Many farmers across the world are starting out on their precision farming journey and are looking to make better use of inputs and increase profitability.
However, there are some basics to consider before setting out, which can remove a lot of the pain later on. This could be the hassle of setting up A-B lines for a guidance system every time a tractor goes into a field or the problem of duplication of field records, because imported data did not match field records.
In my experience, data-handling problems can be a source of much frustration, even leading to growers to abandon precision farming. Here are 5 key elements to consider.
Consider why you are collecting data. Basically, if you don’t measure it, you can’t manage it. It’s a term farmers have heard many times, but how much attention has been paid to it and more importantly action taken, based on the results?
Getting the tape measure out is as important now as it was seven years ago when I started advising farmers on the fundamentals of precision farming. Taking a few measurements in the field and on farm would make a huge difference to the success of any precision farming investment.
Many times I have heard farmers saying: “My tramlines aren’t 24m wide, I must invest in a steering system to help become more accurate.” In theory, this was an understandable observation, but the fundamental measurement hadn’t been taken; the width of the drill.
On 75% of occasions, the drill wasn’t as wide as the farmer thought it was. For example, a 4m drill came out to be closer to 3.8m rather than 4m. On that basis, the tramlines were never going to reach 24m, so the farmer may have been 100% accurate without realising it. Getting the fundamental measurements for all equipment will form the basis for improving accuracy on the farm.
Precision farming is a very broad area, but I have found the best way to help farmers break this huge topic down is into 3 manageable sections.
These sections are:
The idea behind breaking precision farming into these sections is to help farmers manage their precision farming operation efficiently and effectively.
In my experience, farmers have knowledge and confidence in 2 out of the 3 sections, but a successful operation will have to ensure all 3 sections are covered in detail for the whole system to work successfully.
I don’t know many farmers who are experts in all 3, so the key is to work with trusted partners such as machinery suppliers, agronomy companies and data-management companies to fill in the gaps.
Of all the work I do across the world, there is one aspect that frequently undermines the success of the precision farming operation – the link between the physical farm and the digital farm.
Problems arise when the list of field names in a farm management software system is not the same. All the successful data managers I’ve worked with have one thing in common, they have a list of digital fields that is exactly the same as the physical fields on the farm.
What do I mean by this? Over the years when a new monitor arrives on farm, on the combine for example, the operator will add the list of fields directly on the monitor to gather the yield data. Another monitor arrives on a tractor and another list of field names is created, but there are subtle differences. A capital letter, an extra space, a full stop or a different name altogether.
When the manager or owner imports it to the software, all the data is merged together and the number of digital fields explodes with duplicate entries due to the slight differences in the field names.
If the digital farm or management tree is different, then changes have to be made to ensure it aligns to the physical farm exactly. All farm management systems (FMS) are based on the core principle of a hierarchical list of growers, farms and fields and these principles are exactly the same, regardless of the system being used.
The problem can be compounded if a farmer is using an external agronomy or precision farming service provider to gather and interpret their agronomy information. They may use a different naming system and will not link together as seamlessly as they could or should.
In my experience, about 90% of digital farms don’t match the physical farm in their structure and design, resulting in the adoption of precision farming coming to a standstill.
How often do operators have to create new reference A-B lines in a field for their operation? What if the same A-B lines were on all the vehicles on the farm? Again, 60-70% of operators I’ve worked with say they have to create new ones because the A-B Lines created before haven’t been named or identified clearly.
How much time is wasted by operators having to create A-B lines for each field operation? What if all the A-B lines were the same across all monitors? All named the same and all in the correct field? How much easier would that be for the operator?
As more and more farmers consider improved tramline management techniques, such as Controlled Traffic Farming (CTF), reliable A-B Line management will help manage the vehicles efficiently. The key to CTF is keeping the vehicle movements to the smallest area of the field and the process is made easier, but not exclusively, by robust A-B line management.
I’m really excited about the potential for precision agronomy using the vast array of sensors and tools available. However, as with the rest of the precision farming process is keeping it as simple as possible.
The deciding factor on resolution is the application equipment, which is going to be used to apply the agronomic knowledge. If, for example, the fertiliser spreader has a spread width of 36m, why look at creating variable rate maps with a resolution of 12m? In many instances less will be more.
As fields have got larger and larger, with hedges and fences being pulled out, this is an obvious place to start to understand the zones in the new field. Historically, hedges would have been put in to define areas of different soil textures.
In the past 50 years, those physical boundaries have been removed and we’ve treated the field evenly even though the field will have agronomic differences. So all we’re doing now is putting virtual hedges back where the physical ones used to be.
The hardest part of the precision agronomy aspect of precision farming is deciding on the input you want to vary. Do I put on more or less fertiliser, seed or spray?
Once the agronomy aspect has been decided, you can turn the knowledge into a variable rate application map for the machine controller. The majority of monitors don’t make decisions on the go without some input from the operator. Processes are only automatic once they’ve been programmed to do so – they then work automatically within the parameters given to them.