What is the Impact of Field Shape on Planting Time?
Yesterday we announced the release of machine data functionality – our customers can finally put their precision data to good use by importing it into Granular for streamlined, machine-verified tracking of input usage and yield. That means that our Data Science team can use equipment data to help our customers more easily track productivity and profitability. We also use this data to create models and analyses that help them make smart decisions, like planning planting activities taking into account factors like field shape and size.
It’s no secret that field shape and size have an impact on how fast you can farm it. The smaller and more irregular your fields are, the fewer acres you can plant per day. But with machine data now automatically imported into our system, we’re able to know approximately how much longer it can take to farm a field that’s not perfectly shaped.
We used field area-to-perimeter ratios to describe ‘shape’, and normalized all fields in our database to answer the question, “For the same tractor/implement, how much slower is a field to plant based on its shape?” We calculated the relative planting speed by field for all unique tractor/implement, crop, year, and grower combinations in our database, and plotted the results in the graphic below. The smallest/most complex fields can be up to 40% slower than the largest/least complex fields, which saturate at about 10% above average speed. Not surprisingly, larger fields tend to be farmed with larger implements, and hence have an ‘extra’ planting speed advantage. To account for this, we compared only different fields farmed by the same implement.
Being able to farm faster means using less fuel, farming more acres, or using less equipment – and now we know how a field’s shape can directly play into that, and your overall profitability. These metrics can help you manage and allocate costs on a field level, and have a more accurate sense of how much a field is really worth.
Are the results of these analysis consistent with what you see on your fields? We’d love to hear from you. If you have any suggestions, questions, or ideas for future analyses, please email us at [email protected].