Km Christie

Potential of deficit irrigation to increase marginal irrigation response of perennial ryegrass (Lolium perenne L.) on Tasmanian dairy farms

In the cool temperate dairy regions of Tasmania, there is heavy reliance on irrigation to maximise pasture performance by ensuring that plants do not suffer water stress. Consequently, irrigation water has often been applied at a greater amount than plant water requirements, resulting in low efficiencies. An irrigation experiment was undertaken in north-western Tasmania between October 2007 and April 2008, examining the effect of deficit irrigation treatments on pasture growth and water-use efficiency. A rainfall deficit (potential evapotranspiration minus rainfall) of 20 mm was implemented to schedule irrigation, at which point 20, 16, 12, 8, or 0 mm of irrigation water was applied, referred to as treatments I100%, I80%, I60%, I40%, and I0%, respectively. The trial was a randomised complete block design with 4 replications. There were 21 irrigation events between October and April. The experimental area was grazed by 60 Holstein Friesian heifers at a grazing interval coinciding with emergence of 2.5–3.0 new ryegrass leaves/tiller of the I100% treatment. Cumulative pasture consumption for the irrigated period was 9.2, 8.9, 7.6, 6.9, and 3.7 t dry matter (DM)/ha for the I100%, I80%, I60%, I40%, and I0% treatments, respectively. The resulting marginal irrigation water-use index (MIWUI; marginal production due to irrigation) was 1.29, 1.54, 1.55, and 1.87 t DM/ML, for the I100%, I80%, I60%, and I40% treatments, respectively. The results of this study were modelled using the biophysical model DairyMod, with strong agreement between observed and modelled data. DairyMod was then used to simulate the MIWUI for 5 differing dairy regions of Tasmania using 40 years of climatic data (1968–2007) under 3 differing nitrogen management strategies by the 5 irrigation treatments. The modelling indicated that a MIWUI greater than 2 t DM/ML can be achieved in all regions. The current study has shown that the opportunity exists for irrigated pastoral systems to better manage an increasingly scarce resource and substantially improve responses to irrigation.

Whole-farm systems analysis of Australian dairy farm greenhouse gas emissions

The Australian dairy industry contributes ~1.6% of the nation’s greenhouse gas (GHG) emissions, emitting an estimated 9.3 million tonnes of carbon dioxide equivalents (CO2e) per annum. This study examined 41 contrasting Australian dairy farms for their GHG emissions using the Dairy Greenhouse Gas Abatement Strategies calculator, which incorporates Intergovernmental Panel on Climate Change and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions included were pre-farm embedded emissions associated with key farm inputs (i.e. grains and concentrates, forages and fertilisers), CO2 emissions from electricity and fuel consumption, methane emissions from enteric fermentation and animal waste management, and nitrous oxide emissions from animal waste management and nitrogen fertilisers. The estimated mean (±s.d.) GHG emissions intensity was 1.04 ± 0.17 kg CO2 equivalents/kg of fat and protein-corrected milk (kg CO2e/kg FPCM). Enteric methane emissions were found to be approximately half of total farm emissions. Linear regression analysis showed that 95% of the variation in total farm GHG emissions could be explained by annual milk production. While the results of this study suggest that milk production alone could be a suitable surrogate for estimating GHG emissions for national inventory purposes, the GHG emissions intensity of milk production, on an individual farm basis, was shown to vary by over 100% (0.76–1.68 kg CO2e/kg FPCM). It is clear that using a single emissions factor, such as milk production alone, to estimate any given individual farm’s GHG emissions, has the potential to either substantially under- or overestimate individual farms’ GHG emissions.

Modelling pasture management and livestock genotype interventions to improve whole-farm productivity and reduce greenhouse gas emissions intensities

Livestock greenhouse gas (GHG) emissions form the largest proportion of emissions from agriculture. Here we seek intervention strategies for sustainably intensifying the productivity of prime lamb enterprises without increasing net farm emissions. We apply a biophysical model and an emissions calculator to determine the implications of several interventions to a prime lamb farm in south-eastern Australia. We examine the effects of lamb liveweight or age at sale, weaning rate, maiden ewe joining age, genetic feed-use efficiency, supplementary grain feeding according to green pasture availability, soil fertility and botanical composition. For each intervention, stocking rates were optimised to the lesser of a minimum ground cover threshold or a maximum supplementary grain feeding threshold. Total animal production of the baseline farm was 478 kg clean fleece weight plus liveweight (CFW+LWT)/ha.annum and ranged from 166 to 609 kg CFW+LWT/ha.annum for interventions that replaced existing pastures with annual ryegrass or increased soil fertility respectively. Annual GHG emissions intensity of the baseline farm was 8.7 kg CO2-e/kg CFW+LWT and varied between 7.7 and 9.2 kg CO2-e/kg CFW+LWT for interventions that reduced maiden ewe joining age or increased sale liveweight, respectively. Stocking rate primarily governed total animal production, and in many cases production drove emissions, so interventions that increased production did not always reduce emissions intensity. Indeed, replacing existing perennial ryegrass/subterranean clover mixed pastures with perennial legume swards caused large reductions in both production and emissions, and interventions that increased soil fertility via phosphate addition caused large increases in production and emissions; as a consequence, both strategies had little effect on emissions intensity. Implementing several beneficial interventions simultaneously further increased production and reduced emissions intensity relative to implementing individual interventions alone. Baseline production increased by 61% by increasing soil fertility, improving feed-use efficiency and reducing the joining age of maiden ewes, while baseline emissions intensity was reduced by 17% by improving feed use efficiency, reducing the joining age of maiden ewes and supplementary grain feeding. We demonstrate that imposing several strategies on existing sheep farming systems simultaneously is more conducive to sustainable agricultural intensification than is imposing any single intervention alone, provided individual strategies were beneficial in their own right. The best strategies for both sustainably increasing production and reducing emissions intensity are those that decouple the linkage between production and emissions such as interventions that shift the balance of the flock away from adults and towards juveniles while holding average annual stocking rates constant.

Using a modelling approach to evaluate two options for improving animal nitrogen use efficiency and reducing nitrous oxide emissions on dairy farms in southern Australia

Ruminant livestock are generally considered inefficient converters of dietary nitrogen (N) into animal product. Animal nitrogen use efficiency (NUE) is a measure of the relative transformation of feed N into product and in dairy systems this is often expressed as milk N per unit of N intake (g milk N/100 g N intake). This study was a theoretical exercise to explore the relative potential efficacy and value proposition of breeding versus feeding to improve NUE, reduce urinary N excretion and associated environmental impact in pasture-based dairy systems. The biophysical whole farm systems model DairyMod was used across three dairying regions of south-eastern Australia representing a high-rainfall cool temperate climate (HRCT), a high-rainfall temperate climate (HRT) and a medium-rainfall temperate climate (MRT) to examine the two theoretical approaches of (1) maintaining the same amount of N exported in milk from a reduced N intake; and (2) increasing the amount of N exported in milk for the same amount of dietary N intake. Sixteen scenarios were explored for each site; these include four supplementary feed N (SN) concentrations (ranging from 1% to 4% N) combined with four milk N (MN) concentrations (ranging from 0.50% to 0.65% N). Reducing the SN concentration from 4% to 1% increased the 30-year mean model-predicted NUEs from ~16 g milk N/100 g N intake at all three sites to between 23 and 28 g milk N/100 g N intake, with the least and greatest improvements in NUE occurring for the HRCT and MRT sites, respectively. Corresponding to this improved NUE through reduced SN concentrations, model-predicted N2O emissions declined from 3.0 to 1.3 t carbon dioxide equivalents (CO2-e)/ha.annum for the HRCT site, from 4.2 to 2.1 t CO2-e/ha.annum for the HRT site and from 4.4 to 2.1 t CO2-e/ha.annum for the MRT site, representing a decline of between 50% and 57%. In contrast, increasing the MN concentration from 0.50% to 0.65% increased the 30-year mean model-predicted NUEs from 17 to 22 g milk N/100 g N intake for the HRCT site, from 18 to 23 g milk N/100 g N intake for the HRT site and from 18 to 24 g milk N/100 g N intake for the MRT site. Corresponding to the improved NUE through increased MN concentrations, model-predicted N2O emissions declined from 2.3 to 2.0 t CO2-e/ha.annum for the HRCT site, from 3.3 to 3.1 t CO2-e/ha.annum for the HRT site and from 3.4 to 3.2 t CO2-e/ha.annum for the MRT site; representing a decline of between 7% and 11%. These results suggest that improving animal NUE to reduce associated N2O losses holds much more promise if achieved through a reduction in the amount of N in supplementary feed than through increasing N exported in milk. This is an important finding for the Australian dairy industry, since manipulation of dietary N to better balance the energy to protein ratio would be much easier to implement than manipulation of N concentration in milk through genetics.

Use of modelling to identify perennial ryegrass plant traits for future warmer and drier climates

Abstract. Potential exists to select pasture species better adapted to anticipated warmer temperatures and lower rainfall, associated with increasing atmospheric carbon dioxide (CO2) and other greenhouse gas concentrations, to maximise pasture yields and persistence. This study assessed the effect of increasing three plant traits in perennial ryegrass (Lolium perenne L.) to adapt to future climates: root depth; heat tolerance, defined as the ability of plant to grow at high temperatures; and responsiveness to elevated CO2 concentrations. Pasture production was simulated using the Sustainable Grazing Systems Pasture model at three sites with temperate climates in south-eastern Australia: Hamilton, Victoria (medium rainfall); Ellinbank, Victoria (high rainfall); and Elliott, Tasmania (high rainfall). Two future climate scenarios were created at each site by scaling the historical climate (1971–2010) by +1°C with –10% rain (435 ppm CO2) and +2°C with –20% rain (535 ppm CO2). A genotype × environment interaction suggested that the plants traits most effective at increasing pasture yield differed depending on the local climate. Increased root depth was the most effective change in a single trait that increased pasture harvested at Elliott, increased heat tolerance was most effective at Ellinbank, whereas increasing all three individual traits was similarly effective at Hamilton. At each site, the most effective traits increased pasture growth during the period between late spring and mid-summer compared with the current cultivar. When all three traits were increased at the same time, the pasture production advantage was greater than the additive effects of changing single traits at Hamilton and Ellinbank. Further consideration of the feasibility of selecting multiple traits and the effects of a broader range of climate projections is required. Nonetheless, results of this study provide guidance to plant breeders for selection of traits adapted to future climates.

Assessing the reliability of dynamical and historical climate forecasts in simulating hindcast pasture growth rates

A priori knowledge of seasonal pasture growth rates helps livestock farmers plan with pasture supply and feed budgeting. Longer forecasts may allow managers more lead time, yet inaccurate forecasts could lead to counterproductive decisions and foregone income. By using climate forecasts generated from historical archives or the global circulation model (GCM) called the Predictive Ocean Atmosphere Model for Australia (POAMA), we simulated pasture growth rates in a whole-farm model and compared growth-rate forecasts with growth-rate hindcasts (viz. retrospective forecasts). Hindcast pasture growth rates were generated using posterior weather data measured at two sites in north-western Tasmania, Australia. Forecasts were made on a monthly basis for durations of 30, 60 and 90 days. Across sites, forecasting approaches and durations, there were no significant differences between simulated growth-rate forecasts and hindcasts when our statistical inference was conducted using either the Kolmogorov–Smirnov statistic or empirical cumulative distribution functions. However, given that both of these tests were calculated by comparing growth-rate hindcasts with monthly distributions of forecasts, we also examined linear correlations between monthly hindcast values and median monthly growth-rate forecasts. Using this approach, we found a higher correlation between hindcasts and median monthly forecasts for 30 days than for 60 or 90 days, suggesting that monthly growth-rate forecasts provide more skilful predictions than forecast durations of 2 or 3 months. The range in monthly growth-rate forecasts at 30 days was less than that at 60 or 90 days, further reinfocing the aforementioned result. The strength of the correlation between growth-rate hindcasts and median monthly forecasts from the historical approach was similar to that generated using POAMA data. Overall, the present study found that (1) statistical methods of comparing forecast data with hindcast data are important, particularly if the former is a distribution whereas the latter is a single value, (2) 1-month growth-rate forecasts have less uncertainty than forecast durations of 2 or 3 months, and (3) there is little difference between pasture growth rates simulated using climate data from either historical records or from GCMs. To test the generality of these conclusions, the study should be extended to other dairy regions. Including more regions would both enable studies of sites with greater intra-seasonal climate variability, but also better highlight the impact of seasonal and regional variation in forecast skill of POAMA as applied in our forecasting methods.

Modelling enteric methane abatement from earlier mating of dairy heifers in subtropical Australia by improving diet quality

Milking cows typically dominate dairy farm greenhouse gas (GHG) emissions, but replacement heifers also contribute to farm emissions and can increase the emission intensity of milk production. In northern Australia, heifers generally graze poorer-quality subtropical pastures and in the absence of energy-dense supplementary feed during periods of low pasture growth, liveweight (LW) gain can be restricted. This modelling study examined the time required and enteric methane (CH4) emissions produced in raising dairy heifers to a target LW for first mating by feeding a diet assuming either constant (static) or variable (dynamic) nutritive values. Using a static approach (Australian Feeding Standards methodology), and assuming a target mating LW of 360 kg, growing heifers reached their target LW at ~18 months of age while consuming C4 grasses with a constant metabolisable energy content of 9.5 MJ/kg dry matter (DM) or 11 months of age on a diet of 11.0 MJ/kg DM. Enteric CH4 emissions were 1.2 and 0.8 t of carbon dioxide equivalents/heifer over the 18- and 11-month periods, respectively. To explore the extent with which climatic conditions influence seasonal pasture availability and nutritive value with a dynamic approach, we used a whole-farm biophysical model (SGS pasture model) to simulate diets with mean metabolisable energy values of 9.5 and 10.9 MJ/ kg DM. On average (±s.d.), heifers required 22 ± 4 and 17 ± 1 months, respectively, to reach target LW, with cumulative enteric CH4 emissions of 1.22 ± 0.20 and 0.72 ± 0.04 t carbon dioxide equivalents, respectively. The dynamic approach resulted in slower LW gain due to the variable nutritive value of the diet throughout the year, resulting in seasonal periods of LW plateauing or decline. Maintaining heifers on high-quality diets in subtropical northern Australia should result in increased daily LW gain, lower enteric CH4 emissions to mating LW and earlier calving. Together, these factors reduce their lifetime emission intensity of milk production.

A review of whole farm-system analysis in evaluating greenhouse-gas mitigation strategies from livestock production systems

Recognition is increasingly given to the need of improving agricultural production and efficiency to meet growing global food demand, while minimising environmental impacts. Livestock forms an important component of global food production and is a significant contributor to anthropogenic greenhouse-gas (GHG) emissions. As such, livestock production systems (LPS) are coming under increasing pressure to lower their emissions. In developed countries, LPS have been gradually reducing their emissions per unit of product (emissions intensity; EI) over time through improvements in production efficiency. However, the global challenge of reducing net emissions (NE) from livestock requires that the rate of decline in EI surpasses the productivity increases required to satisfy global food demand. Mechanistic and dynamic whole farm-system models can be used to estimate farm-gate GHG emissions and to quantify the likely changes in farm NE, EI, farm productivity and farm profitability as a result of applying various mitigation strategies. Such models are also used to understand the complex interactions at the farm-system level and to account for how component mitigation strategies perform within the complexity of these interactions, which is often overlooked when GHG mitigation research is performed only at the component level. The results of such analyses can be used in extension activities and to encourage adoption, increase awareness and in assisting policy makers. The present paper reviews how whole farm-system modelling has been used to assess GHG mitigation strategies, and the importance of understanding metrics and allocation approaches when assessing GHG emissions from LPS.

Revised greenhouse-gas emissions from Australian dairy farms following application of updated methodology

Every year since 1990, the Australian Federal Government has estimated national greenhouse-gas (GHG) emissions to meet Australia’s reporting commitments under the United National Framework Convention on Climate Change (UNFCCC). The National Greenhouse Gas Inventory (NGGI) methodology used to estimate Australia’s GHG emissions has altered over time, as new research data have been used to improve the inventory emission factors and algorithms, with the latest change occurring in 2015 for the 2013 reporting year. As measuring the GHG emissions on farm is expensive and time-consuming, the dairy industry is reliant on estimating emissions using tools such as the Australian Dairy Carbon Calculator (ADCC). The present study compared the emission profiles of 41 Australian dairy farms with ADCC using the old (pre-2015) and new (post-2015) NGGI methodologies to examine the impact of the changes on the emission intensity across a range of dairy-farm systems. The estimated mean (±s.d.) GHG emission intensity increased by 3.0%, to 1.07 (±0.02) kg of carbon dioxide equivalents per kilogram of fat-and-protein-corrected milk (kg CO2e/kg FPCM). When comparing the emission intensity between the old and new NGGI methodologies at a regional level, the change in emission intensity varied between a 4.6% decrease and 10.4% increase, depending on the region. When comparing the source of emissions between old and new NGGI methodologies across the whole dataset, methane emissions from enteric fermentation and waste management both increased, while nitrous oxide emissions from waste management and nitrogen fertiliser management, CO2 emissions from energy consumption and pre-farm gate (supplementary feed and fertilisers) emissions all declined. Enteric methane remains a high source of emissions and so will remain a focus for mitigation research. However, these changes to the NGGI methodology have highlighted a new ‘hotspot’ in methane from manure management. Researchers and farm managers will have greater need to identify and implement practices on-farm to reduce methane losses to the environment.