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Activity of Insecticides on Stone Fruit Pests, 2018

This table provides information on the relative activity of insecticides and miticides labelled on stone fruit in Ontario. The ratings are a result of the review of various U.S. extension publications, scientific journal articles, Canadian Pest Management Research Reports, and Arthropod Management Reports (ESA).

Ratings in shaded cells indicate the disease is listed on the product label for control or suppression. Please see the product label or crop calendars for registered uses. Use insecticide only for the crop and pest combinations listed on the product label. Additional information is provided in this table to assist the grower in choosing the best insecticide for the pests listed on the product label.

Click on the link below to access a printable version of this table.

Insecticide Activity on Stone Fruit 2018 (PDF)

Source : ONfruit

Trending Video

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

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.