Journal list menu

Early View
ORIGINAL ARTICLE

Predicting grass proportion in fresh alfalfa: Grass mixtures using a hand-held near-infrared spectrometer

Rink Tacoma-Fogal

Rink Tacoma-Fogal

Department of Animal Science, Cornell University, Ithaca, New York, USA

Contribution: Data curation, Formal analysis, ​Investigation, Project administration, Software, Validation, Writing - original draft, Writing - review & editing

Search for more papers by this author
May Boggess

May Boggess

Cornell Statistical Consulting Unit, Cornell University, Ithaca, New York, USA

Contribution: Data curation, Formal analysis, ​Investigation, Writing - original draft

Search for more papers by this author
Jerome. H. Cherney

Jerome. H. Cherney

Department of Soil and Crop Science, Cornell University, Ithaca, New York, USA

Contribution: Conceptualization, Funding acquisition, Methodology, Resources, Writing - review & editing

Search for more papers by this author
Mathew Digman

Mathew Digman

Department of Biological Systems Engineering, University of Wisconsin, Madison, Wisconsin, USA

Contribution: Formal analysis, Writing - review & editing

Search for more papers by this author
Debbie J. R. Cherney

Corresponding Author

Debbie J. R. Cherney

Department of Animal Science, Cornell University, Ithaca, New York, USA

Correspondence

Debbie J. R. Cherney, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA. Email: [email protected]

Contribution: Conceptualization, Formal analysis, Funding acquisition, ​Investigation, Methodology, Project administration, Supervision, Visualization, Writing - review & editing

Search for more papers by this author
First published: 06 May 2024

Assigned to Associate Editor Miguel Castillo.

Abstract

Technological advancements have made hand-held near infrared (NIR) spectrometers more affordable and more accurate, creating interest in on-farm application for forage management. The objective of this study was to evaluate the ability of a hand-held NIR spectrometer to predict grass percentage within fresh alfalfa (Medicago sativa L.):grass mixtures. Forage samples were collected at a range of maturities and varieties during the 2021 and 2022 growing seasons from multiple locations in New York. Fresh forage samples were chopped, and pure species were combined into known proportions on a dry matter basis, resulting in 534 samples. Analysis was carried out on NIR spectra collected from a hand-held NeoSpectra spectrometer using stationary and sliding scanning techniques. Development of calibration models was completed using partial least squares regression with cross validation. The best performing calibration model using absorbance was from the sliding scanning technique with preprocessing consisting of mean-centering (R= 0.89, root mean square error of prediction [RMSEP] = 13.7%, and ratio of prediction to deviation = 2.53). A total of 84% of the samples were correctly classified when the grass component was lower than 40%. For samples with the grass component above 40%, a total of 94% of the samples were correctly classified. Correct sample classification is critical considering that the extension recommendation in New York is to reseed alfalfa fields when the grass component exceeds 40% of the sward on a botanical composition basis. This research demonstrates that NIR technology has potential to provide the agricultural industry with rapid, non-destructive, and affordable information to allow farmers and consultants to predict grass proportion within alfalfa:grass fresh forage mixtures in real time.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.