Rye Cover Crops In Corn Production On Irrigated Sands

Jan 09, 2018

By Natalie Ricks

In Minnesota, approximately 500,000 acres of irrigated farmland are highly productive but susceptible to nitrate leaching to groundwater. Irrigated sandy soils are especially vulnerable to leaching. A recent study from University of Minnesota, with support from Pope County SWCD and the Minnesota Department of Agriculture, evaluated the use of winter rye as a cover crop in corn production. Early results show that a rye cover crop can help reduce nitrogen leaching in a corn after soybean rotation by 45 percent.

We conducted this study in 2016, an optimal year for corn, and 2017, a wetter and colder year. At the end of each season, the data were analyzed to determine what effect treatments had on nitrogen leaching, yield and economic optimum nitrogen rates (EONR). The addition of rye to a continuous corn (CC) rotation had no effect on nitrate leaching. However, under a corn/soybean (CSb) rotation, rye reduced nitrate leaching by 45 percent for 2016-17. This is likely because rye residue is tying up residual nitrate early in the spring when there is high precipitation and little crop uptake. As the season continues, the rye decomposes and nitrogen is released into the system when the plants are consuming nutrients. This allows the plants to utilize the nutrients, rather than losing them through leaching.

The preliminary data show promising results with rye decreasing nitrate leaching without negatively impacting yield or EONR. We found that rye had no effect on corn yield in either rotation. The presence of rye had an inconsistent effect on the EONR. In 2016, the EONR for rye plots in CSb was 50 lbs N/ac less than the conventional (no rye) treatments. In 2017, the EONR for rye plots in CSb was 17 lbs N/ac greater than for no rye treatments.

Rye Cover Crops In Corn Production On Irrigated Sands

So, why winter rye? Rye can survive the cold Minnesota winters, it is a vigorous nitrogen scavenger during critical nutrient loss (spring and fall) and it can be managed with chemical and mechanical termination.

Along with the rye cover crop, the crops received urease treated urea in four split applications at corn development stages V2, V6, V8, and V12. Nitrogen rates of 0, 90, 180, 225, and 270 lbs N/ac were used to predict the EONR.

What does this mean for your operation? These management practices will not fit every farm. They will work best on irrigated farms with sandy soils in CSb rotations. Fertigation systems and systematic weed management will also help in developing these practices. Researchers continue to evaluate new practices for nitrogen and water quality management. With the goal of helping farms perform more efficiently and reducing agricultural pollution, we will continue to investigate best management practices.

Source: umn.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.