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Soil water is the single most important resource for pasture and crop production in New Zealand farms. Because soil water is difficult to measure, however, the ability to predict soil water status from daily weather data is valuable, and has application for on-farm irrigation, stocking, and supplementation decisions. In this paper a practical water balance model is presented. The model uses daily rainfall and potential evapotranspiration (PET) estimates to predict changes in the water content in two overlapping soil zones: a rapidly recharged (and depleted) zone of unspecified depth, and the total plant rooting zone. The use of two zones improves predictions of actual evapotranspiration and plant stress compared with models that use only one zone. An important factor determining the success of soil water models is the ability to predict actual evapotranspiration, AET. In this model actual evapotranspiration, AET, is calculated as the lesser A00039 Received 4 August 2000; accepted II December 2000of potential evapotranspiration, PET, and total readily available water (RAW) per day. RAW is defined as all of the water in the rapidly recharged surface zone plus a proportion of the water in the remainder of the soil profile. By validation against 11 historical data sets, the model is shown to give accurate predictions of soil water deficit across a range of New Zealand flat-land pastoral soils. The model parameters can be easily estimated from commonly available soil properties (soil order classification, and available water holding capacity) without the need for additional site-specific calibration. This model provides an easily used, practical decision tool for the management of drought, allowing early prediction of decline in pasture growth and estimates of required irrigation.

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Pressure on New Zealand's largely pasture-based dairy industry has grown with a drive to increase production, expansion into new regions and demand for farmers to mitigate environmental impacts e.g., leaching of excess urinary nitrogen. A 3-year trial in the Waikato investigating the use of mixed pasture (e.g. perennial ryegrass, white clover, prairie grass, lucerne, chicory and plantain) showed similar annual dry matter (DM) production to standard pasture (perennial ryegrass and white clover) with greater yields of mixed pasture during summer (December, January, February) when lucerne and chicory grew better than perennial ryegrass in the warm, dry conditions. However, this yield advantage did not persist during the winter (June, July, August). Milk yields from cows grazing the mixed and standard pasture were similar. The mixed pasture retained a high level of species diversity and, while a single "magic bullet" is an unlikely solution to the challenges facing dairy farmers, increased species diversity could reduce risks and increase pasture stability. Keywords: pasture species diversity, dry matter yield, milk, nitrogen

Simulation modelling is a methodology that appears highly suitable for use in farming systems innovation. However, if the aim is to improve farming systems by supporting farmer behaviour change, model-based approaches seem to have delivered surprisingly little observable benefit to date. This paper identifies the problems underlying this apparent lack of impact, and proposes better approaches to improve on-farm benefits from farming systems modelling. A key principle that has been neglected in farm simulation modelling is intimate involvement of clients (defined as "the individuals or groups whose approval is needed for change to be implemented") throughout the innovation process. We argue that client participation is essential in the problem definition, model design and testing and policy design and evaluation phases of model-based research projects. The role of a simulation model within the innovation process, then, is to be a jointlycreated "virtual world" wherein experiments may be conducted to facilitate learning about the relevant system. We argue that whole farm simulation models that use decision rules to specify alternative farm management strategies are the best available form of virtual world models of farming systems. Besides appropriate client input, high quality models require excellent software development practices and strenuous attention to building user confidence. The latter should include analysing the model to assess its stability and sensitivity properties, before using it to simulate experiments that compare several management alternatives under a range of environmental and local conditions. This approach allows estimation of the variations in farm system performance that are likely to result from interactions between initial farm state, farm management policy and future weather and markets. On the other hand, using simulation models to discover "optimal" farm systems would usually be inappropriate due to the complexity arising from multiple-stakeholder views, multiple-criteria, and the dynamic nature of farming systems problems. An improved systems modelling methodology is proposed that should be better able to provide benefits into farming practice.