Estimation of dry‐matter intake in lambs via field‐based NIR proximal sensing

Abstract

Dry‐matter intake (DMI) is difficult to measure. The potential of field‐based near‐infrared (NIR) proximal sensing as a means to quantify DMI is a logical extension of the findings of several researchers who demonstrated that benchtop NIR spectroscopy could be used to predict animal intake. Our objectives were to predict DMI from the relative feed value and the Relative Forage Quality equations and to compare these estimates to that measured in lambs. Proximally, sensed spectra of live forage were used to develop predictions of neutral detergent fibre, acid detergent fibre, in vitro true digestibility (IVTD) and crude protein which were used as inputs to the DMI equations. These prediction equations accounted for 68%, 87%, 84% and 71% of the variation in their measured counterparts, and when used in the DMI equations, accounted for about 80% and 79%, respectively, of the variation of that calculated using laboratory‐measured values. However, neither the laboratory‐ nor proximally sensed estimates of DMI were correlated with that measured DMI in the lambs. Proximally sensed IVTD was incorporated into a sheep‐based intake model where from 7% to 36% of the variability in measured DMI, depending on breed, was explained. This improvement in estimated DMI suggests: 1) that DMI equations based only on forage nutritive values are unlikely to provide acceptable estimates, and 2) that proximally sensed forage nutritive values incorporated into a more comprehensive model, such as that described by Freer, et al. (1997, Agric Syst, 54, 77) may provide a timely grazing decision support system.

Editorial: Grass and Forage Science – 75 years of impact and service to the science of grasslands

“…The tradition of publishing papers that describe new methods for measuring existing traits such as lignin (van Parijs et al, 2018) or water-soluble carbohydrates (Maharjan et al, 2018) in the laboratory in more appropriate or efficient ways has continued, along with the extension of laboratory-based techniques such as near-infrared spectroscopy to direct application in the field (Starks & Brown, 2018) area of science, with potential application across the continuum from breeding to precision farm management, should issues around data management, storage and analysis be resolved.…”

“…The tradition of publishing papers that describe new methods for measuring existing traits such as lignin (van Parijs et al, 2018) or water‐soluble carbohydrates (Maharjan et al, 2018) in the laboratory in more appropriate or efficient ways has continued, along with the extension of laboratory‐based techniques such as near‐infrared spectroscopy to direct application in the field (Starks & Brown, 2018), due to the availability of sensors that can be deployed under field conditions. The use of remote or proximal sensor‐based technologies to measure dry matter content, yield, composition and nutritive characteristics of grass and grasslands (Borra‐Serrano et al, 2019; Svinurai et al, 2018; Wachendorf et al, 2018) is clearly an emerging area of science, with potential application across the continuum from breeding to precision farm management, should issues around data management, storage and analysis be resolved.…”