# Report on developing bottom-up Marginal abatement cost curves (MACCS) for representative farm type

Vera Eory, M. Macleod, Philippe Faverdin, D. O'Brien, R. de Oliveira Silva, L. G. Barioni, T. Z. Albertini, K. Topp, F. A. Fernandes, D. Moran, et al.

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Vera Eory, M. Macleod, Philippe Faverdin, D. O'Brien, R. de Oliveira Silva, et al. Report on developing bottom-up Marginal abatement cost curves (MACCS) for representative farm type. [Contract] 11.2, UE;Union Européenne. 2015, 129 p. hal-01611406

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ANIMALCHANGE

SEVENTH FRAMEWORK PROGRAMME

THEME 2: FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGIES

<figure><img src="image_2.png" /></figure>

SEVENTH FRAMEWORK PROGRAMME

Grant agreement number: FP7-266018

DELIVERABLE 11.2

Report on developing bottom-up Marginal Abatement Cost Curves (MACCS) for representative farm types

Abstract:

Developing efficient policy instruments and incentive schemes to promote the uptake of greenhouse gas mitigation measures requires some kind of prioritisation of the mitigation measures. An important consideration in this process is the estimated cost and cost-efficiency the measures. The high number of reports done in developed countries show a high variability in the country-level cost-effectiveness estimates, and suggest that approaches providing higher granularity at the spatial and farm type could suit better to the purpose of regional policy development. At the same time, there is still a gap in our understanding of economic mitigation potential of agriculture in developing and newly industrialised countries.

To address these questions this report presents three studies. The first is a literature review of the cost-effectiveness estimates of mitigation measures published in the past 15 years, discussing the variability in these estimates. The second study reports on marginal abatement cost curves for beef cattle production in Brazil. Finally, the last report presents the conceptual basis of a tool to assess the financial implications of the mitigation measures to be used in parallel with the FarmAC model, ultimately providing mitigation measure cost-effectiveness estimates specific to individual farms. Additionally, it describes the selection of mitigation measures which have been assessed at the farm level in Component 3 of the AnimalChange project.
Due date of deliverable: M30

Start date of the project: March 1st, 2011

Organisation name of lead contractor: SRUC

Revision: V1

Dissemination level: PU

Authors:

Eory, V. (SRUC), M. MacLeod (SRUC), P. Faverdin (INRA), D. O'Brien (TEAGASC), R. de Oliveira Silva (SRUC), L. G. Barioni (EMBRAPA), T. Z. Albertini (EMBRAPA), K. Topp (SRUC), F. A. Fernandes, D. Moran (SRUC), N. Hutchings (AU), M. Stienezen (DLO), L. Shalloo (TEAGASC), R. M. Rees (SRUC), Lisbeth Mogensen (AU), Peter Lund (AU), Maike Brask (AU), M. Doreau (INRA), F. Garcia-Launay (INRA), J. Y. Dourmad (INRA), A. B. Bendahan (EMBPRAPA), R. F. Veloso (EMBRAPA), R. D. Sainz Gonzalez (EMBRAPA)

Actual submission date: M50

Duration: 48 months
# Table of Contents

1. INTRODUCTION 5

2. LITERATURE REVIEW OF THE COST-EFFECTIVENESS ESTIMATES OF GHG MITIGATION MEASURES 7

2.1 INTRODUCTION 7

2.2 METHODOLOGY 7

2.3 RESULTS 8

2.4 DISCUSSION 16

3. MARGINAL ABATEMENT COST CURVES FOR BEEF PRODUCTION IN THE BRAZILIAN CERRADO 19

3.1 INTRODUCTION 19

3.2 METHODOLOGY 19

3.3 RESULTS AND DISCUSSION 20

4. COST-EFFECTIVENESS ASSESSMENT OF MITIGATION OPTIONS ON MODEL FARMS 21

4.1 INTRODUCTION 21

4.2 IDENTIFICATION AND DESCRIPTION OF THE MITIGATION OPTIONS FOR MODELLING 21

4.2.1 Initial selection for modelling 21

4.2.2 Selection of five best mitigation options by farm experts 22

4.2.3 Selection for modelling across farm types 26

4.2.4 Description of mitigation options for biophysical and economic modelling 29

4.3 METHODOLOGY 41

4.3.1 Economic assessment 41
<table><tr><td>4.3.2</td><td>Description of the farms and the financial data</td><td>42</td></tr><tr><td>4.4</td><td>RESULTS</td><td>47</td></tr><tr><td>4.5</td><td>CONCLUDING REMARKS</td><td>52</td></tr><tr><td colspan="2">REFERENCES</td><td>54</td></tr><tr><td colspan="2">APPENDIX 1: LIST OF STUDIES REVIEWED</td><td>57</td></tr><tr><td colspan="2">APPENDIX 2: LONG LIST OF MITIGATION MEASURES</td><td>60</td></tr><tr><td colspan="2">APPENDIX 3: OVERVIEW OF THE STUDIES</td><td>63</td></tr><tr><td colspan="2">APPENDIX 4: MARGINAL ABATEMENT COST CURVES FOR BEEF PRODUCTION IN THE BRAZILIAN CERRADO</td><td>65</td></tr><tr><td colspan="2">APPENDIX 5: MITIGATION OPTIONS - BRIEF DESCRIPTIONS FOR BIOPHYSICAL AND ECONOMIC MODELLING</td><td>82</td></tr><tr><td colspan="2">APPENDIX 6: DETAILED DESCRIPTION OF MITIGATION OPTIONS FOR BIOPHYSICAL AND ECONOMIC MODELLING</td><td>89</td></tr><tr><td colspan="2">REDUCING N FERTILISATION</td><td>89</td></tr><tr><td colspan="2">NITRIFICATION INHIBITORS: THE ROLE OF DCD IN REDUCING N<sub>2</sub>O EMISSIONS AND NO<sub>3</sub><sup>-</sup> LEACHING</td><td>96</td></tr><tr><td colspan="2">MORE LEGUMES IN GRASS SWARDS</td><td>101</td></tr><tr><td colspan="2">FEEDING MORE MAIZE AND LESS GRASS</td><td>105</td></tr><tr><td colspan="2">FEEDING MORE FAT WITH RUMINANTS</td><td>109</td></tr><tr><td colspan="2">ADDITIVE NITRATE</td><td>112</td></tr><tr><td colspan="2">BALANCE AMINO ACIDS AND REDUCE CP IN PIGS</td><td>115</td></tr><tr><td colspan="2">GENETIC IMPROVEMENT OF DAIRY CATTLE</td><td>119</td></tr><tr><td colspan="2">AGROFORESTRY</td><td>122</td></tr><tr><td colspan="2">RESTORING DEGRADED LAND</td><td>125</td></tr></table>

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# 1. Introduction

Agriculture is expected to play a role in reducing greenhouse gas (GHG) concentrations globally. The sector has capacity to contribute to this goal via GHG emission reduction, carbon (C) sequestration and renewable energy (bioenergy production). It is estimated that a total mitigation of 1.5-15.6 Gt CO₂e/year (3-31% of the global anthropogenic GHG emissions of 50 Gt CO₂e/year (IPCC 2014)) could be achieved in agriculture and the land use and forestry sectors (Smith et al. 2013), with the economic potential in agriculture alone (and excluding bioenergy production) being 1.5-4.3 Gt CO₂e/year (Smith et al. 2008). This latter mitigation is to be achieved on farms by modifying current management practices, like improving nutrient management, restoring soils, administering feed additives to livestock and increasing the rate of genetic improvement both crops and livestock. However, agri-environmental policies are necessary to be developed to encourage the uptake of mitigation measures by farmers, either supporting the voluntary uptake or introducing compulsory regulations.

The heterogeneity of farms and farming conditions and the difficulty in estimating the mitigation on farms mean that market-based instruments (like carbon tax) can be very costly to set up in the agricultural sector. Reinforced by the complexity of international negotiations, this makes voluntary or targeted obligatory regulations favoured over market-based instruments (Beddington et al. 2012, Kasterine and Vanzetti 2010). Such policy instruments (often developed at national or sub-national level) require some kind of prioritisation of the mitigation measures, and an important input in this process is the cost-efficiency estimates of the measures, preferably specific to country/region, farm type, farm size and biophysical conditions (e.g. soil and climate).

A number of reports and scientific papers have estimated the mitigation potential and cost-efficiency of various measures, usually at a country-level and often using either economic modelling or bottom-up cost-engineering approaches to calculate the financial consequences of the implementation of the measures (see a typology and a short assessment of these approaches in (Vermont and De Cara 2010)). The measures are usually ranked according to their cost-effectiveness, and those are considered to be economically efficient of which cost-effectiveness is lower than the marginal benefits from emission reduction. This is approximated by a carbon price threshold, for example, in the UK by the social price of carbon (Price et al. 2007).

The bottom-up cost-engineering approach is well suited to give a detailed assessment of the mitigation measures (Kesicki and Strachan 2011), and therefore give specific advice to policy development. Still, the amount of evidence about the cost-efficiency of mitigation measures, especially when disaggregated to farm types or geographical regions, is small, as most of the studies work on the basis of country averages. Examples for the latter include numerous work on MACCs for agriculture in the European Union, New Zealand, France, Ireland and UK (Bates et al. 2009, MacLeod et al. 2010, Pape et al. 2008, Pellerin et al. 2013, Schulte et al. 2012), while papers presenting evidence on the difference between regions or farm types are less abundant (see examples for the US (Biggar et al. 2013) and France (De Cara and Jayet 2000, Dequiedt et al. 2014)).

A literature review in this deliverable investigates the cost-effectiveness estimates of mitigation measures published in the past 15 years, discussing the variability in these estimates and highlighting research gaps (Section 2). Furthermore, a study is presented in the deliverable aiming to advance our knowledge on mitigation measures beyond the developed countries, reporting on marginal abatement cost curves for beef cattle production in Brazil (Section 3).
The wide differences in the cost-effectiveness estimates across the studies suggest that regional, farm type level, or even individual farm level assessments might better suit to inform regional policy development and decision makers on farms about potential mitigation options. However, given the well-known trade-off between complexity and accuracy, modelling tools which can be widely used in the farming community would be less accurate in their emission and cost estimates than models requiring detailed information. One approach to this problem is a tool which can be operated by people with expertise in both modelling and in the farming practices of a region. Such a tool, namely the FarmAC model, has been developed in the AnimalChange project to assess the nutrient flows and GHG emissions of the farms. Section 4 of this deliverable presents the conceptual basis of a tool to assess the financial implications of the mitigation measures to be used in parallel with the FarmAC model, ultimately providing mitigation measure cost-effectiveness estimates specific to individual farms.
## 2. Literature review of the cost-effectiveness estimates of GHG mitigation measures

This section is partially based on (MacLeod and Eory 2014).

### 2.1 Introduction

The potential role agriculture can play in the global climate change mitigation effort is now well known. Some improvements in the emissions intensity of agricultural output have already been made as a result of increased resource use efficiency and regulations to lower nitrogen and phosphorous emissions. Still, identifying technically effective, economically efficient, and socially acceptable mitigation options remains a challenge. The cost-effectiveness of mitigation practices is often highly variable, being very dependent on location, weather, existing farm practices, etc. This section reviews the international literature on the cost-effectiveness of GHG mitigation measures, focusing on supply-side technical measures to be implemented on farm.

### 2.2 Methodology

A literature review was carried out to analyse the cost-effectiveness estimates of agricultural mitigation measures published within the past 15 years. The review included studies which assessed a set of mitigation measures at the same time as opposed to evaluating a single mitigation measure. Studies varied in terms of their geographical scope and sectoral scope, some being multi-sectoral, others analysing agriculture only. The list of the 37 studies reviewed is presented in Appendix 1: List of studies reviewed.

The following characteristics of the studies were recorded:

- geographical scope (e.g. global, EU-15, the Netherlands)

- geographical resolution (e.g. world regions, countries, sub-national regions, farms)

- boundaries of emission sources (on-farm versus life cycle analysis (LCA))

- interactions between measures considered or not

- MACC methodology (using the classification of Vermont and De Cara (2010), i.e. equilibrium models, micro-economic modelling, bottom-up cost-engineering MACCs)

- Cost-effectiveness (CE) results for individual measures are available or not

262 agricultural activities were identified as mitigation measures. This list was based on the long mitigation measure list compiled in Moran *et al.* (2008), complemented with any additional mitigation measure found in the studies reviewed. The mitigation measures were aggregated into 8 categories, and 22 sub-categories were also introduced to give a better overview of the measures. The categories were the following (the number of subcategories within each category is in brackets): cropland management (6), grazing land management (3), management of organic soils (0), restoration of degraded lands (0), livestock management (5), livestock housing and manure (3), land use change (0) and energy efficiency (5).

To avoid duplication, mitigation measures which were applicable both to cropland and grazing land were allocated to cropland management (in most cases these measures are not disaggregated in the studies). Due to the difference in the scope of the mitigation measures between studies, in some cases aggregate mitigation measures are listed alongside the more specific ones, for example both 'Dietary additives in general' and 'Ionophores' are measures. The list of the mitigation measures is presented in Appendix 2: Long list of mitigation measures.
The mitigation measures included in each of the studies were recorded to obtain a frequency table, showing in how many studies each mitigation measure was included. In some specific cases a mitigation measure analysed in a study was a composite of two or three different, though related mitigation measures. In this case it was recorded under all of the relevant mitigation measures, assuming that the CE value, if provided, applied to all mitigation measures.

To obtain the range of the CE results for mitigation measures, those studies had to be selected which reported data on the CE of individual mitigation measures. Due to the methodological differences, mitigation measure specific CE estimates were only available from bottom-up cost-engineering studies. Those mitigation measures for which three or more studies made CE estimates available were selected for further data collection, whereby the CE values were recorded. If a study provided more than one CE estimate for a mitigation measure, then the lowest and highest estimates were recorded. Otherwise the single value was recorded as both the lowest and the highest value. Finally, the CE data were converted to a common metric (EUR/tCO₂e). From this part of the analysis the rice cultivation measures were excluded, as they have very low relevance to the regions the AnimalChange project was looking at (i.e. Europe, Sub-Saharan Africa and South America).

## 2.3 Results

From the 37 studies reviewed, 10 were global, 17 targeted the European Union or European countries, three the United States, four Australia or New Zealand, two reviewed Kyoto Annex I countries and one reported on Asian countries (see Appendix 3: Overview of the studies). Regarding the geographical resolution, most of the studies (21) were reporting at a country or sub-national level, while 10 studies presented results at the global and regional scale and 6 other studies were farm-level work. The majority of the studies (30) considered on-farm emissions only. About half of the studies (19) addressed the issue of interactions between the measures (i.e. when the mitigation potential and/or cost of a measure changes either positively or negatively if another mitigation measure is also implemented on the farm), either accounting for the synergies and trade-offs directly (13 studies), or allocating the mitigation measures so that they are assumed not to be implemented simultaneously on the same farms (6 studies). Seven studies did not take into account interactions, and a further 11 did not specify if or how interactions had been dealt with.

Five studies used equilibrium models to calculate the mitigation potential, 8 applied supply-side micro-economic models, and the rest (24) used some form of bottom-up cost-engineering method (this category included studies relying on cost estimates produced by whole-farm bio-economic models and also studies drawing from expert opinion). CE estimates of individual mitigation measures were only available in 11 bottom-up cost-engineering studies, as the rest presented results in an aggregated way, so CE data on individual measures could not be derived from it. Those eleven studies (see their characteristics Table 1) were used to analyse the range of CE estimates for selected mitigation measures.

<table><caption>**Table 1** Overview of the eleven studies where CE data were available for individual mitigation measures</caption><thead><tr><td>ID</td><td>Scope</td><td>Resolution</td><td>Emission boundaries<sup>a</sup></td><td>Interactions<sup>b</sup></td><td>GHG gases/sinks considered</td></tr></thead><tbody><tr><td>2</td><td>Global</td><td>World region</td><td>On-farm</td><td>Yes</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>10</td><td>NW Germany</td><td>Farm</td><td>On-farm</td><td>Not specified</td><td>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub></td></tr><tr><td>11</td><td>EU-27</td><td>Country</td><td>On-farm</td><td>No simultaneous implementation</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>14</td><td>New Zealand</td><td>Country</td><td>On-farm</td><td>Not specified</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>15</td><td>UK</td><td>Country</td><td>On-farm</td><td>Yes</td><td>N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub> and soil C</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Scope</th><th>Resolution</th><th>Emission boundaries<sup>a</sup></th><th>Interactions<sup>b</sup></th><th>GHG gases/sinks considered</th></tr></thead><tbody><tr><td>18</td><td>EU-27</td><td>World region</td><td>On-farm</td><td>Not specified</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>22</td><td>Global</td><td>World region</td><td>On-farm</td><td>Yes</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>23</td><td>Kyoto Annex I countries</td><td>Country</td><td>Some LCA elements</td><td>Yes</td><td>N<sub>2</sub>O and CH<sub>4</sub></td></tr><tr><td>28</td><td>Ireland</td><td>Country</td><td>On-farm and LCA</td><td>Yes</td><td>N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub> and soil C</td></tr><tr><td>32</td><td>USA</td><td>Farm</td><td>On-farm</td><td>Not specified</td><td>N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub> and soil C</td></tr><tr><td>33</td><td>France</td><td>Country</td><td>Some LCA elements</td><td>Yes</td><td>N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub> and soil C</td></tr></tbody></table>

The eleven selected studies reported on 9 to 26 mitigation measures each, all combined assessing 100 different mitigation measures out of the 262 identified measures. The mitigation measures' frequency table (Table 2) shows how many mitigation measures within a category or sub-category were evaluated by a particular study. It also shows how many mitigation measures a category/sub-category contained (column 'MM all') and how many of these measures were mentioned in the studies (column 'MM assessed'). The one but last column ('Total frequency') presents the number of mitigation measures assessed across the eleven studies, i.e. the sum of the eleven study columns. The last column ('Relative frequency') shows how well a category/sub-category is represented (Relative frequency = Total frequency / MMs all). For example, the sub-category 'Agronomy' contains 13 different mitigation measures ('MM all'); out of these 13 measures 8 appeared in at least one of the eleven studies ('MM assessed'). Five studies assessed measures belonging to 'Agronomy': Study 11, Study 15, Study 28, Study 32 and Study 33 (e.g. Study 11 evaluated one, Study 33 evaluated three). Altogether, the sub-category 'Agronomy' appeared 9 times in the studies ('Total frequency'), so the representation of this sub-category is 1.1.

There is a wide range in how well categories and sub-categories are represented. The most frequently appearing sub-category is 'Nutrient management' on croplands: from its 36 mitigation measures 22 are included in the eleven studies, altogether assessed 52 times. Another three well-represented sub-categories are 'Feeding practices', 'Anaerobic digestion and CH<sub>4</sub> capture' and 'Rice management'; the mitigation measures within these sub-categories appear 22, 19 and 18 times, respectively, across the studies. On the other end of the spectrum are categories and sub-categories which are not at all, or just marginally explored in these studies: 'Structural and management changes' (Cropland), 'Orchards', 'Fire management', 'Management of organic soils', 'Restoration of degraded lands', 'Animal health', 'Housing', 'Land use change' and 'Energy and waste'.

Particular mitigation measures were popular and appeared in multiple studies, like measures in the sub-category 'Anaerobic digestion and CH<sub>4</sub> capture': the four mitigation measures listed there were together assessed 19 times ('On-farm AD' was evaluated in all of the eleven studies, and 'Centralised AD' were assessed in six studies), thus the relative frequency of this sub-category was 3.8. Also popular were measures from the sub-categories 'Nutrient management', 'Grazing intensity and timing', 'Rice management' and 'Feeding practices'. However, almost two thirds of the mitigation measures (62 out of the 100) were assessed only in a single study. 18 measures were assessed in two studies and another 18 in three to six studies, while two measures appeared in 10 or 11 out of the eleven studies (these measures were 'Nitrification inhibitors' and 'On-farm AD').

<footnote><sup>a</sup> 'On-farm': emissions within the farm gate are considered, On-farm + other: some off-farm emissions are considered, 'LCA': off-farm emissions (usually pre-farm or up to the retailers) are considered</footnote>

<footnote><sup>b</sup> 'Yes': interactions between mitigation measures are taken into account, 'Measure allocation': mitigation measures are allocated so that simultaneous implementation of measures do not happen, 'No': interactions are not taken into account, 'Not specified': the study does not specify whether interactions are considered</footnote>
<table><caption>Table 2 Frequency table of the mitigation measures as they appear in the eleven selected studies</caption><thead><tr><th rowspan="2">Mitigation category / sub-category</th><th rowspan="2">MM all<sup>a</sup></th><th rowspan="2">MM assessed<sup>b</sup></th><th colspan="12">Frequency of MMs within each category/sub-category by study<sup>c</sup></th><th rowspan="2">Total frequency<sup>d</sup></th><th rowspan="2">Relative frequency</th></tr><tr><th>ID: 2</th><th>10</th><th>11</th><th>14</th><th>15</th><th>18</th><th>22</th><th>23</th><th>28</th><th>32</th><th>33</th></tr></thead><tbody><tr><td><strong>Cropland management total</strong></td><td><strong>81</strong></td><td><strong>47</strong></td><td>9</td><td>5</td><td>7</td><td>1</td><td>15</td><td>4</td><td>15</td><td>7</td><td>6</td><td>9</td><td>12</td><td><strong>90</strong></td><td><strong>1.1</strong></td></tr><tr><td>Agronomy</td><td>13</td><td>8</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>2</td><td>3</td><td>9</td><td>0.7</td></tr><tr><td>Nutrient management</td><td>36</td><td>22</td><td>3</td><td>4</td><td>4</td><td>1</td><td>11</td><td>4</td><td>6</td><td>4</td><td>4</td><td>4</td><td>7</td><td>52</td><td>1.4</td></tr><tr><td>Structural and management changes</td><td>4</td><td>2</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0.5</td></tr><tr><td>Soil and water management</td><td>11</td><td>4</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>1</td><td>0</td><td>1</td><td>2</td><td>2</td><td>9</td><td>0.8</td></tr><tr><td>Rice management</td><td>15</td><td>11</td><td>6</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>8</td><td>3</td><td>0</td><td>0</td><td>0</td><td>18</td><td>1.2</td></tr><tr><td>Orchards</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td><strong>Grazing land management total</strong></td><td><strong>21</strong></td><td><strong>11</strong></td><td>1</td><td>1</td><td>0</td><td>2</td><td>1</td><td>3</td><td>0</td><td>0</td><td>2</td><td>2</td><td>5</td><td><strong>17</strong></td><td><strong>0.8</strong></td></tr><tr><td>Grazing intensity and timing</td><td>7</td><td>6</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>1</td><td>0</td><td>3</td><td>9</td><td>1.3</td></tr><tr><td>Increased productivity</td><td>13</td><td>5</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>2</td><td>8</td><td>0.6</td></tr><tr><td>Fire management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td><strong>Management of organic soils total</strong></td><td><strong>6</strong></td><td><strong>1</strong></td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td><strong>1</strong></td><td><strong>0.2</strong></td></tr><tr><td><strong>Restoration of degraded lands total</strong></td><td><strong>9</strong></td><td><strong>0</strong></td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td><strong>0</strong></td><td><strong>0.0</strong></td></tr><tr><td><strong>Livestock management total</strong></td><td><strong>76</strong></td><td><strong>24</strong></td><td>11</td><td>3</td><td>1</td><td>5</td><td>7</td><td>4</td><td>2</td><td>1</td><td>2</td><td>0</td><td>4</td><td><strong>40</strong></td><td><strong>0.5</strong></td></tr><tr><td>Feeding practices</td><td>21</td><td>10</td><td>6</td><td>3</td><td>1</td><td>1</td><td>3</td><td>2</td><td>0</td><td>1</td><td>0</td><td>0</td><td>3</td><td>20</td><td>1.0</td></tr><tr><td>Specific agents and dietary additives</td><td>25</td><td>5</td><td>1</td><td>0</td><td>0</td><td>2</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>7</td><td>0.3</td></tr><tr><td>Animal health</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td>Structural and management changes</td><td>10</td><td>3</td><td>2</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4</td><td>0.4</td></tr><tr><td>Animal breeding, genetics, herd structure</td><td>15</td><td>6</td><td>2</td><td>0</td><td>0</td><td>1</td><td>3</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>9</td><td>0.6</td></tr><tr><td><strong>Housing and manure total</strong></td><td><strong>36</strong></td><td><strong>13</strong></td><td>2</td><td>5</td><td>2</td><td>1</td><td>3</td><td>3</td><td>4</td><td>2</td><td>1</td><td>5</td><td>3</td><td><strong>31</strong></td><td><strong>0.9</strong></td></tr><tr><td>Housing</td><td>10</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0.1</td></tr><tr><td>Manure storage and handling</td><td>21</td><td>9</td><td>0</td><td>3</td><td>0</td><td>0</td><td>1</td><td>1</td><td>2</td><td>0</td><td>0</td><td>3</td><td>1</td><td>11</td><td>0.5</td></tr><tr><td>Anaerobic digestion and CH<sub>4</sub> capture</td><td>5</td><td>3</td><td>2</td><td>1</td><td>2</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>2</td><td>2</td><td>19</td><td>3.8</td></tr><tr><td><strong>Land use change total</strong></td><td><strong>13</strong></td><td><strong>2</strong></td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td><strong>2</strong></td><td><strong>0.2</strong></td></tr><tr><td><strong>Energy and waste</strong></td><td><strong>20</strong></td><td><strong>2</strong></td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td><strong>2</strong></td><td><strong>0.1</strong></td></tr><tr><td>Transport</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.5</td></tr><tr><td>Heating and electricity</td><td>7</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.1</td></tr><tr><td>Waste</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td>Electricity generation</td><td>8</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td>Other energy and waste</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.0</td></tr><tr><td><strong>TOTAL</strong></td><td><strong>262</strong></td><td><strong>100</strong></td><td>23</td><td>14</td><td>11</td><td>9</td><td>26</td><td>14</td><td>21</td><td>10</td><td>12</td><td>17</td><td>26</td><td><strong>183</strong></td><td><strong>0.7</strong></td></tr></tbody></table>

<sup>a</sup> Number of mitigation measures grouped into the category/sub-category; MM: mitigation measure

<sup>b</sup> Number of mitigation measures in the category/sub-category mentioned at least once across the eleven studies

<sup>c</sup> Number of mitigation measures within a category or sub-category which were evaluated by a particular study

<sup>d</sup> Frequency showing the number of mitigation measures assessed across the eleven studies (including repetitions)
<figure><img src="image_4.png" /><figcaption>Figure 1 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 4 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 5 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6 Cost-effectiveness of mitigation measures (dotted line: notional carbon price, n: number of studies providing information)</figcaption></figure>
Lowest and highest cost-effectiveness estimates were recorded for those mitigation measures (less rice cultivation related measures) which appeared at least in two studies, i.e. for 31 mitigation measures. These estimates are presented on Figure 1 – Figure 6 (note the log scale on the y axis). The dotted horizontal line represents a notional carbon price of € 50 tCO₂e⁻¹, and the number of studies providing estimates for each measure is indicated in the legend under the x axis. The range of cost-effectiveness estimates for almost all mitigation measures is strikingly wide; the majority of the mitigation measures have both positive and negative values (negative values meaning cost savings), and in most cases the estimates can differ by two or three orders of magnitude or more. There are a number of reasons for the variability between studies. One is the differences in the modelling and calculation approaches the studies use. For example, LCA calculations might attribute very different GHG mitigation potential to certain measures than on-farm GHG calculations do. Another methodological difference which can result in high variability in the cost-effectiveness estimates is the way the interactions are considered. In some studies the mitigation potentials of the measures targeting the same emission source are reduced cumulatively, thus increasing the cost-effectiveness of the measures. An example is the measure ‘Biological fixation’ in Moran et al. (2008), which has a cost-effectiveness of £43 tCO₂e⁻¹ in 2022 if considered on its own (‘stand-alone cost-effectiveness’), but becomes very expensive (€13,435 tCO₂e⁻¹) if other measures targeting soil N₂O emissions are implemented prior to it. In studies where the issue of interactions is dealt with an allocation assumption (i.e. assuming that one each farm only one mitigation measure is implemented to tackle a particular emission source) the mitigation potential of the measures and therefore their cost-effectiveness is not affected. Other drivers of the variability between studies are the difference in geographical locations they cover, the different farm sizes they might use as an average, and further differences in key assumptions about the biophysical efficiency and the financial implications of the measure.

All of the studies reports more than one cost-efficiency estimates for at least one-third of the mitigation measures. In 4 studies all the mitigation measures have more than one cost-effectiveness estimate. The disaggregation is most often done on a spatial basis, e.g. for world regions in global studies (Graus et al. 2004, Hasegawa and Matsuoka 2010), for countries in EU studies (Amann et al. 2008, Bates et al. 2009, Hoglund-Isaksson et al. 2010) or for states in the case of United States (Biggar et al. 2013). It is also common to differentiate – at least for some mitigation measures, like those related to animal feeding – between livestock types, e.g. dairy versus beef cattle (Bates et al. 2009, Graus et al. 2004, Moran et al. 2008, Pape et al. 2008, Pellerin et al. 2013). Some further bases for disaggregation include farm types (Weiske and Michel 2007), crop type (Biggar et al. 2013), year of mitigation (Moran et al. 2008), discount rate (Moran et al. 2008, Pape et al. 2008), GHG accounting methodology (Schulte et al. 2012). Pellerin et al. (2013) also reports a range for some of the cost-effectiveness estimates, though the origin of this range is not well explained. Interestingly, the range between the lowest and highest estimates can be wide even within a study, for example, out of the ten mitigation measures recorded from (Biggar et al. 2013) the lowest range of cost-effectiveness estimates is €77 tCO₂e⁻¹ (‘Covering slurry stores’), while the widest range is close to €5,000 tCO₂e⁻¹ (‘Nitrification inhibitors’). The widest range within one study is €7,800 tCO₂e⁻¹ for the mitigation measure ‘Reduced tillage’ (Moran et al. 2008).

The mitigation measures can be grouped into three categories according to the range their cost-effectiveness estimates cover:

1. Negative and/or small positive cost-effectiveness: these measures are estimated to have either negative cost-effectiveness (providing savings to the farmers), or have a positive cost-effectiveness which is still lower than a threshold carbon price, meaning that though their implementation would cost money, it would be still a cost-effective way to reduce GHG emissions. Here a carbon price threshold of €50 tCO₂e⁻¹ is used for the purpose of discussion.

<figure><img src="image_10.png" /></figure>
2. Small positive and high positive cost-effectiveness: these measures are estimated to cost money to the farmers, and while in some cases still they can be a cost-effective, they might have cost-effectiveness beyond the threshold carbon price, indicating that their implementation might not only be expensive, but would not be economically efficient – though further consideration of additional impacts might make them desirable.

3. Cost-effectiveness estimates in the whole range (negative, small positive, high positive): the cost-effectiveness values reported for these measures span across the whole range, though for some mitigation measures there is a visible tendency with the estimates falling more into one or the other part of the range.

<table><caption>**Table 3 Grouping of the mitigation measures according to their cost-effectiveness estimates**</caption><thead><tr><th>Cost-effectiveness group</th><th>Mitigation measures (n)<sup>a</sup></th></tr></thead><tbody><tr><td>1. Negative and/or small positive</td><td><ul><li>Full allowance of manure supply (2)</li><li>Improved maintenance of fertiliser spreaders (3)</li><li>Extended grazing season (2)</li><li>Take stock off from wet ground (2)</li><li>High starch diet (3)</li><li>Ionophores (2)</li><li>Selection for reduced enteric CH<sub>4</sub> emissions (2)</li><li>Improved genetic potential – productivity (3)</li></ul></td></tr><tr><td>2. Small positive and high positive</td><td><ul><li>Catch/cover crops (2)</li><li>Biological N fixation in rotations (2)</li><li>Fertiliser free zone on field edges (3)</li><li>No-till (3)</li><li>Propionate precursors (2)</li><li>CH<sub>4</sub> capture and combustion (2)</li></ul></td></tr><tr><td>3. Whole range</td><td><p><strong>Mostly negative or small positive:</strong></p><ul><li>Avoiding N excess (adjusting yield targets) (4)</li><li>Reduce N fertiliser (5)</li></ul><p><strong>Mostly high positive:</strong></p><ul><li>Precision farming (4)</li><li>Placing N precisely in soil (3)</li><li>Improved timing of manure application (3)</li><li>Nitrification inhibitors (10)</li><li>Feeding more concentrates (3)</li><li>High fat diet (4)</li><li>Centralised anaerobic digesters (6)</li></ul><p><strong>No clear tendency:</strong></p><ul><li>Improved timing of mineral N application (3)</li><li>Reduced tillage (4)</li><li>Biological N fixation on pastures (2)</li><li>Reduce stocking rates (2)</li><li>Improved feed intake (3)</li><li>Bovine somatotrophin (2)</li><li>Covering slurry stores (3)</li><li>On-farm anaerobic digesters (11)</li></ul></td></tr></tbody></table>

<footnote>a n: number of studies where cost-effectiveness data were available</footnote>
Approximately half of the mitigation measures' cost-effectiveness does not spread very wide, seemingly defining clearly whether the measure provides savings (Group 1), or costs money but can be cost-effective (Group 2). The remaining measures have cost-effectiveness estimates in the whole range (Group 3). Among these latter two seem the be mostly cost-effective: 'Avoiding N excess (adjusting yield targets)' and 'Reduce N fertiliser', while four are usually estimated to be too expensive ('Precision farming', 'Placing N precisely in the soil', 'Nitrification inhibitors', 'Centralised anaerobic digesters'). The remaining seven measures in this group show no clear tendency.

Important to note that those mitigation measures which fall into Group 1-2 have only a very limited number of estimates recorded, as they are represented only in two, maximum three studies. On the other hand, the mitigation measures which have been assessed in four or more studies all belong to Group 3, i.e. the range of estimates always spreads from negative to high positive.

## 2.4 Discussion

This review reveals that there are three important areas and issues which are often neglected in studies on the marginal abatement costs and cost-effectiveness of agricultural GHG mitigation. First, mitigation in developing countries have hardly been assessed so far, only a few studies offered estimates for developing countries as part of a global assessment, and one study reported on mitigation in rice production in three Asian countries (Wassmann and Pathak 2007). Still, a significant growth in agricultural production and related GHG emissions are likely to occur in developing nations (Steinfeld et al. 2006), and currently the emission intensity of production in these countries is often higher than in developed countries (MacLeod et al. 2013, Opio et al. 2013). At the same time, food security and poverty alleviation in the face of changing climate and growing population is of utmost importance in these regions (Foresight 2011). Thus, assessing opportunities for mitigating GHG emissions in these regions is a pressing need.

Second, the majority of the studies looked at impacts on emissions within the farm gate, even though changes on farm can have effects on emissions from other parts of the supply chain (like emissions from fertiliser or livestock feed production). Admittedly, exploring sectoral emissions is an important part of planning national and international emission budgets, and by keeping the analysis within the farm gate double-counting of mitigation can be avoided in such an exercise. However, ignoring off-farm emissions poses the risk of emission leakage, for example reducing on-farm enteric methane emissions on a cattle farm by increasing the amount of grains in the feed might result in a net emission increase due to the increased CO₂ emissions from related land-use change (Vellinga and Hoving 2011). Similarly, as demonstrated by Schulte et al. (2012), the cost-effectiveness of the mitigation measures can be very different with different system boundaries; increasing emphasis on cost-effectiveness work done by LCA approaches in the future would help to raise awareness to this problem at the policy level.

Finally, not all agricultural emission sources and sinks are considered equally across the studies. Possibly because of the dominance of N₂O and CH₄ emissions in the agricultural sector of the national inventories many studies focus only on these two gases, ignoring CO₂ emissions and C sequestration effects. Some studies look at CO₂ emissions as well, and a growing number of studies also take into account long term changes in soil C stock. CO₂ emissions related to agricultural energy use are important, specifically within some farm types, like dairy cattle and horticulture. Even more important is the soil C stock change related to agricultural activities, and very often mitigation measures targeting soil C have positive effects on soil structure and climate change adaptation. Agricultural MACCs cannot be complete without these effects and mitigation measures.

<figure><img src="image_11.png" /></figure>
As presented in Table 2, certain categories and subcategories of mitigation measures are very much favoured across the bottom-up cost-engineering agricultural MACCs, e.g. cropland nutrient management, rice management, grazing intensity and timing, livestock feeding practices, and, particularly, anaerobic digestion and CH₄ capture. Others, like organic soils, animal health, waste and energy use have been very much neglected so far. The popularity of the subcategories is only partly a consequence of the perceived efficacy of the mitigation measures, it is also a function of historic research focus (e.g. many nitrogen related measures have been earlier identified as ways of reducing ammonia emissions and nitrogen leaching) or perceived novelty of the measure (e.g. anaerobic digestion and nitrate feeding). The research gaps highlighted in this review highlight the need for further research. Animal health related mitigation measures can be addressed in bottom-up cost-engineering approaches within the boundaries of the farm gate, while work on organic soils and degraded land necessitates looking beyond current agricultural areas, and waste reduction measures can be best addressed by methods looking at the whole supply chain (cradle to grave LCA analysis).

The cost-effectiveness estimates of the mitigation measures vary greatly both within and between studies. Many drivers of the variability can be explored by comparing the results and the methodologies of the studies, but most of these differences are not clearly stated in the methodology sections of the studies. Beyond the need for clearer description of the studies boundaries and methodologies, an important omission should also be addressed in the future: the lack of uncertainty analysis and sensitivity analysis (though disaggregation by the discount factor can be considered as a sensitivity analysis to the discount factor).

Two measures were identified which showed consistently favourable cost-effectiveness estimates across more than three studies: 'Avoiding N excess (adjusting yield targets)' and 'Reduce N fertiliser'. The latter measure refers to a combination of suboptimal fertilisation (i.e. reducing the N fertiliser amount below the economic optimum) and proportional fertiliser reduction (e.g. 10%, 20%), where the study did not specify whether this reduction is a reduction only in the excess N or beyond that. Admittedly there is a difficulty in drawing clear boundaries between the mitigation measures in general, particularly in some sub-categories, like 'Nutrient management'. The difficulty is exacerbated by the often inaccurate description of the mitigation measures and the assumptions related to them.

On the other side of the spectrum, still amongst the measures where data are available at least in four studies, are four measures ('Precision farming', 'Nitrification inhibitors', 'High fat diet', 'Centralised anaerobic digesters') which, though have a wide cost-effectiveness estimate range, but most often are estimated to have too high cost-effectiveness to be considered economically efficient. All of them have either high upfront costs or high running costs. Examples are the machinery and computer system in 'Precision farming', the extended slurry storage for 'Improved timing of manure application', the significant investment required for a 'Centralised anaerobic digesters', the annual cost of chemicals used as 'Nitrification inhibitors', or the increased feed costs in 'Feeding more concentrates' and in 'High fat diet'.

However, many measures with high upfront or running costs still have estimates indicating that the measure can be cost-effective and, in some cases, might provide financial savings. Examples of such measures were 'Reduced tillage', 'No-till', 'Covering slurry stores', and, most notably, 'On-farm anaerobic digester'.

The limitation of this review is that it considers only those studies which look both at the environmental and economic effects of mitigation, thus it narrows down the evidence base considerably. This means that potentially cost-effective measures (currently in Group 1 and Group 2) might be failed to be identified as consistently cost-effective measures or
consistently non-cost-effective measures, due to the current lack of information. The same applies to the long list of measures which are not assessed in any of these studies.

The range of the cost-effectiveness estimates is so large that, on the global scale, no firm conclusions can be drawn on which mitigation measure is better than the other. Mitigation measures which are assessed in more than three studies show a wide range of cost-effectiveness estimates, and *vice versa*, the low number of observations for many measures can be the main reason why they can be categorised more easily. At the same time, the low number of studies limits conducting analysis at a smaller spatial scale or disaggregating the studies in other ways. However, a meta-analysis of the estimates could shed light on some of the drivers of the variability in the estimates. Also, it can be possible to draw some conclusions about individual mitigation options at a regional level. This might be possible by looking at the accumulating evidence on the biophysical effects as analysed in meta-analyses and, at the same time, exploring the private and public cost elements and non-financial barriers in more detail.

The range of estimates is driven only partly by the variability in the systems studied: uncertainty also has an important role. Unfortunately, this uncertainty has not been explored in any of the reviewed studies. Therefore it is of paramount importance that the results of any single study have to be considered only as an indicative value. Additionally, as the cost-effectiveness metric only integrates two features of a mitigation measure (namely costs and mitigation potential), a range of other aspects have also to be considered in a policy development process, for example positive and negative co-effects, the cost and complexity of policy instruments needed to promote uptake and the behavioural barriers to uptake.

Going further, beyond filling in the research gaps identified above, there is a need for developing regional and tailored policy instruments. This could include increasing the availability of farm-specific advice, equipping the farmers and advisors with regionally appropriate information and decision support tools, and, at the same time, promoting the development of monitoring and verification mechanisms, like the use of proxies and simple modelling tools.

However, the value of these studies lies mostly in drawing up an inventory of the likely consequences of the GHG mitigation on farms, discussing possible policy instruments to promote behavioural change and raising awareness among stakeholders by contributing to the public discourse on GHG mitigation.
# 3. Marginal abatement cost curves for beef production in the Brazilian *Cerrado*

This section is a brief summary of Oliveira Silva *et al*. (2015), attached in Appendix 4: Marginal abatement cost curves for beef production in the Brazilian *Cerrado*.

## 3.1 Introduction

The expected growth in livestock production poses significant additional pressure on natural resources. Sustainable management will require increasing yields and efficiency in existing ruminant production systems, minimizing competition of land used for food and feed, while maximizing ecosystem services, including mitigation of greenhouse gas (GHG) emissions (Gerber *et al.* 2013, Soussana *et al.* 2013, Thornton and Herrero 2010). Brazil is the second world's largest beef producer of the world, with a production predominantly based on pastures mostly in humid and sub-humid tropical climate.

Tropical regions are implicated as potentially offering major opportunities to increase beef productivity and emissions mitigation, as current productivity levels are still relatively low and emission intensities correspondingly high (Gerber *et al.* 2013, Opio *et al.* 2013). More productive pastures can increase soil carbon stocks, providing one of the largest terrestrial carbon sinks (Follett and Reed 2010, Neely *et al.* 2009). In addition, increasing productivity through feed supplementation may significantly reduce direct methane emissions (Berndt and Tomkins 2013, Ruviaro *et al.* 2014).

This paper investigates the cost-effectiveness of livestock mitigation measures applicable in the *Cerrado* core (Central Brazilian Savannah). The paper offers the first MACC analysis using an optimization model for Brazilian beef production. The evaluated measures are pasture restoration, feedlot finishing, supplement concentrates and protein, nitrification inhibitors and pasture irrigation. The analysis uses the outputs of a multi-period Linear Programming model (the Economic Analysis of Greenhouse Gases for Livestock Emissions model- EAGGLE (Oliveira Silva 2013)) to develop a bottom-up or engineering MACC. The analysis accounts for both the private and social costs and benefits (e.g. including avoided deforestation) and provides new insights for regional policy intervention.

## 3.2 Methodology

In our analysis we use a marginal abatement cost curve (MACC) approach to rank the cost-effectiveness of a range of mitigation measures. Agricultural MACCs are used to compare the relative costs of implementing these measures and the amount of mitigation they offer under average farm conditions. MACCs can be used to determine the relative cost-effectiveness of each measure in terms of cost per tonne of CO₂ mitigated. With this information it is possible to define the lowest cost ways to achieve a given GHG reduction target. In our case, the MACC is derived using output from a multi-period linear programming model. We represent the *Cerrado* region as a single production unit (or farm) and seek to maximize production value subject to economic and biophysical constraints.

**Overview:** The EAGGLE model (Oliveira Silva 2013) is a multi-period linear programming model that represents the complete production cycle (cow-calf, stocking and finishing) on a beef farm. The model allocates farm resources optimally to meet demand projections while maximizing profit. In this analysis, the *Cerrado* beef production system is treated as a single farm.
**Outputs:** Profit (gross margin) and net GHG emissions are obtained by running the model for a given beef demand projection and associating the resulting animal numbers with standard emissions coefficients, land use conversion emissions (i.e. loss of biomass in terms of CO2e) and changes in soil organic carbon stocks.

**Building the MACC:** By assuming the adoption of a mitigation measure 'm scenario', the values of the outputs (profit and net emissions) are evaluated relative to a baseline without measure adoption. The abatement potential is calculated as the difference between GHG emissions under the two scenarios. Cost-effectiveness is calculated as the difference between profit, divided by the difference between emissions under the 'm scenario' and the baseline.

**3.3 Results and discussion**

<figure><img src="image_12.png" /><figcaption>**Figure 7 Estimated marginal abatement cost schedule for mitigation measures in Cerrado livestock production, 2006 to 2030**</figcaption></figure>

* Not in scale

By implementing negative-cost measures identified in the MACC, by 2030, regional emissions could be reduced by 27.8 Mt CO2e yr⁻¹, while the abatement potential of all measures shown by the MACC is 28.2 Mt CO2e yr⁻¹. Pasture restoration, involving avoided deforestation, offers the largest contribution to these results.

The results who that pasture restoration is a very promising mitigation measure, with a total annual abatement potential of 27 Mt CO2e at negative costs, i.e. providing savings to livestock farmers. Increasing the amount of concentrates and protein in the feed can, similarly, generate savings on the beef farms. Adopting these measures could reduce the Cerrado beef production emissions by 23.7% by 2030.
# 4. Cost-effectiveness assessment of mitigation options on model farms

## 4.1 Introduction

Component 3 (Work Packages 9-11) of the AnimalChange project was assessing mitigation options on model and showcase farms in Europe, South America and Africa both from the GHG emission perspective and from their financial performance. The work included the following steps (more details of these steps can be found in research deliverables indicated in brackets):

- Development of FarmAC (Deliverables 9.1, 9.2, 9.3, 9.4)

- Selection of model farms and showcase farms (Deliverables 10.1 and 10.5)

- Selection of mitigation measures (described below)

- Detailed description of mitigation measures for biophysical and economic modelling (described below)

- Biophysical modelling of mitigation measures in FarmAC (Deliverables 10.4 and 10.3)

- Development of the economic assessment tool (described below)

The section is reporting on the selection and description of the mitigation options and on the development of the economic assessment tool. For the latter the methodology is explained, with a future prospect of using this tool to gain deeper insight into the differences in the economic efficiency of GHG mitigation on European farms.

## 4.2 Identification and description of the mitigation options for modelling

### 4.2.1 Initial selection for modelling

Work reported in D8.1 'Qualitative overview of mitigation and adaptation options and their possible synergies & trade-offs' developed a list of mitigation and adaptation options, containing 42 mitigation options. On a workshop attended by whole-farm GHG modelling experts 27 of these mitigation options were selected for consideration for biophysical modelling (Table 4), based on three criteria:

- High GHG abatement,

- Whole farm effect as opposed to isolated effect on a single emission source and

- Low perceived difficulty in modelling.

<table><caption>Table 4 Mitigation options initially selected for modelling</caption><thead><tr><td>Mitigation option</td></tr></thead><tbody><tr><td>Fertilisation rate</td></tr><tr><td>Nitrification inhibitors</td></tr><tr><td>Grass-legume swards</td></tr><tr><td>Legumes in the rotation</td></tr><tr><td>Cover crops</td></tr><tr><td>Irrigation</td></tr></tbody></table>
<table><thead><tr><td>Mitigation option</td></tr></thead><tbody><tr><td>Restoring degraded lands</td></tr><tr><td>Improving pastures</td></tr><tr><td>Improving roughage quality</td></tr><tr><td>Feeding more maize and less grass</td></tr><tr><td>Feeding more fat</td></tr><tr><td>Additive nitrate</td></tr><tr><td>Balancing amino acids and reduce CP</td></tr><tr><td>Increasing housing (grass constant)</td></tr><tr><td>Replacement rate cattle</td></tr><tr><td>Cover slurry stores</td></tr><tr><td>Manure acidification</td></tr><tr><td>Anaerobic digestion</td></tr><tr><td>Agroforestry</td></tr><tr><td>Genetic improvement in dairy cattle</td></tr><tr><td>Start the feedlot fattening period at a younger age</td></tr><tr><td>Change the grazing management</td></tr><tr><td>Clean the pasture from unwanted species</td></tr><tr><td>Integrate livestock and crop production</td></tr><tr><td>Reducing age at first calving</td></tr><tr><td>Optimising calving dates</td></tr><tr><td>Using enzymes (phytase)</td></tr></tbody></table>

### 4.2.2 Selection of five best mitigation options by farm experts

The next step was a further selection, whereby showcase and model farm experts (i.e. researchers in AnimalChange Component 3 with in-depth knowledge of regional farming systems) picked the best five options out of the 27 options listed above, considering how they perceive the mitigation options in relation to the following criteria for their particular farms:

- High GHG abatement

- Low cost

- High likely uptake

Farm experts were asked not to consider other criteria at this stage, like scientific relevance to the project, modelling capabilities or data availability. The rankings are presented on Table 6 and Table 7, for European and for South American and African farms, respectively.

Out of the 27 options only one option was not selected by any expert, and that was 'Nitrification inhibitors'. 21 options were selected for at least two farms, and the most popular choices were the following:
<table><caption>Table 5 Most popular mitigation options from the '5 best' exercise</caption><thead><tr><td>Overall</td><td>Additionally in Europe</td><td>Additionally in South America and Africa</td></tr></thead><tbody><tr><td>Improving pastures</td><td>Feeding more maize and less grass</td><td>Change grazing management</td></tr><tr><td>Improving roughage quality</td><td>Feeding more fat</td><td>Restoring degraded lands</td></tr><tr><td>Fertilisation rate</td><td>Reducing age at first calving</td><td>Agroforestry</td></tr><tr><td>Legumes in the rotation</td><td></td><td></td></tr><tr><td>Grass-legume swards</td><td></td><td></td></tr><tr><td>Replacement rate cattle</td><td></td><td></td></tr></tbody></table>

The majority of the most popular mitigation options increase the production efficiency of farms, either by more efficient use of nutrient in crop and livestock production ('Improving pastures', 'Improving roughage quality', 'Fertilisation rate', 'Legumes in rotation', 'Grass-legume swards'), or by higher productivity at the herd level ('Replacement rate cattle', 'Reducing age at first calving'). Note that in the option 'Fertilisation rate' was usually understood differently in the regions, in Europe it was considered as decreasing the fertilisation rate while in South America and Africa the opposite was assumed, as current fertilisation rates are very low, and increasing them would increase area-based productivity more than the area-based GHG emissions. All of these options are generally regarded to have low costs, mostly requiring a change in management approach rather than additional investment or annual expenses. The mitigation options most popular in South America and Africa but not in Europe were those which benefit soil quality and soil C stocks – corresponding to the existing agronomic problems in these areas. Interestingly, two mitigation options which are usually estimated to incur increased annual costs were also selected, albeit only in Europe: 'Feeding more maize' and 'Feeding more fat'. Though the first is regarded to result in productivity benefits, which might be higher than the increased costs, the second is mostly considered as an option rather to decrease GHG emissions than to increase productivity. On the other hand, the mitigation option 'Nitrification inhibitors' were not selected by any experts, even though it is commonly considered to have a high mitigation potential – though at an increased annual expense.
<table><caption>Table 7 The best five the mitigation options by farm as selected by farm experts in South America and Africa</caption><tr><td>Livestock</td><td>Brazil, Amazon</td><td>Sub-humid/humid</td><td>Fertilisation rate</td><td>Nitrification inhibitors</td><td>Grass-legume swards</td><td>Legumes in the rotation</td><td>Cover crops</td><td>Irrigation</td><td>Restoring degraded lands</td><td>Improving pastures</td><td>improving roughage quality</td><td>Feeding more maize and less grass</td><td>Feeding more fat</td><td>Additive nitrate</td><td>Balancing amino acids and reduce CP</td><td>Increasing housing (grass constant)</td><td>Replacemnt rate cattle</td><td>Cover slurry stores</td><td>Manure acidification</td><td>Anaerobic digestion</td><td>Agroforestry</td><td>Genetic improvement in dairy cattle</td><td>Start the feedlot fattening period at a younger age</td><td>Change the grazing management</td><td>Clean the pasture from unwanted species</td><td>Integrate livestock and crop production</td><td>Reducing age at first calving</td><td>Optimizing calving dates</td><td>Using enzymes (Phytase)</td></tr><tr><td>Intensive dairy</td><td>Brazil</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td>x</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intensive beef</td><td>Brazil</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Crop livestock</td><td>Burkina Faso</td><td>Semi-arid grassland</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Crop livestock pastoral</td><td>Burkina Faso</td><td>Sub-humid</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive / semi-intensive</td><td>French Guian</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>Brazil</td><td>Sub-humid/humid</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>South Africa</td><td>Semi-arid grassland</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>Brazil, Cerra</td><td>Arid</td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>Brazil, Amazon</td><td>Sub-humid/humid</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>Senegal</td><td>Semi-arid grassland</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>
### 4.2.3 Selection for modelling across farm types

The objective of this step was to suggest a combination (matrix) of mitigation option x farm for subsequent modelling, based on:

- Experts' choices of five best options

- Maximising the overlap of mitigation options between farm type

- Reducing possible modelling and data availability difficulties

- Considering time and resource constraints

Two additional criteria were also considered: in the final combination one option ('Fertilisation rate') should be modelled on all farms, and two farms (maritime grass-based dairy, maritime mixed dairy) will be modelled with a high number of mitigation options, thus achieving a horizontal and a vertical cross-section. The aim was to achieve a balance in the matrix, trying to keep the experts' choices as far as possible while increasing overlap between farms, and balancing this with the modelling (time and effort) constraints.

4 different mitigation option x farm combinations were developed and evaluated at a workshop by the farm experts and the model developers (Table 8):

- '7 best' was aiming to retain the most from the farm expert's 'five best' choices and proposed to evaluate 7 mitigation options on each farm.

- '4 best local' was proposing to evaluate 4 options on each farm with an emphasis on keeping the expert's original choices.

- '4 best overlap' was also proposing to evaluate 4 options/farm, but with a focus on maximising the overlaps between the farms, i.e. to have more options evaluated on multiple farms.

- '3 best easy' was suggesting to assess 3 options on each farm aiming for those which are relatively easier to model.

The final matrix was agreed by the CP3 farm experts and modellers. It was based on the '4 best local' and '4 best overlap' combinations, proposed to have 20 options evaluated, on average 6.5 options/farm (ranging from four to nine options/farm), achieving a good overlap of mitigation options on farms, though at the expense of high total number of modelling runs and the inclusion of a number of the more complex mitigation options. This final matrix is presented on Table 9, and served as the starting point for the farm assessment work. The farm experts used it as a starting point in their modelling and had a choice of adding or leaving out options if needed. The final list of modelled options x farms can be found in Deliverable 10.3.
<table><caption>Table 8 Evaluation of mitigation option x farm combinations</caption><thead><tr><th></th><th>Number of<br>options to be<br>modelled</th><th>Modelled on at<br>least three farms</th><th>Number of<br>difficult<br>measures</th><th>Total number<br>of runs (farm<br>type X option)</th><th>Overlap with '5<br>best options'</th></tr></thead><tbody><tr><td><strong>Europe</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7-local</td><td>24</td><td>15</td><td>7</td><td>108</td><td>77</td></tr><tr><td>4-local</td><td>20</td><td>9</td><td>6</td><td>64</td><td>54</td></tr><tr><td>4-overlap</td><td>13</td><td>8</td><td>3</td><td>64</td><td>46</td></tr><tr><td>3-easy</td><td>8</td><td>5</td><td>1</td><td>48</td><td>29</td></tr><tr><td>Final</td><td>16</td><td>13</td><td>3</td><td>112</td><td>50</td></tr><tr><td><strong>South America and<br>Africa</strong></td><td>Number of<br>options to be<br>modelled</td><td>Modelled on at<br>least three farms</td><td>Number of<br>difficult<br>measures</td><td>Total number<br>of runs (farm<br>type X option)</td><td>Overlap with '5<br>best options'</td></tr><tr><td>7-local</td><td>17</td><td>10</td><td>8</td><td>63</td><td>50</td></tr><tr><td>4-local</td><td>11</td><td>6</td><td>6</td><td>36</td><td>31</td></tr><tr><td>4-overlap</td><td>9</td><td>7</td><td>5</td><td>36</td><td>29</td></tr><tr><td>3-easy</td><td>5</td><td>4</td><td>1</td><td>27</td><td>21</td></tr><tr><td>Final</td><td>10</td><td>8</td><td>6</td><td>51</td><td>35</td></tr><tr><td><strong>Overall</strong></td><td>Number of<br>options to be<br>modelled</td><td>Modelled on at<br>least three farms</td><td>Number of<br>difficult<br>measures</td><td>Total number<br>of runs (farm<br>type X option)</td><td>Overlap with '5<br>best options'</td></tr><tr><td>7-local</td><td>25</td><td>23</td><td>8</td><td>171</td><td>127</td></tr><tr><td>4-local</td><td>22</td><td>15</td><td>7</td><td>100</td><td>85</td></tr><tr><td>4-overlap</td><td>17</td><td>13</td><td>6</td><td>100</td><td>75</td></tr><tr><td>3-easy</td><td>11</td><td>7</td><td>2</td><td>75</td><td>50</td></tr><tr><td>Final</td><td>20</td><td>18</td><td>6</td><td>163</td><td>85</td></tr></tbody></table>
<table><caption>Table 9 Final mitigation option x farm combination (x: mitigation option for that farm to be assessed)</caption><thead><tr><th></th><th></th><th></th><th>Fertilisation rate</th><th>Nitrification inhibitors</th><th>Grass-legume swards</th><th>Legumes in the rotation</th><th>Cover crops</th><th>Irrigation</th><th>Restoring degraded lands</th><th>Improving pastures</th><th>improving roughage quality</th><th>Feeding more maize and less grass</th><th>Feeding more fat</th><th>Additive nitrate</th><th>Balancing amino acids and reduce CP</th><th>Increasing housing (grass constant)</th><th>Replacemnt rate cattle</th><th>Cover slurry stores</th><th>Manure acidification</th><th>Anaerobic digestion</th><th>Agroforestry</th><th>Genetic improvement in dairy cattle</th><th>Start the feedlot fattening period at a younger age</th><th>Change the grazing management</th><th>Clean the pasture from unwanted species</th><th>Integrate livestock and crop production</th><th>Reducing age at first calving</th><th>Optimizing calving dates</th><th>Using enzymes (Phytase)</th></tr></thead><tbody><tr><td>Mixed dairy</td><td>Europe</td><td>Maritime</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mixed dairy</td><td>Europe</td><td>Maritime</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mixed dairy</td><td>Europe</td><td>Continental</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mixed dairy</td><td>Europe</td><td>Mediterranean</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mixed beef</td><td>Europe</td><td>Maritime</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mixed beef</td><td>Europe</td><td>Continental</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland dairy</td><td>Europe</td><td>Maritime</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland beef</td><td>Europe</td><td>Maritime</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland beef</td><td>Europe</td><td>Continental</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland beef</td><td>Europe</td><td>Mountain</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland sheep</td><td>Europe</td><td>Mountain</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland sheep</td><td>Europe</td><td>Mediterranean</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland sheep</td><td>Europe</td><td>Mediterranean</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland sheep</td><td>Europe</td><td>Mediterranean</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pig</td><td>Europe</td><td>N-Eu</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pig</td><td>Europe</td><td>S-Eu</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>Brazil, Amazon</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intensive dairy</td><td>Brazil</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intensive beef</td><td>Brazil</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Crop livestock</td><td>Burkina Faso</td><td>Semi-arid grassland</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Crop livestock pastoral</td><td>Burkina Faso</td><td>Sub-humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive / semi-intensive</td><td>French Guiana</td><td>Sub-humid/humid</td><td>x</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>Brazil</td><td>Sub-humid/humid</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>South Africa</td><td>Semi-arid grassland</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extensive beef</td><td>Brazil, Cerrado</td><td>Arid</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>Brazil, Amazon</td><td>Sub-humid/humid</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>Senegal</td><td>Semi-arid grassland</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

FINAL SET
### 4.2.4 Description of mitigation options for biophysical and economic modelling

The biophysical modelling work in Component 3 was done by farm experts, therefore a common understanding of the mitigation options had to be achieved. This common understanding had to cover the mitigation rationale, the agronomic details and the cost elements of the mitigation option. Additional to that, agreement had to be made on the boundaries of the mitigation options (e.g. in case an option would reduce the animal feed available on farm, is the whole-farm response to reduce stocking rates or to increase the amount of feed bought in?), and, as a continuous process, the fine details of the biophysical modelling implementation had to be shared amongst the experts.

While acknowledging that the mitigation measures would be implemented somewhat differently on the farms, common guidelines on the mitigation measures were developed to help to make the results as comparable as possible. A brief description of the options was prepared (see 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.

#### 2.1 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.

<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>

<footnote n="1">¹ Estimated using the models published by Tonato *et al.* (2010)</footnote>
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 $lu \in \{A, B, C, D, E, F, Soybeans, Corn(silage), Corn(graing)\}$. 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).

## 2.2 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:

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

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).

<figure><img src="image_13.png" /></figure>

<footnote n="2">² According to Parton (1987)</footnote>
## 2.3 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.

## 2.4 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 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).

## 2.5 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.

<figure><img src="image_14.png" /><figcaption>Table 3: Selected livestock mitigation measures</figcaption></figure>
<table><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>

### 2.5.1 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>

<figure><img src="image_15.png" /></figure>

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

### 2.5.2 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.

### 2.5.3 Nitrification Inhibitors

The measure works by avoiding a proportion of the Nitr fertilizer or manure being converted into N₂O, i.e. nitrification and denitrification.35 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 systemwhere it is unfeasible to apply inhibitors to animal excreta.

### 2.5.4 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 finished herd, the measure can be stated as: increasing by 50% over the baseline adoption rate.

## 2.6 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 (APm) is calculated as the annual average of the difference between the

<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>
business-as-usual (baseline) total GHG emissions ($E_{BAU}$) and the total emissions under the mitigation measure scenario ($E_m$) during the production period $T$:

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

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

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

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.

# 1. Results

## 3.1 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_16.png" /><figcaption>Figure 1: *Cerrado* baseline emissions 2010-2030</figcaption></figure>

<figure><img src="image_17.png" /></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_18.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 $10^3$ 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).

## 3.2 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_19.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⁻¹.yr⁻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⁻¹.yr⁻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⁻¹, but the measure cost-effectiveness US$ 13.32 t CO₂e⁻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 maximize the gross margin in the model.

<caption>Table 5: Mitigation measures adoption rate.</caption>
<table><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>%<sup>1</sup></td></tr><tr><td>Supplementation: protein</td><td>2.2</td><td>%</td></tr><tr><td>Pasture restoration</td><td>314.7</td><td>10<sup>3</sup> ha.yr<sup>-1</sup></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<sup>-1</sup>.yr<sup>-1</sup></td></tr></tbody></table>

## 2. 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 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

<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>
other mitigation studies, e.g. Moran *et al.* (2013), which need to be addressed before wider
measure adoption can be expected.

3. Conclusion

This paper highlight show 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

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Appendix 5: Mitigation options – brief descriptions for biophysical and economic modelling), and more detailed agronomic/biophysical descriptions were also prepared for ten options (see Appendix 6: Detailed description of mitigation options for biophysical and economic modelling).

## 4.3 Methodology

### 4.3.1 Economic assessment

For the economic assessment of the mitigation options a calculator approach is used, as the method which can align most with the FarmAC modeling. It is a static approach, estimating the on-farm net technical cost of the mitigation option as the difference in costs and income streams between the baseline and the mitigation scenario. The cost-effectiveness of the mitigation option is calculated by dividing the net technical cost with the GHG emission difference between the mitigation and the baseline scenario:

$$CE_m = \frac{\sum_i Costs_{m,i} - \sum_j Income_{m,j}}{GHG_m - GHG_0}$$

Where $CE_m$: cost-effectiveness of mitigation option m
Costs<sub>m</sub>: main technical costs of implementing mitigation option *m*

Income<sub>m</sub>: main income streams of implementing mitigation option *m*

GHG<sub>m</sub>: GHG emissions when implementing mitigation option *m*

GHG<sub>0</sub>: GHG emissions in the baseline scenario

The calculator uses GHG emission and resource use and production data directly from FarmAC. These data are complemented with additional information on some management aspects not available in FarmAC and also with income and cost information. The farm experts are used as the primary source of the information on cost and selling/buying prices, or, if data are not available from them then data from the Eurostat database is used where possible. For mitigation options requiring capital investment the annualised capital cost and the annual maintenance cost are also included in the total costs.

Most of this information is provided by the farm experts, while some can be derived from Eurostat database. The livestock herd data are stock data, with no flow information, i.e. the income and cost calculations are based on the average annual number of animals in the herd. The variable costs (including veterinary costs, insemination costs, water and energy costs and excluding feed costs and labour costs), the labour requirement and the labour costs are provided by the farm experts for each livestock category. The milk and meat production data are derived from FarmAC at an aggregate level. The milk, livestock and meat selling prices are sourced from the Eurostat database or, if available, can be provided by the experts. The feed costs are calculated on a whole farm level, as explained below.

The crop activities are modelled as rotations in FarmAC, and the economic tool calculates the annual average crop activities for each crop based on the length of the rotation and the frequency of a crop used in the rotation. The nitrogen fertiliser use is derived from FarmAC, and the price is either provided by farm experts or Eurostat data are used. Other variable cost elements and labour requirements are provided by farm experts. The variable costs include seed costs, other fertiliser costs, plant protection costs, machinery and fuel costs (including on-farm processing, like crop drying and silage and hay production costs) and insurance. The amount of the exported and imported crop products (including animal feed) is available at the aggregated level from FarmAC. Corresponding buying or selling prices are either provided by farm experts or sourced from the Eurostat database and are used to calculate the cost of crop products purchased and the income from the sold crop products.

### 4.3.2 Description of the farms and the financial data

Cost-effectiveness assessment was carried out on two maritime grass-based cattle farms from the Component 3 model farms, with the following mitigation options as seen in Table 10:

* M-EU-004 Maritime Grassland Dairy – Irish Average National Dairy

* M-EU-002 Maritime Grassland Beef – Irish Average National Beef

<table><caption>**Table 10 Mitigation options assessed on the two maritime model farms**</caption><thead><tr><td>Irish Average National Dairy</td><td>Irish Average National Beef</td></tr></thead><tbody><tr><td>Reducing inorganic N fertiliser use</td><td>Reducing inorganic N fertiliser use</td></tr><tr><td>Introducing white clover into the sward</td><td>Introducing white clover into the sward</td></tr><tr><td>Increasing grass and grass silage quality</td><td>Increasing grass and grass silage quality</td></tr><tr><td>Improving total genetic merit of dairy cows</td><td></td></tr><tr><td></td><td>Earlier finishing of beef heifers and steers</td></tr></tbody></table>
<table><thead><tr><td>Irish Average National Dairy</td><td>Irish Average National Beef</td></tr></thead><tbody><tr><td>Application of nitrification inhibitors</td><td>Application of nitrification inhibitors</td></tr><tr><td>Extending the length of the grazing season</td><td>Extending the length of the grazing season</td></tr><tr><td>Increasing inorganic N fertiliser use</td><td>Increasing inorganic N fertiliser use</td></tr><tr><td>Combined measure: genetic improvement and longer grazing</td><td>Combined measure: earlier finishing and longer grazing</td></tr><tr><td>Combined measure: white clover and reduced N fertiliser use</td><td>Combined measure: white clover and reduced N fertiliser use</td></tr></tbody></table>

The detailed description of the farms and the GHG, N cycle and C cycle results are reported in AnimalChange Deliverable10.3. Key farm data are presented in Table 11 and Table 13, for the dairy and beef farm, respectively. Key production and emission FarmAC results can be found in Table 13 and Table 14, for the dairy and beef farm, respectively.
<table><caption>Table 11 Key farm data of the Irish Average National Dairy Farm by mitigation options</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Genetic impr.</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Genetic imp. + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>Farm size [ha]</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td></tr><tr><td>Grazed pasture [ha]</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td></tr><tr><td>Grass silage [ha]</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td></tr><tr><td>Number of cows [head]</td><td>66.00</td><td>66.00</td><td>74.91</td><td>66.00</td><td>66.00</td><td>67.98</td><td>66.00</td><td>67.69</td><td>66.00</td><td>73.02</td></tr><tr><td>Urea used [kg N/yr/farm]</td><td>2,532</td><td>2,401</td><td>2,532</td><td>2,532</td><td>2,532</td><td>2,637</td><td>2,530</td><td>2,653</td><td>2,530</td><td>2,401</td></tr><tr><td>CAN used [kg N/yr/farm]</td><td>2,686</td><td>2,547</td><td>2,686</td><td>2,686</td><td>2,686</td><td>2,798</td><td>2,684</td><td>2,815</td><td>2,684</td><td>2,547</td></tr><tr><td>Concentrate imported [kg DM/y/farm]</td><td>49,126</td><td>49,126</td><td>55,754</td><td>49,126</td><td>49,126</td><td>50,599</td><td>49,501</td><td>50,383</td><td>49,501</td><td>54,348</td></tr><tr><td>Grass silage imported [kg DM/y/farm]</td><td>1,851</td><td>3,932</td><td>0</td><td>1,851</td><td>1,851</td><td>1,901</td><td>2,182</td><td>1,891</td><td>2,182</td><td>0</td></tr></tbody></table>

<table><caption>Table 12 Key farm data of the Irish Average National Beef Farm by mitigation options</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Earlier finishing</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Earlier finishing + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>Farm size [ha]</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td><td>35.2</td></tr><tr><td>Grazed pasture [ha]</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td><td>21.8</td></tr><tr><td>Grass silage [ha]</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td><td>13.4</td></tr><tr><td>Number of cows [head]</td><td>66.00</td><td>66.00</td><td>74.91</td><td>66.00</td><td>66.00</td><td>67.98</td><td>66.00</td><td>67.69</td><td>66.00</td><td>73.02</td></tr><tr><td>Urea used [kg N/yr/farm]</td><td>2,532</td><td>2,401</td><td>2,532</td><td>2,532</td><td>2,532</td><td>2,637</td><td>2,530</td><td>2,653</td><td>2,530</td><td>2,401</td></tr><tr><td>CAN used [kg N/yr/farm]</td><td>2,686</td><td>2,547</td><td>2,686</td><td>2,686</td><td>2,686</td><td>2,798</td><td>2,684</td><td>2,815</td><td>2,684</td><td>2,547</td></tr><tr><td>Concentrate imported [kg DM/y/farm]</td><td>49,126</td><td>49,126</td><td>55,754</td><td>49,126</td><td>49,126</td><td>50,599</td><td>49,501</td><td>50,383</td><td>49,501</td><td>54,348</td></tr><tr><td>Grass silage imported [kg DM/y/farm]</td><td>1,851</td><td>3,932</td><td>0</td><td>1,851</td><td>1,851</td><td>1,901</td><td>2,182</td><td>1,891</td><td>2,182</td><td>0</td></tr></tbody></table>
<table><caption>Table 13 Key production and emission results for the Irish Average National Dairy Farm by mitigation options (source: FarmAC, D10.3)</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Genetic impr.</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Genetic impr. + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>Milk produced [t/yr/farm]</td><td>328</td><td>328</td><td>388</td><td>338</td><td>344</td><td>338</td><td>332</td><td>337</td><td>349</td><td>378</td></tr><tr><td>Meat exported [t LW/farm/y]</td><td>0.660</td><td>0.660</td><td>0.749</td><td>0.660</td><td>0.660</td><td>0.680</td><td>0.660</td><td>0.677</td><td>0.660</td><td>0.730</td></tr><tr><td>Total protein exported [t protein/farm/y]</td><td>10.9</td><td>10.9</td><td>12.9</td><td>11.2</td><td>11.5</td><td>11.3</td><td>11.2</td><td>11.2</td><td>11.7</td><td>12.6</td></tr><tr><td>GHG emissions [kg CO2e/farm/y]</td><td>336,667</td><td>341,616</td><td>354,562</td><td>336,280</td><td>336,011</td><td>337,517</td><td>336,925</td><td>337,835</td><td>333,883</td><td>352,126</td></tr><tr><td>NH3 emissions [kg N/farm/y]</td><td>1,543</td><td>1,533</td><td>1,534</td><td>1,535</td><td>1,534</td><td>1,564</td><td>1,526</td><td>1,571</td><td>1,516</td><td>1,506</td></tr><tr><td>N leaching [kg N/farm/y]</td><td>3,883</td><td>3,892</td><td>2,600</td><td>3,855</td><td>3,816</td><td>3,816</td><td>3,939</td><td>3,877</td><td>3,822</td><td>2,591</td></tr><tr><td>GHG mitigation [kg CO2e/farm/y]</td><td>-4,950</td><td>-17,895</td><td>386</td><td>655</td><td>-851</td><td>-258</td><td>-1,168</td><td>-1,168</td><td>2,784</td><td>-15,459</td></tr><tr><td>GHG mitigation % [% of baseline]</td><td>-1.5%</td><td>-5.3%</td><td>0.1%</td><td>0.2%</td><td>-0.3%</td><td>-0.1%</td><td>-0.1%</td><td>-0.3%</td><td>0.8%</td><td>-4.6%</td></tr><tr><td>NH3 mitigation [kg N/farm/y]</td><td></td><td>11</td><td>9</td><td>9</td><td>9</td><td>-20</td><td>18</td><td>-27</td><td>27</td><td>38</td></tr><tr><td>N leaching mitigation [kg N/farm/y]</td><td></td><td>-10</td><td>1,283</td><td>27</td><td>67</td><td>67</td><td>-56</td><td>6</td><td>61</td><td>1,292</td></tr></tbody></table>
<table><caption>Table 14 Key production and emission results for the Irish Average National Beef Farm by mitigation options (source: FarmAC, D10.3)</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Earlier finishing</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Earlier finishing + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>Milk produced [t/yr/farm]</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Meat exported [t LW/farm/y]</td><td>21.871</td><td>21.871</td><td>26.740</td><td>22.311</td><td>21.867</td><td>22.552</td><td>22.198</td><td>22.156</td><td>22.193</td><td>26.390</td></tr><tr><td>Total protein exported [t protein/farm/y]</td><td>4.7</td><td>4.5</td><td>5.8</td><td>4.8</td><td>4.6</td><td>4.9</td><td>4.6</td><td>4.8</td><td>4.5</td><td>5.7</td></tr><tr><td>GHG emissions [kg CO2e/farm/y]</td><td>319,072</td><td>318,928</td><td>286,181</td><td>318,966</td><td>299,122</td><td>313,309</td><td>314,264</td><td>314,918</td><td>295,307</td><td>286,126</td></tr><tr><td>NH3 emissions [kg N/farm/y]</td><td>1,272</td><td>1,272</td><td>1,426</td><td>1,268</td><td>1,175</td><td>1,326</td><td>1,253</td><td>1,313</td><td>1,152</td><td>1,402</td></tr><tr><td>N leaching [kg N/farm/y]</td><td>2,934</td><td>2,897</td><td>2,560</td><td>2,934</td><td>2,728</td><td>2,845</td><td>2,858</td><td>2,924</td><td>2,646</td><td>2,507</td></tr><tr><td>GHG mitigation [kg CO2e/farm/y]</td><td></td><td>144</td><td>32,891</td><td>106</td><td>19,950</td><td>5,763</td><td>4,808</td><td>4,154</td><td>23,765</td><td>32,946</td></tr><tr><td>GHG mitigation % [% of baseline]</td><td></td><td>0.0%</td><td>10.3%</td><td>0.0%</td><td>6.3%</td><td>1.8%</td><td>1.5%</td><td>1.3%</td><td>7.4%</td><td>10.3%</td></tr><tr><td>NH3 mitigation [kg N/farm/y]</td><td></td><td>-1</td><td>-154</td><td>4</td><td>97</td><td>-54</td><td>19</td><td>-41</td><td>120</td><td>-130</td></tr><tr><td>N leaching mitigation [kg N/farm/y]</td><td></td><td>37</td><td>374</td><td>1</td><td>207</td><td>90</td><td>76</td><td>11</td><td>289</td><td>428</td></tr></tbody></table>
The relevant cost elements and their respective values are presented in Table 15.

<table><caption>Table 15 Financial elements</caption><thead><tr><td></td><td>Unit</td><td>Value</td><td>Scenario</td></tr></thead><tbody><tr><td>Urea price</td><td>EUR(2011)/t N</td><td>878</td><td>All scenarios</td></tr><tr><td>CAN price</td><td>EUR(2011)/t N</td><td>1,185</td><td>All scenarios</td></tr><tr><td>Reseeding frequency</td><td>/year</td><td>0.125</td><td>All, except 'Pasture quality'</td></tr><tr><td>Reseeding frequency</td><td>/year</td><td>0.13</td><td>Scenario 'Pasture quality'</td></tr><tr><td>Reseeding cost</td><td>EUR(2011)/ha</td><td>250</td><td>All scenarios</td></tr><tr><td>Clover seed amount</td><td>kg/ha</td><td>5</td><td>Scenario 'Clover'</td></tr><tr><td>Clover seed price</td><td>EUR(2011)/kg</td><td>8</td><td>Scenario 'Clover'</td></tr><tr><td>DCD application rate</td><td>kg/ha/y</td><td>10</td><td>Scenario 'Nitrification inhibitors'</td></tr><tr><td>DCD application rate</td><td>kg/ha/y</td><td>7</td><td>Scenario 'Nitrification inhibitors'</td></tr><tr><td>DCD price</td><td>EUR(2011)/kg</td><td>7</td><td>Scenario 'Nitrification inhibitors'</td></tr><tr><td>Concentrate price</td><td>EUR(2011)/t fresh matter</td><td>284</td><td>All scenarios</td></tr><tr><td>Grass silage price</td><td>EUR(2011)/t fresh matter</td><td>30</td><td>All scenarios</td></tr><tr><td>Milk price</td><td>EUR/kg, actual butterfat</td><td>0.34</td><td>All scenarios</td></tr><tr><td>Average heifer/steer (200-250kg) price</td><td>EUR/kg LW</td><td>1.90</td><td>All scenarios</td></tr><tr><td>Urea price</td><td>EUR(2011)/t N</td><td>878</td><td>All scenarios</td></tr></tbody></table>

## 4.4 Results

The results from the economic assessment are presented in Table 16 and Table 17. The GHG mitigation, mitigation cost and emission intensity results are also shown on figures 8 and 9.
<table><caption>Table 16 Key costs, cost-effectiveness results and emission intensities for the Irish Average National Dairy Farm by mitigation options</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Genetic impr.</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Genetic impr. + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>N fertiliser costs [EUR(2011)/farm/y]</td><td>5,408</td><td>5,128</td><td>5,408</td><td>5,408</td><td>5,408</td><td>5,634</td><td>5,404</td><td>5,667</td><td>5,404</td><td>5,128</td></tr><tr><td>Feed costs [EUR(2011)/farm/y]</td><td>21,115</td><td>21,427</td><td>23,973</td><td>21,115</td><td>21,115</td><td>21,747</td><td>21,281</td><td>21,654</td><td>21,281</td><td>23,362</td></tr><tr><td>Income from milk [EUR(2011)/farm/y]</td><td>113,087</td><td>113,087</td><td>133,745</td><td>116,335</td><td>118,730</td><td>116,476</td><td>114,483</td><td>115,980</td><td>120,196</td><td>130,375</td></tr><tr><td>Income from meat [EUR(2011)/farm/y]</td><td>1,254</td><td>1,254</td><td>1,423</td><td>1,254</td><td>1,254</td><td>1,292</td><td>1,254</td><td>1,286</td><td>1,254</td><td>1,387</td></tr><tr><td>Income from crops [EUR(2011)/farm/y]</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Crop net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>-280</td><td>176</td><td>44</td><td>0</td><td>2,690</td><td>-3</td><td>260</td><td>-3</td><td>-104</td></tr><tr><td>Livestock net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>312</td><td>-17,658</td><td>-3,248</td><td>-5,644</td><td>-2,725</td><td>-1,231</td><td>-2,327</td><td>-6,944</td><td>-14,929</td></tr><tr><td>Net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>32</td><td>-17,482</td><td>-3,204</td><td>-5,644</td><td>-36</td><td>-1,234</td><td>-2,068</td><td>-6,948</td><td>-15,033</td></tr><tr><td>GHG CE [EUR(2011)/t CO2e]</td><td>NA</td><td>NA</td><td>NA</td><td>-8,293</td><td>-8,613</td><td>NA</td><td>NA</td><td>NA</td><td>-2,495</td><td>NA</td></tr><tr><td>NH3 CE [EUR(2011)/t N]</td><td>NA</td><td>3,008</td><td>-1,847,271</td><td>-366,106</td><td>-610,478</td><td>NA</td><td>-69,062</td><td>NA</td><td>-256,723</td><td>-398,029</td></tr><tr><td>N leaching CE [EUR(2011)/t N]</td><td>NA</td><td>NA</td><td>-13,629</td><td>-117,274</td><td>-84,602</td><td>-532</td><td>NA</td><td>-374,656</td><td>-113,764</td><td>-11,639</td></tr><tr><td>GHG EI [kg CO2e/kg protein]</td><td>30.80</td><td>31.25</td><td>27.43</td><td>29.91</td><td>29.29</td><td>29.99</td><td>30.21</td><td>30.16</td><td>28.54</td><td>27.98</td></tr><tr><td>GHG EI change % [% of baseline]</td><td></td><td>1.5%</td><td>-10.9%</td><td>-2.9%</td><td>-4.9%</td><td>-2.6%</td><td>-1.9%</td><td>-2.1%</td><td>-7.3%</td><td>-9.2%</td></tr></tbody></table>
<table><caption>Table 17 Key costs, cost-effectiveness results and emission intensities for the Irish Average National Beef Farm by mitigation options</caption><thead><tr><th></th><th>Baseline</th><th>Reduced N</th><th>Clover</th><th>Pasture quality</th><th>Earlier finishing</th><th>Nitrification inhibitors</th><th>Longer grazing</th><th>Increased N</th><th>Earlier finishing + Longer gr.</th><th>Clover + Reduced N</th></tr></thead><tbody><tr><td>N fertiliser costs [EUR(2011)/farm/y]</td><td>3,815</td><td>3,653</td><td>3,815</td><td>3,815</td><td>3,530</td><td>4,106</td><td>3,707</td><td>3,961</td><td>3,424</td><td>3,653</td></tr><tr><td>Feed costs [EUR(2011)/farm/y]</td><td>10,878</td><td>11,255</td><td>12,466</td><td>10,878</td><td>9,708</td><td>11,204</td><td>10,695</td><td>11,006</td><td>9,708</td><td>12,314</td></tr><tr><td>Income from milk [EUR(2011)/farm/y]</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Income from meat [EUR(2011)/farm/y]</td><td>41,554</td><td>41,554</td><td>50,805</td><td>42,392</td><td>41,548</td><td>42,849</td><td>42,175</td><td>42,097</td><td>42,166</td><td>50,141</td></tr><tr><td>Income from crops [EUR(2011)/farm/y]</td><td>1,063</td><td>875</td><td>1,230</td><td>1,063</td><td>953</td><td>1,100</td><td>988</td><td>1,083</td><td>953</td><td>1,210</td></tr><tr><td>Crop net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>27</td><td>69</td><td>59</td><td>-174</td><td>3,558</td><td>-33</td><td>126</td><td>-280</td><td>-72</td></tr><tr><td>Livestock net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>377</td><td>-7,484</td><td>-837</td><td>-1,163</td><td>-931</td><td>-804</td><td>-399</td><td>-1,782</td><td>-6,990</td></tr><tr><td>Net costs of mitigation [EUR(2011)/farm/y]</td><td>0</td><td>404</td><td>-7,415</td><td>-778</td><td>-1,338</td><td>2,628</td><td>-836</td><td>-272</td><td>-2,062</td><td>-7,062</td></tr><tr><td>GHG CE [EUR(2011)/t CO2e]</td><td>NA</td><td>2,806</td><td>-225</td><td>-7,353</td><td>-67</td><td>456</td><td>-174</td><td>-66</td><td>-87</td><td>-214</td></tr><tr><td>NH3 CE [EUR(2011)/t N]</td><td>NA</td><td>NA</td><td>NA</td><td>-205,304</td><td>-13,739</td><td>NA</td><td>-44,484</td><td>NA</td><td>-17,151</td><td>NA</td></tr><tr><td>N leaching CE [EUR(2011)/t N]</td><td>NA</td><td>10,874</td><td>-19,818</td><td>-1,499,010</td><td>-6,477</td><td>29,314</td><td>-10,982</td><td>-25,380</td><td>-7,143</td><td>-16,507</td></tr><tr><td>GHG EI [kg CO2e/kg protein]</td><td>67.58</td><td>70.48</td><td>49.55</td><td>66.55</td><td>65.01</td><td>64.40</td><td>67.76</td><td>65.71</td><td>65.45</td><td>50.34</td></tr><tr><td>GHG EI change % [% of baseline]</td><td></td><td>4.3%</td><td>-26.7%</td><td>-1.5%</td><td>-3.8%</td><td>-4.7%</td><td>0.3%</td><td>-2.8%</td><td>-3.2%</td><td>-25.5%</td></tr></tbody></table>
<figure><img src="image_22.png" /><figcaption>Figure 8 GHG mitigation, mitigation cost and emission intensity effects of the mitigation options (Irish Average National Dairy Farm). Note that negative mitigation values mean increase in GHG emissions; positive cost values mean costs to the farmers.</figcaption></figure>
Figure 9 GHG mitigation, mitigation cost and emission intensity effects of the mitigation options (Irish Average National Beef Farm). Note that negative mitigation values mean increase in GHG emissions; positive cost values mean costs to the farmers.

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>
As D10.3 highlighted already, these grass based farms there is not much scope for significant GHG abatement. The results presented here show that on the dairy farm all but two options actually increase GHG emissions: Improving pasture quality and Genetic improvement (also in combination with Longer grazing period) decrease emissions by 0.1-0.8%. Nevertheless, as these options, with the exception of Reducing N use, increase production more than they increase GHG emissions, the emission intensity of the farm improves in all cases, apart from the Reducing N use option. The GHG emissions are reduced by every option on the beef farm, the effects is between 0.03% and 10.3%, for Improving pastures and Clover-grass mixture, respectively. The emission intensity of the farm also improves for all options except Reduced N use and Longer grazing season, as in these cases a slight drop in crop production counter-balances the small improvement in GHG emissions.

These farm management options are usually accompanied with financial savings, according to the cost calculations, except for Reduced N use, where the loss in income results in a total loss to the farmer and Nitrification inhibitors, where the additional expense on DCD makes the option expensive on the beef farm (€2,628/ha) and just about cost neutral on the dairy farm (€32/ha). Regarding the emission intensity improvements and the financial implication, the Clover option (and the Clover and Reduced N combination) are the most promising options, though on the dairy farm they increase total emissions. The biggest (still very small) mitigation on the dairy farm and the second biggest mitigation on the beef farm can be achieved by Genetic improvement and Earlier finishing, respectively, both of them at negative costs.

## 4.5 Concluding remarks

The mitigation option selection by farm experts revealed that there are big differences between farms (and also amongst farmer experts) regarding which mitigation option is considered the best in terms of GHG efficacy, costs and likely uptake. These differences origin mostly from the climatic, soil, agronomic differences, differences in the financial structure of the farms and in the regional prices, but also from the different personal perceptions. Such ranking exercise can reveal big heterogeneity among farmers as well, with surprisingly low number of explanatory factors identifiable behind the ranking (Glenk et al. 2014). The heterogeneity in the experts' choices of mitigation measrues to be modelled on farms and in their implementation of mitigation measrues in the biophysical modelling (see Deliverables 10.4 and 10.3).

The calculator approach in the economic assessment has certain benefits compared to other methodologies, like whole farm economic modelling. This approach follows the management changes in FarmAC directly, thus providing strong consistency between the biophysical and the eocnomic results. Particularly when comparing mitigation options implemented on different farms, this approach makes it easier to understand the finanical and GHG implications of the underlying agronomic and farm management processes. Additionally, as the farm experts are heavily involved in the assessment process, the cost elements of the mitigation options can be represeneted in details. On the other hand, obtaining detailed information on local prices can prove challenging, in which case the assessment has to rely on regional or national statistical data.

This approach is capable of estimating the finanical implications of the mitigation *ceteris paribus*, i.e. assuming that the farm activities change only as much as to accomodate the mitigaiton option. For example, if due to a mitigation option the gross margin of a particular crop drops below a substitute crop, in reality the farmer might switch, still, this assessment assumes that the cropping structure is kept and the gross margin falls. Thus, it gives the pure technical cost of the mitigation option, without any opportunity costs. Due to these particular features, this approach does not predict potetnial structural changes happening on farms,
neither does it assess potential shifts in agricultural production at the regional or national scale.

Most of the mitigation options assessed on these two farms were low-cost options, often providing high savings in costs (e.g. fertiliser costs) or increased revenues (e.g. increased milk production). Not surprisingly, the cost-effectiveness results are mostly negative, indicating potential 'win-win' options, if at not at the farm GHG level, but at least regarding emission intensity and the finances. The existence of win-win options have been debated though, as profit maximising theory suggests that farms should be already operating at the maximum efficiency, there is no action should exist which provides net savings. There are various explanations why these options appear repeatedly in various assessments, and there is a need for further exploring the potential barriers or hidden costs associated with these options.

The scientific literature on agricultural GHG mitigation and the cost-effectiveness of mitigation options reveals that there is great variability in the economic assessment of agricultural mitigation, partly as a result of the biophysical, agronomic and economic differences between regions and farms, and partly due to the uncertainties and the consequent differences in modeling assumptions. Whole farm GHG modelling and accompanying economic assessment might shed light on the practicalities on the implementation of the options on farms. It can reveal important details about potential synergies and trade-offs between on-farm GHG emission sources, while providing an improved assessment of the financial knock-on effects of implementation, i.e. consequences on resource requirements and production. Additionally, the specificities of farm types and farming systems are presented in a focused way with such assessments.

The whole-farm effects are often only very roughly estimated in the regional and national GHG cost-effectiveness assessments, as there is a research gap in looking at whole farm GHG mitigation. Moreover, there is very limited information available on the GHG synergies and trade-offs of mitigation options at the farm level, which is a key information in the marginal abatement cost curves. Such synergies and trade-offs have to be considered to avoid the double-counting of mitigation effects at the national scale. Future research agenda should continue focusing on whole-farm assessments, partly as a way to feed into economic modelling at a bigger spatial scale, and partly as a way to inform regional policy makers about the potential differences between farm types and the most cost-effective mitigation option packages.

<figure><img src="image_25.png" /></figure>
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# Appendix 1: List of studies reviewed

<table><caption>Table 18 List of studies reviewed</caption><thead><tr><td>ID</td><td>Reference</td><td>Short reference</td></tr></thead><tbody><tr><td>1</td><td>Schneider, U.A. (2000) Agricultural sector analysis on greenhouse gas mitigation in US agriculture and forestry. Dissertation, Texas A&M University</td><td>Schneider 2000</td></tr><tr><td>2</td><td>Graus, W., Harmelink, M. and Hendriks, C. (2004) Marginal GHG-abatement curves for agriculture, Report No EEP03039, Ecofys, Utrecht, the Netherlands</td><td>Graus et al. 2004</td></tr><tr><td>3</td><td>Perez Dominguez, I. (2004). Greenhouse gases: Inventories, abatement costs and markets for emission permits in European agriculture - A modelling approach, EU, Rheinischen Friedrich-Wilhelms-Universitet zu Bonn</td><td>Perez Dominguez 2004</td></tr><tr><td>4</td><td>De Cara, S., Houze, M. and Jayet, P. A. (2005) Methane and nitrous oxide emissions from agriculture in the EU: A spatial assessment of sources and abatement costs. Environmental & Resource Economics 32 (4), 551-583</td><td>De Cara et al. 2005</td></tr><tr><td>5</td><td>DeAngelo, B. J., de la Chesnaye, F. C., Beach, R. H., Sommer, A. and Murray, B. C. (2006) Methane and nitrous oxide mitigation in agriculture. Energy Journal 27, 89-108</td><td>DeAngelo et al. 2006</td></tr><tr><td>6</td><td>Hediger, W. (2006) Modeling GHG emissions and carbon sequestration in Swiss agriculture: An integrated economic approach. International Congress Series 1293, 86-95</td><td>Hediger 2006</td></tr><tr><td>7</td><td>Lucas, P. L., van Vuuren, D. P., Olivier, J. G. J. and den Elzen, M. G. J. (2007) Long-term reduction potential of non-CO2 greenhouse gases. Environmental Science & Policy 10 (2), 85-103</td><td>Lucas et al. 2007</td></tr><tr><td>8</td><td>Schneider, U. A., Mccarl, B. A. and Schmid, E. (2007) Agricultural sector analysis on greenhouse gas mitigation in US agriculture and forestry. Agricultural Systems 94, 128-140</td><td>Schneider et al. 2007</td></tr><tr><td>9</td><td>Wassmann, R. and Pathak, H. (2007) Introducing greenhouse gas mitigation as a development objective in rice-based agriculture: II. Cost-benefit assessment for different technologies, regions and scales. Agricultural Systems 94 (3), 826-840</td><td>Wassman & Pathak 2007</td></tr><tr><td>10</td><td>Weiske, A. and Michel, J. (2007) Greenhouse gas emissions and mitigation costs of selected mitigation measures in agricultural production, Report No MEACAP WP3 D15a, Institute for Energy and Environment (IE)</td><td>Weiske & Michel 2007</td></tr><tr><td>11</td><td>Amann, M., Hoglund-Isaksson, L., Winiwarter, W., Tohka, A., Wagner, F., Schopp, W., Bertok, I. and Heyes, C. (2008) Emission scenarios for non-CO2 greenhouse gases in the EU-27 - Mitigation potentials and costs in 2020, IIASA</td><td>Amann et al. 2008</td></tr><tr><td>12</td><td>Beach, R. H., DeAngelo, B. J., Rose, S., Li, C., Salas, W. and DelGrosso, S. J. (2008) Mitigation potential and costs for global agricultural greenhouse gas emissions. Agricultural Economics 38 (2), 109-115</td><td>Beach et al. 2008</td></tr><tr><td>13</td><td>Environmental Protection Agency - Queensland Office of Climate Change (2008) An enhanced Queensland marginal abatement cost curve Queensland</td><td>Queensland EPA 2008</td></tr><tr><td>14</td><td>Pape, D., Moffroid, K. and Thompson, V. (2008) Analysis of the potential and costs for greenhouse gas emission reductions within the New Zealand agricultural sector, ICF International, Ministry of Agriculture and Forestry</td><td>Pape et al. 2008</td></tr><tr><td>15</td><td>Moran, D., MacLeod, M., Wall, E., Eory, V., Pajot, G., Matthews, R., McVittie, A., Barnes, A., Rees, R., Moxey, A., Williams, A. and Smith, P. (2008) UK marginal abatement cost curves for the agriculture and land use, land-use change and forestry sectors out to 2022, with qualitative analysis of options to 2050, Report No RMP4950, Committee on Climate Change, SAC</td><td>Moran et al. 2008</td></tr><tr><td>16</td><td>Neufeldt, H. and Schafer, M. (2008) Mitigation strategies for greenhouse gas emissions from agriculture using a regional economic-ecosystem model. Agriculture Ecosystems & Environment 123 (4), 305-316.</td><td>Neufeldt & Schafer 2008</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Reference</td><td>Short reference</td></tr></thead><tbody><tr><td>17</td><td>Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., McCarl, B., Ogle, S., O'Mara, F., Rice, C., Scholes, B., Sirotenko, O., Howden, M., McAllister, T., Pan, G., Romanenkov, V., Schneider, U., Towprayoon, S., Wattenbach, M. and Smith, J. (2008) Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society B-Biological Sciences 363 (1492), 789-813</td><td>Smith et al. 2008</td></tr><tr><td>18</td><td>Bates, J., Brophy, N., Harfoot, M. and Webb, J. (2009) Agriculture: methane and nitrous oxide, Report No Sectoral Emission Reduction Potentials and Economic Costs for Climate Change (SERPEC-CC), AEA Energy & Environment</td><td>Bates et al. 2009</td></tr><tr><td>19</td><td>Golub, A., Hertel, T., Lee, H. L., Rose, S. and Sohngen, B. (2009) The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry. Resource and Energy Economics 31 (4), 299-319</td><td>Golub et al. 2009</td></tr><tr><td>20</td><td>Naucler, T. and Enkvist, P. A. (2009) Pathways to Low Carbon Economy: Version 2 of the Global Greenhouse Gas Abatement Cost Curve, McKinsey & Company</td><td>Naucler & Enkvist 2009</td></tr><tr><td>21</td><td>Durandeau, S., Gabrielle, B., Godard, C., Jayet, P. A. and Le Bas, C. (2010) Coupling biophysical and micro-economic models to assess the effect of mitigation measures on greenhouse gas emissions from agriculture. Climatic Change 98 (1), 51-73</td><td>Durandeau et al. 2010</td></tr><tr><td>22</td><td>Hasegawa, T. and Matsuoka, Y. (2010) Global methane and nitrous oxide emissions and reduction potentials in agriculture. Journal of Integrative Environmental Sciences 7 (sup1), 245-256</td><td>Hasegawa & Matsuoka 2010</td></tr><tr><td>23</td><td>Hoglund-Isaksson, L., Winiwarter, W., Wagner, F., Klimont, Z. and Amann, M. (2010) Potentials and costs for mitigation of non-CO₂ greenhouse gas emissions in the European Union until 2030. Results. Report to the European Commission, DG Climate Action Contract no. 07.030700/2009/545854/SER/C5, IIASA</td><td>Hoglund-Isaksson et al. 2010</td></tr><tr><td>24</td><td>Leip, A., Weiss, F., Wassenaar, T., Perez, I., Fellmann, T., Loudjani, P., Tubiello, F., Grandgirard, D., Monni, S. and Biala, K. (2010) Evaluation of the livestock sector's contribution to the EU greenhouse gas emissions (GGELS) - Final Report, Administrative Arrangements AGRI-2008-0245 and AGRI-2009-0296, Joint Research Centre, European Commission</td><td>Leip et al. 2010</td></tr><tr><td>25</td><td>De Cara, S. and Jayet, P. A. (2011) Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement. Ecological Economics 70 (9), 1680-1690</td><td>De Cara & Jayet 2011</td></tr><tr><td>26</td><td>Vellinga, T. V., de Haan, M. H. A., Schils, R. L. M., Evers, A. and 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 (1-3), 185-195</td><td>Vellinga et al. 2011</td></tr><tr><td>27</td><td>Perez Dominguez, I., Fellmann, T., Witzke, H. P., Jansson, T., Oudendag, D., Gocht, A. and Verhoog, D. Fellmann, T. (ed) (2012) Agricultural GHG emissions in the EU: an exploratory economic assessment of mitigation policy options, Report No 25288 EN, European Commission, Joint Research Centre, Institute for Prospective Technological Studies</td><td>Perez Dominguez et al. 2012</td></tr><tr><td>28</td><td>Schulte, R. P., Crosson, P., Donnellan, T., Farrelly, N., Finnan, J., Lalor, S. T., Lanigan, G., O'Brien, D., Shalloo, L. and Thorne, F. Schulte, R. P. and Donnellan, T. (ed) (2012) A marginal abatement cost curve for Irish agriculture, Teagasc</td><td>Schulte et al. 2012</td></tr><tr><td>29</td><td>Van Amstel, A. R. (2012). Methane: Its role in climate change and options for control, global, Wageningen University</td><td>van Amstel 2012</td></tr><tr><td>30</td><td>Wagner, F., Amann, M., Borken-Kleefeld, J., Cofala, J., Hoglund-Isaksson, L., Purohit, P., Rafaj, P., Schopp, W. and Winiwarter, W. (2012) Sectoral marginal abatement cost curves: implications for mitigation pledges and air pollution co-benefits for Annex I countries. Sustain Sci 7 (2), 169-184</td><td>Wagner et al. 2012</td></tr></tbody></table>

<figure><img src="image_27.png" /></figure>
<table><thead><tr><td>ID</td><td>Reference</td><td>Short reference</td></tr></thead><tbody><tr><td>31</td><td>Cornet, M., Duerinck, J., Laes, E., Lodewijks, P., Meynaerts, E., Pestiaux, J., Renders, N. and Vermuelen, P. (2013) Scenarios for a low carbon Belgium by 2050, CLIMACT, VITO</td><td>Cornet et al. 2013</td></tr><tr><td>32</td><td>Biggar, S., Man, D., Moffroid, K., Pape, D., Riley-Gilbert, M., Steele, R. and Thompson, V. (2013) Greenhouse gas mitigation options and costs for agricultural land and animal production within the United States, Report No Contract No. AG-3142-P-10-0214, ICF International, U.S. Department of Agriculture Climate Change Program Office Washington, DC</td><td>Biggar et al. 2013</td></tr><tr><td>33</td><td>Pellerin, S., Bamiere, L., Angers, D., Beline, F., Benoit, M., Butault, J. P., Chenu, C., Colnenne-David, C., De Cara, S., Delame, N., Dureau, M., Dupraz, P., Faverdin, P., Garcia-Launay, F., Hassouna, M., Henault, C., Jeuffroy, M. H., Klumont, K., Metay, A., Moran, D., Recous, S., Samson, E. and Savini, I. (2013) How can French agriculture contribute to reducing greenhouse gas emissions? Abatement potential and cost of ten technical measures, INRA</td><td>Pellerin et al. 2013</td></tr><tr><td>34</td><td>Reisinger, A. and Ledgard, S. (2013) Impact of greenhouse gas metrics on the quantification of agricultural emissions and farm-scale mitigation strategies: a-áNew Zealand case study. Environmental Research Letters 8, 025019</td><td>Reisinger & Ledgard 2013</td></tr><tr><td>35</td><td>EPA (2013) Global mitigation of non-CO2 greenhouse gases: 1990-2030, Report No EPA-430-R-13-011, United States Environmental Protection Agency, Office of Atmospheric Programs, Washington DC</td><td>EPA 2013</td></tr><tr><td>36</td><td>Whittle, L., Hug, B., White, S., Heyhoe, E., Harle, K., Mamun, E. and Ahammad, H. (2013) Costs and potential of agricultural emissions abatement in Australia, Report No ABARES technical report 13.2, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), Canberra</td><td>Whittle et al. 2013</td></tr><tr><td>37</td><td>Lengers, B., Britz, W. and Holm-Muller, K. (2013) Comparison of GHG-Emission Indicators for Dairy Farms with Respect to Induced Abatement Costs, Accuracy, and Feasibility. Applied Economic Perspectives and Policy 35 (3), 451-475</td><td>Lengers et al. 2013</td></tr></tbody></table>
# Appendix 2: Long list of mitigation measures

<table><caption>## Table 19 List of mitigation measures</caption><thead><tr><th colspan="2">Cropland management</th></tr></thead><tbody><tr><td><strong>Agronomy</strong></td><td>Applying organic input on cropland instead of on grassland</td></tr><tr><td>Improved crop varieties</td><td>Improved maintenance of fertiliser spreaders</td></tr><tr><td>Extending the perennial phase of crop rotations</td><td>Modify soil microbial communities to reduce N<sub>2</sub>O into N2 (e.g. incorporation of Rhizobia strains)</td></tr><tr><td>Reducing bare fallow</td><td>Do not apply fertiliser at high-risk areas</td></tr><tr><td>Adding nutrients when deficient</td><td>Fertiliser free zone on field edges</td></tr><tr><td>Adopting systems less reliant on inputs (nutrients, pesticides etc)</td><td>Optimisation of fertiliser distribution geometry</td></tr><tr><td>Catch/cover crops</td><td>Water buffer strips</td></tr><tr><td>Keep pH at an optimum for plant growth (e.g. liming)</td><td>Other</td></tr><tr><td>Reduce liming</td><td><strong>Structural and management changes</strong></td></tr><tr><td>Changing from winter to spring cultivars</td><td>Tightening the N cycles (regionally optimised plant and animal production)</td></tr><tr><td>Agroforestry (with low tree density)</td><td>Relocate high N-input cropping to drier, cooler areas</td></tr><tr><td>Hedges</td><td>Less monoculture</td></tr><tr><td>Cultivated crops to increase soil C (e.g. deep-rooted or permanent plants)</td><td>Other</td></tr><tr><td>Other</td><td><strong>Soil and water management</strong></td></tr><tr><td><strong>Nutrient management</strong></td><td>Reduced tillage</td></tr><tr><td>Precision farming</td><td>No-till</td></tr><tr><td>Avoiding N excess (adjusting yield targets)</td><td>Retain crop residues</td></tr><tr><td>Use a fertiliser recommendation system</td><td>Plough in early spring, spread crop residues evenly and control compaction</td></tr><tr><td>Analyse manure prior to application</td><td>Avoid burning of residues</td></tr><tr><td>Full allowance of manure N supply</td><td>Burying biochar</td></tr><tr><td>Use the right form of mineral N fertiliser</td><td>Improved irrigation</td></tr><tr><td>Split fertilisation (baseline amount of N fertiliser but divided into smaller increments)</td><td>Land drainage</td></tr><tr><td>Reduce N fertiliser</td><td>Loosen compacted soils / Prevent soil compaction</td></tr><tr><td>Improved timing of manure application</td><td>Prevent soil erosion</td></tr><tr><td>Improved timing of mineral N application</td><td>Other</td></tr><tr><td>Improved timing of slurry and poultry manure application</td><td><strong>Rice management</strong></td></tr><tr><td>Separate slurry applications from fertiliser applications by several days</td><td>Aeration of rice-growing soil (e.g. reduce the depth of paddy fields, empty them several times per year)</td></tr><tr><td>Use composts, straw-based manures in preference to slurry</td><td>Shallow flooding of rice</td></tr><tr><td>Mix nitrogen rich crop residues with other residues of higher C:N ratio</td><td>Mid-season drainage of rice</td></tr><tr><td>Placing N precisely in soil</td><td>Off-season rice straw</td></tr><tr><td>Trailing hose</td><td>Alternate flooding of rice</td></tr><tr><td>Trailing shoe</td><td>Rice straw compost</td></tr><tr><td>Injection</td><td>Application of phosphogypsum to rice</td></tr><tr><td>Incorporate manure after application</td><td>Direct wet seeding of rice</td></tr><tr><td>Low trajectory slurry application</td><td>Replace urea with AN for rice</td></tr><tr><td>Increasing rate of infiltration into soil (dilution of manure, application of water after spreading)</td><td>Sulphate amendments for rice</td></tr><tr><td>Controlled release fertilisers</td><td>Changing fertiliser type for rice (in general)</td></tr><tr><td>Nitrification inhibitors</td><td>Other uses of rice straw</td></tr><tr><td>Production of natural nitrification inhibitors by plants</td><td>Low methane rice cultivars</td></tr><tr><td>Application of urease inhibitor</td><td>Continuous flooding of rice</td></tr><tr><td>Plant varieties with improved N-use efficiency</td><td>Other</td></tr><tr><td>Genetically improve the efficacy of nitrogen uptake and use by plants</td><td><strong>Orchards</strong></td></tr><tr><td>Biological N fixation (legumes in rotations)</td><td>Convert to trellis system</td></tr><tr><td></td><td>Other</td></tr></tbody></table>
## Grazing land management

### Grazing intensity and timing

- Reduce N on the most intensive permanent and temporary grasslands

- Increase stocking density on medium productivity grassland

- Intensive grazing (cattle are frequently rotated between pastures)

- Extended grazing season

- Take stock off from wet ground

- Reduce stocking density

- Other

### Increased productivity

- In general

- Fertilisation

- Pasture renovation

- Avoid fertiliser applications prior to pasture renovation

- Biological N fixation (grass-legume mixtures)

- Species introduction (including legumes)

- New forage plant varieties for improved nutritional characteristics

- New forage plant varieties to buffer grass growth

- Increasing digestibility (forage quality)

- Higher sugar content grasses

- Increase the lifespan of temporary grassland

- Improved irrigation

- Other

### Fire management

- Fire management

## Management of organic soils

- Avoid drainage of wetlands / conversion of peatlands

- Avoiding row crops and tubers

- Avoiding deep ploughing

- Maintaining a shallower water table - peat

- Maintaining a shallower water table - arable

- Other

## Restoration of degraded lands

- In general

- Erosion control

- Revegetation

- Nutrient amendments

- Organic amendments (manures, biosolids, composts, etc.)

- Reducing tillage

- Retaining crop-residues

- Conserving water

- Other

## Livestock management

### Feeding practices

- Feeding more concentrates (replacing forages)

- High starch diet

- Balance diet for energy and protein (e.g. reducing protein, increasing carbohydrates)

- Reduce protein intake (without AA supplementation)

- Reduce protein intake and provide AA supplementation

- Target specific livestock nutrient requirements

- High fat diet (dietary lipids)

- Estimating potential CH₄ production from feeds

- Mechanical treatment of feed

- Chemical treatment of low quality feed

- Feeding total mixed ration

- Precision feeding (+ feed analysis)

- Multiphase feeding

- Improved feed conversion (increasing energy content and digestibility)

- Continuing conventional dietary improvement

- Increase body fat at slaughter

- Food industry co-products as feed

- Improved feed intake

- Bovine somatotropin

- Increased milking frequency

- Other

### Specific agents and dietary additives

- In general

- Higher salt content of the diet

- Plant extracts

- Essential oils

- Tannins

- Saponins

- Ionophores (e.g. monensin)

- Antibiotics

- Propionate precursors

- Nitrate

- Nitrification inhibitors fed directly

- Hexose partitioning

- Directly fed probiotic microbes (e.g. yeast products)

- Manipulation of rumen archaea and bacteria

- Directly fed microbes (acetogens, CH₄ oxidisers)

- Antimethanogens

- Genetic modification of rumen microflora

- Vaccination against methanogens

- Halogenated methane analogues

- Defaunating agents

- Naturally occurring plant compounds (new species/GM)

- Chicory

- Allicin

- Glycerol

- Other

### Animal health

- Better health planning

- Improve hygiene & supervision at lambing

- Improve ewe nutrition in late gestation to increase lamb survival

- Anti-parasitics

- Other

### Structural and management changes

- Reduction in the number of replacement heifers / Improved fertility management

- Multi use of cows (milk, calves and meat)

- More feed production on farm scale or local level

- Organic farming

- Winter management of cattle (collected and re-utilised excreta)

- Increase of grazing in comparison to housing

- Increase of housing in comparison to grazing

- Reduce stocking rates

- Skipping the stocker phase

- Other
Breeding, genetics, herd structure

Improved genetic potential in general

Selecton for reduced methane emissions

Selection for non-productive traits (e.g. longevity, fertility)

Improved genetic potential - productivity

Use semen of high economic breeding value

Cloning

GM livestock

Artificial insemination

Using sexed semen

Twinning

Transgenic manipulation

Use cows with lower yield but which can produce beef calves

Develop mixed breeds or industrial cross-breeding

Switching breeds

Other

Housing and manure

Housing

New low-emission livestock and poultry housing systems

Filtration of animal house emissions

Decreasing of air velocity above manure

Cooling the manure covered surfaces

Cages and aviaries instead of floor systems for layer hens

Keeping surfaces, manure and animals dry

Partly or fully slatted floors

Deep litter systems

Switch between solid manure and slurry systems

Other

Manure storage and handling

Cooling of manure storage

Frequent manure removal to outside (cooler) storage

Covering manure heaps

Lowering the filling level of slurry storage

Covering slurry stores

Semi-permeable

Impermeable

Allowing the build-up of natural crust on cattle slurry

Separating solids from slurry

Composting solid manure (also after slurry separation)

Switch from anaerobic to aerobic facilities

Minimising of stirring slurry

Manure acidification

Reducing the surface per unit volume of slurry or FYM (e.g tanks instead of lagoons)

Increasing the carbon content of the manure (adding straw)

Compaction of FYM

Comminution of FYM

Increased frequency of slurry spreading

Drying of manure (esp. poultry)

Incinerate poultry litter

Other

Anaerobic digestion and CH4 capture

AD

Centralised AD

On-farm AD

Methane capture and combustion

Other

Land use change

Shelter belts/riparian zones/buffer strips

Arable to grassland

Arable to woodland

Arable to set-aside

Grassland to woodland

Drained croplands - wetlands

Afforestation

Permanent revegetation of set-aside

Restoration of peatlands

Avoid conversion of woodlands

Biomass crops

Sylvopastoral systems

Other

Energy and waste

Transport

Increased fuel efficiency

Other transport

Heating and electricity

Increased heating efficiency (livestock houses and greenhouses)

Move heated greenhouses to areas where the can utilise waste heat

Improved crop-drying

Efficient cooling of milk

Efficient ventilation and cooling

Efficient lighting

Other

Waste

Reduce waste during transport, processing and storage

Other

Electricity generation

Use of renewable electricity

Biomass combustion

Solar energy

Wind power

Solar water heating

Small-scale hydro-electric power

Ground-source or air-source heat pumps

Other

Other energy and waste

Other energy and waste
# Appendix 3: Overview of the studies

<table><caption>Table 20 Overview of the studies' characteristics</caption><thead><tr><td>ID</td><td>Scope</td><td>Resolution</td><td>Emission boundaries<sup>a</sup></td><td>Interactions<sup>b</sup></td><td>Methodology<sup>c</sup></td><td>CE available?<sup>d</sup></td></tr></thead><tbody><tr><td>1</td><td>US</td><td>Sub-national region</td><td>On-farm</td><td>Not specified</td><td>2</td><td>No</td></tr><tr><td>2</td><td>Global</td><td>World region</td><td>On-farm</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>3</td><td>EU-15</td><td>Sub-national region</td><td>On-farm</td><td>No</td><td>1</td><td>No</td></tr><tr><td>4</td><td>EU-15</td><td>Sub-national region</td><td>On-farm</td><td>No</td><td>1</td><td>No</td></tr><tr><td>5</td><td>Global</td><td>World region</td><td>On-farm</td><td>Measure allocation</td><td>3</td><td>No</td></tr><tr><td>6</td><td>Switzerland</td><td>Sub-national region</td><td>On-farm</td><td>Not specified</td><td>1</td><td>No</td></tr><tr><td>7</td><td>Global</td><td>World region</td><td>On-farm + some LCA elements</td><td>No</td><td>3</td><td>No</td></tr><tr><td>8</td><td>US</td><td>Sub-national region</td><td>On-farm</td><td>No</td><td>2</td><td>No</td></tr><tr><td>9</td><td>Philippines, China, India</td><td>Sub-national region</td><td>On-farm</td><td>Not specified</td><td>3</td><td>No</td></tr><tr><td>10</td><td>NW Germany</td><td>Farm</td><td>On-farm</td><td>Not specified</td><td>3</td><td>Yes</td></tr><tr><td>11</td><td>EU-27</td><td>Country</td><td>On-farm</td><td>Measure allocation</td><td>3</td><td>Yes</td></tr><tr><td>12</td><td>Global</td><td>Global</td><td>On-farm</td><td>Measure allocation</td><td>3</td><td>No</td></tr><tr><td>13</td><td>Queensland (Australia)</td><td>Sub-national region</td><td>On-farm</td><td>Yes</td><td>2</td><td>No</td></tr><tr><td>14</td><td>New Zealand</td><td>Country</td><td>On-farm</td><td>Not specified</td><td>3</td><td>Yes</td></tr><tr><td>15</td><td>UK</td><td>Country</td><td>On-farm</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>16</td><td>Germany</td><td>Sub-national region</td><td>On-farm</td><td>Yes</td><td>1</td><td>No</td></tr><tr><td>17</td><td>Global</td><td>World region</td><td>On-farm</td><td>Not specified</td><td>3</td><td>No</td></tr><tr><td>18</td><td>EU-27</td><td>World region</td><td>On-farm</td><td>Not specified</td><td>3</td><td>Yes</td></tr><tr><td>19</td><td>Global</td><td>World region</td><td>On-farm</td><td>No</td><td>2</td><td>No</td></tr><tr><td>20</td><td>Global</td><td>Global</td><td>On-farm</td><td>Not specified</td><td>3</td><td>No</td></tr><tr><td>21</td><td>France</td><td>Sub-national region</td><td>On-farm</td><td>Yes</td><td>1</td><td>No</td></tr><tr><td>22</td><td>Global</td><td>World region</td><td>On-farm</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>23</td><td>Kyoto Annex I countries</td><td>Country</td><td>On-farm + some LCA elements</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>24</td><td>EU-27</td><td>Sub-national region</td><td>LCA</td><td>Yes</td><td>1</td><td>No</td></tr><tr><td>25</td><td>EU-24</td><td>Sub-national region</td><td>On-farm</td><td>No</td><td>1</td><td>No</td></tr><tr><td>26</td><td>the Netherlands</td><td>Farm</td><td>On-farm</td><td>No</td><td>3</td><td>Yes</td></tr><tr><td>27</td><td>EU-27</td><td>Sub-national region</td><td>On-farm</td><td>Yes</td><td>1</td><td>No</td></tr><tr><td>28</td><td>Ireland</td><td>Country</td><td>LCA</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>29</td><td>Global</td><td>Global</td><td>On-farm</td><td>Yes</td><td>2</td><td>No</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Scope</td><td>Resolution</td><td>Emission boundaries<sup>a</sup></td><td>Interactions<sup>b</sup></td><td>Methodology<sup>c</sup></td><td>CE available?<sup>d</sup></td></tr></thead><tbody><tr><td>30</td><td>Kyoto Annex I countries</td><td>Country</td><td>On-farm + some LCA elements</td><td>Measure allocation</td><td>3</td><td>No</td></tr><tr><td>31</td><td>Belgium</td><td>Country</td><td>On-farm</td><td>Not specified</td><td>3</td><td>No</td></tr><tr><td>32</td><td>US</td><td>Farm</td><td>On-farm</td><td>Not specified</td><td>3</td><td>Yes</td></tr><tr><td>33</td><td>France</td><td>Country</td><td>On-farm + some LCA elements</td><td>Yes</td><td>3</td><td>Yes</td></tr><tr><td>34</td><td>New Zealand</td><td>Farm</td><td>LCA</td><td>Not specified</td><td>3</td><td>No</td></tr><tr><td>35</td><td>Global</td><td>Country</td><td>On-farm</td><td>Measure allocation</td><td>3</td><td>No</td></tr><tr><td>36</td><td>Australia</td><td>Farm</td><td>On-farm</td><td>Measure allocation</td><td>3</td><td>No</td></tr><tr><td>37</td><td>Germany</td><td>Farm</td><td>On-farm</td><td>Yes</td><td>3</td><td>No</td></tr></tbody></table>

<footnote>a 'On-farm': emissions within the farm gate are considered, On-farm + other: some off-farm emissions are considered, 'LCA': off-farm emissions (usually pre-farm or up to the retailers) are considered</footnote>

<footnote>b 'Yes': interactions between mitigation measures are taken into account, 'Measure allocation': mitigation measures are allocated so that simultaneous implementation of measures do not happen, 'No': interactions are not taken into account, 'Not specified': the study does not specify whether interactions are considered</footnote>

<footnote>c 1: micro-economic modelling, 2: equilibrium models, 3: bottom-up cost-engineering</footnote>

<footnote>d Whether cost-effectiveness estimates for individual mitigation measures are presented or not</footnote>
# Appendix 4: Marginal abatement cost curves for beef production in the Brazilian *Cerrado*

## Developing a Nationally Appropriate Mitigation Measure from the greenhouse gas GHG abatement potential from livestock production in the Brazilian *Cerrado*

Rafael de Oliveira Silva<sup>a,d</sup>, Luis Gustavo Barioni<sup>b</sup>, Tiago Zanett Albertini<sup>c</sup>, Vera Eory<sup>d</sup>, Caristiona F. E. Topp<sup>d</sup>, Fernando A. Fernandes<sup>e</sup>, Dominic Moran<sup>d</sup>

## Abstract

Brazil is one of the first major developing countries to commit to a national greenhouse gas (GHG) emissions target that requires a reduction of between 36.1% and 38.9% relative to baseline emissions by 2020. The country intends to submit agricultural emissions reductions as part of this target, with livestock production identified as offering significant abatement potential. Focusing on the *Cerrado* core (central Brazilian savannah), this paper investigates the cost-effectiveness of this potential, which involves some consideration of both the private and social costs and benefits (e.g. including avoided deforestation) arising from specific mitigation measures that may form part of Brazil's definition of Nationally Appropriate Mitigation Measures (NAMAs). The analysis was made using the EAGGLE optimization model (Economic Analysis of Greenhouse Gases for Livestock Emissions), which helps to define abatement costs. A baseline projection suggests that, the region will emit 2.6 Gt from 2010 to 2030, the equivalent of 9% of the country's total net emissions. By implementing negative-cost measures identified in a marginal abatement cost curve (MACC), by 2030, regional emissions could be reduced by around 24%. Pasture restoration, involving avoided deforestation, offers the largest contribution to these results. As the Brazilian *Cerrado* is seen as model for transforming other global savannahs, the results offer a significant contribution by identifying alternatives for increasing productivity whilst minimizing national and global external costs.

**Keywords:** climate change; marginal abatement cost curves; mitigation measures; sustainable intensification; grassland restoration; linear programming.

## Highlights

- [ ] Around 66% of emissions in the region are due to enteric fermentation in livestock.

- [ ] 24% of emissions can be reduced by adopting negative-cost (cost-saving) measures.

- [ ] Pasture restoration has the biggest abatement potential (27.8 Mt CO₂e.yr⁻¹).

<footnote><sup>a</sup>School of Mathematics, The University of Edinburgh, Mayfield Road, Edinburgh, EH9 3JZ, Scotland</footnote>

<footnote><sup>b</sup>Embrapa Agriculture Informatics, CEP 13083-886 Campinas-SP, Brazil</footnote>

<footnote><sup>c</sup>University of SaoPaulo (ESALQUSP), CEP 13418-900, Piracicaba, SP, Brazil</footnote>

<footnote><sup>d</sup>Research Division, SRUC, West Mains Road, Edinburgh, EH9 3JG, Scotland</footnote>

<footnote><sup>e</sup>Embrapa Pantanal, CEP 79320-900, Corumbá-MS, Brazil</footnote>
## 4. Introduction

Global demand for livestock products is projected to grow by 70 per cent by 2050 (Gerber et al., 2013). This is expected to generate significant additional pressure on producers and on natural resources. Sustainable management (or intensification) will require increasing yields and efficiency in existing ruminant production systems, minimizing competition of land used for food and feed, while maximizing ecosystem services, including mitigation of greenhouse gas (GHG) emissions (Gerber et al., 2013; Soussana et al., 2013; Thornton and Herrero, 2010).

Tropical regions are implicated as potentially offering major opportunities to increase beef productivity and emissions mitigation, as current productivity levels are still relatively low and emission intensities correspondingly high (Opio et al., 2013; Henderson and Steinfeld, 2013).

More productive pastures can increase soil carbon stocks, providing one of the largest terrestrial carbon sinks (Follett and Reed, 2010; Neely et al., 2009), in a pool that is a more stable form than the aerial components of forests (Soussana et al., 2010). But potential carbon sequestration in soils under grasslands far from offset the loss of above ground vegetation in the majority of tropical areas, and therefore natural vegetation should be preserved.

Brazil is the world's second largest beef producer – 9.3 Mt.yr⁻¹ (14.7 % of the world's total), and the largest exporter in 2012-13 (FAO, 2014). Production is predominantly pasture-based in a grassland area of approximately 170 M ha (IBGE, 2014), mostly in a humid or sub-humid tropical climate.

But beef production can entail significant trade-offs, that must be managed to minimize external costs. These include the controlled expansion of agricultural area, associated deforestation, cost-effective greenhouse gas mitigation, and land competition between food and biofuels.

Analysis of historical data (Martha et al., 2012) and scenario studies conducted by the World Bank (Gouvello et al., 2010) suggests that improving beef productivity has the highest potential to buffer the expansion of other agricultural activities, avoiding further deforestation. Increasing pasture productivity can also boost soil carbon sequestration, particularly when carried out in currently degraded grasslands (Bras et al., 2013; Ruviaro et al., 2014). In addition, increasing productivity through feed supplementation may significantly reduce direct methane emissions (Berndt and Tomkins, 2013; Ruviaro et al., 2014).

In this context and based on its previous National Plan on Climate Change, at the Conference of the Parties 15 (COP 15), Brazil has proposed Nationally Appropriate Mitigation Actions (NAMAs) as part of its commitment to the United Nations Framework Convention on Climate Change (http://www.mmechanisms.org/e/namainfo/index.html). Over the period 2010-2020, the NAMAs establish targets for the reduction of Amazon deforestation by 80% and by 40% in the *Cerrado* (Brazilian Savannah), through the adoption of pasture recovery (15 million ha), and from integrated crop-livestock-forestry systems (4 million ha). With these cattle-related measures, Brazil expects to reduce net emissions by between 101 and 126 Mt CO₂-e, by 2020, which account for 61% -73% of all mitigation in agricultural practices by the NAMA route. The NAMA proposal is enacted as part of the ambitious ABC (Agricultura de Baixo Carbono - Low Carbon Agriculture) program, which offers low interest credit lines to farmers adopting mitigation technologies (Mozzer, 2011). This paper investigates the cost-effectiveness of these livestock mitigation measures applicable in the *Cerrado* core (Central Brazilian Savannah); a region that contains around 35% of the Brazilian herd (Anualpec, 2010). The region is considered as central in Brazil's ascendance in global production (The Economist, 2010; The New York Times, 2007) and is still regarded as the most important region for expanding beef production in Brazil (Ferraz and Felício, 2010). It is seen as a potential model for transforming other savannahs (Morris et al., 2012; The World Bank, 2009).

The analytical focus is significant because there is currently little research clearly demonstrating that mitigation through livestock management can be delivered at relatively low cost. The paper offers the first bottom-up cost-effectiveness analysis using an
optimization model for Brazilian beef production. The measures evaluated are pasture
restoration, feedlot finishing, supplement concentrates and protein and nitrification inhibitors.
The analysis uses the outputs of a multi-period Linear Programming model (the Economic
Analysis of Greenhouse Gases for Livestock Emissions model-EAGGLE; Oliveira Silva,
2013), to develop a bottom-up or engineering marginal abatement cost curve (MACC), to
represent the relative cost-effectiveness of measures and their cumulative abatement
potential above a baseline of business as usual (Moran et al., 2010). The analysis examines
the direct emissions of measures enacted within the notional farm gate rather than wider life
cycle impacts, and accounts for both the private and social costs and benefits (e.g. including
avoided deforestation).

The paper offers new insights for regional policy and is structured as follows. Section two outlines the modelling structure and relevant optimization assumptions underlying the cost-effectiveness analysis. Section three describes the MACC calculation, while section four sets out results. Sections five and six offer a discussion and conclusions.

5. Modelling methods for mitigation cost-effectiveness

2.7 Model Overview

Abatement potential and cost-effectiveness of measures were derived using the EAGGLE model; a Multi-Period Linear Programming model that simulates a whole cycle (cow-calf, stocking and finishing) beef production farm, accounting for: (i) herd dynamics, (ii) financial resources, (iii) feed budgeting, (iv) Land use: pasture recovery dynamics and crops, and (v) Soil carbon stock dynamics.

The model optimizes the use of the farm resources (capital, cattle, land) while meeting
demand projections and maximizing profit. In this context EAGGLE is used to simulate beef
production treating the *Cerrado* region as a single farm. The farm activities (i-iii) are modelled
using monthly time steps, while (iv&v) are modelled using annual time steps. EAGGLE
represents animals in age cohorts *k*; a steer of age cohort *k*=1, is a calf aged 6 months, and
189 kg of live weight (LW). After 3 months in the system, age cohort *k* is transferred to age
cohort *k*+1, now with 222 kg of LW. The final weight is 454kg, corresponding to *k*=9 (33
months), when the animal is sold and removed from the system.

The same cohorts apply to heifers, although these can also accommodate breeding rates, where a heifer generates 1 calf per 18 month cycle, comprising 9 months of pregnancy, 6 months of lactation (Millen et al., 2011), plus 3 months of non-lactation and non-pregnancy. Half of the calves born are allocated to steers and the other half are allocated to heifers, both of age cohort k=1. After 4 cycles, the cows are removed from the system and slaughtered, i.e., used to meet demand.

EAGGLE also simulates feedlot finishing, and thus allows the reduction of the finishing time.
The model can remove steers from exclusive grazing, inserting the animals into feedlot
systems; generally only males are confined in Brazil (Millen et al., 2009; Costa Junior et al.,
2013). For all cattle categories, i.e., male, female, male in feedlot and breeding females, the
corresponding age cohort is associated with specific parameters: weight, death rate, dry
matter (DM) intake, selling and purchase prices, emissions factors for CH₄ from enteric
fermentation and emissions factors for N₂O from excreta. The associated coefficient values
are detailed in Table S1 and Table S2.

The gross margin of the *Cerrado* single region farm is maximized and calculated as the
difference between the income and expenses. Income derives exclusively from the sale of
finished cattle, 454kg of LW for steers and 372 of LW for heifers. Farm expenses are
composed of investment and maintenance costs. Maintenance costs are (i) farm
maintenance and (ii) animal non-feed maintenance. Costs for (i) include working animals,
machinery and equipment, veterinary equipment telephone device, fuel, taxes and fees,
totalling US$ 25.00 ha⁻¹.yr⁻¹ (See Table S8 details). Costs for (ii) were calculated for each
age cohort and it is composed of cost of mineral salt and expenses with health (vaccines), and animal identification (Table S1).

## 2.8 Land use dynamics

The model simulates land use dynamics by allocating the total area across pastures or crops; the latter being used for grain and silage production to be used for the formulation of ration for feedlot and supplementation for grazing cattle. EAGGLE allocates land into pasture, soybeans and corn. In the case of pasture, the model allocates land into different productivity levels. Pasture degradation and restoration rates are key model processes that have a bearing on overall system productivity and hence emissions intensity of production.

### 2.8.1 Grassland degradation

Pasture degradation can be defined as the loss of vigour and productivity of forage. To represent the degradation process, EAGGLE defines six levels of Dry Matter Productivity (DMP) levels categories (Table 2): A, B, C, D, and F, where level A is pasture of highest productivity, and level F is fully degraded. If no action is taken to maintain or improve productivity of a fraction of the area in a given category, it is relocated to a lower productivity category. So, after a period of time (assumed as two years herein) category A degrades to category B, B degrades to C, and so on, until pasture F, thus completing a 10-year full degradation (with no management interventions).

The DMP of the pastures levels were calculated exogenously using a model that estimates seasonal pasture growth according to soil, species and climate conditions (Tonato *et al.*, 2010).

### 2.8.2 Land use change and pasture restoration

To offset the degradation process the model can allow for grassland restoration through improved forage quality by direct restoration (by chemical and mechanical treatment) or indirect restoration (by rotating with crops). For example, in a given year a pasture A will degrade to B, the optimal solution might be letting half of pasture A to degrade, and half be maintained to level A. Furthermore, EAGGLE works simultaneously with a composition of pasture DMP levels; e.g., in a given year *t*, the composition can be 4% of A, 10% of B, 85% of C, and 1% of soybeans. Then, at year *t+1*, the composition can change by any combination among the pasture DMP levels and crops.

For each type of land use change or restoration, there is an associated cost (Table 1). Costs were calculated accounting for the amount of inputs (e.g., nitrogen, limestone, micronutrients, forage seeds, and machinery) needed to maintain or increase the DMP level in the target pasture production category. For details of applied inputs, see Table S3-S7.