**4. Results and discussion**

Alberta had the highest rangelands ESSR by virtue of being the only province with the Foothills Fescue (FF) Ecoregion, which had the highest ESSR value (Table 1). Because it had the largest share of the Dry Mixed Grass (DMG) Ecoregion, with the lowest ESSR of the four ecoregions, Saskatchewan had the lowest provincial ESSR value. The Mixed Grass (MG) and the Parkland-Northern Fescue (PNF) ecoregions, with similar ESSR values, are more or less evenly divided between Saskatchewan and Alberta. Table 2 shows that Manitoba has only 7% of the prairie rangeland while Alberta has 50%. The yields of forage from Prairie rangelands (Table 2) were roughly one tenth of those of tame hay (Table 4). However, in both cases these forage yields were general approximations since these yield statistics are not regularly surveyed in the prairie region.

All of the statistics for annual crops (Table 3) showed considerable variability over the three provinces and the two census years, which helps to explain some of variability in the results of this assessment. The area in canola in the Prairies grew between 2006 and 2011 while the area in feed grains (barley and oats) shrank by a similar proportion (Table 3). The dry matter yield of two feed grains was 62% higher than the yield of canola. Even though the proposed expansion area for canola is only a little over 10% of the total canola crop land in the Prairies, Table 4 shows that the proposed expansion could lead to the conversion of roughly 80% of the rangeland to full time grazing by domestic ruminants in order to make up for the feed lost to the canola expansion.

Figure 3 demonstrated the importance of the province of Alberta to the CF of the beef industry in the Prairie Provinces. Alberta beef generated as much of the total GHG emissions as did Saskatchewan and Manitoba combined. Methane was the dominant type of GHG in the western Canadian beef industry. Not only was CH4 the type of GHG with the highest quantity, it is the GHG that will increase the most if cattle are fed a more roughage based diet. This is because most of this gas is enteric methane which is the direct result of the ruminant digestion of roughage, the dominant component of the diet when cattle are displaced from a feed grain diet. This trend is partly counteracted, however, by the decreases in both N2O and fossil CO2 emissions when cattle are less intensively managed (as in Scenario 3), or when sheep are substituted for the feedlot finished cattle (Scenario 4). This feedback effect is accounted for in ULICEES.

Because the emission rates for Scenarios 3 and 4 in Table 5 were estimated from differences between the scenario simulations and the baseline, they were, not surprisingly, rather unstable. This instability carried over into Table 6. The wider spread among provinces for Scenario 4 in Table 5 indicates that they were a bit less stable than Scenario 3. For the Prairies, Scenario 3 was closer to the baseline ULICEES simulations (the baseline being zero in this regard) than Scenario 4. Even with Manitoba having a negative difference, the Scenario 4 emissions rate for the Prairies was more than double the Scenario 3 emissions rate for the Prairies.

The Prairie average live weights (W) used in ULICEES were 495 for steers and slaughter heifers, 506 for replacement heifers, 616 for culled cattle, and 380 for slaughter calves, while the live

Alberta had the highest rangelands ESSR by virtue of being the only province with the Foothills Fescue (FF) Ecoregion, which had the highest ESSR value (Table 1). Because it had the largest share of the Dry Mixed Grass (DMG) Ecoregion, with the lowest ESSR of the four ecoregions, Saskatchewan had the lowest provincial ESSR value. The Mixed Grass (MG) and the Parkland-Northern Fescue (PNF) ecoregions, with similar ESSR values, are more or less evenly divided between Saskatchewan and Alberta. Table 2 shows that Manitoba has only 7% of the prairie rangeland while Alberta has 50%. The yields of forage from Prairie rangelands (Table 2) were roughly one tenth of those of tame hay (Table 4). However, in both cases these forage yields were general approximations since these yield statistics are not regularly surveyed in the

All of the statistics for annual crops (Table 3) showed considerable variability over the three provinces and the two census years, which helps to explain some of variability in the results of this assessment. The area in canola in the Prairies grew between 2006 and 2011 while the area in feed grains (barley and oats) shrank by a similar proportion (Table 3). The dry matter yield of two feed grains was 62% higher than the yield of canola. Even though the proposed expansion area for canola is only a little over 10% of the total canola crop land in the Prairies, Table 4 shows that the proposed expansion could lead to the conversion of roughly 80% of the rangeland to full time grazing by domestic ruminants in order to make up for the feed lost to

Figure 3 demonstrated the importance of the province of Alberta to the CF of the beef industry in the Prairie Provinces. Alberta beef generated as much of the total GHG emissions as did Saskatchewan and Manitoba combined. Methane was the dominant type of GHG in the western Canadian beef industry. Not only was CH4 the type of GHG with the highest quantity, it is the GHG that will increase the most if cattle are fed a more roughage based diet. This is because most of this gas is enteric methane which is the direct result of the ruminant digestion of roughage, the dominant component of the diet when cattle are displaced from a feed grain diet. This trend is partly counteracted, however, by the decreases in both N2O and fossil CO2 emissions when cattle are less intensively managed (as in Scenario 3), or when sheep are substituted for the feedlot finished cattle (Scenario 4). This feedback effect is accounted for in

Because the emission rates for Scenarios 3 and 4 in Table 5 were estimated from differences between the scenario simulations and the baseline, they were, not surprisingly, rather unstable. This instability carried over into Table 6. The wider spread among provinces for Scenario 4 in Table 5 indicates that they were a bit less stable than Scenario 3. For the Prairies, Scenario 3 was closer to the baseline ULICEES simulations (the baseline being zero in this regard) than

weights were 57 for ewes and 48 kg and for lambs.

**4. Results and discussion**

366 Biofuels - Status and Perspective

prairie region.

the canola expansion.

ULICEES.

Being normalized to the same area basis, the prairie-wide GHG emission rates were almost equal for the two scenarios. In Figure 4 both scenarios show lower values for the prairie region than the respective rates in Table 5 because the rates in Figure 4 included the differences between GHG emission rates from the expanded canola and the respective CO2 sequestration rates, which were negative quantities. For both Figure 4 and Table 5 (Columns 7 and 8), Scenario 3 in Manitoba was the only negative emission rate difference. In Figure 4, the only province where Scenario 4 was greater than Scenario 3 was Alberta.

The difference between Columns 4 and 5 in Table 5 (Scenarios 1 and 2) shows that the potential contribution by canola meal to ruminant diets could decrease the requirement for replacement perennial forage by over a third in the Prairie region. The inter-provincial variations in GHG emission intensities from the differences between the assumed livestock systems in Scenarios 3 and 4 were several times higher than the inter-provincial variations in just the direct emission intensities of the expanded canola crop (Column 1 in Tables 5 and 6). All of the GHG emission intensities from Table 5 showed considerable inter-provincial variations with Saskatchewan having the lowest GHG emission intensities for the crops of the three provinces, but the highest intensity differences from the baseline simulations for the two livestock scenarios. This interprovincial variation was evident even before the livestock GHG emission rates were normal‐ ized to the canola areas.

The areas in feed grain production in the BCC that were freed to expand canola production were 1.75 Mha in Scenario 4 and 1.01 Mha in Scenario 3. The regional total feed grain area was 2.28 Mha.

Hence, the expanded canola areas were smaller than the feed grain areas in Scenario 3 but greater in Scenario 4. Thus, normalizing from feed grain to canola areas (for Table 6) would inflate the rates in Column 7 of Table 5 and deflate the rates in Column 8. The greater area changes associated with Scenario 4 helps to explain why Scenario 4 was more sensitive to the inclusion of GHG emission from the new canola and the sequestered CO2 under the new forage area.

Without considering the livestock in Table 6, Scenarios 1 and 2 would actually appear to reduce the net CF of the expanded canola. Additionally, when CO2 sequestration is considered, Scenario 2 suggests that just the growing of canola reduces GHG emissions without having to consider the fossil CO2 emission offset potential. The ranking of the two livestock scenarios reversed in Table 6 compared to Table 5, with Scenario 3 having the greater net CF for expanded canola. Manitoba showed the biggest difference between the two livestock scenarios, whereas Alberta (with the largest beef industry) showed the least difference. The offset fossil CO2 emission intensities (Table 6) were highest in Alberta because of the higher canola yields in that province. The net CF of the expanded canola in Table 6 was below the offset fossil CO2 emission intensities (Column 7) for Scenario 3 in Alberta and for Scenario 4 in Saskatchewan. At the prairie region scale, however, the potential fossil CO2 emissions offset by canola oil was less than the net CF of the expanded canola for Scenario 3. The potential fossil CO2 emissions offset for the prairie region exceeded net CF of the expanded canola for Scenario 4, but not by a high enough to meet the EC directives [1].

To understand the role of CO2 sequestration in the net CF of the expanded canola, a sensitivity test was run on the soil carbon storage rate [35] with a plus or minus 20% range. For Scenario 3 the range on the net CF was from 1.7 to 2.4 tCO2e/ha, for a range about 2.1 tCO2e/ha of ±16%. For Scenario 4 the range was from 0.9 to 1.5 tCO2e/ha, for a range about 1.2 tCO2e/ha of ±27%. Whereas a 20% increase in soil CO2 sequestration rate would change Scenario 4 to 51% below the fossil CO2 emission offset by canola, the result for Scenario 3 would only be 3% below that offset level. If the expanded canola described in this chapter were considered to be a continu‐ ation of the current operation of Canadian canola production, rather than a new installation, Scenario 4 might be deemed to just barely qualify for export to the EU [1] with soil carbon sequestration made 20% higher than reported by [35]. The increased sensitivity of Scenario 4 compared to Scenario 3 was due to the greater area of feed grain that was freed from the BCC for expanded canola in Scenario 4.

The protein based emission intensities in Figure 5 were close to equal for the two livestock scenarios in the Prairie region. Saskatchewan had the highest protein based GHG emission intensities in Scenario 3, while Alberta had the highest intensity for Scenario 4, but only slightly higher than Saskatchewan for Scenario 4. Scenario 3 exceeded Scenario 4 in Manitoba and Saskatchewan, while Scenario 4 was higher in Alberta. For the region, both scenario protein based emission intensities were higher than the baseline intensities for both beef and sheep, although only slightly higher than for sheep.
