Pasture use efficiency - East Coast

There has been relatively widespread adoption of innovations such as guidance and auto-steer and to a lesser extent yield monitoring, remote sensing and site specific management (SSM) of inputs in the grains and horticultural industries. In contrast, the grazing-based livestock industries, primarily the red-meat, wool and milk production systems, have yet to fully explore the potential of similar “precision agriculture” (PA) technologies. There are a number of reasons for a lack of development and adoption of these innovations. One standout issue is the complexity that graziers face when managing the interactions between soil, plant and animal systems that make up pasture and rangeland operations. In those industries which have taken up PA innovations, the focus is largely on monitoring and managing the spatial and temporal variation found in the soil and plant systems. Pasture and rangeland livestock producers have to deal with variability in the soil and plant systems, but also face the added complexity that the animal system brings as it interacts with these factors. Monitoring and managing the spatial and temporal variability in the animal system in terms of its interaction with the landscape remains one of the most challenging issues for graziers, however it also offers an opportunity to increase operational efficiency. Furthermore, PA livestock systems provide opportunities to increase production through increased monitoring of individual animal productivity and better management of animal health and nutrition. This project within the CRCSI sought to address several of these challenges. As part of the CRCSI Biomass Business Project the “Pasture utilization – high rainfall high input pastures” project sits within the Activity 2 “Tools for improved pasture use efficiency” section. This project sought to explore the following broad objectives and research questions: Objective (#2 of BB) - Create large and small scale, spatially-enabled, measurement and interpretation protocols, and a knowledge/data access system, for managing stocking rate on monoculture and composite grazing lands (including rangelands) based on measures of pasture growth and availability, as well as time-based growth and grazing demand models. Question (#2 of BB) - How may remote and proximal biomass sensing technologies, spatiallyreferenced livestock grazing behaviour data, and pasture production/grazing demand models be deployed to improve whole of landscape management efficiency, productivity and sustainability in high-rotation/high-input and rangeland pastures? To address these broad objective a coordinated series of projects were developed based around three key work packages: 1. Spatial variability in grazing systems and the implications for management; 2. Spatially enabled livestock management; and 3. Calibration of Active Optical Sensors for pasture biomass. The following summary outlines the research questions, key findings, industry relevance and future directions of each work package