Forage Fertility: Where We Are and Why it Matters

Jul 07, 2021

By Garth Ruff and Greg LaBarge et.al

Hay and haylage crops are grown on just over 1 million acres in Ohio (NASS, 2019) and are grown on more Ohio farms (44% of all farms) than any other crop (Becot et al., 2020). In addition, there are over 1.3 million acres of pastureland on nearly 39,000 farms (50% of all farms) in the state of Ohio (NASS, 2017). Fertilizer costs represent 40 to 60% of the variable input costs of forage hay production (Ward et al., 2016, 2018), and so managing these costs is key to an Ohio forage producers’ ability to stay competitive. Furthermore, water quality issues in the state underscore the need for Ohio farmers to manage on-farm nutrients as efficiently as possible. A farmer’s ability to find this optimal balance between meeting crop nutrient requirements without over-application is highly reliant on the best available information.

In order to make better and up to date forage fertility recommendations, we want to hear back from producers as to what current practices are already implemented on farms across the state. Understanding current practices and limitations to forage fertility will guide us in determining the type and kind of related research to conduct in order to revise current recommendations.

Please take this short voluntary survey regarding current forage fertility practices. This survey is part of a research effort conducted by The Ohio State University and should take 10 minutes or less to complete. Once again your feedback is appreciated as we evaluate current forage fertility guidelines.

Source : osu.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.