Uncertainty analysis of greenhouse gas (GHG) emissions is becoming increasingly necessary in order to obtain a more accurate estimation of their quantities. The Monte Carlo simulation (MCS) and non-parametric block bootstrap (BB) methods were tested to estimate the uncertainty of GHG emissions from the consumption of feedstuffs and energy by dairy cows. In addition, the contribution to variance (CTV) approach was used to identify significant input variables for the uncertainty analysis. The results demonstrated that the application of the non-parametric BB method to the uncertainty analysis, provides a narrower confidence interval (CI) width, with a smaller percentage uncertainty (U) value of the GHG emission model compared to the MCS method. The CTV approach can reduce the number of input variables needed to collect the expanded number of data points. Future studies can expand on these results by treating the emission factors (EFs) as random variables.

The study objective was to develop a method for an uncertainty analysis of the greenhouse gas (GHG) emission model output based on consecutive use of an analytical and a stochastic approach. The contribution to variance (CTV) analysis followed by the data quality analysis are the main feature of the procedure. When a set of data points of a certain input variable has a high CTV, but its data quality indicator (DQI) is good, then there is no need to iterate data collection of this input variable. This is because the DQI of this data set indicates that there is no room for the reduction of its variance, and the high variance must be its inherent attribute. Through the CTV analysis and data quality analysis, the identified input variables were selected as the input variables for the data from the iteration of data collection. The statistical parameters of the GHG emissions of the model were calculated using the Monte Carlo simulation (MCS). In the case study of a cattle dairy farm, the relative reduction in the CV value was 47.6%. In this study, a procedure was developed for the selection of the input variables for iteration of data collection to reduce their variance and subsequently reduce the uncertainty in the model output. The dairy cow case study showed that the uncertainty in the model output was decreased by the iteration of data collection, indicating that CTV analysis can be used to identify the input variables, contributing considerably to the uncertainty in the model output.