Evaluation and development of prediction models for enteric methane emissions from cattle in India

India is home to more than 525 million ruminants, which are major contributors to global warming via enteric methane (Ent_CH4) emissions. Various mitigation strategies exist to reduce Ent_CH4 emissions but accurate emission estimates are needed to establish the true potential of these strategies. Measuring Ent_CH4 emissions is expensive and unrealistic at such a large scale, so an urgent need exists for accurate Ent_CH4 prediction models. The present study evaluated the accuracy of various existing models and developed a new model to predict Ent_CH4 emissions from cattle in India. Six Ent_CH4 prediction models based on either DMI or gross energy intake (GEI) were identified as applicable to and suitable for the Indian context. Models based on DMI and GEI were derived from various works, including those of the Intergovernmental Panel on Climate Change and others (designated as IPCC_DMI, IPCC_GEI, Ribeiro_DMI, Ribeiro_GEI, Patra_DMI, and Patra_GEI). These were evaluated using 2 independent datasets characterizing 528 lactating (dairy) and 122 nonlactating (nondairy) cattle from 15 and 13 studies, respectively, under different management practices across 13 Indian states. Furthermore, the same datasets were combined to develop an empirical Ent_CH4 prediction model using a linear mixed-effects framework. The relative prediction error (RPE) and mean bias error (MBE) were used to evaluate model accuracy. A model's prediction was considered acceptable when RPE was <20%. None of the 6 models predicted Ent_CH4 for nondairy cattle with an RPE <20%. None of the 6 models predicted Ent_CH4 for both dairy and nondairy cattle with an RPE <20%. For dairy cattle, only the Ribeiro_DMI and Patra_DMI models approached this threshold, producing RPE values of 22.6% and 22.9%, respectively. The linear mixed-effects model (Alam's model, described herein: Ent_CH4 [g/d per head] = 15.45 + 1.91 × DMI [kg/d], conditional R² = 0.94), developed for both dairy and nondairy cattle, achieved a substantially lower RPE (9.48%) than any of the 6 previously tested models. Whereas the Ribeiro_DMI and Patra_DMI models could acceptably predict Ent_CH4 emissions from dairy cattle in India, none of the evaluated models were suitable for nondairy cattle. Our linear mixed-effects model provides more accuracy than the latter 2 in estimating emissions for dairy cattle and also offers a suitable option for nondairy cattle in India.