P.PSH.1340 - Data driven system optimising the forage base for sustainable beef production

Summary

This project was undertaken to address the challenge of optimising grazing management in Australian beef production systems by developing a scalable, data driven model that integrates real time animal performance data with environmental and pasture metrics. The goal was to understand paddock level productivity in the Northwest region of NSW using animal liveweight gain as the primary indicator. The Paddock Performance Benchmark (PPB) tool was developed, using a combination of remotely collected liveweight data from in-field weigh stations (Optiweigh), CiboLabs biomass data (Total standing dry matter, TSDM), publicly available climate and soil datasets, and pasture quality indicators to describe how animal growth varies within and between farms.

The project acquired data from three commercial properties in North West NSW under routine rotational grazing. Across farms large within farm divergence in paddock ADG was observed, ranging from 0.44 to 1.03 kg/day across farms. These differences were influenced by forage composition, pasture quality, seasonal conditions and rainfall, aligning with the previous literature and the complexity of ground-based measurements for estimating productivity. The biomass of forage crops showed stronger correlations with animal performance than perennial pastures, likely due to variation in growth stage and feed quality at the time of measurement. This reinforces that biomass alone is insufficient to explain animal gain and that ADG can operate as a proxy for diet relevant intake.

A gross margin template was created to link biological and economic performance, where paddock-level ADG is used as the measure of productivity. The PPB can be used as a workflow to enable producers to benchmark, diagnose and intervene with paddock-level grazing decisions, stocking strategies and feedbase selection to optimise farm productivity. Forecasting is positioned as long term, conditional work requiring longer, region specific datasets and improved remote proxies for diet relevant quality and pasture utilisation.

This project established the data architecture to integrate datasets from remote platforms. This method using real time animal growth data to benchmark paddock productivity, could be incorporated into existing farm management platforms, supporting adoption through training and producer led demonstration.

Objectives

•    Develop a data-driven model linking cattle liveweight gain to pasture, soil, and climate data.
•    Validate the model across multiple commercial farms and test its predictive capabilities.
•    Evaluate integration of the model into existing platforms to inform grazing decisions.
•    Conduct extension activities to support adoption by producers.

Key findings

Substantial within farm divergence in ADG, ranging from 0.44 to 1.03 kg/day across farms,” highlighting strong variability in paddock productivity.

Biomass alone is not a reliable predictor of animal performance, with relationships between biomass and ADG varying depending on feedbase and grazing dynamics. 

Pasture quality metrics crude protein (CP) and metabolisable energy (ME) varied greatly (CP 3.8–26.1%; ME 8.11–11.35 MJ) and were positively associated with animal growth.

The project demonstrated that integrating animal, pasture and environmental data enables producers to benchmark, diagnose and intervene to improve productivity and decision-making.

Benefits to industry

This project demonstrates the value of real-time, automated data in transforming grazing management and lays the groundwork for further work to enable on-farm adoption. Specifically, the project:

•    Enables producers to benchmark paddocks based on animal performance which informs data -driven decision making on grazing duration, stocking density, and forage selection
•    Provides a practical gross margin template that links biological performance to per paddock financial outcomes.
•    Establishes the data architecture and methods for automated extraction, geospatial alignment and benchmarking, creating a practical pathway to embed PPB outputs into existing platforms (e.g., Optiweigh, CiboLabs, AgriWebb) and extension programs.
•    Supports MLA’s strategic goals around digital innovation, climate resilience, and sustainable production.

MLA action

MLA has supported the development of a data-driven system to optimise forage utilisation and grazing management” and the Paddock Performance Benchmark (PPB) tool.

The project has “established the data architecture  to embed PPB outputs into existing platforms (e.g., Optiweigh, CiboLabs, AgriWebb) and extension programs.
MLA is using these outcomes to support “training and producer-led demonstration” and to enable integration into “existing farm management platforms,” creating pathways for adoption and informing future investments.

Future research

•    Extend multiyear datasets across additional agroclimatic zones, forage systems and cattle classes to strengthen the transferability, robustness and adoption of the PPB framework.
•    Improve utilisation and pasture quality proxies. Emerging approaches such as remote or automated sensing (e.g., drone-based imagery, faecal NIRS) require careful validation.
•    Forecasting should be treated as a conditional, longer-term evaluation. It is only feasible where region-specific datasets and validated proxies exist. 
•    Expand economic and sustainability components. This includes gross margin-based scenario testing and carbon and natural capital metrics. 
•    Establish demonstration sites to support scalable adoption with collaborative, producer-led innovation.