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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #350799

Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

Location: Agroclimate and Natural Resources Research

Title: Estimation of dry-matter intake in lambs via field-based NIR proximal sensing

Author
item Starks, Patrick - Pat
item Brown, Michael - Retired ARS Employee

Submitted to: Grass and Forage Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2018
Publication Date: 8/13/2018
Citation: Starks, P.J., Brown, M.A. 2018. Estimation of dry-matter intake in lambs via field-based NIR proximal sensing. Grass and Forage Science. p. 1-11. doi: 10.1111/gfs.12381.
DOI: https://doi.org/10.1111/gfs.12381

Interpretive Summary: Voluntary dry matter intake (DMI) is difficult to measure as it is influenced by environmental, animal, and forage factors, and is especially difficult to measure under grazing conditions. The potential of proximal sensing as a means to quantify DMI under grazing conditions is a logical extension of the findings of several researchers who demonstrated that bench-top near-infrared spectroscopy could be used to predict animal intake. Our objectives were to predict DMI using equations rooted in the development of the Relative Feed Value (RFV) and the Relative Forage Quality (RFQ) and to compare these DMI estimates to measured DMI of lambs. Proximally sensed spectra were used to develop and validate prediction equations for neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro dry matter disappearance (IVDMD), and crude protein (CP) which were used as inputs for the DMI equations. The prediction equations accounted for 63, 69, 90, and 81% of the variation in ADF, IVDMD, NDF, and CP in the validation data set. When used in the RFV and RFQ DMI calculations, the proximally sensed-based DMIs accounted for about 80 and 79%, respectively, of the variation of that calculated using laboratory measured values. However, neither the laboratory- nor the proximally sensed-based estimates of DMI were correlated with the measured DMI of the lambs used in our experiment.

Technical Abstract: Voluntary dry matter intake (DMI) is difficult to measure as it is influenced by environmental, animal, and forage factors, and is especially difficult to measure under grazing conditions. The potential of using field portable remote sensing devices as a means to quantify DMI under grazing conditions is a logical extension of the findings of several researchers who demonstrated that bench-top near-infrared spectroscopy could be used to predict animal intake. Our objectives were to predict DMI using equations rooted in the development of the Relative Feed Value (RFV) and the Relative Forage Quality (RFQ) and to compare these DMI estimates to measured DMI of lambs. Remotely sensed spectra were used to develop and validate prediction equations for neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro dry matter disappearance (IVDMD), and crude protein (CP) which were used as inputs for the DMI equations. The prediction equations accounted for 63, 69, 90, and 81% of the variation in ADF, IVDMD, NDF, and CP in the validation data set. When used in the RFV and RFQ DMI calculations, the remotely sensed-based DMIs accounted for about 80 and 79%, respectively, of the variation of that calculated using laboratory measured values. However, neither the laboratory- nor the proximally sensed-based estimates of DMI were correlated with the measured DMI of the lambs used in our experiment.