Lead Authors:
Alexander N. Hristov, The Pennsylvania State University
Jane M. E. Johnson, USDA Agricultural Research Service
Contributing Authors:
Charles W. Rice, Kansas State University
Molly E. Brown, University of Maryland
Richard T. Conant, Colorado State University
Stephen J. Del Grosso, USDA Agricultural Research Service
Noel P. Gurwick, U.S. Agency for International Development
C. Alan Rotz, USDA Agricultural Research Service
Upendra M. Sainju, USDA Agricultural Research Service
R. Howard Skinner, USDA Agricultural Research Service
Tristram O. West, DOE Office of Science
Benjamin R. K. Runkle, University of Arkansas
Henry Janzen, Agriculture and Agri-Food Canada
Sasha C. Reed, U.S. Geological Survey
Nancy Cavallaro, USDA National Institute of Food and Agriculture
Gyami Shrestha, U.S. Carbon Cycle Science Program and University Corporation for Atmospheric Research
Science Lead:
Sasha C. Reed, U.S. Geological Survey
Review Editor:
Rachel Melnick, USDA National Institute of Food and Agriculture
Federal Liaisons:
Nancy Cavallaro, USDA National Institute of Food and Agriculture
Carolyn Olson (former), USDA Office of the Chief Economist

Agriculture

5.7.1 Inventory Uncertainties

As previously discussed, enteric and manure fermentation are the sources of livestock CH4 emissions. These two sources are affected by different factors and carry different levels of uncertainties. The U.S. EPA estimated 95% confidence interval lower and upper uncertainty bounds for agricultural GHG emissions at –11% and +18% (CH4 emissions from enteric fermentation) and –18% and +20% and –16% and +24% (CH4 and N2O emissions from manure management, respectively; U.S. EPA 2018). Whereas emissions from enteric fermentation are relatively well studied and predictable, there is larger uncertainty regarding manure CH4 emissions and net effects of different intensities and types of grazing (see also Ch. 10: Grasslands). Large datasets have established CH4 emissions from enteric fermentation at 16 to19 g per kg dry matter intake for dairy cows (higher-producing cows have lower emissions per unit of feed intake) to 21 to 22 g per kg dry matter intake for beef cows on pasture (Hristov et al., 2013b). Levels of manure CH4 emissions, however, largely depend on the type of storage facility, duration of storage, and climate (Montes et al., 2013). Emissions from certain dairy manure systems (e.g., flush systems with settling ponds and anaerobic lagoons) can be higher than estimates used by current inventories. So-called top-down approaches have suggested that livestock CH4 emissions are considerably greater than EPA inventories. Miller et al. (2013) and Wecht et al. (2014) proposed that livestock CH4 emissions may be in the range of 12 to 17 Tg per year, which is roughly 30% and 85% greater than EPA’s estimate for 2012 (U.S. EPA 2016). Thus, future research is needed to address these discrepancies and reconcile top-down and bottom-up estimates.

Large uncertainties in GHG emissions from agricultural systems also exist because of their high spatial and temporal variability, measurement methods, cropping systems, management practices, and variations of soil and climatic conditions among regions (Hristov et al., 2017b, 2018). Uncertainty in GHG measurements often exceeds 100% (Parkin and Venterea 2010). Finally, there is considerable uncertainty in soil carbon accumulation and emissions from soils under different conditions and management practices, all of which are complicated by uncertainties about the total amount of land area under different management practices (see Ch. 12: Soils for more information on soil carbon balance).

5.7.2 Modeling and Modeling Uncertainties

Whole-farm models representing all major farm components and processes provide useful tools for integrating emission sources to predict farm-scale GHG emissions (Del Prado et al., 2013). By predicting emission processes and their interactions, models can provide a better understanding of production system emissions and be used to explore how different management decisions could affect GHG emissions. This approach has been used to estimate the carbon footprint of common U.S. dairy production systems at around 1 ± 0.1 kg CO2e per kg fat- and protein-corrected milk produced, in which about half of these emissions come from enteric CH4 emissions (Rotz and Thoma 2017). With a similar approach, the carbon footprint of beef cattle production was found to be 18.3 ± 1.7 kg CO2e per kg carcass weight, with about 60% of emissions in the form of enteric and manure management CH4 (Rotz et al., 2015).

Uncertainty exists in any measurement or projection of GHG emissions. The uncertainty of farm-scale projections is related to the uncertainty in projecting emissions from individual sources (Chianese et al., 2009). The IPCC (2006) suggested a ±20% uncertainty in predicting both enteric and manure management CH4 emissions. Through the use of process-based models representing common management strategies for the United States, the uncertainty for predicting enteric emissions may be reduced to ±10%, but uncertainty for manure management likely will remain around ±20% (Chianese et al., 2009). Considering these uncertainties along with those of other agricultural emission sources, total GHG emissions can be determined with an uncertainty of ±10% to ±15%. As process-level models improve, verified with accurate measurements, this uncertainty can be reduced. As with inventories, uncertainties also are great for modeling agricultural carbon fluxes related to soil processes. Improving the modeling of these processes and incorporating them into large-scale carbon flux models will help increase understanding and reduce uncertainties in carbon models for agricultural lands.


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