What question did this study set out to answer?

The research aims to eliminate prediction bias in CO2 emissions by integrating dry matter intake alongside other factors.

March 21, 2026Open Access

Eliminating prediction bias in CO₂ emission models for lactating cows by incorporating feed intake: Accurate quantification of methane-reducing effects using a CO₂-based method, demonstrated by a case study on 3-nitrooxypropanol

Key Points

The research aims to eliminate prediction bias in CO2 emissions by integrating dry matter intake alongside other factors.
Developed CO2 emission prediction models incorporating dry matter intake, metabolic body weight, and energy-corrected milk.
Used 219 records from previous experiments involving Holstein cows with respiration chambers and headboxes.
Evaluated model performance through cross-validation and assessed bias linked to energy utilization efficiency.
Incorporating dry matter intake improved the predictive accuracy of CO2 emissions.
Models excluding dry matter intake displayed energy utilization efficiency bias, which was resolved when included.
Models without dry matter intake systematically underestimated the methane-reducing effect of 3-nitrooxypropanol.

Abstract

Eliminating prediction bias in CO2 emission models for lactating cows by incorporating feed intake: Accurate quantification of methane-reducing effects using a CO2-based method, demonstrated by a case study on 3-nitrooxypropanol ABSTRACT Objective: The methane (CH4) emission prediction method, using predicted CO2 emissions and the CH4:CO2 concentration ratio, faces challenges in evaluating the efficacy of CH4-reducing feed additives due to CO2 prediction bias associated with energy utilization efficiency.We hypothesized that incorporating dry matter intake (DMI), along with metabolic body weight (MBW) and energy-corrected milk (ECM) as explanatory variables, would eliminate this bias.The primary objective was to compare the performance of CO2 emission models with and without including DMI.The secondary objective was to assess the CO2-based method's applicability for quantifying CH4-reducing effects, through a case study of 3-nitrooxypropanol (3-NOP).Methods: Prediction models for CO2 emissions were developed including DMI, MBW, and ECM as explanatory variables, based on 219 records obtained from previous experiments with Holstein cows using respiration chambers or headboxes.Model performance was evaluated using cross-validation.Bias associated with energy utilization efficiency was assessed.The applicability of the CO2-based method to quantify the CH4-reducing effect of 3-NOP was assessed using data obtained from the literature, including 10 studies with 22 treatment and A c c e p t e d A r t i c l e control mean comparisons.The agreement between the observed and predicted CH4 reductions was assessed.Results: Incorporating DMI along with MBW and ECM improved the predictive performance of CO2 emissions.While the models without DMI showed bias associated with energy utilization efficiency, the bias was eliminated when DMI was incorporated.Applicability assessment demonstrated that the models without DMI systematically underestimated the CH4-reducing effect of 3-NOP.In contrast, the models that included DMI showed smaller discrepancies between observed and predicted CH4 reductions. Conclusion:This study highlights the importance of incorporating DMI as an explanatory variable to achieve accurate and unbiased predictions of CO2 emissions.These findings would contribute to the appropriate application of the CO2-based method for evaluating the CH4reducing effects of feed additives.

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