Towards standardising irrigation DSS inputs data formats through adaptation of the WDTF/WaterML

Abstract

Irrigators use many data sources such as automatic weather stations (AWS), flow metres, soil moisture probes etc. to inform them when they try to make objective decisions at the farm level. Crop models and irrigation Decision Support Systems (DSS) that they could use to help them in this are hindered by their ability to use the data from all of these sources which diminishes their relevance to irrigators and reduces their uptake. Factors causing this hindrance are: • Physical connectivity access to data; • Data ownership restrictions; • Incompatibility of the data formats; • Unsatisfactory or non-existent quality assurance and metadata for reliable use. The Internet and wide radio and cellular network coverage across Australia's irrigation districts has greatly improved the physical connectivity aspect of data access while ownership restriction on access have no technical solution. This paper addresses the remaining issues through discussing on-farm irrigation-specific adaptations or extensions, particularly relating to evapotranspiration, of the Bureau of Meteorology's (BoM) currently implemented Water Data Transfer Format (WDTF) described by Walker et al. (2009), the international WaterML project described by Zaslavsky et al. (2007) and their joint future WaterML 2.0 standard as described by Taylor (2009). These projects have evolved rapidly since the authors' previous work on irrigation data standardisation (see Car et al. (2009)) and are now seeing large-scale implementation in Australia and thus immediate adaptation and adoption is now possible. A trial adoption of the extended standard is described in this paper whereby by data from two different AWS networks are able to be used by a previous DSS built by one of the authors and others (see Hornbuckle et al. (2009) for the DSS details). In addition to the technical implementation of data formats at the network scale, implementation by irrigation-related hardware vendors is discussed. This is crucial to the acceptance of standards for use at the farm scale. Irrigation sensors and other hardware must be natively standards compliant - i.e. not requiring further software layers to be so - as irrigation model and DSS users at this scale will not have the ability to undertake further development. Specifically, on-farm DSS, irrigation model and WDTF/WaterML background is given in Section 1, conceptual work relating to on-farm irrigation model requirements in Section 2, test implementations of format extensions in Section 3 and case studies of hardware vendor adoption in Section 4. This proposed exercise in standardisation is part of a larger project by the authors looking to improve the utilisation of on-farm DSS through both technical and non-technical methods. Increased DSS use for on-farm irrigation management decisions not only assists irrigators to use more science in their decisions but also enables decision data to be captured which can then be used post-event by researchers for analysis.