Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland1

Abstract

Monitoring the nutritional status of range cows is difficult. Near-infrared spectroscopy (NIRS) of feces has been used to predict diet quality in cattle. When fecal NIRS is coupled with decision support software such as the Nutritional Balance Analyzer (NUTBAL PRO), nutritional status and animal performance can be monitored. Approximately 120 Hereford and 90 CGC composite (50% Red Angus, 25% Tarentaise, and 25% Charolais) cows grazing in a single herd were used in a study to determine the ability of fecal NIRS and NutbalPro to project BCS (1 = thin and 9 = fat) under commercial scale rangeland conditions in central Arizona. Cattle were rotated across the 31,000 ha allotment at 10 to 20 d intervals. Cattle BCS and fecal samples (approximately 500 g) composited from 5 to 10 cows were collected in the pasture approximately monthly at the midpoint of each grazing period. Samples were frozen and later analyzed by NIRS for prediction of diet crude protein (CP) and digestible organic matter (DOM). Along with fecal NIRS predicted diet quality, animal breed type, reproductive status, and environmental conditions were input to the software for each fecal sampling and BCS date. Three different evaluations were performed. First, fecal NIRS and NutbalPro derived BCS was projected forward from each sampling as if it were a "one-time only" measurement. Second, BCS was derived from the average predicted weight change between 2 sampling dates for a given period. Third, inputs to the model were adjusted to better represent local animals and conditions. Fecal NIRS predicted diet quality varied from a minimum of approximately 5% CP and 57% DOM in winter to a maximum of approximately 11% CP and 60% DOM in summer. Diet quality correlated with observed seasonal changes and precipitation events. In evaluation 1, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.1 to 1.1 BCS in Herefords and 0.0 to 0.9 in CGC. In evaluation 2, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.00 to 0.46 in Hereford and 0.00 to 0.67 in CGC. In evaluation 3, the range of differences between observed and projected BCS was 0.04 to 0.28. The greatest difference in projected versus observed BCS occurred during periods of lowest diet quality. Body condition was predicted accurately enough to be useful in monitoring the nutrition of range beef cows under the conditions of this study.

Elevated CO2 induces substantial and persistent declines in forage quality irrespective of warming in mixedgrass prairie

“…Monitoring of forage quality could occur both via plant tissue samples collected using protocols that control for phenology, and via livestock fecal samples, which already are collected and analyzed in a consistent manner across much of the western US (Craine et al. , Tolleson and Schafer ). Detecting directional long‐term trends in forage quality will be challenging in the variable climates that characterize most rangelands worldwide, emphasizing the need to establish consistent and cost‐effective monitoring protocols as soon as possible.…”

“…Finally, we suggest that tactical monitoring of forage quality and quantity should play an increasingly important role in livestock production systems, and be used to guide adjustments in stocking rates and the application of other adaptive management strategies. Monitoring of forage quality could occur both via plant tissue samples collected using protocols that control for phenology, and via livestock fecal samples, which already are collected and analyzed in a consistent manner across much of the western US (Craine et al 2010, Tolleson andSchafer 2014). Detecting directional long-term trends in forage quality will be challenging in the variable climates that characterize most rangelands worldwide, emphasizing the need to establish consistent and cost-effective monitoring protocols as soon as possible.…”

Long-term declines in dietary nutritional quality for North American cattle

“…Samples having poor NIRS prediction statistics were also removed where the H-value > 8. The H-value is the Mahalanobis distance between the spectrum of the fecal sample and the mean spectrum of the calibration dataset (Tolleson and Schafer 2014). Data were averaged across any samples that were collected at a particular location on the same date.…”

Predictability of growth performance in feedlot cattle using fecal near infrared spectroscopy

“…The use of NIRS by the livestock industry has permitted nutritional information of the diet (primarily of grazing ruminants) to be obtained from feces, allowing researchers and nutritionists to rapidly improve management strategies. Currently most reports for predicting performance of cattle using NIRS are restricted to free-ranging cattle (Dixon and Coates 2009;Tolleson and Schafer 2014), but recently fecal NIRS has been used to predict net energy of gain and ADG in feedlot cattle (Jancewicz et al 2017b). Near-infrared spectroscopy calibrations have also been directly applied to finding associations between measured fecal parameters and performance, and to predicting ADG and G:F using small data sets in which grain type, processing, or grain proportion were deliberately altered (Jancewicz et al 2017b).…”