<table><caption>Table 1: Costs of land use change, pasture maintenance and restoration. The table can be read as "the cost from land use X to land use Y", where X and Y can be any pasture DMP level or crops.</caption><caption>Cost of land use change/pasture restoration Cost<sup>1</sup> (US $ 2012.ha<sup>-1</sup>)</caption><thead><tr><th rowspan="2"></th><th colspan="7">To land use</th><th rowspan="2">Corn (Silage)</th><th rowspan="2">Corn (Grain)</th><th rowspan="2">Soybeans</th></tr><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th><th>F</th></tr></thead><tbody><tr><td>A</td><td>112.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1352.6</td><td>600.0</td><td>345.4</td></tr><tr><td>B</td><td>149.9</td><td>72.7</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1502.5</td><td>749.9</td><td>495.3</td></tr><tr><td>C</td><td>399.3</td><td>249.4</td><td>15.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1751.9</td><td>999.3</td><td>744.7</td></tr><tr><td>D</td><td>630.0</td><td>480.0</td><td>230.7</td><td>9.4</td><td>0.0</td><td>0.0</td><td>1982.6</td><td>1229.9</td><td>975.3</td></tr><tr><td>E</td><td>724.6</td><td>574.6</td><td>325.2</td><td>94.6</td><td>5.6</td><td>0.0</td><td>2077.2</td><td>1324.5</td><td>1069.9</td></tr><tr><td>F</td><td>767.0</td><td>617.1</td><td>367.7</td><td>137.1</td><td>42.5</td><td>5.6</td><td>2119.6</td><td>1367.0</td><td>1112.4</td></tr><tr><td>Corn (Silage)</td><td>269.8</td><td>200.9</td><td>125.1</td><td>125.1</td><td>125.1</td><td>125.1</td><td>1630.7</td><td>1060.6</td><td>971.8</td></tr><tr><td>Corn (Grain)</td><td>269.8</td><td>200.9</td><td>125.1</td><td>125.1</td><td>125.1</td><td>125.1</td><td>1736.4</td><td>981.9</td><td>992.6</td></tr><tr><td>Soybean</td><td>269.8</td><td>200.9</td><td>125.1</td><td>125.1</td><td>125.1</td><td>125.1</td><td>1736.4</td><td>981.9</td><td>1017.7</td></tr></tbody></table>

Land use change (including deforestation), degrading or restoring pasture will affect the soil carbon (C) stocks. These changes are calculated by EAGGLE: the model estimating the annual C stock under pasture and crops for each land use. The total accumulated C under soils is given by the sum of the C stock of each pasture DMP levels, soya and corn.

## 2.9 Carbon sequestration through pasture management

Depending on the DMP, the C flux may change significantly. The EAGGLE model works with equilibrium values of the C stock for each type of pasture and crops. The higher the pasture productivity, the higher the C equilibrium value (Table 2). The equilibrium values were calculated exogenously, using simulations from the CENTURY model (Parton et al., 1987) applied to *Cerrado* biophysical characteristics and using the annual DMP calculated for each pasture category.

<footnote n="1">¹See Appendix S1 for calculation details.</footnote>
<table><caption>Table 2: Annual dry matter productivity and equilibrium C stock values.</caption><thead><tr><th>Pasture/Crops</th><th>DM<sup>1</sup><br>(t.ha<sup>-1</sup>.yr<sup>-1</sup>)</th><th>Carbon stock equilibrium<sup>2</sup><br>(t.ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>A</td><td>19.6</td><td>84.3</td></tr><tr><td>B</td><td>17.6</td><td>82.7</td></tr><tr><td>C</td><td>12.6</td><td>62.3</td></tr><tr><td>D</td><td>8.7</td><td>45.2</td></tr><tr><td>E</td><td>5.8</td><td>32.4</td></tr><tr><td>F</td><td>3.9</td><td>26.1</td></tr><tr><td>Corn (Silage)</td><td>3.8</td><td>45.0</td></tr><tr><td>Corn(Grain)</td><td>9.0</td><td>40.0</td></tr><tr><td>Soybean</td><td>2.5</td><td>45.0</td></tr></tbody></table>

EAGGLE accounts for the annual carbon stocks per each land use in column 1, Table 2. The model transfers the accumulated carbon from year *t-1* to year *t* and calculates the variation of soil C in year *t*.

Letting $C_{t,lu}$ be the soil carbon stock (tons) under the land use $lu$, where $lu \in \{A, B, C, D, E, F, Soybeans, Corn(silage), Corn(Grain)\}$. Then $C_{t,lu}$ can be expressed by:

$$C_{t,lu} = \varphi(t,lu) + \Delta C_{t,lu} \quad (\text{Eq. 1})$$

And

$$\Delta C_{t,lu} = f(\varepsilon_{lu}, C_{t-1,lu}) \quad (\text{Eq. 2})$$

Eq. (1) is composed of the carbon transference term, $\varphi(t,lu)$, and the C sequestration term, $\Delta C_{t,lu}$. The term $\varphi(t,lu)$ accounts the transference of C from other uses to land use $lu$ in year $t$; e.g., if $lu$ is equal pasture $B$, and one hectare of soybeans is converted in year $t$ into one hectare of pasture level $B$, the carbon previously stocked under soybeans has to be transferred to pasture $B$. Similarly, if some hectares are converted from pasture $B$ to pasture $A$, or degraded to $C$, then part of the C stock from $B$ has to be proportionally transferred from $B$ to these other uses. The sequestration term, $\Delta C_{t,lu}$ is written as a function of the distance between the previous C stock $C_{t-1,lu}$, and the C stock equilibrium value, $\varepsilon_{lu}$. Hence the further the previous stock is from the equilibrium value, the more C will be up taken. Conversely, if due to the land use change, or degradation, the C stock becomes greater than the equilibrium value, there will be negative C sequestration, i.e., a loss of C stock. These modelling approaches follow the concepts suggested by Eggleston *et al.* (2006) and Vuichard *et al.* (2007). The extended version of Eq. (1) and (2) are presented in Oliveira Silva (2013).

## 2.10 Deforestation due to cattle ranching

For pasture area we use the projections published by (Gouvello *et al.*, 2010) combined with an endogenous deforestation term. Let $LU_t$ be the total area at year $t$; $a_t$ the Gouvello *et al.* (2010) projections; and $D_t$ the endogenous term that represents further area expansion. Then for every year:

$$LU_t = a_t + D_t \quad (\text{Eq. 3})$$

<figure><img src="image_28.png" /></figure>

<footnote n="1">¹ Estimated using the models published by Tonato *et al.* (2010)</footnote>

<footnote n="2">² According to Parton (1987)</footnote>
The deforested area will cause a loss of carbon stocks in natural vegetation and influence soil C; and directly influences the transference term in eq. (1), i.e., loss of soil organic matter (SOM). Both vegetation carbon stocks and SOM are accounted by EAGGLE to represent the emissions associated with deforestation.

There is limited quantitative research on the dynamics of pasture productivity following deforestation. In accordance with the best available information, the model allocates new converted areas into the system in pasture category C(the highest without nitrogen fertilization), as soil carbon also can increase or decrease values after deforestation (Maia et al., 2009) and pasture productivity is relatively high after conversion due to higher soil organic matter mineralization (Martha Jr, 2007).In this analysis, we assumed the cost of opening new areas is zero because the cost of conversion the *Cerrado* into pastures can be offset by timber sales and land value appreciation(Bowman, 2013).

Another assumption is that the model cannot discard land endogenously, neither does it allow fallow in any year of the planning period. This assumption is based on the fact that cattle ranchers are not allowed to let their properties be unproductive; otherwise the land can be confiscated by the government for Agrarian Reform (Federal Law 8.629 - www.planalto.gov.br/ccivil_03/leis/l8629.htm).

## 2.11 Baseline construction

Land use change scenarios need to be mapped onto a plausible baseline for land use activity. The baseline scenario is based on national forecasts of beef demand and grassland area for Brazil, from 2006 to 2030 (Gouvello et al., 2010). The assumption is that the attributable *Cerrado* pasture area and beef demand share are a fixed proportion of the national projections. In 2006, the *Cerrado* pasture area represented 34% of the national total(IBGE, 2014).The model then assumes that *Cerrado* pasture area corresponds to 34% of Brazil's pasture area, and this proportion is constant during the studied period (2006-2030). Similarly, as there is no data for regional demand, we assumed demand to be proportional to area, i.e., demand for *Cerrado* is also equivalent to 34% of national demand, this percentage is very close to the 35% figure estimated by Anualpec (2010).

In the model, increased productivity occurs by means of investments in technologies, e.g., pasture restoration, supplementation and feedlot animals. The baseline scenario has limited adoption of these measures, implying constant productivity. We assumed that pasture restoration is allowed in the baseline only to avoid degradation, but it is constrained to maintain productivity at 2006 levels (10 t-DM.ha⁻¹.yr⁻¹) (Appendix S2). Combining this constraint with projected increased demand pushes the model to open new areas if it is necessary to meet the growing demand for beef.

The current adoption rate of feedlot finishing in Brazil is around 10% of the total herd (Anualpec, 2010). We assumed this proportion to be constant in the baseline, a rate that is in counterpoint to a higher level of penetration of this measure in a mitigation counterfactual.

## 2.12 GHG emissions sources

The EAGGLE model calculates GHG emissions using emissions factors for activities within the farm gate. GHG emissions associated with the farm activities are: (a) CH₄ from cattle enteric fermentation (CH₄ from excreta is not accounted); (b) N₂O from cattle excreta; (c) N₂O direct emissions from N fertilization; (d) CO₂ from deforestation; and (e) CO₂ from pasture degradation and land use change from pasture to crops.

Items (a) and (b) depend on herd composition: each age cohort of males and females (heifer or cow) has an associated emission factor of CH₄ and N₂O calculated using Tier 2 methodology (Eggleston et al., 2006), see Table S1 and Table S2. Due to the lack of studies

<figure><img src="image_29.png" /></figure>
in Brazilian conditions, for (c), we used the Tier 1 IPCC default factor of 1% (Eggleston et al., 2006). The emissions from (d) are calculated using coefficient of loss of natural vegetation per deforested area. The average carbon loss of natural vegetation due to deforestation was estimated as 34.6 tons of C per hectare, in accordance to Eggleston et al. (2006) and Bustamante et al. (2012). For (e), the emissions are calculated according to Eq. (1) and (2).

## 2.13 Mitigation Measures

The selection of GHG mitigation measures was based on literature review and expert opinion regarding the relevance and applicability of the technologies to Brazilian livestock production and conditions. The measures evaluated are: concentrate supplementation, protein supplementation, pasture restoration, nitrification inhibitors and feedlot finishing. Although the latter is already in the baseline, we investigated a higher adoption rate of this technology. Modelling assumptions for these measures related to the effects the measures have upon the gross margin and emissions are detailed in Table 3.

<table><caption>Table 3: Selected livestock mitigation measures</caption><thead><tr><th>Mitigation measure</th><th>Description</th><th>Cost<sup>1</sup></th><th>Unit</th><th>Reduces emissions by:</th><th>Adoption rate target</th></tr></thead><tbody><tr><td>Feedlot finishing</td><td>When cattle weight is around 80% of the slaughter weight it is removed from pasture and grass to feedlot on a diet with ration of balanced protein and energy content</td><td>9.12</td><td>$.head<sup>-1</sup>.mth<sup>-1</sup></td><td>Shorter animal life cycle by increasing weight gain</td><td>15% of the total finished animals</td></tr><tr><td>Nitrification inhibitors</td><td>Application of Agrotain Plus® together with urea used as fertilizer; 3g per Kg of applied nitrogen<sup>2</sup></td><td>61.44</td><td>$.t<sup>-1</sup></td><td>Reduced conversion of nitrogen to the GHG nitrous oxide (nitrification)</td><td>Optimized</td></tr><tr><td>Pasture restoration</td><td>Improving pasture forage productivity by soil chemical and mechanical treatment. As described in Section 2.1</td><td>Table 1</td><td>$.ha<sup>-1</sup></td><td>Avoiding the need for additional pasture land and increasing organic carbon sequestration</td><td>Optimized</td></tr><tr><td>Supplementation concentrate</td><td>Feeding cattle via grazing and a ration with a high energy content. Grazing steers with 421 kg of LW can be selected for concentrate supplementation. The supplementation takes 2 months and the final weight is 490 kg</td><td>3.07</td><td>$.head<sup>-1</sup>.mth<sup>-1</sup></td><td>Shorter animal life cycle by increasing weight gain</td><td>Optimized</td></tr><tr><td>Supplementation protein</td><td>Feeding cattle via grazing and a ration with a high protein content. Calves (189 kg) can be selected (only in March) to be supplemented with protein. The steers are finished after 15 months, with 481 kg</td><td>1.15</td><td>$.head<sup>-1</sup>.mth<sup>-1</sup></td><td>Shorter animal life cycle by increasing weight gain</td><td>Optimized</td></tr></tbody></table>

<footnote n="1">¹Supplementations non-feed cost, for feed costs (ration formulation, see Table 4)</footnote>

<footnote n="2">²Cost and Application ratio suggested manufacturer (http://www.agrotain.com/us/home).</footnote>
## 2.13.1 Concentrate and protein supplementation

Both measures involve supplementing the feed of grazing steers; e.g., feed is composed of forage and supplements. It is expected that these measures reduce emissions since animals gain weight faster and take less time to be finished.

<table><caption>Table 4: Rations (supplements) formulation and costs.</caption><thead><tr><th rowspan="2">Crop</th><th colspan="3">Ration Formulation (%)<sup>1</sup></th><th rowspan="2">Cost<sup>2</sup> (US$.kg<sup>-1</sup>)</th></tr><tr><th>Feedlot</th><th>Concentrate</th><th>Protein</th></tr></thead><tbody><tr><td>Corn (grain)</td><td>83</td><td>80</td><td>15</td><td>PBF</td></tr><tr><td>Corn (Silage)</td><td>11</td><td>0</td><td>0</td><td>PBF</td></tr><tr><td>Soybeans</td><td>5</td><td>17</td><td>39</td><td>PBF</td></tr><tr><td>Urea</td><td>0</td><td>2</td><td>12</td><td>1.19</td></tr><tr><td>Mineral Salt</td><td>1</td><td>1</td><td>19</td><td>0.84</td></tr><tr><td>NaCl</td><td>0</td><td>0</td><td>15</td><td>1.19</td></tr></tbody></table>

Biological coefficients, e.g., mortality rate, weight, DM intake, and emissions factor for steers fed with supplementations can be found in Table S2.

## 2.13.2 Pasture restoration

This measure works in the model by avoiding deforestation and because restoration boosts carbon soil uptake. Details of the modelling and Costs are explained in section 2.2.2. In contrast to the baseline scenario, to evaluate this measure, the fixed DMP baseline constraint was removed.

## 2.13.3 Nitrification Inhibitors

The measure works by avoiding a proportion of the Nitrification or manure being converted into N₂O, i.e. Nitrification and denitrification process (Abbasi and Adams, 2000). To date there have been no studies detailing the reduction in N₂O emissions for Brazilian pastures when nitrogen inhibitors are applied. A 50% reduction of direct N₂O emissions is assumed in this paper -as found by Giltrap *et al.* (2011) for a New Zealand study. We assumed that this measure is applicable only over the N used for pasture and crops fertilization. The reason is that most of the Brazilian herd is based on a grazing system where it is unfeasible to apply inhibitors to animal excreta.

## 2.13.4 Feedlot finishing

Like supplementation, this measure works by reducing the cattle finishing time since feedlot animals are fed only by ration (with the formulation described in Table 4). Only steers can be selected to model in the feedlot system. The adoption rate was arbitrarily assumed to be 15% of the total finished herd, since in the baseline the adoption rate is 10% of the total

<footnote n="1">¹Rations were formulated by using the software Invernada (minimum cost ration formulator) (Barioni, 2011)</footnote>

<footnote n="2">²PBF = Produced By the Farm, i.e., the crops produced by EAGGLE (according to the dynamics in section 2.2.2) are stored and used to formulate the rations.</footnote>
finished herd, the measure can be stated as: increasing by 50% over the baseline adoption rate.

## 2.14 Marginal abatement cost curve

A MACC can be used to represent the relative cost-effectiveness of different abatement options and the total amount of GHG that can be abated by applying mitigation measures over and above a baseline scenario. The aim is to identify the most economically efficient manner to achieve emissions reduction targets, where the cheapest units of greenhouse gas should be abated first (Moran et al., 2010).

MACC analysis can be derived by means of a top-down analysis – which usually makes use of a general equilibrium model and emissions are calculated endogenously, or by a bottom-up or engineering analysis (MacLeod et al., 2010). This paper takes a bottom-up approach, where the individual abatement potential of measures and their costs are individually modelled using the EAGGLE detailed equations.

The MACC can be presented in form of a histogram, where the C abatement potential lies on the x-axis, and the cost per tons of abatement in the y-axis. The abatement potential of a measure $m$ ($AP_m$) is calculated as the annual average of the difference between the business-as-usual (baseline) total GHG emissions ($E_{BAU}$) and the total emissions under the mitigation measure scenario ($E_m$) during the production period $T$:

$$AP_m = \frac{E_{BAU} - E_m}{T} \quad (\text{Eq.4})$$

The cost-effectiveness of measure $m$ ($CE_m$), therefore, is calculated by:

$$CE_m = \frac{GM_{BAU} - G_m}{AP_m} \quad (\text{Eq. 5})$$

Where $GM_{BAU}$ and $GM_m$ are, respectively, the gross margin in the baseline scenario and the gross margin in the scenario with the measure $m$ implemented.

As observed in Eq.4 and Eq.5, $AP_m$ and $CE_m$ are average values across the planning period.

# 6. Results

## 6.1 Baseline Emissions

In the baseline scenario, livestock production in the *Cerrado* accounts for an average of 121.5Mt CO₂e.yr⁻¹, from 2010 to 2030. This value includes enteric fermentation, animal waste (emissions from excreta), soil fertilization emissions, pasture (due to the loss in C stocks), and deforestation driven by cattle production (Fig. 1). The accumulated emissions from 2010 to 2020 account for about 1,249Mt CO₂e or 2,551Mt CO₂e from 2010 to 2030.

In relative terms, enteric fermentation makes the biggest contribution to the total: 66% of emissions, followed by deforestation, with 26%. The results also show that pasture degradation is a considerable source 100 of emissions, accounting for an average of 8.35 Mt CO₂e.yr⁻¹ (an average of 0.06 t CO₂e.ha⁻¹.yr⁻¹), the equivalent to 4% of emissions or the same proportion as animal waste (Fig.2).

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /><figcaption>Figure 1: *Cerrado* baseline emissions 2010-2030</figcaption></figure>

Gouvello *et al.* (2010) suggests that total national GHG emissions from energy, transport, waste, livestock and agriculture, will be around 1.70 Gt CO2e, for 2030. The results presented here suggest that beef production in the *Cerrado* will be responsible for about 152 Mt CO2e in 2030, corresponding to 9% of total national GHG emissions.

<figure><img src="image_32.png" /><figcaption>Figure 2: Share of emissions from 2010 to 2030 for the Brazilian *Cerrado*.</figcaption></figure>

In the baseline scenario, without increasing productivity, an average deforestation rate of 246.1 10³ ha.yr⁻¹ would be required to meet the beef demand.
Emissions attributed to the use of fertilizers were not significant, accounting for an average of 0.2 Mt CO₂e.yr⁻¹. This was expected, since small amounts of N are used to fertilize *Cerrado* pasture soils (Martha Jr *et al.*, 2007; Cederberg *et al.*, 2009).

## 6.2 Cost-effectiveness analysis

For policy purposes it is important to detail the relative cost of emissions mitigation measures. Three of the five mitigation measures simulated, - concentrate supplementation, protein supplementation, and pasture restoration - have negative cost-effectiveness: US$-8.01. kt CO₂e⁻¹, US$-2.88. kt CO₂e⁻¹ and US$-0.05. kt CO₂e⁻¹, respectively (Figure 3). Adopting these measures implies cost savings while reducing emissions. These measures work by balancing the loss of DM production during the dry months. The *Cerrado* biome is predominantly seasonal tropical, meaning dry winters and rainy summers, with lower pasture productivity during the dry months. If cattle are supplemented with concentrates or protein they can be finished earlier, thereby reducing emissions.

Due to the large applicable area (approximately 60 M ha), and given the current low productivity of 10 t DM.ha⁻¹.yr⁻¹, pasture restoration provides the biggest opportunity for reducing emissions in the region.

<figure><img src="image_33.png" /><figcaption>Figure 3: Marginal abatement cost curve: mitigation measures for *Cerrado* livestock production from 2006 to 2030. * Not in scale</figcaption></figure>

The abatement potential (AP) for pasture restoration is 26.9 Mt CO₂e.yr⁻¹, comprising of two components: C sequestration and avoided deforestation, the latter accounting for 96% of this AP. Pasture restoration would improve the *Cerrado* average productivity from 10 to 11.2 t DM.ha⁻¹.yr⁻¹, an increase of 12% relative to the baseline. This increase would lead to an
average C sequestration rate of 0.32 t CO₂e.ha⁻<fnref n="1" />.yr⁻<fnref n="1" />. This is a low C uptake potential when compared to values found by Maia *et al.* (2009), which showed that C sequestration rates of 2.24 t CO₂e.ha⁻<fnref n="1" />.yr⁻<fnref n="1" /> can be achieved in well-managed pastures in *Cerrado*. The carbon sequestration rate however, reflect the 2006-2030 period, after which, and in the long term, as pastures are intensified it will eventually reach equilibrium and therefore no more carbon is likely to be sequestered.

The AP of feedlot finishing is 470 kt CO₂e.yr⁻<fnref n="1" />, but the measure cost-effectiveness US$ 13.32 t CO₂e⁻<fnref n="1" /> is high relative to t supplementation.

Nitrification inhibitors are the least cost-effective measure considered. But this analysis only considered the application to N used for pasture and crops fertilization and excluded the application to animal excreta.

The results indicate that restoring degraded lands is the biggest opportunity for reducing emissions in the *Cerrado*. The AP of this measure is about 20 times greater than all the other measures combined.

An important assumption underpinning the MACC relates to the assumed measure adoption rates. With exception of feedlot finishing, the adoption rates are optimized, meaning the rates that maximizes the gross margin in the model.

<table><caption>Table 5: Mitigation measures adoption rate.</caption><thead><tr><td>Mitigation Measure</td><td>Adoption rate</td><td>Unit</td></tr></thead><tbody><tr><td>Supplementation: concentrate</td><td>12</td><td>%<fnref n="1" /></td></tr><tr><td>Supplementation: protein</td><td>2.2</td><td>%</td></tr><tr><td>Pasture restoration</td><td>314.7</td><td>10³ ha.yr⁻<fnref n="1" /></td></tr><tr><td>Feedlot finishing</td><td>15</td><td>%</td></tr><tr><td>Nitrification inhibitors</td><td>12.78</td><td>g.ha⁻<fnref n="1" />.yr⁻<fnref n="1" /></td></tr></tbody></table>

## 7. Discussion

To meet increasing domestic and export demand, the government of Brazil recognizes the need to foster sustainable agricultural intensification, which implies increased resource productivity while minimizing significant domestic and global external costs implicit in GHG emissions and deforestation. The results presented here suggest that a significant contribution to this objective can be made by targeting specific measures to improve yield. Specifically, pasture restoration, supplements and feedlot measures could reduce sector emissions by 24.1% by 2030. Moreover, by adopting only negative-cost measures (Fig. 3), it is possible to abate about 23.7% of baseline livestock emissions in the *Cerrado*, up to 2030. According to our results the restoration of degraded pastures offers the greatest abatement potential, involving the restoration of an average of 314.7 10³ ha.yr⁻<fnref n="1" /> in *Cerrado* grasslands.

Currently, it has been estimated that 50 % to 80 % of pastures in the Amazon and *Cerrado* are degraded (Macedo *et al.*, 2014; Peron & Evangelista, 2004; Zimmer *et al.*, 1994). Achieving a higher rate is likely to entail some initial investment costs to promote modified

<footnote n="1">¹Adoption rates for feedlot, protein and concentrate supplementation are calculated as the percentage of the total finished animals. The adoption rate of pasture restoration is the annual average area of restored pasture.</footnote>
production practices and this is the purpose of the government's ABC program. ABC is an ambitious plan created to stimulate farmers and ranchers to adopt mitigation measures including restoration of degraded pastures, helping the country to meet the reduction targets presented at COP 15. ABC is the biggest sustainable agriculture fund running in Brazil, with a key objective of disbursing subsidized credit to the agricultural sector. The plan currently targets the recovery of 15 Mha in 10 years, which will lead to reductions up to 104 Mt CO₂e, roughly 64% of the program total mitigation potential. But it does not include other relevant measures such as feed supplementation measures, which would normally be considered as privately profitable anyway.

The outcome of the ABC plan remains to be evaluated, but initial indications suggest that uptake of credit has been slower than anticipated (Claudio, 2012). Recent evidence from the Amazon Environmental Research Institute suggests that several institutional barriers have retarded the program, including a lack of publicity and information about the aims and the benefits of the program, difficulties in complying with program requirements, a lack of technical assistance, and producer scepticism about the private economic benefits of measures that are predominantly designed to address global external costs (Stabile et al., 2012).

Producers also perceive transaction costs in program compliance and a lack of basic infrastructure (Rada, 2013) that is needed to support increased productivity. In short, the ABC plan is confronting similar behavioural barriers in relation to non-adoption, identified in other mitigation studies, e.g. Moran et al. (2013), which need to be addressed before wider measure adoption can be expected.

## 8. Conclusion

This paper highlights resource efficiency measures can be enacted (notionally within farm gate) in the *Cerrado* biome to help reconcile competing objectives of private yield improvements and the reduction of external costs. The analysis responds to the need to demonstrate the possibilities for sustainable intensification, allowing Brazil to meet economic growth ambitions for the sector.

The key finding from the use of the EAGGLE economic optimization model is the representation of the cost-effectiveness of key mitigation measures. Specifically, that pasture restoration is the most promising mitigation measure in terms of abatement potential volume and that it offers a cost saving for the livestock sector. By adopting these measures – pasture restoration, concentrate and protein supplementations – the *Cerrado* could reduce 23.7% of its emissions by 2030, while the total abatement potential of adopting all measures is 24.1%.

The analysis presented here has a number of caveats that potentially warrant further research. These include a more detailed representation of the biophysical heterogeneity of the *Cerrado* biome, more detailed treatment of the deforestation (and hence land sparing) processes and relaxation of the assumed equilibrium supply and demand conditions in the optimization model.

Nevertheless by highlighting cost-effective policy options, this paper contributes to our understanding of sustainable intensification processes as relevant to Brazilian livestock production.

## Acknowledgement

We acknowledge our financial supporters: the project Identifying pathways to support climate compatible agricultural development through NAMAs, financially supported by the Climate & Development Knowledge Network (CDKN), under the project reference RSGL-0018A, the Scottish Government Rural and Environmental Science and Analytical Services division through ClimatexChange (http://www.climatexchange.org.uk/) and AnimalChange, financially
supported from the European Community's Seventh Framework Programme (FP7/2007-2013) under the grant agreement number 266018. Rafael Silva acknowledges CAPES Foundation for the scholarship no. 10180/13-3.

**Appendix. Supplementary material**

Appendix S1: Description of inputs and costs

Appendix S2: Model calibration

Supplementary Tables: Tables S1-S8

**References**

Abbasi, M.K., Adams, W.A., 2000. Estimation of simultaneous nitrification and denitrification in grassland soil associated with urea-N using 15 N and nitrification inhibitor. Biol. Fertil. Soils 31, 38-44.
doi:10.1007/s003740050621

ANUALPEC, 2010. Anualpec: anuário da pecuária brasileira. São Paulo, São Paulo.

Barioni, L.G., 2011. Embrapa Invernada.

Berndt, A., Tomkins, N.W., 2013. Measurement and mitigation of methane emissions from beef cattle in tropical grazing systems: a perspective from Australia and Brazil. Anim. An Int. J. Anim. Biosci. 7 Suppl 2, 363-372. doi:10.1017/S1751731113000670292

Bowman, M.S., 2013. Essays on Externalities and Agricultural in the United States and Brazil. University of California, Berkeley.

Bras, S.P., URQUIAGA, S., SANTOS, C.A. dos; PINHEIRO, E.F.M., JANTALIA, C.P., BODDEY, R.M., 2013. Soil Carbon Stocks under Productive and Degraded Brachiaria Pastures in the Brazilian Cerrado. Soil Sci. Soc. Am. J. VO-77 914.

Bustamante, M.M.C., Nobre, C. a., Smeraldi,R., Aguiar, A.P.D., Barioni, L.G., Ferreira, L.G., Longo, K., May, P., Pinto, A.S., Ometto, J.P.H.B., 2012. Estimating greenhouse gas emissions from cattle raising in Brazil. Clim. Change 115, 559-577. doi:10.1007/s10584-012-0443-3

Cederberg, C., Meyer, D., Flysjö, A., 2009. Life cycle inventory of greenhouse gas emissions and use of land and energy in Brazilian beef production.

Claudio, A., 2012. Brazil's fund for low-carbon agriculture lies fallow. Nature.
doi:10.1038/nature.2012.11111

Costa Junior, C., Goulart, R.S., Albertini, T.Z., Feigl, B.J., Cerri, C.E.P., Vasconcelos, J.T., Bernoux, M., Lanna, D.P.D., Cerri, C.C., 2013. Brazilian beef cattle feedlot manure management: A country survey. J. Anim. Sci. 91, 1811-1818.

Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., 2006. IPCC guidelines for national greenhouse gas inventories. Hayama, Japan.

FAO, 2014. The Statistics Division of the FAO (Food and Agriculture Organization of the United Nations) [WWW Document]. URL http://faostat.fao.org/ (accessed 3.17.14).

Ferraz, J.B.S., Felício, P.E. De, 2010. Production systems--an example from Brazil. Meat Sci. 84,
238-43. doi:10.1016/j.meatsci.2009.06.006

FGV, 2012. General Price Index -Internal Availability. Brasília, DF.

Follett, R.F., Reed, D.A., 2010. Soil Carbon Sequestration in Grazing Lands: Societal Benefits and Policy Implications. Rangel. Ecol. Manag. 63, 4-15.
Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. & Tempio, G., 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Rome.

Giltrap, D.L., Saggar, S., Singh, J., Harvey, M., McMillan, A., Laubach, J., 2011. Field-scale verification of nitrous oxide emission reduction with DCD in dairy-grazed pasture using measurements and modelling. Soil Res. 49, 696. doi:10.1071/SR11090

Gouvello, C. de, Filho, B.S.S., Hissa, L., Nassar, A., Harfuch, L., Moreira, M.M.R., Luciane Chiodi, Antoniazzi, L.B., Antoniazzi, B.B., Barioni, L.G., Junior, G.M., Sainz, R.D., Alves, B.J.R., Lima, M.A. de, Martins, O., Branco, M.C., Toledo, R., Verde, M.R.L., Marques, F., Ferreira, R., Goulart, L., Mendes, T., Moreira, A., Farinelli, B., Chang, J.M., Pinto, R., Hato, J., Pacca, S., Freitas, S.R., Longo, K.M., Siqueira, R.A. de, 2011. Brazil Low Carbon Case study: Technical Synthesis Report. The World Bank Group, Washington, DC.

Gouvello, C. de, Filho, B.S.S., Nassar, A., Schaeffer, R., Alves, F.J., Alves, J.W.S., 2010. Brazil Low-carbon Country Case Study, The World Bank Group.

IBGE, 2014. Agricultural Census 2006 [WWW Document]. Brazilian Inst. Geogr. Stat. URL http://www sidra.ibge.gov.br/bda/acervo/acervo2.asp (accessed 3.17.14).

IEA, 2012. Survey of monthly average prices received by farmers. São Paulo, SP.

MacLeod, M., Moran, D., Eory, V., Rees, R.M., Barnes, A., Topp, C.F.E., Ball, B., Hoad, S., Wall, E., McVittie, A., Pajot, G., Matthews, R., Smith, P., Moxey, A., 2010. Developing greenhouse gas marginal abatement cost curves for agricultural emissions from crops and soils in the UK. Agric. Syst. 103, 198–209. doi:10.1016/j.agsy.2010.01.002

Maia, S.M.F., Ogle, S.M., Cerri, C.E.P., Cerri, C.C., 2009. Effect of grassland management on soil carbon sequestration in Rondônia and Mato Grosso states, Brazil. Geoderma 149, 84–91. doi:10.1016/j.geoderma.2008.11.023

Martha, G.B., Alves, E., Contini, E., 2012. Land-saving approaches and beef production growth in Brazil. Agric. Syst. 110, 173–177. doi:10.1016/j.agsy.2012.03.001

Martha Jr, G.B., 2007. Cerrado: uso eficiente de corretivos e fertilizantes em pastagens, 1st ed. Planaltina -DF.

Millen, D.D., Pacheco, R.D.L., Arrigoni, M.D.B., Galyean, M.L., Vasconcelos, J.T., 2009. A snapshot of management practices and nutritional recommendations used by feedlot nutritionists in Brazil. J. Anim. Sci. 87, 3427–39. doi:10.2527/jas.2009-1880

Millen, D.D., Pacheco, R.D.L., Meyer, P.M., Rodrigues, P.H.M., De Beni Arrigoni, M., 2011. Current outlook and future perspectives of beef production in Brazil. Anim. Front. 1, 46–52. doi:10.2527/af.2011-390 0017

Moran, D., Lucas, A., Barnes, A., 2013. Mitigation win-win. Nat. Clim. Chang. 3, 611–613. doi:10.1038/nclimate1922

Moran, D., MacLeod, M., Wall, E., Eory, V., McVittie, A., Barnes, A., Rees, R.M., Topp, C.F.E., Pajot, G., Matthews, R., Smith, P., Moxey, A., 2010. Developing carbon budgets for UK agriculture, land-use, land-use change and forestry out to 2022. Clim. Change 105, 529–553. doi:10.1007/s10584-010-9898-2

Morris, M., Binswanger, H., Byerlee, D., Staatz, J., 2012. A Breadbasket for Africa: Farming in the Guinea Savannah Zone. Solutions 3, 44–49.

Mozzer, G.B., 2011. AGRICULTURE AND CATTLE RAISING IN THE CONTEXT OF A LOW CARBON ECONOMY, in: Seroa da Motta, R. (Ed.), CLIMATE CHANGE IN BRAZIL Economic, Social and Regulatory Aspects. IPEA, Brasília, DF, p. 358.
Neely, C., Bunning, S., Wilkes, A., 2009. Review of evidence on drylands pastoral systems and climate change / Implications and opportunities for mitigation and adaptation. L. Water Discuss. Pap.

Oliveira Silva, R. de, 2013. EAGGLE: A linear programming model for optimizing mitigation strategies of greenhouse gases emissions in beef cattle production systems. University of Campinas. http://www.bibliotecadigital.unicamp.br/document/?code=00091778417 4&fd&lg=en_US

Opio, C., Gerber, P., Mottet, A., Falcucci, A., Tempio, G., MacLeod, M., Vellinga, T., Henderson, B., & Steinfeld, H., 2013. Greenhouse gas emissions from ruminant supply chains – A global life cycle assessment. Rome.

Parton, W.J., Schimel, D.S., Cole, C. V., Ojima, D.S., 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci. Soc. Am. J.

Peron, A.J., Evangelista, A.R., 2004. Degradação de pastagens em regiões de cerrado. Ciência e Agrotecnologia.

Rada, N., 2013. Assessing Brazil's Cerrado agricultural miracle. Food Policy 38, 146–155. doi:10.1016/j.foodpol.2012.11.002

Ruviaro, C.F., de Léis, C.M., Lampert, V. do N., Barcellos, J.O.J., Dewes, H., 2014. Carbon footprint in different beef production systems on a southern Brazilian farm: a case study. J. Clean. Prod. doi:10.1016/j.jclepro.2014.01.037

Sousa, D.M.G. de, Lobato, E., 2004. Cerrado: correção do solo e adubação. Planaltina: Embrapa Cerrados.

Soussana, J.F., Tallec, T., Blanfort, V., 2010. Mitigating the greenhouse gas balance of ruminant production systems through carbon sequestration in grasslands. Animal 4, 334–50. doi:10.1017/S1751731109990784

Soussana, J.F., BARIONI, L.G., ARI, T.B., CONANT, R., GERBER, P., HAVLIK, P., ICKOWICZ, A., HOWDEN, M., 2013. Managing grassland systems in a changing climate: the search for practical solutions, in: Revitalising Grasslands to Sustain Our Communities: Proceedings. Sydney, p. 22.

Stabile, M.C.C., Azevedo, A., Nepstad, D., 2012. BRAZIL'S "LOW-CARBON AGRICULTURE" PROGRAM: BARRIERS TO IMPLEMENTATION. Brasília.

The Economist, 2010. The miracle of the cerrado; Brazilian agriculture. Econ. VO -396 59.

The New York Times, 2007. Scientists Are Making Brazil's Savannah Bloom. New York Times VO - 157 3.

The World Bank, 2009. Awakening Africa's sleeping giant / prospects for commercial agriculture in the Guinea Savannah zone and beyond.

Thornton, P.K., Herrero, M., 2010. Potential for reduced methane and carbon dioxide emissions from livestock and pasture management in the tropics. Proc. Natl. Acad. Sci. 107, 19667–19672. doi:10.1073/pnas.0912890107

Tomé Junior, J.B., 1997. Manual para interpretação de análise de solo / Análise de solo. Livraria e Editora Agropecuária, Guaíba, RS, Brazil.

Tonato, F., Barioni, L.G., Guilherme, C., Pedreira, S., 2010. de forragem em pastagens tropicais. Pesq. agropec. bras. 45, 522–529.

Vuichard, N., Ciais, P., Viovy, N., Calanca, P., Soussana, J.-F., 2007. Estimating the greenhouse gas fluxes of European grasslands with a process-based model: 2. Simulations at the continental level. Global Biogeochem. Cycles 21, n/a–n/a. doi:10.1029/2005GB002612
## General assumptions

* We are modelling the current situation, so no forecasts are needed.

* When the total production of feedstuff changes at farm, we will adjust the stocking rate accordingly rather than buying in/selling feed or changing the milk yield or weight gain rate.

* We're going to use typical farms rather than best practice farms, as i) not much difference can be seen for many mitigation measures if the farm is already best practice, ii) "average"/"typical" farms are more useful for policy development.

## Fertilisation rate

Change mineral N application ±50 kg N ha⁻¹ y⁻¹ relative to current practice. We only consider inorganic N in this mitigation measure, leaving the organic N practice unchanged. (Note that subsequently manure N is going to change due to the change in yield, so the organic N will be different.) The stocking rate is adjusted to the yield. If there is a need to increase P or K fertilisation, information on that is needed too (amount and cost).

Input variables/parameters affected:

* Grass production, crop yield

* Machinery and fuel use for fertilisation (including N, P and K)

* Labour requirement for fertilisation (including N, P and K)

## Nitrification inhibitors

Nitrification inhibitors (NIs) are applied onto cropland or pasture to slow down the conversion of NH₄⁺ to NO₃⁻ in the soil and thus reduce N leaching and N₂O emissions from inorganic and organic fertilisers and grazing. Commonly used NIs include dicyandiamide (DCD) and 3,4-dimethylpyrazole phosphate (DMPP). The application rate of DCD and DMPP usually range between 7 to 30 kg ha⁻¹ and 0.5 to 5 kg ha⁻¹, respectively, and in this modelling exercise it should be defined based on the baseline N fertilisation rate. We assume no change in the fertilisation rate. NI application might increase yield and the N content of the plant.

Input variables/parameters affected:

* Machinery and fuel use for fertilisation

* Labour requirement for fertilisation

* Grass production, crop yield

* Crop/grass N content

## More legumes in grass swards

Clover (or other appropriate legumes) is sown along with grass, so that the legume content of the grass will be kept at a constant level of 20-30 % DM. The measure is applicable on rotational, temporary and permanent grasslands. We will assume stitching the legumes into the grass – where it is not feasible, we can model immediate reseeding, and assume that
reseeding was due anyway. As the legume content of the sward might be decreasing over time, more frequent reseeding/stitching in might be required. The N fertilisation is kept constant if the baseline is less than 75 kg ha⁻¹, or reduced to 75 kg ha⁻¹ (one application in the spring) if it's more than that. We'll assume no change in the N content, energy content and digestibility of the grass mix. Yield is reduced, so stocking rate is reduced.

The measure is going to affect soil N₂O emissions, cattle/sheep diet and rumen emissions, manure N and VS content (so storage emissions), N₂O emissions from manure spreading.

Input variables/parameters affected:

* Type of legume

* Grass yield

* Seed cost

* Frequency of reseeding

* N fertiliser amount

* Machinery and fuel use for fertilisation and extra seeding

* Labour requirement for fertilisation and extra seeding

## Legumes in the rotation

The proportion of legumes (could be grain legumes or fodder legumes, e.g. alfalfa) in the rotation is increased to 10% of the land area. The measure is applicable on arable land. Legumes will either replace a crop in the rotation or reduce the amount of all the other crops. The N fertilisation requirements within the rotation are reduced. The legumes are either used to replace imported N in the animal feed, or if the animals are already eating local protein sources then the legumes are sold. In the first case the N-content of the diet has to be balanced by reducing the N-content of the concentrates, replacing soya at the first place (LCA for related emissions).

The measure is going to affect soil N₂O emissions, cattle diet and rumen emissions, manure N and VS content (so storage emissions), N₂O emissions from manure spreading.

Input variables/parameters affected:

* Rotation pattern, crop outputs

* Yield (legume and subsequent crop)

* Seed cost (both the legumes and the crop it is replacing)

* N fertiliser amount

* Machinery and fuel use for fertilisation and changed rotation pattern

* Labour requirement for fertilisation, changed rotation pattern and changed feed mix

* Diet energy and N content

* Off-farm emissions from concentrates replaced (changes in concentrates and GHG emissions from concentrates production)

## Cover crops

Cover crops are sown in between main crops. The applicability of this measure depends on the rotational pattern. The type and timing of the cover crop depends on local circumstances. 100% of the crop is ploughed in when the main crop is seeded. The N fertilisation need of the next crop decreased.
The measure is going to affect soil N₂O emissions and NO₃⁻ leaching in winter (leaching reduced, but N₂O might increase) and soil C stocks.

Input variables/parameters affected:

* Crop pattern

* Yield of subsequent crop

* Seed cost

* Field operations from seeding (machinery, labour, fuel)

* N fertiliser amount of subsequent crop

* Machinery and fuel use for fertilisation and cover crops

* Labour requirement for fertilisation and cover crops

## Irrigation

Can be applied to crops or grass, but the type of land (whether grassland or arable land) should be kept constant. Water harvesting and storage should also be considered where applicable. The changes in the fertilisation needs are to be defined.

Input variables/parameters affected:

* Yield

* Water cost (installation of water storage if applicable)

* Irrigation installation, maintenance and fuel costs

* Labour requirement

* Fertilisation needs

## Restoring degraded lands

This option is only considered for the non-European regions. The main focus is on improving the soil quality and soil C content.

### Improving permanent pastures

We consider three main ways of improving pastures:

a. Changing the species mix or the varieties.

b. Removing unwanted species.

c. Supplying P and K if needed.

These can be implemented in combination or alone, depending on the local circumstances.

### Improving roughage quality

We consider two main ways of improving roughage quality:

a. Changing the species mix or the varieties.

b. Better cutting regime.

These can be implemented in combination or alone, depending on the local circumstances.
We consider this option only for the non-European regions.

## Feeding more maize/sorghum and less grass

Increase the part of area dedicated to maize/sorghum production on farm so that the DM intake from maize:grass is I) 50:50 and II) 25:75. Maintain constant the area dedicated to roughage production. Adapt stocking rate (and thereby milk/meat production) according to the change in the forage production and quality.

This measure will only be about maize or sorghum, not silage wheat or barley. For farms where it is not economic to grow these crops, we don't model this measure.

This option can change many GHG emissions: enteric CH₄, manure CH₄, N₂O emissions, ammonia emissions, C sequestration, CO₂ fossil source.

Input variables/parameters affected:

* Change in land use (grass and maize/sorghum areas)

* Diet (grass, maize and concentrates), energy and N content

* Off-farm emissions from concentrates replaced (changes in concentrates and GHG emissions from concentrates production)

* Mineral fertilization and manure production

* Milk production and composition

* Average selling price of milk

* Time spent indoor

## Feeding more fat

Increase the proportion of specific unsaturated fats to 5% in the concentrate compounds to reduce the enteric methane emission of ruminants. With a moderate addition of lipids in the diets, no negative effects on digestibility, rumen ecosystem or milk yield are expected. Diet energy and N content is assumed to be constant. Milk composition might change (increase in fat content). The supplement must be fed with the concentrates, in substitution to another concentrate compound. We assume that the additive is bought in (too special to be easily produced at farm). LCA emissions will be assigned to the additive.

Input variables/parameters affected:

* Type and amount of supplement

* Cost of feed (including the cost of replaced feedstuff)

* Milk composition (fat)

* Average price of milk

## Additive nitrate

Add 1% nitrate to the diet, replacing either urea or other N sources. Diet energy and N content is assumed to be constant.

Input variables/parameters affected:

* Type and amount of supplement

<figure><img src="image_34.png" /></figure>
* Cost of feed

* Enteric $CH_4$ emission factor

## Balance amino acids and reduce CP in pigs

Reduce the CP content of the diet without changing animal performance by using phase feeding and amino acids supplementation. Diet energy and N content is assumed to be constant.

The expected change in GHG emissions concerns mainly $N_2O$ either directly or indirectly ($NH_3$).

Input variables/parameters affected:

* Feed composition

* Cost of feed

## Increasing housing while keeping the proportion of grass in the feed constant

Animals spend more time indoors, though not 100%. (Still to decide on the target housing rate.) The cut grass will be fed fresh, and if that is not feasible, then as hay or silage, depending on local circumstances. The manure collected in houses will be spread, plus a little bit more inorganic N to compensate for gaseous losses through storage. Leaching on the field is assumed to be constant.

The expected change in GHG emissions concerns mainly manure management ($N_2O$, $CH_4$, $NH_3$) and C sequestration. Fossil energy is also concerned ($CO_2$).

Input variables/parameters affected:

* Time spent indoor

* Proportion of grass grazed / cut / hay / silage

* Diet energy and N content

* Mineral fertilization and manure production

* Machinery, fuel and energy use for fertilisation and cutting grass (and preparing hay/silage)

* Labour requirement for fertilisation and cutting grass (and preparing hay/silage)

## Replacement rate dairy cattle

Increase stocking rate of cows and decrease the stocking rate of heifers (still to decide the target replacement rate). The total area of forage production is kept constant. This option will only be considered in dairy farms, otherwise it will be necessary to consider that the heifers will be reared in another herd. Milk yield is kept constant, but the production per cow has to consider the change in primiparous cows due to a lower replacement rate.

Many emissions can be changed ($CH_4$, $N_2O$, $NH_3$, $CO_2$), but productions (milk and meat) are also modified.

Input variables/parameters affected:

<figure><img src="image_35.png" /></figure>
* Replacement rate

* Change in concentrate and forage system according to the diet the different animal classes, and, accordingly, change in land use (grass versus crops)

* Off-farm emissions from concentrates replaced (changes in concentrates and GHG emissions from concentrates production)

* Diet energy and N content

* Milk production and composition

* Average milk selling price

## Cover slurry stores/manure heaps

Covering of slurry tanks or manure heap by an impervious cover.

Impacts: C degradation, NH₃ emission, N₂O emission & N₂ emission from storage, CH₄ emission from storage.

Input variables/parameters affected:

* Type of cover

* Investment and maintenance of cover

* Storage CH₄, NH₃, N₂O and N₂ emission factors

## Manure acidification

Acidify slurry to pH 5.5 using sulphuric acid, using add-on technology to existing stores. Applicability is affected by the type of the concrete in the livestock house and by the type of the manure store.

Impacts: C degradation in storage, NH₃ emission, N₂O emission & N₂ emission from storage, CH₄ emission from storage, NH₃, N₂O and N₂ emission from field-applied manure.

Input variables/parameters affected:

* Investment and maintenance of acidification equipment

* Labour requirement

* Storage CH₄, NH₃, N₂O and N₂ emission factors

* Manure spreading NH₃ emission factor

## Anaerobic digestion

Anaerobically digest slurry and/or farm yard manure. (Still to decide the size of the digester, whether manure is imported (i.e. centralised anaerobic digestion) or not (on-farm anaerobic digestion), whether any supplementary substrate is used, and whether the supplementary substrate is bought in or home-grown.)

Impacts: C degradation, CH₄ emission from storage, NH₃ emission, N₂O emission & N₂ emission in storage, NH₃ emission from field-applied manure.

Input variables/parameters affected:

* Size of digester

<figure><img src="image_36.png" /></figure>
o Investment, maintenance and income (electricity price and subsidies) from AD (including labour costs)

o Land use and stocking rate changes (if supplementary substrate is used and grown on farm)

o Amount and type of supplementary substrate bought in

o Manure import (if assumed), distance of manure imported from

o Storage CH$_4$, NH$_3$, N$_2$O and N$_2$ emission factors

o Manure spreading NH$_3$ emission factor

o AD technical details, like

o Farm electricity use (baseline)

o Farm gas use which can be replaced by electricity

o Farm oil use which can be replaced by electricity

o Lifetime of the AD plant

o % of biogas as CO$_2$

o % of biogas as CH$_4$

o Methane leakage

o Efficiency of electricity production in the AD plant

o Efficiency of heat production in the AD plant

o Typical GHG emissions per kWh of electricity generation (grid average)
# Appendix 6: Detailed description of mitigation options for biophysical and economic modelling

## Reducing N fertilisation

For farm modelling in the Animal Change project, Component 3

Authors: Bob Rees and Vera Eory (SRUC)

### Questions

*Do we assume a change in N₂O EF, i.e. higher EF for N above Navailmax?*

A number of recent studies have shown that when N₂O emissions are scaled according to yield, minimum levels of emission intensity can be identified at nitrogen fertiliser rates that that may differ from the economic optimum (Figure 4) (Hoben et al., 2011; Pappa et al., 2011). However, such relationships are not consistent in some studies have shown fairly linear relationships between emission intensity of N₂O and fertiliser application rates (Thorman et al., 2013).

*How to set NavailMax? Is NavailMax expected to be constant within an AEZ?*

### Approach 1

$$NavailMax = N(\text{economic optimum}), NyieldMax = Nyield(\text{economic optimum})$$

Rational: As FarmAC is using a linear N-response function with a NyieldMax at NavailMax. N response curves are more or less exponential, and the argument in this mitigation option is that though in the baseline situation available mineral N is likely to be below the point where the yield is maximised (because the economic N optimum is always below that point), still the N response curve's slope is low enough to allow for only a small decrease in the yield given a decrease in N mineral N. As the exponential curve will not be reproduced in FarmAC, it is important to set NavailMax in a way that it captures this. Therefore it might be needed to set NavailMax not at the maximum yield, but slightly below, e.g. at the economic optimum (available in fertiliser recommendations).
<figure><img src="image_37.png" /><figcaption>Figure 1. Effects of defining NavailMax on the changes in Nyield as the function of Navailable</figcaption></figure>

In this case experts don't need to give any information to FarmAC on this measure.

Financial information is still needed (especially in the case of increasing fertilisation in non-European countries) on whether there is change in the cost of other fertilisers and the cost of spreading (or machine runs).

## Approach 2

Another alternative is to ask experts to provide a value about how much the yield changes given a ±50 kg N/ha change in the available N (see an example below). But that would imply that this information has to overwrite the original response function in FarmAC. Would it be feasible?

We should ask these data anyway to crosscheck them with the modelled yields.

## Data to be provided by regional experts

Estimate the economic optimum N application (kg N available / ha) and the corresponding yield (kg DM / ha) for each crop in the agroecological zone. Provide these as the FarmAC model parameters NavailMax and NyieldMax, respectively:

$$NavailMax = N-available(economic optimum)$$

$$NyieldMax = Nyield(economic optimum)$$

These values might be available in fertiliser recommendations, or in scientific papers.

NOTE: the NavailMax parameter of FarmAC refers to total available mineral N, and not only to the applied N. The fertiliser recommendations might provide the N(economic optimum) as
applied N. In this case, find estimates about the corresponding soil nitrogen supply (SNS), and calculate NavailMax as:

$$NavailMax = SNS + N-applied(economic optimum).$$

For reference, make a note on the fertiliser N price: grain N price ratio (it is a key assumption in the economic optimum calculations).

Explaining SNS: SNS is the amount of nitrogen in the soil that is available for uptake by the crop throughout its entire life, taking account of nitrogen losses. The SNS includes Soil Mineral Nitrogen (SMN):

$$SNS = SMN + \text{estimate of mineralisable soil nitrogen}$$

SMN is the nitrate-N plus ammonium-N content of the soil within the potential rooting depth of the crop, allowing for nitrogen losses.

Mineralisable soil nitrogen is the estimated amount of nitrogen which becomes available for crop uptake from mineralisation of soil organic matter and crop debris during the growing season after sampling for SMN. (Defra, 2011)

EXAMPLE (winter wheat, UK)

<table><thead><tr><td>Parameter</td><td>Value</td><td>Unit</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>NavailMax, winter wheat</td><td>324</td><td>kg N ha<sup>-1</sup></td><td>All farms, EU maritime</td><td>(Kindred et al., 2010; Kindred et al., 2012; Sylvester-Bradley et al., 2009)</td></tr><tr><td>NyieldMax, winter wheat</td><td>195</td><td>kg N ha<sup>-1</sup></td><td>All farms, EU maritime</td><td></td></tr></tbody></table>

Calculations used:

$$NavailMax = N-applied/opt) + SNS = 324 \text{ kg N ha}^{-1}$$

$$SNS = 110 \text{ kg N ha}^{-1} \text{ (assumption, based on (Kindred et al., 2012))}$$

$$N-applied/opt) = [\ln(k - c) - \ln(b * \ln(r))] / \ln(r) = 214 \text{ kg N ha}^{-1} \text{ (Kindred et al., 2010)}$$

where k: fertiliser N price : grain N price ratio of 5, k = 0.005

c: the slope of the response beyond the region of maximum curvature, c = -0.002

b: the change in yield from the maximum if no fertiliser N was applied, b = -6.0

r: the shape of the response in the region of maximum curvature, r = 0.99

Note, that though NavailMax = 324 kg N ha⁻¹, the optimum N applied is 214 kg N ha⁻¹

$$NyieldMax = \text{Yield}(opt) * 1000 \text{ kg/t} * \text{GrainN} = 162 \text{ kg N ha}^{-1}$$

$$\text{Yield}(opt) = 0.85 * (a + b * rN + c * N) = 7.94 \text{ t N ha}^{-1} \text{ (Kindred et al., 2010)}$$
where 0.85: conversion factor (85% DM to 100% DM)

a: the asymptote, or maximum achievable yield, a = 10.47 t N ha⁻¹

N: N applied, here N = N-applied/opt) = 214 kg N ha⁻¹

GrainN: grain N content (%DM), GrainN = 2.04% (assumption, based on (Sylvester-Bradley et al., 2009))

**Data to be provided by local experts**

1. Estimate how much the yield changes (kg DM ha⁻¹) given a ±50 kg N ha⁻¹ change in the applied N (synthetic + organic). The change should be relative to the farm baseline.

2. Is there any change in the cost of other fertilisers and the cost of spreading (or machine runs)? (Especially in regions where N is currently under-applied, and the +50 kg N ha⁻¹ increase is substantial.

**EXAMPLE (winter wheat, UK)**

1. Yield change:

<figure><img src="image_38.png" /><figcaption>Figure 2. Response curves (NOTE: N applied, not N available) from wheat trials in 2005-7 (modern varieties), and an 'average' curve (bold line) fitted to their average yields (Sylvester-Bradley *et al.*, 2008)</figcaption></figure>

'Average' yield curve (Kindred *et al.*, 2010):

Y=10.47-6.0*[(0.99)]^N-0.002*N (see above for more on the parameters)

where Y: yield at 85% DM

N: N applied (kg N ha⁻¹)

If the baseline N applied (manure + inorganic) = 200 kg N ha⁻¹, and yield = 7.88 t DM ha⁻¹:
$$N \text{ applied} = 150 \text{ kg N ha}^{-1} \rightarrow Y = 7.52 \text{ t DM ha}^{-1} (-4.6\%)$$

$$N \text{ applied} = 250 \text{ kg N ha}^{-1} \rightarrow Y = 8.06 \text{ t DM ha}^{-1} (+2.4\%)$$

2. The cost of other fertilisers and the cost of spreading the N are assumed to be constant.

## Objective

Nitrous oxide emissions from soils are known to be highly sensitive to the amounts of nitrogen fertilisers applied. The objective of this measure is to reduce N fertiliser application rates in order to reduce N₂O emissions without having a significant impact on yield. The expected change in GHG emissions would occur as a consequence of reduced direct and indirect N₂O loss and by reduced CO₂ emissions associated with N fertiliser manufacture.

## Assumptions for farm modelling

* The measure is applicable to grass and arable cropping.

* Small changes in applications of fertiliser N can be made without significant loss of crop yield.

* Cropland soil management assumed to follow best practice. This would include full allowance for residual soil N and N content of slurries of manure prior to the application of mineral N. It would also assume that soil is maintained in good condition i.e. that drainage is maintained and at first soil conditions such as compaction are avoided.

* It is assumed that emissions of methane and carbon dioxide from the soil would be unaffected by this measure

## Further guidance for the farm experts

* Formulation:

* This measure is applicable to all forms of mineral nitrogen fertiliser. The cost of ammonium nitrate purchased in May 2013 is £270/t.

* Costs:

* The effects on cost of directly proportional to the cost of fertiliser N.

## Model variables and parameters affected

<table><thead><tr><td>Parameter</td><td>Unit</td><td>Likely range of change</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>Crop yield</td><td></td><td>Reduced by up to 5%</td><td>All farms</td><td></td></tr><tr><td>Mineral N fertiliser</td><td></td><td>Reduced by up to 20%</td><td>All farms</td><td></td></tr></tbody></table>

## Rationale and mechanism of action

Nitrous oxide emissions are strongly influenced by N fertiliser applications (Bouwman et al., 2002; Rees et al., 2013). This is often considered to be the single most important factor influencing emissions from agricultural soils, although its effects interact with local environmental conditions such as soil wetness and temperature. However, recent studies have shown that the relationship between N₂O emissions and end fertiliser application is often non-linear, with a slow increase in emissions at low fertiliser application rates, followed by a much more rapid increase in emissions at fertiliser rates at or above the economic
optimum (Cardenas *et al.*, 2010; Pappa *et al.*, 2011). This highlights the importance of avoiding over fertilisation as a mitigation measure, since N applied at these high rates please to disproportionally large quantities of N₂O release.

Most fertiliser recommendation systems use economic optimum to set fertiliser rates. This is defined as the point at which additional inputs of fertiliser N cost more than the financial benefits gained by additional yield. Although fertiliser recommendations tend to be fixed within particular regions, in reality the economic optimum would vary from year to year as a consequence of variations in the costs of fertiliser N and crop yield. A further complication in precisely defining the economic optimum is that crop yield tends to be much less sensitive to fertiliser application at high levels of addition than it is at lower levels. Recent studies in the UK have confirmed the relatively flat response in terms of yield at N application rates close to the recommended economic optimum and have been able to demonstrate that a 10% reduction in fertiliser application rates would lead to less than 1% reduction in yield (Sylvester-Bradley *et al.*, 2009). Other studies suggest that such fertiliser reduction strategies would be applicable more widely across Europe and globally, with the opportunity to reduce global fertiliser use by 20% without significant impacts on crop yield (Good *et al.*, 2011). It is anticipated that such changes in management could lead to significant environmental benefits, and address serious concerns about the environmental impacts of excessive N use in Europe (Anon., 2011; Galloway *et al.*, 2002).

<figure><img src="image_39.png" /><figcaption>Figure 3. A typical nitrogen response curve, used to develop fertiliser N application guidelines for UK crops (Defra, 2011)</figcaption></figure>

Therefore at fertiliser recommendations close to the economic optimum, there is a relatively low sensitivity in yield response, but a high sensitivity changes in nitrous oxide emission. A number of recent studies have shown that when N₂O emissions are scaled according to yield, minimum levels of emission intensity can be identified at nitrogen fertiliser rates that may differ from the economic optimum (Figure 4). This leads to the interesting observation that in order to achieve the minimum N₂O intensity is important to avoid both low and high levels of fertiliser N application (Hoben *et al.*, 2011; Pappa *et al.*, 2011). However, such relationships are not consistent in some studies have shown fairly linear relationships between emission intensity of N₂O and fertiliser application rates (Thorman *et al.*, 2013).
<figure><img src="image_40.png" /></figure>

Figure 4. Relationships between fertiliser N application to spring barley and N₂O emission intensities, (Pappa *et al.*, 2011)

## Proposed actions to reduce fertiliser nitrogen application rates

It is proposed that fertiliser application rates recommended by N fertiliser recommendation systems should be reduced by 10% in all circumstances. It is anticipated that this would have negligible effects on yield whilst contributing significantly to a reduction in both direct and indirect nitrous oxide emissions. The measure should then lead to a 10% or greater reduction in N₂O emissions.

## Other effects of the action

Reducing fertiliser application rates will be associated with reduced direct and indirect emissions of N₂O. There will also be an associated reduction in losses by leaching and in some cases volatilisation. A further reduction in GHG emissions would be associated with reduced energy consumption from fertiliser manufacture

## Effects neglected

This measure is unlikely to have any significant impacts on increasing greenhouse gas emissions elsewhere in the farming system or supply chain

## Cost data

-

## References

* The European Nitrogen Assessment, 2011. Sutton, M.A., Howard, C.M., Erisman, J.W., Billen, G., Bleeker, A., Grennfelt, P., van Grinsven, H., and Grizzetti, B. (Eds.), Cambridge University Press, Cambridge.

* Bouwman, A.F., Boumans, L.J.M., and Batjes, N.H., 2002. Emissions of N₂O and NO from fertilized fields: Summary of available measurement data. Global Biogeochemical Cycles. 16.

* Cardenas, L.M., Thorman, R., Ashleee, N., Butler, M., Chadwick, D., Chambers, B., Cuttle, S., Donovan, N., Kingston, H., Lane, S., Dhanoa, M.S., and Scholefield, D., 2010. Quantifying annual N₂O emission fluxes from grazed grassland under a range of inorganic fertiliser nitrogen inputs. Agriculture Ecosystems & Environment. 136, 218-226.

* Defra, 2011. Fertiliser manual (RB209). Defra, London.

* Galloway, J.N., Cowling, E.B., Seitzinger, S.P., and Socolow, R.H., 2002. Reactive nitrogen: Too much of a good thing? Ambio. 31, 60-63.
Good, A.G. and Beatty, P.H., 2011. Fertilizing nature: a tragedy of excess in the commons. Plos Biology. 9, e1001124.

Hoben, J.P., Gehl, R.J., Millar, N., Grace, P.R., and Robertson, G.P., 2011. Nonlinear nitrous oxide (N₂O) response to nitrogen fertilizer in on-farm corn crops of the US Midwest. Glob Change Biol. 17, 1140-1152.

Kindred, D. R., Knight, S., Berry, P., Sylvester-Bradley, R., Hatley, D., Morris, N., Hoad, S., and White, C., 2012. Establishing best practice for estimation of Soil N Supply. Report no. Project Report No. 490. HGCA.

Kindred, D.R. and Sylvester-Bradley, R., 2010. Routes to reducing the N requirements of high yielding wheat crops. Water and nitrogen use efficiency in plants and crops, 105 ed. AAB.

Pappa, V., Thorman, R., Rees, R., and Sylvester-Bradley, R., 2011. Effects of nitrog fertiliser use on minimizing N₂O intensities of arable crop products. European Geophysical Union, Vienna.

Rees, R.M., Augustin, J., Alberti, G., Ball, B.C., Boeckx, P., Cantarel, A., Castaldi, S., Chirinda, N., Chojnicki, B., Giebels, M., Gordon, H., Grosz, B., Horvath, L., Juszczak, R., Klemedtsson, A.K., Klemedtsson, L., Medinets, S., Machon, A., Mapanda, F., Nyamangara, J., Olesen, J.E., Reay, D.S., Sanchez, L., Cobena, A.S., Smith, K.A., Sowerby, A., Sommer, M., Soussana, J.F., Stenberg, M., Topp, C.F.E., van Cleemput, O., Vallejo, A., Watson, C.A., and Wuta, M., 2013. Nitrous oxide emissions from European agriculture - an analysis of variability and drivers of emissions from field experiments. Biogeosciences. 10, 2671-2682.

Sylvester-Bradley, R., Kindred, D. R., Blake, J., Dyer, C., and Sinclair, A. H., 2008. Optimising fertiliser nitrogen for modern wheat and barley crops. Report no. Project Report No. 438. HGCA.

Sylvester-Bradley, R. and Kindred, D.R., 2009. Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency. Journal of Experimental Botany. 60, 1939-1951.

Thorman, R.E., Pappa, V.A., Smith, K.E., Rees, R.M., Chauhan, M., Bennett, G., Denton, P., Munro, D., and Sylvester-Bradley, R., 2013. Recent evidence of nitrous oxide emissions from arable cropping in the UK. Proceeding 710 ed. International Fertiliser Society, Cambridge, UK.

# Nitrification inhibitors: the role of DCD in reducing N₂O emissions and NO₃⁻ leaching

*For farm modelling in the Animal Change project, Component 3*

*Authors: Laurence Shalloo (TEAGASC)*

This review is dealing predominantly with dairy cows on grazed grassland.

## Nitrogen Loss

The rate of utilisation by the sward of N deposited in urine is dependent on the timing of deposition. Nitrogen deposited in the spring has a high level of utilisation due to high demand by the growing plant, while in late autumn N use efficiency is low as grass is not actively growing and the N is available for loss through one form or another. Nitrogen in urine is in the form of urea, however, once applied N is converted to NO₃⁻ through the nitrification process. Nitrate leaching and N₂O emissions are key environmental loss pathways from grazed grassland as a result of urine deposition by grazing livestock. There are numerous strategies to mitigate N losses in grazing systems including the use of nitrification inhibitors such as dicyandiamide (DCD).

## Dicyandiamide (DCD)

Dicyandiamide is a white crystalline nitrogenous powder naturally broken down in the soil (Amberger, 1989). Nitrification inhibitors such as DCD are compounds that delay the bacterial oxidation of the ammonium ion (NH₄⁺) by reducing the activity of the edaphic Nitrosommas spp. bacteria (Irigoyen et al., 2003). Dicyandiamide has been shown to reduce
N losses from urine patches (Di and Cameron, 2005; Dennis *et al.*, 2012). The performance of nitrification inhibitors such as DCD is influenced by factors such as soil temperature, rainfall, and time and rate of application of the DCD.

## Herbage production

Dicyandiamide has been shown to increase herbage production in a number of studies in New Zealand (Di and Cameron, 2002; Di and Cameron, 2005; Moir *et al.*, 2012), however, the application of DCD to pastures to increase herbage production has proved inconsistent in Internationally with a number of studies in Ireland showing only small agronomic benefits (Dennis *et al.*, 2012; O'Connor *et al.*, 2012, 2013a) while in some New Zealand studies a similar affect was found (Monaghan *et al.*, 2009). Di and Cameron (2005) reported that the application of DCD gave an annual average increase in herbage production of 33% when the DCD was applied directly to the urine patch. This type of application would prove extremely difficult at the paddock application level. Monaghan *et al.* (2009) observed that DCD application had no significant effect on annual or seasonal pasture production in any measurement year. Dennis *et al.* (2012) reported that there was an inconsistency in the increase in herbage production when DCD was applied following urine application to lysimeters in Irish soils; O'Connor *et al.* (2012; 2013a) found inconsistent herbage production responses and when a positive effect occurred it was very small.

Di and Cameron (2005) reported that the application of DCD increased herbage production when urine was applied to grassland in autumn by an average of 49%, while there was also an increase of 18% in spring. This gave an annual average increase in herbage production of 33%. A study completed in New Zealand by Di and Cameron, (2007) showed that an application of DCD to urine treatments can increase herbage production by an average of 25% across all treatment, an application of urine at a rate of 700 kg N ha⁻¹ yr⁻¹ with DCD increased herbage production from 13.90 t DM ha⁻¹ yr⁻¹ without DCD to 18.71 t DM ha⁻¹ yr⁻¹, an increase in herbage production of 35%, an application of urine at a rate of 1000 kg N ha⁻¹ yr⁻¹ increased herbage production from 19.74 t DM ha⁻¹ yr⁻¹ without DCD to 23.24 t DM ha⁻¹ yr⁻¹ with DCD, an increase in herbage production of 18%. A national series of farm trials was conducted in New Zealand by Carey *et al.* (2012) to investigate the effectiveness of DCD at increasing pasture DM yield in a paddock situation. The results from the study found that DCD effectiveness in increasing herbage production varied between north and south islands, with a herbage response in the north island varying between 4-27% and in the south island by 12-31%. Monagahan *et al.* (2009) observed that application of DCD had no significant effect on annual or seasonal pasture production in any measurement year.

Therefore it can be concluded that the application of DCD to soil gives a region specific response based on climate and soil type in any particular region, however there could also be a within paddock specific affect which relates to the application of DCD to urine patches within paddocks.

## Environmental benefit

In New Zealand the application of DCD at a rate of 15 kg ha⁻¹ to urine patches in autumn resulted in total NO₃⁻-N leaching losses being reduced by 76% compared to urine only treatments (Di and Cameron, 2002). Dicyandiamide applied to Irish soils reduced NO₃⁻-N leaching by an average of 45% (Dennis *et al.*, 2012). Selbie *et al.* (2011) also reported that the application of DCD at a rate of 15 kg ha⁻¹ to urine patches (1000 kg N ha⁻¹) reduced N₂O emissions in the winter season by 70% on Irish soils. In Ireland, Dennis *et al.* (2010) reported that the application of DCD to urine treatments reduced NO₃⁻-N leaching by up to 45%. In New Zealand, the application of DCD to urine patches was shown to reduce NO₃⁻-N leaching by 60-65% (Singh *et al.*, 2009). Qui *et al.* (2010) reported that applying 15 kg DCD ha⁻¹ to dairy cow urine patches in winter and summer reduced N₂O-N emissions by 40 and 69%,

<figure><img src="image_41.png" /></figure>
respectively. A study carried out by Di et al. (2007) concluded that the application of DCD, reduced N₂O emissions from urine patches by 70%, from 9.2 kg N₂O-N ha⁻¹ without DCD to 2.8 kg N₂O-N ha⁻¹. Inter-urine patches have had total N₂O emissions reduced by 25% from 0.48 kg N₂O-N ha⁻¹ to 0.36 kg N₂O-N ha⁻¹ (Di et al., 2007). A study completed in the Basque region of Northern Spain has reported that the addition of DCD to cattle slurry can reduce N₂O emissions by 60% (Merino et al., 2002).

## Nitrate leaching

Di and Cameron (2007) reported that the application of DCD with urine (1000 kg N ha⁻¹) led to a reduction in NO₃⁻ leaching of 45%. Total NO₃⁻ leaching loss of 254.9 kg ha⁻¹ yr⁻¹ in the treatment that received 1000 kg N ha⁻¹ was observed, with total NO₃⁻ leaching loss of 139.0 kg N ha⁻¹ yr⁻¹ with the treatment receiving DCD at a rate of 10 kg ha⁻¹. When urine was applied in autumn without DCD, total NO₃⁻ leaching loss was equivalent to 516 kg N ha⁻¹ yr⁻¹, with the application of DCD (15 kg ha⁻¹) this was reduced to 128 kg N ha⁻¹ yr⁻¹, this is equivalent to a reduction of 76% in autumn applied urine (Di and Cameron, 2002). Dennis et al. (2012) reported that DCD applied to Irish soil reduced NO₃⁻ leaching by an average of 45%, however this may be a significant reduction, it is not as impressive as reductions observed in New Zealand (Di and Cameron, 2002). Shepherd et al. (2010) observed that DCD was effective in reducing NO₃⁻ leaching from silt (61%) and clay (36%) but not from sand soils, this suggests that soil-type is also a factor in affecting the effectiveness of DCD reducing NO₃⁻ leaching. Monagahan et al. (2009) observed that the application of DCD reduced NO₃⁻ losses in drainage by between 21 and 56%. Zaman et al. (2010) and Cookson and Cornforth (2002) reported that the application of DCD to soils with and without urine did not reduce the amount of NO₃⁻ being leached.

## Nitrous oxide emissions

On average, the application of DCD to soils reduced total N₂O emissions from the urine by 70%, from 9.2 kg N₂O-N ha⁻¹ without DCD to 2.8 kg N₂O-N ha⁻¹ (Di et al., 2007). Di and Cameron (2003) reported that the use of DCD can have a positive effect on reducing N₂O emissions by treating grazed pasture soil, including animal urine patch areas, the N₂O flux can be reduced by 76% for the autumn urine application and by 78% for the spring urine application.

The average amount of N₂O emitted (5.1 kg N₂O-N ha⁻¹) after DCD application was only slightly above background emissions (1.4 kg N₂O-N ha⁻¹), measured in New Zealand, where no N fertiliser or urine was applied in clover based pastures. Qui et al. (2010) reported that the application of DCD at a rate of 10 kg ha⁻¹ to urine patch receiving 1000 kg N ha⁻¹ reduced N₂O emissions in the winter season by 69% and by 40% reduction in N₂O emissions were achieved during the summer measurement period. Selbie et al. (2011) reported that the application of DCD at a rate of 15 kg ha⁻¹ to urine patch receiving 1000 kg N ha⁻¹ reduced N₂O emissions in the winter season by 70% on Irish soils. Zaman et al. (2009) observed that urine applied with DCD reduced N₂O emissions, DCD does not reduce nitrification indefinitely, and it is dependent on soil moisture, temperature, microbial activity and pH. These results suggest that treating urine patches with DCD in dairy pastures provides an effectiveness method of mitigating N₂O emissions in agricultural systems.

## Using the animal to apply DCD

Ledgard et al. (2008) successfully infused DCD into the rumen of sheep and 86% of DCD was recovered in the excreted urine. O'Connor et al. (2013b) infused DCD into the rumen of dairy cows and found that 82.3% was recovered in urine and 2.1% in faeces. When the urine collected from the cows infused with DCD into the rumen was applied to lysimeters N₂O

<figure><img src="image_42.png" /></figure>
emissions on were reduced by 84 and 57% on a free draining soil type and a on a poorly drained soil, respectively, compared to applying urine from non-treated cows (O'Connor, 2012). Similarly, NO₃⁻ leaching was reduced by 91 and 86% on a free draining soil type and a on a poorly drained soil, respectively, when the urine collected from the cows infused with DCD into the rumen was applied to lysimeters compared to applying urine from non-treated cows (O'Connor, 2012). Ledgard et al. (2012) examined the possibility of application of DCD in water troughs for consumption by dairy cows. The authors reported a 44% reduction in N₂O emissions and 40% reduction in nitrate leaching from grazed grassland when DCD was added to dairy cows drinking water.

## Economic benefit

This product is not commercially available in Europe but with the expected varied agronomic response, the expected economic benefit of applying DCD would be limited on farm. In comparison to the purchase of chemical nitrogen at €1.05/kg of N and 15 kg of herbage DM per kg of N versus €5/kg of DCD or €75/ha, there would have to be an agronomic response of 1,125 kg DM/ha associated with the DCD to justify using it over chemical nitrogen.

## Human Health

A recent food scare in relation to the widespread use of DCD in New Zealand has resulted in the banning of its use on commercial farms. Traces of DCD were found in whole milk powder in China. The median lethal dose of a substance (LD50) is used to determine the quantity of the substance that is likely to cause death to 50% of the population (Barth et al., 2002). Amberger (1989) reported that DCD had an LD50 of 10g/kg liveweight and DCD is classed as a non-toxic substance.

## References

* Amberger, A. (1989). Research on dicyandiamide as a nitrification inhibitor and future outlook. Communications in Soil Science and Plant Analysis. 20, 1933 - 1955.

* Barth, R.C., George, P.D., Hill, R.H. and American Industrial Hygiene, A. (2002). Environmental health and safety for hazardous waste sites. American Industrial Hygiene Association, Fairfax, Va.

* Carey, P.L., Jiang, S., Roberts, A.H., 2012. Pasture dry matter responses to the use of a nitrification inhibitor: a national series of New Zealand farm trials. New Zealand Journal of Agricultural Research 55, 63-72.

* Cookson, W.R. and Cornforth, I.S. (2002). Dicyandiamide slows nitrification in dairy cattle urine patches: effects on soil solution composition, soil pH and pasture yield. Soil Biology and Biochemistry. 34, 1461-1465.

* Dennis, S.J., Cameron, K.C., Di, H.J., Moir, J. and Richards, K. (2010). Dicyandiamide (DCD) reduces nitrate losses from Irish soils. In Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, International Union of Soil Sciences: Brisbane, Australia. pp. 42-45.

* Di, H.J. and Cameron, K.C. (2002). The use of a nitrification inhibitor, dicyandiamide (DCD), to decrease nitrate leaching and nitrous oxide emissions in simulated grazed and irrigated grassland. Soil Use and Management. 18, 395-403.

* Di, H.J. and Cameron, K.C. (2003). Mitigation of nitrous oxide emissions in spray-irrigated grazed grassland by treating the soil with dicyandiamide, a nitrification inhibitor. Soil Use and Management. 19, 284-290.

* Di, H.J. and Cameron, K.C. (2005). Reducing environmental impacts of agriculture by using a fine particle suspension nitrification inhibitor to decrease nitrate leaching from grazed pastures. Agriculture Ecosystems and Environment. 109, 202-212.

* Di, H.J. and Cameron, K.C. (2007). Nitrate leaching losses and pasture yields as affected by different rates of animal urine nitrogen returns and application of a nitrification inhibitor—a lysimeter study. Nutrient Cycling in Agroecosystems. 79, 281-290.

* Di, H.J., Cameron, K.C. and Sherlock, R.R. (2007). Comparison of the effectiveness of a nitrification inhibitor, dicyandiamide, in reducing nitrous oxide emissions in four different soils under different climatic and management conditions. Soil Use and Management. 23, 1-9.
Dennis, S.J., Richards, K., Cameron, K.C., Di, H.J., Moir, J.L., Staples, V. and Sills, P. (2012). Reducing nitrate losses from simulated grazing on grassland lysimeters in Ireland using a nitrification inhibitor (Dicyandiamide). *Biology and Environment: Proceedings of the Royal Irish Academy.* 112b.

Irigoyen, I., Muro, J., Azpilikueta, M., Aparicio-Tejo, P. and Lamsfus, C. (2003). Ammonium oxidation kinetics in the presence of nitrification inhibitors DCD and DMPP at various temperatures. *Australian Journal of Soil Research.* 41, 1177-1183.

Legard, S.F., Welten, B.G. and Luo, J. (2012) Animal delivery of the nitrification inhibitor DCD as a new effective method for reducing nitrogen losses from grazed pastures. In: *Proceedings of the 17th IOnternational Nitrogen Workshop 'Innovations for sustainable use of nitrogen resources', 26th – 29th June 2012, Wexford, Ireland*. p. 272-273.

Ledgard, S.F., Menneer, J. C., Dexter, M. M., Kear, M. J., Lindsey, S., Peters, J. S. and Pacheco, D. (2008). A novel concept to reduce nitrogen losses from grazed pastures by administering soil nitrogen process inhibitors to ruminant animals: A study with sheep. *Agriculture, Ecosystems & Environment.* 125, 148-158.

Merino, P., Estavillo, J.M., Graciolli, L.A., Pinto, M., Lacuesta, M., Muñoz-Rueda, A. and Gonzalez-Murua, C. (2002). Mitigation of N₂O emissions from grassland by nitrification inhibitor and Actilith F2 applied with fertiliser and cattle slurry. *Soil Use and Management.* 18, 135-141.

Moir, J.L., Malcolm, B.J., Cameron, K.C. and Di, H.J. (2012). The effect of dicyandiamide on pasture nitrate concentration, yield and N offtake under high N loading in winter and spring. *Grass and Forage Science*. pp.1-12.

Monaghan, R.M., Smith, L.C. and Ledgard, S.F. (2009). The effectiveness of a granular formulation of dicyandiamide (DCD) in limiting nitrate leaching from a grazed dairy pasture. *New Zealand Journal of Agricultural Research.* 52, 145-159

O'Connor, P.J., M.B. Lynch, E. Cahalan, M. O'Donovan and D. Hennessy. (2013a) The effect of the nitrification inhibitor dicyandiamide (DCD) on spring and annual herbage production in urine patches when applied in late summer or early autumn. *Grass and Forage Science* http://onlinelibrary.wiley.com/doi/10.1111/gfs.12011/pdf

O'Connor, P.J., D. Hennessy, M.B. Lynch, H. Slattery, E. Lewis. (2013b) The effect of pulse-dosing non-lactating dairy cows with the nitrification inhibitor dicyandiamide (DCD): the recovery of DCD and DCD effects on blood metabolites, rumen function and diet digestibility. *Livestock Science.* 155: 30-37.

O'Connor, P.J., Hennessy, D., Brophy, C., O'Donovan, M. and Lynch, M.B. (2012) The effect of the nitrification inhibitor dicyandiamide (DCD) on herbage production when applied at different times and rates in the autumn and winter. *Agriculture, Ecosystems and Environment*, 152: 79-89

O'Connor, P.J. (2012) An investigation into the agronomic and environmental benefits of applying the nitrification inhibitor dicyandiamide (DCD) on Irish grasslands as fine particle suspension (FPS) or excreted with urine from non-lactating dairy cows. PhD. Thesis, University College Dublin, Ireland. 193 pages.

Qiu, W., Di, H.J., Cameron, K.C. and Hu, C. (2010). Nitrous oxide emissions from animal urine as affected by season and a nitrification inhibitor dicyandiamide. *Journal of Soils and Sediments*. 10, 1229-1235.

Selbie, D.R., Lanigan, G., Di, H.J., Moir, J.L., Cameron, K.C. and Richards, K.G. (2011). Improving nitrogen efficiency using a nitrification inhibitor on urine-affected soil - a grassland lysimeter study. In: *Proceedings of the Agricultural Research Forum. Tullamore, Co. Offaly*. p. 2.

Shepherd, M., Wyatt, J., Welton, B. and Ledgard, S. (2010). Forms of nitrogen leaching from dairy cow urine and effectiveness of dicyandiamide: not all soils are equal. In: *19th World Congress of Soil Science, Soil Solutions for a Changing World*. R.J. Gikes, and Prakongkep, N., (Eds). *International Union of Soil Sciences: Brisbane, Australia*. p. 32-35.

Singh, J., Saggar, S. and Bolan, N.S. (2009). Influence of dicyandiamide on nitrogen transformation and losses in cow-urine-amended soil cores from grazed pasture. *Animal Production Science*. 49, 253-261.

Zaman, M., Saggar, S., Blennerhassett, J. D. and Singh, J. (2009). Effect of urease and nitrification inhibitors on N transformation, gaseous emissions of ammonia and nitrous oxide, pasture yield and N uptake in grazed pasture system. *Soil Biology and Biochemistry*. 41, 1270-1280.

Zaman, M. and Blennerhassett, J.D. (2010). Effects of the different rates of urease and nitrification inhibitors on gaseous emissions of ammonia and nitrous oxide, nitrate leaching and pasture production from urine patches in an intensive grazed pasture system. *Agriculture, Ecosystems and Environment*. 136, 236-246.
# More legumes in grass swards

For farm modelling in the Animal Change project, Component 3
Authors: Laurence Shalloo (TEAGASC)

## Introduction

White clover (Trifolium repens L.) is the most important pasture legume in temperate regions of the world (Frame and Newbould 1986; Whitehead 1995) because of its wide climatic range, high nutritional quality and digestibility, and the significant contribution it makes to a perennial ryegrass white clover pasture through the fixation of atmospheric nitrogen (N). It grows well in association with perennial ryegrass. It is tolerant of frequent defoliation and grazing as its growing point is at or just below ground level. In recent years there has been renewed interest in white clover use on farms because of the cost/price squeeze at farm level, and increased importance of environmental sustainability at farm level.

## Herbage production

White clover can play a role in the development of grass based milk systems by reducing the costs of N fertiliser and to some extent flattening the curve of annual DM herbage production (Frame and Newbould, 1986; Riberio Filho et al., 2003). Productivity of white clover in mixed pastures with perennial ryegrass is influenced by a number of climatic, soil and management factors. Within management factors N fertiliser use and the control of sward height by grazing livestock are two vital factors (Ledgard et al., 1995). The proportion of white clover in a sward is generally highest in the summer and lowest in the winter. White clover and perennial ryegrass have different temperature responses and different seasonal growth patterns (Davies, 1992). As for most plants, white clover growth and plant morphology are influenced by cultivar, climate, soil type, companion species, radiation, day length, temperature, soil water, farm management practices (rotational grazing or set stocking, fertiliser regimes, etc.) and grazing effects (Frame and Newbould, 1986; Harris, 1994). White clover has a lower growth rate than perennial ryegrass at temperatures below 10°C, but its growth rate continues to increase up to 24°C, whereas perennial ryegrass peaks at 15 – 20°C (Davies, 1992; Frame and Newbould, 1986). Growth rates of perennial ryegrass peak in mid-summer in temperate regions and subsequently decline, while white clover growth rate reaches a maximum in late summer, coinciding with the reduction in perennial ryegrass growth (Davies, 1992). This results in a considerable increase in the proportion of clover in the harvested DM material (Davies, 1992) at this time. As a consequence of this low clover growth before mid-summer, the strategic use of N fertiliser on grass clover swards in the spring is commonly practiced to increase total herbage yields in spring, compared with swards reliant solely on N fixation (Humphreys et al., 2009). However, this N fertiliser application may reduce sward clover content if correct grazing management techniques are not practiced. White clover content reduction in mixed swards is usually more severe when high herbage masses are allowed to accumulate (Laidlaw et al., 1992). Harris and Clark (1996) have shown that frequent and tight grazing can enhance white clover content and yield in mixed swards.

White clover monocultures can grow up to 12 t DM/ha in temperate regions; however, clover monocultures are unable to supply the energy requirements of milk solids production in intensive dairy systems and those yields are highly unrealistic in intensive grazing systems. Greater yields are obtained from mixed perennial ryegrass and white clover swards, and even these can fail to meet the energy requirements of lactating dairy cows, especially in early spring and autumn, and so require N fertiliser application and concentrate supplementation.

<figure><img src="image_43.png" /></figure>
## Nitrogen saving versus increased stock carrying capacity

Many studies (Harris et al., 1996; Clark and Harris, 1996) have found that high N fertiliser application levels can reduce sward clover content. Perennial ryegrass is more efficient at taking up N applied as fertiliser than white clover and as a result perennial ryegrass will grow at a faster rate than the clover and the competition between species particularly for light, results in shading of the clover and may also result in water and nutrient stress (Blackman and Black, 1959). Over a three year period Ledgard et al. (1995) found that N fertiliser application reduced the proportion of white clover in the sward by as much as 30%. Ledgard et al. (1995) and Harris et al. (1996) found that white clover content was not affected by N fertiliser application during the spring and summer, suggesting that N fertiliser application did not have an effect on white clover content of the swards.

## Environmental impact

Artificial fertiliser N requires a considerable amount of energy for its manufacture and use, which has an environmental consequence. Increased use of forage legumes offers some potential to mitigate these effects. The substitution of forage legumes for inorganic N fertiliser will reduce the quantities of non-renewable resources required to manufacture and distribute fertiliser (Rochon et al., 2004).

### N Leaching

Overall N leached from pasture is related to level and intensity of the defoliation activities placed on the sward and whether they are cut or grazed (Wachendorf et al., 2004). Generally, in swards that are fertilised or where there is a grass clover component to the sward there are significant N surpluses. The level of leaching depends on the soil type and level of surplus organic N built up in the soil. However, the level of surplus N is only an indicator of the N leached from the sward with leaching significantly less than the surplus N. A number of studies have shown relatively low rates of nitrate leaching from beneath legume swards; however the evidence is not conclusive (Owens et al., 1994; Fillery 2001). Some studies comparing clover/grass and N fertilised grass only pastures at similar levels of total N input have indicated similar levels of nitrate leaching losses (Ledgard et al., 2009).

### Nitrous oxide

Nitrous oxide (N₂O) emissions from grazed pastures due to N cycling from excreta are similar for clover/grass and N fertilised grass-only pastures at similar total N inputs (Ledgard et al., 2009). However, when artificial N is applied to grassland N loss in the form of N₂O emissions from surplus N, and so higher N₂O losses are associated with artificial fertiliser application in grass based systems compared to systems relying on N fixation from clover. The N₂O losses reported from grazing dairy systems in New Zealand and Australia range between 6 to 11 kg N₂O -N per hectare per year (Luo et al., 2008). It would be expected that at comparable levels of production it is likely that N₂O emissions resulting from N cycling from animal excreta would be similar for both clover/grass and grass pasture, however additional losses N losses associated with the fertiliser would be expected where fertiliser was being applied =. In Australia the N₂O losses increased from 6 kg/ha/year to 13 and 15kg/ha/year when approximately 200kg of chemical N was applied in differing formulations. In New Zealand the losses increased from 3.4 kgN/ha to 19.3 kg/ha when there was 400 kg of N fertiliser applied (Ruz Jerez, White and Ball, 1994). The level of loss is multiplied when the soil becomes compacted and there is a build-up of moisture in the soil. There is very poor information available on the actual N losses associated with N₂O in the form of clover N or artificial N in the literature.

<figure><img src="image_44.png" /></figure>
## Methane

Enteric CH₄ fermentation (hereafter referred to as CH₄ emissions) accounts for up to 49% of the total greenhouse gas (GHG) emissions (Casey and Holden, 2005). Previous studies have reported that net CH₄ emissions can be reduced on a per product basis if animals can be more efficient (Eckard et al., 2010; Buddle et al., 2011), e.g. achieving greater output for same feed intake. Methane emissions related to gross energy intake of animals fed with legumes are lower than animals fed with grasses (Waghorn et al., 2006; Beauchemin et al., 2008). However, while some work has shown that clover inclusion into pasture can reduce dairy cow CH₄ emissions (Lee et al., 2004) others have found no effects (Van Dorland et al., 2007). In an Irish study, it was shown that cows grazing grass clover swards produced 3.0 g CH₄/kg dry matter intake (DMI) less than the cows grazing perennial ryegrass swards (Enriquez-Hildalgo et al., 2013).

## Clover and the interaction with the rumen

White clover has a higher nutritive value than perennial ryegrass due to structural carbohydrate content, higher digestible protein and a faster rate of passage through the rumen. These qualities can result in higher herbage DMI and subsequently increased milk production compared to pure perennial ryegrass swards (Harris et al, 1997; Woodfield and Clark, 2009). White clover has a higher nutritive value per unit DM than that of perennial ryegrass (Clark and Harris, 1996). For grazing animals, white clover compares favourably with grass, being high in protein and minerals and low in structural fibre, and unlike grass it maintains a high level of digestibility as it matures (less stemy material) (Davies, 1992). The cell wall content of white clover is approximately half that of perennial ryegrass of similar digestibility, and the principle difference between perennial ryegrass and white clover is that the hemi-cellouse is a fraction of the cell wall (Thomson, 1984). Research has shown a benefit of white clover over perennial ryegrass swards for milk production, particularly in the second half of lactation (Riberio Filho et al., 2003; Harris et al., 1998). This increase in milk production is due to a combination of both feed quality and intake factors (Harris et al., 1998; Clark and Harris, 1996). The superiority of white clover over perennial ryegrass for milk production occurs under ad libitum and restricted levels of feeding (Clark and Harris, 1996). Harris et al. (1997) found that cows fed diets containing 65% clover had milk yields equivalent to 98% of the milk yield produced by cows fed 100% clover. With the high nutritive value of perennial ryegrass during the spring and early summer along with high growth rates (Harris et al., 1997; Brereton, 1995) the benefit from increased white clover content during spring and early summer is likely to be relatively small (Harris et al., 1997). The lower structural fibre/cell wall of white clover seems to be a main reason for the higher DMI relative to grass when feeding with white clover (Davies, 1992). Thus in a grazing situation increasing clover content in the diet may result in increased DMI in the second half of lactation. Cows grazing white clover spend less time ruminating (Harris et al., 1998) due to clovers lower retention time in the rumen, lower bulk density, and the relatively higher amount of herbage that enters the small intestine, and can therefore graze for a larger proportion of the day (Clark and Harris, 1996; Harris et al., 1998). White clover in the sward can result in a higher rate of passage through the rumen; this faster rate of passage is explained by the lower structural fibre/cell wall and lower resistance to mechanical breakdown by chewing. Some studies (Harris et al., 1998; Riberio Filho et al., 2003) have shown that cows grazing white clover swards have higher voluntary DMIs than those on perennial ryegrass only swards, although the difference in intake between the two diets varies, depending on stage of lactation and the energy requirements of the animal.

## Economic benefit

Chemical fertiliser N costs have increased dramatically over the past number of years driven by increases in demand and increased energy costs. Including clover has the potential to

<figure><img src="image_45.png" /></figure>
allow an increase in stock carrying capacity and/or reduced use of chemical N fertiliser. Recent research showing the potential for clover when managed in combination with chemical N at higher stocking rates indicates that there is significant potential for clover to act in combination with chemical N fertiliser to increase the profitability of grass based ruminant production systems.

## References

Beauchemin, K. A., M. Kreuzer, F. O'Mara, and T. A. McAllister. 2008. Nutritional management for enteric methane abatement: a review. Aust. J. Exp. Agr. 48(2):21-27.

Buddle, B. M., M. Denis, G. T. Attwood, E. Altermann, P. H. Janssen, R. S. Ronimus, C. S. Pinares-Patiño, S. Muetzel, and D. Neil Wedlock. 2011. Strategies to reduce methane emissions from farmed ruminants grazing on pasture. Vet. J. 188(1):11-17.

Casey, J. W. and N. M. Holden. 2005. Analysis of greenhouse gas emissions from the average Irish milk production system. Agric. Syst. 86(1):97-114.

Davies D.A. 1992. Potential of white clover in the less favoured areas of the United Kingdom. Herba, 5, 52-55

Enriquez-Hidalgo D., Gilliland T., O'Donovan M., Deighton M.H., Hennessy D. 2013. The effect of grass clover swards on herbage production, herbage intake, methane emissions and milk production of dairy cows. Journal of Dairy Science (Submitted)

Eckard, R. J., C. Grainger, and C. A. M. de Klein. 2010. Options for the abatement of methane and nitrous oxide from ruminant production: A review. Livest. Sci. 130(1-3):47-56.

Fillery I.R.P. 2001. The fate of biologically fixed nitrogen in legume based dryland farming systems: a review. Australian Journal of Experimental Agriculture, 41, 361-3811

Frame, J. and P. Newbould. 1986. Agronomy of white clover. Adv. Agron. 40(1):88.

Garwood, E. A. 1969. Seasonal tiller populations of grass and grass/clover swards with and without irrigation. Grass Forage Sci. 24(4):333-343.

Harris, S. L. and D. A. Clark. 1996. Effect of high rates of nitrogen fertiliser on white clover growth, morphology, and nitrogen fixation activity in grazed dairy pasture in northern New Zealand. N. Z. J. Agric. Res. 39(1):149-158.

Harris, S. L., D. A. Clark, M. J. Auldist, C. D. Waugh, and P. G. Laboyrie. 1997. Optimum white clover content for dairy pastures. Proc. N. Z. Grassl. Assoc. 59:29-33.

Harris, S. L. 1994. White clover growth and morphology in dairy pasture in the Waikato region of northern New Zealand. N. Z. J. Agric. Res. 37(4):487-494.

Humphreys, J., I. A. Casey, and A. S. Laidlaw. 2009. Comparison of milk production from clover-based and fertilizer-N-based grassland on a clay-loam soil under moist temperate climatic conditions. Ir. J. Agric. Food Res. 48(2):189-207.

Laidlaw, A. S., N. G. Teuber, and J. A. Withers. 1992. Out-of-season management of grass clover swards to manipulate clover content. Grass Forage Sci. 47(3):220-229.

Ledgard, S. F., M. S. Sprosen, K. W. Steele, and C. P. West. 1995. Productivity of white clover cultivars under intensive grazing, as affected by high nitrogen fertiliser application. N. Z. J. Agric. Res. 38(4):473-482.

Ledgard S., Schils R., Eriksen J., Luo J. 2009. Environmental impacts of grazed clover/pastures. Irish Journal of Agricultural and Food Research 48: 209-226.

Lee, J. M., S. L. Woodward, G. C. Waghorn, and D. A. Clark. 2004. Methane emissions by dairy cows fed increasing proportions of white clover (Trifolium repens) in pasture. Pages 151-155 in Proc. N. Z. Grassl. Assoc.

Luo J., Ledgard S.F., De Klein C.A.M., Lindsey S.B. and Kear M. 2008. Effects of dairy farming intensification on nitrous oxide emissions. Plant and Soil 309: 227-237

Owens L.B., Edwards W.B., and Vankeuren R.W. 1994. Groundwater nitrate levels under fertilised grass and grass-legume pastures. Journal of Environmental Quality, 23, 752-758.

Ribeiro Filho, H. M. N., R. Delagarde, and J. L. Peyraud. 2003. Inclusion of white clover in strip-grazed perennial ryegrass swards: herbage intake and milk yield of dairy cows at different ages of sward regrowth. Anim. Sci. 77:499-510.

Rochon J.J., Doyle C.J., Greef J.M., Hopkins A., Molle G., Sitzia M., Scholefield D. and Smith C.J. 2004. Grazing legumes in Europe: a review of their status, management, benefits, research needs and future prospects. Grass and Forage Science, 59, 197-214.

<figure><img src="image_46.png" /></figure>
Ruz-Jerez B.E., White R.E. and Ball P.R. 1994. Long-term measurement of denitrification in three contrasting pastures grazed by sheep. Soil, Biology and Biochemistry 26: 29-39.

Thomson, D. J. 1984. The nutritive value of white clover. Pages 78-92 in Forage legumes. Ocassional Symposium of the British Grasland Society. Vol. 16. D. J.

Van Dorland, H., H. R. Wettstein, H. Leuenberger, and M. Kreuzer. 2007. Effect of supplementation of fresh and ensiled clovers to ryegrass on nitrogen loss and methane emission of dairy cows. Livest. Sci. 111(1):57-69.

Wachendorf M., Buchter M., Trott H. and Taube F. 2004. Performance and environmental effects of forage production on sandy soils.II. Impact of defoliation system and nitrogen input on nitrate leaching losses. Grass and Forage Science 59: 56-68.

Waghorn, G. C., S. L. Woodward, M. Tavendale, and D. A. Clark. 2006. Inconsistencies in rumen methane production - effects of forage composition and animal genotype. Int. Congr. Ser. 1293:115-118.

Whitehead, D. C. 1995. Grassland nitrogen. CAB international.

Woodfield D.R. and Clark D.A. 2009. Do forage legumes have a role in modern dairy farming systems? Irish Journal of Agricultural and Food Research 48: 137-147.

# Feeding more maize and less grass

*For farm modelling in the Animal Change project, Component 3*

Authors: Lisbeth Mogensen, Peter Lund, Maike Brask (AU, Denmark)

## Objective

Increase the part of area dedicated to maize production and decrease that to grass on farm and at the same time feed more maize and less grass.

(It was originally suggested that DM intake from maize:grass silage should be I) 50:50 and II) 25:75 respectively).

This option can change many GHG emissions: enteric CH$_4$, manure CH$_4$, N$_2$O emissions, ammonia emissions, C sequestration, CO$_2$ fossil source.

## Assumption for farm modelling

* Maintain constant the area dedicated to roughage production.

* Adapt stocking rate (and thereby milk/meat production) according to the change in the forage production and quality.

(This measure will only be about maize silage, not silage from wheat or barley). For farms where maize is not economic to grow, we don't model this measure.

## Input variables/parameters affected

* Change in land use to obtain higher DM production from maize and lower from grass silage

* Diet changed (decreased grass silage, increased maize silage and type of concentrates changes (for example cereals to soy bean/rape seed cake to maintain crude protein content in the ration))

* Mineral fertilization and manure production changes will automatically be calculated by Farm AC, when area grown with grass and maize as well as the feed ration are changed

<figure><img src="image_47.png" /></figure>
* Milk production and composition are as a starting point assumed to be unchanged or alternatively, can be adjusted by the local expect according to what is expected due to the chances in nutrients caused by change in the feed ration

* Average selling price of milk are assumed to be unchanged

* Time spent indoor are assumed to be unchanged as the same total amount of roughage and grass grazed is used in the feed ration

## Major changes in GHG emissions

### Enteric CH₄

Enteric methane is produced when especially carbohydrates but also amino acids and glycerol are digested in the rumen. Amount of methane produced can be manipulated by changes in the feed composition. Starch causes a lower methane production than sugar, whereas it is difficult to show differences in methane production depending on type of starch, even though degradation and rumen digestibility differ. Starch from roughage also causes a reduced methane production, where maize silage causes a lower methane production than grass silage (Johannes et al., 2011).

In a newly conducted Danish study, Brask et al. (2013) compares three different silages in a 6x4 incomplete Latin square also including the effect of adding supplement with or without cracked rapeseed (extra fat):

* EG: Early grass clover, harvested 26th may (329 g NDF/kg DM)

* LG: Late grass-clover, harvested 15th of June (484 g NDF/kg DM)

* MS: Maize silage (390 g NDF/kg DM).

The TMRs contain 64% forage (of DM) from one of these silages.

As can be seen in Table 1, a late cut of grass and a higher content of fiber and lower digestibility, result in a higher CH₄ production. Whereas for early cut of grass, and maize silage CH₄ production was reduced by 9 and 17% respectively compared to late cut grass silage. This indicates that highly-digestible grass silage can be as favourable as maize silage for reducing CH₄ emission.

<table><caption>Table 1. GHG from enteric fermentation of grass versus maize silage in TMRs contain 64% forage (of DM) from one of these silages (Johannes et al., 2011).</caption><thead><tr><th></th><th>EG</th><th>LG</th><th>MS</th></tr></thead><tbody><tr><td>DM intake, kg/d</td><td>17.6</td><td>16.0</td><td>17.6</td></tr><tr><td>ECM, kg/d</td><td>24.8</td><td>21.8</td><td>26.0</td></tr><tr><td>CH₄, L/d</td><td>539</td><td>542</td><td>495</td></tr><tr><td>CH₄, L/kg total DM intake</td><td>29.0</td><td>31.8</td><td>26.5</td></tr><tr><td>CH₄, l/kg ECM</td><td>21.7</td><td>24.9</td><td>19.0</td></tr><tr><td>Relative CH₄/kg DMI</td><td>91</td><td>100</td><td>83</td></tr><tr><td>Energy loss as CH₄, % of GE intake</td><td>6.1</td><td>6.7</td><td>5.4</td></tr></tbody></table>

However, some earlier studies (McCourt et al., 2007; Doreau et al., 2011) found that grass hay or silage and maize silage did not differ in CH₄ production, but Staerfl et al. (2012) found
in agreement with the Danish study a lower CH₄ production per kg DMI in fattening bulls receiving maize silage compared with grass silage (cf. Brask *et al.*, 2013)

### 4.2.6.2. C sequestration

Enhancing carbon (C) sequestration in soil is a way to reduce GHG emissions and increase soil fertility. C sequestration happens when we add biomass, however the carbon will not stay in the soil forever. Amount of C in the soil depends on the balance between C input and C output. C sequestration is stimulated by crop residues left in soil, use of manure, especially deep litter, perennial grass and catch crops.

However, the size of the sequestration potential is debatable. As a first estimate, GHG contribution from C sequestration was calculated according to Vleeshouwers & Verhagen (2002). Later Farm AC will be updated to account for changes in soil carbon taking into account results from Animal Change.

Vleeshouwers & Verhagen (2002) assume that growing grass works as a sink for C, whereas growing other crops is causing a release of C from soil. Carbon sequestration in grassland crops were estimated to 0.52 Mg C/ha/year (191 g CO₂/m²/year), whereas growing other crops is assumed to cause release of C from soil corresponding to 0.84 Mg C/ha/year (308 g CO₂/m²/year) (Vleeshouwers & Verhagen, 2002).

Table 2 shows contribution from soil C (based on Vleeshouwers & Verhagen, 2002) using crop productivity for growing grass and maize under Danish condition (Mogensen *et al.*, 2011).

<table><caption>Table 2. GHG from changes in soil C from grass versus maize silage production</caption><thead><tr><th></th><th>Grass-clover silage</th><th>Maize silage</th></tr></thead><tbody><tr><td>Crop yield, kg DM/ha</td><td>8300</td><td>11300</td></tr><tr><td>Manure input, kg N/ha</td><td>170</td><td>170</td></tr><tr><td>Fertilizer input, kg N/ha</td><td>118</td><td>39</td></tr><tr><td>Carbon foot print from growing, g CO₂/kg DM</td><td>389</td><td>210</td></tr><tr><td>Contribution from soil C <sup>1)</sup> g CO₂/kg DM</td><td><strong>-231</strong></td><td><strong>272</strong></td></tr><tr><td>Total CF including soil C, g CO₂/kg DM</td><td>158</td><td>482</td></tr></tbody></table>

### Change in manure CH₄, N₂O emissions, ammonia emissions, CO₂ fossil source

The changes will be calculated by the Farm AC model when the above changes in input variable has been done

### Combined effect on GHG emissions of grass versus maize silage in the feed ration

Under Danish condition, GHG contribution from growing 1 kg DM of maize silage is lower than from growing 1 kg DM of grass-clover silage (Mogensen *et al.* 2011, Table 1, before taking into account soil C) (this will be calculated by Farm AC). Furthermore, CH₄ from enteric fermentation of maize silage is 8% lower than from grass clover silage assuming an average digestibility of grass silage (Table 1).

<footnote n="1">¹) based on Vleeshouwers & Verhagen, 2002: positive number means C sequestration whereas negative number means C release</footnote>
<figure><img src="image_48.png" /><figcaption>Figure 1. GHG from feed and CH₄ enteric, kg CO₂-eq/kg ECM (Mogensen et al. 2011)</figcaption></figure>

However, if taking into account contribution from soil carbon as suggested in Table 2 this will more than set off the positive effects on GHG of growing and feeding maize silage, thereby the ration with grass-clover silage will have the lowest GHG emission. As can be seen in Figure 1, the maize silage ration ends up causing 16% higher GHG emissions per kg ECM (including GHG from growing the feed and enteric fermentation of the feed).

Finally, it has been shown that reduced enteric CH₄ production can increase the methanogenic potential of the slurry (Külling et al., 2001) and a study with fecal samples from the Danish study by Brask et al. (2013) conducted by Møller (2012) showed that this valid. Therefore, reducing CH₄ emission by reducing digestibility can be counteracted by increased fermentation in the slurry, which is only favourable if the slurry is used for biogas (Brask et al., 2013).

Therefore, the effect of maize versus grass as a mitigation option is highly depended on the assumptions, the boundaries of the study and the method used to include contribution from soil C. But also the quality of the grass clover silage and the regional relation between crop yields in grass and maize will be very important for which effect should be assumed..

## References

Brask, M., Lund, P., Hellwing, A.L.F., Poulsen, M., Weisbjerg, M. 2013. Enteric methane production, digestibility and rumen fermentation in dairy cows fed different forages with and without rapeseed fat supplementation. (Submitted).

Doreau, M., Werf, H.M.G. van der, Micol, D., Dubroeucq, H. Agabriel, J., Rochette, Y., Martin, C. 2011. Enteric methane production and GHG balance of diets differing in concentrate in the fattening phase of a beef production system. J. Anim. Sci. 89. 2518-2528.

Johannes, M., Lund, P., Hellwing, A.L.F., Weisbjerg, M.R. 2011. Hvad betyder fodring for metanemission?. In. Kvæg og klima. Videnskabelig rapport nr. 1., DCA, Danish Center for Food and Agriculture, Aarhus University. 53-63. (In Danish)

Külling, D.R., Dohme, F., Menzi, H., Sutter, F., Lischer, P., Kreuzer, M. 2002. Methane emissions of differently fed dairy cows and corresponding methane and nitrogen emissions from their manure during storage. Environ. Mon. Asses. 79, 129-150.

McCourt, A.R., Yan, T., Mayne, C.S. 2007. Effect of forage type on methane production from dairy cows. Proc. Br. Soc. Anim. Sci. BSAS, Penicuik, p 48.

Mogensen, L., Kristensen, T., Nguyen, T.L.T., Knudsen, M.T. 2011. Udledning af klimagasser fra dyrkning, forarbejdning og transport af foder. In (Kristensen, T. & Lund, P., red.). Kvæg og klima. Videnskabelig rapport nr. 1., DCA, Danish Center for Food and Agriculture, Aarhus University. P. 73-90. (In Danish)
Møller, H.B. 2013. Nye fodringsstrategier giver mere biogas. (In Danish).

Staerfl., S.M., Zeitz, J.O., Kreuzer, M., Solvia, C.R. 2012. Methane conversion rate of bulls fattened on grass or maize silage as compared with IPCC default values, and the long-term methane mitigation efficiency of adding acacia tannin, garlic, maca and lupine. Agric. Ecosyst. Environ. 148, 111-120.

Vleeshouwers, L.M., Verhagen, A., 2002. Carbon emission and sequestration by agricultural land use: a model study for Europe. Global Change Biologi. 8, 519-530.

# Feeding more fat with ruminants

For farm modelling in the Animal Change project, Component 3

Authors: P. Faverdin (INRA PEGASE), M. Doreau (INRA UMRH)

## Objective

Reduce CH₄ emissions due to rumen fermentation by the addition of lipids in the diet.

## Assumptions for farm modelling

* The measure is only applied to ruminants.

* The animal performance are changed, which has to be considered.

* Diet formulation increases the proportion of unsaturated fat as a substitute to other sources of energy with an appropriate choice of compounds to reach a 5% lipids content in the diet.

* Consider if the distribution of these new compounds is possible for each specific farming system. Generally, distribution of concentrates should exist in the reference situation.

* Formulation is based on local circumstances and available feedstuffs.

* CH₄ emissions from manure are assumed to be constant.

## Further guidance for the farm experts

* Formulation:

* If the supplement of concentrate with the fatty acid content of the diet is less than 5% replace conventional concentrated concentrate enriched oils to a value of 5% of fatty acids in the diet.

* With the formulation established for concentrated food, calculate the change in quantities of grain and meal.

* Calculate the fatty acid supplement versus conventional operation, and after the mitigation of methane on the basis of 4% reduction (mean value) for 1% added fat. The lower limit and upper limit are 3.2 and 4.8% loss to 1% added fat.

* Costs:

* Feed costs of the concentrate have to be recalculated according to the change in the formulation and the price of the different compounds. The cost of extruded compounds is generally increased. Other costs or benefits could be neglected if the formulation considers the recommendations proposed in this action.
## Model variables and parameters affected

<table><thead><tr><td>Parameter</td><td>Unit</td><td>Likely range of change</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>Proportion of feed components</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Cost of feed</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Enteric CH<sub>4</sub> emission factor</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Milk production and composition</td><td></td><td>To be defined by local farm experts only if the maximum proposed of 5% lipids is exceeded.</td><td></td><td></td></tr></tbody></table>

## Rationale and mechanism of action

Carbohydrate fermentation in the rumen is achieved by a complex ecosystem of bacteria and protozoa. It produces volatile fatty acids, which are the main source of energy for ruminants, and also carbon dioxide and hydrogen. Methane is normally produced in the rumen by methanogenic archaea, microorganisms that convert hydrogen to methane. The conversion of carbohydrates into volatile fatty acids is done in two concurrent paths, the first leads to the formation of acetate and butyrate and produces hydrogen, the latter results in the formation of propionate and consumes hydrogen. There is a net production of hydrogen, because the path of acetate and butyrate is more important than the way propionate. This orientation is related to the balance of the bacterial population in the rumen (flora cellulolytic or amylolytic) and protozoa population. Diets rich in cellulose (grass-products rich in plant cell walls) preferentially produce acetate, diets rich in starch (cereals) preferentially produce propionate. In addition, protozoa are major hydrogen producers. Unsaturated fats (especially polyunsaturated) reduce cellulolytic bacterial populations and / or protozoa, and thus the production of hydrogen.

## Proposed actions to increase the proportion of unsaturated lipids in the diet

Lipids, whatever they are, are digested in the intestine and are not producing methane in the rumen, unlike carbohydrates they replace in the diet. In addition, some sources of fat (unsaturated among others) affect the microbial ecosystem in a direction towards a reduction in methane emissions (Popova et al., 2011).

Some sources of fat are more effective to reduce methane than others. In order from best to least effective: medium chain fatty acids, linolenic acid, linoleic acid, saturated fatty acids 16 and 18 carbons and oleic acid. But it has not been shown that the average efficiency differences are statistically significant. The magnitude of the effect is related to changes in the microbial ecosystem under the influence of lipids. These are the strongest for fatty acids and medium chain linolenic. For oleic acid, the results are highly variable. The dose-response effect is unclear. According to a test conducted with flax seed, the effect would be small when adding fat is less than 2%, but this does not appear in a quantitative analysis of the literature (Doreau et al, 2011.). It should be noted that the effect of fat seems to maintain over the long term (Grainger and Beauchemin, 2011). This was especially observed for linolenic acid flaxseed after a year of distribution by Martin et al. (2011).

It is proposed to prefer the use of a mixture of oilseeds (rapeseed and linseed half each) extruded, and secondly oils (soybean and rapeseed half each) to incorporate in the concentrates.

<figure><img src="image_49.png" /></figure>
To simplify the problem, it is proposed to take a common reduction in the average efficiency of enteric methane emission for all sources of fat for two reasons: firstly the variability in the experimental data and the imprecision on separate for each lipid source estimates, other from greater flexibility in the use of different sources of fat according to their market availability and cost. If the estimations of Grainger and Beauchemin (2011) and Doreau et al. (2011) are used, this average value is lower than 4% methane per unit of added fat in the feed, expressed as% of dry matter. The maximum intake of unsaturated fat is limited in part by the risk of decreased digestibility of the ration in particular cellulosic fraction (and therefore animal performance) related to changes in the microbial ecosystem, other share the risk of excessive growth of trans mono-unsaturated fatty acids as possible deleterious effect on the nutritional value of milk and meat. If a reasonable increase of 3.5 percentage points of lipids (e.g. ration from 1.5 to 5% of fatty acids in the dry matter) is fixed, it is possible to exclude these negative effects of fat intake. This increase results in an average reduction of 14% methane. In practice, lipids cannot be distributed in controlled amounts in all cases. It is possible when the ruminants are in barn, or at pasture but return every day to the barn (case of dairy cows). When the small amounts of concentrate are distributed, it will be difficult or impossible to reach 3.5% more lipids in practice. Extruded mixtures contain about 25% fat. It is proposed to distribute first the mixture of extruded up to 10%, and if necessary complete with concentrates rich in oils. The final formulation does not to exceed 5% of fatty acids in the diet. However, other formulation can be considered if local reasons support other solutions.

## Other effects of the action

If the use of lipids in substitution with other sources of energy in concentrates is carried out as proposed (concentrate with the same energy and protein values), no significant change in milk or meat production is expected, except an increase in the proportion of unsaturated fat, which sometimes can increase the value of products. Otherwise, if the experts prefer to add more lipids for specific reasons they have to consider with good references all the possible impacts on intake, digestion, production and composition of products.

## Effects neglected

The effect of lipids on the other greenhouse gases is largely unknown. The weak effect of fat intake on the digestibility of rations linked to an apparent digestibility only slightly higher than its rations supplemented with lipids (Doreau and Ferlay, 1994) suggests that non-digestible organic matter varies little, so that the production of methane from manure is little changed. Similarly, the fat intake low interfering with the digestion of nitrogen, fecal or urinary nitrogen losses are little changed. A study on beef and dairy cattle on a study recently showed that the introduction of flaxseed in the diet had a very small impact on other greenhouse gas emissions and other environmental impacts (Nguyen et al., 2012, 2013), but to our knowledge there has been no study with other oilseeds. In the current state of knowledge, it can be assumed that unsaturated fats have no significant effect found on greenhouse gas emissions other than methane.

As the calculation of the emissions in this WP does not included the emissions due to the inputs before its use on farm, the substitution between cereals and products rich in lipids will not be considered. The experts have to avoid the substitution of cereals produced on farm by extruded seeds produced outside. Part of the change of the emissions will only be change to the change in the boundaries of the system.

## Cost data

The cost of this action is mainly linked to the cost of the feeds. The changes in the cost of the diet have to be adapted according to the price of feeds in each country. However, if the
experts prefer to add more lipids for specific reasons that the 5% proposed, they have to
estimate the other cost on production and consumption.

Suggested references

Beauchemin K.A., McAllister T.A., McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources. 4 (No. 035). CABI, Wallingford, Royaume-Uni, 1-18.

Doreau M., Martin C., Eugène M., Popova M, Morgavi D.P., 2011. Leviers d'action pour réduire la production de méthane entérique par les ruminants. INRA Prod. Anim., 24, 461-474.

Doreau, M., Ferlay A., 1994. Digestion and utilisation of fatty acids by ruminants. Animal Feed Science and Technology 45, 379-396.

Givens, D.I. (2010) Milk and meat in our diet: good or bad for health? Animal 4, 1941-1952

Grainger, C., Beauchemin, K.A., 2011. Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology 166-167, 308-320.

Martin C., Morgavi D.P., Doreau M., 2010. Methane mitigation in ruminants: from microbe to the farm scale.
Animal, 4, 351-365.

Martin, C., Rouel, J., Jouany, J.P., Doreau, M., Chilliard, Y., 2008. Methane output from dairy cows in response to dietary supplementation of crude linseed, extruded linseed or linseed oil. Journal of Animal Science 86, 2642-2650.

Martin, C., Pomiès, D., Ferlay, A., Rochette, Y., Martin, B., Chilliard, Y., Morgavi, D.P., Doreau, M., 2011. Methane output and rumen microbiota in dairy cows in response to long-term supplementation with linseed or rapeseed of grass silage- or pasture-based diets. Proceedings of the New Zealand Society for Animal Production 71, 242-247.

Nguyen, T.T.H., van der Werf, H.M.G., Eugène, M., Veysset, P., Devun, J., Chesneau, G., Doreau, M., 2012a. Effect of type of ration and allocation methods on the environmental impacts of beef-production systems. Livest. Sci., 145, 239-251.

Popova, M., Morgavi, D.P., Doreau, M., Martin, C., 2011. Production de méthane et interactions microbiennes dans le rumen. In : Gaz à effet de serre en élevage bovin : le méthane. Doreau M., Baumont R., Perez J.M. (Eds). Dossier, INRA Productions Animales 24, 447-460.

Sauvant, D., Giger-Reverdin, S., Serment, A., Broudiscou, L., 2011. Influences des régimes et de leur
fermentation dans le rumen sur la production de méthane par les ruminants. INRA Productions Animales 24, 433-
446.

Additive nitrate

For farm modelling in the Animal Change project, Component 3
Authors: P. Faverdin (INRA PEGASE), M. Doreau (INRA UMRH)

Data to be provided by local experts

Formulate new diet with 1.5% nitrate (= 2.37% Calcinit (Ca(NO₃)₂), equivalent of 0.369% N in the diet), replacing other N sources, prioritising urea:

* if urea ≥ 0.8%, then replace 0.8% urea

* if no urea in the diet, then reduce the protein content by 2.31% (Ask Ph F again about this)

* if 0% ≤ urea ≤ 0.8%, then replace all urea and also other protein sources (e.g. soybean meal, rapeseed meal) proportionally

Finances: most probably local nitrate price is not available, so there is no need for this information from the experts.

<figure><img src="image_50.png" /></figure>
## Objective

Reduce CH₄ emissions due to rumen fermentation by the addition of nitrate in the diet.

## Assumptions for farm modelling

* The measure is only applied to ruminants.

* The animal performance (weight gain) is not changed.

* The diet's energy content is constant.

* Diet formulation incorporates nitrates as a substitute to other sources of highly fermentable proteins or to non protein nitrogen compounds.

* Consider if the distribution of this small amount of nitrate is possible to distribute to the animals for the particular farming system (difficult in ranching situation for example). It will be assumed that the option will be applied if the individual dose can be controlled (individual dosing or careful mixing with roughage).

* Feed formula is set to minimise price. Formulation is based on local circumstances.

* CH₄ emissions from manure are assumed to be constant.

* If some farming systems are producing animal products with some specific rules (PDO, AOP...) specifying that the use of urea (or other similar compound) is not allowed, the use of nitrate should not be considered as an option.

## Further guidance for the farm experts

* Formulation:

* As nitrate could toxic for the animal and is not authorized in many countries today, the possible method to use it is still unknown. Nitrates will be substituted to other sources of highly fermentable proteins or to non protein nitrogen compounds at a maximum dose of 1.5% nitrate.

* Costs:

* Feed costs of this feed compound is largely unknown today because it is not sold for this purpose. It will be assumed that its use will generally be limited to farming systems where equipment for the supplements distribution exists (substitution to other feedstuffs). If not, this cost should be added.

## Model variables and parameters affected

<table><thead><tr><td>Parameter</td><td>Unit</td><td>Likely range of change</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>Proportion of feed components</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Cost of feed</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Enteric CH<sub>4</sub> emission factor</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr></tbody></table>

## Rationale and mechanism of action

Carbohydrate fermentation in the rumen is achieved by a complex ecosystem of bacteria and protozoa. It produces volatile fatty acids, which are the main source of energy for ruminants, and also carbon dioxide and hydrogen. Methane is normally produced in the rumen by
methanogenic archaea, microorganisms that convert hydrogen to methane. The conversion of carbohydrates into volatile fatty acids is done in two concurrent paths, the first leads to the formation of acetate and butyrate and produces hydrogen, the latter results in the formation of propionate and consumes hydrogen. There is a net production of hydrogen, because the path of acetate and butyrate is more important than the way propionate. Nitrate is rich in oxygen, easily used in an anaerobic system like the rumen. With nitrates, oxygen is replaced by hydrogen with formation of ammonia, preferably with methane being formed. In short, it is possible to consider that nitrates prevent the formation of methane. The review of Leng (2008) presents a comprehensive background on the use of nitrates in different ruminant species.

The role of nitrate on enteric methane emissions has been demonstrated in vivo in several trials effect, including the long term (Van Zijderveld et al., 2011). The level of emission reduction achieved in vivo following an intake of nitrate is also quite similar from one test to another. Until recently, all published trials came from the same team, but a recent trial involving researchers from different countries has been achieved with consistent results (Hulshof et al., 2012).

## Proposed actions to incorporate nitrate in the diet

The proposed action consists to replace the different sources of highly degradable proteins or nitrogen with the use of nitrate. Nitrates will be substitute to other sources of highly fermentable proteins or to non-protein nitrogen compounds at a maximum dose of 1.5% nitrate. The new diet should be balance to maintain constant the energy and metabolizable protein content of the diet, and to reach the same level of degradable nitrogen if this level is close to the recommendation or higher in the reference situation, to increase it up to the recommendation, if there is a deficit in the reference situation. The calculation should normally be made by the experts using their favourite feeding systems available for protein (PDI, DVE and OEB). In theory, it is difficult to consider with diet based on forages rich in protein (grass, grass silage). In this case, the addition of nitrate will lead to a too high content of degradable protein, which could be a problem. The maize or sorghum based diets are probably the best candidate for this option, but not only (poor roughage can also be considered). If it is allowed, it is possible to increase the proportion and protected protein (formaldehyde treated meal for example) to increase the proportion of nitrate in the diet.

No commercial form of nitrate for animal nutrition is available today. It is propose to base the calculation on the virtual use of the Calcinit (trade name Calcinit®, including 63% of nitrates) a product derived from ammonium calcium nitrate double salt and normally used for fertilization. As the value of PDIN kg urea is 1472 g, it could be assessed that the value of Calcinit® is 589 g / kg of dry matter, 489 g / kg crude, based on the number of nitrogen atoms which will be integrated into ammonia molecules. Distribution of 1% of nitrate in the diet corresponds to 1.58% of Calcinit®. It corresponds to a reduction of 0.53% urea.

Different tests have shown that a dose of 2.2 to 2.6% of nitrates added to the diet (NO₃⁻ equivalents) resulted in a decrease in methane production from 16 to 31%. Considering a linear response, the expected reduction of enteric CH₄ emission will be close to 10% for 1% of nitrates in the diet. As a maximal dose of 1.5% is proposed to avoid possible risks of nitrates on animal health, the maximal response expected should not exceed 15%.

## Other effects of the action

If the use of nitrates increases the N content of the diet, it must be considered for the other effects associated to an increase of N excretion (see mitigation option on the reduction of CP content of the diet). However, if the nitrates are used in substitution to other sources of degradable nitrogen as recommended, this effect has not to be considered.
## Effects neglected

The emissions of the feed components are not considered in this package. However, the CO₂ emission associated to the production of nitrate is unknown, but could probably be considered negligible compared to the CO₂ cost of the diet, which is very likely. Moreover, the nitrates in theory replace a portion of the urea ration, so another additive industrial production.

Nitrate is able to produce nitrite which is known to react with haemoglobin in an irreversible manner (methaemoglobin). At high doses, this can of course induce health problems and generate some change in animal production. However, it will be assumed than the doses given and the feeding process will limit the problem and that the production will not be affected.

## Cost data

The cost of nitrates is difficult to assess because it is not yet commercially available for introduction in the diet of animals. Of course, artificial fertilizers cannot be added to the ration just like that, due to the great risks of undesirable other substances. Fertilizer producer Yara from Norway has set up a special animal feed line for the production of nitrate that is suitable as feed additive for ruminants (Newsletter KTC De Marke, No 4, May 2013), but the price is unknown. An idea of price could be extrapolated from the fertilizer with a supplement due to the preparation for animal. The product sold under the name Calcinit® is located at prices ranging between 230 and 2050 € / t for deliveries from 20 to 25 T (www.alibaba.com website) without the possibility of have information on the quality of the product. It is proposed to use an average price similar to the cost of urea, which is about 860 € / t in France. The changes in the cost of the diet have to be adapted according to the price of feeds in each country.

## Suggested references

Grainger, C., Beauchemin, K.A., 2011. Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology 166-167, 308-320.

Hulshof, R. B. A., Berndt, A., Gerrits, W. J. J., Dijkstra, J., Van Zijderveld, S. M., Newbold, J. R., Perdok, H. B. 2012. Dietary nitrate supplementation reduces methane emission in beef cattle fed sugarcane based diets. Journal of Animal Science 90, 2317-2323.

Leng, R.A., 2008. The potential of feeding nitrate to reduce enteric methane production in ruminants. Report, Commonwealth Government of Australia, Canberra.

Popova, M., Morgavi, D.P., Doreau, M., Martin, C., 2011. Production de méthane et interactions microbiennes dans le rumen. In : Gaz à effet de serre en élevage bovin : le méthane. Doreau M., Baumont R., Perez J.M. (Eds). Dossier, INRA Productions Animales 24, 447-460.

Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B. A., Perdok, H.B., 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. Journal of Dairy Science 94, 4028-4038.

## Balance amino acids and reduce CP in pigs

For farm modelling in the Animal Change project, Component 3

Authors: P. Faverdin, F. Garcia-Launay, J. Y. Dourmad (INRA PEGASE)

### Objective

Reduce the crude protein (CP) content of the diet without changing the animal performance, by using phase feeding and amino acids supplementation. Diet energy content is assumed to

<figure><img src="image_51.png" /></figure>
be constant. The expected change in GHG emissions concerns mainly N₂O either directly or indirectly (NH₃).

## Assumptions for farm modelling

* The measure is only applied to pigs.

* The animal performance (weight gain) is not changed.

* The diet's energy content is constant.

* Two-phase feeding using synthetic essential amino acids in the formula to reduce the CP content of the diet.

* Feed formula is set to minimise price. Formulation is based on local circumstances.

* CH₄ emissions from manure are assumed to be constant.

* Adjustment of mineral N fertilization has to be considered according the farm practices (important in systems where the conservation of excreted nitrogen until spreading is high, and if the additional mineral fertilization is exactly adjusted to the nitrogen supply to the crops or grass), with the objective to maintain crop production.

## Further guidance for the farm experts

* Formulation:

* L-Lysine HCL, L-threonine, DL-méthionine, L-tryptophane and Valine are currently available for feed formulation.

* INRAporc (2006) is a free available to formulate pig diet.

* Costs:

* Feed costs for Europe are proposed in Table 1 if no other data are available.

## Model variables and parameters affected

<table><thead><tr><td>Parameter</td><td>Unit</td><td>Likely range of change</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>Proportion of feed components</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Cost of feed</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr><tr><td>Mineral N fertilisation of crops</td><td></td><td>to be defined by local farm experts</td><td></td><td></td></tr></tbody></table>

## Rationale and mechanism of action

Nitrous oxide (N₂O) emissions associated with manure management have a significant impact in GHG emission and are produced in the animal building, during manure storage and manure spreading. They come from food nitrogen not retained by the animal, but excreted in the faeces (in a relatively stable form), and – mainly – in the urine as urea. The amount of urinary nitrogen is high in monogastric animals, representing from 70 to 80% of the total nitrogen excreted. Urea is very unstable and is converted into N₂O and ammonia (NH₃) which itself can lead to N₂O emissions. The evolution of NH₄⁺ in NH₃ or N₂O depends on the building type and method of effluent management.

These emissions can be reduced by better adjusting the amount of protein to the needs of animals, and improving the quality of these proteins and thus their use efficiency. The
decreases in nitrogen intake in pigs effectively reduce excretion: a decrease in the protein content from 20% to 12% can reduce 67% NH₃ emissions during manure storage.

The aim is to implement this strategy with little or no impact to production. The action is applied to pigs, which receive large protein food that is easy to control.

## Proposed actions to reduce CP content of the diet

The nitrogen excretion can be reduced without the loss of production by two ways:

1. Limiting the total CP in the diet while ensuring coverage of the essential amino acids (AA) (what the body does not produce and must be present in the feed). Many studies have shown that a diet for fattening pigs of reduced protein content decreased the nitrogen excretion and does not affect weight gain or feed efficiency if the energy content and the content essential amino acids are maintained. The industrial production of amino acids is able to deliver more and more types of essential amino acids, which gives new opportunities to reduce CP content of the diet without changing the animal performance.

2. In current practice the animals usually receive a single type of food throughout his life (single-phase feeding) or two types of food, each adapted to a development phase (two-phase feeding). Adjusting the composition of the food ten times during the life of the animal (multi-phase feeding) can reduce the overall amount of protein distributed. The two phase feeding system is the easiest to manage and does not imply new investment. The multiple phase feeding is more efficient but requires special investment to adjust the diet of pigs several times combining two main compounds.

Considering the action for animal change, the more realistic option available consists in a two phase feeding using more synthetic essential amino acids in the formula to reduce the CP content of the diet. L-Lysine HCL, L-threonine, DL-methionine, L-tryptophane and Valine are currently available for feed formulation. INRAporc (2006) is a free available to formulate pig diet.

Using formulation tools, it is possible to assess the change in the composition of compounds, but several approaches can be used to formulate them (Mosnier et al. 2012). It is possible to minimize the price, the carbon footprint or the CP content of the diet. As the option is considered at farm level, a simple approach of minimizing the price is consistent. It could be oriented by the feedstuff available on farm for pig feeding.

## Other effects of the action

Reducing protein intake may affect the fertilizer value of manure: it decreases the total nitrogen in the effluent which can lead to the farmer increasing the use of mineral fertilizers. In practice, the measured nitrogen availability for plants remains high even with a reduced protein content of the diet, suggesting that this change in food has little impact on the fertilizer value of the effluent. However, the adverse effect could be more important in systems where the conservation of excreted nitrogen until spreading is high, and if the additional mineral fertilization is exactly adjusted to the nitrogen supply to the crops or grass. Adjustment of mineral fertilization has to be considered according the farm practices, with the objective to maintain crop production.
## Effects neglected

CH₄ emissions could increase due to an increase in fermentation of manure, which could be facilitated by the decrease in NH3 (pH shift). This effect will be neglected because the effect of NH3 is still poorly documented.

## Cost data

<table><caption>Table 1: Cost of ingredients (€/t)</caption><thead><tr><td>Ingredients</td><td>Average price 2010-2011 (€/t)</td><td>Source</td></tr></thead><tbody><tr><td>Wheat</td><td>191</td><td>IFIP</td></tr><tr><td>Maize</td><td>201</td><td>IFIP</td></tr><tr><td>Barley</td><td>175</td><td>IFIP</td></tr><tr><td>Wheat bran</td><td>130</td><td>IFIP</td></tr><tr><td>Peas</td><td>233</td><td>IFIP</td></tr><tr><td>Rapeseed meal</td><td>212</td><td>IFIP</td></tr><tr><td>Rapeseed oil</td><td>848</td><td>La Dépêche</td></tr><tr><td>Soyabean meal</td><td>326</td><td>IFIP</td></tr><tr><td>Extruded Soyabean</td><td>414</td><td>La Dépêche</td></tr><tr><td>L-lysine.HCl</td><td>1800</td><td>Ajinomoto Eurolysine</td></tr><tr><td>L-threonine</td><td>1950</td><td>Ajinomoto Eurolysine</td></tr><tr><td>L-tryptophane</td><td>9500</td><td>Ajinomoto Eurolysine</td></tr><tr><td>L-valine</td><td>10000</td><td>Ajinomoto Eurolysine</td></tr><tr><td>DL-methionine</td><td>3600</td><td>Ajinomoto Eurolysine</td></tr><tr><td>Lactoserum Powder</td><td>752</td><td>FranceAgrimer</td></tr><tr><td>Phytase</td><td>9500</td><td>Relevés mensuels IFIP</td></tr><tr><td>Monocalcium Phosphate</td><td>650</td><td>Relevés mensuels IFIP</td></tr><tr><td>Salt</td><td>100</td><td>Mosnier et al. 2011</td></tr><tr><td>CaCO3</td><td>50</td><td>Relevés mensuels IFIP</td></tr></tbody></table>

## Suggested references

Aarnink, A.J.A., Verstegen, M.W.A., 2007. Nutrition, key factor to reduce environmental load from pig production. Livestock Science 109, 194-203.

Ajinomoto Eurolysine S.A.S., 2011. Formulating pig grower diets with no minimum crude protein - Essential amino acids requirements; energy systems and low protein diets. Go to essentials, Information N°37, 39 p. Téléchargeable sur http://ajinomoto-eurolysine.fr/telechargement-bulletins-techniques.html

Ball, R.O., Möhn, S., 2003. Feeding strategies to reduce greenhouse gas emissions from pigs. Advances in Pork Production 14, 301-311.

Bourdon, D., Dourmad, J.Y., Henry, H., 1995. Réduction des rejets azotés chez le porc en croissance par la mise en oeuvre de l'alimentation multiphase, associée à l'abaissement du taux azoté., Journées de la Recherche Porcine, Paris, pp. 269-278.

Canh, T.T., Aarnink, A.J.A., Schutte, J.B., Sutton, A., Langhout, D.J., Verstegen, M.W.A., 1998. Dietary protein affects nitrogen excretion and ammonia emission from slurry of growing-finishing pigs. Livestock Production Science 56, 181-191.

Dourmad J.Y., Ryschawy J., Trousson T., Bonneau M., Gonzalez J., Houwers H.W.J., Hviid M., Zimmer C., Nguyen T.L.T., Morgensen L., 2012. Evaluation of the environmental sustainability of different European pig production systems using life cycle assessment. Submitted to Animal.
Dourmad, J.Y., Ryschawy, J., Trousson, T., Bonneau, M., Gonzalez, A.M., Houwers, H.W.J., Hviid, M., Zimmer, C., Nguyen, T.L.T., Morgensen, L., 2012. Evaluation of the environmental sustainability of different European pig production systems using life cycle assessment. In: 8th International Conference on LCA in the Agri-Food Sector, Saint-Malo, France, p. 98.

Garcia-Launay, F., van der Werf, H., Nguyen, T.T.H., Le Tutour, L., Dourmad, J.Y. 2013. L'incorporation d'acides aminés dans les aliments permet de réduire les impacts environnementaux de la production porcine. 45èmes Journées de la Recherche Porcine, 5-6 février 2013.

Garcia-Launay, F., van der Werf, H., Nguyen, T.T.H., Le Tutour, L., Dourmad, J.Y. Evaluation of the environmental implications of the utilization of feed-use of amino acids in pig production using Life Cycle Assessment. Soumis à Livestock Science.

InraPorc, 2006. Un outil pour évaluer des stratégies alimentaires chez le porc. Version 1.0.4.0. INRA-UMR SENAH, www.rennes.inra.fr/inraporc

Jarret G., Martinez J., Dourmad J.Y., 2011. Pig feeding strategy coupled with effluent management - fresh or stored slurry, solid phase separation - on methane potential and methane conversion factors during storage. Atmospheric Environment, 45, 6204-6209.

Le, P.D., Aarnink, A.J.A., Jongbloed, A.W., 2009. Odour and ammonia emission from pig manure as affected by dietary crude protein level. Livestock Science 121, 267-274.

Leek, A.B.G., Hayes, E.T., Curran, T.P., Callan, J.J., Beattie, V.E., Dodd, V.A., O'Doherty, J.V., 2007. The influence of manure composition on emissions of odour and ammonia from finishing pigs fed different concentrations of dietary crude protein. Bioresource Technology 98, 3431-3439.

Mosnier, E., van der Werf, H.M.G., Boissy, J., Dourmad, J.-Y., 2011. Evaluation of the environmental implications of the incorporation of feed-use amino acids in the manufacturing of pig and broiler feeds using Life Cycle Assessment. Animal 5, 1972-1983.

Osada, T., Takada, R., Shinzato, I., 2011. Potential reduction of greenhouse gas emission from swine manure by using a low-protein diet supplemented with synthetic amino acids. Anim. Feed Sci. Technol. 166-67, 562-574.

Portejoie S., Dourmad J.Y., Martinez J., Lebreton Y., 2004. Effect of lowering dietary crude protein on nitrogen excretion, manure composition and ammonia emission from fattening pigs. Livestock Production Science, 91, 45-55.

Quiniou, N., Primot, Y., Peyronnet, C., Quinsac, A., 2011. Interest of using synthetic amino acids, including L-Valine, for formulating low crude protein pig diets based on rapeseed meal. Annual Meeting of the European Association of Animal Production, Stavanger, p. 17.

Schils R. L. M., Eriksen J., Ledgard S. F., Vellinga Th. V., Kuikman P. J., Luo J., Petersen S. O. and. Velthof G. L., 2012. Strategies to mitigate nitrous oxide emissions from herbivore production systems. animal, Available on CJO2011 doi:10.1017/S175173111100187X

Sørensen, P., Fernandez, J.A., 2003. Dietary effects on the composition of pig slurry and on the plant utilization of pig slurry nitrogen. The Journal of Agricultural Science 140, 343-355.

Vedrenne F., Beline F., Dabert P., Bernet N., 2008. The effect of incubation conditions on the laboratory measurement of the methane producing capacity of livestock measurement wastes. Bioresource Technology, 99, 146-155.

Vellinga, T.V., de Haan, M.H.A., Schils, R.L.M., Evers, A., van den Pol-van Dasselaar, A., 2011. Implementation of GHG mitigation on intensive dairy farms: Farmers' preferences and variation in cost effectiveness. Livestock Science 137, 185-195.

# Genetic improvement of dairy cattle

For farm modelling in the Animal Change project, Component 3

Authors: D. O'Brien and L. Shalloo (TEAGASC)

## Objective

Increase genetic merit of dairy cattle by improving genetic traits for fertility, survival, milk yield and milk composition (fat and protein). The strategy will affect the number of replacement heifers, diet requirements, manure chemical composition, mineral fertiliser requirements and effect most emissions including CH$_4$, N$_2$O, NH$_3$, CO$_2$.

<figure><img src="image_52.png" /></figure>
## Assumptions for farm modelling

* The measure affects dairy cows and heifers.

* The fertility, survival, milk yield, milk composition and meat performance of dairy cows will all be changed or individually.

* The diet's energy and N content is affected.

* Concentrate and forage feeding is changed according to changes in dairy cow performance and changes in replacement heifers.

* Concentrate feed formulation is set to minimise price. Formulation is based on local circumstances.

* Changes in concentrate and forage feeding will change manure chemical composition and mineral fertiliser requirements

* All emissions will be affected.

* The indirect effects of changes in meat yield from dairy systems on meat production from other sectors such as suckler or monogastric systems will not be considered

## Rationale

In general, research studies (e.g. Schils et al., 2005; Lovett et al., 2006; Weiske et al., 2006; Beukes et al., 2010) indicate that improving the genetic merit of dairy cows for fertility, survival and milk yield reduces emissions per unit of milk. For instance, previous analysis in the Netherlands by Schils et al. (2005) showed that increasing milk yield per cow by 500kg to 8,600 kg reduced greenhouse gas (GHG) emissions per unit of product by 4%, because the total number of cows required to produce a fixed volume of milk was reduced. Similarly, Rotz et al. (2010) reported that an increase in milk yield of 16% reduced the carbon footprint per unit of milk by 8% of USA dairy systems in Pennsylvania, but the reduction was due to an increase in feed conversion efficiency (kg of milk/kg of feed) and not a reduction in animal numbers.

Nevertheless, it is important to stress that Schils et al. (2005) and Rotz et al. (2010) assumed that the increase in milk yield only affected the level of forage and concentrate feeding and did not result in a change in the number of replacements heifers required to maintain the dairy herd. In contrast, Lovett et al. (2006), who compared Holstein-Friesian cows with different genetic potential for milk, fed different quantities of concentrate, reported Holstein-Friesian cows with higher milk yields increased emissions per unit of product relative to Holstein-Friesian cows with lower yields. This was because Holstein-Friesian cows with higher milk yields had lower herd fertility rates, which led to a higher ratio between productive and non-milk producing animals, which increased emissions per unit of milk (Lovett et al., 2006).

O'Brien et al. (2010) also evaluated the effect of genetic merit of dairy cows on carbon footprint per unit of milk using the phenotypic results of Horan et al. (2004, 2005) and McCarthy et al. (2007) and Moorepark Dairy System Model (Shalloo et al., 2004). In short, the study assessed GHG emissions from 3 strains of Holstein-Friesian cows differing in genetic merit, blocked across 3 grazing treatments; high grass allowance (2.5 cows/ha with 325kg DM concentrate/cow); high concentrate (2.5 cows/ha with 1,445kg DM concentrate/cow) and a high stocking rate (2.74 cows/ha with 325kg DM concentrate/cow). The results showed that the carbon footprint unit of milk varied by up to 15% between cows genotypes. This was because dairy cows selected solely for milk production were not as fertile as cows selected on the basis of fertility, survival and milk performance. Consequently, higher yielding cows increased emissions from non-milk producing animals and had reduced expected herd lifetime milk performance given the greater proportion of primiparous cows in the herd.
Thus, these findings indicate that it is important to consider a combination of genetic traits to reduce GHG emissions from milk production. However, apart from Lovett *et al.* (2006) and O'Brien *et al.* (2010) few have considered the antagonistic genetic relationship for milk production and fertility traits (Pryce and Veerkamp, 2001). Weiske *et al.* (2006) and Beukes *et al.* (2010) also modelled the effect of improving genetic merit on dairy systems GHG emissions, but only considered the effect of improving fertility. The results of these studies indicated that improvements in fertility reduced the replacement heifer requirement by 10% and decreased GHG emission per unit of milk by an average of 14%. This decrease in GHG emissions occurred due to a decline in emissions from non-milk producing animals. Similarly, Vellinga *et al.* (2011) reported that this strategy reduced GHG emissions per unit of product on commercial dairy farms in the Netherlands. However, modelled reductions were lower than previous studies because the strategy is also determined by farm management (Vellinga *et al.*, 2011).

## Mechanism of action

The results of current research demonstrate that a balancing breeding program that improves several economically important genetic traits has a positive effect on emissions per unit of milk. This can mainly be explained by the following factors, which are relevant to grass-based systems and in part to confinement systems:

* Improving fertility, which reduces calving intervals and replacement rates, thus reducing enteric CH₄ emissions per unit of product

* Increasing milk yield per unit of forage and improving milk composition. This increases the efficiency of production, which decreases emissions (Martin *et al.*, 2010)

* Shorter calving interval to increase the proportion of grazed grass in the diet and reduce culling and to increase lifetime herd performance.

* Improved survival and health to reduce deaths and disease, which increases efficiency and reduce emissions

## Other effects of the action

Altering the genetic merit of dairy cows also affects meat production from dairy systems. The carbon footprint of meat produced from dairy systems is significantly lower than suckler systems but greater than monogastric systems (Williams *et al.*, 2006; Flysjö *et al*. 2012). Thus, a reduction in the production of meat from milk production systems can have a negative or positive affect on emissions from total milk and meat production. For instance, Zehetmeier *et al*. (2012) showed that to meet a constant demand for milk and meat, increasing milk yield per cow from 6,000 kg to 10,000 kg increased the requirement to produce beef from suckler beef systems, which caused net emissions from milk and meat production to increase.

## Effects neglected

The effect a change in meat production from dairy systems has on overall meat production emissions is generally not considered. The main reasons this is not considered is the uncertainty associated with determining the meat and quality of meat that dairy systems replace and the uncertainty in projecting demand for different meat products.

<figure><img src="image_53.png" /></figure>
## References

Beukes, P.C., Gregorini, P., Romera, A.J., Levy, G., Waghorn, G.C., 2010. Improving production efficiency as a strategy to mitigate greenhouse gas emissions on pastoral dairy farms in New Zealand. Agric. Ecosyst. Environ. 136, 358-365.

Flysjö, A., C. Cederberg, M. Henriksson, and S. Ledgard. 2012. The interaction between milk and beef production and emissions from land use change - critical considerations in life cycle assessment and carbon footprint studies of milk. Journal of Cleaner Production 28:134-142.

Horan, B., Mee, J.F., Rath, M., O'Connor, P., Dillon, P., 2004. The effect of strain of Holstein-Friesian cow and feeding system on reproductive performance in seasonal-calving milk production systems. Anim. Sci. 79, 453-467.

Horan, B., Dillon, P., Faverdin, P., Delaby, L., Buckley, F., Rath, M., 2005. The interaction of strain of Holstein-Friesian cows and pasture-based feed systems on milk yield, body weight and body condition score. J. Dairy Sci. 88, 1231-1243.

Lovett, D.K., Shalloo, L., Dillon, P., O'Mara, F.P., 2006. A systems approach to quantify greenhouse gas fluxes from pastoral dairy production as affected by management regime. Agric. Syst. 88, 156-179.

Martin, C., D. P. Morgavi, and M. Doreau. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351-365.

O'Brien, D., L. Shalloo, C. Grainger, F. Buckley, B. Horan, and M. Wallace. 2010. The influence of strain of Holstein-Friesian cow and feeding system on greenhouse gas emissions from pastoral dairy farms. Journal of Dairy Science 93:3390-3402.

Pryce, J.E., Veerkamp R.F., 2001. The incorporation of fertility indices in genetic improvement programmes. Br. Soc. Anim. Sci. Occasional Publ. 26, 237-249.

Rotz, C.A., Montes, F., Chianese, D.S., 2010. The carbon footprint of dairy production systems through partial life cycle assessment. J. Dairy Sci. 93, 1266-1282.

Schils, R.L.M., Verhagen, A., Aarts, H.F.M., Šebek, L.B.J., 2005. A farm level approach to define successful mitigation strategies for GHG emissions from ruminant livestock systems. Nutr. Cycl. Agroecosyst. 71, 163-175.

Shalloo, L., Dillon, P., Rath, M., Wallace, M., 2004. Description and validation of the Moorepark Dairy System Model. J. Dairy Sci. 87, 1945-1959.

Vellinga, T.V., de Haan, M.H.A., Schils, R.L.M., Evers, A., van den Pol-van Dasselaar, A., 2011. Implementation of GHG mitigation on intensive dairy farms: Farmers' preferences and variation in cost effectiveness. Livestock Science 137, 185-195.

Weiske, A., Vabitsch, A., Olesen, J.E., Schelde, K., Michel, J., Friedrich, R., Kaltschmitt, M., 2006. Mitigation of greenhouse gas emissions in European conventional and organic dairy farming. Agric. Ecosyst. Environ. 112, 221-232

Williams, A.G., Audsley, E., Sandars, D.L., 2006. Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities. Main report. Defra Research Project IS0205. Cranfield University and Defra, Bedford.

Zehetmeier, M., J. Baudracco, H. Hoffmann, and A. Heißenhuber. 2012. Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach. Animal 6:154-166.

## Agroforestry

*For farm modelling in the Animal Change project, Component 3*

*Authors: Amaury Burlamaqui*

System combining in the same area, intercropping associations, including trees, annual and perennials crops, forages and pastures for livestock

Using the word “agro-pastoral” or “agro-sylvo-pastoral” system focuses more on the animal component integrated to the farming system.
Using "Sylvo" makes reference to the forest as an ecosystem (natural or not), or something different than plantations (cultivation) of trees.

## Objective

Increase the C rate sequestered in the whole farm system and increase the production of biomass per unit area. The carbon sequestration process occurs into the soil and in the trees biomass planted in the system.

## Assumptions for farm modelling

* Address to agroforestry systems including animal component (agro-pastoral, silvopastoral or agro-sylvo-pastoral systems).

* GHGE should be reduced. May be these will increase in the first steps(1), then they will decrease more due to the C stock in the system.

* GGE will increase in the first steps related to the larger use of inputs (fertilizers, agricultural machinery, chemicals) and the higher number of animals in the system.

* The weight gain of cattle doesn't change.

* The animal gain per unit area in the silvipastoral systems can be decreased and it can be increased in the agrosilvipastoris and agro-pastorales systems.

* The total biomass of the system increases.

* The biomass increase per unit area can contribute to GHGE reductions, due to the sequestration in the soil.

## Further guidance for the farm experts

* The N fertilization has to apply to corn and rice crops. Soybean and cowpea don't require nitrogen fertilizers, due to inoculants use.

* Costs: costs for Amazonia are proposed in Table 1 if no other data are available.

## Model variables and parameters affected

<table><thead><tr><td>Parameter</td><td>Unit</td><td>Likely range of change</td><td>Farm type/AEZ</td><td>Reference</td></tr></thead><tbody><tr><td>Livestock Unit/ha</td><td>Unit/ha</td><td>450kg – 1350kg</td><td>Amazonia</td><td></td></tr><tr><td>Plant protection cost</td><td>EUR/year/ha</td><td>1.09 – 165.38</td><td>"</td><td></td></tr><tr><td>Machinery cost</td><td>EUR/year/ha</td><td>8.75 – 273.73</td><td>"</td><td></td></tr><tr><td>Labour requirement</td><td>man-day/year/ha</td><td>0.004 – 0.034</td><td>"</td><td></td></tr><tr><td>Seed buying price</td><td>EUR/ha</td><td>0 – 92.31</td><td>"</td><td></td></tr><tr><td>N fertiliser cost</td><td>EUR/ha</td><td>0 – 115.40</td><td>«</td><td></td></tr><tr><td>Amount of N fertiliser used</td><td>kg N/year/ha</td><td>0 – 90</td><td>"</td><td></td></tr><tr><td>Number of animals</td><td>head/ha</td><td>0.66 – 3</td><td>"</td><td></td></tr><tr><td>Feed composition - grazed grass</td><td>t/year</td><td>15 – 60</td><td>"</td><td></td></tr><tr><td>Wood quantity</td><td>m3/ha</td><td>0 – 15</td><td>"</td><td></td></tr><tr><td>grains production</td><td>Kg/ha/an</td><td>0 – 8000</td><td>"</td><td></td></tr><tr><td>Liveweight gain cattle</td><td>Kg/ha/an</td><td>126 – 600</td><td>"</td><td></td></tr><tr><td>Soil Carbon seq.</td><td>t/ha/an</td><td>??</td><td></td><td></td></tr></tbody></table>
# Rationale and mechanism of action

## Increase carbon sequestration

The C sequestration in the system occurs firstly in the trees, secondly in the soil. That depends on the tree species, spatial arrangements and mainly by management of the system.

## Increase the production of biomass

In silvopastoral system the biomass increase occurs mainly in the trees.

In agrosilvopastoral the biomass increase is the sum of the three components (trees, crops and cattle).

## Proposed actions to increasing the fixation of C in the soil and the production of biomass

Use of forage more adapted to shadow.

Use of trees which interact with grass, legume trees which are good options.

Use spatial cropping designs which optimize the productivity of pasture, the production of total biomass by the system, and C sequestration in the trees and the soil.

Use of pasture management strategies that better respect the cycles of forages.

## Other effects of the action

The intensification process based on the higher use of fertilizers and chemicals should increase GGE.

On another hand, the use of no-productive or degraded areas could contribute to deforest for food production.

## Cost data

<table><caption>Table1 : Cost of ingredients (€/t)</caption><thead><tr><td>Ingredients</td><td>Average price 2010-2011 (€/t)</td><td>Source</td></tr></thead><tbody><tr><td>Rice (2009)</td><td>270</td><td>local market Roraima</td></tr><tr><td>Soybean (2010-2011)</td><td>308</td><td>CEPEA</td></tr><tr><td>Tecthona grandis (2008)</td><td>1000 €/m3</td><td>AIMEX</td></tr><tr><td>Liveweight cattle (2010)</td><td>1282</td><td>CEPEA</td></tr></tbody></table>

# Suggested references

Andrade, C.M.S. de, Valentim, J.F., Carneiro, J. da C., 2002. Baginha trees (Stryphnodendronguianense (Aubl.) Benth.)in cultivated pasture ecosystems in the Western Amazon. Rev. Bras. Zootec. 31, 574–582.

De Moraes Piau, A., 2010. Mudanças na matéria orgânica do solo causadas pelo tempo de adocão de um sistema agrosilvopastoril com eucalipto. Rev. Bras. Ciência Solo 34, 43–57.
Machado, V.D., Santos, M.V., Santos, L.D.T., Mota, V.A., dos Santos Júnior, A., 2012. SISTEMAS AGROFLORESTAIS.

Müller, M.D., Fernandes, E.N., Castro, C.R.T. de, Paciullo, D.S.C., Alves, F.D.F., 2010. Estimativa de Acúmulo de Biomassa e Carbono em Sistema Agrossilvipastoril na Zona da Mata Mineira. Pesqui. Florest. Bras.

Nair, P.K.R., 1993. An introduction to agroforestry. Kluwer Academic Publishers in cooperation with International Centre for Research in Agroforestry, Dordrecht; Boston.

Neves, C.M.N., Silva, M.L.N., Curi, N., Macedo, R.L.G., MOREIRA, F. de S., d' Andréa, A.F., 2009. Indicadores biológicos da qualidade do solo em sistema agrossilvipastoril no noroeste do Estado de Minas Gerais. Ciência E Agrotecnologia 33, 105-112.

Oliveira, T.K. de, Macedo, R.L.G.M., Venturin, N., Higashikawa, E.M., 2010. Desempenho Silvicultural e Produtivo de Eucalipto sob Diferentes Arranjos Espaciais em Sistema Agrossilvipastoril. Pesqui. Florest. Bras. 0.

Rhoades, C.C., 1996. Single-tree influences on soil properties in agroforestry: lessons from natural forest and savanna ecosystems. Agrofor. Syst. 35, 71-94.

Sanguino, A.C., 2011. Custos de implantação e rentabilidade econômica de povoamentos florestais com teca no Estado do Pará. Rev. Ciências Agráriasamazonian J. Agric. Environ. Sci. 52, 61-78.

Vergutz, L., Novais, R.F., Silva, I.R. da, Barros, N.F. de, Nunes, T.N., Piau, A.A. de M., 2010. Mudanças na matéria orgânica do solo causadas pelo tempo de adoção de um sistema agrossilvipastoril com eucalipto. Rev. Bras. Ciência Solo 34, 43-57.

Young, A., 1989. Agroforestry for soil conservation. C.A.B. International; International Council for Research in Agroforestry, Wallingford, Oxon, U.K.

Restoring degraded land

For farm modelling in the Animal Change project, Component 3

Authors: R. F. Veloso, R. D. Sainz Gonzalez, L. G. Barioni (EMBRAPA)

Areas of natural vegetation and anthropogenic land in the Cerrado biome (Figure 1 and Table 1), have incorporated over 54 million hectares of planted pasture, large parts of which are in some stage of degradation (Figure 2; Barcellos et al., 2001; Sano et al., 2008).

<figure><img src="image_54.png" /><figcaption>Figure 1 – Areas of natural and anthropic vegetation in the Cerrado biome in 2002. (Source:</figcaption></figure>

http://mapas.mma.gov.br/geodados/brasil/v egetacao/vegetacao2002/cerrado/docume ntos/relatorio_final.pdf
<table><caption>DISCRIMINAÇÃO DE ÁREAS OCUPADAS POR DIFERENTES CLASSES DE COBERTURA VEGETAL EM DIFERENTES REGIÕES HIDROGRÁFICAS DO CERRADO</caption><thead><tr><th rowspan="2">Região<br>Hidrográfica</th><th rowspan="2">Área Total (ha)</th><th colspan="3">Cobertura Natural (Fisionomia)</th><th colspan="3">Cobertura Antrópica</th></tr><tr><th>Florestal</th><th>Savânica</th><th>Campestre</th><th>Pastagem<br>Cultivada</th><th>Cultura<br>Agrícola</th><th>Reflorestamento</th></tr></thead><tbody><tr><td>Atlántico Nordeste</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Oriental</td><td>12.608</td><td>12.229</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Atlántico Sudeste</td><td>164.823</td><td>39.337</td><td>12.898</td><td>39.661</td><td>71.637</td><td>0</td><td>765</td></tr><tr><td>Atlántico Leste</td><td>3.324.836</td><td>258.122</td><td>1.412.885</td><td>754.958</td><td>343.246</td><td>58.354</td><td>488.169</td></tr><tr><td>Atlántico Nordeste</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ocidental</td><td>12.477.471</td><td>9.382.857</td><td>1.762.400</td><td>59.606</td><td>1.190.119</td><td>57.629</td><td>24.947</td></tr><tr><td>Parnaíba</td><td>15.573.672</td><td>3.990.222</td><td>9.628.843</td><td>197.330</td><td>820.281</td><td>531.373</td><td>3.603</td></tr><tr><td>Amazônica</td><td>15.679.538</td><td>4.613.834</td><td>6.529.726</td><td>45.176</td><td>1.747.723</td><td>3.216.557</td><td>587</td></tr><tr><td>Paraguai</td><td>18.025.034</td><td>2.982.228</td><td>5.998.838</td><td>290.150</td><td>6.721.159</td><td>1.961.617</td><td>73.197</td></tr><tr><td>São Francisco</td><td>36.513.093</td><td>5.372.339</td><td>16.816.462</td><td>2.122.813</td><td>8.796.498</td><td>2.367.198</td><td>802.921</td></tr><tr><td>Paraná</td><td>43.013.213</td><td>4.361.310</td><td>4.626.543</td><td>1.687.526</td><td>19.739.907</td><td>10.022.347</td><td>1.743.860</td></tr><tr><td>Tocantins</td><td>59.865.603</td><td>9.308.907</td><td>28.553.023</td><td>2.822.305</td><td>14.636.989</td><td>3.277.530</td><td>27.606</td></tr><tr><td>TOTAL</td><td>204.649.891</td><td>40.321.386</td><td>75.341.619</td><td>8.019.525</td><td>54.067.559</td><td>21.492.604</td><td>3.165.656</td></tr></tbody></table>

Table 1

(Source:

http://mapas.mma.gov.br/geodados/brasil/vegetacao/vegetacao2002/cerrado/documentos/relatorio_final.pdf)

Reviews on animal production systems that have incorporated pasture (planted and natural) areas have shown that traditional beef cattle production systems persist in Brazil in some regions because even with low (about 50 kg of meat/ha-year) productivity, their low cost makes them still competitive in economic terms as long as the price of land is low.

The production base is crucial for improving the technical and financial efficiency of such systems. This involves aspects such as: soil type, relief, climate, infrastructure (machinery, equipment, buildings, facilities), subdivisions of paddocks, types and conditions of fences, geographic location and logistics (road network access, proximity to consumer centers and suppliers of raw materials, and availability of service providers), and availability and quality of skilled labour. Stocking rate or grazing method and management practices are basic elements for better animal performance (Mendes Peixoto et al., 2003; Silveira Pedreira, 2005; Carneiro da Silva et al., 2009).

According to the agricultural subsidies analysis carried out by Nassar et al. (2009), in 2009 Brazil invested around 4% (US$ 56 billion) of the agricultural Gross Domestic Product as green box expenditure. At the same time the total amount available for the EU-15 was 12% (US$ 205 billion). Since then the Brazilian government has undertaken specific domestic initiatives to foster sustainable economic development while reducing GHG emissions.
<figure><img src="image_55.png" /><figcaption>Figure 2: Dynamics of degradation of planted pasture. Adapted from Barcellos *et al.* (2001).</figcaption></figure>

In 2011, the Brazilian Government established the ABC Low Carbon Agriculture Program aiming to promote technological changes in traditional beef cattle production systems and especially for restoration of degraded pastures by the incorporation of new grain crops (soybean and Phaseoulus beans), cereals (such as: maize and sorghum) and trees (eucalyptus) into crop-pasture rotations. Basically, following environmental criteria, government-subsidized credit (R$ 4.5 billion) has been made available for investments and crop maintenance. Interest rates are very low (5% per year) when compared to the Brazilian capital market. In practice, however, the implementation of the ABC program has been limited due to complex environmental legislation and the bureaucracies that rural producers must face to obtain any agricultural credit in the local public banks.

Restoration of degraded pastures may be carried out directly (i.e., removal of existing vegetation, incorporation of fertilizer and soil amendments and reseeding new pasture species) or indirectly (i.e., through integration of crops and livestock, by cropping degraded lands for 1-3 years (with, e.g., maize and/or soybeans) and then interseeding a new pasture with the last crop). In general, restoration of degraded pastures is best accomplished by the second method, as the direct restoration generally is not economically feasible because its high cost cannot be recovered through livestock production alone. Indirect pasture restoration, on the other hand, can be economically advantageous because income from the grain crop subsidizes the new pasture establishment. Cost-benefit analyses are complicated due to the almost infinite variations of crops, sequences and time periods that may be used. For simplicity, the values below would apply for direct restoration of degraded pasture, without the cropping component.

The restoration of degraded pastures in a traditional beef cattle production system located in the "Cerrado (savannah) biome, would require at least the following inputs to start a technically sustainable and ecologically sound beef production cycle:

Inputs

<figure><img src="image_56.png" /></figure>
* Removal of weed species (including shrub regrowth); variable costs depending on state of pasture and technology used (e.g., mechanical, chemical, fire).

* Lime (2 tons/ha, US$150/ha) – applied every 5 years.

* Phosphogypsum (CaSO4 - 500 kg/ha) – applied each 5 years.

* Fertilizer (300 kg 10-10-10/ha.year-1,+ FTE (80/1080),US$200/ha) applied in the first and the second years. However, the fertilization rate after the second year does not require additional FTE (Zn, Cu, B, Mn and Mo micronutrients). In the case of additional N application the urea fertilizer costs US$ 650/ton.

* Tilling ($90/ha) – every 5 years.

* Reseeding pasture species (20 kg/ha, US$120/ha + US$ 30/ha for sowing seeds) – applied every 5 years.

## Outputs

* Increased stocking rate (1 AU*/ha to 3 AU/ha) (1 AU = 450 kg live weight)

* Increased individual performance (50 kg carcass eq/animal/year to 150 kg carcass eq/animal/year at US$3/kg carcass eq)

For small properties, after the first year, the reseeding and fertilization practices could be carried out by using the equipment shown in Figure 3 which requires less fuel than any tractor.

<figure><img src="image_57.png" /><figcaption>Figure 3: Low cost application equipment. Further information available at http://www.youtube.com/watch?v=l1IleF2-RPY</figcaption></figure>

## References

Barcellos, A. O. et al., (2001) Desafios da pecuária de corte a pasto na Região Cerrado, Documentos 31, Embrapa Cerrados, ISSN 1517-5111.

Nassar et al. (2009). "Agricultural subsidies in the WTO green box: opportunities and challenges for development countries." Agricultural subsidies in the WTO Green Box. http://ictsd.org/downloads/2012/02/agricultural-subsidies-in-the-wto-green-box-ensuring-coherence-with-sustainable-development-goals.pdf

http://www.iadb.org/en/news/news-releases/2013-04-05/low-carbon-agriculture-in-brazil,10406.html

http://siteresources.worldbank.org/BRAZILEXTN/Resources/Brazil_LowcarbonStudy.pdf

SIMPÓSIO SOBRE MANEJO DA PASTAGEM, 20., 2003, Piracicaba. Produção animal em pastagens: situação atual e perspectivas: anais. Piracicaba: FEALQ, 2003. 354 p. Editado por Eristeu Mendes Peixoto, José Carlos de Moura, Sila Carneiro da Silva e Vidal Pedroso de Faria.
SIMPÓSIO SOBRE MANEJO DA PASTAGEM, 22., 2005, Piracicaba. Teoria e prática da produção animal em pastagens; anais Piracicaba: FEALQ, 2005. 403 p. Editado por Carlos Guilherme Silveira Pedreira, José Carlos de Moura, Sila Carneiro da Silva, e Vidal Pedroso de Faria.

SIMPÓSIO SOBRE MANEJO DA PASTAGEM, 25., 2009, Piracicaba. Intensificação de sistemas de produção animal em pasto: anais. Piracicaba: FEALQ, 2009. 278 p. Editado por Sila Carneiro da Silva, Carlos Guilherme Silveira Pedreira, José Carlos de Moura e Vidal Pedroso de Faria.

MAPEAMENTO de cobertura vegetal do bioma Cerrado. Brasília: [s.n.], 2007. 93 p. Relatório final do Projeto Executivo B.02.02.109, edital Probio 02/2004. Coordenação técnica do projeto: Edson Eyji Sano

SANO, E. E.; ROSA, R.; BRITO, J. L. S.; FERREIRA, L. G. Mapeamento da cobertura vegetal do bioma Cerrado. Planaltina, DF: Embrapa Cerrados, 2008. 60 p. il. Color. (Embrapa Cerrados. Boletim de Pesquisa e Desenvolvimento, 205).