Assessment of nitrogen (N) loss forms and pathways from farming systems is important for improving our understanding of potential off-farm impacts on high value environmental assets. The objective of this study was to predict N losses in different pathways in dairy systems across the range of climate, soil and farm management found in west Gippsland (Victoria, Australia), and to characterise the sensitivity of the adopted predictive model parameters. To estimate the nutrient loss pathways from dairy systems this study combined the point scale models DairyMod and Howleaky to estimate Dissolved N (DN) and Particulate N (PN) losses in runoff, and N leaching (LN) in deep drainage from representative dairy farms covering a range of climate, soil and farm management practices in west Gippsland, Victoria, Australia. Monte Carlo error propagation with Latin hypercube sampling was performed to identify sensitive model parameters and assess uncertainty in N loss predictions. The combined model was capable of simulating climate-soil-animal-pasture management interactions and estimating DN, PN and LN at an annual scale. Estimated mean annual N losses from the representative dairy farms were up to 312 kg-N ha-1 as LN, 18 kg-N ha-1 as DN and 15 kg-N ha-1 as PN. Soil type and farm management explained much of the variability (up to 76%) observed in LN and DN losses, whereas climate and soil type had significant influence on PN losses (62-77%). Compared to other N fluxes, the DN was poorly explained by the climate, soil and farm management differences. Year to year variation, particularly under dry conditions had a marked influence on N losses. Due to high sensitivity, soil N content, vegetation cover, rooting depth and soil maximum drainage rate must be well characterised in order to reduce potentially high uncertainty in the estimation of N losses in heterogeneous catchments.

THAYALAKUMARAN Thabo;  ROBERTS Anna M.;  BEVERLY Craig;  VIGIAK Olga;  SORN Norng;  STOTT Kerry; 

2016-09-23

ELSEVIER SCIENCE BV

JRC98290

0378-3774,   

http://www.sciencedirect.com/science/article/pii/S0378377416303407,    https://publications.jrc.ec.europa.eu/repository/handle/JRC98290,   

10.1016/j.agwat.2016.09.008,