E. Charmley

Methane yields from Brahman cattle fed tropical grasses and legumes

In the national greenhouse inventory, methane emissions from the Australian tropical beef herd are derived from cattle fed two diets. In the experiments reported here, methane production was measured by open-circuit gas exchange from 13 Brahman cattle offered 22 diets from combinations of five tropical grass species and five legumes, with a minimum of three steers per diet. All diets were offered daily ad libitum, with the exception of three legume diets fed without grass and leucaena (Leucaena leucocephala) mixed with grass, which were offered at 15 g dry matter per kg liveweight. Diets were fed as long-chopped dried hay, with the exception of leucaena, which was harvested and fed within 2 days. For the data from cattle fed diets of grass and grass mixed with legumes, methane production could be predicted as 19.6 g/kg forage dry matter intake (residual standard deviation 12.3). Observed methane yields were not predictable from a stoichiometry, which used volatile fatty acid proportions in rumen fluid. Mean methane emission rates across all diets were equivalent to 8.6–13.4% of digestible energy intake, and 5.0–7.2% of gross energy intake. The latter values are comparable to IPCC (2006) recommendations (5.5–7.5%) for large ruminants fed low-quality crop residues and by-products. Methane yields per unit of ingested dry matter or digested organic matter were variable across diets but were related to digestibility and contents of fibre and protein. These results constitute a significant downward revision of the methane emissions attributable to the northern Australian beef herd grazing tropical pastures.

A universal equation to predict methane production of forage-fed cattle in Australia

The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (±0.28) × DMI (kg/day) (R2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (±0.008) × GEI (MJ/day) (R2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia’s national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO2-e reduction in calculated annual emissions from Australian cattle.

Wireless sensor networks to study, monitor and manage cattle in grazing systems

Monitoring and management of grazing livestock production systems can be enhanced with remote monitoring technologies collecting information with high temporal and spatial detail. However, the potential benefits of such technologies have yet to be realised and challenges still exist with hardware, and data analysis and interpretation. The objective of this paper was to propose analytical methods and demonstrate the value of remotely collected liveweight (LW) and behaviour of beef cattle grazing tropical pastures. Three remote weighing systems were set up at the water troughs to capture LW of three groups of 20 animals for 341 days. LW data reflected short-term effects following the first rain event (>50 mm) at the end of the dry season, which resulted in LW losses of 22 ± 8.8 kg of LW at a rate of –1.54 ± 0.46 kg/day (n = 60). This period was followed by a peak daily LW change (LWC) of +2 kg/day. The remote weighing system also captured longer environmental effects related to seasonal changes in forage quality and quantity with highest LWC during the wet season and weight loss during the dry season. Effects of management on LW and LWC were observed as a result of moving animals to paddocks with more edible forage during the dry season when the negative trend in LWC was reversed after rotating animals. Behavioural monitoring indicated that resting and ruminating took place at camping sites, and foraging resulted in grazing hotspots. Remotely collected LW data captured both short- and long-term temporal changes associated with environmental and management factors, whereas remote monitoring collars captured the spatial distribution of behaviours in the landscape. Wireless sensor networks have the ability to provide data with sufficient detail in real-time making it possible for increased understanding of animal biology and early management interventions that should result in increased production, animal welfare and environmental stewardship.

Determining the effect of stocking rate on the spatial distribution of cattle for the subtropical savannas

With the commercial development of the global positioning system (GPS), it is now possible to monitor the distribution of free ranging cattle and derive measures to describe landscape use. Animal GPS data can be integrated with a geographic information system (GIS) detailing topography, vegetation, soil type and other landscape features. Combining GPS and GIS information is useful for understanding how animals respond to spatial variability. This study quantified land-type preferences for Brahman cross steers over three time periods, from October 2004 to March 2006 in a replicated trial, under heavy (4 ha/AE; animal equivalent of ~450 kg steer) and light (8 ha/AE) stocking in four, ~105 ha paddocks of subtropical semi-arid savanna near Charters Towers, Queensland, Australia. The grazing trail was conducted at a scale much less than would be found in commercial situations. Consequently, the spatial pattern of cattle reported here may not represent what occurs at a commercial scale and implications are discussed. Results were analysed in terms of the spatial distribution of steers fitted with GPS devices in each of the four paddocks and for each stocking rate to provide insight into cattle distribution and land-type preferences. Steers walked in excess of 6 km per day, regardless of stocking rate, and exhibited diurnal patterns of movement, with peak activity around dawn (0500–0700 hours) and dusk (1800–2000 hours). The spatial distribution of the collared steers was not uniform and appeared to be strongly influenced by the prevailing drought conditions and location of water points within each paddock. A hierarchy of drivers for distribution was identified. With the exception of drinking water location, land subtype based on soil-vegetation associations influenced animal distribution. Preference indices (ŵi) indicated that steers selected sites associated with heavy clay and texture contrast soils dominated by Eucalyptus coolabah Blakely & Jacobs (ŵi = 5.33) and Eucalyptus brownii Maiden & Cambage (ŵi = 3.27), respectively, and avoiding Eucalyptus melanophloia F.Muell. ridges (ŵi = 0.26) and Eucalyptus cambageana Maiden (ŵi = 0.12) on sodosols. The results suggest that spatial variation in cattle distribution within a paddock may be more critical than overall stocking rate in influencing the pattern of biomass utilisation. However, to quantifying the effects of different grazing land management practices on animal distribution on a commercial scale, additional studies in extensive paddocks are required.

Evaluating dispersion modeling options to estimate methane emissions from grazing beef cattle.

Enteric methane (CH) emission from cattle is a source of greenhouse gas and is an energy loss that contributes to production inefficiency for cattle. Direct measurements of enteric CH emissions are useful to quantify the magnitude and variation and to evaluate mitigation of this important greenhouse gas source. The objectives of this study were to evaluate the impact of stocking density of cattle and source configuration (i.e., point source vs. area source and elevation of area source) on CH emissions from grazing beef cattle in Queensland, Australia. This was accomplished using nonintrusive atmospheric measurements and a gas dispersion model. The average measured CH emission for the point and area source was between 240 and 250 g animal d over the entire study. There was no difference ( > 0.05) in emission when using an elevated area source (0.5 m) or a ground area source (0 m). For the point-source configuration, there was a difference in CH emission due to stocking density; likewise, some differences existed for the area-source emissions. This study demonstrates the flexibility of the area-source configuration of the dispersion model to estimate CH emissions even at a low stocking density.

Development of a remote method for the recording of cattle weights under field conditions

Remote weighing of cattle is advantageous because it is less labour-intensive and less invasive for the animal than conventional methods. An automated system was built using ‘off the shelf’ technology which allowed for cattle to be weighed automatically on pasture when accessing the device. Data collected over a 70-day grazing period was compared with data collected at 2-week intervals by transferring cattle to a centralised weighing facility. Liveweights recorded using the remote system were some 20 kg higher than those measured conventionally. However the daily rate of change was similar for conventional and remote weighing methods, being 1.25 v. 1.20 kg/day, respectively (P = 0.48). This study demonstrated that remote weighting of cattle on pasture is feasible and gives satisfactory results compared with conventional methods. This relatively simple method relies on technologies currently available to the cattle industry and could readily be adapted to a range of commercial situations.

The use of image analysis to determine the number and position of cattle at a water point

This work assessed the accuracy of animal counts and positions using image analysis.Counting accuracy decrease with distance to camera.Positional accuracy of independent points was 0.8?0.5m.Positional accuracy did not change with distance to the camera. This study assessed the application of an image analysis method to accurately determine the number and position of cattle which are critical inputs for enteric methane emission calculations using micrometeorological methods. Animal imagery was collected with three synchronised time-lapse cameras located at 7, 35 and 77m from a 20×30m water point enclosure containing 20 steers, recorded over three consecutive days. Four independent observers counted the number of animals visible in each of 516 images. The counting error increased with distance from the enclosure (0.1%, 3.7% and 15.4% of total animals) as a result of increased overlapping and decreased clarity of the animals on the image. Animal positions were estimated using a polynomial transformation of image coordinates (pixels) to map coordinates. The average location error (distance between estimated and actual position) of independent targets was 0.8?0.5m and did not change with distance to the camera. We conclude that the analysis of 12MP images from time lapse cameras can provide reliable and accurate estimates of the position and the number of animals located within 55m from the camera.

Modelling methane emissions from remotely collected liveweight data and faecal near-infrared spectroscopy in beef cattle

The objective of the present study was to develop a model-data fusion approach using remotely collected liveweight (LW) data from individual animals (weighing station placed at the water trough) and evaluate the potential for these data from frequent weighing to increase the accuracy of estimates of methane emissions from beef cattle grazing tropical pastures. Remotely collected LW data were used to calculate daily LW change (LWC), i.e. growth rate on a daily basis, and then to predict feed intake throughout a 342-day grazing period. Feed intake and diet dry matter digestibility (DMD) from faecal near-infrared spectroscopy analysis were used to predict methane emissions using methods for both tropical and temperate cattle as used in the Australian national inventory (Commonwealth of Australia 2014). The remote weighing system captured both short- and long-term environmental (e.g. dry and wet season, and rainfall events) and management effects on LW changes, which were then reflected in estimated feed intake and methane emissions. Large variations in all variables, measured and predicted, were found both across animals and throughout the year. Methane predictions using the official national inventory model for tropical cattle resulted in 20% higher emissions than those for temperate cattle. Predicted methane emissions based on a simulation using only initial and final LW and assuming a linear change in LW between these two points were 7.5% and 5.8% lower than those using daily information on LW from the remote weighing stations for tropical and temperate cattle, respectively. Methane emissions and feed intake can be predicted from remotely collected LW data in near real-time on a daily basis to account for short- and long-term variations in forage quality and intake. This approach has the potential to provide accurate estimates of methane emissions at the individual animal level, making the approach suitable for grazing livestock enterprises wishing to participate in carbon markets and accounting schemes.

Towards an integrated approach to stochastic process-based modelling: with applications to animal behaviour and spatio-temporal spread.

Using example applications from our recent research we illustrate the development of an integrated approach to modelling biological processes based on stochastic modelling techniques. The goal of this programme of research is to provide a suite of mathematical and statistical methods to enable models to play a more central role in the development of scientific understanding of complex biological systems. The resulting framework should allow models to both inform, and be informed by data collection, and enable probabilistic risk assessments to reflect inherent variability and uncertainty in current knowledge of the system in question. We focus on discrete state-space Markov processes as they provide a general and flexible framework both to describe and infer the behaviour of a broad range of systems. Unfortunately the nonlinearities required to model many real world systems typically mean that such discrete state-space stochastic processes are intractable to analytic solution necessitating the use of simulation and analytic approximations. We show how to formulate stochastic process-based models within this framework and discuss the representation of spatial and temporal heterogeneity. Simple population models are developed and used to illustrate these concepts. We describe how to simulate from such models, and compare them with their deterministic counterparts. In addition, we discuss two methods, closure schemes and linearization about steady-states, which can be used to obtain analytic insights in to model behaviour. We outline how to conduct parameter estimation for such models when, as is typically the case for biological and agricultural systems, only partial observations are available. Having focussed on familiar population level models in introducing our integrated approach its wider applicability is illustrated by two contrasting applications from our recent research. The first example combines the development and analysis of an agent-based model describing grazing in heterogeneous environments, with parameter inference based on data generated using a transponder system in a behavioural experiment on diary cows. The second example makes use of large-scale data describing bio-geographical features of the landscape and the spatio-temporal spread of an alien plant to estimate the parameters of a stochastic model of dispersal and establishment.

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle

Grazing systems represent a significant source of enteric methane (CH), but available techniques for quantifying herd scale emissions are limited. This study explores the capability of an eddy covariance (EC) measurement system for long-term monitoring of CH emissions from grazing cattle. Measurements were made in two pasture settings: in the center of a large grazing paddock, and near a watering point where animals congregated during the day. Cattle positions were monitored through time-lapse images, and this information was used with a Lagrangian stochastic dispersion model to interpret EC fluxes and derive per-animal CH emission rates. Initial grazing paddock measurements were challenged by the rapid movement of cattle across the measurement footprint, but a feed supplement placed upwind of the measurements helped retain animals within the footprint, allowing emission estimates for 20% of the recorded daytime fluxes. At the water point, >50% of the flux measurement periods included cattle emissions. Overall, cattle emissions for the paddock site were higher (253 g CH m adult equivalent [AE] d, SD = 75) and more variable than emissions at the water point (158 g CH AE d, SD = 34). Combining results from both sites gave a CH production of 0.43 g kg body weight, which is in range of other reported emissions from grazing animals. With an understanding of animal behavior to allow the most effective use of tower placement, the combination of an EC measurement platform and a Lagrangian stochastic model could have practical applications for long-term monitoring of fluxes in grazing environments.