Bridging the gap between modelling advice and irrigator solutions through empirical reasoning techniques

Abstract

Science has led to increased on-farm irrigation productivity in the last decade in Australia through the widespread adoption of better crop varieties, management practices and mechanical tools. However, Decision Support Systems (DSS) for farmers, despite proved productivity benefits Inman-Bamber et al. (2005), have seen poor adoption. Inman-Bamber and Attard (2005) noted the vast majority of purpose-built irrigation DSS saw poor or no commercial adoption after development. Australian authors, particularly McCown (2001, 2002b,a), have noted a science-industry 'gap' between science researchers and DSS designers and the agriculture industry resulting in systems not entirely fit for practical pu..