What really drives profitability on pastoral farms – the right story

What really drives pastoral farm profitability?

This is the second article in a series reflecting on the building blocks of profitability on pastoral farms in New Zealand. My particular interest is in sheep and beef farming, occupying 70% of New Zealand’s pastoral land.

This article focusses on the farm system – a fascinating animal-pasture game of matching the ever-changing pasture growth with a livestock business.

I am lucky to have spent time with farmers who are far ahead in thinking: my role has been to understand this thinking and how it can be incorporated into a system. Theirs is a whole story because it includes the battle to get the time to think when everything else is happening – farm succession, regional council consents and farming with your brother…

Helping a farmer involves either supporting them to find the right story and or helping them make the right choices with the story they have chosen. The first is strategy and second is tactics, and this article is about strategy (my next article is about tactics).

Let’s pause for a moment to discuss the animal-pasture game and the size of the prize. In quantifying the value of precise knowledge in pasture management on dairy farms Beukes et al (2019) estimated $385/ha can be gained with improving knowledge of pasture biomass (average 15% error) – also referred to as pasture cover, and a further $155/ha could be achieved with perfect knowledge of paddock biomass, at a price of $6.33/kg of milk solids – a 27% increase in profit.

The study found a 26% increase in pasture production between poor and perfect knowledge. Can we translate this to NZ sheep and beef farms? We can assume an average sheep and beef farmer is dairy’s poor category[1] so the move to perfect knowledge would increase pasture productivity of 6.5tDM/ha by 1.7tDM/ha. A farmer should be able to convert this into revenue that would double the profit of average hill country sheep and beef farms. And, that is what we see the top farmers are doing in almost every benchmarking scheme I have seen or been part of.

The dairy sector knows exactly how they would use perfect knowledge – using a feed wedge and the Spring Rotation Planner (developed by DairyNZ) to manage grazing rotations. Sheep and beef farms are more complicated with many more mobs all with different metabolic demands running on different landforms with different pasture composition, quality and soil fertility.

So, can a sheep and beef farm double its profit through more perfect knowledge? I think this is theoretically possible and entirely worth the effort. I see three preconditions:

  1. Pasture cover measurements – easy and accurate
  2. The farm makes good seasonal decisions – when required, with good measurement
  3. The farm system is well design – and can adapt to its climatic variability.

Let’s work through these:

1. Pasture cover measurements – easy and accurate

There are great farmers dedicated to measuring their pasture covers paddock by paddock limited only by the accuracy of their technique and state of the technology. But that dedication is beyond the mettle of the remaining 92% of farmers.

Accurate measures of pasture cover on New Zealand sheep and beef farms is extremely important if we want to win the game and double profit. One example is the decisions made in the autumn season. New Zealand farms minimise expensive supplements by accumulating pasture cover to use in winter, while leaving enough to set stock (open the gates) at the start of lambing and calving.

Too little pasture cover pushed forward, and lambs and calves are born underweight on bare pasture – keeping the pasture bare slows regrowth as pastures exhaust their root reserves, reducing annual pasture production by 20%. But the worst effect is that it prolongs the period where mothers cannot wrap their mouths round enough pasture, they roam, and lambs follow an udder that is not full. If a dry summer follows, the farmer starts with poor condition animals.

Too much pasture cover and pastures quickly enter the reproductive phase with too much pasture length: this will mostly be lost to decay and up to 30% of the annual pasture production will not consumed. If the grazing rotations have not been well planned, then the dead and decaying pasture will not be isolated but will instead be through most of grazing area, slowing lamb growth rates.

Getting this right should not be mysterious, it requires good measurement, good information to base predictions and good computation (thinking). Not having an easy and accurate pasture cover measurement and no quantifiable probabilities of future pasture growth rate creates the mystery.

2. The farm makes good seasonal decisions – when required with good measurement

Farms are biological systems and decisions are based on keeping things both alive and thriving. A farmer must manage pasture to keep it vegetative while enabling it to restore root reserves. The complexity comes with maintaining a whole livestock business that supports the management required of pastures.

If a farmer does this without measuring things, they will be on the spectrum between lousy and good.

  • The good intuitive decision-maker really interests me. In my rather informal study, I have found they look closely at things: for example, they drive over their pastures regularly, perhaps mentally calculating what is coming up in their mental grazing plan. They also have a good memory, particularly for how the farm’s situation compares to previous years and in those situations what they did (or should have done).
  • The other three quarters of farmers also interest me. Often the other things going on in their story are just too distracting and may be a higher priority.

3. The farm system is well designed – can adapt to its climatic variability

I’m going to discuss this topic in more detail.

Designing for variability is all about understanding the strategy of the game. This is best illustrated to me by farmers who always find variability traumatic. The farm seems to operate well less than half the time. In good years there is a big catch-up to be made and the farm is not setup to capture the onrush of pasture. In poor years stock are not sold in good condition and markets are poor. The solution is usually to invest in thinking – why is the farm so brittle?

The highest value from investing in strategy is in farms with diversity in landforms and variability in climate and who have not yet optimised their systems. This is perhaps half our sheep and beef farming area.

The first step in analysing any farm system is to create a good electronic map of the farm, using this to map out the different landforms. New Zealand has seen many attempts at mapping products but most are designed by people who are not closely engaged with sheep and beef farmers in formulating strategy, so there is no concept of things such as area, effective developable area, effective area excluding scrub, and area in puggable soils.

The next step is matching each area (as above) with its seasonal pasture growth profile. Almost no farmer knows the seasonal growth profile for each landform. Given the size of the prize in winning this game we may be underinvesting.

The next few steps depend on the farmers. They involve looking at changes to the way the livestock policies work, or even replacing these with different stock that the farmer may have no experience with. A moderately complicated sheep and beef farm may have three enterprises, some having several mobs such as ewes, hoggets, sale lambs – so understanding what can feasibly run on a farm is complex.

How do you go into the unknown?

Fortunately for New Zealand farmers in the early 80’s a doctoral student called David McCall went about creating a computer model called Stockpol. Stockpol evolved into the Farmax farm modelling software which supports precisely these questions. Dave’s brilliance was in designing it at the right level of biological complexity to answer most optimisation questions.

But Farmax is only as good as it’s professional user, who may be anywhere on the talent-spectrum from costing a farmer through the resulting bad decisions, through to stories that are transformational.

Good farm modellers have the knowledge to both reduce the number of assumptions made and know where to look for opportunities. The assumption-gap is widest in the knowledge of local pasture growth on each landform. Closing this gap is a worthy cause.

A strategy creates direction, and an example of this would be:

  • Strategy: Reduce the variability in ewe productivity by managing condition score 
  • Direction: “At weaning we will prioritise getting ewes to 3 condition score and adjust the number of finishing lambs accordingly”

I love it when farmers have bold strategies that are based on solid logic. Such strategies spell out what needs to be measured. These farmers talk in metrics that line up with the decisions they expect to make. I have visited many farmers who are measuring things without knowing what they will do with the results. I like farmers who have these side hobbies, but if they are measuring just because they are guessing at what might be useful, this article is encouraging them to invest in strategy first.

The farmers story is bigger than their farms profitability.

Farmers may discount a direction because they have no affinity to it and know they will muck it up: some farmers do not like trading (buying then selling) stock for example. This discount may push it well down the list despite its apparent profitability – I am always amazed but I respect a farmer who is living the dream.

So, how can technology help New Zealand sheep and beef farmers to formulate the strategy needed to double farm profitability? Here are a few things that would excite me:

  1. Hill country pasture cover measurement
  2. Calibration of pasture cover measurements
  3. Pasture database library
  4. Improved pasture modelling
  5. Weighing ewes
  6. Condition scoring ewes

Hill country pasture cover measurement

As rising plates and other devices are not useable on hill country. You just cannot buy enough remote sensing devices to get accurate measurements for each paddock. Farmers are limited to visual assessment with a sward stick. This takes considerable confidence, is not accurate on diverse landscapes and takes at least a day to do well. If satellite measurement technologies can prove accurate this is the prime candidate.

Pasture cover measurement calibration

No matter how good the RBG satellite image is it will not pick up much of the dead material. This may need some in-paddock calibration. The prime candidate for this is a mobile phone and artificial intelligence to give accurate measures of biomass and the proportion in dead material.

Pasture database library

There were hundreds of pasture growth rate trials during the old MAF and DSIR days and there continues to be more. But it is a grovel to get these data, most is still in hardcopy. It’s good to hear of progress being made to build such a resource.

Improved pasture modelling

The Pasture Growth Forecaster and Farmax simply needs more investment in handling the dynamics of dead and reproductive material across more locations and sward types. Some work is being done here with lucerne but more needs to be done with the gradient from ryegrass/clover to Browntop, Yorkshire fog, kikuyu pastures.

Weighing ewes

New Zealand companies lead the world in yarding, handling, and weighing technologies. The holy grail on the animal side is real-time measurement of liveweight change in a sample of a mob. I do not think this needs to be calibrated to actual grams of liveweight gain but if shepherds could know when animals are going down when they should be going up. There are some exciting satellite based technologies being trialled.

Condition scoring ewes

Condition score is directly related to ovulation rate (and weaning percentage) while human eye assessment of a ewes lightest has been shown to be poorly related. The solution is grasping ewes in the small of the back and feeling how much fat is there – so plenty of room for operator error and very time consuming. Lifting a 2.5 CS ewe to 3 CS is one of the highest return investments on farm – a technology that drafted accurately as sheep flew through the yards would be an exciting development.

What’s next?

This article covered strategy in the pasture-animal game. My third article will take you to the frontline. We have learned what moves a knight can make; it will be time to make the right moves within a farm’s season.

References

Beukes PC, McCarthy S, Wims CM, Gregorini P, Romera AJ. 2019. Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science 59: 359–367

[1] Sheep and beef farmers are limited by accuracy of pasture cover measurements on diverse landforms and in-paddock variability (soils, slope, stock camps).

What really drives profitability in pastoral agriculture?

Unique, profitable, and complex

New Zealand pastoral agriculture is uniquely based around grazing pasture in the paddock while it is growing. In most countries agriculture is relentlessly dependent on diesel, machinery and chemicals to establish forage crops, spray weeds, harvest them, transport them, wrap them in plastic for storage, then fed them out often in capital intensive feeding systems.

If one side of the coin is fossil fuel hungry, high cost, monocultural farming then New Zealand is on the other side of that coin. Our farmers are skilled shepherds who ensure pastures are abundant and nutritious through their understanding of the ecology of perennial pastures as livestock follow a natural grazing pattern that meets their changing requirements with room to feel freedom and contentment.

We often define farmers who are in the top 5% in profitability. We can also define a group of farmers that have grazing systems that are well designed and are implemented with precision. There is no group of pastoral farmers who have sub-optimal grazing systems, make mediocre grazing decisions and who then find themselves in the top 5% for farm profit – that combination don’t exist.

I have been fascinated by this fact. It has taken me on a unique journey through 35 years of farm consultancy, farm system analysis and development of models to help grazing decisions. When computers became common-place in the late 1980’s I started using spreadsheets to perform tasks that I had performed long-hand with a calculator. I developed the view that if I did a task regularly it was worth developing a spreadsheet and as my interest developed, develop a simple software application.

The true potential of software is in handling complexity and in the mid-1990’s I managed AgResearch’s Decision Support Group and we built industry platforms that are now standard in New Zealand agriculture. These perform tasks that were previously impossible such as managing nutrients (Overseer), modelling farms (Farmax), and evaluating genetic potential (SIL).

In 2004 we left AgResearch and formed Rezare Systems. We loved working with the inspired people New Zealand agriculture seems to be good at producing. They have a spirit about them that I think comes from being part of a biological system. We needed to develop a way of working that could keep pace with them and shares their inspiration. To achieve this we work hard to grasp how software development technologies can be relevant to their biological systems and core staff who have a good grounding in what drives profitability on a farm.

Growing and managing plants that thrive in the environment

This is the first of three blog posts for readers who are interested both in the technologies and the business of farming:

  • Growing and managing plants that thrive in the environment (this post)
  • Designing a feed demand that fit with changes in feed supply
  • Making timely meaningful decisions

The fourth pillar is the importance of people, and I’ll incorporate this topic in each of the posts.

In my experience sheep and beef farmers are stock people who understand their farms through observing the behaviour and performance of their stock. When I get down on my knees and start looking at their pastures few can name more than five pasture species, and none can name all 20 that probably make up their sward. When I studied for my degree in agriculture our agronomy lecturer, Parry Matthews showed how the presence and absence of species and their condition was a lens through which we can understand the environment, soil fertility, past management practices and future growth potential. Indeed, the pastures on a farm are perfectly adapted to the farmer – it is the result of everything that is being done, both the good and the bad.

In most farm systems the pasture species that can support a highly profitable farm system are present on-farm, in the sward or on a neighbour’s farm. To encourage these to be more productive involves understanding their ecology and therefore how we must manage them. In a pastoral system management can only control two factors: soil nutrients and the frequency and intensity of grazing. We could add to this control of competing plants but if you cannot get the first two right then competing plants become a bigger problem than they need be.

I spent some years working in the drier regions of the South Island. It seemed obvious to me that lucerne was a plant that thrived in high pH, well-drained soils. After a drought it would rise like a phoenix from the burned landscape providing abundant high-quality feed well before any other plant had woken up. And, amazingly the drier the environment the longer it lived – a lifespan of 15 years being commonplace. Did farmers see this? Many farmers were planting grass/clover swards. Many were choosing drought tolerant ryegrass. But on the spectrum of drought tolerance across all pasture plants grass and lucerne do not even overlap.

Example of system change – Marlborough

I started working with Doug and Fraser Avery on a project called the Starborough-Flaxbourne Project initiated by Don Ross of the Landcare Trust. Doug was emerging from a period where he had been beaten by drought and unable to see the opportunities directly under his control (his words).

The project was initially focussed on establishing saltbush on eroded sunny facing slopes. It seemed too easy to say “there is the problem, those eroded sunny facing hill slopes – lets fix it by growing saltbush there”. My biggest contribution was in questioning this focus. On the team was New Zealand’s expert on grazing shrubs. He was passionate about saltbush, but he had no understanding or interest in the farm system. He couldn’t tell me how much it would cost to establish and how much forage it would supply.

It also seemed that for grazing to suit the physiology of saltbush on New Zealand farms, it couldn’t be eaten when you needed it and you had to graze it when you had ample feed elsewhere. When we started to develop estimates of the cost of establishment and the forage produced it seemed to me an illogical investment in the farms most unproductive soils and in my rough estimation I concluded the more you planted the broker you got.

In comparison lucerne produced five times as much forage, at the right time, of higher quality and for a tenth of the establishment cost. I simply asked Doug and Fraser how much area could they possibly grow in lucerne – why not base the farm system round this plant. In reviewing livestock grazing habits they concluded lucerne could be grown in the same paddocks that included uncultivatable hill slopes. This increased the potential area from about 8% to 20%. Given the cultivable 20% produces threes time the pasture on hill slopes, it would provide around half of the feed supply.

They worked with Professor Derrick Moot from Lincoln University to understand the plants ecology and how they could base a grazing system around it. Derrick has an incredible knowledge of the lucerne plant and, what is most important a real desire to understand how it fits into the grazing system. I co-authored a paper that describes this system (Avery et al, 2008[1]) so I won’t go into further detail here.

Systems in the North Island

In the North Island where I now reside hill country pastures are based around browntop which thrives and can out-compete all other species if it is poorly managed, particularly if soils are low in phosphate, more acidic and particularly if this acidity causes a high level of aluminium. Browntop has a deep rooted aggressive rooting system that if allowed will completely take over the root zone tying up available moisture and nutrients.

Management is about controlling the aggressive nature of browntop so that more productive ryegrass and clover can thrive through grazing frequency and intensity. To do this well a farm needs paddock sizes that match the size of mobs – ideally, a mob should enter a paddock well before the pasture starts producing reproductive stems and take no more than four days to graze the pasture down to the required pasture height – called the post graze residual, which keeps the sward in a vegetative state.

Under the right management browntop is kept at less than 50% of the sward and ryegrass and clover can thrive. At this point a good financial return is achieved from increasing soil fertility (particularly phosphate) and soil pH. The difference between a sward that is well controlled and one dominated by browntop is an increase in feed production of 30-40%, proportionally more of this extra production being grown in the shoulders of the season when it is most valuable – autumn, winter and early spring. This is equivalent to a 400ha farm purchasing a further 140ha but at a tenth of the cost.

In summary, if a farm system is based on plants that thrive in your environment and management is based on the plants ecology then it allows nature to work with you. If it isn’t nature will beat you at every turn.

Technology and ecology

How has technology helped farmers understand plants that thrive on farm and their management requirements? I would conclude this is not where digital technology has helped. What does help is being able to see what other farmers are doing then trialling these in a meaningful way. It is then no coincidence that farmers who are out-going, ask plenty of questions, participate in farmer groups and are keen to experiment are more successful in changing their systems. Clearly, there is a whole mindset involved in change and Doug Avery’s[2] book The Resilient Farmer describes this better than anything I can write.

People and ecology

When I read Doug’s book it seemed clear to me the Avery’s had been growing lucerne successfully for many decades mainly for making winter supplements and feeding lambs. It wasn’t until Doug and Fraser (with the help of Professor Derrick Moot) really studied the plants ecology and how it could provide half of all livestock grazing that the farm’s profitability soared. So, who in the farm business needs to understand the ecology of the plants it is based on? Is it enough to send the shepherd on a pasture management course?

Understanding the ecology of the plants that a business is based on is a lifetime endeavour for the farm owner and everyone who makes decisions about grazing.

It may seem curious that as a technologist I have started this article series by concluding the first step in optimising a farm system does not involve digital technology. However, in my 35 years as a farm systems analyst I have never seen a highly successful business based on plants that don’t thrive in the farm environment – so, it cannot be ignored.

In the next article I’ll discuss ‘Designing a feed demand that fits with a changing feed supply’. In this step we start to enter the world of digital agriculture. There are good programs that can assist but there is so much more that can be achieved – and I’ll discuss where technologies are being developed.

[1] Avery D., Avery F., Ogle G.I., Wills B.J., Moot D.J. 2008 Adapting farm systems to a drier future. NZ Grasslands Association Proceedings 70: 13-18.

[2] The Resilient Farmer 2017, Penguin Books, 288pp, ISBN-13 97801437707787

The case for smarter tech in cattle and sheep breeding

My first job in livestock performance recording was with the Genetics Section, as it was called, at Ruakura Research Centre in New Zealand. I worked part time while studying at university, transferring research trial data off the government mainframe on reel-to-reel tape, and writing inbreeding coefficient calculation software.

The genetics section was based in an old converted house, where we sat around at large, wooden, public service desks, surrounded by high stacks of computer printouts, all painstakingly bound and labelled for future use. We were the leading edge of genetic improvement and livestock performance recording.

That was nearly thirty years ago of course, and the face and capability of modern technology has radically changed. Interestingly however, many of the practices in livestock recording industries still reflect that past golden age, and it is only recently that the software tools and databases of – let’s be generous and say – 15 years ago have started to be refreshed.

In this, the first of two articles about technology in livestock breeding, I propose that we could make much more effective use of smart technologies to increase the rate of genetic progress and address commercially important, but hard to measure, animal characteristics. In my next post, I’ll examine how technology could reduce the cost of phenotype collection (I might even explain what a phenotype is), and encourage better use of improved genetics by commercial producers.

Measure what you can’t see

In our traditional performance breeding tools, we focused on things that farmers could readily measure: kilograms and counts. Numbers of live progeny, and kilograms of liveweight, milk, and wool. Good news, most of those production traits are heritable and we’ve made good progress over the last 30+ years.

So how do you measure characteristics that are important in modern farming systems?

  • Meat eating quality, so that consumers can repeatably have a great eating experience;
  • Feed conversion efficiency, converting inputs into product more efficiently, reducing greenhouse gas emissions per unit of product, and making the farming system more profitable;
  • For that matter, greenhouse gas emissions (where this is driven by livestock genetics rather than inoculation by a specific set of gut microorganisms);
  • Urine nitrate concentration, and hence one key environmental impact of extensive livestock farming;
  • Disease resistance and the response of animals to a variety of disease and parasite challenges;
  • Behaviour of animals around people and other livestock, including how they handle stressful environments such as being moved; and
  • Longevity, the ability of female animals to raise progeny season after season, reducing the substantial cost of replacement animals.

There are proxies for many of these measures of course. Breeding for growth rates or milk production have arguably improved greenhouse gas efficiency for example, but in some breeding systems a change in mature weight of animals has increased emissions. Progeny tests and laboratory measures have been used in key programmes, but they may not help us with routinely identifying the genetic outliers that will lead the next leap in genetic progress.

New measurement and sensing technologies offer real potential to help with these “hard to measure” areas of animal performance in the coming years. Accelerometer and microphone technologies can identify individual animal eating habits, heats and parturition (birth) dates. 3D and multispectral cameras tell us about carcass and meat product characteristics, and additional characteristics of milk. Increasingly, this data will be collected in-line or in near-real-time, providing a rich stream of data that could be analysed for many purposes.

The next generation of animal recording and genetic analysis systems must be built to handle this variety of real-time, stream data: or at least the results of analysing it.

Fewer errors, more progress

A primary driver of any livestock recording and animal evaluation system is to enable breeders and commercial producers to make better decisions about the animals they use in breeding. Computers don’t select animals: people do. Where a producer chooses an animal because they like the look of its eyes, or its stance, or its colour, and ignores the potential impact of the animal on their herd, the results will be at best random, and often detrimental.

Formal breeding schemes with EBVs and indexes seek to inform better decisions about the breeding merit of animals, but EBVs can be limited by the information available:

  • Accuracy of recording parentage and animal relationships;
  • Incorrect allocation of records to the wrong animals;
  • Transposition and recording errors when capturing data; and
  • Failing to account for the impact of environmental effects such as the feeding and management regimes of groups of animals, the age of the mother, or whether an animal was reared as a single or twin.

Technology is playing a substantial role in improving the accuracy of EBVs, notably through genomic DNA analyses resolving the fraught process of parentage recording and contributing substantially more information, earlier in each animals’ life-cycle. Better facilitation and handling of genomic data collection is well overdue in animal recording systems, and I’m pleased to see this being addressed.

In addition to genomics, electronic identification (EID) and automated recording systems can remove many identification and data capture areas, and the ability to feed this data seamlessly into modern evaluation systems without having to manually manipulate data will provide another leap forward.

Recording management groups properly has been a real limiting factor in many breeding programmes, and is one of the key hesitations in extending these to commercial producers. I believe that sensors that identify eating and movement behaviours, and location or proximity to other animals, will help us to automatically and transparently solve the problem of recording management groups and regimes. This will provide another substantial step forward in removing the noise of environmental effects.

Of course, more accurate EBVs is still only a piece of the puzzle. Helping producers to make use of this information effectively is another, and something I’ll address in my next post.

 

Rezare Systems is a bespoke software design and development company specialising in the agriculture sector. We have special expertise in building livestock recording and management systems, and tools for data collection and integration. Learn how Rezare Systems can assist your business.

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/.
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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.

Getting the most from farm data

Increasing pressure from commodity returns, input costs and environmental compliance means that farming today relies on consistent, quality decision-making. Good information, viewed properly to gain insights, is the life-blood of great farm decisions.

Unfortunately, the most useful data is often hardest to collect and interpret. Pasture information relies on pasture walks (or drives); stock condition must be assessed manually or using advanced equipment; and even understanding growth rates of cattle or sheep requires pulling them off feed and into yards where the risk of transferring disease increases.

Many advisors from fertiliser and feed planning to finance and animal health now have tools that help with visualising outcomes and supporting decisions. In turn, these tools are also hungry for data – sometimes detailed and sometimes high-level farm information. Some farmers tell me they feel every second person up their driveway needs to ask “twenty questions”.

So how can we satisfy our craving for more and better data, without turning farmers into field technicians or survey gurus?

Start with making better use of the data we have

This might include the farmer’s own records in their tool of choice – whether that’s a feed planning tool, paddock recording system, or their financial management system (which often capture product quantities and inventory as well as sale and purchase records). At the moment this existing data is in silos – unable to be accessed because it is locked away, or perhaps in a different format.

Where forward-looking software vendors have made some data available, it is often unable to be directly applied to answer other questions – at least without a human to interpret. Take the example of one tool asking information about calving dates and peak milking numbers, while another asks for monthly cows in milk. With experience and farm system knowledge, a human can readily translate one from the other – but these inferences are hard to automate.

The Farm Data Standards are the New Zealand industry’s approach to getting a common vocabulary, so that our computer systems will be able to meaningfully re-use data. This vocabulary is supported by the Data Linker (a DairyNZ and Red Meat Profit Partnership project), creating standard protocols so that software tools can share farm data through APIs, with explicit farmer permission. Organisations in the Data Linker early-adopter group are building streamlined processes so farmers can re-use their data with little or no overhead.

Towards more automated collection

The “Internet of Things” (IoT) promises to connect sensors and measurement devices from the farm to farm software and databases, making the most of recent advances in consumer electronics to reduce the cost of the electronics, enhance reliability and improve battery life.

IoT devices now available include remote monitoring and alerts for your water supply, pumps and tanks, as well as devices monitoring the state and efficacy of electric fences and effluent spreaders. There have been electronic solutions in this space for quite some time, but improved mobile and on-farm wireless networks, along with smaller and lower-cost electronics, are now making them more attractive.

Coming IoT devices may monitor water quality in real time, assess pasture cover, assist with matching dams and progeny, or with diagnosing animal health challenges. A key for farmers will be ensuring that they can access this data and re-use it for a wider range of purposes where it makes sense.

Filling in the gaps with remote sensing

Lately I’ve been privileged to meet farmers and technology companies in the United States and Australia, where broad-acre cropping of corn, soybeans, and wheat are the predominant farming practice. Farmers are starting to make great use of multispectral and hyperspectral imagery regularly captured from aircraft, low-earth orbit satellites, and even drones (though the range of most drones is too short for larger farms).

Image analysis from these platforms has been around for a long time now (using normalised vegetation difference index or NVDI, for example), but instead of just displaying images and leaving the farmer to guess what is going on, companies are now applying machine learning to correlate the patterns in the images with known crop issues and yields. For large enterprises, this remote sensing data “fills in the gap” between what the farmer observes by walking in the fields, and the wider enterprise. Hyperspectral imaging that captures additional wavelengths will support more sophisticated analysis, and I look forward to seeing some new crop-specific analyses in the future.

Weather and climate data from MetService and NIWA can also be considered remote sensing data to can support decision making, even for those without their own on-farm weather station. The NIWA Virtual Climate Station Network (VCSN) provides a grid of historic climate data and weather data across New Zealand, and that data is combined with soil drainage and fertility information in the Pasture Growth Forecaster. Other countries provide similar climate data services.

A word to the wise regarding Pasture Growth Forecaster: free regional averages are just that – averages over a broad area and a range of soils. You’ll get better mileage by paying the trivial amount each month to get a custom forecast based on your location and soils.

Bringing it all together

I’ve painted a bright picture of how the data available from a number of sources – existing databases and suppliers or customers, small in-field devices connected with the Internet of Things, and remote sensing data – could reduce the overhead that currently puts many farmers off collecting data.

The challenge for farmers and their service providers is now to bring those assorted pieces of data together to provide information and insight for better decisions.

For service providers (including software developers such as Rezare Systems) that means lifting our sights from simplistic tools that regurgitate input data in pretty graphs, to providing predictions, visualisation, and insights that support decisions which matter to farmers. For farmers, that will mean grasping technologies that show potential to address future farming needs, and challenging vendors to make systems as open, connected, and useful as possible.

 

Agricultural Tech Reloaded

MobileTECH 2016 in Rotorua, New Zealand was a showcase and discussion forum for the adoption of smart technology in primary industries: agriculture, horticulture, and forestry. 300 attendees from a broad spread of primary sector organisations and technology companies spent two days discussing the application of all things sensor, wireless, and cloud.

I thought it worth summarising four major themes from my perspective.

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Four ways to make your farming app engaging

So you’ve decided to build a mobile app for your rural customers or users. You have spent time on the overall value proposition, deciding how your app will deliver enough value to your users that they will spend the requisite time – and money – to use your service. No doubt you’re also modelling the likely market penetration and adoption curves, because you want to make sure you can get a return for all the investment in software development, testing, and marketing.

You know that up to 20% of mobile apps are downloaded, used once, and then never touched again (perhaps uninstalled when the user runs out of space). How can you avoid your app ending up in that category? Better than that, can you provide an experience sufficiently valuable and engaging that users tell their friends?

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