V.DIG.2022 - Regrow - MLA’s Digital Livestock 4.0 Pilot at Romani Pastoral Co

Summary

The project was undertaken to demonstrate the range and application of “ag tech” products available to MLA’s membership, with the ultimate goal of increasing adoption of such technologies to improve the industry’s productivity. Remote sensing can help solve many challenges in pastures and broadacre crops, helping producers and agronomists to monitor large-scale operations effectively and efficiently.

Regrow makes this possible by applying the latest in remote sensing, agricultural science, machine learning and AI across all relevant layers of data to create efficient agronomic workflows and provide timely, accurate, and actionable insights. What makes FluroSense analytics unique is the transformation of raw data (satellite imagery, weather) into agronomic metrics, easily accessible and usable by agronomists to make better-informed decisions.

Objectives

The project is using the FluroSense analytics engine to accurately and more importantly, numerically, track pasture growth parameters (such as biomass, chlorophyll content and other vegetation cover performance indices) in order to maximise the forage production and consumption efficiency. Specific objectives that the project focused on:

• Optimising stocking rates through dry matter variability mapping for improved grazing.
• Detecting trends and anomalies in pasture growth parameters through remote sensing data and weather monitoring.
• Improving crop and pasture productivity through variable-rate fertiliser management.
  
  

Key findings

• The accuracy of the planting date for an accurate growth stage detection.
• The importance of Dry Matter detection for livestock pasture management.
• The importance of a sampling tool to optimise Nitrogen applications.
  
  

Benefits to industry

Digital AgTech providers often make fictious claims about where their technologies and solutions are up to. Digital farms play an important role in vexing these claims and determining what Red Meat Producers can deploy today and the value proposition behind each.

• Optimise the nitrogen strategy for pastures and broadacre crops, by optimising tissue sampling locations based on the leaves chlorophyll content and simplifying the creation of Variable rate application maps.
• Be aware of abnormal crop development and crop stresses in broadacre crops and pastures, to optimise production, conduct more efficient scouting and adjust management practices.
  
  

MLA action

The learnings from the Romani Digital demonstration farm project has helped shape the MLA Digital Agriculture business plan. A need has been identified to further test AgTech which is market ready with producers in real world situations to identify the use cases and value propositions of the solutions beyond the simple demonstration of them. This is guiding the current and future MLA investments in this space.

Future research

Future research and development should be focused on expanding the wireless sensor ecosystem. In particular, water quality and animal tracking. Additional research in data analytics of the collected data, to explore water consumption and animal health, is also of significant interest.