**3. Methodology**

of expanding canola production on beef production in the Prairie Provinces of Canada. The scope of this assessment excludes any substitution of beef with non-ruminant livestock in response to canola expansion. The second goal was to determine the feedback effect from this

The primary impact of canola expansion was expected to be the displacement of other grains and oilseeds from the land most capable of growing annual crops. The assessment in this chapter did not consider the small areas of canola that might be seeded into lower quality land because the expectation is that most of that land would not support the cultivation of canola. Since the expansion of canola between 1986 and 2006 happened concurrently with, and possibly as a consequence of, the shrinkage of summerfallow [2], there is little indication that canola expansion to date has caused the direct conversion of land under permanent year-round

Without this conversion of perennial forage areas to canola, a potential decrease in soil carbon could be ignored. However, the converse cannot be ruled out if beef production is forced to shift to a more forage (roughage) based diet. Because ruminants have the option of feeding on roughages, the land base that supports these livestock is likely to shift to more permanent cover if their feed grain supply is displaced by canola. Although beef cattle are the dominant ruminants in Canada, some consideration has been given to the potential expansion of sheep production [3]. The impacts of canola expansion on ruminant livestock production can be treated as secondary effects. An environmental effect is considered secondary when one environmental component is affected by another environmental component when the second component has been affected by a human activity [4, 5]. The activity being assessed in this chapter is the continued expansion of canola in Western Canada at the expense of livestock

The Western Canadian beef industry is an intensive system that relies on finishing animals destined for slaughter in feedlots with a diet that is high in feed grains [6, 7]. Canadian lamb production is similarly intensive in this regard [3]. The conversion of these systems to extensive systems that are mainly based on grazing and hay consumption could be one of the indirect effects of canola expansion [8]. The main impact on the CF of beef production will be greater enteric methane emissions due to a higher share of roughage in the diet [7]. While Dyer et al. [8] qualitatively assessed the impacts on biodiversity from this potential land use change, a quantitative assessment of the GHG emissions from beef cattle displaced from a highly grain based diet into improved pasture or rangeland, and greater dependence on hay has not yet

The GHG emission budgets of biofuel feedstock and livestock production have already been shown to strongly interact [9]. Instead of converting beef production from intensive to extensive production (as proposed in this chapter), Dyer et al. [9] replaced part of the beef population with hogs which, being non-ruminants, reduced enteric methane emissions. That

impact on the Greenhouse Gas (GHG) emission cost of canola biodiesel.

**2. Background**

352 Biofuels - Status and Perspective

cover into annual crops.

feed grains.

been carried out.

While the expansion of canola can also displace baking quality grains or other food crops for humans, this assessment will only deal with the canola that displaces livestock feed grains, which would lead to changes in the livestock diet. The first set of impacts from canola expansion into ruminant livestock production will be on land use. The expected output variables from the analysis of land use changes included the weight and area of feed grain that will be displaced by the expanded canola crop, the areas of roughage crops, including, hay (for winter feed), pasture (improved) and rangeland (unimproved pasture), and the number of displaced grazing animals computed from the roughage crop yields and stocking rates [11, 7]. This chapter also considers the net changes in the GHG emissions budget for canola and the implications for protein supply.

### **3.1. Rangeland forage availability**

If part of the increased forage in the ruminant diets is to come from more grazing, then one of the pools of available land would likely be rangelands. Therefore, the first land use change question addressed in this chapter will be how much rangeland could be allocated to grazing the livestock that are taken off feed grain due to the canola expansion. In addition to the impact on biodiversity [8], overgrazing would make the forage digestibility on rangeland less than the forage digestibility on tame pasture [12], which effectively lowers forage yields. Therefore, it is essential to set stocking rakes at a population density that is sustainable.

The Ecological Sustainable Stocking Rate (ESSR) was an essential indicator in quantifying the rangeland grazing resources. ESSR values have been quantified for rangelands in most of the agro-ecological sub-regions of the Prairie Provinces [11, 13-15]. The fraction of each agroecological sub-region in each of the three Prairie Provinces was extrapolated by Dyer et al. [16]. Integrating the ESSR fractions for these regions in each province gave the approximate provincial ESSR values shown in Table 1.

Each ESSR represents the sustainably grazed forage by one Animal Unit Month (AUM) from a given area of rangeland. One Animal Unit (AU) was defined as being equal to one 454 kg cow with calf, or five breeding sheep (ewes and their lambs), based on equivalent forage consumption [11, 17, 18]. One AUM is, therefore, a measure of forage production. Provincial ESSR and rangeland areas were combined to approximate the rangeland forage yields and the Total Sustainable Animal Units (TSAU) in each province shown in Table 2. To determine yields, an AUM must be converted to the required quantity of feed for each AU. The ESSR from Table 1 were converted to the required areas of rangeland per AU over six months (Column 3). The ESSR (Column 1) were used to derive forage yield estimates for the three Prairie Provinces (Column 4). The hay needed to over-winter one AU (one breeding cow and her calf or five ewes and their lambs) must approximately equal the forage that a typical cow would have grazed from the rangeland during the six-month summer period [19].


**Table 1.** Provincial Ecological Sustainable Stocking Rate (ESSR) factors for Canadian Prairie rangeland interpolated from ecoregion ESSR estimates

One AU has a daily requirement of 11.8 kg of dry matter forage [17]. Therefore, one AU-month (AUM) equals 355 kg of dry matter forage (30 days times the daily forage requirement). Six AUM (half year of feed) would equal 2.13 t of dry matter per AU. Forage yields in each province (Column 4 of Table 2), in t per ha, were the product of each provincial ESSR (Table 1) and 0.355 t dry matter. Table 2 also shows the rangeland area needed to support one AU for six months of summer grazing (Column 3), the total rangeland area (Column 2) and the TSAU (Column 5) in each province.


1 , Yield = Dry matter yield of forage from rangeland

2 , TSAU = Total Sustainable Animal Units

**Table 2.** Areas, carrying capacities and sustainable forage yields of rangeland in the Prairie Provinces of Canada.

### **3.2. Changes in arable land use**

ESSR (Column 1) were used to derive forage yield estimates for the three Prairie Provinces (Column 4). The hay needed to over-winter one AU (one breeding cow and her calf or five ewes and their lambs) must approximately equal the forage that a typical cow would have

**0.37 0.84 1.68 0.88 ESSR**

**/ha**

grazed from the rangeland during the six-month summer period [19].

**Ecoregions DMG MG FF PNF ESSR (AUM/ha)**

Canadian Prairie Ecoregions:

1

1

2

, AUM = Animal Unit Months

354 Biofuels - Status and Perspective

from ecoregion ESSR estimates

5) in each province.

, Yield = Dry matter yield of forage from rangeland

, TSAU = Total Sustainable Animal Units

DMG = Dry Mixed Grass FF = Foothills Fescue

MG = Mixed Grass PNF = Parkland-Northern Fescue

**Table 1.** Provincial Ecological Sustainable Stocking Rate (ESSR) factors for Canadian Prairie rangeland interpolated

One AU has a daily requirement of 11.8 kg of dry matter forage [17]. Therefore, one AU-month (AUM) equals 355 kg of dry matter forage (30 days times the daily forage requirement). Six AUM (half year of feed) would equal 2.13 t of dry matter per AU. Forage yields in each province (Column 4 of Table 2), in t per ha, were the product of each provincial ESSR (Table 1) and 0.355 t dry matter. Table 2 also shows the rangeland area needed to support one AU for six months of summer grazing (Column 3), the total rangeland area (Column 2) and the TSAU (Column

**Province AUM/ha ha × 106 ha/AU {6 months} t/ha AU × 106** Manitoba 0.88 0.72 6.9 0.31 0.10 Saskatchewan 0.71 4.55 8.4 0.25 0.54 Alberta 0.97 5.29 6.2 0.34 0.85

**Table 2.** Areas, carrying capacities and sustainable forage yields of rangeland in the Prairie Provinces of Canada.

**ESSR Rangeland Summer forage Yield1 TSAU2**

**Province Share of province in each Ecoregion AUM1**

Manitoba 100% 0.88 Saskatchewan 30% 35% 35% 0.71 Alberta 20% 30% 25% 25% 0.97 The next phase of this chapter considers the impact of expansion of canola on the areas seeded to livestock feed crops. Since they account for roughly 90% of the grains in cattle diets in western Canada, a mix of barley and oats was taken as representing a typical ration of feed grain in the prairie region. The area currently used to grow feed grain (oats and barley) and canola is shown in Table 3 in each province for the two most recent census years (2006 and 2011). The total provincial production and yields for these crops are also shown in Table 3. The respective yields were used to determine how much feed grain area would be displaced by expanded canola. The dry matter weights of production in Table 3 were used to determine how much new area in perennial forage would be needed the replace the lost livestock feed.

This assessment was based on four scenarios of how expanded canola could impact beef production (described in Section 3.3). The land use changes that are the basis of these scenarios are shown in Table 4. These changes include the expansion of canola, the feed grain displaced by canola and the areas of additional hay needed to replace the displaced feed grain. This table represents a dynamic area balance calculation for testing the quantitative response to assumed expanded areas of canola to be of the crops being displaced by canola. The controlling parameter for this table was the total area of new canola across all three provinces. While this table is a dynamic tool that changes depending on what value for this parameter is selected, the version of this table shown in this chapter assumed a total area of 0.7Mha for both census years. This canola area total was then distributed among the three provinces so that the rangeland in any one province would not be exceeded. The 0.7Mha of expanded canola was the maximum new canola area that the rangeland could have sustainably replaced the required forage.


**Table 3.** Crop production comparisons for canola (biofuel feedstock) and barley (livestock feed grain) in the Canadian Prairie Provinces during two census years.

The crop type and year-specific crop yields from Table 3 were used to convert areas to production quantities. The computation sequence in this balance was:


Since this chapter allowed for the contribution of canola meal to the ruminant diet, the lost feed grain production was reduced by the weight of canola meal from the expanded area of canola. The weight of extractable oil from canola is 39% of the harvested crop weight, which means that 61% of the harvested canola dry matter weight is available as livestock feed supplement [18, 19].

As a general rule for sheep and cattle, 1.8 kg of average quality hay can replace approximately one kg of barley or oats [22-24]. This broadly accepted rule of thumb allowed land under feed grains and under perennial forage to be equated on the basis of nutrient energy for ruminant livestock. This approximation also allowed the land diverted away from the feed grains into canola production to be expressed in terms of the additional tame hay or grazing land that ruminant livestock would need to maintain their dietary energy intake.


**Table 4.** Changes in areas and production resulting from displacement of barley by 700, 000 ha of canola needed for biodiesel feedstock, and the areas of tame hay, improved pasture or rangeland to grow enough forage to replace the lost feed grain for cattle (represented by barley and oats) during two census years in the Prairie Provinces.

Tame hay yields are less accurately reported (by survey) than the yields of annual field crops. A typical yield of about 4.1 t/ha across Canada, however, has been estimated [25]. Bootsma et al. [26] demonstrated that perennial forage yields on improved land vary with regional climate and soil types. For simplicity, it was assumed that the spatial variance among these tame hay yields (Column 6 of Table 4) would be the same as among the rangeland forage yields (Column 8) and that the provincial tame hay and improved pasture yields could be scaled to the rangeland yields (Column 8 of Table 4 or Column 4 of Table 2). The steps in the above computation sequence relate to the column numbers in Table 4 as follows: Step 1 is in Column 1, Step 2 (allowing for canola meal) is in Column 3, Step 3 is in Column 4, Step 4 is in Column 5, and Step 5 is in Column 7 for tame hay and Column 9 for rangeland. Table 4 also shows the canola production in Column 2.

### **3.3. Defining the canola expansion scenarios**

The crop type and year-specific crop yields from Table 3 were used to convert areas to

Since this chapter allowed for the contribution of canola meal to the ruminant diet, the lost feed grain production was reduced by the weight of canola meal from the expanded area of canola. The weight of extractable oil from canola is 39% of the harvested crop weight, which means that 61% of the harvested canola dry matter weight is available as livestock feed

As a general rule for sheep and cattle, 1.8 kg of average quality hay can replace approximately one kg of barley or oats [22-24]. This broadly accepted rule of thumb allowed land under feed grains and under perennial forage to be equated on the basis of nutrient energy for ruminant livestock. This approximation also allowed the land diverted away from the feed grains into canola production to be expressed in terms of the additional tame hay or grazing land that

**2006** Manitoba 109 197 70 218 393 4.22 93 0.31 720 Saskatchewan 315 473 201 511 919 3.43 268 0.25 4,184 Alberta 276 524 171 532 958 4.65 206 0.34 2,792 Prairies 700 1,193 442 1,261 2,269 4.10 567 0.29 7,695 **2011** Manitoba 63 101 38 94 169 4.22 40 0.31 543 Saskatchewan 301 542 192 584 1,052 3.43 307 0.25 4,164 Alberta 336 739 208 734 1,321 4.65 284 0.34 3,852 Prairies 700 1,382 438 1,412 2,541 4.10 631 0.30 8,559

**Table 4.** Changes in areas and production resulting from displacement of barley by 700, 000 ha of canola needed for biodiesel feedstock, and the areas of tame hay, improved pasture or rangeland to grow enough forage to replace the lost feed grain for cattle (represented by barley and oats) during two census years in the Prairie Provinces.

**New canola Displaced feed grain Required Tame hay/pasture Rangeland area production area production forage yield area yield area 000,ha 000,t 000,ha 000,t 000,t t/ha 000,ha t/ha 000,ha**

**2.** let the area of canola define the displaced area of feed grain (barley and oats),

production quantities. The computation sequence in this balance was:

**3.** convert displaced feed grain area to lost feed grain production,

**4.** define the required production of forage to replace feed grain, and

**5.** determine the new forage area from the required forage production.

ruminant livestock would need to maintain their dietary energy intake.

**1.** set the area to produce canola,

356 Biofuels - Status and Perspective

supplement [18, 19].

The second goal of this chapter was to determine the change in the GHG emissions budget for the ruminants undergoing a diet. Prior to this determination for livestock, two preliminary scenarios were considered for the additional forage crop resulting from canola expansion. The difference between not including canola meal in the diet of displaced beef cattle (Scenario 1) and including canola meal (Scenario 2) served to demonstrate the feedback effect of canola meal in partially mitigating the secondary effects of canola expansion into the feed supply for beef cattle.

Two additional scenarios were used to assess the secondary impact of the canola expansion on livestock production. The first (Scenario 3) involved relocating the displaced feedlot cattle to pasture and rangeland, and a diet much richer in hay. The second (Scenario 4) assumed that the steers and heifers destined for finishing in feedlots would be butchered as veal at the calf or pre-yearling life stage, rather than being relocated to feedlots, or to pasture and hay. In order to avoid a major drop in protein supply in this scenario, these pre-yearlings would be replaced with sheep to be grazed and wintered on hay.

Six age-gender categories define the lifecycle of western Canadian beef cattle based on the feed intake and live weight differences among these categories [7, 27]. This grouping put breeding bulls and cows in one category. Figure 1 defines the age-gender categories and shows the ages and their intake of annual feed grains. This grouping ignores the newborn calves because at this age these animals do not consume grain. The bottom three categories in Figure 1, which include the animals destined for slaughter, consume proportionally more feed grain than do the replacement categories. This dietary difference was essential to the GHG emission assessment described below.

To help understand the two livestock scenarios, the structure of the beef cattle population in the three Prairie Provinces is shown in Figure 2. The breeding stock included 0.7, 1.6 and 2.1 million head of cattle in Manitoba, Saskatchewan and Alberta, respectively in 2006, making up 46% of the beef cattle population of the Prairie Provinces. Bulls account for 5% of these breeding cattle. The animals that are less than one year old are split almost equally between bull calves and young heifers. About 7% of the animals shown as steers and slaughter heifers category in Figure 2 are slaughter calves. Although the younger age-gender categories

**Figure 1.** Annual feed grain consumption in each age-gender category of beef in the Prairie Provinces.

**Figure 2.** Age-gender based population distributions and total cattle populations (in millions of head (Mhd)) of the beef industry in the three Prairie Provinces of Canada during 2006.

fluctuate depending on market prices, the relative populations of the steers and slaughter heifers indicate that there is a net flow of these animals to the feedlots which are mostly located in southern Alberta.

In order to fully understand the full impact of livestock production on GHG emissions, the GHG emissions from the areas that provide livestock feed, not just emissions of enteric methane, must be included in this budget. The Livestock Crop Complex (LCC) defines the crop areas required to feed Canada's livestock populations [27, 28]. Five specific crop com‐ plexes have been defined in Canada, including the BCC, DCC, PCC, ACC and SCC, respec‐ tively, for beef, dairy, pork, poultry (avian) farms [7, 20, 29, 30] and, most recently, for sheep [3]. The LCC concept has been used to quantify the cropland that was not used to support livestock in Canada [28]. This LCC-excluded land concept is similar to the LCC application in this chapter, since the land designated for canola expansion was removed from the BCC.

The crop complex area includes both the roughage and grain crops in the animal diet. Only the BCC, DCC and SCC in the Prairie Provinces include land in perennial forage. The grain area in each LCC is the product of population, diet and the yield of each feed grain, integrated over all grain crops in the livestock diet, although (as in Table 3) feed grain in this region is mostly a mix of barley and oats. In this chapter the potential changes in the BCC due to anticipated canola expansion were assessed for each of the three Prairie Provinces and for the Prairie Province region of Canada.

### **3.4. GHG emissions budget for ruminants**

fluctuate depending on market prices, the relative populations of the steers and slaughter heifers indicate that there is a net flow of these animals to the feedlots which are mostly located

**Figure 2.** Age-gender based population distributions and total cattle populations (in millions of head (Mhd)) of the

**Figure 1.** Annual feed grain consumption in each age-gender category of beef in the Prairie Provinces.

0% 20% 40% 60% 80% 100%

beef industry in the three Prairie Provinces of Canada during 2006.

**Shares of total beef cattle population by age-gender categories in each Prairie Province**

0.0 0.5 1.0 1.5 2.0 2.5

Manitoba

Alberta

Saskatchewan

**t {feed grain} / head**

Bulls & Breeding Cows

Reproduction Heifers > 1 year Heifers & Bull Calves < 1 year Steers & Slaughter calves & Heifers

In order to fully understand the full impact of livestock production on GHG emissions, the GHG emissions from the areas that provide livestock feed, not just emissions of enteric methane, must be included in this budget. The Livestock Crop Complex (LCC) defines the crop areas required to feed Canada's livestock populations [27, 28]. Five specific crop com‐ plexes have been defined in Canada, including the BCC, DCC, PCC, ACC and SCC, respec‐ tively, for beef, dairy, pork, poultry (avian) farms [7, 20, 29, 30] and, most recently, for sheep

in southern Alberta.

Alberta (5.4 Mhd)

Saskatchewan (2.9 Mhd)

Manitoba (1.4 Mhd)

Steers & Slaughter Heifers

Reproduction Heifers > 1 year

358 Biofuels - Status and Perspective

Bulls & Breeding Cows

Slaughter Calves

Heifers < 1 year

Bull Calves < 1 year

The GHG emissions for the two livestock scenarios were simulated for 2006 with the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES) model [27]. ULICEES was created by assembling the five sets (discussed above) of livestock-specific GHG compu‐ tations from the Canadian beef, dairy, pork and poultry industries [7, 20, 29, 30] in one spreadsheet model. Figure 3 shows the total GHG emissions for beef production in each prairie province calculated by ULICEES for 2006. The livestock GHG emission assessments include fossil CO2, CH4 and N2O. Since these calculations provided a baseline for Scenarios 3 and 4, separate totals for the three GHGs are shown in Figure 3. These emissions are expressed as fossil CO2 emission equivalent quantities.

**Figure 3.** Total Greenhouse Gas (GHG) emissions from the beef industry in the Prairie Provinces of Canada during 2006.

ULICEES uses the Tier 2 methodology from IPCC [31], modified for Canadian conditions [32], to estimate nitrous oxide emissions for each age-gender livestock category. Methane emissions from enteric fermentation and manure storage were calculated separately by ULICEES [27]. Both methane source estimates also relied on IPCC Tier 2 methodology [31]. Both types of methane emissions were then calculated on a per-head basis for each age-gender category and multiplied by each respective category population. Using the six farm energy terms defined in [33], the provincial fossil CO2 emission rates for 2006 were simulated by Dyer et al. [34]. These estimates were incorporated into ULICEES [27]. Unlike the CH4 and N2O mission estimates, the fossil CO2 emission estimates were not distributed over age-gender categories within each livestock type.

Three ULICEES simulations for beef and lamb production in the three Prairie Provinces were required to describe the two livestock scenarios. The first ULICEES simulation was the baseline set of GHG emissions by the beef industry with no assumed changes in the population structure of the industry. To run the two additional ULICEES simulations, the changes in the age-gender livestock populations described above were implemented in the inputs to the ULICEES model. For ULICEES to implement the grass beef scenario (#3), the replacement heifers were used as an analog for grass beef because their diet is mostly forage [7, 27]. This meant that in the grass beef scenario (#3) the populations of steers, slaughter heifers and slaughter calves in ULICEES were transferred to the replacement heifer age-gender category. To apply ULICEES to the veal/lamb scenario (#4), the populations of steers, slaughter heifers and slaughter calves were transferred to the newly born calves' category, to which ULICEES attributes no GHG emissions [27]. In addition, the sheep populations had to be expanded to consume the forage no longer consumed by those animals that were converted to veal production. This was achieved by inflating the sheep populations by the ratios of meat animals in the beef industry to the sheep population expressed as protein in each province.

Before the reallocation of steers and slaughter heifers to the reproduction heifer category (Scenario 3), these populations were redistributed to match the distribution of breeding cows among the provinces. This was done to remove the influence of the concentration of feedlots in southern Alberta, to which the cattle destined for finishing for market before slaughter tend to gravitate. Before inflating the sheep populations in Scenario 4, the GHG emissions from sheep were redistributed to match the distribution of GHG emissions from beef cattle given by ULICEES. This was done to reduce instability caused by the populations of sheep in western Canada being very small relative to beef cattle.

### **3.5. Changes in the Carbon Footprint (CF) of canola**

The CF of expanded canola must combine initial GHG emission costs of actually growing the canola crop with the secondary impact assessment of the crops being displaced by the canola. In addition, it must include potential benefits stemming from the shift from annual to perennial ground cover for both scenarios. The change in beef production (from feed grain to hay) would mean that the soil surface is never bare between crops which would cause atmospheric CO2 to be sequestered as soil carbon. For the Prairie Provinces the average yearly carbon storage would be approximately 0.55 t{carbon}/ha [35], or 2.02 t/ha of sequestered CO2. In this chapter when the CF determination takes all of these terms into account, it is then deemed to be the net CF of canola.

Table 5 shows the GHG emission rates used for all four scenarios normalized to areas of expanded canola so that all of these coefficients have the same area basis. The first two columns show the emission rates for canola and feed grain [36], while the last two columns show the changes in the GHG emission rates of the two livestock scenarios (#3 and #4). Columns 3, 4 and 5 of Table 5 all represent emission rates for the new areas of hay in the expansion scenarios, with Column 3 showing the rates as reported by [36]. Columns 4 and 5 have been normalized to the areas in expanded canola. The differences between these two columns demonstrate the importance of substituting canola meal for part of the displaced feed grain. Column 6 of Table 5 gives the sequestration rate for CO2 by the conversion to perennial forage normalized from the forage area to the expanded canola area in each province. The negative signs on these values illustrate that sequestration flux direction is apposite that of GHG emissions. Columns 7 and 8 include the increase in GHG emissions compared to the baseline GHG simulations normal‐ ized to the area remaining in feed grains in each respective scenario. Columns 7 and 8 do not include the emission cost of growing canola (Column 1) or the benefit of sequestered CO2 (Column 6).


1 , GHG emission intensity of hay per unit area of hay grown and harvested, not normalized to canola.

2 , GHG emission intensity of forage with no substitution by canola meal, normalized to canola area.

3 , GHG emission intensity of forage with substitution by canola meal, normalized to canola area.

4 , fossil CO2 sequestered by new forage, normalized to canola area.

Both methane source estimates also relied on IPCC Tier 2 methodology [31]. Both types of methane emissions were then calculated on a per-head basis for each age-gender category and multiplied by each respective category population. Using the six farm energy terms defined in [33], the provincial fossil CO2 emission rates for 2006 were simulated by Dyer et al. [34]. These estimates were incorporated into ULICEES [27]. Unlike the CH4 and N2O mission estimates, the fossil CO2 emission estimates were not distributed over age-gender categories

Three ULICEES simulations for beef and lamb production in the three Prairie Provinces were required to describe the two livestock scenarios. The first ULICEES simulation was the baseline set of GHG emissions by the beef industry with no assumed changes in the population structure of the industry. To run the two additional ULICEES simulations, the changes in the age-gender livestock populations described above were implemented in the inputs to the ULICEES model. For ULICEES to implement the grass beef scenario (#3), the replacement heifers were used as an analog for grass beef because their diet is mostly forage [7, 27]. This meant that in the grass beef scenario (#3) the populations of steers, slaughter heifers and slaughter calves in ULICEES were transferred to the replacement heifer age-gender category. To apply ULICEES to the veal/lamb scenario (#4), the populations of steers, slaughter heifers and slaughter calves were transferred to the newly born calves' category, to which ULICEES attributes no GHG emissions [27]. In addition, the sheep populations had to be expanded to consume the forage no longer consumed by those animals that were converted to veal production. This was achieved by inflating the sheep populations by the ratios of meat animals

in the beef industry to the sheep population expressed as protein in each province.

Canada being very small relative to beef cattle.

net CF of canola.

**3.5. Changes in the Carbon Footprint (CF) of canola**

Before the reallocation of steers and slaughter heifers to the reproduction heifer category (Scenario 3), these populations were redistributed to match the distribution of breeding cows among the provinces. This was done to remove the influence of the concentration of feedlots in southern Alberta, to which the cattle destined for finishing for market before slaughter tend to gravitate. Before inflating the sheep populations in Scenario 4, the GHG emissions from sheep were redistributed to match the distribution of GHG emissions from beef cattle given by ULICEES. This was done to reduce instability caused by the populations of sheep in western

The CF of expanded canola must combine initial GHG emission costs of actually growing the canola crop with the secondary impact assessment of the crops being displaced by the canola. In addition, it must include potential benefits stemming from the shift from annual to perennial ground cover for both scenarios. The change in beef production (from feed grain to hay) would mean that the soil surface is never bare between crops which would cause atmospheric CO2 to be sequestered as soil carbon. For the Prairie Provinces the average yearly carbon storage would be approximately 0.55 t{carbon}/ha [35], or 2.02 t/ha of sequestered CO2. In this chapter when the CF determination takes all of these terms into account, it is then deemed to be the

within each livestock type.

360 Biofuels - Status and Perspective

5 , livestock GHG emission intensities not including areas of expanded canola and not normalized to canola areas.

**Table 5.** GHG emission intensities per unit area for canola, feed grains (represented by oats and barley combined) and hay, rates of CO2 sequestration under new forage areas and two ruminant production scenarios, (area basis of intensities shown as footnotes) for the Prairie Provinces during 2006.

Figure 4 shows the changes in GHG emissions that can be attributed to the land use changes induced by the proposed expansion of canola. These changes were measured by the differences between the two scenario simulations and the baseline simulations shown in Figure 3. These differences were expressed as emission rates per unit area of feed grains in the baseline ULICEES simulations. Unlike the emission differences shown in Columns 7 and 8 of Table 5, those in Figure 4 include the cost of growing the expanded canola crop and the new forage crop, and the CO2 sequestered by the land use change from feed grains to perennial forage. The rates shown in Figure 4 were also normalized to baseline feed grain areas so that they have the same area base.

The GHG emission cost of growing canola and additional forage, and the sequestration of CO2 under the new forage area were added to the assessment after the ULICEES simulation process. This was necessary because ULICEES computes the forage component of ruminant diets in the BCC and SCC (as well as the DCC) by partitioning areas from fixed pools of land in hay and in improved pasture to the regional beef, sheep and dairy populations [27]. Unlike the grain components of those diets, ULICEES cannot, therefore, create new areas of forage to meet changes in the BCC. Adding both of these terms to the simulations from ULICEES, required them to be expressed on the basis of the expanded area of canola (as shown in Table 5). Of the 2.3 Mha of feed grain area used in ULICEES to support cattle and sheep in the Prairie Provinces, Scenario 3 converted 44% to expanded canola while Scenario 4 converted 77% to expanded canola.

**Figure 4.** GHG emission intensity estimates for the two livestock based scenarios (including GHG emissions from the canola expansion and the CO2 sequestration under forage) normalized to the area of feed grains in the diet of the base‐ line beef cattle populations in the Prairie Provinces during 2006.

### **3.6. Carbon footprints of the expansion scenarios**

To calculate the GHG emissions budget for each scenario, the emission coefficients shown in Columns 4, 5, 7 and 8 from Table 5 were each integrated separately with the difference between the canola and feed grain emission coefficients, and the sequestration of CO2 (Columns 1, 2 and 6 of Table 5). Table 6 shows the GHG emission rates from the four secondary impact scenarios for expanding canola in the three Prairie Provinces. Scenarios 2, 3 and 4 assume that canola meal can compensate for part of the displaced feed grain in the ruminant diet.

The GHG emissions intensities (EI1-2) of the two scenarios based on crop differences (#1 and #2) were the result of straight forward addition of emission terms from feed grain (barley and oats combined) and perennial forages to the CF of canola. The inclusion of the CO2 sequestra‐ tion rates (SR) from Table 5 in Equation 1 reduced these GHG emissions intensity estimates. These terms were summarized as follows.

The rates shown in Figure 4 were also normalized to baseline feed grain areas so that they have

The GHG emission cost of growing canola and additional forage, and the sequestration of CO2 under the new forage area were added to the assessment after the ULICEES simulation process. This was necessary because ULICEES computes the forage component of ruminant diets in the BCC and SCC (as well as the DCC) by partitioning areas from fixed pools of land in hay and in improved pasture to the regional beef, sheep and dairy populations [27]. Unlike the grain components of those diets, ULICEES cannot, therefore, create new areas of forage to meet changes in the BCC. Adding both of these terms to the simulations from ULICEES, required them to be expressed on the basis of the expanded area of canola (as shown in Table 5). Of the 2.3 Mha of feed grain area used in ULICEES to support cattle and sheep in the Prairie Provinces, Scenario 3 converted 44% to expanded canola while Scenario 4 converted 77% to

Manitoba Saskatchewan Alberta Prairies

**Figure 4.** GHG emission intensity estimates for the two livestock based scenarios (including GHG emissions from the canola expansion and the CO2 sequestration under forage) normalized to the area of feed grains in the diet of the base‐

To calculate the GHG emissions budget for each scenario, the emission coefficients shown in Columns 4, 5, 7 and 8 from Table 5 were each integrated separately with the difference between the canola and feed grain emission coefficients, and the sequestration of CO2 (Columns 1, 2 and 6 of Table 5). Table 6 shows the GHG emission rates from the four secondary impact scenarios for expanding canola in the three Prairie Provinces. Scenarios 2, 3 and 4 assume that

The GHG emissions intensities (EI1-2) of the two scenarios based on crop differences (#1 and #2) were the result of straight forward addition of emission terms from feed grain (barley and oats combined) and perennial forages to the CF of canola. The inclusion of the CO2 sequestra‐

canola meal can compensate for part of the displaced feed grain in the ruminant diet.

grass beef veal/lamb

the same area base.

362 Biofuels - Status and Perspective

expanded canola.


line beef cattle populations in the Prairie Provinces during 2006.

**3.6. Carbon footprints of the expansion scenarios**


0.0

0.5

**∆ t CO2e / ha {feed grain}**

1.0

1.5

2.0

$$\text{EI}\_{1-2\text{ }t\text{-}anal,net} = \text{EI}\_{canda} - \text{EI}\_{fed\text{ }gain} + \text{EI}\_{forg\text{ }e} - \text{SR}\_{forg\text{ }e} \tag{1}$$

The crop-specific EI values in Equation 1 for each province and the region were taken from Columns 1 and 2, and either 4 or 5 of Table 5, depending on whether canola meal was assumed to be a feed supplement.

Using Columns 1 and 2 of Table 5, and either Column 7 for the grass beef (#3) scenario or Column 8 for the veal/lamb (#4) scenario, the GHG emissions intensities (EI3-4) of the two livestock based scenarios (#3 and #4) can be represented symbolically by the following equation, which includes the same SR*forage* term as Equation 1 and the emission cost of the additional hay (EI*forage*) in each livestock scenario.

$$\text{EI}\_{3-4, \text{camda}, \text{net}} = \text{EI}\_{\text{camda}} - \text{EI}\_{\text{fuel gain}} + \Delta \text{EI}\_{\text{livestack}} + \text{EI}\_{\text{fvage}} - \text{SR}\_{\text{fvage}} \tag{2}$$

However, the feed grain EI values in Equation 2 were generated as part of the ULICEES simulation of the livestock-specific Scenarios 3 and 4. The emission cost of the additional hay production appears as a separate term in Equation 2 because it had to be calculated externally from ULICEES. Since EI values generated by ULICEES were expressed on the basis of the scenario feed grain areas, it was necessary to convert the EI for canola and the SR for the new forage from Table 5 back to the feed grain area basis. This was done by multiplying the canola GHG emission rates from [36] and the SR terms from Table 5 by the areas freed from feed grain and adding the difference between these two GHG emission quantities to the respective GHG emission differences.

These new GHG emissions (for canola plus livestock and additional hay, minus the seques‐ tered CO2) were then divided by the respective new canola areas to give the emission rates shown in Table 6 to represent the net CF for canola under the two livestock scenarios. To convert from the feed grain to the new canola area basis, the area ratios of feed grain to canola were taken for the whole Prairie region for both livestock scenarios as a way of smoothing these normalized estimates over the three provinces.

Scenarios 1 and 2 were useful for demonstrating the role of canola meal in minimizing the crop displacement by the expanded canola area. As well, Column 4 of Table 6 (for Scenario 2) showed what the inclusion of carbon sequestration without accounting for GHG emissions from livestock would mean for the CF of canola. The difference between the sequestered soil carbon shown in Column 6 of Table 5 and Column 4 of Table 6 was that in Table 5 the sequestration rates did not include the GHG emission costs of growing the new forage. It was the CO2 sequestration rates from Table 6 that were incorporated into Scenarios 3 and 4.


1 , meal = canola meal after oil extraction which is available as substitute livestock feed.

2 , includes CO2 sequestered by the land use change from annual feed grain to perennial forage.

**Table 6.** Area based GHG emission intensity estimates for canola, and four canola expansion impact scenarios normalized to the area of the expanded canola crop, and potential the fossil CO2 emissions offset by canola oil as a biodiesel feedstock in the Prairie Provinces during 2006.

Only Scenarios 3 and 4 represent the net CF of the expanded canola because the secondary impact on ruminant livestock production was incorporated by ULICEES into these two scenarios. The measure of this impact and the net CF of the new canola was a comparison with fossil CO2 emissions that were expected to be offset by the expanded canola. The offset fossil CO2 emission intensities (FI) are shown in each province and for the Prairies in the last Column of Table 6. They are also shown as negative values to reflect the opposite direction from the net CF. These fossil CO2 emission offsets vary with provinces because their calculations accounted for the variations in the provincial canola yields (Ycanola). Each prairie yield was the production-weighted average from the 2006 and 2011 census years (Table 4). The offset fossil CO2 emission intensities (FI) per ha of canola were calculated as follows.

$$\text{FI} = 2.8 \times 88\% \times 0.39 \times \text{Y}\_{\text{causal}} \tag{3}$$

The diesel fuel to fossil CO2 conversion factor is 2.8 kgCO2/kg of fuel [36]. Equation 3 also took account of the 12% difference in energy content between petrodiesel and biodiesel [37] and the 39% by weight of canola yield (kg oil/kg canola seed) that is canola oil.

#### **3.7. Protein based GHG emission intensities of scenario livestock**

The assessment of canola expansion must also take into account the CF of the protein produc‐ tion from the proposed new distributions of age-gender categories of the ruminant livestock industries. The differences in GHG emission intensities between the two scenarios were assessed on the basis of protein supply and compared to baseline simulations for this indicator from ULICEES [3]. In this context, protein is taken to include only human edible protein (excluding blood meal, pet food, edible offal and leather). This comparison did not allow for potential nutritional differences between the protein derived from beef and lamb. Figure 5 shows the protein based GHG emission intensities for both livestock scenarios. As a reference baseline for this comparison, the 2006 protein based intensities of beef and lamb [3] are also shown in Figure 5. For this indicator, the actual GHG emission intensity simulations, rather than the differences from baseline GHG emissions, were used.

**Expanded Required forage Livestock Fossil CO2 canola no with with meal scenarios offset by only meal1 meal1 and soil C2 grass beef2 veal/lamb2 canola**

**t CO2e/ha{canola}**

**Scenario # 1 2 2 3 4**

, meal = canola meal after oil extraction which is available as substitute livestock feed.

biodiesel feedstock in the Prairie Provinces during 2006.

, includes CO2 sequestered by the land use change from annual feed grain to perennial forage.

CO2 emission intensities (FI) per ha of canola were calculated as follows.

39% by weight of canola yield (kg oil/kg canola seed) that is canola oil.

**3.7. Protein based GHG emission intensities of scenario livestock**

**Table 6.** Area based GHG emission intensity estimates for canola, and four canola expansion impact scenarios normalized to the area of the expanded canola crop, and potential the fossil CO2 emissions offset by canola oil as a

Only Scenarios 3 and 4 represent the net CF of the expanded canola because the secondary impact on ruminant livestock production was incorporated by ULICEES into these two scenarios. The measure of this impact and the net CF of the new canola was a comparison with fossil CO2 emissions that were expected to be offset by the expanded canola. The offset fossil CO2 emission intensities (FI) are shown in each province and for the Prairies in the last Column of Table 6. They are also shown as negative values to reflect the opposite direction from the net CF. These fossil CO2 emission offsets vary with provinces because their calculations accounted for the variations in the provincial canola yields (Ycanola). Each prairie yield was the production-weighted average from the 2006 and 2011 census years (Table 4). The offset fossil

The diesel fuel to fossil CO2 conversion factor is 2.8 kgCO2/kg of fuel [36]. Equation 3 also took account of the 12% difference in energy content between petrodiesel and biodiesel [37] and the

The assessment of canola expansion must also take into account the CF of the protein produc‐ tion from the proposed new distributions of age-gender categories of the ruminant livestock industries. The differences in GHG emission intensities between the two scenarios were assessed on the basis of protein supply and compared to baseline simulations for this indicator from ULICEES [3]. In this context, protein is taken to include only human edible protein

FI = 2.8 × 88% × 0.39 × Y*canola* (3)

1

364 Biofuels - Status and Perspective

2

Manitoba 1.30 0.80 0.55 -1.17 2.41 -0.69 -1.64 Saskatchewan 1.03 0.87 0.68 -1.03 3.32 1.48 -1.63 Alberta 1.28 0.96 0.72 -0.78 1.50 2.12 -2.00 Prairies 1.16 0.88 0.67 -0.97 2.05 1.20 -1.77

**Figure 5.** Protein based GHG emission intensities for the two livestock based canola expansion scenarios and for the baseline beef and sheep populations in the Prairie Provinces during 2006.

To calculate the protein based GHG emission intensities for Scenarios 3 and 4, the weight of animal Protein (P) was computed from the number of head (H) and the live weight (W) of the age-gender categories involved in simulating the two livestock scenarios that provide slaugh‐ ter animals. The age-gender categories involved in the assumed population redistributions were steers and slaughter heifers (*s&sh*), replacement heifers (*rh*), culled cattle (*cc*), slaughter calves (*sc*), culled ewes (*ce*) and slaughter lambs (*sl*). The live weight conversions to protein were 6.4% for slaughter lambs [38, 39] and 8.3% for slaughter steers and heifers [10, 39]. Because breeding cows were culled every six years and ewes were culled every 4.5 years [3], reduction factors of 0.17 and 0.22 were applied to culled cow and ewe populations, respectively, in Equations 4 and 5.

$$\mathbf{P}\_{\text{gauss leaf}} = 0.083 \left( \mathbf{H}\_{\text{ads}} \times \mathbf{W}\_{rh} + 0.17 \times \mathbf{H}\_{\text{cr}} \times \mathbf{W}\_{\text{cr}} \right) \tag{4}$$

$$\mathbf{P}\_{\text{real/amb}} = 0.083 \left( \mathbf{H}\_{\text{ads}} \times \mathbf{W}\_{\text{sc}} + 0.17 \times \mathbf{H}\_{\text{cr}} \times \mathbf{W}\_{\text{ar}} \right) \\ + \ 0.064 \left( \mathbf{H}\_{\text{sl}} \times \mathbf{W}\_{\text{sl}} + 0.22 \times \mathbf{H}\_{\text{ac}} \times \mathbf{W}\_{\text{ar}} \right) \tag{5}$$

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