Andrew P. Smith

Farm-scale nitrogen, phosphorus, potassium and sulfur balances and use efficiencies on Australian dairy farms

Efficient and effective nutrient management decisions are critical to profitable and sustainable milk production on modern Australian dairy farms. Whole-farm nutrient balances are commonly used as nutrient management tools and also for regulatory assessment on dairy farms internationally, but are rarely used in Australia. In this study, nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) imports and exports were measured during a standardised production year on 41 contrasting Australian dairy farms, representing a broad range of geographic locations, milk production, herd and farm size, reliance on irrigation, and soil types. The quantity of nutrients imported varied markedly – with feed and fertiliser generally the most substantial imports – and were principally determined by stocking rate and type of imported feed. Milk exports were the largest source of nutrient exports. Nitrogen balance ranged from 47 to 601 kg N/ha.year. Nitrogen-use efficiency ranged from 14 to 50%, with a median value of 26%. Phosphorus balance ranged from –7 to 133 kg P/ha.year, with a median value of 28 kg P/ha. Phosphorus-use efficiencies ranged from 6 to 158%, with a median value of 35%. Potassium balances ranged from 13 to 452 kg K/ha, with a median value of 74 kg K/ha; K-use efficiency ranged from 9 to 48%, with a median value of 20%. Sulfur balances ranged from –1 to 184 kg S/ha, with a median value of 27 kg S/ha; S-use efficiency ranged from 6 to 110%, with a median value of 21%. Nitrogen, P, K and S balances were all positively correlated (P < 0.001) with stocking rate and milk production per ha. Poor relationship between P, K and S fertiliser inputs and milk production from home-grown pasture reflected the already high soil fertility levels measured on many of these farms. The results from this study demonstrate that increasing milk production per ha will be associated with greater nutrient surpluses at the farm scale, with the potential for greater environmental impacts. We suggest that simplified and standardised nutrient balance methodologies should be used on dairy farms in Australia to help identify opportunities for improvements in nutrient management decisions and to develop appropriate industry benchmarks and targets.

Nitrate leaching from temperate perennial pastures grazed by dairy cows in south-eastern Australia

Nitrate (NO3-N) leaching losses were measured over 3 years from a temperate grass/clover pasture with and without 200 kg N fertiliser/ha, applied as ammonium nitrate or urea, using a system of moles and tile drains. Fertiliser was applied in 4 split dressings of 50 kg N/ha in each of the 4 seasons of each year. Drainage was collected continuously and NO3-N concentrations in drainage water were measured in subsamples collected using a flow-proportioned sampler. Pastures were rotationally grazed with dairy cows at stocking rates equivalent to 1.9 or 2.8 cows/ha for the unfertilised and fertilised treatments, respectively. Soil water deficit (SWD) varied markedly between seasons and years, with drainage occurring in the cooler, wetter months (April–October) and not at all through the summer. There were no significant differences between treatments in SWD, drainage events, or drainage volumes. Peak NO3-N concentrations were 19, 50, and 17 mg/L for the control, ammonium nitrate, and urea treatments, respectively. Mean annual flow-weighted NO3-N concentrations over the 3 years were 1.7 and 2.2 times higher from the ammonium nitrate treatment than from the urea and control treatments, respectively. Annual NO3-N leaching loads (kg N/ha) were 3.7–14.6 from the control treatment, 6.2– 22.0 from the urea treatment, and 4.3–37.6 from the ammonium nitrate treatment, for the lowest and highest drainage years, respectively. The experiment confirmed that the application of N fertiliser prior to periods of substantial drainage can result in high losses of NO3-N through leaching. More efficient and environmentally sound use of N fertiliser can be achieved by not combining high N fertiliser rates, high stocking intensity, and nitrate-containing fertilisers prior to periods when there is a risk of substantial drainage occurring.

The impact of extreme climatic events on pasture-based dairy systems: a review

Abstract. Extreme climatic events such as heat waves, extreme rainfall and prolonged dry periods are a significant challenge to the productivity and profitability of dairy systems. Despite projections of more frequent extreme events, increasing temperatures and reduced precipitation, studies on the impact of these extreme climatic events on pasture-based dairy systems remain uncommon. The Intergovernmental Panel on Climate Change has estimated Australia to be one of the most negatively impacted regions with additional studies estimating Australian production losses of around 16% in the agricultural sector and 9–19% between the present and 2050 in the south-eastern dairy regions of Australia due to climate change. Here we review the literature on the impact of climate change on pasture-based dairy systems with particular focus on extreme climatic events. We provide an insight into current methods for assessing and quantifying heat stress highlighting the impacts on pastures and animals including the associated potential productivity losses and conclude by outlining potential adaptation strategies for improving the resilience of the whole-farm systems to climate change. Adapting milking routines, calving systems and the introduction of heat stress tolerant dairy cow breeds are some proposed strategies. Changes in pasture production would also include alternative pasture species better adapted to climate extremes such as heat waves and prolonged periods of water deficit. In order to develop effective adaptation strategies we also need to focus on issues such as water availability, animal health and associated energy costs.

Modelling of lucerne (Medicago sativa L.) for livestock production in diverse environments

Abstract. Several models exist to predict lucerne (Medicago sativa L.) dry matter production; however, most do not adequately represent the ecophysiology of the species to predict daily growth rates across the range of environments in which it is grown. Since it was developed in the late 1990s, the GRAZPLAN pasture growth model has not been updated to reflect modern genotypes and has not been widely validated across the range of climates and farming systems in which lucerne is grown in modern times. Therefore, the capacity of GRAZPLAN to predict lucerne growth and development was assessed. This was done by re-estimating values for some key parameters based on information in the scientific literature. The improved GRAZPLAN model was also assessed for its capacity to reflect differences in the growth and physiology of lucerne genotypes with different winter activity. Modifications were made to GRAZPLAN to improve its capacity to reflect changes in phenology due to environmental triggers such as short photoperiods, declining low temperatures, defoliation and water stress. Changes were also made to the parameter governing the effect of vapour pressure on the biomass-transpiration ratio and therefore biomass accumulation. Other developments included the representation of root development and partitioning of canopy structure, notably the ratio leaf : stem dry matter. Data from replicated field experiments across Australia were identified for model validation. These data were broadly representative of the range of climate zones, soil types and farming systems in which lucerne is used for livestock grazing. Validation of predicted lucerne growth rates was comprehensive owing to plentiful data. Across a range of climate zones, soils and farming systems, there was an overall improvement in the capacity to simulate pasture dry matter production, with a reduction in the mean prediction error of 0.33 and the root-mean-square deviation of 9.6 kg/ha.day. Validation of other parts of the model was restricted because information relating to plant roots, soil water, plant morphology and phenology was limited. This study has highlighted the predictive power, versatility and robust nature of GRAZPLAN to predict the growth, development and nutritive value of perennial species such as lucerne.