With all the advanced planting technology available today, numerous planting metrics including population, singulation, and spacing quality can be displayed and mapped simultaneously on the planter displays, also commonly referred to as seed monitors. In precision ag, planting data is gaining more interest recently as it can provide valuable insights into planter performance in the field as well as serve as an important data layer that can be referenced for making crop management decisions during the season. With additional capabilities on modern planting systems to precisely control and monitor other inputs such as fertilizer and pesticides, as-applied data from a planter can also provide useful information on what, where, and how much of different products were applied in the furrow, all of which is highly important information when troubleshooting stand establishment or emergence issues in a field.
While all that sounds pretty good, it is important to mention, and if you haven’t heard it already from somewhere, the insights provided by the planting data are only as good as the quality of the data collected. This is true for any type of ag data collected on a farm today as information from erroneous data can lead to some poor management decisions and could even end up being costly in some cases. For growers utilizing planting data in their farm operation in one way or the other, it is important to ensure that the data being collected is accurate and of highest quality.
Here are few considerations to avoid some common errors and ensure quality data collection during planting:
Planter Configuration and Section Control: In order to collect and map accurate data while planting, proper planter configuration including the length and width of the planting equipment, total number of rows, and row width in the seed monitor is critical. If section or individual row control capabilities are present on the planter, it is important to verify that the number of sections and width of each section are entered correctly, and auto section/row control is enabled. All this should be a part of pre-plant technology inspection and can easily be done before getting out in the field.
GPS Accuracy and Offsets: Since any ag data including planting is spatially mapped using real-time GPS position (latitude and longitude) in the field, the setup and accuracy of the GPS system plays a big part in how accurately the planting data is being displayed and recorded during planting. Similar to the planter configuration, GPS offsets including exact location of the GPS on the tractor and from the planter should be entered correctly to prevent any errors such as data logging out of the field boundary, unnecessary overlap or skips between the passes in the planting data.
Calibration: Planted population – one of the main planting metrics – is measured using a seed sensor installed on the seed tube. It is common to have a seed sensor malfunction or provide inaccurate readings due to an obstruction in the seed tube. Hydraulic driven planters also require a correct gear ratio to be entered into the planter display to control and achieve target seeding rate. Most modern planting systems have an option to perform a static calibration test to check the accuracy of seed metering system for the whole planter and even for individual row units in some cases. This step also helps in verifying if the correct crop kit including the seed disc for the crop being planted is installed in the seed meters and is functioning properly. If utilizing planter display to meter and place any other inputs, make sure to calibrate and verify the accuracy of those systems as well.
Field Names and Jobs: One of the most common issues with as-planted data is the presence of data from multiple fields together in a single job or under the same field name. This makes it harder to visualize data for each field separately and often requires some sort of post-processing to split and assign data to individual fields. It is always a good practice to name each field distinctly as well as to start and end the planting job within that field to keep data clean and organized. This becomes even more important when planting multiple varieties and/or seeding rates across the farm as one of the main benefits of planting data is being able to track planting metrics including crop varieties and seeding rates from one field to another.
Planting Prescriptions: If using any planting prescriptions to automatically vary seeding rates within the fields, proper equipment setup along with GPS offsets (as mentioned above) are crucial for the planter to successfully apply the assigned seeding rates within each zone. When loading prescription maps, make sure it in the correct file format and the appropriate rate column (with right units) is selected in the planter display to read the planting prescription correctly. Appropriate look-ahead distance based on the planting speed and size of the seeding rate zones should also be checked and entered correctly for planter to transition smoothly between the prescribed rates.
Data Transfer: Ensuring proper data quality does not end in the field with planting but should be followed all the way through until the data has been successfully transferred into a data management software or an application. If enabled and active, most new planter displays have capabilities of wirelessly transferring planting data into their own respective data management software’s, whether available as an online web or desktop application. If this functionality is not present or enabled, data should be transferred using an external storage device from the seed monitor to a computer. While the specific timeline for transferring this data depends on what and how this data will be utilized, it is generally recommended that the sooner the better.
In summary, planting data can be a useful layer when used appropriately for evaluating planter performance and/or assessing crop stand in the field. The quality of planting data is an important aspect and should always be taken into consideration, especially when making any management decisions based on this data.