Linda Lilburne

A prototype DSS to evaluate irrigation management plans

Inefficient irrigation strategies delivering an excess of irrigation water can result in pesticides and nitrates being leached to groundwater. However, information on the environmental impact of irrigation strategies is not readily available to either growers or the local authority responsible for water consents. Improved irrigation practices can be promoted by making this information more readily available in the form of a decision support system (DSS) linked to the water allocation process. Under this approach, growers would be required to submit to the local authority an irrigation management plan (IMP) which details how they intend to irrigate the crop in each of their management blocks. Sufficient information about the soil, crop, irrigation system and scheduling mechanism would have to be supplied in the IMP to allow it to be evaluated from an environmental impact perspective. The IMP is evaluated by a water allocation consent officer with the help of the decision support system, in which is incorporated environmental impact knowledge. This DSS integrates a simulation model SWIM, a decision tree, and scientific soil hydraulic data. The simulation model is used to estimate the likely water requirement of the grower under the IMP. The decision tree represents expert heuristics on the effects of the various irrigation strategies. The soil hydraulic data provides soil hydraulic properties to SWIM and to the decision tree. Local authorities and growers can use the DSS to learn about the likely environmental impact and water requirements of each grower.

The Scale Matcher: a procedure for assessing scale compatibility of spatial data and models

It is becoming easier to combine environmental data and models to provide information for problem-solving by environmental policy analysts, decision-makers, and land managers. However, the scale dependencies of each of these (data, model, and problem) can mean that the resulting information is misleading or even invalid. This paper describes the development of a systematic framework (dubbed the ‘Scale Matcher’) for identifying and matching the scale requirements of a problem with the scale limitations of spatial data and models. The Scale Matcher framework partitions the complex array of scale issues into more manageable components that can be individually quantified. First, the scale characteristics of data, model, and problem are separated into their scale components of extent, accuracy, and precision, and each is associated with suitable metrics. Second, a comprehensive set of pairwise matches between these components is defined. Third, a procedure is devised to lead the user through a process of systematically comparing or matching each scale component. In some cases, the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated by randomly simulating data and model imprecision and inaccuracy. Finally, a conclusion is drawn as to the scale compatibility of the Data–Model–Problem trio based on the overall procedure result. Listing the individual match results as a set of scale assumptions helps to draw attention to them, making users more aware of the limitations of spatial modelling. Application of the Scale Matcher is briefly illustrated with a case study, in which the scale suitability of two sources of soil map data for identifying areas of vulnerability to groundwater pollution was tested. The Scale Matcher showed that one source of soil map data had unacceptable scale characteristics, and the other was marginal for addressing the problem of nitrate leaching vulnerability. The scale-matching framework successfully partitioned the scale issue into a series of more manageable comparisons and gave the user more confidence in the scale validity of the model output.

GIS, expert systems, and interoperability

Integrating a GIS has been a common way to combine the functionality of two or more systems for some time. A three-dimensional model of integration is described which shows the range of linkages that can be achieved. Extremely flexible and dynamic linkages between systems can now be created through the recent advances of client/server and object-oriented technology. An expert system shell is coupled with a GIS to create a generic spatial rule-based toolbox called SES (spatial expert shell). An expert system developer using this toolbox can transparently access spatial data and relationships from a GIS by linking application objects to spatial classes. These spatial classes include methods that format and send requests to the GIS server. Thus the linkage is determined at run-time allowing a flexible interwoven interaction between the expert system and the GIS.

Soil Quality Indicators On The World Wide Web

New Zealand is highly dependent on its soil resource for continued agricultural production, natural ecosystem health, and ecosystem services. To avoid depleting this resource, we need to be able to identify those soils that are being managed unsustainably. We report on a science program that is developing indicators for monitoring soil quality and incorporating these indicators into decision support software to assist land managers and administrators identify at-risk soils, and provide information for state of the environment reporting. The research comprised (a) identifying those soil measurements that best discriminated changes in soil quality and reducing these through principal component analysis, (b) statistically analysing soil databases for comparative purposes and (c) designing an interactive tool which integrates soil quality expertise with a readily interpretable assessment of the quality of a soil sample.