Do we really know what’s coming?

One of the questions I am often asked is: “How does farming in NZ compare with the UK”?

Right now I think it’s a slightly loaded question with all the Brexit talk – subsidies and all that. But in reality given the context of the question is usually in the knowledge I head up a UK-based subsidiary of an NZ agri-software business, what many are really asking is: “How will technology change what we are doing, and is NZ ahead of the UK”?

Now this is a harder question to answer. I guess at a high level I would say adoption of technology in the NZ dairy sector is some years ahead of the UK, but equally, there are big advances in UK arable and hort which one might say are further ahead than NZ. One thing I would say is that NZ farmers are, more typically, open to change and innovation and less wedded to the way it is.

But I think there is something bigger going on than simply comparing one country with another. Sure NZ is a focus for our sector just now because of the way it has, in a generation, turned itself into a very globally focused and innovative economy; one that tops the global rankings for ease of doing business (and one that I would say punches well above its weight, and that’s not just the All Blacks!). No. I think we are witnessing the early stages of an utterly transformative period in global agriculture.

And that’s why I ask the question: “Do we really know what’s coming?” By this I mean, how is technology (and maybe digital and data in particular) going to change the sector?

In short, from where I sit, I would say those of us in the tech world do have a good hunch about what’s coming and the potential impact it will have. But I am not at all convinced the “average farmer” (which is a horrid term) does.

To me it is inconceivable that a farming business (whether in the UK or elsewhere) will be in any way competitive without the use of data-driven decision support tools in the future. The level of accuracy and objectivity that data will deliver (and we are seeing this already) simply puts subjective observation in the second tier of good decision making.

That isn’t to say good husbandry and farming experience have no place in the future (of course they do – I know some brilliant, intuitive and innovative farmers) but those who apply that experience with the latest technological tools will become the Premier League while others languish in the lower divisions.

Give me an example I hear you cry? Ok! A couple of weeks ago I sat down with the CEO of an innovative dairy cow data capture company (based in the UK) that is effectively putting Fitbits on cows. The volumes of behavioural data they are collecting from those animals is now substantial. But it’s what they are doing with it that so impressed me.

By using clever algorithms to understand normal and outlier behaviour of animals they are achieving two great things. The first is the ability to provide alerts flagging animals that are not exhibiting typical behaviour. In other words, “go look at those ones, that’s where you should prioritise your time”.

But the second is what really excites me. Who’d have thought that by analysing cow behaviour data it would be possible to identify lameness, mastitis and other disorders days (even weeks) ahead of when the clinical signs might be observed? I don’t care if you are the best herdsman in the world, it is hard to compete with decision support from data that is identifying things well before they are ever observable by the human eye.

This “power” has the potential to transform the way we run our farms. The application of digital technology will not only potentially save time and labour, it will enable better focus on meeting market requirements, predicting and avoiding problems, and increasingly importantly, be able to provide a substantial evidence base to back and improve welfare standards and all sorts of other production areas currently under scrutiny.

But this future is a far cry from where many on our farms sit currently. Sure there are those that are the early adopters, but I think there is a large majority who simply don’t see this massive change coming, or if they do are in denial.

There are many analogies over the years of where technological change has been transformative and where at the time many did not see it coming: Henry Ford and so on. But it’s the sheer scale of change from tech-driven ag that I think we underestimate at our peril.

The upside is that all this talk of agriculture being a high-tech industry that our children and students should be enthused about is not just talk. It is absolutely true. The more we can find demonstrable examples of great (even cool) innovation, the better it will be for our farming sector, not only because we can farm better, but because we can also excite the right people into the industry.

In my 25-plus years in the ag world in the UK and NZ, never have I felt there is a better time and more opportunity for non-farming people to get involved in the industry, whether that’s in agribusiness, science or on the farm.

And if, as I suspect, we see a reasonably aggressive scaling back of direct farm support in the UK (assuming we Brexit!), that could open the door to a new generation of tech-driven farmers, unencumbered by the past and able to deliver from the potential of the land and associated technology alone. They will be the new competition.

Can’t see it coming? The iPhone is only a little over 10 years old. Things will look very different a decade from now in agriculture. That’s really not very far away. Are you on the train or is it leaving without you?

How on-farm data and analysis can support credence attributes

Can on-farm technologies and “big data” support food and fibre product attributes that consumers value?

In a previous article I noted a Hartman Group study that suggested that consumers are interested in attributes other than just the look and price of a product, wanting to know:

  • What ingredients are in the food or beverage product (64%);
  • How a company treats animals used in its products (44%); and
  • From where a company sources its ingredients (43%).

We call these informational aspects of a product “credence attributes”, meaning that they give credence to our decision to purchase (or not purchase) a product or service, but can’t be directly assessed from the product itself, either before purchase (on the basis of colour or feel) or after purchase (on the basis of taste, for instance).

Characteristics such as “organic”, “environmentally responsible”, “grass-fed”, and “naturally raised” relate to the story behind a product. A product may communicate these through advertising, packaging, and other ways of telling the product story.

But consumers are also looking for authenticity and integrity in their food and other products. There’s a consumer backlash when the product story on the pack is in conflict with other data sources – such as claims in news articles or secret video footage.

We’ve been exploring ways that feeds of data from on-farm technology could be used to support the product provenance and credence story – or at least signal to farmers and their supply chain partners where checks and improvements should be considered. Here are a couple of examples.

Monitoring carbon footprint

Carbon life-cycle assessments (LCAs) are used to understand the extent to which production, manufacture, and distribution of a product impacts on climate change through deforestation or release of greenhouse gases such as carbon dioxide, methane, and nitrous oxide. We learn some interesting things from these, sometimes showing that shipping food products from the other side of the world can have a lower impact than growing products locally if the local environment is less hospitable.

Importantly, producing a Life-cycle assessment creates a model – a series of equations and if-then logic that describes the calculation. We can use this model with appropriate local farm and supply chain data to understand how management decisions and activities, timing and stock or crop productivity impact on emissions.

Automated systems on farms that capture data about crop production, livestock weights and production, and farm activities can also deliver data for a custom life-cycle assessment. Benchmark data across multiple farms and it becomes possible to identify the patterns of complete vs missing data, to understand how climatic constraints change emissions, or to identify outliers that need to be more closely examined.

A note of caution here: as we’ve learned from nutrient budgeting, farm systems can be varied and life-cycle assessment models are frequently based on the “typical”. An outlier result may indicate greater variation than the model can handle, rather than a more or less efficient farming system.

Demonstrating animal welfare

Animal welfare and the ability to live a healthy and natural life is another area of concern to consumers. Here too, metrics collected on-farm can be the subject of automated analysis to demonstrate good practices are followed.

In Europe where a premium is payable for “grass-fed” dairy in some regions, farmers are experimenting with the use of monitoring devices – smart tags and neck bands for example. These devices capture data that provide farmers with early warning of heats and potential animal health issues – raised temperatures, more or less movement, and reduced eating for example – but can also be analysed for patterns that only show up in outdoor grazing.

In other jurisdictions, veterinary product purchase, use, and reordering records can help to demonstrate compliance with animal health plans worked out between farmers and veterinarians, and hence demonstrate good welfare practices and appropriate use of medicines. Paper records have been used for this purpose for many years, but software technologies and automated data analysis can reduce the burden of data collection and the need for manual audits and analysis.

Practical application

Some producers will find the thought of such automated systems invasive and potentially threatening. Certainly, given the potential for outliers, for good practices that just don’t quite fit the expected mould, and for technology glitch or human error, you couldn’t use these measures as legal baselines that determine “rights to farm”.

Nevertheless, application of technology and analytics such as these can help us as we seek to improve farming practice and improve the integrity of our food supply chains. A good starting point might be to apply these as tools for committed producer groups that are already aligned with supply of a premium product or market.

 

This article was first published at http://rezare.com/blog/.
Contact us to learn how

we apply software and models to agricultural data.