No abstract

Little is known about the impact of ranging on laying performance and egg quality of free-range hens. The aim of this study was to characterise egg production of commercial free-range laying hen sub-populations of low-, moderate- and high-range use at an early age. A total of five flocks with 40,000 hens/flock were investigated where 1875 hens/flock were randomly selected at 16 weeks of age, monitored for their range use and subsequently grouped into “stayers” (the 20% of hens that spent the least time on the range), “roamers” (the 20% of the hens that used the range more than stayers but less than rangers) and “rangers” (the 60% of the hens that spent the most time on the range). Eggs from the individual groups were collected in 10-weekly intervals until hens were 72 weeks of age, commercially graded and tested for several quality parameters. Significant differences were noted for hen-day production. For example, at 22 weeks of age, rangers enjoyed a laying rate of 88.0% ± 1.1%, while stayers performed at 78.2% ± 1.9% but at 72 weeks of age egg production of rangers was 85.1% ± 0.9% and of stayers was 95.5% ± 0.9% (p < 0.05). Range use was of minor importance to the egg quality.

ASKBILL is a web-based program that uses farm measurements, climate data and information on genetics to predict pasture growth, animal performance and animal health and climate risks. The program uses several biophysical models, which are customised by user inputs, localised daily weather updates and a dynamical probabilistic 90-day climate forecast to enhance sheep well-being and productivity. This approach can minimise the requirement for manual, auto and remote measurements, thus reducing labour requirements and complexity. In this article, the animal growth model provides an example of a biophysical model used to provide predictions. This is an energy-based model and the model parameterisation is designed to be physiologically meaningful and able to be customised for the genetic merit of the animal using a growth coefficient that calibrates growth of body components and energy requirements. A key feature of the animal growth model is its forecast projections, which are based on an ensemble of simulations. The model can estimate supplementary feeding rates required to achieve target liveweights and body condition scores and stocking rates required to achieve target pasture levels. The model can be customised for a farm and its livestock and is updated daily in response to climate data. This dynamic feature enables it to provide early stage alerts to users when animal production targets are unlikely to be met.

Free-range laying hens are provided with the opportunity to access various structural areas, including open floor space, feed areas, water lines, next boxes, perches, aviary tiers, winter gardens and ranges. Different individual location preferences can lead to the development of hen subpopulations that are characterised by various health, welfare and performance parameters. Understanding the complexity of hen movement and hen interactions within their environment provides an opportunity to limit the disadvantages that are associated with housing in loose husbandry systems and aids in decision-making. Monitoring hen movement using modern technologies such as radio-frequency identification (RFID), optical flow patterns, image analysis and three-dimensional (3D) cameras allows the accumulation of big data for data mining, clustering and machine learning. Integrating individual-based management systems into modern flock management will not only help improve the care of under-performing hens, but also ensure that elite hens are able to use their full genetic potential, allowing an ethical, sustainable and welfare friendly egg production. This review highlights the dynamics and impact of hen movement in free-range systems, reviews existing knowledge relevant for feeding hens in non-cage systems, and outlines recent technological advances and strategies to improve the management of free-range laying hens.