Welfare, smart farming and artificial intelligence in the poultry sector

British biologist and professor of ethology at the University of Oxford, Marian Dawkins, said for technology to deliver on its promise of being able to improve the lives of animals, 3 conditions needed to be met. These are:

(1) Both the public and the agricultural industry must be satisfied that automated measures of welfare can capture what is meant by ‘good welfare’.

(2) There is scientific evidence that the technology genuinely improves animal welfare when deployed on commercial farms.

(3) There are demonstrable financial, environmental and other benefits as well as welfare so that industry is commercially worthwhile.

Dawkins said smart or precision farming was a blanket term that covered a range of different techniques that use computers in agriculture and could be considered under 3 headings, namely:

(1) Using sensors at individual or group level to provide information about production, welfare, health and disease outcomes and environmental variables, replacing or supplementing measurements currently made by auditors, veterinary or farm staff.

(2) Understanding the dynamic spread of diseases both within and between farms and collecting evidence on what makes for ‘best practice’ for achieving optimum health and production outcomes.

(3) Using computer-based decision-making and targeted interventions at all levels from management decisions to decisions at the level of groups or individual animals.

Technology, she argues, has the potential to optimise living conditions for animals, save labour costs, detect and treat disease at an early stage, minimise waste and lead to higher farm incomes.

However, Dawkins warned that precision farming could lead to unsafe actions if there was total reliance on machines to control farming systems. So people need to recognise the inherent limitations found in artificial intelligence. Machine learning, for example, uses general purpose learning algorithms to find patterns in large data sets, but if the data sets are composed of biased or heterogenous data, the results will be misleading.

The full paper published in Applied Animal Behaviour Science can be read at www.sciencedirect.com.