Predicting nitrogen dynamics in a dairy farming catchment using systems synthesis modelling
The intensity of milk production from pasture based dairy farming systems is often related to nitrogen (N) losses. This paper investigates dairy farming systems in the Scotts Creek catchment, an intensive farming region in south-eastern Australia, to assess aspects of productive potential and environmental footprint as a result of nutrient loss. A detailed survey of farming systems was coupled with regional data and a dairy farming systems simulation model (DairyMod) to better understand the N flows in farming systems over a range of different input and production intensities. Simulations of representative farming systems – farming system typologies – were run for 100years to provide information on the consistency and proportion of N loss in various pathways over a range of different weather and soil moisture conditions. The typologies covered a very wide range in intensity of stocking rate, inputs (e.g. concentrates, fertiliser). In general N transformations, plant and animal production and the efficiency of conversion of N into animal and plant products were related to the level of N inputs of the farming systems and milk production, or intensity. The long-term average total predicted N loss (via leaching, denitrification and volatilisation) ranged from 24(±6) to 227(±73)kgN/ha/yr and increased relative to the intensity of milk production (L/ha). Over the long term, annual average N losses by leaching ranged from 4 to 143kgN/ha/yr, volatilisation from 17 to 66kgN/ha/yr and denitrification from 2 to 18kgN/ha/yr. In an average system there were similar amounts of N exported from the farming system by leaching (44% of total N), as lost through volatilisation (47%), whereas losses by denitrification were comparatively minor (9%). The inputs were principally related to a potential milk production capacity of the farming system which was not wholly related to stocking rate. The efficiency of N utilisation for milk synthesis (L milk/kgN-consumed) increased with system intensity, however the relative loss (gN loss/kg milk N) also increased. With the current farming system typologies, improvements in animal efficiency due to intensification appear to be offset by the capacity of the system to retain surplus N. In order to meet regional production targets, there are various potential ways to further intensify the current farming systems. However the implications of achieving such targets on N losses (via leaching, denitrification and volatilisation) are important when considering the environmental implications of further intensification.
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- Chapman, D.F. & Kenny, S.N. & Beca, D. & Johnson, I.R., 2008. "Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 1. Physical production and economic performance," Agricultural Systems, Elsevier, vol. 97(3), pages 108-125, June.
- Chapman, D.F. & Kenny, S.N. & Beca, D. & Johnson, I.R., 2008. "Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 2. Inter-annual variation in forage supply, and business risk," Agricultural Systems, Elsevier, vol. 97(3), pages 126-138, June.
- Bryant, J.R. & Snow, V.O. & Cichota, R. & Jolly, B.H., 2011. "The effect of situational variability in climate and soil, choice of animal type and N fertilisation level on nitrogen leaching from pastoral farming systems around Lake Taupo, New Zealand," Agricultural Systems, Elsevier, vol. 104(3), pages 271-280, March.
- Wilcock, Robert J. & Monaghan, Ross M. & Thorrold, Bruce S. & Meredith, Adrian S. & Betteridge, Keith & Duncan, Maurice J., 2007. "Land-water interactions in five contrasting dairying catchments: issues and solutions," Land Use and Water Resources Research, University of Newcastle upon Tyne, Centre for Land Use and Water Resources Research, vol. 7.
- Saam, H. & Mark Powell, J. & Jackson-Smith, Douglas B. & Bland, William L. & Posner, Joshua L., 2005. "Use of animal density to estimate manure nutrient recycling ability of Wisconsin dairy farms," Agricultural Systems, Elsevier, vol. 84(3), pages 343-357, June.
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