Updated 2017 Registered Pesticide List For Michigan Chestnut Producers

Feb 27, 2017

By Erin Lizotte

In an effort to assist chestnut growers in making pesticide and nutrient management decisions, an updated “Michigan Chestnut Management Guide 2017” has been created and is available at the Michigan State University Extension Chestnuts page. The packet includes a listing of registered pesticides, nutrient management recommendations and a guide to seasonal pest occurrence in Michigan.

To protect yourself, others and the environment, always read the label before applying any pesticide. Although efforts have been made to check the accuracy of information presented in the “Michigan Chestnut Management Guide,” it is the responsibility of the person using this information to verify it is correct by reading the corresponding pesticide label in its entirety before using the product.

Reference to commercial products or trade names does not imply endorsement by MSU Extension or bias against those not mentioned. Information presented here does not supersede the label directions.

This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under Agreement No. 2015-09785. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

Source: msu.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.