P.PSH.1299 - Livestock Data Services App (LDSA) Pairtree – Moving Agtech from decision support to decision making

Summary

This Livestock Data Services App project tested the unproven theory of whether Business-as-Usual (BAU) data collection could be repurposed and then recognised as evidence for compliance, stewardship and integrity programs.

As Agtech adoption increases along with the rising issues of what is the true value proposition of the purchase, this Project aimed to understand the potential value of additionality to the original Agtech provider's value proposition.

Thus if data can be integrated and repurposed for evidence and research extension, farmers have an increased reason to continue purchasing solutions that fulfil several requirements for the business.

Objectives

There were two main objectives of this Project firstly, to utilse more Agtech (especially IoT sensors and apps) and secondly, to establish new income revenue options for stewardship.

This was to be achieved through populating the Livestock Production Assurance (LPA) and then also the Natural Capital option of the Sheep/ Beef – Greenhouse Accounting Framework (SB-GAF, researched by Dr Richard Eckard).

By using BAU data to fulfil these tools was hoped to increase efficiencies of overhead compliance and also provide greater integrity at an industry level.

Key findings

Pairtree successfully proved that BAU data collection can be utilised within the context of compliance, integrity and stewardship outcomes.
Through the engagement of a wide range of industry stakeholders, the Project proved an appetite to continue the approach to streamline compliance and also improve the accuracy of the evidence provided.

Benefits to industry

The key benefits to the industry will be short to medium term, where new compliance and extension programs can be better delivered in a connected approach. Thus saving time for farmers, increasing the accuracy of compliance and increasing value propositions for Agtech.

MLA action

Evaluate other opportunities to mine data analytics on Business-as-Usual (BAU) data collection could be repurposed and then recognised as evidence for compliance, stewardship and integrity programs and/or reduced double handling of data.

Future research

There are two key projects that can extend from this Project Firstly, a second stage testing of the SB-GAF tool for an industry-level benchmarking and baselining approach. Secondly, a project to understand the value and likely accuracy of Behavioural Practice Evidence (BPE), which is intrinsically linked to BAU data collection.