Reducing nutrient losses to fresh water
There are a wide range of mitigation practices currently available that can reduce the impact of intensive farming on water quality. However, each mitigation measure differs in its effectiveness, cost and likely impact on those waters. This depends on factors that include soil type, climate, topography and the regional sensitivity of water bodies. Consequently, it can be difficult for land managers to select a mitigation measure or combination of mitigation practices most appropriate for their farm.
To address this problem, Dr Ross Monaghan is leading the development of a Toolbox of Good Management Practices that provides an assessment of the cost and effectiveness of a suite of mitigation options. It also provides an indicative ranking of where expenditure should be prioritised to ensure that maximum benefit is obtained for each dollar invested. Given the very large capital costs associated with some mitigation measures, this ranking process is becoming increasingly important as farmers come under greater pressure to reduce farming footprints in nutrient-sensitive catchments.
Research by social scientists shows that providing economic information coupled with information about the effectiveness of each mitigation option is an important step in aiding the adoption of environmental technologies. This research has also shown us that land users have shown a strong preference for selecting from a range of mitigation options available to them, as opposed to more prescriptive approaches.
Some of the mitigation measures currently in the Toolbox include improved methods for applying farm dairy effluent to land, the use of restricted grazing strategies and herd shelters, riparian protection, wetlands, vegetated buffer strips and improved nutrient balances in animal diets. The algorithms behind the Toolbox are continually up-graded as new research results become available. It is anticipated that some of the functionality of the Toolbox will eventually be incorporated into future releases of the Overseer model.