TY - GEN
T1 - Using Bayesian networks to advise NRM agencies how to influence the adoption of water use efficiency practices by groundwater license holders
AU - Ticehurst, Jenifer
AU - Curtis, Allan
N1 - Publisher Copyright:
© International Congress on Modelling and Simulation, MODSIM 2013.All right reserved.
PY - 2013
Y1 - 2013
N2 - Many new agricultural practices need to be modified if they are going to be sustainable into the future, and this requires adoption by rural landholders. Adoption is a complex process and is typically different for different practices, landholders and contexts. However, it is possible to identify factors that are influential and amenable to influence by natural resource management agencies, and those that are less amenable, but which need to be considered for effective engagement. In this paper we develop a Bayesian network model to explore the influences on the adoption of a set of water efficient management practices, in response to climate change and water entitlement policy, in the Namoi Catchment, an irrigation region of New South Wales, Australia (Figure 1). The management practices included are spray irrigation, soil moisture mapping and modification of flood irrigation methods, measuring dam evaporation and deepening dams, buying water on the temporary or permanent water markets, and changing the crop type and rotation frequencies. A survey of groundwater license holders gathered the data that form the basis of the model (Sharp and Curtis, 2012). Through statistical analysis, those researchers identified a set of variables (from values and beliefs, to property characteristics) that are correlated to the uptake of these practices. The Bayesian network is used to explore the causal relationships between these, and then prioritise which factors have the most influence over adoption. Of the management practices included, groundwater license holders' were most likely to adopt changing crop types and rotation frequency, and least likely to buy water on the temporary or permanent markets. This is most likely to be a reflection of the variability in the financial cost, simplicity and perceived level of risk in adopting these practices. A key influence in the level of uptake of the various management practices was the type of license holder, whereby those who were More Committed to the Farming Business (MCFB) were more likely to adopt the management practices discussed here, compared to those who were More Committed to Environmental Sustainability (MCES). However, these characteristics are inherent, rather than easily changed. The real value of this research is in suggesting more effective ways to engage these license holders, which included through their industry groups (e.g. short courses) and by appeals to the importance of long-term community and business viability.
AB - Many new agricultural practices need to be modified if they are going to be sustainable into the future, and this requires adoption by rural landholders. Adoption is a complex process and is typically different for different practices, landholders and contexts. However, it is possible to identify factors that are influential and amenable to influence by natural resource management agencies, and those that are less amenable, but which need to be considered for effective engagement. In this paper we develop a Bayesian network model to explore the influences on the adoption of a set of water efficient management practices, in response to climate change and water entitlement policy, in the Namoi Catchment, an irrigation region of New South Wales, Australia (Figure 1). The management practices included are spray irrigation, soil moisture mapping and modification of flood irrigation methods, measuring dam evaporation and deepening dams, buying water on the temporary or permanent water markets, and changing the crop type and rotation frequencies. A survey of groundwater license holders gathered the data that form the basis of the model (Sharp and Curtis, 2012). Through statistical analysis, those researchers identified a set of variables (from values and beliefs, to property characteristics) that are correlated to the uptake of these practices. The Bayesian network is used to explore the causal relationships between these, and then prioritise which factors have the most influence over adoption. Of the management practices included, groundwater license holders' were most likely to adopt changing crop types and rotation frequency, and least likely to buy water on the temporary or permanent markets. This is most likely to be a reflection of the variability in the financial cost, simplicity and perceived level of risk in adopting these practices. A key influence in the level of uptake of the various management practices was the type of license holder, whereby those who were More Committed to the Farming Business (MCFB) were more likely to adopt the management practices discussed here, compared to those who were More Committed to Environmental Sustainability (MCES). However, these characteristics are inherent, rather than easily changed. The real value of this research is in suggesting more effective ways to engage these license holders, which included through their industry groups (e.g. short courses) and by appeals to the importance of long-term community and business viability.
KW - Adoption
KW - Bayesian network
KW - Groundwater
UR - http://www.scopus.com/inward/record.url?scp=85080906323&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
SP - 2987
EP - 2993
BT - Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
A2 - Piantadosi, Julia
A2 - Anderssen, Robert
A2 - Boland, John
PB - Modelling and Simulation Society of Australia and New Zealand Inc (MSSANZ)
T2 - 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013
Y2 - 1 December 2013 through 6 December 2013
ER -