Subsoil Moisture Impacts in 2000

Nov 25, 2020

By Paul Kassel

A quick review of subsoil moisture during the fall of 1999 and the spring of 2000 shows that subsoil moisture did not recharge much from October 30, 1999 to April 10 of 2000 (table 1). The five subsoil moisture sites gained from 0.6 to 4.2 inches of moisture during the late fall and early spring.

Table 2 shows the April 11 to September 30 rainfall was almost normal in Milford, Estherville, Spencer and Pocahontas. However, rainfall in Storm Lake was 9.5 inches below normal.

This information shows the effect on corn yields from below normal subsoil moisture (4.3 inches at Newell site on April 10) and below normal summer rainfall in Storm Lake . The USDA NASS Buena Vista county corn yield was about 20 bushel per acre below the levels in other area counties (table 3).

Source : iastate.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.