What explains yield variability within a field, and from one field to another? That question is at the heart of all precision data analysis, says Dan Frieberg, president of Premier Crop Systems, West Des Moines, Iowa, a precision agriculture software company. Finding answers involves a process that Frieberg describes as “a collision between technology and agronomy.”
Turn data into knowledge
The first step in turning geo-referenced field data into useful knowledge is comparing productivity across years and crops, says Kevin Kruize, precision ag manager at Central Valley Cooperative in Owatonna, Minn.
In this 62.5-acre field, Kruize measured the productivity of each 60 x 60-foot square using Premier Crop Systems software. The question he and the grower wanted to answer: Would this field benefit from site-specific management?
To compare productivity variations across years and crops, Kruize charted yields in each square as a percentage of the top yield in the field.
Six years of relative yield analysis revealed significant spatial variability. In addition, variability was fairly consistent from year-to-year and from crop-to-crop, Kruize says. “First we look for the areas that are consistently high and low-producing.” In this field, he found that for both corn and soybeans, the lowest yielding 10% of the field (red squares) were only one-third to one-half the yield the highest yielding 10% of the field (dark green squares).
Differences in soil moisture-holding capacity explain much of the yield variability in this field, Kruize says. The sandy-soiled center section of the field usually dries out in July and August, stressing the crop. The higher organic-matter soils in the upper and left portions of the field have better moisture-holding capacity.
Significant and consistent variability from a known cause makes this field an excellent candidate for site-specific management, Kruize says. Using yield and soil data, plus input from the grower, he defined four management zones within this field: A (high productivity), B (above average), C (below average) and D (low productivity). Two to four zones per field is typical, he adds.
Make data pay
The two high-productivity zones in this field will benefit from intensive management, Kruize says. These sweet spots have a better chance of a high return on investment. For example, in some years, the grower targets the A and B zones for a fungicide application. The two lower-productivity zones, where droughty soils limit yields, should be managed conservatively, in order to avoid wasting inputs, he says.
In 2012, the grower varied corn plant populations and nutrients by zone. N applications, for example, varied from 145 pounds per acre in Zone D, to 200 pounds per acre in a small section of Zone A. The B and C zones received from 155 to 175 pounds per acre. Plant populations varied from 38,000 seeds per acre in Zone A to 28,000 in Zone D.
The grower also planted two 1-acre population check plots, or “Learning Blocks:” one in Zone B and one in Zone D. These check blocks are easy to do and can help you fine-tune site-specific management, Kruize says.
In Learning Block 1, the grower cut the population by 3,600 seeds per acre, compared to the rest of Zone B. The lower population Learning Block out-yielded the surrounding zone by 10 bushels per acre in 2012. “That tells us we may want to drop the population in that area next time,” Kruize says.
In Learning Block 2, the grower cut the population another 2,000 seeds per acre, compared to the rest of Zone D, to 26,000. Yield in the Learning Block dropped 13 bushels per acre. “That shows us that we may not need to drop the population further in the D zone,” Kruize says.