Refining Cattle Nutrition Predictions: Texas Study Shows Regional Calibration Improves Accuracy of FNIRS-Based Monitoring

A recent article published in Rangelands examined region-specific calibrations in improving the accuracy of nutritional monitoring systems for beef cattle grazing rangelands (1). This study, which was led by Douglas R. Tolleson, an associate professor at Texas A&M AgriLife Research, demonstrates the utility of near-infrared spectroscopy of feces (fNIR) in estimating diet quality and predicting body condition in cattle raised under typical management conditions. This study combined fNIRS monitoring with the Nutbal nutritional model, and this integration revealed how it can improve nutritional monitoring systems (1).

What is the Nutbal Nutritional Model?

Nutbal stands for nutritional balance analyzer model, and it is used to determine a pasture’s nutritional status (2).

What was the experimental procedure?

As part of the experimental procedure, the researchers focused on a small herd of 24 Angus crossbred beef cows that were aged between 4–6 years and were maintained at the Texas A&M AgriLife Read Ranch in Crockett County (1). These cows were managed using practices common to the Edwards Plateau, including grazing native rangeland, traveling modest distances for water access (typically between 0.3 to 1.25 miles), and receiving daily protein supplementation. The cows were naturally bred with a Hereford bull, calved in the spring, and calves were weaned in the fall. To encourage cattle movement and ease monitoring, a protein-rich supplement, which contained 32% crude protein and 78% total digestible nutrients, was administered daily, increasing during the winter months for nutritional management (1).

In their study, the research team used the FNIRS-Nutbal system to analyze the fecal samples from the cows and project body condition scores (BCS) up to 30 days in advance (1). However, discrepancies were observed between predicted and actual outcomes, underscoring the limitations of relying on broad-scale calibration models for dietary predictions in localized rangeland systems.

Under the study conditions, the researchers found that using a regional FNIRS calibration significantly improved the accuracy of diet quality estimates, particularly in predicting crude protein (CP) values (1). This finding suggests that while the FNIRS-Nutbal system is a powerful tool, it is most effective when tailored to the specific environmental and management context of the cattle (1).

What did the researchers accomplish with the FNIRS-Nutbal system?

The researchers used the FNIRS-Nutbal system to accurately predict cow body condition within ±0.5 BCS units approximately 80% of the time, which is sufficient for making informed management decisions in most cases (1). The team meticulously followed sample collection protocols, ensuring that over 50% of the herd was represented in each composite fecal sample (1). Proper handling during collection and transport was confirmed, and laboratory analysis further validated the consistency of CP and digestible organic matter (DOM) predictions, minimizing the likelihood of sample contamination or handling errors (1).

In their study, the researchers acknowledged several potential sources of FNIRS-Nutbal prediction errors. These sources included limitations in the diet quality prediction itself, misrepresentation of the herd by the sample, contamination or mishandling, outdated nutritional requirement databases, or inaccurate data input into the Nutbal model (1).

What are the implications of this study?

The researchers highlight the need for nutritional models to be updated. The reason for this is that the models need to reflect modern cattle genetics and evolving grazing practices (1). Importantly, researchers suggested incorporating enteric emissions data into nutritional assessments, supporting broader sustainability goals in livestock management (1).

By aligning monitoring tools more closely with the local environment, rangeland managers can make better-informed nutritional decisions, ultimately benefiting both animal welfare and ranch productivity (1). For best results, the researchers recommend adherence to rigorous sampling protocols, accurate data input, and integration of field-based observations. Adaptive management will remain essential as the industry works to refine these technologies for broader use across diverse landscapes.

References

1. Tolleson, D. R.; Fox, W. E.; Pinchak, W. E. Nutritional Monitoring of Rangeland Beef Cattle in the Edwards Plateau of Texas Using Region-specific Fecal Near-infrared Spectroscopy Predictions of Diet Quality. Rangelands 2025, 47 (2), 109–117. DOI: 10.1016/j.rala.2024.11.004
2. U.S. Department of Agriculture, Forage Sampling and Nutritional Balance Analyzer (NUTBAL). USDA.gov. Available at: https://www.nrcs.usda.gov/state-offices/south-dakota/forage-sampling-and-nutritional-balance#:~:text=NUTBAL%20is%20a%20fecal%20sampling,if%20additional%20supplementation%20is%20needed.&text=NUTBAL%20Resources%3A,cattle%2C%20sheep%2C%20and%20goats. (accessed 2025-06-12).