Tools & data

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How to manage and interpret yield data: 5 steps

You’ve collected the combine yield data – now what? One of the hardest aspects of precision farming is to start using the data and make a difference to the farming operation. These 5 steps will get you started.

Import the yield data

The first challenge is finding the time to import the yield data in to a Farm Management Information System (FMIS). I see so much yield data safely stored on USB sticks, data cards or just left on the monitor because time hasn’t been set aside to look at it.

Many farmers just hand it over to their agronomists and they manage it for them. There’s definitely nothing wrong with that, but are we getting to a time where farmers should be looking at the data themselves as well?

So why is data management lower down on the list of priorities than it should be? It comes down to 2 things:

  1. A lack of time to learn and understand how to use the FMIS to interpret the information
  2. Not trusting the data from the combine and having the confidence to turn it into something useful

If the data has been collected in an organised way, all the yield data will be in the correct fields. If it has been mislabelled or not labelled at all, it may be spread across the farm or, more worryingly, be in one field.

The FMIS can remove any incorrect high or low yield points, effectively cleaning the data.

Yield data analysis

Now it’s time to analyse it. Unfortunately, there is not one clear and concise way of looking at yield data and then turning it into a map that can be converted in to a variable rate application map, for example.

In any analysis, software legends or bands (which set the range, thereby remove outliers) can be set to analyse yield data to help ensure consistency across crop type, variety or any other banding approach. Avoid using a single template, as there will be no consistency in the data being viewed.

Photo credit: Tim Scrivener

Photo credit: Tim Scrivener

Therefore, a grower should ensure the scaling or bandings for winter wheat, for example, are the same across winter wheat in all fields and across years. Otherwise the data starts to become misleading. Is there any point in comparing canola yields with wheat yields using the same scale? Not really.

However, comparing average yields for canola and wheat against barley in the same field over a number of years is vital to the performance toolkit available to farmers and agronomists.

Does the same part of the field always yield highly? And by how much compared with the low yielding parts of the field, regardless of the crop type. This is good, solid performance-related information.

The natural step is then to ask why there is a consistent yield difference. Once the cause has been identified, a variable rate map can be created to manage the consistent variability in the field.

How many zones?

To start with, my preferred approach is to work on the basis that less is more. The fewer zones you have the more manageable they are. So a field with 3 clearly defined yield zones will be easier to manage than a field with 8 or 9 zones where the difference is less defined.

We also have to take in to account the accuracy or otherwise of the yield monitor. The yield can vary across the width of a header by a significant amount and all the data is effectively an average from across the header. Focusing on the last few kilogrames of yield is not going to affect the final analysis in terms of understanding trends.

In many instances, it is easier to understand and manage three or four zones compared with eight or nine, but over time the resolution can be increased if needed.

Equally, resolution can be determined by the width of the seed drill, sprayer or fertiliser spreader. The wider the machine or the fewer controllable sections it has, the less resolution you require.

Multi-year use of yield data

I get asked a lot if a yield map can be used after one year. As long as the data has been collected accurately, it can definitely be used after the first year: it can be used as a replacement map to replace the nutrients that have been removed to grow the crop.

This method won’t account for any in-soil nutritional requirements, it will just replace what has been taken out of the soil to grow the crop.

An interesting comparison to make against these yield zones is the positioning of historical fence lines or field boundaries. In many instances the historical fence line will be the old fashioned way of splitting up a field.

Photo credit: Tim Scrivener

Photo credit: Tim Scrivener

With the old fence lines removed, technology is putting digital fence lines back in to allow farmers to utilise the larger, more efficient equipment. The principles, though, are very similar.

The old fashioned boundary approach can be carried forward to soil texture zones as well. In many cases the zones are the same, which helps a grower decide if conductivity scanning is required or not. Looking at yield maps and old farm and field maps will help produce a soil management strategy.

There’s a great amount of work that can be done before money is spent on conductivity to understand what type of sensing technique should be used.

Look for trends, not the detail

The advantages of using software analysis tools is the ability to look at a field in many different ways. It allows a farmer and agronomist to understand what was done in the past and whether the same or a new approach is needed to help grow crops successfully.

It brings together a range of information such as nutritional data, yield data, soil conductivity data and weather data, among others. The challenge is not to look at the detail in the information, but the trends.

It’s the trends in the data over time that bring the most valuable information. One wet or dry year doesn’t or shouldn’t stimulate a sudden change in agronomic strategy. Continual poor zones in fields should encourage farmers and growers to identify why the variation is there and understand how to manage it.