Modern science is revealing that the number on the scale is a deceptively simple figure, and adjusting how we use it is revolutionizing our ability to predict dry matter intake (DMI)—the single most important factor in feedlot efficiency and profitability.
For centuries, cattle producers have relied on a simple tool to manage their herds: the scale. Body weight has been the cornerstone of predicting how much a feedlot steer or heifer will eat, and in turn, how efficiently it will grow. But what if this fundamental measurement is leading us astray?
At first glance, using an animal's body weight to predict its feed intake seems logical. Larger animals require more energy for maintenance and growth. For decades, standard equations, like those from the National Research Council (NRC), have used metabolic body weight (BW⁰·⁷⁵) and dietary energy concentration to forecast DMI 5 9 .
Standard models have a known tendency to over- and underpredict intake depending on specific animal and dietary conditions 5 .
However, these models have a known tendency to over- and underpredict intake depending on specific animal and dietary conditions 5 . The reason is that a single body weight measurement does not distinguish between what is actually the animal and what is just passing through.
The live weight of a cow is a composite of several components 2 :
This variation in gut fill is a major source of error. A "shrunk" weight after 16 hours of fasting provides a more accurate baseline, but even this can be influenced by the animal's physiological state 2 . Furthermore, as an animal fattens, its intake often declines relative to its size, a feedback mechanism that simple weight-based equations struggle to capture 9 . Relying solely on unadjusted body weight is like trying to budget for a family without knowing how many guests are coming for dinner—it's an incomplete picture that leads to poor planning.
To understand the true drivers of intake, researchers are delving deeper into the components of body weight. A pivotal 2015 study aimed to develop approaches to estimate the actual weight of maternal tissues in pregnant cows, adjusting for both feeding status and stage of gestation 2 .
The experiment introduced the concept of the "pregnant compound" (PREG), which represents the weight genuinely related to pregnancy (gravid uterus minus the non-pregnant uterus) 2 . By subtracting the PREG from the live weight of a pregnant cow, researchers could isolate the weight of only the maternal tissues. This allowed for a direct comparison of a cow's body condition across different stages of pregnancy.
A key finding was that udder weight did not increase significantly until the very late stages of pregnancy (up to 238 days) 2 . The study successfully established non-linear functions to predict the relationships between fasted, non-fasted, and empty body weight for both pregnant and non-pregnant cows.
This work provided the first quantitative framework for disentangling the confounding effects of pregnancy and gut fill on live weight in Bos indicus cattle. It demonstrated that without these adjustments, any prediction model—for nutrient requirements or dry matter intake—would be fundamentally flawed. The ability to estimate the weight of a cow's body constituents allows for a true "apples-to-apples" comparison throughout its reproductive life, forming a more reliable basis for precision feeding.
| Parameter | Finding | Significance for DMI Prediction |
|---|---|---|
| Pregnant Compound (PREG) | Quantifiable weight from pregnancy tissues | Allows isolation of maternal tissue weight for accurate intake modeling in breeding herds. |
| Udder Weight Accretion | Not significant until very late gestation (< 238 days) | Simplifies early- and mid-gestation models; focus can be on uterine growth. |
| Feeding Status Impact | Strong non-linear relationship between fed, fasted, and empty weight | Highlights the critical need for standardized weighing protocols (e.g., always using shrunk weight). |
| Adjustment Equations | Developed for pregnant & non-pregnant cows | Provides tools to translate a single live weight into a more physiologically relevant metric. |
While refining body weight measurements is a crucial first step, the science of predicting DMI has expanded to include a wider array of tools and variables. The advent of machine learning (ML) and big data is allowing scientists to model the complex, non-linear interactions that traditional statistics could not fully capture.
A 2023 study compared different ML algorithms for predicting daily DMI in feedlot cattle using simple, available inputs: initial body weight, days on feed, and the average proportion of dietary concentrate 1 .
The traditional statistical approach that models a linear relationship between inputs and output.
Builds many "decorrelated" decision trees and averages the results.
Sequentially builds trees, with each new tree correcting the errors of the previous ones.
Advanced algorithms that sequentially build models to correct errors.
The results showed that while all models performed similarly, the LGBR and GBR algorithms slightly outperformed the others, particularly in predicting intake for heifers 1 . This suggests that even with basic inputs, machine learning can uncover subtle patterns related to gender and other factors.
Other studies are proving that DMI can be predicted with remarkable accuracy (within 0.75 kg) using a wider set of proxies that are easier to measure in a group setting, such as water intake, drinking behavior, average daily gain (ADG), and climatic conditions 3 .
Function in Research: Precisely measure individual feed intake in group-housed settings (e.g., GrowSafe Systems).
Provides the high-quality "ground truth" data needed to build and validate predictive models 3 .
Function in Research: Uses a scale at the water trough to collect daily partial weights, from which full body weight is imputed.
Enables frequent, non-invasive weight monitoring without handling stress, capturing dynamic weight changes 3 .
Function in Research: Uniquely identifies each animal when it visits the feed bunk or water trough.
Links intake and weight data to the specific individual, enabling large-scale data collection 3 .
The journey to perfectly predict a feedlot animal's appetite is ongoing. However, the path is clear: the industry is moving beyond a naive reliance on the number displayed on a scale. The future of nutritional management lies in adjusted body weights that account for gut fill and physiological state, combined with sophisticated modeling techniques like machine learning that can process complex, real-time data.
Reliance on simple body weight measurements and standard equations that often over- or under-predict intake 5 9 .
Development of adjusted weight metrics (EBW, PREG) and implementation of machine learning algorithms for more accurate predictions 1 2 .
Integration of real-time data from multiple sources (water intake, behavior, climate) with advanced analytics for precision feeding 3 .
This evolution promises a more efficient, sustainable, and profitable beef industry. By understanding what the scale is truly telling us, we can formulate diets with unparalleled precision, reduce feed waste, and minimize environmental impact.
The humble body weight is being dethroned, not discarded, replaced by a richer, more nuanced understanding of the animal within.