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Digital data in the agri-food supply chain

Established companies and technology start-ups are all racing to create solutions that better manage agricultural data in supply chains. Does this mean radical new transparency for farmers and producers? And have we thought through the implications for data collection, management, and ownership?

In this post:

What is agricultural data?

Definition of agricultural data;

The facts, metrics, and statistics that describe elements of one or more farming or agriculture operations.

Most farms collect data in some form. Much of this may be in personal notebooks and mandatory compliance forms. Precision farming equipment, machinery, and mobile and desktop apps also collect agricultural data.

Most farmers collect information to:

  • Support improvements to farm management;
  • Follow government directives; or
  • Have something interesting to talk about in the pub.

So why are processors, food service and retailers, and dozens of internet start-ups becoming more interested in on-farm data?

How rich digital data can benefit agri-food supply chains

Three ways that digital data can benefit consumers, retailers and processors in the agri-food supply chain are:

  1. Traceability and tracebacks;
  2. Forecasting and efficiency; and
  3. Supporting product claims.

1.      Traceability and tracebacks

Tracking animals and crops through the supply chain helps the entire chain to respond to concerns about food safety or disease. This is especially important in livestock industries where animals move between farms, often through shared facilities.

Even for crops, on-farm records can establish linkages between the fertilisers and slurries, pesticides and herbicides used, and the resulting product.

2.      Forecasting and efficiency

Purchasing and processing goods from biological systems carries uncertainty and risk. Crop yields, dry-matter, or flavour will vary from the sector “average”. Animals may not be ready when first predicted or vary in how they meet processing specs.

If on-farm data were available before harvest or delivery, processors and retailers could predict the likely quality, timing, and specification of supply.

With enough lead time, processors and marketers could better match demand and processing capacity to supply. A dairy processor might vary the mix of UHT, cheese, and powder products based on expected quantities, fat, protein, and calcium levels. A fruit marketer could negotiate different market commitments based on predicted ripeness and flavour profiles.

Connected data may allow market signals to flow the other direction also. With the right information, producers could adjust harvest dates or livestock delivery to achieve target specifications and match market demand.

3.      Supporting product claims

Consumer interest is driving the creation of differentiated products, which make claims about what they do or do not contain. Examples might include:

  • “free from x”,
  • “organic”,
  • “naturally produced”,
  • “grass-fed”,
  • “local”,
  • “A2 beta-casein only”, or
  • “higher welfare”.

Consumers can see differentiation like “chocolate flavour” or gold kiwi fruit. “Credence attributes” are types of differentiation that can’t be seen. Consumers can only evaluate these based on trust and the story that supports the claims.

Small-scale producers can single-source from one or two farms that they own and closely control. For supply at scale, the evidence and controls to support credence attribute claims must be based on data and audits. And even audits make substantial use of agricultural data collected on farm.

Challenges of data in agri-food supply chains

Making effective use of agricultural data to benefit the supply chain is a worthy goal. In our experience, it is not necessarily straightforward. If you intend to use on-farm data to support an agri-food supply chain, there are four key challenges to consider:

  1. Data collection effort and methods;
  2. Data quality and completeness;
  3. Data flow between organisations; and
  4. Data ownership or control.

1.      Data collection effort and methods

With some exceptions, farmers have not traditionally been proponents of formal data collection. A few agribusinesses have built a culture of data gathering and analysis, but many farms would collect the minimum possible.

Recording has often been informal. Data to support a decision might appear on paper, in a notebook, or on an embedded device. After the on-farm decision, data may be discarded, having never been transcribed or centrally stored.

Apps are a great improvement over desktop software for data collection. But, collecting agricultural data is not as simple as rolling out a new app. Design effort needs to go into establishing when, how, and why data will be collected. You need to consider appropriate incentives and support.

A powerful data collection incentive is to immediately return useful insights to support on-farm decisions. For instance, a tool tracking mobs of animals for a processor might graphically show small changes the producer might make to improve their returns.

Remote sensing, image processing, and Internet of Things (IOT) devices promise to take farmer effort out of data collection. In our opinion, this could be transformative. At present the cost of some devices (compared to their perceived benefits) is still a challenge, as is network connectivity. Rollout of 5G networks may improve this!

2.      Data quality and completeness

Data quality issues in agricultural data don’t always arise from insufficient validation of input boxes. Sometimes just the opposite! Issues include:

  • Software and tools that are too clumsy to use or take too long, so don’t get used.
  • Overly tight validation that forces farmers to lie or “fudge” data to get it accepted.
  • Farmers who record results they believe that they should be getting, rather than what is really occurring. A farmer once told me about lamb growth rates that matched industry best benchmarks: I only to discovered later that they did not own any weigh scales.
  • Farmers recording data “just to tick the boxes”, so records are abbreviated, approximated, or (potentially) fabricated.

Transcription errors are another common cause of problems with data quality. We can understand this where data is captured on paper and later transcribed (and certainly in-field data collection can reduce errors). We have also seen real cases of manual transcription between software systems – with an advisor placing their laptop by the farmer’s computer so they can manually re-enter data from one screen to the other.

For supply chain data to be timely and useful to all parties, careful attention needs to be paid to the underlying design issues that cause missing and inaccurate data.

3.      Data flow between organisations

Supply chain networks face potential challenges in managing the flow of data between organisations. For example, farmers may potentially make use of several similar-but-different tools that capture data on farm. Or supply chain partners may request that a grower or farmer use their preferred tool – which can be challenging if the grower sends produce to multiple markets with different preferences!

In an ideal world, producers would not be locked into a single software tool or equipment manufacturer. Use of global standards would allow farmers, growers, processors and retailers to “mix and match”, selecting the best tool for their circumstances with confidence of compatibility. 

Such e-commerce standards have existed between large supply chain partners for many years. Consider electronic ordering, ship notifications and invoices exchanged in the automobile supply chain, for instance. Equivalent progress in the agricultural market has been slow and fragmented, although initiatives such as ICAR, DataLinker, and AgGateway are changing this.

4.      Data ownership or control

As supply chains start to leverage agricultural data, a key question that needs to be asked is “who owns or controls this data?”. Is it the producer, the manufacturer of on-farm equipment, a software vendor, or the processor or market partner who receives data?

It may be tempting to take the approach of “possession is nine tens of the law”. If the data has made it into our database, surely it is ours to use?

With some exceptions, rights to control data fall under copyright law. This leaves the “ownership” decisions about who can use data, and for what purpose to the party who invested time or money to create it – unless changed by a contract.

Surveys show that farmers worry about who controls and uses their data. Surveys of US farmers from 2014 and 2016 showed that 77% of farmers were concerned or very concerned about which entities could access their data, and whether it could be used for regulatory purposes. The November 2018 Farm Credit Canada survey showed similar results.

These concerns motivated the US Farm Bureau to draft its Privacy and Security Principles for Farm Data, and the NZ pastoral farming industry to create the NZ Farm Data Code. The position of these codes has been that organisations and farmers should explicitly agree what data is shared, and for what purposes, and that the starting point should support farmers rights to data about their businesses.

When we work with supply chain and agritech companies, we recommend that organisations are definite about the uses to which they will put data, and that they communicate this clearly and trustfully with producers.

In summary

There are compelling reasons why supply chain organisations in procurement, processing, marketing and retail, are looking to make greater use of agricultural data. Effective use offers greater forecasting accuracy and supply chain efficiency, as well as supporting differentiated product claims. If this is your vision, you’ll also want to consider how you will tackle the challenges of agricultural data – collection, quality, connectivity between organisations, and rights to data.

Rezare Systems helps organisations collect and make sense of supply chain data. We focus on your intended outcomes, rather than a single technology. We use design-led processes to collaboratively look across the issues of collection, quality, connectivity and rights – to identify what must be tackled, and when. If this resonates with you, let’s discuss.

Family eating dinner

Are you making authentic supply chain promises?

If you’re in the food business (whether that’s retail, food service, processing, farming, or supply), consumers are asking questions about your supply chain.

Of course, they may not be asking you directly, and they may not be asking your retail or food service partner, but they are asking: on social media, on recommendation sites such as TripAdvisor and Yelp, even over drinks at their local.

Are you providing the information they need to be confident about the quality and safety of your product? Do you have a substantiated story around provenance, animal welfare and the environment?

Safeguards such as DNA testing lasagna are “bottom of the cliff” activities, an attempt to rebuild broken trust and arguably too limited and late in the supply chain.

Future product preference and even acceptance relies upon a supply chain that can show ethical practices: in how environmental impacts are managed, natural biodiversity is encouraged, animal welfare is maintained, anti-microbial resistance is avoided, and workers and communities are treated.

Activist groups and the power of social media means that our response to these demands must be much more solid than a promise or a declaration form. We must have the systems and measures to back up our words – and to demonstrate as much to auditors and our supply-chain partners.

For those of us at the confluence of technology and agriculture, this means we must do more than just record activities and calculate gross margins. We must step up with tools that capture rich data in support of farming activities, and which actively encourage good decisions that improve both profitability and sustainability.

All this needs to be done with minimal additional effort by farmers and their staff, and aligned to real-world processes on farm.

I’ll be speaking at MobileTech 2017, the annual summit for technology innovations in the primary sector, reflecting on these challenges. I’ll summarise some of the work Rezare Systems is doing in this space, and suggest ways the industry could apply technology to the opportunity.

This article was first published at www.rezare.com/blog  

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://www.rezare.com/blog/.
Contact us to learn how

we apply software and models to agricultural data.

What do consumers know about your supply chain?

Consumers. A jaded and cynical bunch. I include myself in that statement.

Just last weekend, a lovely salesperson was extolling the praises of a new smoothie product (“would you like to try it sir, it’s packed with fruit”), while I was remembering comments from my children about the level of sugar in smoothies and trying to see what was on the ingredients panel.

Studies by the Hartman Group would suggest that consumers are interested in more than just what a product’s packaging looks like, instead 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%).

Of course, that’s not to say we are always completely logical and analytical. When I buy Bella Pane bread at our local farmers’ market, I don’t ask to see the ingredients list, or the best-before date, or ask when it was made. Probably Mike has already told me he got up at 3am to bake the day’s bread, but even if he hasn’t done so, I gain a level of confidence and trust from his local proximity, previous discussions, and the farmers’ market brand story.

That level of trust and confidence in product quality, source, and ingredients is what supports positioning of premium food products. A large North American corporate recently discovered that promising “Food with Integrity” was only a start, and those promises needed to be backed with processes and checks to maintain confidence in their products.

I’ve spent a while recently considering how the information we collect on farm can support the broader story about premium protein products. The Hartman Group research would tell us that consumers in the US are interested in:

  • Hormone free (52%);
  • Free of antibiotics (49%);
  • Artificial (48%);
  • GMO-free (41%); and
  • Organic (31%).

When it comes to animal welfare consumers want to know that companies avoid inhumane treatment of animals – and while they may not know the details of what that means, the proportion of people who care is rising:

  • Other animals are not harmed in capture/raising (e.g. bycatch) (68%);
  • Animals are raised in as natural environment as possible (65%);
  • Animals are not used for product safety testing (65%);
  • Animals are not given hormones or antibiotics (63%);
  • Company supports animal welfare causes/organisations (51%);
  • No animals at all used in products (45%); and
  • Animals fed only organic food (33%).

We know that products and processes that meet these criteria – and more importantly, have a compelling story in these areas – may command a premium in the market, and are in a position to build stronger, more defensible brands.

Consumers expect products and brands to live up to the brand story they are told. When lack of integrity in process or supply chain is exposed, consumers act angrily, as though we have been “tricked” (read Seth Godin’s “All Marketers are Liars” to learn more of how this works).

For that reason, any claims we make about our agricultural products having green origins or being “very pure indeed” need to be backed up by guides, processes and records that demonstrate our commitment to those brand values. Claims of greenness or purity are potentially for naught if we don’t have both safeguards and evidence in place.

Hence the importance of Farm Assurance or Good Agricultural Practice programmes, and the need for audits and for simple to use, on-farm record keeping tools that back up the story. We’re working on some of the latter with our partners. It’s hard work, because farmers are busy people with limited finance. In order for supply programmes to really deliver the benefits promised by the brand, I think we need to do two key things:

Link the activities to the brand story

Make sure everyone who has a role in the supply chain understands how their role contributes to the brand and to the consumer experience. Spell out how actions on farm impact the supply chain: safety, provenance, and in-market claims. Ensure staff know the risks to the business if product integrity fails.

Make it easier to comply than not

Most audit schemes today run on paper – recording pages in a paper book or filling in forms. For practical reasons, these are filled in at the farm office, and often updated just before the auditor arrives. We remove a substantial barrier if it is easy to capture information in the field rather than spending evenings in the office. Reusing information captured for farm assurance records to provide insights for farm management aligns goals and makes adoption more likely.

Your thoughts?

Consumer expectations have been changing over the last decade. Our supply chains and production systems are evolving to meet those expectations. This will require a greater commitment from us all to transparency and integrity, making sure what we do lines up with what we claim.

Do you manage a supply programme, or participate as a farmer, grower or processor? We’re interested in your thoughts. Drop me a note in the comments, or contact me directly.