Apply the model to determine the quantity and spatial density of biomass resources available for biofuel production
After applying the GIS model and determining areas with the highest levels of biomass yield, it is evident that Perth county, with or without the applied constraints, has the highest biomass density which can be observed in Figure 6. Therefore, if a biorefinery were to be located in the most optimal location, it would be placed in Perth County. The least optimal location for a biorefinery would be Peel County due to the heavy presence of urban infrastructure and forest land cover.
For the entire study area, the spatial density maps suggest that applying constraints affected the overall spatial density of biomass resources. The first density map developed includes no constraints to display the overall output of biomass yields (Figure 6). Three additional density maps were produced, each map containing a different constraint. These maps can be observed in (Figure 7, Figure 8, and Figure 9). In Figure 7, the constraint applied is removing fallow land (switchgrass) from total biomass resources. The second density map (Figure 8) has no forest biomass included in total biomass, while the third density map (Figure 9) involves the sustainability threshold constraint, which does not include any crop yields that produce less than 61 bushels per acre.
Figure 10. Comparison of all the density maps with respective contraints.
After observing the spatial density maps, it can be suggested that the only constraint that resulted in a significant difference was the map which had the sustainability threshold applied to it. Excluding forest and fallow land made a slight difference, however, it was much less significant than the sustainability threshold. Excluding forest and fallow land from the total biomass resources resulted in approximately 2.3% percent reduction in total biomass yield. Therefore, it can be concluded that fallow land and forest would not be an efficient source of biomass resources. The sustainability threshold (ST) did however reduce total biomass resources by 4.1%. If fallow land is excluded in conjunction with the sustainability threshold, approximately 5.3% of total resources would be eliminated. Table 9 is a summary of the magnitude that certain criteria have on the overall biomass resources available.
Table 9. Results of implementing criteria and the affect on total biomass yields
The results of the biomass estimation equations revealed varying levels of biomass yield with crop type. Figure 11 below shows which crops produced the most biomass in tonnes per acre. Fallow land, area devoted to the production of switchgrass produced the most mass per unit area. Grain corn, wheat and barely had the highest yields per acre respectively. It is apparent from the graph below that the yields of individual crops do not vary significantlyfrom county to county. Further statistical testing should be performed to determine whether determining yields within each county is required.
Figure 11. Average biomass yields for each county.
The sustainability threshold applied to the model results in several crop classes being eliminated from the total biomass resources. Across the entire study area, spring wheat, rye and mixed grain consistently produced under the sustainability threshold of 61 bu/ac. Exclusion of low yielding crops leads to reduction in total resources, however, the reduction is not significant. With the sustainabilitiy threshold applied, the total contribution of biomass resources can be determined for the entire study area. Corn and winter wheat contribute 95% of total biomass resources with the sustainability threshold applied (Figure 13). Therefore, bioenergy projects do not require diverse biomass resources.
Figure 12. Determination of agricultural crops below the sustainability threshold of 61 bushel per acre.
Figure 13. Percent contribution to total biomass yields within the study area with the sustainability threshold applied.
Determine whether siting a biofuel refinery within the boundaries of the city of Guelph would be feasible.
After implementing the GIS model, the constraints placed on each particular density map can be observed and analyzed in terms of their overall impact on biofuel density within the study area. It can be concluded that resources with the Sustainability Threshold (ST) constraint and exclusion of fallow land and forest resources represents the most realistic estimate of biomass resources accessible to the City of Guelph. The ST ensures that biomass extraction does not impact growing conditions, thus decreasing yields. Therefore the potential resources available for the City of Guelph are estimated using these constraints. The two service area distances of 100km (Figure 14) and 50km (Figure 15) provide information required to estimate the total yield in biomass resources and subsequent estimation of electricity available. The total biomass resources within these service areas and the associated electricity potential can be seen in Table 10.
Figure 14. Map of 100 km service distance for biomass resources.
Figure 15. Map of 50km service distance for biomass resources.
Table 10. Estimation of electricity production based on service distance