Rachel J. Brooks, Douglas R. Tolleson, G. Ruyle, D. Faulkner
{"title":"A production-scale evaluation of nutritional monitoring and decision support software for free-ranging cattle in an arid environment","authors":"Rachel J. Brooks, Douglas R. Tolleson, G. Ruyle, D. Faulkner","doi":"10.1071/rj20116","DOIUrl":null,"url":null,"abstract":"Range cattle in semi-arid regions are commonly limited by lack of nitrogen and other nutrients from grazing low-quality forage, with managers needing to monitor diet quality to address nutrient limitations. Near-infrared spectroscopy of faecal samples (FNIRS) is an accurate method used to determine diet quality in grazing animals. When combined with a nutritional balance software such as the Nutritional Balance Analyser (NUTBAL), FNIRS can monitor nutritional status and estimate weight change. We aimed to test the ability of NUTBAL to predict animal performance as represented by body condition score (BCS) in cattle grazing on a semi-desert rangeland. BCS and faecal samples were collected from a Red Angus herd (n = 82) at the Santa Rita Ranch (June 2016–July 2017). Standing biomass and botanical composition were measured before each grazing period, and relative utilisation was measured following each grazing period. During the midpoint of grazing in each pasture, 30 BCS and a faecal composite of 15 samples were collected. Faecal derived diet quality varied between a maximum of 10.75% crude protein (CP) and 61.25% digestible organic matter (DOM) in early August 2016, to a minimum value of 4.22% CP and 57.68% DOM in January 2017. Three NUTBAL evaluations were conducted to determine the likelihood of accurately predicting animal performance: one with typical user defined inputs; one with improved environment and herd descriptive inputs; and one with these improvements plus the use of metabolisable protein in the model. This third evaluation confirmed the ability of FNIRS:NUTBAL to predict future BCS within 0.5 BCS more than 75% of the time. With this information, cattle managers in semi-arid regions can better address animal performance needs and nutrient deficiencies.","PeriodicalId":20810,"journal":{"name":"Rangeland Journal","volume":" ","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Rangeland Journal","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1071/rj20116","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 2
Abstract
Range cattle in semi-arid regions are commonly limited by lack of nitrogen and other nutrients from grazing low-quality forage, with managers needing to monitor diet quality to address nutrient limitations. Near-infrared spectroscopy of faecal samples (FNIRS) is an accurate method used to determine diet quality in grazing animals. When combined with a nutritional balance software such as the Nutritional Balance Analyser (NUTBAL), FNIRS can monitor nutritional status and estimate weight change. We aimed to test the ability of NUTBAL to predict animal performance as represented by body condition score (BCS) in cattle grazing on a semi-desert rangeland. BCS and faecal samples were collected from a Red Angus herd (n = 82) at the Santa Rita Ranch (June 2016–July 2017). Standing biomass and botanical composition were measured before each grazing period, and relative utilisation was measured following each grazing period. During the midpoint of grazing in each pasture, 30 BCS and a faecal composite of 15 samples were collected. Faecal derived diet quality varied between a maximum of 10.75% crude protein (CP) and 61.25% digestible organic matter (DOM) in early August 2016, to a minimum value of 4.22% CP and 57.68% DOM in January 2017. Three NUTBAL evaluations were conducted to determine the likelihood of accurately predicting animal performance: one with typical user defined inputs; one with improved environment and herd descriptive inputs; and one with these improvements plus the use of metabolisable protein in the model. This third evaluation confirmed the ability of FNIRS:NUTBAL to predict future BCS within 0.5 BCS more than 75% of the time. With this information, cattle managers in semi-arid regions can better address animal performance needs and nutrient deficiencies.
期刊介绍:
The Rangeland Journal publishes original work that makes a significant contribution to understanding the biophysical, social, cultural, economic, and policy influences affecting rangeland use and management throughout the world. Rangelands are defined broadly and include all those environments where natural ecological processes predominate, and where values and benefits are based primarily on natural resources.
Articles may present the results of original research, contributions to theory or new conclusions reached from the review of a topic. Their structure need not conform to that of standard scientific articles but writing style must be clear and concise. All material presented must be well documented, critically analysed and objectively presented. All papers are peer-reviewed.
The Rangeland Journal is published on behalf of the Australian Rangeland Society.