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Pulling Smarter Fertilizer Decisions from Soil Sampling Technology


Photo Courtesy of Rogo Ag

Robotic collection and broader use of soil profile data can help farmers be more economical and accurate with fertilizer purchases and application.


Soil sampling isn’t traditionally considered one of the most precise precision ag practices. Variability in depth, timing, and volume of samples taken in a field are pain points that can limit the reliability of results.


While lab analysis of soil is highly accurate – less than a 3% margin for error – results are only as reliable as the collection method. Inconsistent or incomplete samples can translate to mismanagement of nutrients, an outcome farmers can’t afford given rising fertilizer prices.


“There is no precision agriculture without a precise soil sample,” says Stephen Roswarski, vice president of sales and marketing at Rogo Ag. “Farmers depend on their soil sampling results when purchasing fertilizer. But how confident are they in the accuracy of those samples today to make those decisions?”


Recent advancements in soil sampling technology are providing more repeatable collection methods and broader application of soil profile data to minimize error and maximize the economic value today and in the future.


Closing the Variability Gap

Taking a 4-wheeler into the field with a soil probe every few years might give farmers a surface understanding of soil health. But with the USDA estimating $212 in fertilizer costs per acre of corn, improving the quantity and quality of sampling can have a significant economic impact.


One of the limiting factors in leveraging soil samples to make fertilizer decisions has been a need for repeatability and accuracy with the collection, Roswarski says. “This is especially true with fertilizer prescriptions and farmers not believing in the promises they were sold with variable-rate,” he says. “It can help farmers maximize fertilizer placement, which can impact yield. But not if fertilizer recommendations are based on soil data that had too wide of a variability gap.”


To narrow that gap, Rogo Ag developed the SmartCore robotic soil sampler. The mounted system is pre-programmed with field boundary information, and an automated probe pulls a specified volume of samples at depths of 4-10 inches in the field.


“The most reliable method for taking soil samples had been by hand, and even that had an 11% margin of error,” he says. “We’re at 2.4%, so every time the probe goes into the ground, it’s within 3 millimeters of the target depth and comes out with a full profile of the soil.”


A third-party research study comparing 100 acres of robotically sampled results with those taken by hand found that almost a quarter of the area sampled by hand was over-applied with lime, phosphorus (P) and potassium (K).


Analysis of multiple samples taken in the same field and on the same day revealed that the accuracy of the Rogo Ag system helped shift fertilizer that was overapplied by 24% to areas of the field that were underapplied.


Dependable soil sampling results that allow farmers to reduce or reallocate nutrients to more productive fields proactively could also drive “situational” sampling, says Dave Swain, owner of Vision Technology Management.


Farmers may need more time or the economic flexibility to soil sample every year, so a history of accurate soil test data provides another layer of information to sample specific acres and get timely results efficiently.


“I can see farmers being more strategic in their sampling based on their confidence in the data they’ve accumulated over time,” Swain says. “Instead of taking 50 samples across a field every year, maybe they take 10-15 in a specific zone they know could be a potential problem.”


Building a Smarter Soil Profile

With more data supporting on-farm performance, biologicals have become an economical alternative to commercial fertilizers for some farmers. The momentum also creates an opportunity to utilize detailed soil sampling data differently.


“There is a need for more spatial application of soil data,” says Tyler Lund, sales and marketing director with Veris Technologies. “We get pretty robust information from the lab to guide our pH, P and K decisions, but there’s an underutilized opportunity to apply soil data beyond commercial fertilizer applications.”


The hardware and sensing technology are in place to help farmers better capitalize on strategic applications of biologicals, nitrogen stabilizers or herbicides. The missing piece, according to Lund, is more soil-specific recommendations.


“There are products that might be cost-prohibitive for farmers to apply farm-wide, or they are unwilling to apply at label rate,” he says. “But those products have a better ROI in certain parts of the field. For example, nematicides can cost $60 per acre to apply, but certain nematodes are only prominent in sandy soils. A Veris map can identify those target areas to apply in a cost-effective manner.”


As farmers pursue more identity-preserved crops, the carbon credit market matures, and the regenerative ag movement evolves, Lund adds that soil sampling technology will also need to evolve.


“If we as farmers are going to take advantage of some of these precision ag practices that are focused on better stewardship and have the potential to grow more at a lower cost and sell to certain buyers at higher price, soil sampling is the starting point,” he says.

Seeding Potential with Variable-Rate Technology

Get a from-the-field perspective on how to capitalize on variable-rate seeding technology from Morgan Seger, host of the Precision Points podcast. From sorting data to create zones to populating those areas, she discusses what she has seen work for growers in the field.


She also suggests that you do not rank all your zones by productivity. Seger breaks down the different types of data that are most commonly used in variable-rate seeding scripts today, including yield data, soil maps, satellite imagery, and elevation maps.


“I often see people spend painstaking hours developing their zones and quickly throw populations into those areas,” she says. “I think we need to slow down and really think about what caused the differentiation between areas and how hybrids and varieties will respond to that area. Understanding the minimum and maximum population the specific product can handle is a good place to start.


We recently shared a list of several insights you should have before populating your fields to point you in the right direction.

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