Search

Livestock have long been integral to food production systems, often not by choice but by need. While our knowledge of livestock greenhouse gas (GHG) emissions mitigation has evolved, the prevailing focus has been-somewhat myopically-on technology applications associated with mitigation. Here, we (1) examine the global distribution of livestock GHG emissions, (2) explore social, economic and environmental co-benefits and trade-offs associated with mitigation interventions and (3) critique approaches for quantifying GHG emissions. This review uncovered many insights. First, while GHG emissions from ruminant livestock are greatest in low- and middle-income countries (LMIC; globally, 66% of emissions are produced by Latin America and the Caribbean, East and southeast Asia and south Asia), the majority of mitigation strategies are designed for developed countries. This serious concern is heightened by the fact that 80% of growth in global meat production over the next decade will occur in LMIC. Second, few studies concurrently assess social, economic and environmental aspects of mitigation. Of the 54 interventions reviewed, only 16 had triple-bottom line benefit with medium-high mitigation potential. Third, while efforts designed to stimulate the adoption of strategies allowing both emissions reduction (ER) and carbon sequestration (CS) would achieve the greatest net emissions mitigation, CS measures have greater potential mitigation and co-benefits. The scientific community must shift attention away from the prevailing myopic lens on carbon, towards more holistic, systems-based, multi-metric approaches that carefully consider the raison d'etre for livestock systems. Consequential life cycle assessments and systems-aligned 'socio-economic planetary boundaries' offer useful starting points that may uncover leverage points and cross-scale emergent properties. The derivation of harmonized, globally reconciled sustainability metrics requires iterative dialogue between stakeholders at all levels. Greater emphasis on the simultaneous characterization of multiple sustainability dimensions would help avoid situations where progress made in one area causes maladaptive outcomes in other areas.

Many nitrogen (N) fertilizer recommendations for grazing livestock enterprises are based on cutting experiments, excluding the influence of recycled N in excreta. Grazing experiments are expensive to conduct, and so compromise on variables such as number of N fertilizer rates, replication and number of years of investigation. Biophysical modelling provides an efficient and effective approach to address many of the complexities of field studies. Our study, using the biophysical whole-farm systems model DairyMod, examined the effect of a range of N fertilizer rates on pasture production for five dairy sites through south-eastern Australia over 18 years under both cutting and grazing regimes. The study aims were to highlight the variation in pasture N responses between cutting and grazing experiments and compare results to current best management practice (BMP) guidelines for N fertilizer management. Annual and seasonal maximum and optimum pasture production, defined as 90% of maximum production, N fertilizer rate to achieve optimum pasture production and the slope of the response rate curve between two fertilizer application rates were estimated. For all five sites, at the lower N rates, there was a divergence in annual pasture production between the grazing and cutting management regimes. However, once N was no longer limiting pasture production for the cutting regime, annual pasture production under cutting and grazing converged. For most sites and seasons, current BMPs of applying between 20 and 50 kg N ha−1 post grazing will ensure efficient use of N applied, assuming soil moisture is not first limiting growth. However, this study has refined these recommendations across all sites and seasons. For some seasons and sites, there was high variability in pasture N response rate between years that need to be taken into consideration. At Elliott in Tasmania, an irrigated site, there was merit in increasing N fertilizer rates above the current recommendation above 50 kg N ha−1 post grazing during spring and summer. In contrast, at the rainfed sites of Ellinbank and Terang in Victoria, the recommendation would be to not apply N fertilizer during autumn and only in selected wetter summers.

Evidence from farm level studies indicates that there is potential to improve nitrogen (N) use efficiency of the predominately pasture-based dairy farms in Australia. This is possible via several ways which includes modifying the timing and rates of N fertiliser applied to pasture. Traditionally fertiliser strategies have been based on a “recipe” approach where N fertiliser, primarily urea, is applied a set rate following grazing. The aim of this study was to compare the pasture dry matter response, N loss and response rate of fertiliser strategies which used increasing knowledge of plant and soil conditions in different ways. The study was conducted under grazing conditions using the biophysical model, DairyMod and repeated at several locations and farming systems in the dairy regions of Australia. In comparison to set rates this study showed that strategic approaches to N fertiliser have the potential to be more efficient in N use and lower both N inputs and N losses with little impact of pasture production. This was evident across all seasons and locations studied. Strategies that used the plant N status to trigger fertiliser timing and rates were more efficient and had lower environmental N losses than those that used fixed rates or soil N information. Fertilising per plant N requirements was the most efficient – and therefore should be the priority for development – particularly in view of the greater expense of fertilisers that are slow release. Precision fertiliser management strategies have the value in terms of reducing fertiliser use and loss during autumn and to a lesser extent in summer, with the least value in winter. However, for the strategies to be properly evaluated for pasture based dairy farms with grazing, a whole farm analysis needs to be conducted that incorporates other sources of feed. This is a necessary inclusion in any subsequent studies.

Background Soil N mineralisation is the process by which organic N is converted into plant-available forms, while soil N immobilisation is the transformation of inorganic soil N into organic matter and microbial biomass, thereafter becoming bio-unavailable to plants. Mechanistic models can be used to explore the contribution of mineralised or immobilised N to pasture growth through simulation of plant, soil and environment interactions driven by management. Purpose Our objectives were (1) to compare the performance of three agro-ecosystems models (APSIM, DayCent and DairyMod) in simulating soil N, pasture biomass and soil water using the same experimental data in three diverse environments (2), to determine if tactical application of N fertiliser in different seasons could be used to leverage seasonal trends in N mineralisation to influence pasture growth and (3), to explore the sensitivity of N mineralisation to changes in N fertilisation, cutting frequency and irrigation rate. Key results Despite considerable variation in model sophistication, no model consistently outperformed the other models with respect to simulation of soil N, shoot biomass or soil water. Differences in the accuracy of simulated soil NH4 and NO3 were greater between sites than between models and overall, all models simulated cumulative N2O well. While tactical N application had immediate effects on NO3, NH4, N mineralisation and pasture growth, no long-term relationship between mineralisation and pasture growth could be discerned. It was also shown that N mineralisation of DayCent was more sensitive to N fertiliser and cutting frequency compared with the other models. Major conclusions Our results suggest that while superfluous N fertilisation generally stimulates immobilisation and a pulse of N2O emissions, subsequent effects through N mineralisation/immobilisation effects on pasture growth are variable. We suggest that further controlled environment soil incubation research may help separate successive and overlapping cycles of mineralisation and immobilisation that make it difficult to diagnose long-term implications for (and associations with) pasture growth.

Context A key pathway for nitrogen (N) loss, which is poorly quantified in livestock farming systems with grazing, is the ammonia (NH3) volatilisation that occurs from fertilisers, soils, fresh and stored animal excreta. Objective This study used farm systems modelling to understand, the likely losses of N via NH3 volatilisation from different farming systems in diverse locations throughout the geographical extent of the dairy industry in Australia. Methods The systems model DairyMod was used to simulate daily water, energy and N cycles in representative farming systems. DairyMod simulated the flows of N through soils, pastures, feed, animals throughout the whole farm system in order to sensibly simulate the volatilisation from fertilisers, soil and animal excreta across 18 locations. Results and conclusions Average annual NH3volatilisation for the years 2000–2019 was 40 kg N/ha but varied greatly from 10 to 97 kg N/ha depending on year, location and farming system. Across all locations, volatilisation was higher in Spring > Winter > Autumn > Summer. Across the 18 locations annual volatilisation was, on average, 51% greater from the livestock excreta source than from the fertiliser N source, which was 22% greater than from soil N sources over the long term assess how N losses volatilised. Heterogeneity was a feature as the amount of volatilisation changed between seasons and N sources but with no obvious trends across all locations or systems when averaged over all years. Supplemental forage feeding with grass silage made up between 10 ± 6% of total intake on a dry matter basis, but by substituting this for low protein maize silage, the overall diet N concentration was reduced from 3.0% to 2.4% which in turn caused a 47% reduction in volatilisation, from 56 to 30 kg N/ha/year, improvement in whole farm nitrogen use efficiency (NUE) by 65% from 31 to 60 g milk MS/g N-NH3 volatilisation and milk production was uncompromised overall. Significance This is the first study to produce realistic losses of ammonia losses from pasture based dairy farming systems in Australia. The findings indicate if addressed at the whole farm scale, farm NUE can be improved significantly but this needs to be understood in the broader context of the farming system. Due to the highly heterogenous nature and responses of farming systems that include grazing animals, a pervasive and inflexible one-size-fits-all approach by policy to modifying dairy systems to improve environmental air quality would be implausible.

Evidence from farm level studies indicates that there is potential to improve nitrogen (N) fertilizer efficiency of Australian dairy farms. Increasing N fertilizer application rates to drive pasture dry matter production beyond an agronomic or economical optimum has the potential to result in detrimental environmental outcomes. Our study, using the biophysical whole-farm systems model DairyMod, modelled a range of N fertilizer rates on total N loss for five dairy sites through south-eastern Australia, using 18 years of historical climate. Nitrogen accumulation in plant biomass and soil N accumulation within and below the rootzone were estimated. Total N loss, in the form of volatilization, leaching, runoff and denitrification lost to the environment were also estimated. The reduction in N fertilizer inputs required to achieve 90% of relative yield (Y90), relative to maximum pasture production (Ymax), was > 50% across all sites and seasons. The associated reduction in total N loss when fertilizer was reduced from Ymax to Y90, varied between 34% and 74%, depending on site and season. Nitrogen recovery (proportion of N recovered in biomass relative to N fertilizer applied) exceeded 100% with lower N fertilizer rates (< 30 kg N ha-1 month-1) for most sites and seasons. Demand for N was high during spring due to high pasture growth and this was supported via N mineralization and legacy N build-up in winter. Nitrate leaching risk was highest in winter for the four temperate sites and autumn at the subtropical site. This study demonstrated the benefits of developing site and seasonal-specific N fertilizer best management practice guidelines that are both economical and environmentally beneficial. When considering whether to add more fertilizer, the value of additional pasture production needs to be weighed up against environmental N losses and the cost of additional N fertilizer to achieve this. The relationship between seasonal soil and climatic conditions and N loss and recovery were also examined for one rainfed site. As this study does not consider the externalities associated with N loss, recommendations need to be considered and amended in the context of location specificity and seasonal climatic conditions.

This paper explores the carbon footprint of milk produced by dairy cows in Australia and follows the common carbon footprint approach guidelines of the International Dairy Federation and the International Standard for Life Cycle Assessment. Primary data were collected and analysed from 139 farms representing the main dairy regions in Australia. The scope of the study covers all inputs and outputs from cradle to farm gate for the twelve month period from July 2009 to June 2010 (2009/2010). The carbon footprint of average Australian milk for 2009/2010 was calculated to be 1.11 kg carbon dioxide equivalents kg−1 fat and protein corrected milk. Differences in GHG emission per kg of milk produced could be traced back to differences in production practices. The feed conversion efficiency was identified as the most important driver for the reduction of enteric methane emissions. Reduction potentials were also identified for manure management practices.

The Australian dairy industry contributes ∼1.6% of the nation's greenhouse gas (GHG) emissions, emitting an estimated 8.9 million tonnes of CO2 equivalents (t CO2e)/annum ( DCC, 2008 ). This study examined GHG emissions of 60 Tasmanian dairy farms using the Dairy Greenhouse gas Abatement Strategies (DGAS) calculator, which incorporates International Panel on Climate Change (IPCC) and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions including pre-farm embedded emissions associated with key farm inputs (i.e., grains/concentrates, forages and fertilizers) and on-farm emissions from CO2, CH4 and N2O are estimated by DGAS software. A detailed description of GHG calculations and functionality of DGAS software are provided. Total farm GHG emissions of 60 Tasmanian dairy farms, as estimated with DGAS, ranged between 704 and 5839 t CO2e/annum, with a mean of 2811 t CO2e/annum. Linear regression analyses showed that 0.93 of the difference in total farm GHG emission was explained by milk production. The estimated mean GHG emission intensity of milk of production was 1.04 kg CO2e/kg fat and protein corrected milk (FPCM; ranged between 0.83 and 1.39 t CO2e/t FPCM) with a standard deviation of 0.13. Stepwise multiple linear regression analysis showed that feed conversion efficiency (kg FPCM/kg dry matter (DM) intake) and N based fertilizer application rate explained 0.60 of the difference in the GHG emissions due to milk production from these pastoral based dairy systems. Estimated per cow and per hectare emission intensity was 6.9 ± 1.46 t CO2e/cow and 12.6 ± 4.37 t CO2e/ha, respectively. Stepwise multiple linear regression analysis showed that DM intake per cow (t DM intake/cow/lactation) explained 0.86 of the variability in per cow GHG emissions intensity, while milk production/hectare (t FPCM/ha) explained 0.92 of the variability in per hectare GHG emission intensity. Given the influence that feed conversion efficiency and/or N based fertilizer application rates had on all GHG emissions intensities, it is clear that these factors should be key target areas to lower the intensity of emissions associated with dairying in Tasmania. This paper is part of the special issue entitled: Greenhouse Gases in Animal Agriculture – Finding a Balance between Food and Emissions, Guest Edited by T.A. McAllister, Section Guest Editors: K.A. Beauchemin, X. Hao, S. McGinn and Editor for Animal Feed Science and Technology, P.H. Robinson.

The nitrogen (N) nutrition of dairy pasture systems in southern Australia has changed from almost total dependence on legumes in the early 1990s through to almost complete reliance on N fertiliser today. Although some tactical N fertiliser is applied to sheep and beef pastures to boost late winter growth, most N fertiliser usage on pastures remains with the dairy industry. Intensification of the farming system, through increased stocking rates and a greater reliance on N fertiliser, has increased N loading, leading to higher potential N losses through volatilisation, leaching and denitrification. With increasing focus on the environmental impact of livestock production, reducing N loading on dairy farms will become increasingly important to the longer-term sustainability of the dairy industry, possibly with the expectation that Australia will join most of the developed countries in regulating N loading in catchments. This paper examines N usage in modern pasture-based dairy systems, the N cycle and loss pathways, and summarises a series of recent modelling studies and component research, investigating options for improving N use efficiency (NUE) and reducing whole-farm N balance. These studies demonstrate that the application of revised practices has the potential to improve NUE, with increasing sophistication of precision technologies playing an important role. This paper discusses the challenge of sustainably intensifying grazing systems with regard to N loading and what approaches exist now or have the potential to decouple the link between production, fertiliser use and environmental impact.

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.

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.

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.

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.

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.

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.

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.

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.

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.