ABSTRACT
Emissions are a function of diverse factors such as management, climate, geography, and technology, so it may not be fruitful to search for “onesize-fits-all” mitigation policies, since they will not be optimal for the majority of farms or cropping systems (Smith et al. 2008). As such, there has been interest in the development of methods and software tools which estimate emissions at the farm scale (where management decisions are often made). Table 19.1 provides a summary of the types of methods available for agricultural greenhouse gas (GHG) emissions quantification. Summary of agriculture emissions quantification methods https://www.niso.org/standards/z39-96/ns/oasis-exchange/table">
Complexity
Models
Data requirements
Aggregation level/uncertainty
Notes
Tier 1
IPCC Tier 1 default factors
Limited land use and management activity data (e.g., N application rates; acres under no-till); little soil delineation; animal populations Low data inputs
Typically large spatial units; national scale; annual resolution; highest uncertainty when applied at project scale
Suitable for rough overviews and where limited data is available (e.g., indirect N2O emission factor from leaching)
Tier 2
Hybrid approaches – using process or empirical models to develop region-specific empirical equations with emission factors
Intermediate spatial/temporal scale input data; land-use and activity data scaled to the spatial unit of analysis (tillage types, animal classes, N fertilizer type; crop type); Requires longer-term scientific data to develop empirical models or calibrate process models
Finer spatial and temporal resolution than above; can achieve reasonable uncertainty due to ‘averaging’ of modelled results
Can be suitable for project-based accounting and inventory roll-ups to national scale; application will depend on available scientific and management data
Tier 3
Process-based models
Spatially explicit fine-scale data for model variables; detailed land-use and management histories; fine-scale soil maps and daily/weekly climate data; requires extensive scientific information to calibrate models at this scale; field measured data for estimating uncertainty is often limiting factor
Finest spatial scale with representation of environmental and management variables at the individual farm level
Suitable for small-scale applications where local variability can be managed; model parameterization and testing can be done; collection of land-use and verified activity data obtained; systems will be needed to make advanced modeling approaches accessible to project developers
Sampling and Measurement
Highest data requirements; costly to measure and variability high; long sampling intervals and crediting periods for soil carbon; can have best precision
Site scale; may be sub-daily if micrometeorological techniques are used to estimate near-continuous gas emission rates, or every few years with soil carbon stock change; uncertainty can be high if not applied correctly
Level of errors may become overwhelming in sites/projects with high variability without tight sampling and statistical design; can be most costly to implement
Source: Reproduced from Olander et al. 2011.