Garbage In Means Garbage Out

Use Farm Data to Make the Best Decisions

By Paul Hermans

Harvest is happening and we are all eager to get the crop in the bin. What we do from a data collection standpoint this fall can affect our 2023 cropping plans and beyond.

Technology in the agricultural sector has changed a lot in the last 20 years. There are big gains to be had using yield monitors and variable rate applications. The old saying “garbage in means garbage out” applies yearly when using these tools.

You may be asking, why do I say that? Simply put, if we do not do a proper job of calibrating yield monitor equipment, we will be using inaccurate data affecting both farm agronomic and economic implications.

In this article we will explore what happens when we use inaccurate digital information and offer some timely tips to avoid these mishaps.

Variable Rate Seeding

A key input factor for variable rate seed is previous yield maps. Utilizing differences in yield environments (management zones) scripts are written based off varying yield levels. For every 10-bushel difference in yield, your crop advisor would recommend a given prescription rate for corn in that yield environment. If the yield map is off 10 per cent, that could mean a 20-bushel difference in a 200-bushel field. This would send “the wrong signal” planting-rate wise in that management zone.

Variable Rate Fertility

Corn, soybeans, and other crops all have unique crop removal rates when it comes to fertility. There are different ways to apply variable rate fertilizer. One utilizes crop removal based on yield monitor data. Like the seeding example below (Figure 1), if we start with the wrong information during harvest, the variable rate fertility script that will be written will be applying too much or too little fertilizer in each management zone.

crop removal data chart
    Figure 1: Crop removal based on yield monitor data

With current commodity prices and the cost of seed and fertilizer inputs, dialing in correct prescriptions can mean the differences of $20 to $50 per acre or more.

Seed Purchase Decisions

Arguably one of the most important decisions you make yearly is what hybrid/variety to grow on each field. Everyone who knows me, knows I love looking at data. The more the better. Growers have asked me if they can trust yield monitor data to make seed purchase decisions looking at multiple hybrids in one field. Simply put the answer is no. The differences in corn characteristics from moisture to test weight can greatly influence how a yield monitor records data. To prove this point, we conducted a simple trial comparing yield monitor data to weigh wagon data in 2018 (Figure 2). Fourteen hybrids were looked at on this on-farm trial.

chart comparing yield monitor data to weigh wagon data
    Figure 2: Comparing yield monitor data to weigh wagon data in 2018

The total differences between the two weights (combine versus yield monitor) on average was 95.8 per cent. Not a big difference. When we “peeled back the onion” and investigated the data hybrid by hybrid, that is when we noticed some big differences.

On a hybrid-by-hybrid basis the data ranged from 89 to 108 per cent. Interestingly the No. 1 hybrid ranked off the combine was ranked fifth using the weigh wagon. The No. 6 placed hybrid on the combine was ranked No. 1 on the weigh wagon.

I am all for fair game hybrid comparisons amongst companies. However, utilizing yield monitor data to make these seed decisions is very difficult to do accurately with today’s yield monitor technology.

New tools can be used in advance to get estimates for corn on a field-by-field basis prior to harvest to set yield benchmark levels. Yield estimator apps, like Granular Insights, can be used to walk low, medium, and high areas of plant health. Yield estimates can be used to get an average for the field prior to harvest. When setting up the yield monitor, yield estimate numbers can be compared to calibrated monitor data. This will ensure you are in the ballpark when it comes to having accurate data.

I have highlighted three cases where using the wrong data can affect agronomic and economic decision down the road. Luckily, we can take proper steps before harvest to offset wrong decision making.

What are the key steps to avoid having bad data? It all starts with the combine operator and making sure the yield monitor is properly set up.

Before harvest starts there are some simple steps that can be completed.

In the shed:

  • Check tension and paddles on the clean grain elevator. Improper setup at this point will mean data at the yield sensor will not be consistent.
  • Pull the moisture sensor auger and sensor out. Make sure all the old grain is cleaned off and the moisture sensor does not have buildup on it. The biggest factor in inaccurate data is incorrect moisture readings.
  • Check the yield sensor for wear.
  • Clean out old data in the GPS display and verify all measurements.
  • Take time to download data to your computer in a file structured system for future use.
  • Ensure the data is stored in the “raw format” right from the combine. This will allow other users to utilize the data with different software programs.
  • Verify you have the correct speed source setup, and it is accurate. Some machines use transmission speed and others use GPS.
  • Read your monitor’s calibration process to get familiar with it.
  • Take five minutes to go through calibration screens with other users prior to harvest to save critical harvest time.

In the field:

  • Setup the machine for the crop correctly before beginning calibration on the yield monitor
  • For the calibration loads keep them as close to equal sizes as possible
  • Harvest each calibration load at a different ground speed. This helps set the calibration curve by varying the flow across the sensor.
  • If after three loads you are not seeing the numbers very close, clean out all current and old calibration loads in the monitor and start over.
  • After the grain weight calibration is done, adjust the moisture sensor calibration to match a good moisture meter.
  • If the machine was calibrated in high moisture >21 per cent corn another load may be needed later in the season when the moisture is lower.

If the monitor has never been calibrated, expect the first load to be between 15 to 25 per cent off versus accurate scales or weigh wagon. Once the machine is calibrated properly it should be within three to five per cent of the known scales. I have been successful helping growers get combines calibrated to within one to two per cent. On a 200-bushel corn crop that is within a two to four bushel spread.

I was fortunate to listen to a talk on yield monitor accuracy by Dr. Matt Darr, from Iowa State University. Dr. Darr recommends calibrating your yield monitor often. He suggests doing it when changing crops.

Second would be when crop properties change significantly. This means when the crop changes more than four per cent in moisture, has different test weigh components or when tough conditions (like down corn) affect ground speed.

combine moving crop into weigh wagon
    Paul Hermans photo

One of the biggest factors affecting yield monitor error, according to Darr’s studies, was the corn moisture levels at harvest.

When you think about Canada’s climate, we are harvesting corn that is typically a lot wetter that the midwest corn belt. Hence the need to calibrate our combines more often for accurate data.

Yes, monitors have their place on every farm. However, utilizing weigh wagons, grain buggies with weigh scales or simply weighing commodities across a truck scale go a long way to making sure our data helps to make the right decisions going forward.

When Ontario’s harvest is over, let us make sure that at every field level, we are carrying valuable data back to the office (not garbage) that leads to sound management decisions moving forward. BF

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