Field studies were conducted at Lincoln University of Missouri (USA) and Hokkaido University (Japan) to: (i) study the relationships between greenhouse gases emissions and soil properties, (ii) assess the influence of agricultural practices on greenhouse gas fluxes and soil properties and (iii) improve the quantification of greenhouse gases from soil in agricultural fields using geospatial technologies. Results showed that besides soil temperature (T), soil thermal properties such as thermal conductivity (K), resistivity (R) and diffusivity (D) and soil pore spaces indices such as the pore tortuosity factor and the relative gas diffusion coefficient (Ds/Do) are controlling factors for greenhouse gases emissions. Soil thermal properties correlated with greenhouse gases emissions when soil temperature could not. The study has found that predicted Ds/Do and correlate with greenhouse gas fluxes even when the air-filled porosity and the total porosity from which they are predicted did not. We have also showed that Ds/Do and can be predicted quickly from routine measurements of soil water and air and existing diffusivity models found in the literature. Agricultural practices do seriously impact greenhouse gases emissions as showed by the effect of mechanized tillage operations on soil physical properties and greenhouse gas fluxes in a corn and soybean fields. In fact, our results showed that tractor compaction increased soil resistance to penetration, water, bulk density and pore tortuosity while reducing air-filled porosity, total pore space and the soil gas diffusion coefficient. Changes in soil properties resulted in increased CO2, NO and N2O emissions. Finally, our results also confirmed that greenhouse gas fluxes vary tremendously in space and time. As estimates of greenhouse gas emissions are influenced by the data processing approach, differences between the different calculation approaches leads to uncertainty. Thus, techniques for developing better estimates are needed. We have showed that Geographic Information Systems (GIS), Global Positioning System (GPS), computer mapping and geo-statistics are technologies that can be used to better understand systems containing large amounts of spatial and temporal variability. Our GIS-based approach for quantifying CO2, CH4 and N2O fluxes from soil in agricultural fields showed that estimating (extrapolating) total greenhouse gas fluxes using the “standard” approach – multiplying the average flux value by the total field area – results in biased predictions of field total greenhouse gases emissions. In contrast, the GIS-based approach we developed produces an interpolated map portraying the spatial distribution of gas fluxes across the field from point measurements and later process the interpolated map produced to determine flux zones. Furthermore, processing, classification and modeling enables the computation of field total fluxes as the sum of fluxes in different zones, therefore taking into account the spatial variability of greenhouse gas fluxes.
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