Summary

Australian oaten hay export is increasing in response to the growth of overseas dairy production. The value of hay varies according to genotype, environment (including weathering), management processes and industry demand. The value of hay is typically determined once hay is delivered to the plant based on performance for key traits. Therefore, growers usually make decisions based on historical performance and impeding weather to prioritise which paddocks to cut for hay to mitigate and balance on-farm risk.

 

This project involves multidisciplinary experts, who understand the research gaps and industry needs, and have worked on AgriFutures-funded projects, including oaten hay breeding. The project aims to: 1) determine the suitability of remote sensors to predict key hay quality traits, targeting breeding plots and paddock scale; 2) leverage other projects to establish the value of these traits; and 3) custom build a low-cost drone sensor linked to a cloud-based algorithm that can categorise hay quality into Australian Fodder Industry Association (AFIA) grades within 24 hours, and thus provide a relative value of individual hay paddocks at a given date. The outputs will help growers predict the value of their hay and, in conjunction with short-term weather predictions, enable prioritisation of paddocks to be cut for hay to manage risk and maximise their products’ value. Simultaneous sharing of data between growers and exporters will improve matching supply and demand for overall industry price optimisation. This same technology will be applied to breeding plots to improve the rate of genetic gain for the same quality traits; such sensors may also enable prediction of additional high value traits yet to be realised. 

Program

Export Fodder

Research Organisation

The University of Adelaide