Data, one of the most written about topics in agriculture in recent years and the proposed solution for everything from animal welfare to climate change. It is a bankable pitch for Agtech companies trying to entice investors with promises of on farm efficiency gains worth millions or even billions of dollars to the industry. However, data is not the goal in itself, it is the means to an end and how it is presented and ultimately used will decide its utility.
All data is not created equal. There are several attributes that data should have to optimise its use:
Integrity
This is the accuracy, consistency and completeness of data. It is very hard to draw meaningful conclusions from inaccurate or incomplete data sets.
Interoperability
Depending on where the data is being used, this can unlock benefits far beyond the farm gate. In the days of traceability, product provenance and integrated supply chains, having data that flows between IT systems with ease is essential to be able to look at the big picture. If, for example, an ivermectin injection has one code in one database and a different one in another, these codes will have to be mapped across adding friction to the system. A common agreed upon language is needed.
'the data need to interoperate with applications or workflows for analysis, storage, and processing' FAIR Principles - GO FAIR (go-fair.org).
Data sharing and transparency
Sharing of data captured at different points along the supply chain will provide benefits for all participants to help to grow a healthy animal in a sustainable and efficient way to produce a safe end product which fits the market.
A good example is Integrity System's Livestock Data Link. This connects carcase data to NLIS, MSA and a central animal health database allowing the producer to look at individual carcase characteristics as well as any animal health information that has been collected at post-mortem inspection (sheep only at the moment). Use of this information will help a producer to identify and tackle disease and to work out the bloodlines or management practices which are most profitable.
If data is shared, the provider of the data needs to be informed on how it will be used in order to be able to give or withdraw consent for its use. They need to be reassured that the data will be kept in a way that adheres to recommended security practices and need to be able to trust that the data will be used in a way that is honest and legal. In return it is important that the data is trustworthy and complete. If an animal is sold based on past performance such as growth rates, then the buyer needs to be able to trust that the scales used in the weighing were accurate and weights recorded correctly. As livestock purchases are increasingly carried out online, there is a greater need for trustworthy data. Buyers and sellers are less and less likely to be known to each other, or their agents.
Tangible Incentives
The technology and time needed to collect and record accurate data is a significant investment so there needs to be a very tangible reward to incentivise the producer. On farm, this data needs to be presented in such a way that it can be easily acted upon to improve performance and any resulting improvement in performance needs to be quantified. If new technology is to be adopted then it must show more than just a marginal benefit.
Likewise, when this data flows up the supply chain there needs to be a quantifiable benefit to the sharer. If you are producing quality animals of high genetic merit which have been weaned using best practice protocols and have already been vaccinated for respiratory disease AND you can demonstrate this with a complete set of data then there needs to be adequate reward. Just relying on how the animal looks at the point of sale does not tell the whole story. These benefits will be realised by the feedlotter with reduced incidence of disease, greater weight gain efficiency and improved carcase quality. Being able to easily identify and source animals that work well in their system is crucial for them too.
With big enough data sets I believe there will come a point where an accurate price can be put on quality inputs. If, for example, a feedlotter knows they will reliably make $50 extra a head on a certain animal then they can confidently pay the producer $30/head more. This doesn't account for the less tangible benefits of repeat business, reputation or better animal welfare due to better practices. With good marketing and quality assurance schemes these may become tangible too.
Companies to watch in this space:
Breedr- UK based- has a very interesting and potentially disruptive business model
Agriwebb- Australian agritech behemoth
Black Box Company- Winner of the Beef Australia 2021 Pitch in the Paddock. Presents data in visually attractive and easy to read dashboards.
BRD Use Case
Vaccinating animals for bovine respiratory disease prior to feedlot entry, rather than at processing results in lower morbidity and mortality due to pneumonia. It is a constant frustration that this is not more widely carried out. The reason for this can be seen as a market failure. Peel found that in the US, the industry has a need for 'improved economic signals across industry sectors to improve incentives for changing health and management practices'. This certainly rings a bell here in Australia and has been voiced by feedlotters and producers at meetings I have attended. This is a problem where free-flowing data can provide a solution, allowing pre-vaccinated animals to be confidently verified and the benefits to be easily assessed allowing for appropriate incentivisation.
The Effect of Market Forces on Bovine Respiratory Disease. Vet Clin North Am Food Anim Pract. 2020 Jul;36(2):497-508. doi: 10.1016/j.cvfa.2020.03.008. PMID: 32451038.
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