting". To test this hypothesis, power calculations for EVOP design based on sensor measurements of diurnal variation in water use are presented. The calculations are made with typical farm dimensions and are based on estimates of standard deviations and variance components from a setup where water use was measured at pen level.
Original languageEnglish
Title of host publicationPrecision Livestock Farmin '15 : Papers presented at the 7th European Conference on Precision Livestock Farming
EditorsM. Guarino, D. Berckmans
Number of pages7
Publication yearSept 2015
ISBN (print)9788890975325
Publication statusPublished - Sept 2015
EventPrecision livestock farming 2015 - Milano, Italy
Duration: 15 Sept 201518 Sept 2015


ConferencePrecision livestock farming 2015

    Research areas

  • Evolutionary operation, early indicators, tail-biting, optimization

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