by Sally Colby
Farmers are presented with an ever-increasing array of technology aimed at making them better at what they already do. Those who choose to incorporate technology in the field can expect better yields and profits.
“Crop technology hinges on GPS,” said Dr. Robert Nielsen, professor of agronomy at Purdue University. “It’s the driver of everything we do in precision ag.” Nielsen says equipment control, monitoring of equipment, spatial data and GIS software to manage the spatial data are also important components.
However, the farmer’s own experience is a critical part of technology. “The last component is one that is too often overlooked is agronomic knowledge,” said Nielsen. “Too often we become enamored with technology itself and forget that we have to apply agronomic knowledge along with it to gain anything.”
One major component of field technology is spatial data, which Nielsen broadly describes as variability across a landscape. “There are a lot of forms of spatial data,” he said. “Grain yield, imagery, soil sampling, soil types or manually mapped features. Spatial data can be dense or sparse.”
Nielsen explained there are many data points per acre. “It isn’t uncommon to get as many as 300 to 600 data points per acre on a yield map,” he said. “A data set for a typical 2.5 acre grid sample, which is a spatially sparse data set, might have only 0.4 data points/acre.”
Whether data is dense or sparse is critical to accuracy. “When you ask the computer program to draw a map, any area in the field that doesn’t have an data point assigned to it uses non-sampled areas to try to figure out what’s in the holes,” said Nielsen. “The software fills in the spatial holes by mathematically interpolating the values between the known data points.”
Spatial data density affects the accuracy of the interpolated map, which in turn affects the accuracy of any management decisions based on that interpolated map. Because yield (and real money) results can be significantly impacted by management decisions that were based on the map, the accuracy of drawing the map is important. For example, a yield map of a 30-acre field has many data points because data was available for various segments of the field. However, on a magnified view of individual harvest passes, holes in the data are obvious.
“The more data points, the more accurate the information,” said Nielsen. “The spatial density affects the accuracy of the interpolated map. It also affects the accuracy of any management decisions you make based on that interpolated map; and that affects the yield and dollar consequences of the management decisions that you made based on the map.”
Nielsen explained how to use harvest yield data to the best advantage. “Yield data collected every second at 3.5 mph is about one data point every five feet of linear travel,” he said. “That’s equal to about 14,000 data points on 25 acres. It’s a pretty dense data set.” Nielsen noted that dense data results in a map that appears smooth and without missing information because the computer doesn’t have to interpolate as much.” In some cases, Neilson prefers the greater detail that comes with raw data yield maps because it’s easier to see things that might be of interest; such as a low-yielding section of a field.
Data collected from soil mapping is a somewhat different story. “A common pattern for soil sampling is 2.5 acre grids,” said Nielsen. “A typical sampling usually includes 8 to 12 soil core samples pulled within a 40 foot diameter of where the sampler is standing. The samples are combined for a single sample for that point. For a 25 or 30 acre field, there are 12 data points. That’s a very sparse data set. Everything else in that field represents spatial vacuum that has to be interpolated with GIS software. The computer has to figure out what’s between the data points, and that’s a lot of interpolation. If the data are garbage, you’re going to get garbage — even if the model is perfect.”
Soil samples over 2.5 acres are probably better than the one sample per field, but can you afford to soil sample in .5 acre grids? Is there value in getting an accurate baseline from the start? Nielsen suggests that rather than sampling 2.5 acres every three or four years, farmers could sample .5 acre every six or seven years. “Or maybe do a .5 acre sampling once, then go back and target the areas the areas that look ‘weird’. Or maybe supplement those 2.5 acre grids by using yield data or imagery to identify spatially odd areas in the field, then add those to the sample to make sure we’re sampling the weird areas in addition to the patterned 2.5 acres.”
Nielsen says precision ag can improve yield and/or profits. “Using precision ag to improve the operating efficiency of your equipment improves your bottom line, but it has no effect on yields. But because equipment is more efficient, your costs are lower. With row-to-row controls or less overlap of inputs (fertilizer, herbicides), you’re saving on costs and improving efficiency. There is also substantial evidence that operator fatigue is greatly reduced with auto-steering.”
Farmers have always known that fields vary greatly, but can now document those variations with spatial data. “With that, we can identify yield-limiting factors more accurately,” said Nielsen. “Then we have more opportunities to manage spatial variability — that’s how we’re going to improve yields, and hopefully improve profit.”
Nielson manages the KingCorn.org site, which is a clearinghouse for nearly everything there is to know about corn. Growers can reference the site for information about corn growth and development, soil fertility and plant nutrition, diseases and insects.