Rabbits are an introduced species that threaten ecological, agricultural and forestry assets in Australia and New Zealand. Despite decades of control effort, including the release of Rabbit Haemorrhagic Disease Virus (RHDV), rabbits remain a major pest in Australasia. Because eradication of rabbits is not possible, effective management of the rabbit problem requires sustained control, using current best practice application of multiple control tools.
Decision Support Systems (DSS) can help land managers and advisory staff (such as Local Land Service staff) with decisions of when, where and how to manage rabbits, guided by best practice, using interactive tools that provide information, model analysis and decision guides. DSS need to be fit for purpose to ensure their relevance, and use software that is accurate, easy to use, and adaptable, so that new information can be easily incorporated.
The Invasive Animals CRC in collaboration with Landcare Research NZ is developing two DSS to support rabbit management under a participatory approach that focuses on the needs of stakeholders, who are involved at all stages of the development process. The DSS are:
- Conservation land DSS –to support decisions under limited funding allocation for rabbit management in public lands of the Australian Capital Territory (ACT).
- Production land DSS – a learning tool that illustrates the cost-benefits of alternative control protocols on wool production farms in the Central Tablelands of New South Wales (NSW), Australia.
These DSS have been developed for specific end-users, but they are provided as open-source tools so they can be adapted to similar situations elsewhere.
DSSs are important tools that can support decision making, knowledge management, collaboration and learning. However, effective rabbit management also depends on additional social, economic, and legislative factors. For example, community involvement to carry out coordinated landscape-level control programs is more effective than localised efforts; legislation encourages individuals to manage rabbits on their properties; training provides technical experience on how to carry out control. Consideration of these additional factors, not supported by DSS, is important in ensuring the success of a program.
We therefore propose that projects aimed at supporting rabbit management (including DSS development projects) should use an outcomes-based approach for project management and evaluation commonly known as ‘Theory of Change’. This approach guides projects to focus on what difference they are making (outcomes) rather than what they are doing (outputs), and paints a big picture of where the project’s activities and outputs fit to achieve these outcomes. For rabbit management to be effective we need to keep in mind that the ultimate outcomes are protecting and enhancing environmental, social and economic assets, not just killing rabbits.
- Williams K, Parer I, Coman B, Burley J, Braysher M (1995). Managing vertebrate pests: rabbits. Canberra, Australia: Bureau of Resource Sciences and CSIRO Division of Wildlife and Ecology. 284 p.
- Vere DT, Jones RE, Saunders G (2004). The economic benefits of rabbit control in Australian temperate pastures by the introduction of rabbit haemorrhagic disease. Agricultural Economics 30: 143-155. doi: 10.1111/j.1574-0862.2004.tb00183.x
- Williams CK, Moore RJ (1995). Effectiveness and cost-efficiency of control of the wild rabbit, Oryctolagus cuniculus (L.) by combinations of poisoning, ripping, fumigation, and maintenance fumigation. Wildlife Research 22: 253-269. doi: 10.1071/WR9950253
- Cooke B (2002). Rabbit haemorrhagic disease: field epidemiology and the management of wild rabbit populations. Revue scientifique et technique (International Office of Epizootics) 21: 347-358.
- Parkes JP, Glentworth B, Sullivan G (2008). Changes in immunity to rabbit haemorrhagic disease virus, and in abundance and rates of increase of wild rabbits in Mackenzie Basin, New Zealand. Wildlife Research 35: 775-779. doi: 10.1071/WR08008
- Cooke BD (2012). Planning landscape-scape rabbit control. Canberra, Australia: Invasive Animal Cooperative Research Centre. 33 p.
- Hung S-Y, Ku Y-C, Liang T-P, Lee C-J (2007). Regret avoidance as a measure of DSS success: an exploratory study. Decision Support Systems 42: 2093-2106. doi: 10.1016/j.dss.2006.05.006
- Volk M, Lautenbach S, van Delden H, Newham LTH, Seppelt R (2010). How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systems. Environmental Management 46: 834-849. doi: 10.1007/s00267-009-9417-2
- Walker DH (2002). Decision support, learning and rural resource management. Agricultural Systems 73: 113-127. doi: 10.1016/S0308-521X(01)00103-2
- Hayman PT, Easdown WJ (2002). An ecology of a DSS: reflections on managing wheat crops in the northeastern Australian grains region with WHEATMAN. Agricultural Systems 74: 57-77. doi: 10.1016/S0308-521X(02)00018-5
- Shtienberg D (2013). Will decision-support systems be widely used for the management of plant diseases? Annual Review of Phytopathology 51: 1-16. doi: 10.1146/annurev-phyto-082712-102244
- Voinov A, Bousquet F (2010). Modelling with stakeholders. Environmental Modelling and Software 25: 1268-1281. doi: 10.1016/j.envsoft.2010.03.007
- McCown RL (2001). Learning to bridge the gap between science-based decision support and the practice of farming: Evolution in paradigms of model-based research and intervention from design to dialogue. Australian Journal of Agricultural Research 52: 549-471. doi: 10.1071/AR00119
- Jakku E, Thorburn P (2009). A conceptual framework for guiding the participatory development of agricultural development of agricultural decision support systems. Socio-Economics and the Environment in Discussion CSIRO Working Paper Series 2009-12: 1-33.
- McCown RL, Carberry PS, Hochman Z, Dalgliesh NP, Foale MA (2009). Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research. Crop and Pasture Science 60: 1017-1030. doi: 10.1071/CP08455