Summary
The Feedbase Investment Plan (FIP) commissioned by MLA in 2011 defined researchable priorities in all southern agro ecological zones with a focus on increasing the profitability and sustainability of red meat production. The Feedbase R&D Plan then built the next level of detail, in which remote/precision technologies were identified as being critical to addressing fundamental gaps in our understanding of grazing behaviour, animal management and pasture performance. It also acknowledged that increasing application of these technologies could lead to better (more accurate and/or timely) tactical and strategic management decision making. MLA subsequently commissioned this project to review and investigate the potential for a significant R&D investment in the general area of precision agriculture for the southern grazing industries. Their intention was to seek a deeper understanding of the key significant on-farm tactical and strategic decision areas that lead to more efficient use of resources for meat production, and specifically those that could be enhanced through improved data/information collection.
The project focused on three core issues
(1) the key on-farm tactical and strategic decisions that might benefit from better data/information flows,
(2) the potential benefit of that better decision making,
(3) the data/information needs required to capture that potential benefit.
The importance of these decisions were evaluated across four production systems (set stocked, rotationally grazed, mixed farm, intensive rotational grazing) and two operation types (trading/fattening operations, breeding operations). The applicability and significance of these decisions were considered in relation to the various agro ecological zones of southern Australia.
In order to determine the potential alignment of technologies with different on-farm decision needs, two parallel streams of activity were conducted and then assimilated. Firstly, the critical decision points in different production systems were examined, documented and compared across each production system and a short-list of key management decisions that would benefit most from improved data and/or information were detailed. These were defined primarily as being key profit drivers, but the implications for social and/or environmental benefits were also considered. The data needs were determined without consideration of the technology that could be applied. Secondly, a review of possible technologies and information streams was conducted, and a series of discussions held with commercial and R&D stakeholders to fully understand state of development, key issues and constraints, perceived on-farm applications and benefits, and future developments.
In a review of the potential for information technologies to improve decision making for the southern livestock industries commissioned by Meat and Livestock Australia (MLA) (B.GSM.0004 — Potential), Henry et al. (2012) identified the following as significant opportunities for producers in using information technologies:Improved pasture production through soil fertility assessments and variable rate fertiliser applicationImproved feed allocation — allocating appropriate quality and quantity of feed to different classes of stock in a timely mannerPasture yield mapping — understanding, managing and optimising pasture production within and between paddocksFeed prediction - the mitigation of risks associated with adverse climatic conditions and opportunities associated with good seasons
B.GSM.0010 - Biomass Business 11 - Tools for real time estimation in pastures - addresses the second and third opportunities through the development of tools for real time estimation of biomass and allocation of pasture resources to meet feed supply demands. Extension tools such as Prograze highlight the opportunity for farmers to assess pasture biomass and allocate stock to paddocks on the basis of matching feed demand and feed supply. However, the determination of feed availability using the techniques provided through Prograze and other methods can be subjective and time consuming, and still and subject to error. Producers can find it difficult to get accurate estimates of pasture biomass. While some producers have well developed skills in terms of visualizing accurate biomass estimates and can do this across a number of different seasonal conditions it is not a universal skill (Edwards et al. 2011). Even where absolute estimates may be inaccurate, producers can still adequately use their own relative estimates to make appropriate management decisions. However, the differences in experience and the challenges that exist in obtaining objective data from across the industry means that some producers are likely to benefit from more objective measurements. Indeed, it has been estimated that producers are on average achieving 40% of the optimum level in terms of efficient allocation of feed and that precision technologies can increase this to 60%. Economic analysis indicates that an improvement in feed allocation will result in an increase in gross margin / ha (gm/ha) of $96 for sheep and $52 for cattle enterprises (Henry et al. 2012).
While tools to accurately measure pasture biomass exist (e.g. C-DAX or sonar pasture meter) within the grazing sector, they are expensive (i.e. greater than $5000 in cost), require significant regional calibration and/or struggle to delineate the green fraction (most important for predicting animal performance on pasture). The challenge has been to develop a technology which is low cost and is also capable of being deployed from a vehicle and can combine readings with GPS technology to build a pasture biomass map.
Newer technologies such as Active Optical Sensors (AOS) have been developed for use in the cropping industry, ostensibly for inferring crop nitrogen levels. These handheld devices direct a beam of light onto the canopy and an on-board detector records the returning radiation and calculates the optical reflectance of the target canopy in those specific wavelengths. The AOS are neither dependent nor influenced by ambient light, in contrast to a passive sensor. They are relatively low cost, can be deployed from a vehicle and have the potential to be integrated with GPS to provide spatial measures of biomass. Research has shown that AOS have the potential to provide estimates of green pasture biomass that compare favourably with other non-destructive techniques (Teal et al. 2006; Freeman et al. 2007; Trotter et al. 2010; Cabrera-Bosquet et al. 2011; Shaver et al. 2011).
Challenges remain in making this AOS technology commercially available. This includes the need to develop a calibration and data management package that can be easily used by producers. This BB2 project, co-funded by MLA and Co-operative Research Centre for Spatial Information (CRCSI), addresses that need. The project assessed the potential for AOS to provide to objective estimates of pasture biomass. Calibrations of reliable estimates were created and provided in real-time through a mobile device application (MDA).
