Desmanthus for silage

Introduction: Desmanthus is known as a pasture legume where its inclusion can increase animal performance. However, under a cropping scenario desmanthus can produce high yield of good quality forage (Mwangi et al. 2022). This study was initiated to determine if desmanthus could be preserved as silage. Materials and Methods: Progardes desmanthus, cultivars JCU 4, JCU 6 and JCU 9, were established in three irrigated 4 ha blocks in north Queensland (19°35’S 146°54’E) on 21/12/2021. The blocks were slashed and regrowth was mowed after 60 d on 12/4/22. Within 4 hours of mowing, cultivars were round baled and wrapped in 5 to 8 layers of white plastic film wrap. Bales were stored on their ends outside. On 14/9/22 two bales of each cultivar were unwrapped and presented to a group of 15 beef cows for 24 h for monitoring of feeding behaviour. Samples at ensiling and feed-out were analysed by NIR. Results: The dry matter (DM) at mowing was similar, but JCU 9 silage was drier than the other silages (Table 1) leading to more extensive moulding. The loss of water soluble carbohydrate (WSC) in all silages was matched by a reduction in pH and production of fermentation acids. All silages had a restricted heterotactic fermentation typical of round bale silage. Video monitoring revealed that cattle spent more time at cultivars JCU 4 and 6, possibly due to visibly less mounding in these bales. Conclusions: This preliminary study demonstrated that desmanthus can be ensiled. All three silages were of good nutritive value and satisfactory fermentation and should support good levels of animal production. The higher apparent presence of moulds in the drier JCU 9 silages suggests ensiling above ~ 50% DM increases moulding and reduces preference for the silage

A noise robust automatic radiolocation animal tracking system

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m

Impact of communication technologies on pastoralist societies

The rangelands cover approximately 20% of the World's land surface and provide 16% of annual food production as meat and milk for local and distant markets (Holechek, 2013). Food production from rangelands represents an important source of nutrition as global human population is projected to exceed 9 billion by 2050 (United Nations, 2015). There is pressure to increase production from the pastoralism but this has to be done sustainably to ensure the productive capacity is not eroded in the longer term for short term gains. Information technology represents a very real opportunity to improve livelihoods, increase food production and secure environmental outcomes in the pastoral lands. About 70% of the World’s pastoral lands are found in developing and emerging economies where they support indigenous human populations existing in a close synergy with their livestock (Reid et al., 2014). Such societies are driven by cultural mores that often lead to sub-optimal livestock production, over grazing and poor resilience to factors such as climate change and societal upheaval. In developed countries, pastoral lands are under threat from depopulation, loss or lack of infrastructure to support developed production systems and competition for alternative use of the rangelands, such as carbon storage, mining, ecosystem services and tourism (Roxburgh and Pratley, 2015). Against this background then, how can information technologies transform the pastoral lands from marginal production systems to those that are resilient to challenges, sustainable in the long term and deliver optimum levels of livestock production

Redesigning animal agriculture: The challenge of the 21st century

At a time of increased concern over animal welfare and environmental degradation, the global demand for animal-based protein is necessitating the development and use of emerging agricultural technology. Focusing on livestock production systems, this comprehensive text addresses how the growing diversity of global food demands will be met in the future, providing insights into new and emerging scientific areas and the implications for addressing global drivers for change. Contributions from a wealth of international experts cover ethical, philosophical and systemic considerations, the impact of genomics on livestock production, the holistic systems perspective, the complex systems approach using stochastic modelling methods, and how all these factors can be linked to achieve sustainable outcomes

Evaluating an eddy covariance technique to estimate point-source emissions and its potential application to grazing cattle

Measurement of gas emissions from grazing cattle presents a challenging application of the eddy covariance (EC) technique. A cattle herd represents point sources on the landscape, violating the assumptions of spatial homogeneity made in typical EC applications. A proper evaluation of EC fluxes in this case requires an analysis based on the overlap between the EC flux footprint and animal positions. A controlled gas release study was conducted to evaluate the potential of a Lagrangian stochastic (LS) dispersion model to interpret EC fluxes and estimate emissions from point sources. Methane (CH4) gas was released from eight fixed points within a confined area (representing animals in a paddock) while two EC systems monitored CH4 fluxes at two distances downwind of the source area (a near and far tower). Overall accuracy was greater at the far tower location with estimates within 3% of the actual emission rate. The near tower overestimated total emissions by 16%. Deviations from the true emission rate were greatest for night-time and morning periods and least for mid-afternoon to early evening periods when neutral stability and favorable wind directions prevailed. We also investigated the effect of treating the simulated paddock as a homogeneous area emission source. The near tower emission estimate improved with the area source approach (9% overestimation). The far tower suffered a loss of accuracy (17% underestimation), but this was substantially improved (7% underestimation) by reducing the source area to the minimum required to contain the eight release points. Our study suggests that EC can be used to measure animal emissions from grazing cattle on pasture with a level of accuracy similar to other micrometeorological approaches

The Digital Homestead assists rangeland managers to make timely and informed decisions

In 2013 the beef industry was worth $7.4 billion to the Australian economy. For those 10% of Australians who live outside the major cities, information and communication technologies are trickling into the small towns and homesteads of rural Australia. Data and information are key to sustainable businesses including agriculture enterprises and information will be increasingly important for economic and environmental sustainability, animal health and welfare and preserving the right to farm. Producers that can provide evidence of the way their products are produced may command a premium price in a modern society

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle

Grazing systems represent a significant source of enteric methane (CH4), 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 CH4 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 CH4 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 CH4 m-2 adult equivalent [AE]-1 d-1, SD = 75) and more variable than emissions at the water point (158 g CH4 AE-1 d-1, SD = 34). Combining results from both sites gave a CH4 production of 0.43 g kg-1 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

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle

Grazing systems represent a significant source of enteric methane (CH4), 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 CH4 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 CH4 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 CH4 m-2 adult equivalent [AE]-1 d-1, SD = 75) and more variable than emissions at the water point (158 g CH4 AE-1 d-1, SD = 34). Combining results from both sites gave a CH4 production of 0.43 g kg-1 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

Greenhouse gas implications of leucaena-based pastures. Can we develop an emissions reduction methodology for the beef industry?

The perennial legume leucaena (Leucaena leucocephala) is grown across the subtropics for a variety of purposes including livestock fodder. Livestock in Australia emit a significant proportion of the methane produced by the agriculture sector and there is increasing pressure to decrease emissions from beef cattle production systems. In addition to direct productivity gains for livestock, leucaena has been shown to lower enteric methane production, suggesting an opportunity for emissions mitigation and Commonwealth Emissions Reduction Fund (ERF) methodology development, where leucaena browse is adopted for high value beef production. Determining the proportion of leucaena in the diet may be one of the more challenging aspects in attributing mitigation. Current enteric emission relationships for cattle consuming mixed grass-leucaena diets are based on intensive respiration chamber work. Herd-scale methane flux has also been determined using open path laser methodologies and may be used to validate an on-farm herd-scale methodology for leucaena feeding systems. The methodology should also address increased potential for soil organic carbon storage by leucaena grazing systems, and changes in nitrous oxide production. This paper outlines the background, justification, eligibility requirements and potential gaps in research for an emissions quantification protocol that will lead to the adoption of a leucaena methodology by the Australian beef industry. Development of a methodology would be supported by research conducted in Australia

Livestock production in a changing climate: Adaptation and mitigation research in Australia

Climate change presents a range of challenges for animal agriculture in Australia. Livestock production will be affected by changes in temperature and water availability through impacts on pasture and forage crop quantity and quality, feed-grain production and price, and disease and pest distributions. This paper provides an overview of these impacts and the broader effects on landscape functionality, with a focus on recent research on effects of increasing temperature, changing rainfall patterns, and increased climate variability on animal health, growth, and reproduction, including through heat stress, and potential adaptation strategies. The rate of adoption of adaptation strategies by livestock producers will depend on perceptions of the uncertainty in projected climate and regional-scale impacts and associated risk. However, management changes adopted by farmers in parts of Australia during recent extended drought and associated heatwaves, trends consistent with long-term predicted climate patterns, provide some insights into the capacity for practical adaptation strategies. Animal production systems will also be significantly affected by climate change policy and national targets to address greenhouse gas emissions, since livestock are estimated to contribute ~10% of Australia’s total emissions and 8–11% of global emissions, with additional farm emissions associated with activities such as feed production. More than two-thirds of emissions are attributed to ruminant animals. This paper discusses the challenges and opportunities facing livestock industries in Australia in adapting to and mitigating climate change. It examines the research needed to better define practical options to reduce the emissions intensity of livestock products, enhance adaptation opportunities, and support the continued contribution of animal agriculture to Australia’s economy, environment, and regional communities