No abstract

Quantifying and managing diffuse P losses from small catchments or at the farm scale requires detailed knowledge of farming practices and their interaction with catchment processes. However, detailed knowledge may not be available and hence modeling is required. This paper demonstrates two approaches to developing tools that assist P losses from New Zealand or Australian dairy farms. The first is largely empirical and separates sources of P within a paddock into soil, fertilizer, dung, and treading impacts (including damage to grazed pasture). This information is combined with expert knowledge of hydrological processes and potential point sources (e.g., stream crossings) to create a deterministic model that can be used to evaluate the most cost and labor efficient method of mitigating P losses. For instance, in one example, 45% of annual P lost was attributed to the application of superphosphate just before a runoff event for which a mitigation strategy could be to use a less water soluble P fertilizer. The second approach uses a combination of interviews, expert knowledge and relationships to develop a Bayesian Network that describes P exports. The knowledge integration process helped stakeholders develop a comprehensive understanding of the problem. The Network, presented in the form of a "cause and effect", diagram provided a simple, visual representation of current knowledge that could be easily applied to individual circumstances and isolate factors having the greatest influence on P loss. Both approaches demonstrate that modeling P losses and mitigation strategies does not have to cover every process or permutation and that a degree of uncertainty can be handled to create a working model of P losses at a farm or small catchment scale.