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

Research output: Contribution to journalArticle

8 Citations (Scopus)

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 Nut-balPro 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.

Original languageEnglish (US)
Pages (from-to)349-358
Number of pages10
JournalJournal of Animal Science
Volume92
Issue number1
DOIs
StatePublished - Jan 2014

Fingerprint

Near-Infrared Spectroscopy
beef cows
nutritional adequacy
near-infrared spectroscopy
rangelands
body condition
Software
grazing
Diet
monitoring
crude protein
Hereford
organic matter
Nutritional Status
breeds
cows
nutritional status
cattle
sampling
Tarentaise

Keywords

  • Beef cattle
  • Feces
  • Near-infrared spectroscopy
  • Nutritional balance software
  • Nutritional monitoring
  • Rangeland

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Food Science
  • Genetics

Cite this

@article{f6bc3cc29ad845d3a1fbf94dc6e2f83c,
title = "Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland",
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 Nut-balPro 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.",
keywords = "Beef cattle, Feces, Near-infrared spectroscopy, Nutritional balance software, Nutritional monitoring, Rangeland",
author = "Tolleson, {Douglas R} and Schafer, {David W}",
year = "2014",
month = "1",
doi = "10.2527/jas2013-6631",
language = "English (US)",
volume = "92",
pages = "349--358",
journal = "Journal of Animal Science",
issn = "0021-8812",
publisher = "American Society of Animal Science",
number = "1",

}

TY - JOUR

T1 - Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland

AU - Tolleson, Douglas R

AU - Schafer, David W

PY - 2014/1

Y1 - 2014/1

N2 - 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 Nut-balPro 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.

AB - 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 Nut-balPro 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.

KW - Beef cattle

KW - Feces

KW - Near-infrared spectroscopy

KW - Nutritional balance software

KW - Nutritional monitoring

KW - Rangeland

UR - http://www.scopus.com/inward/record.url?scp=84891639361&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891639361&partnerID=8YFLogxK

U2 - 10.2527/jas2013-6631

DO - 10.2527/jas2013-6631

M3 - Article

C2 - 24305871

AN - SCOPUS:84891639361

VL - 92

SP - 349

EP - 358

JO - Journal of Animal Science

JF - Journal of Animal Science

SN - 0021-8812

IS - 1

ER -