Innovation Center for U.S. Dairy

Limiting warming by the end of the century to 1.5°C compared to pre-Industrial times requires reaching and sustaining net zero global carbon dioxide (CO2) emissions and declining radiative forcing from non-CO2 greenhouse gas (GHG) sources such as methane (CH4). This implies eliminating CO2 emissions or balancing them with removals while mitigating CH4 emissions to reduce their radiative forcing over time. The global cattle sector (including Buffalo) mainly emits CH4 and N2O and will benefit from understanding the extent and speed of CH4 reductions necessary to align its mitigation ambitions with global temperature goals. This study explores the utility of an alternative usage of global warming potentials (GWP*) in combination with the Transient Climate Response to cumulative carbon Emissions (TCRE) to compare retrospective and projected climate impacts of global livestock emission pathways with other sectors (e.g. fossil fuel and land use change). To illustrate this, we estimated the amount and fraction of total warming attributable to direct CH4 livestock emissions from 1750 to 2019 using existing emissions datasets and projected their contributions to future warming under three historical and three future emission scenarios. These historical and projected estimates were transformed into cumulative CO2 equivalent (GWP100) and warming equivalent (GWP*) emissions that were multiplied by a TCRE coefficient to express induced warming as globally averaged surface temperature change. In general, temperature change estimates from this study are comparable to those obtained from other climate models. Sustained annual reductions in CH4 emissions of 0.32% by the global cattle sector would stabilize their future effect on global temperature while greater reductions would reverse historical past contributions to global warming by the sector in a similar fashion to increasing C sinks. The extent and speed with which CH4 mitigation interventions are introduced by the sector will determine the peak temperature achieved in the path to net-zero GHG.

The dairy sector contributes to global warming through enteric methane (CH4) emissions. Methane is also a loss of energy to the ruminant. Several studies have developed CH4 prediction models to assess mitigation strategies to reduce emissions. However, the majority of these models have low predictive ability or require numerous inputs that are often not readily available in commercial dairy operations. In this context, the objective of the present paper was to develop CH4 prediction models by using varying levels of information available at the farm level. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only. Models were fitted to 489 individual enteric-CH4 measurements from 30 indirect calorimetry studies and evaluated with an independent database comprising 215 treatment means from 62 studies collected from the literature. Within each complexity level, all possible mixed-effect models were fitted and those with the lowest values of Akaike or Bayesian information criteria were selected using lme4 package in R. Models were evaluated using mean square prediction error (MSPE) based statistic, root MSPE (RMSPE) to observation standard deviation ratio, concordance correlation coefficient and Nash–Sutcliffe efficiency methods. All fitted models performed well with an acceptable error estimates (RMSPE as a percentage of observed mean (RMSPE%) = 16–24%), with more than two-thirds of total error originating from random bias. Overall, models with DMI were more accurate (RMSPE% = 16–20%) than those without (RMSPE% = 20–24%). Although the best prediction model (RMSPE% = 16%) was developed using Level 5 information, a model using Level 2 information is recommended for on-farm methane estimates if DMI is not measured. The proposed models offer easy and practical tools to dairy producers for predicting CH4 emissions and evaluating CH4 mitigation strategies.