There is much existing knowledge about the factors that influence adoption of new practices in agriculture but few attempts have been made to construct predictive quantitative models of adoption for use by those planning agricultural research, development, extension and policy. ADOPT (Adoption and Diffusion Outcome Prediction Tool) is the result of such an attempt, providing predictions of a practice's likely rate and peak level of adoption as well as estimating the importance of various factors influencing adoption. It employs a conceptual framework that incorporates a range of variables, including variables related to economics, risk, environmental outcomes, farmer networks, characteristics of the farm and the farmer, and the ease and convenience of the new practice. The ability to learn about the relative advantage of the practice, as influenced by characteristics of both the practice and the potential adopters, plays a central role. Users of ADOPT respond to 22 questions related to: a) characteristics of the practice that influence its relative advantage, b) characteristics of the population influencing their perceptions of the relative advantage of the practice, c) characteristics of the practice influencing the ease and speed of learning about it, and d) characteristics of the potential adopters that influence their ability to learn about the practice. ADOPT provides a prediction of the diffusion curve of the practice and sensitivity analyses of the factors influencing the speed and peak level of adoption. In this paper the model is described and its ability to predict the diffusion of agricultural practices is demonstrated using examples of new crop types, new cropping technology and grazing options. As well as providing predictions, ADOPT is designed to increase the conceptual understanding and consideration of the adoption process by those involved in agricultural research, development, extension and policy.

High rates of deep drainage (water loss below the root-zone) in Western Australia are contributing to groundwater recharge and secondary salinity. However, quantifying potential deep drainage through measurements is hampered by the high degree of complexity of crop-soil systems as a result of spatial and temporal variability. Simulation models can provide the appropriate means to extrapolate across time and space and supply a new insight into such systems. The Agricultural Production Systems Simulator (APSIM) had been extensively tested with field measurements, including measurements of deep drainage, before it was used to analyse deep drainage under wheat crops in the Mediterranean climate of the central Western Australian wheat-belt. The analyses revealed the extent of the excess water problem that currently threatens the sustainability of the wheat-based farming systems in Western Australia. The simulation results showed that increasing crop production had a minor impact on deep drainage within a growing season. However, increased production reduced the amount of soil water stored at crop maturity, which has an impact on next season's drainage. Simulation scenarios for a catchment indicated that about 50% of the catchment area with the most drainage-prone soil types are required to be re-vegetated with perennials to reduce long-term average drainage rates by 60%. Even more drainage reduction is required to be sustainable and avoid a water table rise.