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Project tests NIR scanners to time silage harvest

Stephen Ausmus/USDA Tractor driving over and moving piles of cut silage
TESTING SILAGE: The moisture content of silage samples tested by Jerry Cherney and his team was estimated reasonably accurately by hand-held NIR units, within about 1.5 percentage units 70% of the time, or within 3 percentage units 95% of the time.
Hand-held NIR scanners can improve forage production and feedout management.

To help dairy farmers add hand-held near-infrared reflectance spectrometry to their on-farm toolboxes, Jerry H. Cherney, a professor of agriculture and crops at Cornell, is testing different NIR scanners that can support quality forage production and feedout management.

“With enough consistency, the use of hand-held NIR will enhance decision-making regarding corn harvest timing. If hand-held NIR estimates of dry matter are accurate enough, there is potential to daily adjust a ration to achieve more milk production, as well as reduce overfeeding,” Cherney says. “One of the greatest issues with production of high-quality corn silage is harvesting at the correct moisture content.”

With a grant from the New York Farm Viability Institute, Cherney’s team collected hand-held NIR estimates for moisture in whole-plant standing corn, and chopped and dried individual plants to determine ear and stalk moisture content over three growing seasons. Results were consistent, with a relatively slow decline in stalk moisture, but a rapid decline in ear moisture before reaching optimum harvest moisture.

“The relationship between ear moisture and the ear DM proportion of the plant was consistent. At about 40% ear moisture, grain accumulation is completed. and the ear DM proportion remains constant,” he explains. “This relationship, along with the fact that stover moisture is constant, indicates a possibility that whole-plant moisture might be estimated from ear moisture.”

Additional work will focus on using NIR estimates to time optimal corn harvest.

Moving vs. stationary scanning

Cherney analyzed moving vs. stationary scanning to determine whether hand-held NIR units with a broader scan range would improve estimating accuracy.

“Moving scans produce a much larger area, which can reduce variability, but it is difficult to maintain a firm contact with the sample during the entire scan, which can increase variability,” he says.

Stationary scanning of corn ears failed to be useful as the small scan area could discriminate between scans directly over a row of kernels vs. scans centered on the gap between rows, causing highly variable results.

“In 2021, rotating the corn ear during scanning captured a much larger scan area and resulted in very consistent, repeatable ear scans with the hand-held unit; however, we have yet to determine whether we can estimate whole-plant moisture using only the ear moisture estimates,” Cherney cautions.

New work underway

Cherney’s NIR research is now testing enhanced models of hand-held NIR scanners, including one with a rotating turntable. This new work will help determine if calibrations can be successfully transferred from one type of unit to another to boost the accuracy of the calibrations.

His work is being funded through a USDA grant with the University of Wisconsin. Collaborators are Debbie Cherney, Cornell professor of animal science, and Matthew Digman, an assistant professor of biological systems engineering at University of Wisconsin.  

Jerry Cherney’s earlier work with hand-held NIR units has provided a basis for this new research with the shared goal of providing farmers with more data to support the potential of enhancing milk production through daily rebalancing of the ration.

He is focused on applying NIR technology to analyze wet, chopped forages.

“This project has provided New York farmers clear guidance about the challenges of and potential for using hand-held NIR readers at this time. I am pleased that our investment provided Dr. Cherney a strong starting point for his USDA-funded project,” says David Grusenmeyer, executive director of the New York Farm Viability Institute.

Dunn writes from her farm in Mannsville, N.Y.

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