Faecal near-infrared reflectance spectroscopy profiling for the prediction of dietary nutritional characteristics for equines

Genetic background of juniper (Juniperus spp.) consumption predicted by fecal near-infrared spectroscopy in divergently selected goats raised in harsh rangeland environments

“…Previous research on the effect of sex on fNIR predictions of juniper consumption reported that, when on the same diet, males had a 50% higher predicted percentage of juniper in the diet compared to females, which may account for some of the difference between populations [ 17 ]. Although not statistically comparable as the animals were not kept in the same pastures, numerically the BS population consumed more juniper than ANG, and previous studies reported similar findings [ 15 , 44 ] that the composite animals [ 45 , 46 ] might be more effective biological agents for juniper control. During droughts, pastoralists are reluctant to reduce their livestock numbers partly because they believe their animals are uniquely suited to the environment and management system they were raised in and believe this adaptiveness is genetic and environmental [ 47 , 48 ].…”

“…One such approach is the application of fecal near-infrared reflectance spectroscopy (fNIRS) to predict diet composition. Compared to standard laboratory procedures, fNIRS brings significant advantages, such as being non-labor-intensive, enabling rapid execution, treating samples non-destructively, and reducing chemical waste [ 15 ]. fNIRS enables the measurement of essential attributes in the diet selected by grazing herbivores, including crude protein content, coefficient of digestibility, botanical composition, and, in some cases, voluntary intake [ 16 ].…”

Predicting fecal composition, intake, and nutrient digestibility in beef cattle consuming high forage diets using near infrared spectroscopy

“…Few prediction equations for fecal mineral content have been developed, likely because minerals that are not part of an organic bond do not absorb energy in the near infrared region and are found in low concentrations. Recently, Ikoyi and Younge (2022) reported R 2 cv statistics in equine feces for Ca and P of 0.26 and 0.71, and SECV of 2.59 and 0.43. The present study R 2 cv statistics for Ca and P of 0.97 and 0.85 with SECV of 0.18 and 0.06 are unusually strong, although Showers et al (2006) reported a fecal P R 2 cv of 0.81 and SECV of 0.04 in white-tailed deer ( Odocoileu virginianus ).…”

Estimating nutrient composition and polyphenol concentration using Near-Infrared Spectroscopy (NIRS) in tropical forages

“…This showed that their absorption value was not disturbed by other chemical component functional groups. Statistical measures, such as the coefficient of determination for validation (R 2 V), Standard Error of Prediction (SEP), Ratio Performance Deviation (RPD), bias, and slope, are essential for evaluating the precision and dependability of NIRS analysis outcomes, as noted by Ikoyi and Younge (2022). Furthermore, R 2 V values are classified as excellent when greater than 0.95, good between 0.9 and 0.95, moderate between 0.8 and 0.9, and acceptable between 0.7 and 0.8, according to Malley et al (2004).…”

“…Lobos et al (2013) stated that an RPD value of at least 2.5 was required for effective NIRS predictions. Ikoyi and Younge (2022) classified RPD into various categories, including excellent, very good, acceptable, and unreliable, when RPD was >3, 2-3, 1.4-2, and <1.4. The calculated RPD values for CP and CF were exemplary (RPD = 3.99) and very good (RPD = 2.44, 2.32), while EE and DM parameters showed low RPD values, marking them as unreliable for NIRS predictive modeling.…”