Are Sulfur Deficiencies Becoming More Common in Ohio?

Feb 06, 2020

By Laura Lindsey, Steve Culman

Sulfur is an essential macronutrient for crop production, often ranked behind only nitrogen, phosphorus, and potassium in importance. Overall, for corn and soybean, deficiencies are fairly rare. However, deficiencies can occur and are most likely on sandy soils with low organic matter (<1.0%). Much like nitrogen, the primary form of sulfur in the soil is found in the organic fraction, and the form taken up by plants (sulfate) is highly mobile. For every 1 percent of organic matter, there is approximately 140 pounds of sulfur, most of which is unavailable. Like nitrogen, sulfur must be mineralized to become plant available. (Plants may exhibit sulfur deficiencies under cool, wet conditions when mineralization is slow.) Historically, sulfur was deposited in large quantities from rainfall primarily due to burning of fossil fuels. However, emission standards have resulted in a sharp decrease in sulfur deposition from the atmosphere. As this trend continues, coupled with higher yielding crops, sulfur fertilization may become more important in the future.

Figure 1. Sulfur deposition maps from 2000-2002 and 2015-2017 (USEPA, 2019).

A common question these days, is ‘Do I need to fertilize with sulfur?’ Table 1 summarizes on-farm sulfur trials conducted in Ohio from 2016 through 2019. Overall, only one trial (out of eight) resulted in a yield increase due to sulfur application (3 bu/acre in soybean). In addition to these on-farm trials, sulfur (applied as gypsum) did not increase yield in sixteen different environments across Ohio in studies conducted in 2013 and 2014. Lack of yield response is likely due to soils with organic matter levels >1%. (In our sixteen-environment study, soil organic matter levels ranged from 2.0 to 5.1%).

Table 1. Summary on on-farm sulfur trials in corn and soybean from 2016-2019.

Year	County	Crop	Sulfur Source, Rate, and Timing	Yield Response?	Reference
2019	Madison	Soybean	Thio-sul at V3	None	Nate Douridas (eFields report)
2019	Crawford	Soybean	Thiosulfate, 20 lb S/acre, starter	+3 bu/acre	Jason Hartschuh (eFields report)
2019	Darke	Soybean	AMS, R1 and R3	None	Sam Custer (eFields report)
2018	Darke	Corn	Starter	None	Sam Custer (On-Farm Report)
2017	Darke	Corn	Starter	None	Sam Custer (On-Farm Report)
2017	Darke	Corn	Ammonium thiosulfate, 20 and 40 lb S/acre, starter and sidedress)	None	Sam Custer (On-Farm Report)
2016	Muskingum	Corn	Starter	None	Clifton Martin & Van Slack (On-Farm Report)
2016	Darke	Corn	Starter	None	Sam Custer (On-Farm Report)

Sulfur deficiency symptoms are similar to nitrogen, but unlike nitrogen, chlorosis (yellowing) is more visible on newer, upper leaves. If you think your crop is deficient in sulfur, plant tissue testing is the best way to assess. (Sulfur soil analysis is not recommended.) If possible, collect plants exhibiting deficiency symptoms and also plants not exhibiting deficiency symptoms for comparison.

Source : osu.edu

Video: Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Plant breeding has long been shaped by snapshots. A walk through a plot. A single set of notes. A yield check at the end of the season. But crops do not grow in moments. They change every day.

In this conversation, Gary Nijak of AerialPLOT explains how continuous crop modeling is changing the way breeders see, measure, and select plants by capturing growth, stress, and recovery across the entire season, not just at isolated points in time.

Nijak breaks down why point-in-time observations can miss critical performance signals, how repeated, season-long data collection removes the human bottleneck in breeding, and what becomes possible when every plot is treated as a living data set. He also explores how continuous modeling allows breeding programs to move beyond vague descriptors and toward measurable, repeatable insights that connect directly to on-farm outcomes.

This conversation explores:

• What continuous crop modeling is and how it works

• Why traditional field observations fall short over a full growing season

• How scale and repeated measurement change breeding decisions

• What “digital twins” of plots mean for selection and performance

• Why data, not hardware, is driving the next shift in breeding innovation As data-driven breeding moves from research into real-world programs, this discussion offers a clear look at how seeing the whole season is reshaping value for breeders, seed companies, and farmers, and why this may be only the beginning.