A production-scale evaluation of nutritional monitoring and decision support software for free-ranging cattle in an arid environment
Rachel J. Brooks (Turner) A E , Douglas R. Tolleson B , George B. Ruyle C and Dan B. Faulkner D
+ Author Affiliations
- Author Affiliations
A Colorado State Land Board, Eaton, Colorado, 80615, USA.
B Texas A&M AgriLife Research Station, Sonora, Texas, 76950, USA.
C University of Arizona School of Natural Resources and the Environment, Tucson, Arizona, 85721, USA.
D University of Arizona Agriculture Research Station, Tucson, Arizona, 85721, USA.
E Corresponding author. Email: rachel.brooks@state.co.us
The Rangeland Journal 43(1) 35-46 https://doi.org/10.1071/RJ20116
Submitted: 12 December 2020 Accepted: 6 July 2021 Published: 23 August 2021
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.
Keywords: cattle, grazing, arid environment, nutritional monitoring, prediction software, diet management.
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