Abstract
This study analyzed individual water and feed consumption related to weight of weaned piglets and their link to diarrhea. Data were collected from 15 batches of 102 piglets each, using specific automata (connected feeders, connected drinkers, automatic weighing stations, RFID ear tags). Analyses were carried out every week on the 138 healthy animals compared by weight category. The average feed consumption had no significant difference between weight categories (light, medium, heavy pigs) whatever the week and was close to 4% of the live weight. For the average water consumption according to weight, it was close to 10%. There was no significant difference between weight groups, except at the end of the period, where the variability of one heavy pig was so high that its own water consumption caused significant difference when compared with the light group. But these overall stable averages do not highlight the high intra-individual variabilities, which was around 40% for both water and feed data at the beginning of trial. At the end, it was almost 16% for feed consumption and 25% for water. The comparison between healthy and diarrheic piglets showed no statistical difference for average water consumption on the day of the first clinical signs and even 1 and 2 days before. In contrast, the average feed consumption had a very significant difference (P ≤ 0.001) for days 5–7 after the weaning and a significant difference for day 8 (P ≤ 0.05). Differences were also significant for data 24 and 48 h before first clinical signs. This means either that diarrheic piglets decrease their feed consumption the first days after weaning or that it is because they eat less that they become diarrheic. So, the hypothesis was that feed consumption could be an interesting indicator to detect early sick animals. Nevertheless, despite this difference, machine learning methods failed in detecting individually diarrheic animals from water and feed consumption related to weight, because of considerable individual variability. To improve these results, one solution could be to collect other data from new sensors like automatic measurement of body temperature or location of piglets in the pen by image analysis.
Highlights
IntroductionThere has been increasing recognition that widespread antimicrobial usage in food animal production might contribute to the development of resistance to antimicrobials commonly used in human medicine (Landers et al, 2012; AidaraKane et al, 2018), largely due to the use of common antimicrobials in food producing animals and humans (Tang et al, 2017)
We found a piglet with very high level of water consumption in the heavy group for weeks four and five
The follow-up of fifteen bands of 102 piglets enabled the creation of a substantial database composed of daily and individual data on water, feed consumption and weight of weaned piglets to study the possibility of early diarrhea detection on post-weaning
Summary
There has been increasing recognition that widespread antimicrobial usage in food animal production might contribute to the development of resistance to antimicrobials commonly used in human medicine (Landers et al, 2012; AidaraKane et al, 2018), largely due to the use of common antimicrobials in food producing animals and humans (Tang et al, 2017). A detailed review, known as ≪ the RONAFA opinion ≫, was published by EMA and EFSA in 2017, to address the need to reduce the use of antimicrobial agents in animal husbandry within the EU [EMA Committee for Medicinal Products for Veterinary Use (CVMP) EFSA Panel on Biological Hazards (BIOHAZ) et al, 2017]. Consistent with the findings of the RONAFA opinion, a broad range of measures are being used across different countries to reduce the need for antimicrobial usage in food animal production (Postma et al, 2015). Good health and a reduced use of antimicrobials can be promoted by disease prevention (biosecurity for instance), disease control (vaccination for instance), and—if necessary—targeted and precise treatment (individual injectable and curative treatment for instance)
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