Use of animal performance and gas flux for estimating dry matter intake in growing steers

Abstract

Dry matter intake (DMI) estimation in ruminants is important for providing a balanced diet, increasing animal performance, and reducing nutrient excretion. Gas flux (CO₂ and CH₄ production) is related to DMI; however, there is limited information regarding the use of gas flux production when estimating DMI in growing steers. This study aimed to 1) determine the relationship of animal growth performance and gas flux variables with DMI of growing steers fed a backgrounding diet, and 2) evaluate the DMI accuracy of eight equations to predict DMI from growing steers fed a forage-based diet. The relationship between DMI, animal growth performance, and gas flux variables was evaluated in 130 backgrounding steers, and two equations were generated to predict DMI. Then, six retrieved equations from the literature and the two new equations were used to determine the prediction accuracy using an independent dataset. Models were compared based on the mean square prediction error (MSPE), the decomposition of the root MSPE (RMSPE), and the concordance correlation coefficient (CCC). In backgrounding steers, DMI had a positive and significant relationship (P < 0.01) with shrunk body weight (SBW), average daily gain, and CO₂ and CH₄ production. The production of CO₂ and CH₄ independently explained 48.1% and 40.9% of the observed DMI in growing steers, respectively. One equation retrieved from the literature had an excellent agreement with the observed DMI, with a CCC value of 0.93 and an RMSPE of 0.19 kg/d, representing 2.5% of the average DMI. That equation used SBW and dietary energy concentration. The use of CO₂ production had adequate agreement with the observed DMI, with a CCC value of 0.73 and an RMSPE of 0.45 kg/d, representing 6% of the average DMI. Other equations had null to moderate agreement with the observed DMI, with CCC values ranging from 0.00 to 0.47 and an RMSPE from 0.51 to 4.40 kg/d. In conclusion, there is a positive relationship between DMI, animal growth performance, and gas flux in growing steers fed a backgrounding diet. In addition, CO₂ production has the potential to be used to predict DMI in growing steers fed a forage-based diet. Future research is required to evaluate the relationship between CO₂ production and DMI, especially under grazing conditions.

There is a positive relationship between dry matter intake, animal growth performance, and gas flux in growing steers fed a backgrounding diet.

The use of CO₂ production has the potential to be used for predicting DMI in growing steers fed a forage-based diet.

Graphical Abstract

Graphical Abstract.

INTRODUCTION

Dry matter intake (DMI) is the most important factor in ruminant production because it defines animal performance and relates to nutrient excretion and negative environmental impacts (Anele et al., 2014; NASEM, 2016). Accurate DMI determination is necessary to provide the required nutrients through a complete ratio or implement supplementation strategies to maximize animal performance and business profitability (Undi et al., 2008; Pereira et al., 2015; NASEM, 2016). Nevertheless, DMI determination is challenging because it is affected by multiple factors, and those factors are difficult to measure, especially in pasture-based systems (Minson and McDonald, 1987; Galyean and Gunter, 2016; Smith et al., 2021).

Different methodological approaches have been reported to estimate DMI under grazing conditions, such as the difference between pre- and post-grazing biomass, animal behavior variables, and the use of internal and external markers (Undi et al., 2008; Smith et al., 2021; Avellaneda-Avellaneda et al., 2022). However, those methodological approaches have greater intrinsic variability, require intense labor, and might affect the normal behavior of grazing ruminants (Undi et al., 2008). New technologies have been proposed as an alternative to estimating DMI in grazing systems (Smith et al., 2021). Under commercial conditions, DMI in grazing ruminants has been defined using general recommendations that vary according to the animal’s physiological state (Galyean and Gunter, 2016) or empirical models where dietary and animal characteristics are used as independent variables (Minson and McDonald, 1987; NASEM, 2016).

Gas flux is the result of metabolism and fermentation in ruminants. Indeed, gas flux is related to energetic losses such as heat production and methane (CH₄) emissions in ruminants (Johnson and Johnson, 1995). Carbon dioxide (CO₂) production and oxygen (O₂) consumption had a positive relationship with DMI (Aubry and Yan, 2015; Arthur et al., 2017). Also, DMI has been reported as an important factor in predicting enteric CH₄ emissions (Herd et al., 2014; Hristov et al., 2018). Using calorimetric chambers, Aubry and Yan (2015) reported that ruminants with greater DMI produced more CO₂ and CH₄. In this regard, it has been proposed that gas flux can be used to predict DMI in ruminants (Pereira et al., 2015; Caetano et al., 2017; Holder et al., 2022).

There is no gold standard methodology for estimating DMI from grazing ruminants. Anele et al. (2014) suggested that DMI determination in cattle consuming forage-based diets under confined conditions could identify critical variables for estimating DMI in grazing ruminants. However, limited information is available on the use of gas flux to estimate DMI in steers consuming forage-based diets (Gunter et al., 2025). This study hypothesized that using animal growth performance and gas flux variables from growing steers consuming a backgrounding diet would allow an accurate estimation of DMI of growing steers feeding a forage-based diet. Thus, this study aimed to 1) determine the performance and gas flux variables associated with DMI of growing steers fed a backgrounding diet, and 2) compare the observed and estimated DMI using gas flux and animal growth performance variables and equations retrieved from the literature in growing steers fed a forage-based diet.

MATERIALS AND METHODS

Ethics Statement

Data from 130 Angus steers, from two different experiments, were used to evaluate the animal growth performance and gas flux during the backgrounding phase at the Climate Smart Research Facility (CSRF) at Colorado State University, CO. Additionally, 13 Angus-crossbred steers fed a forage-based diet were used to determine growth performance and gas flux in confined conditions at the Feed Efficiency Facility (FEF) at North Florida Research and Education Center at University of Florida, Marianna, FL. All procedures involving animals were approved by the Colorado State University and the University of Florida Institutional Animal Care and Use Committees (Animal use protocols 1526 and 202400000484)

Experiment 1: Animals, Feed Management, and Experimental Conditions

A total of 60 Angus steers at approximately 8 months of age and 209 ± 26.5 kg of initial body weight (BW) were evaluated for 60 d at the CSRF. Before evaluating animal growth performance and gas flux, steers were adapted to management and equipment for two weeks. Steers were offered a total mixed ration (TMR,Table 1) using one feed bunk per ten animals. Individual intake was recorded daily using an intake monitoring system (Smartfeed, C-Lock, Rapid City, SD). Feed samples were collected weekly, dried, and preserved for future analysis. Steers were weighed on days -1 and 0 to obtain the initial BW and on days 59 and 60 to record the final BW. In addition, unshrunk BW was obtained every 15 days during the evaluation period.

Table 1.

Ingredient inclusion and chemical composition of the diets offered in the experiments

Item	Exp. 1	Exp. 2	Exp. 3
Ingredient (% of DM)
Corn silage	18.8	30.0	-
Alfalfa hay	12.7	16.2	-
Wheat straw	11.0	10.8	-
Dry distillers grains	19.5	8.6	-
Whole corn	32.7	29.1	-
Mineral supplement1	3.8	3.8	-
Limestone	1.3	1.3	-
Salt	0.2	0.2	-
Rhizoma peanut hay	-	-	100
Composition 2 (% of DM)
Dry matter, % As Fed	66.8	63.6	76.1
Crude protein	10.4	10.2	23.1
Neutral detergent fiber	45.8	46.4	38.3
Acid detergent fiber	29.6	30.6	32.7
Lignin	3.2	5.4	7.4
Non-fiber carbohydrates	33.0	33.3	27.1
Starch	18.7	25.5	ND3
Ether extract	3.4	3.4	2.4
Ash	7.5	6.6	9.1
Total digestible nutrients	69.0	65.0	61.0
Net energy of maintenance, Mcal/kg of DM	1.6	1.4	1.3
Net energy of gain, Mcal/kg of DM	0.9	0.8	0.7

¹MidWest PMS, Englewood, CO.

²Analyzed by a commercial laboratory using a wet chemistry package (Dairy One, Ithaca, NY).

³ND = Non-determined.

Experiment 2: Animals, Feed Management, and Experimental Conditions

A total of 70 Angus steers at approximately 8 months of age and 193 ± 33.3 kg of initial BW were evaluated for 98 days at the CSRF. Before evaluating animal growth performance and gas flux, steers were adapted to management and equipment for two weeks. Steers were offered a TMR (Table 1) using one feed bunk per ten animals. Individual intake was recorded daily using the SmartFeed system. Feed samples were collected weekly, dried, and preserved for future analysis. Steers were weighed on days -1 and 0 to obtain the initial BW and on days 97 and 98 to record the final BW. In addition, unshrunk BW was obtained every 15 days during the evaluation period.

Experiment 3: Animals, Feed Management, and Experimental Conditions

A total of 13 Angus-crossbred steers at approximately 16 months of age and 294 ± 31.9 kg of initial BW were evaluated for 14 d at the FEF. Before evaluating animal growth performance and gas flux, steers were adapted to management and equipment for two weeks. Steers were offered a forage-based diet composed of rhizome peanut (Arachis glabrata, Table 1) using two feed bunks per 7 animals. Individual intake was recorded daily using four Vytelle Sense feed bunks (Vytelle LLC, Lenexa, KS). Feed samples were collected weekly, dried, and preserved for future analysis. Steers were weighed on day 0 to obtain the initial BW and on day 14 to record the final BW. In addition, unshrunk BW was obtained on day 7 during the evaluation period.

Gas Flux Determination

Steers were in pens containing one automated head-chamber system unit (AHCS, GreenFeed, C-Lock, Rapid City, SD) for gas flux collection (CH₄ and CO₂ production). Before using the units, steers individually received radio frequency electronic ear tag ID (Allflex, Merck & Co, Rahway, NJ) using a manual applicator. Steers were exposed to the AHCS unit during an acclimation period of approximately 2 weeks before data collection. After the acclimation period, cattle panels were used to ensure that only one animal had access to the AHCS unit at a time.

In Experiments one and two, steers were allowed to visit the AHCS unit every 4 hours (up to 6 visits per day) and consume up to 6 alfalfa pellet drops (approximately 35 g as-fed/drop) with 30-second intervals between drops during each visit. In Experiment three, steers were allowed to visit the AHCS unit every 2 hours (up to 12 visits per day) and consume up to 10 alfalfa pellet drops (approximately 39 g as-fed/drop) with 10-second intervals between drops during each visit. These settings encouraged steers to visit the units throughout the day and ensured they stayed at the AHCS for an appropriate gas collection.

The emission rate of gases (Qc) was calculated using the following equation (Huhtanen et al., 2015) :

$${\displaystyle \begin{array}{l}{\displaystyle Qc=[Cp\times (Conc-Bconc)\times Qair]÷{10}^6}\end{array}}$$

Where Cp is the fractional capture rate of air, Conc is the concentration of captured gas, BConc is the background concentration of gas, and Qair is the volumetric airflow. Thus, the gas flux (Qm) was calculated using the following equation:

$${\displaystyle \begin{array}{l}{\displaystyle Qm=Qc\times 273.1÷(273.15+Tair)\times GD}\end{array}}$$

Where Tair is the air temperature, and GD is the density of gas at 1 atm and 273.5 K

Recovery tests of CO₂ were performed monthly throughout the experiment and at the beginning and end of each experiment to ensure the gas flux system’s performance. Additionally, the manufacturer performed zero and span calibrations of the CH₄ and CO₂ gas analyzers every three days via an onboard autocalibration system. Raw collection data were validated by C-Lock Inc., which included appropriate head proximity, visit length, and airflow and wind corrections. Data were excluded when the length of the visit was less than 2 min, and the airflow was less than 26 L/s (Arthur et al., 2017; Gunter and Beck, 2018). In addition, the evaluated growing steers visited the AHCS units at least 55 times, as recommended by Vargas et al. (2024).

Models for Predicting Dry Matter Intake in Growing Steers.

In this study, six equations were retrieved from the literature to predict DMI in growing steers (Table 2). Model one uses BW and average daily gain (ADG) to estimate DMI (Minson and McDonald, 1987). Models two and four to seven require shrunk BW (SBW) and net energy for maintenance (NE_m) concentration in the diet (Anele et al., 2014; NASEM, 2016). Model three requires estimating the NE for maintenance and gain, and the concentration of the NE of maintenance and gain in the diet (Anele et al., 2014).

Table 2.

Retrieved and estimated models to determine dry matter intake in growing steers¹

Model	Source	Equation2	R2	RMSPE	AIC
M1	Minson and McDonald, 1987	(1.185 + 0.00454 × BW—0.0000026 × BW2 + 0.315 × ADG)2	-	-	-
M2	NRC, 1996	SBW0.75 × (0.1493 × NEm—0.046 × NEm2 - 0.0196)	-	-	-
M3	Anele et al., 2014	(NEm_req / NEm) + (NEg_req / NEg)	-	-	-
M4	Anele et al., 2014	(1.2425 + 1.9218 × NEm—0.7259 × NEm2) × (SBW / 100)	-	-	-
M5	NASEM, 2016	SBW0.75 × (0.2435 × NEm—0.0466 × NEm2 - 0.1128)) / NEm	-	-	-
M6	NASEM, 2016	SBW0.75 × (0.2435 × NEm—0.0466 × NEm2 - 0.0869)) / NEm	-	-	-
M7	Present	-4.00180 + 0.00185 × CO2	0.481	1.65	513.3
M8	Present	-2.35195 + 0.05021 × CH4	0.409	1.76	523.1

¹R² = Coefficient of determination. RMSPE = root of the mean square prediction error. AIC = Akaike information criteria.

²BW = Body weight, kg. ADG = Average daily gain, kg/d. SBW = Shunk body weight, kg. NE_m = Concentration of the net energy of maintenance, Mcal/kg DM. NE_m_req = Requirement of net energy of maintenance, Mcal/d. NE_g = Concentration of the net energy of gain, Mcal/kg DM. NE_g_req = Requirement of net energy of gain, Mcal/d. CO₂ = Production of carbon dioxide, g/d. CH₄ = Production of methane, g/d.

Calculation and Statistical Analysis

Animal growth was determined by linear regressions of the SBW (0.96 x BW) against time, and the calculated slope was considered the ADG. The DMI was calculated as the average feed intake (i.e., TMR and pellets) corrected by the DM concentration of the TMR and pellets during the evaluation.

The correlation between animal growth performance and gas flux variables from steers under confined conditions was determined using the CORR procedure of SAS 9.4. The main effects of animal growth performance and gas flux variables on DMI were analyzed using the MIXED procedure of SAS 9.4. including the experiment as a random factor (Ellis et al., 2007) and defining the significant variables. The model selection started with all significant variables, and the backward selection approach was applied throughout the different steps, using the procedure REG of SAS 9.4. In addition, the presence of multicollinearity in the selected model was examined based on the variance inflation factor (van Lingen et al., 2019). The model with the smallest Akaike information criterion was selected.

The predictive ability of the selected models and the equations retrieved from the literature for growing steers (Table 2) was compared against the observed DMI from steers fed a forage-based diet using the mean square prediction error (MSPE), calculated as:

$${\displaystyle \begin{array}{l}{\displaystyle MSPE={\displaystyle \underset{i=1}{{\displaystyle \sum ^n}}}{(Oi-Pi)}^2/n}\end{array}}$$

Where Oi is the observed value, and Pi is the predicted value. The root of the MSPE (RMSPE), expressed as a percentage of the observed mean, estimates the overall predicted error. The RMSPE was decomposed into error due to overall bias and error due to deviation of the regression slope from unity (Tedeschi, 2006). Additionally, the concordance correlation coefficient (CCC) analyses were performed (Lin, 1989) and calculated as follows:

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{C}\mathrm{C}\mathrm{C}=\mathrm{P}\times \mathrm{C}\mathrm{b}}\end{array}}$$

Where P is the Pearson correlation coefficient, which is given a measure of precision, and Cb is the bias correction factor, which is given a measure of accuracy. Model evaluation was conducted using R Statistical language (version 4.1) in RStudio version (2024.09.0). The data were analyzed using a linear mixed model fitted with lmer (lme4 package) (Bates et al., 2015).

RESULTS AND DISCUSSION

The relationship between DMI and animal growth performance and gas flux was evaluated in 130 backgrounding steers. Intake of DM had a positive and significant (P < 0.001) relationship with animal performance and gas flux in growth steers under confined conditions (Table 3). Similar to our results, the literature reports a positive relationship between animal growth performance variables such as BW and ADG with DMI in growing steers (NASEM, 2016). Heavier animals often have greater DMI capacity due to a positive relationship between BW and digestive tract weight and volume (NASEM, 2016). In addition, greater DMI, when cattle are fed a similar diet, results in more energy intake; therefore, animals would have more energy available for ADG (NASEM, 2016). It has been reported that ruminants with greater DMI had greater CO₂ and CH₄ production (Aubry and Yan, 2015). In ruminants, the production of CO₂ is the result of metabolic and fermentative processes (Bartley and Black, 1966), while the production of CH₄ is primarily the result of the reduction of CO₂ in the digestive tract (Janssen, 2010; Ungerfeld, 2015). Thus, it is expected that greater DMI provides nutrients for being metabolized or fermented by the ruminants, resulting in greater CO₂ and CH₄ production.

Table 3.

Description of dry matter intake and gas flux in evaluated growing steers fed a backgrounding diet in confined conditions

Variable1	Mean	SD	r
DMI, kg/d	6.5	2.29	-
SBW, kg	246	34.5	0.36*
ADG, kg/d	1.2	0.24	0.37*
CO2, g/d	5695	858.0	0.69*
CH4, g/d	177	26.1	0.64*
O2, g/d	4012	582.7	0.72*

¹DMI = Dry matter intake; SBW = Shrunk body weight; ADG = Average daily gain; CO₂ = Carbon dioxide production; CH₄ = Methane production; O₂, Oxygen consumption; r = Pearson correlation between measured variables and dry matter intake. n = 130. * = P < 0.001.

Previously, Renand et al. (2019) and Giagnoni et al. (2024) reported a positive relationship between CO₂ and CH₄ production in growing heifers and mature dairy cows, respectively. In the current study, the production of CO₂ and CH₄ had a Pearson correlation of 0.94 (P < 0.001; data not shown). Thus, CO₂ and CH₄ should not be independently included in the regression analysis to estimate DMI, avoiding collinearity issues (van Lingen et al., 2019). After performing the regression analysis, M7 and M8 equations explained 48.1 and 40.9% of the variation of the DMI, respectively (Table 2). Previously, equations using BW and dietary NE_m concentration explained between 61.9 and 88.4% of DMI variability in growing cattle (Anele et al., 2014; NASEM, 2016).

Six retrieved equations from the literature (M1 to M6) and the two new equations (M7 and M8) were used to determine the DMI prediction accuracy using independent data from 13 growing steers fed a forage-based diet under confined conditions (Table 4 and Figure 1). For a model evaluation, Proctor et al. (2024) suggested that a value of CCC less than 0 means no agreement, between 0 and 0.39 means slight agreement, between 0.40 and 0.59 means moderate agreement, between 0.60 and 0.80 means adequate agreement, and greater than 0.80 means excellent agreement.

Table 4.

Model evaluation¹ for estimating dry matter intake in growing steers consuming a forage-based diet

Model	Observed	Predicted	RMSPE, %	RMSPE, kg/d	ECT	ER	CCC
M1	7.57	7.84	6.69	0.51	27.29	72.71	0.41
M2	7.57	7.04	8.13	0.62	75.50	24.50	0.47
M3	7.57	11.91	58.09	4.40	97.31	2.69	0.00
M4	7.57	7.64	2.51	0.19	12.40	89.60	0.93
M5	7.57	6.99	8.66	0.66	78.12	21.88	0.43
M6	7.57	8.44	11.93	0.90	92.42	7.58	0.33
M7	7.57	7.13	5.98	0.45	93.27	6.73	0.73
M8	7.57	5.35	29.58	2.24	98.72	1.28	0.07

¹RMSPE = Root of the mean square prediction error (RMSPE). ECT = Error due to overall bias as a percentage of total RMSPE. ER = Error due to deviation of the regression slope from unity as a percentage of total RMSPE. CCC = Concordance correlation coefficient.

Figure 1.

Root mean square prediction error, as a percentage of the observed mean (RMSPE [%], bars), and concordance correlation coefficient (CCC, red dots) from assessed models for predicting dry matter intake in growing steers consuming a forage-based diet.

The estimated DMI using the M4 equation had excellent agreement with the observed DMI in growing steers fed a forage-based diet. In addition, M1, M2, M5, and M6 equations present from slight to moderate agreement with the observed DMI. All these equations require determining the NE_m concentration in the diet (i.e., M2, M4, M5, and M6; Table 2). Those variables could be properly determined in animals under confined conditions where the daily energetic requirements and dietary composition are more controlled. Conversely, it is expected that under grazing conditions, where animal activity and forage quality are less controlled, and animals can increase feed selection, the agreement could be lower between predicted and observed DMI (Vazquez and Smith, 2000). Indeed, forage quality and energy concentration vary throughout the grazing season (Gelley et al., 2016; Avellaneda et al., 2020), potentially resulting in inaccurate determination of the nutritional characteristics (i.e., energy concentration) of the diet. The retrieved equations for predicting DMI were generated using data from animals in confined conditions, except in the M1 equation. Thus, there is limited evaluation of models for estimating DMI using animals under grazing conditions because DMI determination is limited to indirect methods under grazing conditions (Smith et al., 2021). In this regard, other models, including variables such as gas flux, may improve the DMI prediction, especially in animals under grazing conditions.

The use of CO₂ and CH₄ production (M7 and M8) to estimate DMI had an adequate and slight agreement with observed DMI in growing steers fed a forage-based diet, respectively, indicating the potential of using CO₂ as a predictor of DMI in ruminants consuming a forage-based diet (Table 2). As mentioned, CO₂ and CH₄ production are related to DMI (Jonker et al., 2016). The production of CO₂ had a greater relationship with DMI than CH₄ (Table 3), possibly because CO₂ production results from rumen fermentation and animal metabolism. Instead, CH₄ is mainly produced through the reduction of CO₂ (Ungerfeld, 2020). In this regard, a greater agreement between CO₂ and DMI is expected than when using CH₄ production.

Available technologies allow the determination of CO₂ and CH₄ production in grazing conditions, creating a valuable opportunity to estimate DMI. In this study, M4 and M7 equations had good agreement with the observed DMI, resulting in lower MSEP and high CCC values. Thus, the NE_m, SBW, and CO₂ production can be used to predict DMI in growing steers consuming a forage-based diet. The current study has limitations because the relationship between gas flux, animal growth performance, and DMI was defined in growing steers fed a backgrounding diet, and the limited number of evaluated steers fed a forage-based diet. However, the results of the current study suggest an adequate agreement between observed and estimated DMI using CO₂ production in growing steers. Thus, future research is required to evaluate the relationship between CO₂ production and DMI, especially under grazing conditions.

CONCLUSION

Gas flux and animal growth performance variables were positively related to DMI in growing steers. In addition, CO₂ production has the potential to be used to predict DMI in growing steers fed a forage-based diet. Future research is required to evaluate the relationship between CO₂ production and DMI, especially under grazing conditions.

Author Contributions

Juan de J. Vargas (Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing), Maya Swenson (Investigation, Writing - review & editing), Macarena Gomez Salmoral (Investigation, Writing - review & editing), Liza Garcia (Investigation, Writing - review & editing), Eduardo Paula (Investigation, Writing - review & editing), Leo Sitorski (Investigation, Writing - review & editing), Leticia Campos (Formal analysis, Investigation, Writing - review & editing), Pedro Carvalho (Investigation, Writing - review & editing), Kim Stackhouse-Lawson (Conceptualization, Project administration, Writing - review & editing), Nicolas DiLorenzo (Conceptualization, Investigation, Project administration, Writing - review & editing), and Sara Place (Conceptualization, Project administration, Writing - review & editing)

Conflict of Interest Statement

The authors declare no real or perceived conflicts of interest.