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ASKBILL as a web-based program to enhance sheep well-being and productivity

L P Kahn, I R Johnson, J B Rowe, L Hogan, J Boshoff

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)2257-2262
JournalAnimal Production Science
Volume57
Issue number11
DOIs
Publication statusPublished - 31 Dec 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Agricultural Land Planning
  • Agricultural Systems Analysis and Modelling
  • Farm Management, Rural Management and Agribusiness

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