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Degradation of water quality is a major issue in New Zealand, to which the loss of nitrogen, phosphorus and sediment from agriculture into waterways contributes significantly. To predict and manage diffuse pollution from intensive agriculture it is vital that models are able to spatially map the sources, flows and sinks of nutrients in the landscape and spatially target mitigations. This study investigates the application of one such model, the Land Utilisation Capability Indicator (LUCI). Used in conjunction with OVERSEER, LUCI is a powerful tool to support farm scale land management decision-making.  LUCI includes soil, topography and landcover datasets in its analysis. This thesis examines how the quality and resolution of each dataset affects LUCI’s output. Six different case studies are examined, across a range of New Zealand farming systems. This is the most comprehensive study, to date, of LUCI’s sensitivity to input datasets.  The results suggest that LUCI nutrient loading estimates are primarily sensitive to soil order, and therefore to changes in order classifications. Utilising different soil datasets in the LUCI model resulted in varying nutrient load predictions. This sensitivity is primarily attributed to the differing hydraulic and phosphorus retention capabilities of the respective soil orders. To test the sensitivity of LUCI to digital elevation model (DEM) resolution, multiple DEMs with varying spatial and vertical resolution were tested. These results strongly indicate that particularly fine resolution DEMs are required to accurately model flat landscapes.  It was recognised that LUCI was not using all of the relevant data available in Landcare Research’s S-Map database. LUCI was modified to use more of this information, and alternative methods of incorporating sibling level data in both LUCI and OVERSEER were investigated. Finally, avenues for future development are suggested. Overall, this thesis highlights the potential LUCI has to play a key role in farm scale environmental management.

Evaluating the sensitivity of the LUCI model to GIS datasets to enable robust farm management decisions in New Zealand