Widely Used Nanoparticles Enter Soybean Plants From Farm Soil

Feb 07, 2013

Two of the most widely used nanoparticles (NPs) accumulate in soybeans — second only to corn as a key food crop in the United States — in ways previously shown to have the potential to adversely affect the crop yields and nutritional quality, a new study has found. It appears in the journal ACS Nano.

Jorge L. Gardea-Torresdey and colleagues cite rapid increases in commercial and industrial uses of NPs, the building blocks of a nanotechnology industry projected to put $1 trillion worth of products on the market by 2015. Zinc oxide and cerium dioxide are among today’s most widely used NPs. Both are used in cosmetics, lotions, sunscreens and other products. They eventually go down the drain, through municipal sewage treatment plants, and wind up in the sewage sludge that some farmers apply to crops as fertilizer. Gardea-Torresdey’s team previously showed that soybean plants grown in hydroponic solutions accumulated zinc and cerium dioxide in ways that alter plant growth and could have health implications.

The question remained, however, as to whether such accumulation would occur in the real-world conditions in which farmers grow soybeans in soil, rather than nutrient solution.

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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.