# Functional Design of Pig Production System Models: Computerization for Life Cycle Assessment (LCA)

Tesfabirhan Weldegabir Redie

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Tesfabirhan Weldegabir Redie. Functional Design of Pig Production System Models: Computerization for Life Cycle Assessment (LCA). Environmental Sciences. 2015. hal-05175478

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# MASTER FOOD IDENTITY

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Master Thesis
Promotion FI 2013/2015

Tesfabirhan Weldegabir REDIE

## "Functional Design of Pig Production System Models: Computerization for Life Cycle Assessment (LCA)" (Platform MEANS)

**Under the Guidance of:**

<table><tr><td>Academic Supervisor:</td><td>Felix Arion, prof. dr.<br>USAMV, Cluj-Napoca, Romania</td></tr><tr><td>Project Supervisor:</td><td>Julie AUBERGER,<br>Coordinator, Platform MEANS, INRA, France</td></tr></table>

**Groupe ESA**
55 rue Rabelais - BP 30748
49007 ANGERS CEDEX 01
☎ 02 41 23 55 30
www.groupe-esa.com

UMR SAS, INRA Agrocampus Ouest
65 Route saint Brieu CS 84215
35042 Rennes Cedex, France
☎ :+33 (0)2 23 48 59 42
www.renes.inra.fr
**Abstract**

This report is the result of a six months study realized within INRA UMR SAS, Rennes. The project was part of the platform MEANS development in France. Its mission is to provide the scientific community of INRA with a comprehensive and user-friendly LCA tool that conducts multicriteria assessment of Agri-food Systems. The tool helps practitioners to conduct life cycle inventory as well as to interpret the results using the SimaPro LCA software. To accomplish this, an InOut system is under development. The objective of the study is to incorporate pig production system within the platform. For guidance, principles of LCA, methodology developed by AGRIBALYSE and a working methodology specifically developed were used to define system boundaries, design an InOut form, characterize and select emission models. As a result pig production system was fully computerized and made ready for IT developers to build the tool. During the study, three stages of pig production systems were treated. Each stage was taken at a time for inventory. For InOut form development, inputs: feed, water, energy, buildings, and pig technical performance, production outputs: manure, direct emissions and product of interest were conceptualized and computerized and seven menus of inventory system were designed. For emission calculations, models N, NH₃, N₂O, NO, NO₃⁻ and CH₄ were computerized as part of the inventory process. Finally the impact expected from the production activity was made to be allocated to the product of interest using allocation systems. Background data that helps users to conduct during LCI works were built and identified as corresponding inventory list.

**Résumé:**

Ce rapport est le résultat d'une étude réalisée à l'UMR SAS de l'INRA de Rennes Durant 6 mois. Elle s'inscrit dans le cadre du développement de la plateforme MEANS à l'échelle nationale. L'objectif de cette plateforme est de fournir à la communauté scientifique de l'INRA un outil générique et facile d'accès afin de conduire des Analyses de Cycle de Vie de systèmes agricoles. L'outil devrait à terme permettre aux praticiens de l'ACV de réaliser des Inventaires de Cycle de Vie et d'interpréter leurs résultats au sein du logiciel Simapro. L'objectif de cette étude consiste en l'acquisition des données nécessaires à l'analyse des exploitations porcines. Pour ce faire, les principes méthodologiques développés lors du projet AGRIBALYSE ainsi qu'une base élaborée pour la plateforme MEANS ont été utilisés. Ils ont notamment servis à la définition des frontières des systèmes porcins, du développement du modèle InOut et à la sélection des modèles d'émission. Le projet a abouti à l'informatisation complète des systèmes porcins et préparée pour IT aux développeurs de crée l'outil. Durant l'étude, la production porcine a été divisée en trois étapes distinctes. Chacune de ces étapes est considérée séparément lors de l'étape d'inventaire. A partir de l'inventaire des intrants : aliments, eau, énergie, bâtiments, données technique d'animaux, sortants : déjection, émissions et le produit d'intérêt ont été conçus. Pour le calcul d'émissions, modèles azote, NH₃, N₂O, NO, NO₃⁻ et CH₄ ont été informatisé dans le cadre du processus inventaire. Enfin, les impacts de la production porcine sont alloués aux produits d'intérêts via un système 'allocation. Les données nécessaire à la réalisation d'inventaire des productions porcines ont été collectées et mises à disposition des utilisateurs.
Acknowledgement

My heart full gratitude goes to the ERASMUS Mundus program that offered me this great opportunity to study and experience European culture and civilization through the eyes of Master Food Identity. My deep appreciation for sure goes to the program coordinators by the name of Dr. Phillipe MONDONDRY. The coordination and harmony was so great: in France, from west to East, to Italy all the great places we visited, in Romania, such a cool and beautiful city, Spain, the time in Leon and Valladolid, and every visit was wonderful. Those who were with us and made every trip such a colorful and joyful moments have big place in my heart. I thank them all.

Mme. Marie-Claire GILLAIZEAU, she was and is everything for me. She is a mother, always available, always helpful, always caring, always loving, always everything. I thank her for being like my mother.

From Groupe-ESA, I would like to thank Anne-Dominique BUCHE and Catherine PELLEIR, from the office of International relationship for their great assistance from the day I applied for the programme and for the arrangement they did for the French language course and examinations.

My Academic supervisor, Dr. Felix ARION, I thank him for his readiness to guide me for the internship. His leadership quality and his energy were mentoring me far beyond my imagination. I was so glad to work under his guidance.

A wonderful and beautiful mind I encountered during my internship and whom I was welcomed to learn and share from the oceanic knowledge she owns is indeed my project supervisor, Julie AUBERGER! I was so lucky to work under her direct supervision. From the first day I landed in INRA office, her warm welcoming opened the door and remained open till the end. We had great discussions, very friendly and motivating and that led to the success of the work. I will be in shortage of words to say everything she was meant to me but I would like to mention that she at least and tell that she was oxygen to my work.

At the same time, I would like to thank the platform MEANS team members, Caroline Malnoë, Véronique Raimbert, Lucille Chanel. My thanks goes to Hayo M.G. van de Werf, for his briefings and professional guidance. I thank also Thibault Salau, not only his materials but also he was always available. His inputs and brief comments helped me to shape my work. Aurélie Wilfart, I thank her. I tank Thierry Trochet for technical guidance to study SimaPro software.

Angel Avadi, and Julio Rivera Garcia, not only we became close friends but their professional contribution was immense. I tank Julio specially for introducing me with application software TexStudio.

I would like to mention and forward my deep thanks to INRA UMR SAS in general and unit ASAE members in particular. Not only colleagues but we were close friends. Their care and hospitality is wonderful. I was always warm being around them.

Far beyond this, all I did and I achieve so far was because of my family. My wife, Furtuna Asfaha KELATI, her patience, love and encouragement was an internal engine that drives me all along my journey. She took full family responsibility. Her great motherhood and taking care of our beloved daughter, Sophia, is far beyond I can express. I left her for my study when she was expecting and we didn't got a chance to meet so far. It has been three years by now. Yet she is with full hope and always loving. I am lucky for having such a loving and caring wife. We are always hoping to meet with each other sooner.

My thanks goes to my family who were always with me. Encouraging and pushing me to fulfil my dreams. I would like to mention them all: my mother Shashu Hibtizghi, my father Weldegabir, brothers Gebrit, Tesfamichael and Melles, my sisters: Yidega, Kibra and Freweini. They are great gift to me.

My family in Law, father Asfaha KELATI and mother Miliete and to all other family members, who took the care of my wife and baby and who acted on behalf are meant a seed of hope. I thank them for their caring.

The list will be long simply because I have so many people to be thanked. But to all my extended family members, friends and colleagues, I do not forget your good wishes and encouragements.

Master Food Identity Promo 5, they are wonderful classmates. Their love is always full. I am to be among them.

At last, I feel a deep sorrow when can't talk with him how great he was to me. It won't repay but I dedicate this work to him. My brother Simon Weldegabir, who passed away in 2013, whom he gave me even the last word he had. He talked me on phone, and he didn't stay long. I wished he was around me.

Finally I would like to thank God for He is the one who gives me power when I was becoming powerless. Within, I have a spiritual father whom I talk with, Saint Padre Pio. On the name of God I pray through him.
To my brother Simon, whom he gave me everything I have.

“May his soul rest in peace.”
Table of Contents

<table><tr><td>Abstract</td><td>i</td></tr><tr><td>Acknowledgement</td><td>ii</td></tr><tr><td>1. Introduction</td><td>1</td></tr><tr><td>2. Project Context, Mission, Objectives, and Methodologies</td><td>3</td></tr><tr><td>2.1 Project Problem and Justification</td><td>3</td></tr><tr><td>2.2 Mission</td><td>3</td></tr><tr><td>2.3 Objectives</td><td>3</td></tr><tr><td>2.4 Goals</td><td>4</td></tr><tr><td>2.5 Project Materials, Organization and Methodology</td><td>4</td></tr><tr><td>2.5.1 Resources and Materials</td><td>4</td></tr><tr><td>2.5.2 Project Structure and Organization</td><td>4</td></tr><tr><td>3. Methodological Review: Life Cycle Assessment, AGRIBALYSE and Platform MEANS</td><td>7</td></tr><tr><td>3.1 Life Cycle Assessment</td><td>7</td></tr><tr><td>3.1.1 Life Cycle Assessment: Principles and Concepts</td><td>7</td></tr><tr><td>3.1.2 LCA Methodologies and Diversity Issue</td><td>8</td></tr><tr><td>3.1.3 Working Principle of LCA</td><td>9</td></tr><tr><td>3.2 AGRIBALYSE® Methodology</td><td>9</td></tr><tr><td>3.3 Platform MEANS as a Tool for Multi-criteria Assessment</td><td>11</td></tr><tr><td>General Description and Working Principle</td><td>11</td></tr><tr><td>3.4 Platform MEANS Methodology</td><td>12</td></tr><tr><td>3.4.1 Main Features of platform MEANS</td><td>13</td></tr><tr><td>3.4.2 Data and Data Sources in Platform MEANS</td><td>15</td></tr><tr><td>3.4.3 SimaPro as an integral part of Platform MEANS</td><td>15</td></tr><tr><td>3.5 Platform MEANS and AGRIBALYSE</td><td>15</td></tr><tr><td>3.6 Access to Platform MEANS</td><td>15</td></tr><tr><td>4. Conceptualizing Pig Production Systems, System Boundaries and Characteristics</td><td>16</td></tr><tr><td>4.1 Pig Production in France: Status and Production Systems</td><td>16</td></tr><tr><td>4.2 Classification of Pig Production Systems</td><td>17</td></tr><tr><td>4.2.1 Classification based on mode of Production</td><td>17</td></tr><tr><td>4.2.2 Classification systems based on Mode of Operation</td><td>18</td></tr><tr><td>4.2.3 Classification based on Physiological Growth</td><td>18</td></tr><tr><td>4.3 Modeling Pig Production System</td><td>18</td></tr><tr><td>4.4 System Boundaries</td><td>18</td></tr><tr><td>4.3.1 Pig Farm</td><td>20</td></tr><tr><td>4.3.2 Animal Feed</td><td>21</td></tr><tr><td>4.3.3 Model Transport and Process</td><td>22</td></tr><tr><td>4.3.4 Infrastructure</td><td>22</td></tr><tr><td>4.3.5 Water Consumption</td><td>23</td></tr><tr><td>4.3.6 Energy Consumption</td><td>23</td></tr><tr><td>4.3.7 Excretion and Manure Management</td><td>23</td></tr><tr><td>4.3.8 Direct Emissions</td><td>24</td></tr><tr><td>4.3.8 Products and Co-Products</td><td>27</td></tr><tr><td>4.4 Allocation Systems</td><td>27</td></tr><tr><td>5. Design and Development of “InOut” Forms</td><td>28</td></tr><tr><td>5.1 Added Features to the General Documentation</td><td>28</td></tr><tr><td>5.1.1 Project Creation: Hierarchy of Pig production system</td><td>28</td></tr><tr><td>5.1.2 Functional Unit</td><td>28</td></tr><tr><td>5.1.3 Collection Unit</td><td>29</td></tr><tr><td>5.1.4 Age Group (New Feature)</td><td>29</td></tr></table>
<table><tr><td>5.1.5</td><td>Allocation System</td><td>29</td></tr><tr><td>5.2</td><td>Design of Stage Data Collection Form</td><td>29</td></tr><tr><td>5.2.1</td><td>Design of main Menu</td><td>29</td></tr><tr><td>5.2.2</td><td>Herd Technical Performance InOut form</td><td>30</td></tr><tr><td>5.2.3</td><td>Animal Feed InOut form</td><td>32</td></tr><tr><td>4.2.4</td><td>Excretion and Storage of Manure Management Systems InOut Form</td><td>34</td></tr><tr><td>5.2.5</td><td>Buildings Inventory InOut Form</td><td>37</td></tr><tr><td>5.2.6</td><td>Manure Storage Infrastructure Inventory InOut form</td><td>38</td></tr><tr><td>5.2.7</td><td>Energy Inventory InOut Form</td><td>39</td></tr><tr><td>5.2.8</td><td>Water Consumption Inventory InOut Form</td><td>39</td></tr><tr><td>6.</td><td>Emission Models: Selection, Conception and their Functional Design</td><td>40</td></tr><tr><td>6.1</td><td>Nitrogen (N) - Model</td><td>40</td></tr><tr><td>6.1.1</td><td>Nitrogen Model flow Diagram</td><td>41</td></tr><tr><td>6.1.2</td><td>Model Equations</td><td>41</td></tr><tr><td>6.1.3</td><td>N-Model Summary</td><td>46</td></tr><tr><td>6.2</td><td>Ammonia (NH<sub>3</sub>) Model</td><td>47</td></tr><tr><td>6.2.1</td><td>General Concepts of Ammonia Emissions</td><td>47</td></tr><tr><td>6.2.2</td><td>Ammonia (NH<sub>3</sub>) - Model Flow Diagram</td><td>47</td></tr><tr><td>6.2.3</td><td>Factors that affect ammonia emissions from pig farms</td><td>48</td></tr><tr><td>6.2.4</td><td>Models Selected and Equations</td><td>48</td></tr><tr><td>6.3</td><td>Model - NO<sub>3</sub><sup>-</sup></td><td>51</td></tr><tr><td></td><td>Model Equation</td><td>52</td></tr><tr><td></td><td>Model Summary</td><td>52</td></tr><tr><td>6.4</td><td>Model -NO</td><td>52</td></tr><tr><td>6.5</td><td>Model- N<sub>2</sub>O</td><td>53</td></tr><tr><td>6.5.1</td><td>N<sub>2</sub>O Model flow diagram</td><td>53</td></tr><tr><td>6.5.2</td><td>Model Equations</td><td>54</td></tr><tr><td></td><td>Model Summary</td><td>55</td></tr><tr><td>6.6</td><td>Model - Methane (CH<sub>4</sub>)</td><td>56</td></tr><tr><td></td><td>General concepts of methane Gas emissions</td><td>56</td></tr><tr><td>6.6.1</td><td>Methane (CH<sub>4</sub>) - model Flow Diagram</td><td>56</td></tr><tr><td>6.6.2</td><td>Factors that affect methane emissions</td><td>57</td></tr><tr><td>6.6.3</td><td>Models Selected and Equations</td><td>57</td></tr><tr><td>6.6.4</td><td>Model-Summary</td><td>59</td></tr><tr><td>6.7</td><td>Summary of models retained</td><td>60</td></tr><tr><td>7.</td><td>Allocations and Impact Assessment Indicators and Correspondence to Simapro</td><td>61</td></tr><tr><td>7.1</td><td>Allocation Rule Applied for pig production systems</td><td>61</td></tr><tr><td>7.1.1</td><td>Determination of Biophysical Allocation Factor</td><td>61</td></tr><tr><td>7.1.2</td><td>Parameters used for the calculation of Allocation factor</td><td>62</td></tr><tr><td>7.2</td><td>Impact Assessment</td><td>62</td></tr><tr><td>7.3</td><td>Platform MEANS - Simapro Correspondence Inventory List</td><td>63</td></tr><tr><td>8.</td><td>Results, Discussions and Conclusions</td><td>65</td></tr><tr><td>8.1</td><td>Results and Discussions</td><td>65</td></tr><tr><td>8.1.1</td><td>Stage documentation</td><td>65</td></tr><tr><td>8.1.2</td><td>Computerization of the Model Set</td><td>65</td></tr><tr><td>8.2</td><td>Development of Referential System technical data: Data Collect</td><td>66</td></tr><tr><td>8.2.1</td><td>Data Collect Menu</td><td>66</td></tr><tr><td>8.2.2</td><td>Emission Inventory Calculation models</td><td>69</td></tr><tr><td>8.3</td><td>Other Activities</td><td>69</td></tr></table>
<table><tr><td>8.3.1 Allocation System</td><td>69</td></tr><tr><td>8.3.2 Transport Model</td><td>69</td></tr><tr><td>8.4 Application of the concepts developed for pigs to other animal production systems</td><td>70</td></tr><tr><td>8.5 Conclusions</td><td>70</td></tr><tr><td>Abbreviations</td><td>71</td></tr><tr><td>Units and Symbols</td><td>72</td></tr><tr><td>Glossary</td><td>73</td></tr><tr><td>Figures</td><td>74</td></tr><tr><td>Tables</td><td>75</td></tr><tr><td>Equations</td><td>77</td></tr><tr><td>Appendix I: Tables for Chapter 5, 6 and 7</td><td>78</td></tr><tr><td>Appendix II: Emission Models Parameters</td><td>83</td></tr><tr><td>Appendix III: List of Correspondence to SimaPro</td><td>86</td></tr><tr><td>Appendix IV</td><td>90</td></tr><tr><td>Bibliography</td><td>92</td></tr><tr><td>Annexes</td><td>I</td></tr></table>
# 1. Introduction

A life cycle thinking (LCT) and life cycle assessment (LCA) based Product Declaration Rules (PCRs)<fnref n="1" /> are increasingly growing across food production systems as a mechanism to tackle climate change and as move to bring sustainable development programs (ISO, 2006a; Del Borghi, 2013). These regulations require detailed environmental impact assessments of emissions and resource utilization in order to quantify the environmental impacts caused by a unit of product or rendered service. For example; in 2010, European Council called for a quantitative assessment of environmental impacts of products and since 2012², EU recommends producers to have an environmental labeling on agricultural food products (Koch & Salou, 2015). Thus, environmental aspect<fnref n="3" /> has become the center of interest among policy makers, researchers, producers, practitioners, consumers, business people and decision makers.

In response, ISO 14040 and 14044 were developed as an international standard general principles, frameworks and guidance on how to conduct LCA. Though these standards have general guidance, the principles laid are open to wide interpretation thus allowing users to come out with diversified methodologies. So far, different methodologies have been developed by many environmental assessment experts and are continuing to produce enormous publications throughout the world (Van der Werf, et al., 2011). Such prolific works have a potential to mislead claims of product declarations that might create confusion among decision makers. To avoid such risks, harmonization of approaches has become of focus area (Del Borghi, 2013; Lansche, et al., 2014; Colomb, et al., 2013; Koch & Salou, 2015; Emhart, et al., 2014). In principle, PCRs require appropriate and harmonized methodologies that quantify environmental impacts of production systems so that a common consensus can be reached among stakeholders.

Recently, unified, innovative and comprehensive approach that responds questions pertaining quantitative environmental assessments of agricultural products has being of great interest to researchers, developers and practitioners (EC - JRC, 2010; Colomb, et al., 2014). Developing tools that enable researchers and practitioners to conduct thorough environmental assessment with standardized methodology is therefore an indispensable step towards harmonization.

Based on the said above need and demand, INRA took an initiative to develop an innovative and of first in its kind national program called Platform MEANS. The program which was launched in 2012 has its prior objective to create or provide software that helps INRA researchers to conduct multicriteria assessment in crop production systems, animal production systems as well as transformation and processing of food products (Auberger, et al., 2013). Platform MEANS, which is an abbreviated name for platform MulticritEria AssessmeNt of Sustainability, is national platform software designed mainly to researchers that helps them to conduct multi-criteria assessments<fnref n="4" /> and share database.

<footnote n="1">¹ Product Category Rules: ISO 14025 –Environmental Labels and Declarations-Type III. Rules related to environmental declaration of products: ISO 14025: 2006, PAS 2050, BP X30, SMRS, TS 011000, ISO/TS 14067:2013. For more, http://pcr-library.edf.org.tw/ and http://www.environdec.com/en/PCR/</footnote>

<footnote n="2">² French law on environmental: The Grenelle 1 Law of August 3, 2009 Article 54 demands food producers to provide a reliable and complete environmental information of the entity “product and packaging” for consumers.</footnote>

<footnote n="3">³ Element of an organization's activities, products or services that can interact with the environment (ISO 14025: 2006)</footnote>

<footnote n="4">⁴ Environmental, economic and social assessments</footnote>
In general, the program has four phases that are expected to be finalized in 2016 (https://www6.inra.fr/means_eng/presentation, s.d.). So far, two phases of the project are completed. The first phase being on general conceptualization and creation of the platform, on its second phase, it has developed software for “Crop Production Systems” and is now open to INRA users since November 2014. The third phase is focused on “Livestock Production System” for which I was part during my six months internship stay in INRA UMR-SAS<fnref n="5" />, Rennes. Since November 2014, the platform is open to users with its first version V1.0 and has been updated to V1.1 in June (Auberger, 2015)<fnref n="6" />. In the long term, the platform has an ambition to include environmental, social and economic impact assessments but currently its work is limited to environmental impact assessments (Auberger, et al., 2013).

Since 2015, the project has entered its third phase. The main mission of this phase is to develop a tool for “Animal Productions Systems” based on Life Cycle Assessment. I have joined the project at this juncture to have professional experience as a requirement for completion of Master degree in “Food Identity”. The internship focused on conceptualizing and development of technical description of pig production systems, design and development of inventory system, computerization of emission models and henceforth information is delivered to the software developers. The internship was managed and supervised by the Platform MEANS project manager.

The paper is organized into four major parts presented into eight chapters. After brief introduction of the subject matter, the second chapter presents information on project mission and organization. Chapter three contains a thorough review on LCA concepts, methodologies and tools. The fourth chapter mainly focused on pig production systems while conceptualizing the whole project using boundary system approach. The fifth chapter presents the process and development of InOut data inventory system and forms based on the system boundary discussed in chapter 4. Chapter 5 is fully dedicated on model developments and calculation procedures while providing all necessary information required for the development of each model concerned. After having a chapter that deals with allocations, impact assessment indicator characterization and compilation of resource inventory results, the last chapter of this paper has presented discussions and conclusion. Appendixes, Annexes and Bibliography were attached at last.

<footnote n="5">⁵ INRA UMR SAS</footnote>

<footnote n="6">⁶ Introduction to Project Platform MEANS: Working Principle and methodology, progress presentation. Attendants: Julie AUBERGER, Caroline MALNOË, and Tesfabirhan W. REDIE. INRA, UMR SAS, Rennes. 10.03.2015. www.inra-rennes.fr</footnote>
## 2. Project Context, Mission, Objectives, and Methodologies

### 2.1 Project Problem and Justification

As mentioned in the introduction part, life cycle assessment of animal production systems requires large data handling and use of different and numerous models to quantify the impact caused by the activities during the whole life cycle of production process. So far, conducting such assessments has not been easy among practitioners. To create an innovative, user friendly and generic multicriteria assessment tool that base its methodology on practices and know-how of agricultural production systems has been a long quest by researchers and experts.

This search for innovative tool has given birth to the project platform MEANS. According to Auberger et. al (2013), platform MEANS is software dedicated with principal objective to provide a generic and user friendly platform for INRA (and principal stakeholders) researchers to conduct environmental, social as well as economic assessments in agricultural food production systems. Development of such tool requires an integration of multidisciplinary experts to come together and work so that production systems information can be converted into a computer understandable format so that IT engineers can write and program the scientific concepts. The computerization of agricultural production systems is therefore a vital step for creating communication between production practices, tool developers and end users.

It is within this context that the platform has been initiated in 2012 to deliver software that facilitates life cycle data inventory and assessments.

### 2.2 Mission

During the six month internship mission, I joined a project operating team composed of three computer programmers and one project coordinator as an Agronomic Engineer to work on method and system conceptualization and computerization of pig production system models based on a methodology developed by AGRIBALYSE<sup>7</sup>. The information developed was to be delivered to information technology experts and be used for the development of life cycle inventory (LCI) tool.

### 2.3 Objectives

The internship project had two major and one long term objectives.

*Major Objectives:*

a. To conceptualize pig production systems so that each sub-system of the production life cycle is identified, characterized, and developed into a fully computerized data system.

b. To deliver these computerized data to software experts for the information provided is programmed for the development of a generic and multicriteria LCA tool for pig production system.

*Extended Objective*

<footnote n="7"><sup>7</sup> AGRIBALYSE® is a program launched in the year 2009 by the French Environment and Energy Management Agency (ADEME) and has created a database Life Cycle Inventory (LCI) of 137 agricultural products.</footnote>
c. To use the principle and concept developed for pig production systems and apply it for other animal productions systems.

**2.4 Goals**

a. To produce an operating and functional LCA software that will be used for pig productions systems.

b. The system and principle of modeling developed is expected to be adapted as a generic tool for other modes of livestock production systems.

**2.5 Project Materials, Organization and Methodology**

**2.5.1 Resources and Materials**

a. *Human Resources* (Operating Team): one project manager, three computer programming experts and the author of this document.

b. *Methodological Guidelines: AGRIBALYSE®* and platform MEANS methodologies.

c. *Software*: SimaPro, platform MEANS, office packages (Excel and Microsoft word), Free office (Free diagram software), and internet.

d. *Library and reference materials*: INRA UMR SAS library (accessible online). And Reference materials were built on, EndNote, Zotero and Mendeley.

**2.5.2 Project Structure and Organization**

**2.5.2.1 Project Platform MEANS**

Project MEANS is a national project operating under INRA UMR SAS administration with a mission: (a) to develop and make available software tools for the calculation of sustainability indicators, (b) to establish and develop database for multi-criteria analysis, (c) train and support users and (d) to provide scientific and technical monitoring of multi-criteria analysis (Auberger, et al., 2013; Auberger, 2015). Its goal is to develop software and make it operational and deliver to users.

As shown in *Figure 2. /Error! Reference source not found.*, the project involves different actors and stakeholders (researchers, and academic experts), agronomic experts, IT developers, legal advisors and thematic experts working for the development of the platform. Within the project operation team, the platform composes an Agronomic Engineer/expert whose duty is to define methods and systems and conceptualize for developing detailed specification, computerize information and deliver them to IT. Once the software is developed, the agronomist take part in the validation process of the software developed.

The platform development chain includes methods and system definition, detailing, software development and validation of the work (Auberger, 2015). The procedure follows an iterative system of communication between the operation team and the whole project actors.
<figure><img src="image_6.png" /><figcaption>**Figure 2. 1: Structure and Organization of project Platform MEANS and Actors involved in the project**</figcaption></figure>

### 2.5.1.2 Internship Organization and Structure

Based on work flow, the project was organized into five major phases as it is given in Figure 2.2

**I – Methodological Studies:** As working guidelines, a thorough study was done on AGRIBALYSE® methodology and Platform MEANS working methodologies. Project tools<fnref n="8" /> (SimaPro<fnref n="9" /> and InOut software) were also studied based on tutorial materials and exercise.

**II – Conceptualizing Pig Production Systems:** Based on system boundary defined by “Cradle-to-Gate” as per AGRIBALYSE® (V1.2)<fnref n="10" /> detailed models were presented (chapter 6) and include: technical activities of each animal category, animal feed, water and energy consumption, animal buildings, waste management and waste storage infrastructures.

**Phase III – Design and Computerization of InOut Inventory System (chapter 5):** In reference to the system boundary models illustrated, InOut Inventory system was designed and computerized with background information required for the development of the InOut forms.

**Phase IV – Conceptualization and Computerization of Emission Models:** Models are presented in two parts. The first part focuses on N - excretion model. A profound discussion and presentation is done to N - excretion model (see chapter 6, section 1). It is done so because N - Excretion is treated as basic resource input model during emission inventory. It has a strong link between the resource inventory and gas emission models. The second part presents emission models and calculation procedure (NH₃, N₂O, NO and NO₃ and CH₄). Each model is presented with the type of model retained, model diagrams, equations, input

<footnote n="8">⁸ No presentation is done regarding this supporting software. They were studied to understand the working principles and to have a knowhow on how data are stored and processed.</footnote>

<footnote n="9">⁹ SimaPro is the leading LCA tool dedicated to LCA practitioners to collect, analyze and monitor sustainability performance of products and services. More information can be found in http://www.pre-sustainability.com/SimaPro</footnote>

<footnote n="10">¹⁰ AGRIBALYSE® has delivered several versions (2014 V1.1) and (2015, V1.2). Version V 1.2 was released on April 10, 2015 while the project was in progress and the reference material was changed accordingly.</footnote>
parameters and calculation procedure. Each model was presented and delivered to the IT
team in *Excel files*.

Phase V- The last phase of the project concentrated on allocation systems,

<figure><img src="image_7.png" /><figcaption>Figure 2. 2: Project Structure and Organization</figcaption></figure>
# 3. Methodological Review: Life Cycle Assessment, AGRIBALYSE and Platform MEANS

## 3.1 Life Cycle Assessment

### 3.1.1 Life Cycle Assessment: Principles and Concepts

ISO 14040 (2006a) defines Life Cycle Assessment (LCA) as a compilation and evaluation of the inputs and outputs and the potential environmental impacts of a product throughout its life cycle. It is an internationally recognized and standardized method for assessing environmental aspects of resource use and environmental impacts associated with specific product over its life cycle (ISO, 2006a; Cherubini, et al., 2014; Van der Werf, et al., 2011; BSI, 2011; Koch & Salou, 2015). As it is shown in Figure 3.1, in ISO 14040/14044, the framework, principles and application is structured into four main components<fnref n="11" />: Goal and scope definition, inventory analysis, impact assessment and interpretation. (ISO, 2006a; Basset-Mens, et al., 2014; EC - JRC, 2010).

To conduct LCA, the scope and system boundaries relevant to the assessment need to be defined clearly so that no part of the system can be missed from being included (ISO, 2006a). Sufficient data and carefully made assumptions are needed for complete assessment. During LCA, external reviews might be done based on the objectives and nature of end users. And the result obtained has to be communicated with concerned bodies.

LCA is a quantitative assessment method and its use is limited to environmental impacts (ISO, 2006a). It is a powerful method that brings a wide range of environment aspects in a scientific and quantitative manner. The method allows environmental pressures and impact potential to be related to any defined system. Thereby the resource use and emissions over the entire life cycle of the analyzed system are integrated to infer a useful quantitative meaning of the impacts. In addition, LCA results can be used to compare environmental performance of different systems and conduct product declarations.

Often life cycle assessment and life cycle thinking are mentioned together and it is important to distinguish them as they are two different but complementary issues in modern interpretation of life cycle related topics (Recchioni, et al., 2012; UNEP, 2004; EC-JRC and IES, 2010b). Life Cycle Initiative website<fnref n="12" /> describes Life Cycle Thinking (LCT) as about going beyond the traditional focus and production site and manufacturing processes to include environment, social and economic impacts of a product over its entire life cycle. It is a way of thinking based on the concept of “Cradle-to-Grave” where every phase of a product life cycle is looked into for making decisions to reduce product resources and emissions as well as to improve the socio-economic performance of a product.

On the other hand, LCA helps to assess the impacts caused at each product life cycle considered (CSMM, 2014; Recchioni, et al., 2012). While LCT describes a way of considering impacts caused by a product or process throughout its life cycle, LCA accesses the impacts based on LCT. In this regard, LCA is quantitative while LCT can be semi-quantitative or qualitative (Recchioni, et al., 2012). LCT process can be applied throughout the LCA makes them complementary when a complete and comprehensive studies are performed.

<footnote n="11">¹¹ ISO 14044 combines goal and scope as one phase</footnote>

<footnote n="12">¹² http://www.lifecycleinitiative.org/starting-life-cycle-thinking/what-is-life-cycle-thinking/</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 3. 1: Methodological flow of LCA (ISO 14040 and 14044)</figcaption></figure>

3.1.2 LCA Methodologies and Diversity Issue

Despite the existence of ISO guidelines on LCA practices, because of its open interpretation and flexibility, increasing diversification in methodologies is creating a concern among LCA experts and decision makers (Koch & Salou, 2014; Lansche, et al., 2014; Pré Consultants, 2010; Kuczenski, 2014; Emhart, et al., 2014; Recchioni, et al., 2012). In order to develop a harmonized system of LCA, LCA experts coming with several initiatives to develop LCI database and guidelines that are comparable to each other and diverse (Koch & Salou, 2015; BSI, 2011; ISO, 2006a; Pré Consultants, 2010; JRC, 2010; AFNOR, 2011). For example, Lansche et al (2014) listed more than ten<fnref n="13" /> different existing guidelines of conducting LCA worldwide.

To comply with ISO standards, any LCA methodologies are required to adhere to the minimum principles such as careful documentation of goal and scope and interpretation of results (Pré Consultants, 2010; Lansche, et al., 2014). To minimize this concern, different countries are working to develop an LCI database and harmonize the results with other LCA methodologies so that a common way of LCA communication can be achieved (Lansche, et al., 2014). Some of the on-going activities are: ACYVIA<fnref n="14" />, AGRIBALYSE® and Base IMPACTS® (France), PAS 2050¹⁵ (UK), Asian Agri-Food Database (Asia)<fnref n="16" />, Australian LCI Database (Australia)<fnref n="17" />, Chilean Food and Agricultural LCA database (Chile)<fnref n="18" />, Ecoinvent

<footnote n="13">13 BPX 30-323-0, PAS 2050:2011, PEF Guide, Envifood protocol, MTT Guidelines, IDF Guide, IPCC Guidelines, ISO 14025: 2006, ISO 14040:2006, ISO 14044:2006, ISO 14067:2013, ILCD handbook, Shonan Guidance, Ecoinvent data quality guidelines</footnote>

<footnote n="14">¹⁴Analyse de Cycle de vie dans les Industries Agro-alimentaires, works for Environmental product declaration of food transformation processes at national-level in France</footnote>

<footnote n="15">¹⁵ Is a Publicly Available Specifications for assessing product life cycle GHG emissions, prepared by British Standards and co-sponsored by the Carbon Trust and the Department for Environment, Food and Rural Affairs (Defra) (Pré Consultants, 2010)</footnote>

<footnote n="16">¹⁶LCA-Agri-food Asia has been developed with the collaboration of Thailand, Japan, South Korea, Malaysia, Indonesia and Vietnam with the aim of expanding the application of LCA at regional level</footnote>

<footnote n="17">17 A national, public-accessible database of Australian products and services: http://alcas.asn.au/AusLCl/</footnote>

<footnote n="18">¹⁸ A project to develop Chilean EcoBase Food by Fundación Chile and Univesidad de La Frontera (Emhart, et al., 2014)</footnote>
(Switzerland)<fnref n="19" />, ELCD <fnref n="20" />(European Union), ILCD<fnref n="21" />, WFLDB (World Food Database)<fnref n="22" />
(Lansche, et al., 2014; Notarnicola, et al., 2015).

**3.1.3 Working Principle of LCA**

As it is discussed on section 4.1.1 and elaborated in Figure 3.1, LCA is a reference method
for environmental impact assessment of production systems (EC - JRC, 2010). LCA identifies
potential environmental impacts. During the impact assessment phase, LCA method
aggregates inventory data related to the performance of the studied systems into impact
categories such as energy consumption, Eutrophication, acidification, global warming, land
use etc. and produce impact indicator for each category. Convert these indicators into a single
score impact results require the use of value judgments. This holistic approach is the strength
of LCA methods. Conducting LCA demands precise data inventory, appropriate emission
calculation models and aggregation of inventory results according to their impact category
(ISO, 2006a; EC - JRC, 2010). As agricultural production system is of complex in nature in
relation to environmental and its impact, a systematic way of data collection, entry and
processing is required.

**3.2 AGRIBALYSE® Methodology**

AGRIBALYSE® methodology was an outcome of a project launched in response to “Grenelle de l’Environnement” law of environmental labeling policies. The project which was started and coordinated by the French Environmental and Energy Management Agency (ADEME)<fnref n="23" /> as a national research program (2009-2013)<fnref n="24" /> has three major goals: (1) to provide data that contribute to the environmental labeling of food products and (2) to provide benchmark and key figures to the agricultural sector in order to promote environmental assessment projects and reduce the impact of agricultural production systems on the environment and (3) to elaborate an harmonized methodology for LCA in agricultural sector, in order to contribute and improve existing farming systems (Colomb, et al., 2013; Koch & Salou, 2015; www.ademe.fr/agribalyse).

As a result of this mission, the project was able to produce a methodology for agricultural food production systems (crop and animal) that is now adapted and being used by platform MEANS. AGRIBALYSE® bases its methodology on ISO 14040 and 14044, ILCD guidelines and French methodology called BPX30-323²⁵ developed by ADEME and AFNOR (Colomb, et al., 2013; Colomb, et al., 2014; ISO, 2006b)..

In its methodology (Table 3.1), (Koch & Salou, 2015; Koch & Salou, 2015; Colomb, et al., 2013; Colomb, et al., 2014), AGRIBALYSE has defined methods on system boundaries for

<footnote n="19">¹⁹ http://ecoinvent.org/org/database/</footnote>

<footnote n="20">²⁰European Reference Life Cycle Database</footnote>

<footnote n="21">²¹ Internal Life Cycle data Systems http://eplca.jrc.ec.europa.eu/?page_id=86</footnote>

<footnote n="22">²² Dedicated to develop selected agricultural products dataset in the global market (Lansche, et al., 2014)</footnote>

<footnote n="23">23 LOI n° 2009-967 du 3 août 2009 de programmation relative à la mise en œuvre du Grenelle de l'environnement (1)
http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000020949548</footnote>

<footnote n="24">²⁴ Project partners (2010-2013) : ACTA , ADEME, AgroScope-ART, ARVALIS Institut du végétal, CETIOM, CIRAD, CTIFL, IDELE Institut de l'Elevage, IFIP, IFV, INRA, ITAVI, ITB, Terres d'Innovation, UNIP (for more information,
http://www.ademe.fr/en/expertise/alternative-approaches-to-production/agribalyse-program ) --</footnote>

<footnote>Project Partners (2014 - 2018) : ADEME, INRA, CIRAD, ACTA, In Vivo, Ministère de l'Ecologie, du Développement Durable et de l'Energie.</footnote>

<footnote n="25">²⁵ Is a French general principles and a methodology for labeling products with their environmental footprint</footnote>
different production systems, functional unit used, data, data quality control mechanism, and inventory methods for emissions.

<table><caption>Table 3. 1: AGRIBALYSE® Modeling framework and main Methodological Choices</caption><thead><tr><th>Components of AGRIBALYSE methodology</th><th>Description</th></tr></thead><tbody><tr><td>Methodology Guidelines</td><td><ul><li>French Methodology of ADEME-AFNOR, ISO 14040 and 14044, ILCD Guidelines,</li></ul></td></tr><tr><td>Modeling Framework and Tools</td><td><ul><li>Agricultural Production Systems practices data were provided in Excel files called data collection tool</li><li>A calculation chain of emission models were prepared and connected with the data collection tool to calculate direct emissions and resource use.</li><li>Life Cycle Inventory data file (Data Collection Tool) are prepared in Excel format and delivers inputs to emission models (emission calculation chain) also prepared in Excel file.</li><li>Indirect Emissions that are not calculated in AGRIBALYSE project but are part of the inventory process were obtained from other LCI sources like Ecoinvent and are made to be incorporated as part of database.</li><li>Indirect fluxes were then added using file format Ecospold and exported in SimaPro, to obtain LCIA and LCA data.</li></ul></td></tr><tr><td>System Boundaries</td><td><ul><li>“Cradle-to-field-Gate”</li></ul></td></tr><tr><td>Data sources and representativeness</td><td><ul><li>Statistical sources (public and non-public), typical cases, expert opinion, or individual case/estimate</li><li>Representativeness was considered while national averages were taken based on weighted averages.</li></ul></td></tr><tr><td>Functional Unit</td><td><ul><li>Defined in units of mass (1 kg) or volume (1 liter)</li></ul></td></tr><tr><td>Model selection</td><td><ul><li>Models used for calculating direct pollutant emissions that require input data with less difficulty, well published, validated for France and internationally recognized were selected</li></ul></td></tr><tr><td>Emission Calculations</td><td><ul><li>Direct emissions: calculated using models in AGRIBALYSE® (For example: emission models presented for animal production systems are: NH<sub>3</sub>, N<sub>2</sub>O, NO, NO<sub>3</sub><sup>-</sup> and CH<sub>4</sub>)</li><li>Indirect emissions: Based on existing Database mainly Ecoinvent®</li></ul></td></tr><tr><td>Allocation</td><td><ul><li>ISO (2006a and 2006b) recommendations were followed. For animal production: Bio-Physical allocation method</li></ul></td></tr><tr><td>Inventory Data Quality Control</td><td><strong>Three Levels</strong><ul><li>Global quality of LCI</li><li>Individual Inputs</li><li>Emission models</li></ul></td></tr></tbody></table>

(Source: (Koch & Salou, 2015; Colomb, et al., 2014; Colomb, et al., 2013))

As described in Figure 3.2 to produce a life cycle inventory database, the project has also used a tool for the inventory of depletion of resources and of pollutant emissions. This tool is based on separate excel files. The first excel file, called “Data Collection Tool”, and is used for primary input data inventory. This file is coupled with another second excel file called “emission calculation chain” having 15 models built on it to calculate direct emissions. Results obtained from these inventory entry and calculations and other indirect emissions models<sup>26</sup> obtained from other LCI databases like Ecoinvent<sup>27</sup> are exported as separate files in Ecospold format and added to SimaPro<sup>28</sup>. In SimaPro, AGRIBALYSE LCI database are

<footnote n="26"><sup>26</sup> Are obtained from Ecoinvent</footnote>

<footnote n="27"><sup>27</sup> Ecoinvent is the world's leading Life Cycle Inventory (LCI® database. With several thousands and international LCI databases from a number of different fields it finds its use in a broad range of environmental studies. For more information</footnote>

<footnote>http://www.ecoinvent.org</footnote>

<footnote n="28"><sup>28</sup> Is a commercial software</footnote>
stored where they can be used by other actors (French) who are interested in Eco-labeling and other environmental assessment issues.

<figure><img src="image_9.png" /><figcaption>Figure 3. 2: principles of AGRIBALYSE® tools (Source: (Koch & Salou, 2015))</figcaption></figure>

A “cradle-to-farm-gate” system boundary is used for system modeling and emission calculations (Koch & Salou, 2015). Unlike LCA, it is not in the scope of AGRIBALYSE® project to choose a method to interpret the results meaning to calculate impact indicators or to apply normalization or aggregation on these indicators (Koch & Salou, 2015). AGRIBALYSE tool is mainly a calculation tool for emissions based on the production system inputs (Julie AUBERGER, oral presentation)<fnref n="29" />.

## 3.3 Platform MEANS as a Tool for Multi-criteria Assessment

### General Description and Working Principle

Platform MEANS is a set of multi-criteria assessment tools mainly designed for INRA researchers to conduct LCA of agricultural production systems (Auberger, et al., 2013). Its main objective is to provide a generic, simplified and user friendly LCA tool to INRA researchers for environmental impact assessment studies on crop production systems, animal production systems and processing of agricultural products. As an LCA tool, its working principle is based on ISO 14040 and 14044. MEANS platform provides an LCA integrated tool and database of production systems that helps users to conduct multicriteria resource inventory (LCI) and assessment (LCIA) by providing the InOut software (which is a new and innovative feature created by the project) and an external but part of the platform commercial software SimaPro. The combination of the two enables users to produce a complete assessment of an LCA project.

Figure 3.3 gives the whole structure of MEANS platform. The platform provides different impact evaluation methods and each method is provided with different indicators where the user can choose from according to his project objectives. The InOut software enables users to enter data, and calculate direct emission calculations and resource use estimations (such as water consumption, electricity and land use), from the inventory data entered.

To do so, InOut software provides forms to collect detailed information on the studied system, and embedded emission models.

<footnote n="29">²⁹Introduction to Platform MEANS for ASAE stuff members (PPT) by Julie AUBERGER, INRA UMR SAS, 19.05.2015</footnote>
Depending on the system of production and nature of the product output, allocation of impacts is done by embedded allocation system rules. The information created is then exported to SimaPro software using data files called Ecospold. Once data are exported to SimaPro, the user is able to calculate impact indicators and to interpret the data with the help of available features in SimaPro

In addition, from the LCI performed, associated LCI database can be created in SimaPro which can be used as Life Cycle Inventory database references.

Therefore platform MEANS is an advanced tool of conducting life cycle inventory, documentation, life cycle inventory database developer and that enables users to conduct impact assessments and further interpretation using two software through a linkage system created. SimaPro is commercial software and its use requires licensing. For this, platform MEANS provides users a combined license and once one agrees the terms and use of the platform, SimaPro is part of the license agreements. Currently, the InOut software is only accessible through online and to use, the user must have an internet access. Simapro is software that can be installed but it requires permission from the central platform MEANS administrator if it is going to be used with (Auberger, et al., 2013).

<figure><img src="image_10.png" /><figcaption>Figure 3. 3: Integrated Structure of Platform MEANS Tool</figcaption></figure>

## 3.4 Platform MEANS Methodology

In principle, platform MEANS has adapted methodology developed by AGRIBALYSE® (Auberger, et al., 2013). These include: life cycle inventory methods and inventory data quality control, emission models and calculation procedure, allocation system rules, impact assessment indicators methods and related reference data acquisition and sourcing. However, the platform has developed its own methodology for conducting LCA and data acquisition systems and by allowing the user to choose during inventory what emission models to use and what type of indicator to include. In addition, once a project is created, the user is able to use different analysis methods for the same project. This feature gives a chance to create one or several scenarios from which it is possible to make scenario comparisons that are helpful for decision making and production system improvements (Auberger, et al., 2013; van der Werf, et al., 2012).

Conducting LCA using platform MEANS is an eight step process as it is shown in *Figure 3. 4* (Auberger, et al., 2013; Auberger, 2015). As it is illustrated, the page starts with a welcoming page and allows the user to create a new project <fnref n="30" />(or open an existing project). The newly

<footnote n="30">³⁰ In InOut software, a « project » is the workspace of the user.</footnote>
created project has to be defined by choosing the type of sustainability assessment required, methods to be used and indicators of assessment to be utilized for each method selected. In the consequent steps, the user is asked to define the system to be studied, to define indicator algorithms. Then he can describe the system to be studied, calculate impact indicators and interpret the results.

In a single project, different analysis methods can be applied for the project. And once the analysis method is selected, as per the ISO 14040 and 14044 principles, full documentation of inventory is done (for more information on the steps to be followed and information contained in each step, read Table 3.2). Though the platform is designed to conduct multicriteria assessment, it is also possible to run monocriteria assessments. Life cycle inventory inputs (step VI) are done during the stage documentation phase. The input form design varies depending on system of production, scenario or stage selected. The input form is developed in a more systematic, simplified and user friendly manner (Auberger, et al., 2013). Gas emission models are part and parcel of the InOut software from which one has to select for calculations. Then the results are ready in an Ecospold format for export to SimaPro, where impact assessment interpretation is executed.

The user is guided by the platform in Steps I to V and the user chooses from available information according to the project objective and system boundary defined. In Step VI, the platform allows the user to describe the system and to enter inventory input data. Based on the defined parameter and input data, selected model calculate emissions. At last, results are interpreted through pre-defined criteria, but platform MEANS provides no tool to help this interpretation.

<figure><img src="image_11.png" /><figcaption>Figure 3.4: Platform MEANS Working Methodology (Source: (van der Werf, et al., 2012))</figcaption></figure>

### 3.4.1 Main Features of platform MEANS

InOut software simplifies inventory system and guides the user during the whole process. One of the resilient features of InOut is that every step is clearly defined and usually aided with guidelines that are found either in the page opened or by clicking a help button. In addition, there is a complete manual of the platform prepared for users.

During inventory process, the steps are divided into 10 major pages (details are described in Table 3.2). Each step is found in a new page that is following the previous. Once the user finishes his inventory and orders for calculation to proceed, the software calculates the inventory values and makes them ready in Ecospold format for export.
<table><caption>Table 3. 2: Main Features of Platform MEANS and description (source: Platform MEANS –accessible online).</caption><thead><tr><th>Step/Features</th><th>Description</th></tr></thead><tbody><tr><td>Create New project or/and Or open new project</td><td><ul><li>Allows creation of a project of one or several new project Or to open an existing one.</li><li>Information displayed: name of the project, project owner, date of creation, information on updates and the type of system studied.</li></ul></td></tr><tr><td>Analysis creation<sup>31</sup></td><td><ul><li>Under a given project, it allows creation of several analyses that enables a user to use different analysis methods for the same project.</li><li>Analysis methods: Life Cycle Assessment and IDEA diagnosis</li><li>Information displayed: Analysis name, date of creation, last updates and by whom?</li></ul></td></tr><tr><td>Analysis Information (Methods)</td><td><ul><li>Once the stage is created, for each stage, analysis information is selected. These include:<ul><li>a. Suitability dimension<sup>32</sup></li><li>b. Impact assessment methods and</li><li>c. Impact assessment indicators.</li><li>d. Scenario documentation</li></ul></li><li>During method selection, the user is able to select methods indicators to be used for monocriteria and /or multicriteria assessment methods.</li><li>Currently available impact assessment methods and indicators in the platform as given in Annex 1).</li></ul></td></tr><tr><td>Analysis Information (Indicators)</td><td><ul><li>It allows the user to choose impact assessment indicator Common indicators provided include: Carbon Footprint (IPCC 2007 GWP), ILCD, CML 2001, Eco-Indicator 99 (End point and Mid-Point) etc. (for more Annex 1).</li></ul></td></tr><tr><td>Scenario Information</td><td><ul><li>Allows creation of one or several stages within a given scenario.</li><li>It is provided because it allows data collection easier by dividing the scenario into several stages.</li><li>For Example: Pig production system is divided into three stages: breeding sows stage, post-weaning stage and fattening stage</li><li>Information Displayed: Stage name, date of creation, last update and stage type.</li></ul></td></tr><tr><td>Stage Information</td><td><ul><li>Allows the user to have a full access to the next stages: These include: stage features, model sets, referential system technical indicators and results.</li><li>As a control, it shows the user the percentage of tabs filled. (task accomplishment)</li></ul></td></tr><tr><td>Stage Features</td><td><ul><li>Here the user is creating stage documentation. It includes:<ul><li>i. General features: stage name, date created, status<sup>33</sup>, studied production system<sup>34</sup>, stage type studied, name of process, characteristics of production process<sup>35</sup>, description, information about product origin<sup>36</sup>, destination or state of product<sup>37</sup></li><li>ii. Measurement units: Collection Unit<sup>38</sup>, Functional Unit<sup>39</sup></li></ul></li><li>Other features to be filled include: Location, data origin, stage description, included inputs and outputs, Excluded inputs and outputs</li><li>Representativeness<sup>40</sup>: technological, geographical, temporal, completeness, precision/uncertainties</li><li>Method – Type of LCA<sup>41</sup></li><li>Allocation type and unit etc.</li></ul></td></tr><tr><td>Calculation Models</td><td><ul><li>Allows the user to set calculation models to calculate inventory elements which are relevant to the project. The selection process is influenced by the type of data and extent collected.</li><li>As a default set, Models developed used in AGRIBALYSE and models specifically chosen for Platform MEANS are set. The user can set these as default models by selecting one default type at a time or a customized selection can be done as per preferences.</li></ul></td></tr><tr><td>Stage System Referential – Data Collect</td><td><ul><li>Allows the user to technical and farm operation data (normally the main inventory phase). Depending on the type of production system selected and stage, the features may vary but in general they are of generic and follow the same data entry format.</li><li>In each data entry, the platform allows the user to add more data.</li></ul></td></tr><tr><td>Inventory element</td><td><ul><li>This is the last feature to be seen. Every information is filled before and the user has only to order for calculation to proceed. The calculation models are set internally and they run once selected.</li></ul></td></tr></tbody></table>

<footnote n="31">31 In InOut software an analysis is the directory in which the user is able to choose the assessment method he will use</footnote>

<footnote n="32">32 Environmental. In its plan, Platform MEANS has a long term ambition to include the three types of Sustainability dimension: Environmental, Economical and Social.</footnote>

<footnote n="33">33 Status – indicates whether the inventory is validated or not by external experts.</footnote>

<footnote n="34">34 Ex: Animal production system -pigs</footnote>

<footnote n="35">35 Ex: Conventional, Label Rouge, Organic</footnote>

<footnote n="36">36 Ex: in France-Bretagne (local), Brazil (country)</footnote>

<footnote n="37">37 State of product indicates system boundary: Ex: "cradle to gate"</footnote>

<footnote n="38">38 Ex: Herds (for animals)</footnote>

<footnote n="39">39 Ex: 1 kg live body porc for pig products/co-products</footnote>

<footnote n="40">40 Expressed in %.</footnote>

<footnote n="41">41 Attributional, Consequential</footnote>
### 3.4.2 Data and Data Sources in Platform MEANS

According to Auberger et al. (2013), platform uses two different types of data for the two software programs to be functional. These include: reference data and system data obtained during documentation. Reference data are those which are considered as natural or basic (such as climatic data, emission factors, and other studied characteristics). These types of data are obtained from literatures and statistical reports. These data are used by the models for calculation purpose as input parameters. Reference data are part of the platform system. The second type of data can either be data that are entered by the user during inventory or data that are used as references for a known and generic system for a given system. System reference data can also be obtained from other LCI database like AGRIBALYSE, Ecoinvent etc.

### 3.4.3 SimaPro as an integral part of Platform MEANS

As indicated in section 3.4.1 the InOut part of the platform prepares Life Cycle Inventory resources and makes them to be exportable to SimaPro, through an already integrated feature, creating an Ecospold file. SimaPro imports the inventory data and performs LCIA (Malnoë, 2015)<fnref n="42" />. SimaPro LCA software is a licensed, commercial and professional tool that collects, analyze and monitor the sustainability performance data of product and services (www.presustainability.com/simapro).

## 3.5 Platform MEANS and AGRIBALYSE

As noted previously, platform MEANS has adapted AGRIBALYSE methodology but there are some differences on their working principles approach. AGRIBALYSE does not go up to Interpretation of LCI results<fnref n="43" />. It only calculates and exports the result to SimaPro. Unlike AGRIBALYSE, platform MEANS further aides the user to interpret the results using SimaPro by allowing the two tools in one platform. Other differences are: AGRIBALYSE uses Excel sheets for data processing but platform uses software developed and simplified for the user.

## 3.6 Access to Platform MEANS

Platform MEANS is an online accessible LCA tool. It is controlled by INRA UMS SAS administer. The address of the InOut software is: https://pfmeans.inra.fr/means/login.jsp. Once you provide the link, an access page is displayed asking user's name and password. Users who are licensed to access to the web tool have a user's name (login) and a password.

Currently, three users are identified<fnref n="44" />: user from INRA, AGRIBALYSE partners, outside users (from ADEME) and each user is asked to be identified but without priorities (Colomb, 2015)<fnref n="45" />. Whether it will be used by other users or stock holders, it is still undecided. Access is so far limited and non public.

<footnote n="42">⁴² Project presentation by Caroline Malnoë on 18 March, 2015. Madame Malnoë is, IT Team Manager of the platform MEANS Project</footnote>

<footnote n="43">⁴³ Presentation done by Julie AUBERGER on 11.06.2015; (L'ACV for the nulls)</footnote>

<footnote n="44">⁴⁴ On 06,06. 2015, there was a meeting among INRA-UMR SAS, ASAE unit staff members on the futurity of Platform MEANS. The meeting did not decided on whether the platform can be public or not thought there is a chance for INRA partners to use.</footnote>

<footnote n="45">⁴⁵ Internal PPT document available at platform MEANS electronic document archive</footnote>
# 4. Conceptualizing Pig Production Systems, System Boundaries and Characteristics

The objective of this chapter is to give a brief coverage on the main production activities that are considered in this project to develop the InOut platform. After brief introduction of pig production systems in France, system boundary of the production system and what has been included in each sub-boundary will be presented. The information guidance contained in this chapter are used for the development of "InOut" form and emissions models.

## 4.1 Pig Production in France: Status and Production Systems

France is the third largest producer and fourth largest consumer of pig meat products in Europe. In 2013, with a total production of 13,428 million heads, it covered 9.2% of the total European pig production (FranceAgriMer, 2014; FAOSTAT, 2014). According to FranceAgriMer (2014) report, in 2010, 55% of pig herd were owned by 28% of producers and 46% of the total farms were with 100-200 sows. Generally the western part of France has high number of pig population. As shown in Figure 4. 1, Brittany is the leading zone in pig production (57%) followed by Pays de la Loire (12%) and Lower Normandy (4%).

<figure><img src="image_12.png" /><figcaption>Figure 4. 1: French Pig Production in 2013 (in million heads) (Source: (FranceAgriMer, 2014))</figcaption></figure>

With the modernization of pig farming that started after World War II, France has shown a major shift from traditional pig production system to specialized and intensive industrial production systems (IFIP, 2015; Basset-Mens, 2005). For the last two decades, with increasing climate change and environmental degradation, intensive farming systems has created greater concern among decision makers and the public (Basset-Mens & van der Werf, 2005; Dourmad & Casabianca, 2013). In response, environmental thinking aroused questions on how to tackle such challenges. One indispensable solution is a shift from uncontrolled and wild intensification to sustainable farming practices and to sustainable farming systems. This new way of thinking on production systems is affecting policies, production decision making, and consumer's perception on products and environment.

Because of the stated reasons above, for the last two decades sustainable means of pig production systems has become of focal point in decision making and choosing production systems at global level and in France in particular.
## 4.2 Classification of Pig Production Systems

Pig farms are classified based on different criteria and purpose. For example, depending on mode of production, pig farms are classified as: conventional, label rouge or organic farms (Basset-Mens & van der Werf, 2005). Knowing the type of farm helps us to design a generic InOut platform that can serve for all types of pig production systems. For example, if a farm to be studied is oriented mainly for fattening, breeding sow stage and post-weaning stages may not be included in the study. For breeders to fattening farm, three stages of the farm are considered. Similarly, if the mode of production is organic, animal feed input should be of organic material thus affecting the way animal feed ingredients are selected and formulated. In addition, housing management system, time spent inside buildings, surface area required, etc are affected as such.

To simplify inventory for conducting LCA it is good if the farm type is known. Because of the reasons mentioned and relevance they have while conducting LCA, three existing pig production systems classification methods will be discussed below.

### 4.2.1 Classification based on mode of Production

Depending on mode of production, pig farms are divided into: conventional, Label rouge and organic pig farms (Porc Biologique, 2015).

#### 4.2.1.1 Conventional Pig Production System:

Also called “standard” or “Good Agricultural Practices” is a farming system where pigs are raised at high density. Most of the time, pigs are raised in specialized buildings with slatted floor type made of concrete, metallic or plastic (Basset-Mens & van der Werf, 2005). Most buildings are use slatted floors (95%). Bedding material is not common. Because of high animal density, most operations are mechanical. And in this type of farming, feed sources are diverse. 90 to 95% of French pig production system is categorized under this classification.

#### 4.2.1.2 Label Rouge

Also named open air pig farming, Label Rouge was introduced in the year 1960 after the law of agricultural orientation<fnref n="46" />. Label Rouge is defined by INAO<fnref n="47" /> as: a national sign, which refers to products which by their terms of production or manufacture have a higher level of quality compared to other similar products usually marked (www.inao.gouv.fr). Label Rouge guaranties high-quality of products. In 2013, 747 000 pigs were registered in Label Rouge production representing 3% of the total French pig. Mostly they use locally produced feed sources.

#### 4.2.1.3 Organic Pig farming system

Council Regulation (EC) No 834/2007 of 28 June 2007 defines organic production as an overall system of farm management practices, a high level of biodiversity, the preservation of natural resources, the application of high animal welfare standards and a production method in line with the preference of certain consumers for products produced using natural substances and processes (EUROPA, 2007; IFOAM, 2012; IFOAM, 2009). It avoids or

<footnote n="46">⁴⁶ Code rural et de la pêche maritime, art. R.641-1 à R.641-10</footnote>

<footnote n="47">⁴⁷ INAO, Institut National de l'Origine et de la Qualité, (in English the National Institute of Origin) is a public administrative institution responsible for the implementation of French policy on official signs of identification of the origin and quality of agricultural and food products: Appellation d'Origine Contrôlée (AOC), Protected designation of origin (PDO), Protected geographical indication (PGI), Traditional specialty guaranteed (TSG), Label Rouge (LR) and Organic farming (Agriculture biologique (AB)). www.inao.gouv.fr</footnote>
largely reduces the use of synthetic chemical units such as fertilizers, pesticides, additives and medical products. Organic farming puts great emphasis on animal feed (organic origin), animal welfare, space requirement as well as connection with the land (PorkPork Biologique, 2015). The system obliges pigs to have access to outdoor runs. The floor should be slatted floor or provided with bedding material. Pigs are kept indoors, outdoors or mixed (FiBL, et al., 2011). In 2013, it represented only 0.8% of pig production in France (Calvar, 2015).

### 4.2.2 Classification systems based on Mode of Operation

Depending on the mode of operation pig farms are also classified into pig farms with breeding sows and without (GTTT-GTE, 2009). Farms with breeding sows are further classified into (a) farms that sell weaners, (b) breeders, (c) mixed breeders (selling weaners and keep breeders), (d) farms that concentrate on fattening and (e) breeders and fatteners operation. And farms without breeding sows are classified into (a) farms that concentrate on post-weaners, (b) farms that are mainly for fattening and (c) farms that receive weaners and keep until fattening stage (GTTT-GTE, 2009).

### 4.2.3 Classification based on Physiological Growth

Based on physiological growth, pigs are classified as sows, weaners and fatteners (Martel, 2008). Each group is called animal category and forming a stage. Further, each category is divided into animal class based on status, age and management system. Table 4.1 summarizes the classifications explained above.

<table><caption>Table 4.1: Pig Production System Classification</caption><thead><tr><th>Classification System</th><th>Major Classification</th><th>Sub-classification</th><th>Sources</th></tr></thead><tbody><tr><td>Mode of production</td><td>Conventional<br>Label Rouge<br>Organic</td><td></td><td>(Koch & Salou, 2015; Basset-Mens & van der Werf, 2005)</td></tr><tr><td rowspan="4">Physiological growth (also called category or Stage)</td><td rowspan="4">Breeding Sows</td><td>Gestating Sows</td><td rowspan="4">(Martel, 2008), (Koch & Salou, 2015)</td></tr><tr><td>Lactating sows</td></tr><tr><td>Gilts</td></tr><tr><td>Boars</td></tr><tr><td rowspan="2">Weaners</td><td>Piglets</td><td></td></tr><tr><td>Culled sows</td><td></td></tr><tr><td rowspan="2">Fatteners</td><td>Weaners at entry (1st group age)</td><td></td></tr><tr><td>Weaners 2nd group age)</td><td></td></tr><tr><td rowspan="2">Mode of Operation</td><td rowspan="2">Farms with Breeding Sows</td><td>Growers</td><td rowspan="2">(IFIP-GTE, 2009)</td></tr><tr><td>Finishers</td></tr><tr><td rowspan="4">Mode of Operation</td><td rowspan="4">Farms without Breeding Sows</td><td>Breeders that sell weaners</td><td></td></tr><tr><td>Only in breeding activity</td><td></td></tr><tr><td>Mixed (Breeders and sellers at weaning)</td><td></td></tr><tr><td>Fatteners</td><td></td></tr><tr><td rowspan="4">Mode of Operation</td><td rowspan="4">Farms without Breeding Sows</td><td>Breeders and Fattening</td><td></td></tr><tr><td>Keep weaners</td><td></td></tr><tr><td>Fattening activity</td><td></td></tr><tr><td>Mixed (weaning and fattening)</td><td></td></tr></tbody></table>

## 4.3 Modeling Pig Production System

In the process of model development, based on the principle of LCA, defining system boundary is the basic and crucial. Therefore this section is dedicated the system boundaries used for pig production system. Each sub-system within the total system defined will be dealt accordingly.

## 4.4 System Boundaries

Life cycle assessment works require a clearly delineated system boundary to define the scope of the system under study (ISO:14040, 2006; EC - JRC, 2010). According to ISO 14040:
2006 (3.32) definition, system boundary is a set of criteria specifying which unit processes are part of a product system. System boundary clarifies what the scope of the LCA work is and what decisions are done within the scope limit (EC - JRC, 2010; PAS 2050, 2008)Theoretically speaking, once the boundary system is defined, every step that affect the product life cycle has to be studied thoroughly (EC - JRC, 2010; ISO:14040, 2006). However, there are practical reasons that limit the inclusion of every part (SAIC, 2006). If there are excluded steps, there must be valid reasons to do so and their effect should not have a significant impact to the final assessment result. Reasons for excluding some processes may vary depending on the system of study without compromising the scientific bases of the study (SAIC, 2006).

For example, in project AGRIBALYSE, there are processes that are not included during the study. There are circumstances that allow processes not to be included such as: when processes are independent of agricultural production systems, absence of inventory data base and characterization, processes considered with negligible impact and in the absence of input data (Koch & Salou, 2015).

As shown in Figure 4. 2, system boundaries for pig production include pig feed, water consumption (for cleaning and drinking), building (including outdoor runs), energy consumption, and manure production, direct emission of gases as well as products (pigs) and co-products (culled sows).

<figure><img src="image_13.png" /><figcaption>Figure 4. 2: Pig Production system boundary, “Cradle-to-Farm Gate” (Source: (Koch & Salou, 2015))</figcaption></figure>

During documentation of each stage, InOut software provides a space where the user has to define the system boundaries on what processes are included in the project and what are not included but were supposed to be part of the over-all inventory is documented in the platform. This helps the scope of the LCI work.

Major sub-systems within the system boundary are further discussed in the following sections. Each model diagrams illustrate an enlarged and detailed elaboration of each section of the sub-systems. Each unit of the system boundary described the process flow. The purpose for
providing detailed modeling is to characterize each unit within the system boundary so that the informations required during LCI can be identified clearly. Thus, such models help for the development of InOut form (see Chapter 6).

### 4.3.1 Pig Farm

Pig farm denotes here specifically the animal production side of the production system. It includes the breeding stage, post-weaning stage and finishing stage. Within a given stage, animal's technical performance is taken as criteria to characterize the system process. Within the given period of time, animals flow exists and such process is taken into account during (IFIP-GTE, 2009) For example, if purchase for resettlement happens during the study period considered, the presence of those who joined are only counted for their period of presence. These include: resettlement, purchase and selling of animals. Any purchase of gilts or productive sows and joined the unit, their presence is counted from the date they entered the farm until the period considered.

In each stage, technical performance data are required to obtain a complete picture of the unit flow process (IFIP-GTE, 2009). Since the 1960s, INRA has developed a method of pig production system inventory. The system is well developed and makes annual average pig population and related activities (feed consumption, and cost) inventory reports. It is computerized and it is available at farm level. Use of these report system helps to avoid the burden of data source in inventory.

A given pig farm is not necessary to have all types of pig categories as it depends on the mode of operation as it is presented in section 4.2.2. In addition, in a given category the farm may conduct its operation by dividing the animals into various classes. For example, breeding sows are classified into gestating sows, lactating sows, gilts, boars, piglets and those who leave after culling called culled sows.

During farm operation, the management system can be specialized to hold animals per class and each class may have its own feeding management system, buildings and its facilities, manure management system etc. Thus knowing the farm structure is vital part of LCI works.

<figure><img src="image_14.png" /><figcaption>Figure 4. 3: Animal category technical Performance Detail Views</figcaption></figure>

Other technical performance indicator is “the type of reference used” is important for inventory work. Pigs may be referenced based on age or body weight. GTTT-GTE system of referencing for example is Pigs are categorized into two groups: (7-25 kg) and (25-105 kg) and/or (8-30 kg) and (30 -115 kg) based on live body weight. These groupings are used for standardizing animal age<fnref n="48" /> for each category during inventory (IFIP-GTE, 2009).

<footnote n="48">⁴⁸ Pig Ages are standardized based on i. Age at 25 kg and 100kg, or 30 kg and 115 kg. Refer pp: VI-16 to VI-19 from document available online: http://ifip.asso.fr/Pages Statics/resultat/partenaire/tele/criteres%20GTE.pdf</footnote>
### 4.3.2 Animal Feed

About 75% of pig feeds in France are of cereal origin (www.leporcbretagne.com). Pig feed is the main input and source of excreta during the farm activity (IPCC, 2006). The feed types are of diverse in source of origin, content, fabrication system and the way they are feed to the animals. The most important data properties that are needed for inventory are the type of feed and rations given on daily or annual base, the raw materials that constitute the feed and their physicochemical properties (as shown in Table 4. 2), origin and means of transport until the farm, feeding duration for each animal category and class, feeding and management system (Koch & Salou, 2015; Nemecek & Kägi, 2007; Nguyen, et al., 2011; GES'TIM, 2009; Basset-Mens & van der Werf, 2005).

Feed and feedstuff transport plays a central role in the life cycle inventory of animal feed system (Nemecek & Kägi, 2007). In addition, feed processing and the energy demanded is part of the pig production activity, in a LCA modeling.

<table><caption>Table 4. 2: Physicochemical composition of feed used for quantifying Feed Properties</caption><thead><tr><td>Item</td><td>Physicochemical properties</td><td>Abbreviation</td><td>Unit</td></tr></thead><tbody><tr><td>1</td><td>Dry matter Content</td><td>DM</td><td>%</td></tr><tr><td>2</td><td>Total Crude Protein (Expressed in N-Content)</td><td>N-content</td><td>g/kg</td></tr><tr><td>3</td><td>Energy Content</td><td>EC</td><td>Kcal/kg</td></tr><tr><td>4</td><td>Total digestibility</td><td>DF</td><td>%</td></tr><tr><td>5</td><td>Ash</td><td>Ash</td><td>%</td></tr></tbody></table>

Figure 4. 4 is showing the complete animal feed life cycle system. Various feedstuffs which are imported or produced locally require a transportation system either to the factory or to the farm directly. Once they reach to the factory/farm, they are mixed according to the feed formulation and in a given feed mix, the proportion of each feed ingredient determines the content of the each physicochemical properties. Each animal category in a given farm may consume different feed types and depending on the time of feed, the ration is determined.

Other property of animal feed is feeding management system. ITP (now IFIP) (2000) has mentioned about 4 different feeding management systems commonly practiced in France. These include: standard, biphase, triphase and multiphase. In multiphase, a given animal class can be feed based on growth and daily requirements allowing multiple rations. In AGRIBALYSE for example, a biphase system of management has been used; it is a common practice in France (Koch & Salou, 2015).
<figure><img src="image_15.png" /><figcaption>Figure 4. 4: Feed Management System Modeling</figcaption></figure>

In the diagram, transportation of materials from the origin to processing plant and from the processing plant to the farm or directly from the origin of production to the farm is illustrated. This concept is illustrated in more detail in the following section.

### 4.3.3 Model Transport and Process

During feed inventory, transport is treated as part of the system boundary. For this regard, during inventory, feed and feed ingredient transport, the source feed and mode of transport for each one is considered. (Koch & Salou, 2015; Nemecek & Kägi, 2007), For feed sources: the quantity of Feed Fabricated in the Farm (FFF), and quantity of feed purchased, both expressed in percentage out of the total feed consumed are determined. For the origin, it is required to know if it is locally produced or imported. In both cases, the place of fabrication is very important (GES'TIM, 2009; Koch & Salou, 2015).

### 4.3.4 Infrastructure

Nemecek et Kägi (2007) mentioned that infrastructure used for pig production systems create a considerable environmental impacts throughout their life. These include, resources consumed, manufacturing process and construction and dismantling. In general, life cycle inventory is broad in its scope and the main phases are construction phase, utilization phase and waste disposal (Nemecek & Kägi, 2007). In pig farming, pig buildings and infrastructure used for manure storage and treatment are considered.

Other characteristics of buildings such as floor type and ventilation system are part of the inventory work that is counted during inventory works (CITEPA, 2014; EMEP/EEA, 2013).

Due to animal welfare and environmental quality, use of bedding material is increasing from time to time (IFIP, 2012; Guingand, et al., 2010). The type of bedding material used for each animal class as well as quantity, and means of transport are counted during inventory (Nemecek & Kägi, 2007).
### 4.3.5 Water Consumption

Water consumption and use is one of major concern in environmental studies (Flury, et al., 2012; Hörtenhuber, et al., 2014). The quantity consumed and their sources as well as the type of use are center of inventory works. For animal water consumption, the amount consumed is quantified per animal class or per category depending on available information (Flury, et al., 2012). About 93.6% of water consumed by pigs is for drinking (IFIP, 2014). Other areas of water use include cleaning and air cooling systems.

### 4.3.6 Energy Consumption

Energy consumption in pig housing is used for heating, ventilation systems; feed processing and related activities (IFIP, 2008). The main sources of energy (as shown Table 4. 3) for pig farming are electricity, fuel, natural gas, and at small scale wind energy and solar energy (IFIP, 2008; GES'TIM, 2009). The energy consumed by each animal category and its source (expressed on proportion base) and place of use is paramount for inventory.

<table><caption>Table 4. 3: Main energy sources and their conversion Factor of Energy Sources</caption><thead><tr><td>Item</td><td>Source of Energy</td><td>Collection Unit</td><td>Conversion Factor</td></tr></thead><tbody><tr><td>1</td><td>Electricity</td><td>kWh/year</td><td>1 kWh = 3.6 MJ</td></tr><tr><td>2</td><td>Fuel</td><td>l</td><td>1 liter = 9.85 kWh</td></tr><tr><td>3</td><td>Natural Gas</td><td>kg</td><td>1 kg = 12.88 kWh</td></tr><tr><td>4</td><td>Lubricants</td><td>kg</td><td>-</td></tr><tr><td>5</td><td>Propane/butane</td><td>l</td><td>1 liter = 25.9 MJ</td></tr></tbody></table>

### 4.3.7 Excretion and Manure Management

The source of excretion as mentioned in section 4.3.1, is animal feed and Figure 4. 4 has included N-excretion as part of the animal feed end life cycle. In addition, the bedding material used in the buildings contributes to the total excretion. There is a direct link between feed consumption and type as well as excretion produced. The place where excretion took place depends on the timeshared between the building and outside doors (IPCC, 2006; CITEPA, 2014; EMEP/EEA, 2013). Table shows the type of excreta, practice of evacuation systems, types of manure stored and treatments.

<figure><img src="image_16.png" /><figcaption>Figure 4. 5: Excretion and Manure Management Modeling</figcaption></figure>

The following table what type of excreta is evacuated from the animal buildings, what type of evacuation system exists, type of stored manure and system of treatment. Each type or method
has an effect on the emissions that take place. During model development, these characteristics will be parameterized either as emission factors or adjustment factors and used during calculations

<table><caption>Table 4. 4: Type of Excreta, evacuation, Manure stored and Manure Management System commonly practiced in pig farms</caption><thead><tr><th>Item</th><th>Type of Excreta</th><th>System of Management</th><th>Evacuation system</th></tr></thead><tbody><tr><td rowspan="3">1</td><td rowspan="3">Liquid</td><td>Mixed pig slurry</td><td>Slurry removal of the holding tank with a vacuum system</td></tr><tr><td>Fattening pig slurry in the yard</td><td>Evacuation of the manure holding tank with a vacuum system (via manure disposal)</td></tr><tr><td>Gestating sow slurry in the yard<br>Lactating sow slurry in the yard<br>Weaner's slurry in the yard</td><td>Evacuation mechanical (flat scraper)</td></tr><tr><td rowspan="3">2</td><td rowspan="3">Solid</td><td>Straw rich pig manure</td><td rowspan="3">Hydraulic evacuation</td></tr><tr><td>Bedded pack pig slurry</td></tr><tr><td>Straw rich pig slurry</td></tr><tr><td>3</td><td>Type of Manure stored</td><td>Manure system(treatment)</td><td>Management</td></tr><tr><td rowspan="3">4</td><td rowspan="3">Liquid-slurry</td><td>Non-covered outdoor pit without specific treatment of effluents</td><td></td></tr><tr><td>Outdoor covered tank</td><td></td></tr><tr><td>Nitrification-denitrification treatment after phase separation, regardless of the type of storage</td><td></td></tr><tr><td rowspan="3">5</td><td rowspan="3">Solid</td><td>Nitrification-denitrification treatment without phase separation, regardless of the type of storage</td><td></td></tr><tr><td>Methanization</td><td></td></tr><tr><td>Solid manure stored without cover</td><td></td></tr><tr><td rowspan="3">6</td><td rowspan="3">Solid</td><td>Solid manure stored with cover</td><td></td></tr><tr><td>Solid composted without bacterial additives</td><td></td></tr><tr><td>Solid composted with bacterial additives</td><td></td></tr></tbody></table>

(Source: CITEPA, 2014; Koch & Salou, 2015)

### 4.3.8 Direct Emissions

Direct emissions are those considered as a direct product of the activities done during the production process (Nguyen, et al., 2011; Basset-Mens & van der Werf, 2005; EMEP/EEA, 2013; Basset-Mens, 2005). These include (as illustrated in Figure 4. 6) direct emissions from animals (enteric emissions), emissions from excreta dropped and stored inside pig buildings, emissions from storage and during treatments as well as excreta dropped in yards (Koch & Salou, 2015; CORPEN, 2003; CITEPA, 2014; Nguyen, et al., 2011). During quantification of each emitted substance, the inventory considers number of animals, the quantity excreta, location of the farm, and characteristics of the place and time length spent in each place where the emission is emitted. Emissions like methane ($CH_4$) are produced from the enteric fermentation process of the animals while gases like $NH_3$, NO, and $N_2O$ are products of nitrification and denitrification activities. $NO_3^-$-emission occurs from excreta dropped in outdoors and has an impact on areas like water resources.
<figure><img src="image_17.png" /><figcaption>Figure 4. 6: Simplified model for sources of emissions, place of emissions and Type of Emissions</figcaption></figure>

While estimating different emissions models are used. These emission models are characterized by emission factor and other variations that depend on factors like climate, ventilation system, type of floor, treatment methods etc. Rigolot et al. (2010(II)) described these emission factors and emissions using a simplified equation given below.

$$X_{emitted}(kg/pig) = Emission\_Factor \times \prod_{i=1}^{n}(Variation\_Factor)_{i}$$

The equation given above defines general emission model structures that are used in this project for the development of platform MEANS emission calculation models. Emission factors are obtained from reliable resources such as AGRIBALYSE (Koch & Salou, 2015; CITEPA, 2014; IPCC, 2006; EMEP/EEA, 2013). Variation factors mainly considered are ventilation system, bedding material type, evacuation systems and treatment methods which were mainly sourced from tables provided by CITEPA (2014).

To calculate the quantity of gas emitted, different models have been developed, selected and used with different levels of sophistication for a different purpose (EMEP/EEA, 2013; JRC, 2010; Lansche, et al., 2014; IPCC, 2006). While selecting, the objectives and scope of LCA have significant influence henceforth each level can be used for a specific purpose (EC - JRC, 2010; ISO, 2006a). For example (as illustrated in Figure 4. 7, if an average national gas inventory is required for a given system of production, it is not necessarily important to select a model that uses farm specific information rather an average national value can be used as an input data to estimate emissions. On the other hand, if the objective of life cycle inventory is for product declarations or for comparing two production systems so that decisions can be drawn upon, then model that demands more input inventory information and description is selected.

According to their levels of strength, a given model is divided into three levels called “Tiers”<fnref n="49" /> (EMEP/EEA, 2013; IPCC, 2006). First level being the simplest and that uses default values; the third level usually requires country specific information.

Tier 1 is used when there is no enough data and default values are used instead based on literatures or international emission rate values. Tier 2 is mainly for technology specific approach where a mass flow approach is used. The third level, Tier 3 requires country-specific and enough data to be feed. Tier 3 is an extension of Tier 2 with an added adjustment factors. For example, during NH₃ emission calculation (used in section 6.2), Tier 3 requires

<footnote n="49">⁴⁹ Commonly called Tiers (Tier 1, Tier 2 and Tier 3).</footnote>
building and manure storage characterization<fnref n="50" />. As noted in paragraph above, the objectives and scope of inventory should be well defined to choose from existing models.

<figure><img src="image_18.png" /><figcaption>Figure 4. 7: Model Selection Criteria (Source (EMEP/EEA, 2013))</figcaption></figure>

Ideally, platform MEANS is an inventory tool for LCA where the platform provides users a tool that helps them to conduct LCA (Auberger, et al., 2013). For this regard, the objectives and scope of LCI is decided by the user. To respond what has been set during the objectives the platform should be equipped with all levels models. However the platform is currently dedicated to provide models that are most relevant to the main objective of the project. Thus the platform provides carefully selected models based on according to French system of Agricultural practices and level of information provision as it will be presented as a final summary of models used (Table 6. 5). Most embedded models are of Tier 2 type except for NO (more explanation will be presented under section 6.2).

Emissions generated from pig farms<fnref n="51" /> can be calculated using Tier 1, Tier 2 and Tier 3.

Values calculated from emission models are in the form of ammonia-nitrate (NH₃-N), Nitrite-nitrogen (NOₓ-N), Nitrate-Nitrogen (NO₃-N), Nitrous oxide-Nitrogen (N₂O-N) and methane-carbon (CH₄-C) concentrates (Rigolot, et al., 2010(II)). To convert these ionic concentrates in the atmosphere into gas form, a unit conversion is used (Arian, 2015). The conversion factor is a ratio between molecular weight of a given concentrated ion gas to atomic weight of Nitrogen or carbon (Edward, 1986).

<table><caption>Table 4. 5: Nitrogen ions Conversion Factor</caption><thead><tr><td>Estimated Value from models</td><td>Reported Emission</td><td>Conversion Factor</td></tr></thead><tbody><tr><td>Emission of NH<sub>3</sub>-N</td><td>NH<sub>3</sub></td><td>17/14</td></tr></tbody></table>

<footnote n="50">⁵⁰ Such as: type of floor, system of ventilation, evacuation systems (building) and type of treatment and storage structures properties(manure storage)</footnote>

<footnote n="51">⁵¹ Main emissions: NH₃, N₂O, NO, CH₄ and NO₃</footnote>
<table><tr><td>Emission NOx- N</td><td>NOx</td><td>30/14</td></tr><tr><td>Emission N2O-N</td><td>N2O</td><td>44/14</td></tr><tr><td>Emission NO3- N</td><td>NO3-</td><td>62/14</td></tr><tr><td>Emission CH4-C</td><td>CH4</td><td>16/12</td></tr></table>

(Source: Nemecek, et al., 2014; Rigolot, et al., 2010(II); Arian, 2015)

### 4.3.8 Products and Co-Products

The main products and/or products from each sub-system are the main outputs considered from the farm activity (Koch & Salou, 2015). From the breeding sow, piglets leaving the main unit are the products while culled sows are the co-products. In post-weaning stage; there is only one product output called weaners. And from the last stage, pigs are the products.

<table><caption>Table 4. 6: Products and/Co-products from each stage of pig production activity</caption><thead><tr><td>Stage</td><td>Product (Out-put)</td><td>Co-product</td></tr></thead><tbody><tr><td>Breeding Stage</td><td>Piglets</td><td>Culled sow</td></tr><tr><td>Post-weaning stage</td><td>Pork (Weaners)</td><td></td></tr><tr><td>Fattening Stage</td><td>Pork</td><td></td></tr></tbody></table>

(Source: Koch & Salou, 2015)

## 4.4 Allocation Systems

ISO14044 standard defines allocation as partitioning the input or output of flows of a process or a product system between the product system under study and one or more other products (ISO, 2006b). When there is more than one product or co-products produced with similar properties, biophysical allocation system is recommended (Nemecek & Kägi, 2007).

Biophysical allocation system has been recommended by AGRIBALYSE for allocating the impact caused from pig production to the environment throughout the stages (Koch & Salou, 2015).
# 5. Design and Development of “InOut” Forms

Development of the InOut software for pig production system has been divided into two parts. The first part is adding information to the already developed platform when necessary and the second part is design and developing new features for pig production inventory system. Development of new InOut is divided again into three parts: development of Stage System Referential (data collection) platform, Emission Inventory Calculation models Development and Allocation System development. In this chapter, part one and development of Data collection system will be presented. Models and allocation system will be presented in chapter 6 and chapter 7.

## 5.1 Added Features to the General Documentation

MEANS Platform has already developed features for general stage documentation. What has been added are features which are only relevant for pig LCI works. In the following sections, additional informations which are integrated with existing one system will be presented.

### 5.1.1 Project Creation: Hierarchy of Pig production system

Hierarchical classification build-up helps to coordinate production levels for developing and executing inventory activity in a systematic way (EC- JRC and IES, 2010b). Based on the platform hierarchical structure and given in Figure 5. 1, pig production is divided into three mode of production as it has been explained in section previously in *section 4.2.1*. During hierarchy formation, each mode of production selected forms a scenario and depending on type production operation, the scenario is further divided into stages.

<figure><img src="image_19.png" /><figcaption>Figure 5.1: Pig production system hierarchy and InOut flow Analysis Structure (source: Platform MEANS)</figcaption></figure>

During project creation therefore, the platform guides what type of mode of production is to be studied. Once the scenario is selected, each scenario is further divided into animal category<fnref n="52" /> called stages. There are three stages for pig production. These are: breeding sows stage, post-weaning stage and fattening stages. As discussed in *section 0*, it is not necessary for a farm to have all three stages at the same time. In the same way, each stage treated independently. At the same time, if more than one stage is going to be studied, the user has an option to add several stages.

### 5.1.2 Functional Unit

ISO 14040 (2006) defines functional unit as a quantified performance of a product system for use as a reference unit. It should be consistent with the goal and scope of the study.

<footnote n="52">⁵² According to platform terminology, animal category is described as a stage.</footnote>
To quantify the system function and performance, functional unit is required (Koch & Salou, 2015; Basset-Mens & van der Werf, 2005). Functional unit considers the product type and life time of the product (EC - JRC, 2010; Sonneman & Vigon, 2011).

Functional units are defined in units of mass (1 kg) or volume (1 liter). For pigs:

$$ \textit{Functional Unit = 1 kg of live weight} $$

### 5.1.3 Collection Unit

The unit used for collecting technical performance of pig is counted per head. This depends on herd size. For inventory, pig herd information is collected as per herd size.

$$ \textit{Collection Unit = Number of herds} $$

### 5.1.4 Age Group (New Feature)

Age group determines what reference is used during inventory. In AGRIBALYSE, 8-30 kg and 30-110 kg was taken as a reference. In addition to this, age group 7-25 kg/25-105 kg and 8-30 kg/30-115 kg are added. This is a new feature in Platform MEANS.

### 5.1.5 Allocation System

Allocation system is treated in more detail in *chapter 0* but it is worth to mention here as a it is part of the documentation process to mention the type of allocation system used for pig production system. During environmental impact assessment, biophysical allocation system rule is used. In the platform, the user is supposed to fill the form but it is good to provide an option to choose from.

## 5.2 Design of Stage Data Collection Form

### 5.2.1 Design of main Menu

Design of data collection form is based on the processes included in the system as discussed in brief in *chapter 4*, Main components of the system that have been used in the design of the data collection menu<fnref n="53" /> are: Technical performance of herd, animal feed, excreta and manure management, water consumption, energy consumption and two menus for infrastructure, in total forming seven menus as shown in *Figure 5. 2*. As a result, the InOut form is designed to accommodate each sub-systems<fnref n="54" />. Each menu is illustrated in detail in consecutive sections.

<figure><img src="image_20.png" /><figcaption>*Figure 5. 2: Main Menu of Data Collection System designed for pig LCI*</figcaption></figure>

The menus are generic for all stages except “Herd technical Performance”. This was done so because each stage has different technical performance, depending on physiological stage, so it is not easy to design a generic menu for all animal categories.

Each menu will be presented with its main components and data inventory required to build the menu. Each InOut menu proposes cells to be filled with numerical values and also drop-

<footnote n="53">⁵³ Menu is a set of options presented to the user of the application to execute a function</footnote>

<footnote n="54">⁵⁴ Sub-system here represents one unit part of the whole system boundary. Ex: Animal feed is considered as a sub-system</footnote>
down menus, depending on the needed informations. In the following sections, each drop down menu is described with its full detail.

### 5.2.2 Herd Technical Performance InOut form

In section 4.2.1 pig farm was modeled based on each stage. The type and number of animals entering leaving as well as those stayed are used for building an inventory list. And technical Performance menu for these animals is designed to make inventory for one stage at a time. Depending on the stage selected, the format of the form varies as is discussed below.

#### 5.2.2.1 Stage: Breeding Sows

A sow is general term used for breeding pigs. It includes: gilts, gestating sows, lactating sows, culling sows and boars. In a given farm, the average number of breeding sows present in a year is given by the equation

$$ \text{Number of Sows Present} = \text{Number of maiden Gilts} + \text{Number of Gestating Sows} + \text{Number of Lactating Sows} + (\text{Boars if included}) $$

<table><caption>Table 5. 1: Breeding Herd InOut inventory form</caption><thead><tr><th>Item</th><th>Breeding herd/sows</th><th>Collection Unit</th><th>Output/Co-Product</th><th>Application</th><th>Definition/Description</th></tr></thead><tbody><tr><td>1</td><td>Numbers of sows present</td><td>P</td><td></td><td>AAP</td><td>Also called herd size, is the average number of sows present in a year</td></tr><tr><td>2</td><td>Number of maiden Gilts</td><td>P</td><td></td><td>Animal feed</td><td>Average number of females transferred to the breeding herd but stayed "unproductive" throughout the year.</td></tr><tr><td>3</td><td>Number of Gestating sows</td><td>P</td><td></td><td>Animal feed</td><td>Average number of sows that stayed pregnant</td></tr><tr><td>4</td><td>Number of lactating sows</td><td>P</td><td></td><td></td><td>Number of sows providing milk to piglets</td></tr><tr><td>5</td><td>Average Duration of presence time in gestation</td><td>d</td><td></td><td>Animal feed</td><td></td></tr><tr><td>6</td><td>Average Duration of presence time in lactation</td><td>d</td><td></td><td></td><td></td></tr><tr><td>7</td><td>Number of died piglets</td><td>P</td><td></td><td></td><td></td></tr><tr><td>8</td><td>Number of culling sows</td><td>P</td><td>Co-Product</td><td></td><td></td></tr><tr><td>9</td><td>Body weight of culling sow (per head)</td><td>kg</td><td></td><td></td><td></td></tr><tr><td>10</td><td>Number of sows loss*</td><td>P</td><td></td><td></td><td>Are those breeding sows died or taken away during the year</td></tr><tr><td>11</td><td>Body weight of gilt</td><td>kg</td><td></td><td></td><td></td></tr><tr><td>12</td><td>Number of weaned piglets leaving</td><td>P</td><td>OutPut</td><td></td><td></td></tr><tr><td>13</td><td>Number of gilts entered</td><td>P</td><td></td><td></td><td></td></tr></tbody></table>

AAP = Annual Average Population,

p = Collection unit, numbers,

d = days,

**Output:**

Main Product: weaned Piglets

$$ \text{Total number of Piglets leaving breeding stage} = \text{Number of weaned piglets} $$

<footnote>* Is common to the three stages/categories. – indicates the number of maximum places provided for the animals in a given period of time. Each animal has its own place, per stall or per unit area per head (Source: IPCC, 2006) &(CITEPA, 2014)).</footnote>

<footnote>**Losses includes those animals who died and taken because of quarantine reasons from the farm</footnote>

<footnote>Source: (IFIP-GTE, 2009) & (IFIP-GTTT, 2009)) as per AGRIBALYSE methodology of collecting inventory data for pigs (OIS file)⁵⁵</footnote>

<footnote n="55">⁵⁵ More detailed calculation guidelines :</footnote>

<footnote>http://ifip.asso.fr/PagesStatics/resultat/partenaire/tele/criteres%20GTE.pdf &</footnote>

<footnote>http://ifip.asso.fr/PagesStatics/resultat/partenaire/pdf/calculs_criterre.pdf</footnote>
Co-product: Average weight obtained from culled sows

$$ \text{Average live weight of culled Sows (kg live porc)} = \text{Number of culled sows} * \text{Body weight of culled sow} $$

### Correspondence to Simapro:

Full name of culled sow is exported in a format as given to Simapro (Format AGRIBALYSE).

Ex: "Cull sow, conventional, excess slurry treatment, at farm gate/FR U" Annex 1

#### 5.2.2.2 Post-Weaning Stage

According to their growth stage, there are two classes: Weaners 1st age and second weaners 2nd age group. The output obtained from this system is weaners that enter fattening stage. As it was illustrated in Figure 4.3, the only out-put is weaners leaving WS.

The losses are counted during the weaning stage and hence the average annual number of weaners is calculated by:

Annual Average Number of Animals in Weaner's Stage

$$ \text{Average\_Annual\_Weaners} = \frac{(\text{Piglets\_entering\_WS} + \text{weaners\_leaving\_WS})}{2} $$

<caption>Equation 5.1: Average Annual Weaners</caption>

<table><caption>Table 5.2: Weaner's Stage InOut inventory form</caption><thead><tr><th>Item</th><th>Breeding herd/sows</th><th>Unit of Collection</th><th>Output/Co-Product</th><th>Definition/Description</th></tr></thead><tbody><tr><td>1</td><td>Number of weaned piglets entering weaners' stage</td><td>p</td><td>Input</td><td>Weaners who left the breeding stage</td></tr><tr><td>2</td><td>Number of weaned piglets leaving weaners' stage</td><td>P</td><td>Output</td><td>Number of weaners entering minus number of weaned piglets loss (1) – (6)</td></tr><tr><td>3</td><td>Duration of presence of weaners in weaning stage</td><td>d</td><td></td><td></td></tr><tr><td>4</td><td>Body weight of piglets entering Weaners' stage</td><td>kg</td><td></td><td></td></tr><tr><td>5</td><td>Body weight of piglets leaving Weaners' stage</td><td>kg</td><td></td><td></td></tr><tr><td>6</td><td>Number of weaned piglets loss</td><td>P</td><td></td><td></td></tr></tbody></table>

Output from Weaner's Stage - Main Product

$$ \text{Average live weight of porc (kg live porc)} = \text{Number of weaners leaving weaners stage} * \text{Average Body weight weaners leaving} $$

If GTTT-GTE values are used, then the value is given in terms of kilo pig live body weight product

### Correspondence to SimaPro:

Full name of weaners leaving post-weaners stage is exported to Simapro

#### 5.2.2.3 Fattening Stage (FS)

Pigs in this stage are divided into two stages of growth: Growers and Finishers. To conduct inventory, information required are: Number of weaners entering the FS, body weight of growers entering FS, body weight of growers leaving FS, duration of presence in FS, number of pigs losses, %lean meat<fnref n="56" /> as shown in Table 5.3.

<footnote n="56">⁵⁶ %Lean Weight is specially needed for fattening pigs as the meat is expressed qualitatively and is a reference value commonly used for pricing. For more information: http://www.marche-porc-breton.com/pdf/divers/conventiontmp.pdf</footnote>
<table><caption>Table 5. 3: Fattening Stage InOut Inventory form</caption><thead><tr><th>Item</th><th>Breeding herd/sows</th><th>Unit of Collection</th><th>Out-Put/Co-Product</th><th>Definition :Description</th></tr></thead><tbody><tr><td>1</td><td>Number of pigs entering fattening stage</td><td>p</td><td>Input</td><td>Also called growers, are pigs between weaning and sale Weaners who left the PW stage</td></tr><tr><td>2</td><td>Body weight of pigs entering fattening stage</td><td>kg</td><td></td><td></td></tr><tr><td>3</td><td>Duration of presence time in fattening stage</td><td>d</td><td></td><td></td></tr><tr><td>4</td><td>Number of pigs leaving fattening stage</td><td>p</td><td>Output</td><td>Number of weaners entering minus number of weaned piglets loss (1) – (7)</td></tr><tr><td>5</td><td>Body weight of pigs leaving fattening stage</td><td>kg</td><td></td><td></td></tr><tr><td>6</td><td>%Lean meat of Fattened pigs</td><td>%</td><td></td><td>%Lean meat (in French TMP) is used for calculating N fixed in the animal body</td></tr><tr><td>7</td><td>Number of pigs loss</td><td>P</td><td></td><td></td></tr></tbody></table>

Average Annual Number of pigs in Fattening Stage

$$ \text{AverageAnnualNumberof PigsinFS} = \frac{(\text{Number\_pigs\_entering} + \text{Number\_pigs\_leaving})}{2} $$

Equation 5. 2: Average Number of Pigs in Fattening Stage

Product Out-put quantity: kilo live pork

Average live body Weight out put (kilo porc live bodyweight)

$$ = \frac{\text{Number of pigsleaving FS} \times \text{Body weight of pigsleaving FS}}{2} $$

Equation 5. 3: Average live body Weight Pig Output from FS

Products (main) inventory List to be exported to Simapro

Full name of Fattened pigs is exported to Simapro

### 5.2.3 Animal Feed InOut form

The platform is designed to accommodate a multiphase feeding system where the user can enter several feeds per animal class. The user has an option to choose what feeding system is used for technical convenience but this has no effect on the overall structure of the form. The feeding management systems included: Standard, biphase, triphase and Multiphase. This allows for pigs in each category to be treated according to their class group. The user is provided with a drop down list to choose from the animal class. For each animal class the user is able to create several feeds.

For conducting data entry, two options are proposed. The first option is to select from existing database as is given in Table (App.) 5. 3,

Table (App.) 5. 4 and Table (App.) 5. 5. As internal database, animal feed LCI database developed by AGRIBALYSE has been adapted. The second option is to create a new feed data.

For animal feed entry, user is able to create a new animal feed based on known ingredients<fnref n="57" />. These known ingredients can be used from AGRIBALYSE LCI database which is available in AGRIBALYSE Methodological Report V 1.2 (Table 170) and stored as internal database for MEANS or other French LCI databases. The platform can also use its own database which is developed from Crop production systems when they are fully developed and made available to users. In either case, the ingredients that are entered should be with known impact.

<footnote n="57">⁵⁷ Known ingredients are those who are already in LCI database system and with known environmental impacts. For example, cereal crop LCI database developed by platform MEANS users can be used as known inputs.</footnote>
To simplify for the user, at this time the platform has stored feed ingredients taken from AGRIBALYSE® (the list includes about 140 feed ingredients) and the user is able to select from drop-down list (total list of Animal feed raw materials is given in Annex 3).

<table><caption>Table 5. 4: Animal feed Entry form for selecting from an already stored database of animal feeds</caption><thead><tr><th>Item</th><th>Information required</th><th>Unit</th><th>Description</th></tr></thead><tbody><tr><td>1</td><td>Animal category</td><td></td><td>The same as the stage selected</td></tr><tr><td>2</td><td>Animal class name</td><td></td><td>Depending on the stage and animal category the platform provides an option to choose from existing list of animal class</td></tr><tr><td>3</td><td>Name given to the ration</td><td></td><td>Filled by the user.</td></tr><tr><td>4</td><td>Name given to the animal feed</td><td></td><td>Based on AGRIBALYSE list (See, or a new food name can be inserted.</td></tr><tr><td>5</td><td>Feeding Management</td><td></td><td><ul><li>AGRIBALYSE -Biphase system</li><li>MEANS: Standard, biphase, triple phase, multiphase</li></ul></td></tr><tr><td>6</td><td>Feed Consumption rate</td><td>kg/head/day</td><td>Composition of the feed (ingredients and incorporation rate) is known from database. Once the consumption rate is entered, the platform recalls the already stored information and does the calculation of mass of each ingredient (Refer example given in Annex 4).</td></tr><tr><td>7</td><td>Duration of feeding</td><td>days</td><td>To be entered</td></tr><tr><td>8</td><td>Time passed in the buildings</td><td>%</td><td>Is used during N-calculation to divide the site of excretion. When we have a number of entries, the average proportion is calculated based in the duration of feeding and %time passed. (N Model)</td></tr></tbody></table>

<table><caption>Table 5. 5: Animal Feed InOut form for creating a new animal Feed</caption><thead><tr><th>Item</th><th>Information required</th><th>Unit</th><th>Description</th></tr></thead><tbody><tr><td>1</td><td>Animal category</td><td></td><td>The same as the stage selected</td></tr><tr><td>2</td><td>Animal class name</td><td></td><td>Depending on the stage and animal category the platform provides an option to choose from existing list of animal class</td></tr><tr><td>3</td><td>Name given to the ration</td><td></td><td>Filled by the user</td></tr><tr><td>4</td><td>Name given to the animal feed</td><td></td><td>Based on AGRIBALYSE way of naming the feed (name can be inserted).</td></tr><tr><td>5</td><td>Feed consumption rate</td><td>kg/head/day</td><td>Depends on the animal class chosen.</td></tr><tr><td>6</td><td>Feeding Management</td><td></td><td><ul><li>AGRIBALYSE -Biphase system</li><li>MEANS: For other systems of feeding management,</li></ul></td></tr><tr><td>7</td><td>Duration of feeding</td><td>days</td><td>Is used during N-calculation to divide the site of excretion. When we have a number of entries, the average proportion is calculated based in the duration of feeding and %time passed. (N model)</td></tr><tr><td>8</td><td>Time passed in the buildings</td><td>%</td><td>For each ingredient, the %composition must be entered. Making sum total of 100%</td></tr><tr><td>9</td><td>Feed Composition</td><td>%</td><td>Σ(%Composition ingredients) = 100%. It is new feed entry, the software calculates directly once data is stored.</td></tr><tr><td>10</td><td>Raw Materials (Ingredients)</td><td>kg</td><td>A list of known ingredients are stored as a database from which the user can choose from (Annex 3).</td></tr><tr><td>11</td><td>%FFF</td><td>%</td><td>Ingredients are brought directly in the farm from their site of origin. The means of transport, distance and energy consumption are counted.</td></tr><tr><td>12</td><td>Purchased feed</td><td>%</td><td>%Purchased food = 100 - %FFF</td></tr><tr><td>13</td><td>Origin of ingredient</td><td></td><td>From France -all regions, for imported, country list Two cases are considered:</td></tr><tr><td>14</td><td>Distance covered</td><td>km</td><td><ul><li>Distance from the site of ingredient production to the site of 15fabrication</li><li>Distance from the site of fabrication to the farm</li></ul>For distance entry,</td></tr><tr><td></td><td></td><td></td><td><ul><li>Recommendation given by GESTIM can be followed (App.) 5. 8</li></ul></td></tr></tbody></table>
FFF = Feed Fabricated in the Farm

While developing a new feed data, transportation model is used (see Appendix:

### 5.2.3.1 Intermediate Data processing

Once the animal feed data is entered or a new ingredient is selected, the platform summarizes informations to provide an output as listed below. Whether it is feed composite or ingredient entered, each ingredient is treated independently. For the animal feed selected from existing database, each ingredient is having known physicochemical properties (as discussed in section 3.7.3 and presented in Appendix IV.1 Annex 3: Animal feed Physicochemical Properties(French)

and Appendix IV. 1. each ingredient is made ready with its physicochemical properties (DM, N-content, EC, DF, and Ash), percentage of composition, percentage of feed fabricated in the farm and quantity of each feed ingredient used example found in Annex 4

The output data prepared is made ready for N-model

### 5.2.3.2 Out-put data from Animal Feed Menu

<table><caption>Table 5. 6: Output data from animal feed InOut form</caption><thead><tr><th>Item</th><th>Output</th><th>Unit</th><th>Destination</th></tr></thead><tbody><tr><td>1</td><td>Name Given to the ration</td><td></td><td></td></tr><tr><td>2</td><td>Animal class name</td><td></td><td>As known input to Simapro</td></tr><tr><td>3</td><td>Name given to the ration</td><td></td><td>As known input to Simapro</td></tr><tr><td>4</td><td>Name given to the animal feed</td><td></td><td>As known input to Simapro</td></tr><tr><td>5</td><td>Feed consumption rate</td><td>kg/head/d</td><td>Used for calculating Quantity of feed consumed (see table)</td></tr><tr><td>6</td><td>Duration of feeding</td><td>days</td><td>Used for the calculation of total feed consumption</td></tr></tbody></table>

### 4.2.4 Excretion and Storage of Manure Management Systems InOut Form

Inventory of excreted manure and storage management is done at three levels: Excreted manure, storage handling and treatment. Information obtained from this InOut menu are used during NH₃ model, N₂O model and CH₄.

<figure><img src="image_21.png" /><figcaption>Figure 5. 3: System boundary of manure handling system (source: (EMEP/EEA, 2013; CORPEN, 2003; Espagnol, et al., 2014):</figcaption></figure>

For the indoor storage system, the inventory includes type of manure to be evacuated, system of evacuation, and frequency of evacuation as outlined. Once manure is excreted from animals, the drops are kept for some time in the building before evacuation. The frequency
and system of evacuation affects the amount of emission that takes site depending on the type of excreted material. If there is any manure that is spread to the field directly before being stored, the amount (expressed on %) is deducted from the total excreted waste before storage. Another important point that is considered is the type of treatment and the amount treated as described in the following sub-sections.

Outdoor storage inventory includes type of manure and quantity of manure stored. The type of manure is provided in drop down list (Table (App.) 5. 10) and the quantity stored has to be entered directly by the user.

For the treatment, the type of manure, type of treatment and quantity stored are designed as entry form as shown in Table 4. 4

<table><caption>Table 5. 7: InOut form Design of Excretion and Manure Storage Management</caption><thead><tr><th>Item</th><th>Inventory</th><th>Unit</th><th>Description</th></tr></thead><tbody><tr><td colspan="4"><strong>A. Excreta Management</strong></td></tr><tr><td>1</td><td>Type of Excreta</td><td></td><td>Drop-down list. : Solid and/or liquid excreta. Solid excreted can be with or without litter.</td></tr><tr><td>2</td><td>Quantity of manure produced per animal category</td><td>m³/year or ton/year</td><td>Depending on the animal class category, the user inserts value for each animal class and category. Calculation procedure is given in Calculation of Quantity Excreted below. (optional)</td></tr><tr><td>3</td><td>Duration of storage inside the building</td><td>d</td><td>The excreta may stay throughout the presence of the animal in the building (Litter accumulated) or evacuated frequently (litter evacuated). (CORPEN, 2003)</td></tr><tr><td>4</td><td>System of Evacuation</td><td>Type</td><td>Evacuating system of excreta from buildings may vary from on system of management to another as listed in Table 4. 4</td></tr><tr><td>5</td><td>Frequency of Evacuation per year</td><td>Number</td><td>The number of evacuation per year : Entered directly.</td></tr><tr><td>6</td><td>Direct spreading</td><td>%</td><td>Applies when there is direct spread of manure to the farm before storing it. It's value: 0-100%</td></tr><tr><td colspan="4"><strong>B. Manure Storage</strong></td></tr><tr><td>1</td><td>Type of stored manure</td><td></td><td>Liquid/slurry or Solid (same as stored manure)</td></tr><tr><td>2</td><td>Quantity of Stored Manure</td><td>m³, kg/ton</td><td>The total amount of stored manure from the farm, in liquid or solid form or both.</td></tr><tr><td>3</td><td>Dry Matter Content</td><td>%</td><td></td></tr><tr><td>4</td><td>Surface area</td><td>m²</td><td>The surface area of exchange used solid manure</td></tr><tr><td>5</td><td>Storage Duration</td><td>d</td><td></td></tr><tr><td>6</td><td>Evacuation Frequency</td><td>Number</td><td>The number of evacuation per year : Entered directly</td></tr><tr><td colspan="4"><strong>C. Treatment</strong></td></tr><tr><td>1</td><td>System of manure management (Treatment)</td><td></td><td>As given in Manure Management</td></tr><tr><td>2</td><td>Quantity of manure treated</td><td>%</td><td>In a given inventory, there can be different types of treatment. Each must be taken into account to get the final proportional average value (For more refer calculation of %treatment, Equation 5. 7</td></tr></tbody></table>

During excretion and storage of manure, the form is designed to enter several entries for each chosen type. This is done because in a given farm, there could be mixed system of excreta, stored manure and treatment. For example, manure stored in a solid storage form can be treated as solid manure and some portion as Solid manure stored in field and the other remaining manure as Solid composted without bacterial additives. Thus, once the excreta is produced, the inventory allows each phase to be treated independently. The following data process is designed to handle the said above principle.

Evacuation frequency is mentioned two times in the form. The first one evacuation from the building and the second is evacuation from the storage structure.
# Intermediate Inventory Data Process

## 1. Calculation of Quantity of Excreted manure from the animals

Depending on the animal category and animal class, the user must select from a dropdown list, what type of animal class is under inventory. Then, the value is entered accordingly. Finally, the total quantity is calculated by averaging the value in order to get final average quantity of manure excreted. If average value per animal category is given, then there is no need to characterize each animal class.

For C animal class with a given numbers of animals in each category and j types of manure, average value of Excreta is obtained, in kg/head/d C→n, where C is animal class.

$$ \text{Qty}_{\text{Ex\_average\_j}} = \frac{\sum_{C=1}^{n} (\text{Qty}_{\text{ex\_animal\_classC}} \times \text{No}_{\text{ animals\_classC}} \times \%\text{DM}_{(C,j)})}{\sum_{C=1}^{n} (\text{No}_{\text{ animals\_classC}})} $$

<caption>Equation 5. 4: Average Quantity of liquid/solid Manure Production</caption>

**Where:**

<table><tr><td>Qty_{Exc\_average\_j}</td><td>m<sup>3</sup> or m<sup>2</sup></td><td>Average Quantity of manure produced of type j (solid or liquid)</td></tr><tr><td>Qty_{ex\_animal\_class\_C}</td><td>m<sup>3</sup> or m<sup>2</sup></td><td>Average Quantity of manure produced by animal class C</td></tr><tr><td>DM%_{(C,j)}</td><td>%</td><td>Dry matter content of manure produced of type j from animal class C (Table (App.) 5. 10)</td></tr><tr><td>Number_{animal\_class\_C}</td><td>Number</td><td>Average total number of animals in class C</td></tr><tr><td>C</td><td></td><td>Animal Class</td></tr><tr><td>j</td><td></td><td>Type of manure</td></tr></table>

## 2. Calculation of percentage of solid manure, %Solid

%Solid is used as an input parameter during calculation of NH₃, N₂O and NO emissions from manure. While calculating, first, the DM content of each excreta is obtained from the total quantity of excreta and the %DM of each type. Then, from the total sum of dry matter excreta, the %solid and %liquid is determined as equation below. At a given time, there could be different types of excreta. Each therefore must be counted.

To calculate the %Solid,

$$ \%Solid_T = \left[ \frac{\sum (Qty_{ex\_average\_solid})_{(C,i)}}{Qty_{ex\_total}} \right] \times 100\% $$

<caption>Equation 5. 5: %Solid of manure Stored in a Storage Structure</caption>

**Where:**

<table><tr><td>%solid<sub>T</sub></td><td>%</td><td>Average percentage of manure in solid form</td></tr><tr><td>Qty-ex-average-solid</td><td>m<sup>3</sup> or tones t</td><td>Average total of manure stored of each manure type</td></tr><tr><td>Qty-ex-total</td><td>m<sup>3</sup> or tones t</td><td>Total quantity of manure stored</td></tr><tr><td>C</td><td></td><td>Animal Class</td></tr><tr><td>i</td><td></td><td>Animal class type</td></tr></table>

And, %liquid,

$$ \%Liquid = 100 - \%solid $$

## 3. Calculation of fraction of manure treated using a given manure management (treatment)

The fraction manure treated under a given management system is calculated by dividing the quantity of manure treated under system i by the total amount of manure stored from a given animal category.
$$MS_{(T,S)i} = \frac{Qty\_treated\_system\_treatment_i}{Qty\_total\_stored\_manure}$$

Equation 5. 6: Fraction of Manure Treated under a given Manure management System

**Where:**

<table><tr><td>MS<sub>(T, S)i</sub></td><td></td><td>Fraction of total manure stored (solid and liquid) for animal category T that is managed under manure management system i, dimensionless</td></tr><tr><td>Qty_treated_system_treatment</td><td>m<sup>3</sup> or m<sup>2</sup></td><td>Quantity of manure treated using treatment type i</td></tr><tr><td>Qty_total_stored_manure</td><td>m<sup>3</sup> or m<sup>2</sup></td><td>Total quantity of stored manure for animal category T</td></tr><tr><td>S</td><td></td><td>Manure management system (both for solid and liquid)</td></tr><tr><td>i</td><td></td><td>Type of manure management system</td></tr></table>

## 4. Calculation of percentage of manure treated

The %treated is calculated based on the Quantity of manure treated in each system and the total quantity of stored manure.

$$ \%Treated_T = \left[ \frac{\sum (Qty\_treated\_system\_treatment_i)}{Qty\_total\_stored\_manure} \right] \times 100\% $$

Equation 5. 7: Percentage of Manure Treated

**Where:**

<table><tr><td>%Treated<sub>T</sub></td><td></td><td>Percentage of manure treated from manure produced by animal category T</td></tr><tr><td>Qty_treated_system_treatment</td><td>m<sup>3</sup> or t</td><td>Quantity of manure treated using treatment type i</td></tr><tr><td>Qty_total_stored_manure</td><td>m<sup>3</sup> or t</td><td>Total quantity of stored manure for animal category T</td></tr><tr><td>i</td><td></td><td>Type of treatment</td></tr></table>

## InOut System Parameterization

For emission models, parameter values of the manure management characteristics are used to adjust emission inventory when Tier 3 type model is used. For this the type of evacuation and type of treatment utilized are needed to be parameterized. The values are given in Table (App.) 5. 9.

### 5.2.5 Buildings Inventory InOut Form

The InOut form is designed to provide entry of building type used by each animal category. Design of the InOut platform was based on an already existing building database sets developed by CASDAR and used by AGRIBALYSE project. The Platform therefore provides a list building types depending on animal category. The user is expected to choose from existing building types (list of the buildings stored as database in the platform are is given in Table (App.) 5. 11.

Information required for building inventory therefore includes: surface area per animal (the unit measure used per space given, m²), type of floor and ventilation system.

Another points included in the InOut form is bedding material used (type and quantity). Bedding material used inside each building has been designed to be part of building inventory system. Different bedding materials available as LCI database are given in Appendix

The point of making surface area inventory is for calculating impacts of construction activities and land use impact assessment. The total surface area is dependent on the unit surface area
provided for animal and the maximum place allowed in given period of time. The number of places (maximum) input is taken from “Technical performance” inventory form entry.

$$ \text{Land\_occupation\_area}(m^2) = \text{Surface\_area\_per\_unit\_head} \times \text{Number\_Places}(\text{maximum}) $$

<figure><img src="image_22.png" /><figcaption>Figure 5. 4: Simplified Building Characteristics.</figcaption></figure>

<table><caption>Table 5. 8: Building Inventory InOut form outlook (reference: OIS of AGRIBALYSE)</caption><thead><tr><th>Item</th><th>Inventory</th><th>Unit</th><th>Description</th><th>Destination/Description</th></tr></thead><tbody><tr><td colspan="5"><strong>A. Buildings Inventory</strong></td></tr><tr><td colspan="2">Animal Category</td><td></td><td></td><td></td></tr><tr><td colspan="2">Type of Buildings</td><td></td><td>As listed Table (App.) 5. 11</td><td>Simapro</td></tr><tr><td colspan="2">Number of Sites (maximum)</td><td>P</td><td></td><td>Used for land occupation<br/>For NH<sub>3</sub>-model, Tier 3. Floor type has no direct effect by itself. It has a combined effect with types of ventilation and excreta evacuation system from the buildings. For convenience, its value is taken as 1.0 and then can be used during calculations.</td></tr><tr><td colspan="2">Floor Type</td><td></td><td>As listed in Table (App.) 5. 12</td><td></td></tr><tr><td colspan="5"><strong>B. Bedding Material Inventory</strong></td></tr><tr><td colspan="2">Type of bedding material</td><td></td><td></td><td></td></tr><tr><td colspan="2">Quantity of bedding material<br/>(straw or saw dust)</td><td>kg/head/d</td><td>Depending on the animal category and class, the amount may vary. List of bedding materials is given in Table (App.) 5. 13</td><td><ul><li>For export to Simapro and</li><li>For N-model, to quantify the Nitrogen added and then for NH<sub>3</sub>-model as added material for excretion for N-Model</li></ul></td></tr></tbody></table>

### 5.2.6 Manure Storage Infrastructure Inventory InOut form

The design of manure storage infrastructure form takes into account the type of manure stored, the type of the storage structure used for each selected type and the capacity. Pig manure is handled as solid, semi-solid slurry, or a liquid in different structures as given in. The way manure is handled and treated affects structural choices. Each type of storage is treated therefore as an independent input. Some of the storage structures commonly used in France is listed in Table (App.) 5. 15. Each structure varies in size depending on the system of manure management, structure type and other environmental factors.

<table><caption>Table 5. 9: Manure Inventory InOut Form</caption><thead><tr><th>Item</th><th>Inventory</th><th>Unit</th><th>Description</th><th>Destination/Description</th></tr></thead><tbody><tr><td>1</td><td>Animal Category</td><td></td><td></td><td></td></tr><tr><td>2</td><td>Type of manure storage structure</td><td></td><td>As listed in Table (App.) 5. 15</td><td>Simapro</td></tr></tbody></table>
<table><tr><td>3</td><td>Capacity(Liquid manure)</td><td>m<sup>3</sup></td><td>Depends on the total volume of manure stored and frequency of evacuation. In a given farm, there could different systems of manure management and storage systems. The inventory considers and has provided a possibility of manure storage structures as many as possible.</td><td>Simapro</td></tr><tr><td>4</td><td>Surface Area of exchange(solid manure)</td><td>m<sup>2</sup></td><td></td><td>Simapro</td></tr></table>

The information provided in the manure storage structure inventory is exported directly to Simapro as shown in The structures are supplemented with their capacity, volume or surface area of exchange depending on the manure type.

### 5.2.7 Energy Inventory InOut Form

Energy consumption inventory requires information for each stage of growth. The energy consumed for processing animal feed in the farm (if there is fabrication in the farm) is deducted before exporting to SimaPro.

<table><caption>Table 5. 10: Energy Consumption Inventory InOut form</caption><thead><tr><td>Item</td><td>Source of Energy</td><td>Collection Unit</td><td>Unit in Simapro</td><td>Conversion Factor</td></tr></thead><tbody><tr><td>1</td><td>Electricity</td><td>kWh/year</td><td>MJ</td><td>1kWh = 3.6 MJ</td></tr><tr><td>2</td><td>Fuel</td><td>l</td><td>MJ</td><td>1 liter = 9.85 kWh</td></tr><tr><td>3</td><td>Natural Gas</td><td>kg</td><td>MJ</td><td>1 kg = 12.88 kWh</td></tr><tr><td>4</td><td>Lubricants</td><td>kg</td><td>kg</td><td>-</td></tr><tr><td>5</td><td>Propane/butane</td><td>l</td><td>MJ</td><td>1 liter = 25.9 MJ</td></tr></tbody></table>

### 5.2.8 Water Consumption Inventory InOut Form

The assumption used for designing the InOut form is that water consumption LCI database exists from previous inventory works. This implies, the platform provides known water source and the user is supposed to select from listed sources and then enters the consumption rate, type of consumption (drinking, cleaning or cooling process as discussed in section 4.3.1.5 and given in Table (App.) 5. 16 and Table (App.) 5. 17 for the quantity consumed and the source database. Currently, water source is limited to water tap and harvested rain water. For each consumption type, the percentage of each water source is defined by the user.

<table><caption>Table 5. 11: Water Consumption Inventory InOut Form</caption><thead><tr><td>Item</td><td>Water Consumption Inventory</td><td>Unit</td><td>Volume</td><td>Destination/Description</td></tr></thead><tbody><tr><td>1</td><td>Type of Consumption</td><td></td><td></td><td>Simapro</td></tr><tr><td>2</td><td>Consumption rate</td><td>l</td><td></td><td>Simapro</td></tr><tr><td>3</td><td>Source of water</td><td>l</td><td></td><td>Simapro</td></tr><tr><td>4</td><td>Percentage of the source</td><td>%</td><td></td><td>Simapro</td></tr></tbody></table>

l = Liter

Total water consumption is water consumed by each animal class in a given category multiplied by the total number of animals.

$$Total\_water\_Consumed(T) = \sum_{T} \sum_{C} w_{(T,C)_i} \times \%\_S_k$$

Equation 5. 9: Total Water Consumption

**Where:**

<table><tr><td>Total_water_Consumed_k</td><td>1</td><td>Total amount of water consumed from source k</td></tr><tr><td>W<sub>(T, C)_i</sub></td><td>1</td><td>Water Consumption rate by animal class C, animal category T, liters</td></tr><tr><td>S<sub>k</sub></td><td>%</td><td>Quantity of Water consumed from source k, expressed in %</td></tr><tr><td>T</td><td></td><td>Animal Category</td></tr><tr><td>C</td><td></td><td>Animal class</td></tr><tr><td>i</td><td></td><td>Type of consumption</td></tr><tr><td>k</td><td></td><td>Type of water source</td></tr></table>
# 6. Emission Models: Selection, Conception and their Functional Design

The objective of this chapter is to present models that have been selected and used for the development of emission inventory and calculation tool for the InOut platform. These emission models are dedicated to calculate direct emissions generated from pig production activity within the farm environment. The focus of this chapter therefore will be to computerize these emission models. Each model will be presented with briefings and diagram that helps to visualize the process flow and place of emissions.

The methodology developed in AGARIBALYSE® has been used as references and unless there are major changes, for example, using EMEP/EEP 2013 (New version and adapted by platform MEANS) instead of EMEP/EEA 2009 (old version - used by AGARIBALYSE®) and other similar changes, for example in case of N-excretion model where LMC values has been replaced by LMP values because of new law amendments, the rest follows models used by AGARIBALYSE®.

While calculating emissions, collection unit used is number of animals and each model is designed to calculate emissions per head in all cases.

The model presented serves for each stage and is generic for all stages of pig production system.

## 6.1 Nitrogen (N) – Model

This model<fnref n="58" /> is dedicated to calculate Nitrogen excretion from animals after they consume feed. The amount excreted depends on the amount of feed consumed and its nitrogen content as well as the nitrogen retention capacity of the animals.

The principal sources considered for producing Nitrogen excretion are nitrogen excreted from animals and Nitrogen added from bedding material used in the animal (CORPEN, 2003). For Nitrogen excreted from animals, the source is basically animal feed which is contained in the feed consumed by the animals. Some Nitrogen is fixed in the animal body and hence a balance is needed between what is consumed, those fixed in the animal body and the excreted to the environment. This method of estimating Nitrogen excretion is called mass-balance method.

$$N_{EX} = N_{ingested} - N_{fixed}$$

<caption>Equation 6.1: Nitrogen Excreted from Feed</caption>

Where:

<table><tr><td>N<sub>Ex</sub></td><td>kg</td><td>Nitrogen Excreted from a Given animal category</td></tr><tr><td>N<sub>ingested</sub></td><td>kg</td><td>Nitrogen ingested after consuming a given animal feed (Rations)</td></tr><tr><td>N<sub>fixed</sub></td><td>kg</td><td>Net Nitrogen fixed in animal body in a given period of time (Duration of presence)</td></tr></table>

The equation given above is illustrated in the following figures. As Figure 6.1 shows, to calculate N-Ex, live body of pigs (or breeding sows) that enter and leave a given sub-system, or stage, duration of their presence and the amount of feed consumed is taken into account. For more details, the Fig. is explained in following section.

<footnote n="58">⁵⁸ By itself, its role is limited as an intermediate model. It can be considered as a "dedicated model".</footnote>
<figure><img src="image_23.png" /><figcaption>Figure 6. 1: A simplified Model for calculation of N excreted from a farm unit (Sub-system)</figcaption></figure>

### 6.1.1 Nitrogen Model flow Diagram

Figure 6.1 is a simplified model description of Nitrogen model with more detailed and transparent procedure for calculation using the model as shown Figure 6. 2. As shown in the diagram, the model has inputs (animal feed, feed distribution time, average annual number of animals and their technical performance (live body weight and lean meat content) as well as time spent inside or out the buildings. Then the calculation part considers all the inputs and divides the final result between Nitrogen excreted inside the buildings and in the yards. Another input is the material used for bedding inside the buildings. Nitrogen added from bedding material is not directly related with the model but is considered as input only in a building and added directly to solid excretion material.

<figure><img src="image_24.png" /><figcaption>Figure 6. 2: Detailed Illustration of Nitrogen Model (reference: (EMEP/EEA, 2013))</figcaption></figure>

### 6.1.2 Model Equations

Nitrogen model is treated by dividing into two: total Nitrogen ingested and total Nitrogen fixed for a given animal category. To calculate Nitrogen ingested, it was further treated at three levels. First, feed given to a certain animal class at a particular time has been treated by referring its ingredient composition. Each ingredient's nitrogen content is summed to get the total amount of nitrogen for a given feed. Then the total sum of all feed's nitrogen
for the given animal class is obtained. Then to calculate the nitrogen ingested by the given animal category, nitrogen ingested from all animal class that exist within a given animal category is summed up. This gives total Nitrogen excreted in kg per day per head.

To calculate the total nitrogen fixed, body weight at entry and leaving the given animal category stage is considered as illustrated in Figure 6.2. To calculate nitrogen fixed by day, the value is divided by the total duration of the presence of the animal in the category/stage.

Finally, total Nitrogen excreted for a given animal category is obtained by mass balance equation as given above.

a. Total N$_{ingested}$

i. N$_{ingested}$ from one type of animal feed given per day:

N-ingested depends on the amount of animal feed consumed, Nitrogen Content or crude protein content (CP) and Dry matter content (DM, %) of each feed ingredient. The values are obtained from feed composition tables as it was given in chapter 0. Then, the Nitrogen content of a given ration is calculated by multiplying DM (%), %composition and consumption rate (kg/head/d) as given in equation 5.1

Equation:

For a given animal feed with n feed ingredients, the total N-content of the animal feed is calculated by:

$$N_{ing\_sub\_total - j} = \frac{\sum_{i=1}^{n} (N_{i\_content} \times \%DM_i \times \%f_{Ci} \times f_j)}{1000} \times D_{feed\_j}$$

<caption>Equation 6. 2: Ningested from animal feed consumed:</caption>

Where:

<table><tr><td>N<sub>ing-sub-total</sub></td><td>Sub-total of Nitrogen ingested after consuming one type of feed given, kg/head</td></tr><tr><td>N<sub>i_content</sub></td><td>Nitrogen Content of a given feed ingredient i, g/kg</td></tr><tr><td>%DM<sub>i</sub></td><td>Dry Matter content of ingredient i, %</td></tr><tr><td>%f<sub>ci</sub></td><td>Incorporation rate of ingredient i, in a given feed consumed, %</td></tr><tr><td>f<sub>j</sub></td><td>Feed Consumption rate by a given Animal Class of type Formula "j" for a feeding duration, D<sub>f_j, kg/head/d</sub></td></tr><tr><td>D<sub>feed_i</sub></td><td>Duration of feeding feed type j, d</td></tr><tr><td>i</td><td>Feed ingredient contained in feed j</td></tr><tr><td>j</td><td>Formula- Feed given to animal class for a duration D<sub>feed_i</sub></td></tr></table>

In chapter 5, it was indicated that an intermediate data process was presented. The table obtained (Annex 4) is used as an Input value during calculation of N$_{ingested}$ after consumption of a given animal feed. If "m" feeds were given to the animals in a given stage, then total N$_{ingested}$ is calculated by summing all the N$_{ingested}$ after consuming feed j by:

$$Total\_N_{ingested} = \sum_{fj=1}^{m} (N_{inj\_sub\_total\_fj})$$

<caption>Equation 6. 3: Total amount of Nitrogen ingested for one given animal in one category</caption>

Equation 0.1:

Where:

<table><tr><td>Total_N<sub>ingested</sub></td><td>The sum total of Nitrogen ingested after consuming one "m" feeds, kg/head</td></tr><tr><td>m</td><td>Rations - Types of animal feed given to animals in a category</td></tr></table>

b. Net Nitrogen Fixed
To calculate the Net N-fixed, live body weight at entry and leaving from the inventory inputs of “Technical performance” as well as lean meat content (LMP, %) of the animal considered are required. N fixed in the animal body is calculated by subtracting the Nitrogen deposited in the animal body at entry from that of nitrogen deposited while leaving the unit.

When calculating Nfixed, a distinction is needed for which reference value is taken. As it was discussed in *chapter 4*, the law amended by the EU affects which formula is used.

In this project, we have considered the two possibilities. For inventory data collected before 2006, the value of Lean Meat Content (LMC) will be considered and hence Equation 6. 4 any inventory data collected after 2006, Lean Meat Percentage (LMP) values will be used.

For the user, to use LMC is designed to be optional. And when LMC value is selected as an Input value, average values stored in the database are be used for calculation purpose.

N fixed value using LMC(%) –applicable for inventory data collected before to 2006

$$N_{Fixed\_entry/leaving} = \frac{e^{(-0.9385-0.0145 \times LMC)} \times (0.915 \times LW^{1.1009})^{(0.73644+0.0044 \times LMC)}}{6.25}$$

Equation 6. 4: Nitrogen Fixed in animal body (Using LMC values-TVM)

Where:

<table><tr><td>N<sub>Fixed_entry/leaving</sub></td><td>Quantity of Nitrogen fixed in animal body at entry and leaving period, kg</td></tr><tr><td>LW</td><td>Live body weight of the animal considered, kg</td></tr><tr><td>LMC (in French TVM)</td><td>Lean Meat Content at normal slaughter weight, %</td></tr></table>

Nfixed value using LMP (%) –Applicable for inventory data collected after 2006

National Lean Percentage (LMP) average values are provided for breeding sows, weaneres and fatteners Table 6. 1 If a user wants to enter a new value, an option has been provided for fatteners only. This is done because LMP values can be obtained from pig slaughter processing plants<fnref n="59" />. For breeding sows and weaners, national or regional average values can be used as a background data and be recalled during calculations.

$$N_{Fixed\_entry/leaving} = \frac{e^{(-0.9892-0.0145 \times (LMP-2.2))} \times (0.96 \times LW)^{(0.7518+0.0044 \times (LMP-2.2))}}{6.25}$$

Equation 6. 5: Nitrogen Fixed in animal body (Using LMP values – TMP) (Source: (Rigolot, et al., 2010(1); Delaby, et al., 2010)

Where:

<table><tr><td>N<sub>Fixed_entry/leaving</sub></td><td>kg</td><td>Quantity of Nitrogen fixed in animal body at entry and leaving period</td></tr><tr><td>LW</td><td>kg</td><td>Live body weight of the animal considered (kg)</td></tr><tr><td>LMP (in French TMP)</td><td>%</td><td>Lean Meat Percentage at normal slaughter weight (%) (if background data are selected, is used.</td></tr></table>

<footnote n="59">⁵⁹ Pig meat price is determined based on LMC (%) values. Every pig slaughtering house has a report of LMP values and hence it is easy to obtain and use them for inventory purposes.</footnote>
<table><caption>Table 6. 1: : Average values of Lean Meat Content (%) and LMP for different Animal</caption><thead><tr><th>Animal category/Stage</th><th>LMC*, %</th><th>LMP, %</th><th>Type of animal Considered</th><th>Remark</th></tr></thead><tbody><tr><td rowspan="2">Breeding Sows Stage</td><td>61.5</td><td>60.7</td><td>Gilts</td><td><sup>60</sup>Gilts are considered as sows at entry.</td></tr><tr><td>56.5</td><td>60.7</td><td>Sow</td><td><sup>61</sup>Culled sows is considered sow leaving</td></tr><tr><td>Weaners Stage</td><td>61.5</td><td>60.7</td><td>Weaners<sup>62</sup></td><td></td></tr><tr><td>Fattening Pigs Stage</td><td>61.5</td><td>60.7</td><td>Fatteners<sup>63</sup></td><td></td></tr></tbody></table>

## Net Nitrogen fixed value, kg/head

Net Nitrogen fixed value stored in the animal body during the whole period of presence of the animal in the category (Duration of presence). For example, for breeding sows, it is one year. For weaners and fatteners, the value is the duration of the presence of animals in the stage. Its value is calculated by:

$$N_{fixed\_Net} = N_{fixed\_leaving} - N_{fixed\_entry}$$

<table><caption>Equation 6. 6: Net Nitrogen fixed in the animal body</caption><tr><td>N<sub>fixed\_Net</sub></td><td>Net Quantity of Nitrogen fixed n animal body in one day, kg/head</td></tr><tr><td>Duration\_presenceç\_stage</td><td>Length of the presence of pigs, d</td></tr></table>

## Total Nitrogen Excreted, kg/head

$$Total\_Nitrogen\_Ex = Total\_N_{injected} - N_{fixed\_Net}$$

<table><caption>Equation 6. 7: Total Nitrogen Excreted, kg/head</caption><tr><td>Total\_Nitrogen\_Ex</td><td>Net Quantity of Nitrogen fixed n animal body in one day, kg/head</td></tr><tr><td>Duration\_presence\_stage</td><td>Length of the presence of pigs in a given stage, d</td></tr></table>

### c. Time spent inside buildings and the yards

#### i. Time passed inside the buildings, %

Depending on the management system, animals may share their time between the buildings and out-door runs (yards). This divides the excretion between the building and the yards.

<footnote>* Source: AGRIBALYSE</footnote>

<footnote n="60">⁶⁰ Gilts are considered as breeding sows that entered the breeding stage unit.</footnote>

<footnote n="61">⁶¹ Culled sows are assumed as average representative of sows that leave the breeding stage. Hence, Culled Sow body weight it taken as</footnote>

<footnote n="62">⁶² Weaners at entry and leaving the unit are assumed to have the same LMC value.</footnote>

<footnote n="63">⁶³ Pigs that enter and leave the fattening stage unit are assumed to have the same LMC value</footnote>
Accordingly, the amount of excreted nitrogen is divided. The quantity excreted in each site is directly proportional to the time spent inside the building and the yards. To calculate the proportion of time (%), information is obtained from the InOut form (section ) filled during inventory for each feeding duration.

To calculate, the time proportion, the following formula has been used:

$$t_{building\_k} = \frac{\sum_{j=1}^{n} (D_{feed\_j} \times \%t_{buildingi})}{\sum_{j=1}^{n} (D_{feed\_j})}$$

Equation 6. 9: Proportional %time passed inside one building

**Where:**

<table><tr><td>%t<sub>building_k</sub></td><td>Percentage of time passed in building i feeding feed ration j, %</td></tr><tr><td>n</td><td>Number of feeds distributed under management system j</td></tr><tr><td>j</td><td>Feed distributed to a given animal at feeding duration j</td></tr></table>

If there are a number of buildings with different management system, using the same principle, over all proportional percentage of time passed inside the building is calculated by:

$$t_{building\_overall} = \frac{\sum_{k=1}^{z} \left[ \sum_{j=1}^{n} (D_{feed\_j}) \times (t_{building\_k} - t_{building\_overall}) \right]}{\sum_{j=1}^{n} (D_{feed\_j})}$$

Equation 6. 10: Over-all Proportional time Percentage passed Inside Buildings

This formula is used when there is more than one building with different management system in a given animal category. For example, for breeding sows, building and management system for gestating sows can be different from those of lactating sows.

## ii. Time spent in the yards, %

$$t_{yard} = 100 - \%t_{building\_overall}$$

Equation 6. 11: Time spend in the yards, %

## d. Nitrogen excretion site

A distinction between Nitrogen Excreted inside the building and outside yards is an important step for calculation of emissions that take site inside and outside buildings. To determine the value, the proportional time passed inside and out the building and the total amount of Nitrogen excreted is considered.

### i. Excretion inside Buildings

$$N_{Ex\_Building} = Total\_N_{EX} \times t_{building\_overall}$$

Equation 6. 12: Nitrogen Excreted Inside Buildings

**Where:**

<table><tr><td>N<sub>Ex_Building</sub></td><td>Total amount of Nitrogen Excreted inside the buildings, kg/head/d</td></tr><tr><td>t<sub>building_overall</sub></td><td>Over-all time percentage passed inside the building of a Given Animal Category, %</td></tr></table>
## ii. Excretion in the yards

$$N_{Ex\_yards} = Total\_N_{EX} \times (100 - t_{building\_overall})$$

Equation 6. 13: Nitrogen Excreted in the yards

### e. Nitrogen added from Bedding Material

Bedding materials are important source of Nitrogen excretion in pig production systems (CORPEN, 2003; EMEP/EEA, 2013). The quantity added depends on the properties of bedding material. During inventory (section 5.2.5), the user is asked to choose what material is used. Generally, bedding materials are straws from crop and straw dust (see Table (App.) 5. 13)Error! Reference source not found.. Nitrogen content of these materials is either given in tables of feed composition properties or from literatures. The N-value of the bedding material is then used to calculate Nitrogen excreted as below:

$$N_{bedding\_material} = \frac{N_{content\_bedding} \times Quantity\_bedding\_material}{1000}$$

Equation 6. 14: N- Added from Bedding used

**Where:**

N<sub>Bedding_Material</sub> Total amount of Nitrogen added from bedding material used, kg/head/d

N<sub>conetnt_beding material</sub> Nitrogen content of bedding material obtained from “Table of animal Feed Ingredients properties”, g/kg (1000 is used to convert g to kg).

Quantity_beding_material Quantity of bedding material used, kg/head/day

### 6.1.3 N-Model Summary

<table><caption>Table 6. 2: Summary of N-model</caption><thead><tr><th>Phase</th><th>Inventory</th><th>Source</th></tr></thead><tbody><tr><td>1</td><td>Inputs<br>Required Inputs<br>Project type<br>Animal Category<br>Animal Class</td><td>Section 5.2.2</td></tr><tr><td>A<br>Technical Performance Data</td><td>Average number of animals in each class<br>Live Body weight at entry<br>Live Body weight at leaving<br>LMC at entry<br>LMC leaving<br>LMP, at entry<br>LMP at leaving</td><td>Table 6. 1</td></tr><tr><td>B<br>Animal Feed</td><td>Consumption Rate<br>Composition of ingredients<br>Duration of feed distribution<br>Time spent inside the buildings, individual entry</td><td>Table 5. 4</td></tr><tr><td>2<br>Intermediate Outputs</td><td>Calculation of N ingested<br>Subtotal Quantity of N<sub>ingested</sub><br>Total_N_ingested</td><td></td></tr><tr><td>3<br>Time spent</td><td>Average time spent inside the building<br>Overall time spent inside a building<br>Over-all time spent in the yard</td><td></td></tr><tr><td>4<br>Nitrogen excreted</td><td>Nitrogen Excretion<br>N<sub>Ex_Building</sub><br>N<sub>Ex_yards</sub></td><td></td></tr><tr><td>5<br>N added from bedding material</td><td>N<sub>bedding_material</sub></td><td></td></tr></tbody></table>
## 6.2 Ammonia ($NH_3$) Model

An EMEP/EEA (2013) guideline and AGRIBALYSE® (Koch & Salou, 2015) have been used as a reference guidelines to develop $NH_3$-model. Both, Tier 2 and Tier 3 of EMEP/EEA are developed for platform MEANS. In Tier 3, building and storage management systems were considered so that any effect that affects emission quantity has been adjusted depending on their properties.

### 6.2.1 General Concepts of Ammonia Emissions

The first step in Ammonia emission calculations is determining the quantity of Nitrogen excreted from animals has been discussed and presented exhaustively in *section above*. $N_{building}$, $N_{yards}$ as well as $N_{bedding_material}$, quantities were calculated using CORPEN (2003) model. To estimate Ammonia ($NH_3$), a model developed by European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA)<fnref n="64" /> has been adapted to estimate the quantity of ammonia gas emitted from pig excreta as well as manure stored in a storage structure.

Once nitrogen is excreted from animals, a series of biological reactions happen that transform nitrogen into various forms of gases as illustrated in *Figure 6. 3* (CORPEN, 2003; Wikipedia, 2015; MicrobeWiki, 2011). The most important reactions nitrogen fixation, nitrogen mineralization<fnref n="65" />, nitrogen immobilization<fnref n="66" />, nitrification and gentrification processes. Different microorganisms are engaged during such biological transformations. Mineralization plays a negative role by converting organic nitrogen into ammonia and nitrates and immobilization has an opposite and positive role in limiting ammonia emissions to atmosphere. (MicrobeWiki, 2011). These biological reactions are considered during EMEP/EEA model for the role they play in reducing and increasing ammonia emissions.

<figure><img src="image_25.png" /><figcaption>Figure 6. 3: Basic biological reaction of the transformation of Nitrogen excreta (Source: (CORPEN, 2003).</figcaption></figure>

### 6.2.2 Ammonia ($NH_3$) – Model Flow Diagram

Ammonia model has three major components: the input, (excretion), evacuation and storage (as it is given in *Figure 6. 4*, both in liquid and solid phase, where emission starts right after excreta takes site, inside the building and/or yards. Excretion that take place inside the building is affected by the type of floor, ventilation system and system of evacuation from the building (CITEPA, 2014; CORPEN, 2003; CORPEN, 2003; Cherubini, et al., 2014; EMEP/EEA, 2013). Once evacuated, the quantity of excreta stored is also subjected to further

<footnote n="64">⁶⁴ http://www.eea.europa.eu/publications/emep-eea-guidebook-2013</footnote>

<footnote n="65">⁶⁵ Mineralization also called ammonification is a conversion of organic-nitrogen compounds to ammonia thus increasing emissions.</footnote>

<footnote n="66">⁶⁶ Is the conversion of ammonium and nitrate to organic nitrogen, thus decreasing the amount of ammonia emissions to the atmosphere.</footnote>
biological changes producing ammonia. During storage, part of the manure may be treated and affects the emission produced.

<figure><img src="image_26.png" /><figcaption>Figure 6. 4: Ammonia (NH3) Model diagram</figcaption></figure>

### 6.2.3 Factors that affect ammonia emissions from pig farms

Factors that determine amount of NH3 emissions are: (1) number, age, and type of animal, (2) housing design and management, (3) type of manure storage and treatment, (4) Animal feed composition and (5) environmental conditions, (6) Raring system<sup>67</sup> (7) addition of bedding materials and (8) method of manure evacuation from buildings (EMEP/EEA, 2013).

### 6.2.4 Models Selected and Equations

As it is illustrated in Figure 6. 4, ammonia emissions take place at three major sites, emission inside the building, yards (out-door emissions) and from manure storage sites. Emission from sites depend on the amount of Nitrogen excreted (Equation 6. 12 and Equation 6. 13) in the site or amount of ammonia nitrogen stored. Emission factor is a conversion rate of pig excreta into gaseous form (depending on the type of excreta, solid or liquid) ( (IPCC, 2006; EMEP/EEA, 2013; Nguyen, et al., 2011). Equation 0.2 gives the total NH₃ emission from pig farms.

$$E\_NH\_3\_i = N\_Ex\_i \times \%TAN\_pig \times EF\_i\_j$$

Equation 6. 15: Emission calculation from a site i.

$$E\_NH\_3 = E\_building\_NH\_3 + E\_storage\_NH\_3 + E\_yards\_NH\_3$$

Equation 0.2: Total Ammonia Emission (General equation structure) (Source: (CITEPA, 2014))

Where:

<table><tr><td>E\_NH<sub>3</sub></td><td>Total NH<sub>3</sub></td></tr><tr><td>E\_building\_NH<sub>3</sub></td><td>Total NH<sub>3</sub> emission from building sites</td></tr><tr><td>E\_storage\_NH<sub>3</sub></td><td>Total NH<sub>3</sub> emission from manure storage sites</td></tr><tr><td>E\_yard\_NH<sub>3</sub></td><td>Total NH<sub>3</sub> emission from yard/outdoor run sites</td></tr><tr><td>E\_NH<sub>3</sub> i</td><td>NH<sub>3</sub> Emission at site i, kg</td></tr><tr><td>%TAN</td><td>Percentage of Total Ammonia Nitrogen of pig excreta, %</td></tr><tr><td>EF\_i\_j</td><td>Emission factor for pig excreta It site i, type of excreta j dimensionless (Refer)</td></tr></table>

<footnote n="67"><sup>67</sup> Indoor and Outdoor system and the time passed in raring area</footnote>
Steps: EMEP/EEA 2013 Emission (Tier 2 and Tier 3)

<table><caption>Equation 6.16: Step wise calculation procedure of NH3-N emissions (source: (EMEP/EEA, 2013))</caption><thead><tr><th>Excretion Site</th><th>Excretion</th><th>Formula</th><th>Equations</th></tr></thead><tbody><tr><td colspan="4"><strong>1. Quantity of N excreted</strong></td></tr><tr><td>Excretion in buildings</td><td>N Excretion in buildings</td><td>Equation 6.12</td><td></td></tr><tr><td>Excretion in yards</td><td>N Excretion in yards</td><td>Equation 6.13</td><td></td></tr><tr><td colspan="4"><strong>2. Total Ammonia Nitrogen, NH<sub>3</sub>-N</strong></td></tr><tr><td>Buildings</td><td>TAN in buildings</td><td>TAN<sub>buildings</sub> = N<sub>Ex building</sub> ×%<sub>TAN pig</sub></td><td></td></tr><tr><td>Yards</td><td>TAN in Yards</td><td>TAN<sub>yard</sub> = N<sub>Ex yard</sub> ×%<sub>TAN pig</sub></td><td></td></tr><tr><td colspan="4"><strong>3. Quantity of Solid and liquid N and TAN</strong></td></tr><tr><td>Solid Excreta</td><td>TAN in solid Excreta<br>N in solid Excreta</td><td>TAN<sub>building solid</sub> = TAN<sub>building</sub> ×%<sub>solid</sub><br>N<sub>building solid</sub> = N<sub>EX building</sub> ×%<sub>solid</sub></td><td></td></tr><tr><td>Liquid Excreta</td><td>TAN in liquid Excreta<br>N in liquid Excreta</td><td>TAN<sub>building liquid</sub> = TAN<sub>building</sub> × (1 - %<sub>solid</sub>)<br>N<sub>building liquid</sub> = N<sub>EX building</sub> ×%<sub>liquid</sub></td><td></td></tr><tr><td colspan="4"><strong>4. Quantity of NH3-N Emission losses</strong></td></tr><tr><td>Buildings</td><td>NH<sub>3</sub>-N Emissions from solid excreta</td><td>E<sub>building solid NH<sub>3</sub></sub> - N = TAN<sub>building solid</sub> × EF<sub>building solid</sub> - N</td><td></td></tr><tr><td>Buildings</td><td>NH<sub>3</sub>-N Emissions from liquid excreta</td><td>E<sub>building liquid NH<sub>3</sub></sub> - N = TAN<sub>building liquid</sub> × EF<sub>building liquid</sub> - N</td><td></td></tr><tr><td>Yards</td><td>NH<sub>3</sub>-N Emissions in the yards</td><td>E<sub>yard NH<sub>3</sub></sub> - N = TAN<sub>yard</sub> × EF<sub>yard NH<sub>3</sub></sub> - N</td><td></td></tr></tbody></table>
<table><caption>Equation 6. 17: (Table II): Step wise calculation procedure of NH3-N emissions (source: (EMEP/EEA, 2013))</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td colspan="3"><strong>5. Applicable only for Tier</strong></td></tr><tr><td rowspan="4">Buildings</td><td>Adjusted NH<sub>3</sub>-N</td><td rowspan="2">E<sub>building - solid - NH<sub>3</sub> - N</sub> = TAN<sub>building - solid</sub> × EF<sub>building - solid - NH<sub>3</sub> - N</sub> × AF<sub>solid</sub></td></tr><tr><td>Emissions from solid excreta</td></tr><tr><td>Adjusted NH<sub>3</sub>-N</td><td rowspan="2">E<sub>building - solid - NH<sub>3</sub> - N</sub> = TAN<sub>building - solid</sub> × EF<sub>building - solid - NH<sub>3</sub> - N</sub> × AF<sub>liquid</sub></td></tr><tr><td>Emissions from liquid excreta</td></tr><tr><td colspan="3"><strong>6. Bedding material impact</strong></td></tr><tr><td>Nitrogen added from bedding material</td><td>N bedding material</td><td>Equation 6. 14</td></tr><tr><td colspan="3"><strong>7. Effect of Immobilization (imm) – Only affects solid excreta in the buildings</strong></td></tr><tr><td rowspan="2">Solid Excreta</td><td>Stored TAN after imm</td><td>TAN<sub>building - solid after imm</sub> = (TAN<sub>building - solid</sub> - E<sub>building - solid</sub> NH<sub>3</sub> - N) × (1 - f<sub>imm</sub>)</td></tr><tr><td>Stored N</td><td>N<sub>Ex building - bedding - material (solid)</sub> = N<sub>building</sub> + N<sub>beddin - material</sub> - E<sub>building - solid - NH<sub>3</sub> - N</sub></td></tr><tr><td colspan="3"><strong>8. Excreta Stored</strong></td></tr><tr><td rowspan="3">Solid storage</td><td rowspan="3">Solid Stored TAN</td><td>TAN<sub>stored - solid</sub> = (TAN<sub>building - solid after imm</sub>) × (1 - %Direct_Spread)</td></tr><tr><td>N<sub>stored - solid</sub> = (N<sub>building - bedding - material (solid)</sub>) × (1 - %Direct_Spread)</td></tr><tr><td>TAN<sub>stored - liquid</sub> = (TAN<sub>building - liquid</sub> - E<sub>building - liquid - NH<sub>3</sub> - N</sub>) + (TAN<sub>yard</sub> - E<sub>yard</sub> - NH<sub>3</sub> - N) × (1 - %Direct_spread)</td></tr><tr><td rowspan="2">Liquid storage</td><td rowspan="2">Liquid TAN stored</td><td>N<sub>stored - liquid</sub> = (N<sub>building - liquid</sub> - E<sub>building - liquid - NH<sub>3</sub> - N</sub>) + (N<sub>yard</sub> - E<sub>yard</sub> - NH<sub>3</sub> - N) × (1 - %Direct_spread)</td></tr><tr><td>TAN<sub>stored - liquid after mineralization</sub> = TAN<sub>building - liquid</sub> + (N<sub>stored - liquid</sub> - TAN<sub>stored - liquid</sub>) × f<sub>min</sub></td></tr><tr><td colspan="3"><strong>9. Effect of Mineralization</strong></td></tr><tr><td>Final Liquid storage</td><td>Liquid TAN stored after mineralization</td><td></td></tr><tr><td colspan="3"><strong>10. Quantity of NH<sub>3</sub>-N emissions by type of excreta</strong></td></tr><tr><td>Solid</td><td>Emission from liquid storage</td><td>E<sub>stored - solid - NH<sub>3</sub> - N</sub> = TAN<sub>stored - solid</sub> × EF<sub>storage - solid</sub></td></tr><tr><td>Liquid</td><td>Emission from liquid storage</td><td>E<sub>stored - liquid - NH<sub>3</sub> - N</sub> = TAN<sub>stored - liquid after mineralization</sub> × EF<sub>storage - Liquid</sub></td></tr><tr><td>Solid</td><td>Emission after treatment from solid</td><td>E<sub>stored - solid - NH<sub>3</sub> - N after treatment</sub> = E<sub>stored - solid - NH<sub>3</sub> - N</sub> × (100 - AF<sub>treatment</sub>)</td></tr><tr><td>Liquid</td><td>Emission after treatment from solid</td><td>E<sub>stored - liquid - NH<sub>3</sub> - N after treatment</sub> = E<sub>stored - liquid - NH<sub>3</sub> - N</sub> × (100 - AF<sub>treatment</sub>)</td></tr><tr><td>Emission total</td><td>E<sub>total - NH<sub>3</sub> - N</sub> = (E<sub>yard</sub> + E<sub>building - solid - NH<sub>3</sub> - N</sub> + E<sub>storage - solid - NH<sub>3</sub> - N</sub> + E<sub>storage - liquid - NH<sub>3</sub> - N</sub>) × (17/14)</td><td></td></tr></tbody></table>
a. Immobilization and Mineralization

<table><caption>Table 5. 12: Summary of NH₃-model</caption><thead><tr><th>Category</th><th>Inventory</th><th></th></tr></thead><tbody><tr><td>Inputs</td><td></td><td></td></tr><tr><td>1 Input from inventory</td><td></td><td></td></tr><tr><td>Technical performance</td><td>Animal Category<br>Average Annual Number of Animals present</td><td>Section 5.2.2</td></tr><tr><td>Building</td><td>Type of floor<br>Ventilation system</td><td>Table (App.) 5. 12<br>Table (App.) 5. 14</td></tr><tr><td>Excretion Management</td><td>Type of excreta<br>%solid<br>%Direct spread<br>%manure treated</td><td>Section 4.3.1.7<br>Equation 5. 5<br>Table 5. 9<br>Equation 5. 7</td></tr><tr><td>Inputs from N-model</td><td>N<sub>EX_building</sub><br>N<sub>EX_yards</sub><br>Quantity of N added from bedding material</td><td>Equation 6. 12<br>Equation 6. 13<br>Equation 6. 14</td></tr><tr><td>Inputs from Parameter</td><td>%TAN<sub>pig</sub><br>EF<sub>building_solid</sub><br>EF<sub>building_liquid</sub><br>EF<sub>storage_solid</sub><br>EF<sub>storage_liquid</sub><br>EF<sub>yard_solid</sub><br>f<sub>imm</sub><br>f<sub>min</sub></td><td>Table (App.) 6. 3<br>Table (App.) 6. 4<br>Table (App.) 6. 5</td></tr><tr><td>Model _Output</td><td>NH<sub>3</sub> emission in buildings (solid and liquid)<br>NH<sub>3</sub> emission in storage (solid and liquid)<br>NH<sub>3</sub> emission in yards</td><td>Equation 6. 17</td></tr></tbody></table>

6.3 Model – NO₃⁻

Basset-Mens et al, 2007 method is used to calculate NO₃⁻. The emission occurs from excreta dropped in yards. It is an important source of emission to groundwater and surface water resources. An emission factor of 17.5% of the nitrogen produced is used to estimate NO₃⁻ emission (Koch & Salou, 2015).

<figure><img src="image_27.png" /><figcaption>Fig. 0.1: Model NO₃⁻</figcaption></figure>
## Model Equation

$$E\_NO_3\_T = (N\_Ex\_yards \times EF\_pig) \times \frac{62}{14}$$

<caption>Equation 6. 18: NO₃⁻ Emission</caption>

### Where:

<table><tr><td>E\_NO<sub>3</sub><sup>-</sup>_T</td><td>Total NO<sub>3</sub><sup>-</sup> Emission in yard from animal category T, kg/head/d</td></tr><tr><td>N\_ex\_yard</td><td>Nitrogen excreted in the yard, kg/head/d</td></tr><tr><td>EF\_NO<sub>3</sub><sup>-</sup></td><td>Emission Factor of pig(livestock) = 17.5%</td></tr></table>

## Model Summary

<table><caption>Table 6. 3: Model NO3- Output</caption><thead><tr><th></th><th>Inventory</th><th>Input Source</th></tr></thead><tbody><tr><td>1</td><td>Input from inventory<br>Technical performance</td><td>Animal Category<br>Average Annual Number of Animals<br>present</td><td>Table 5. 1, Table 5. 2,<br>Table 5. 3</td></tr><tr><td>2</td><td>Inputs from N-model</td><td>N<sub>EX_yards</sub></td><td>Equation 6. 13</td></tr><tr><td>3</td><td>Parameter Inputs</td><td>EF\_NO<sub>3</sub><sup>-</sup></td><td></td></tr><tr><td>4</td><td>Out-put</td><td>E\_NO<sub>3</sub><sup>-</sup>_T</td><td>Equation 6. 18</td></tr></tbody></table>

## 6.4 Model -NO

NO emissions on outdoor runs and from storage structures are estimated using EMEP/EEA 2013⁶⁸ Tier 1. NO is produced during nitrification process (Figure 6. 3) in the surface layer of stored manure (EMEP/EEA, 2013). The emission factors depend on the type of animal category and type of effluent, number of animals in each class (Koch & Salou, 2015; EMEP/EEA, 2013). To estimate NO emission therefore a weighted value that depends on the duration of the presence of each animal class in a given category.

$$E\_NO\_Total\_T = \left( \left( \frac{D}{365,25} \times Number\_animals \right) * \sum_j \left[ (EF\_NO\_j) \times \frac{Qty\_j}{\sum_T Qty\_j} \right] \right)$$

<caption>Equation 6. 19: NO emission from Lliquid Manure Stored</caption>

### Where:

<table><tr><td>E\_NO\_total<sub>T</sub></td><td>The sum total of NO emission from animal category T, kg/head/d</td></tr><tr><td>EF\_NO\_j</td><td>Emission Factor of manure j, kg NO/AAP</td></tr><tr><td>D<sub>i</sub></td><td>Duration of the presence of animal category in the farm in a year</td></tr><tr><td>Qty<sub>i</sub></td><td>Quantity of manure stored of type j</td></tr><tr><td>T</td><td>Animal category</td></tr><tr><td>i</td><td>Animal class</td></tr><tr><td>j</td><td>Type of manure</td></tr></table>

For use in N₂O model:

<footnote n="68">⁶⁸ AGRIBALYSE used EMEP/EEA 2009. A new version of EMPE/EEA (2013) has been released.</footnote>
Some portion of NO emitted from pigs while they are in the yard is converted into N₂O. To calculate the amount the fraction of time spent in the yard is used.

$$E_{NO\_Total}_{T\_yards} = (E_{NO\_Total}_{T}) \times \frac{(100 - t_{buildings})}{100}$$

<caption>Equation 6.20: Fraction of NO emitted inside the buildings</caption>

<table><caption>Table 6.4: Model Summary and Out-Put</caption><thead><tr><th>1</th><th>Input Source</th></tr></thead><tbody><tr><td><strong>Input from inventory</strong></td><td></td></tr><tr><td>Technical performance</td><td>Animal Category<br>Average Annual Number of Animals present<br>Type of animal class<br>Annual Average number of animals in the class<br>Duration of presence of each animal class</td></tr><tr><td>Input from Excretion Management</td><td>Type of manure stored<br>Qty_manure stored under different management systems</td></tr><tr><td>Output<br>- NO Emission</td><td>Table (App.) 5.10<br>Equation 5.4<br>Equation 6.19</td></tr></tbody></table>

## 6.5 Model- N₂O

N₂O model is dedicated to calculate direct and indirect Nitrous oxide (N₂O) emissions produced as a result of pig farm activity. Direct N₂O emissions produced during manure management (storage and treatment) and from nitrogen excreted in yards is calculated using IPCC 2006 Tier 2 (IPCC, 2006; Koch & Salou, 2015). And IPCC 2006 Tier 1 is used to calculate indirect N₂O produced as from stored manure and yards is the nitrogen excreted from animals. Nitrogen excreted quantities used by the model are calculated—using N - Model (CORPEN 2003).

### 6.5.1 N₂O Model flow diagram

The model illustrated Figure 6.5 shows the natural process of N₂O production. Emissions takes place in storage areas and from excreta dropped in yards. (Kebreab, et al., 2006; IPCC, 2006; EMEP/EEA, 2013).

<figure><img src="image_28.png" /><figcaption>Figure 6.5: N₂O model diagram</figcaption></figure>
## 6.5.2 Model Equations

For direct N₂O emission calculation considers the total nitrogen excreted and stored in storage structures and the nitrogen excreted in yards. N₂O emission from storage structures is influenced by the management system and therefore the type of management and the quantity treated under each system expressed in fractions are considered during calculations. And for emissions that take place from yards the value is calculated from the Nitrogen excreted (N<sub>yard</sub>).

To calculate indirect emission, it is based on nitrogen volatility that is occurred from NH₃ and NO.

### Direct Emissions

#### 6.5.2.1 Direct N₂O emissions – Storage

$$E\_N_2O\_storage = \left[ \sum_S \left[ \sum_T (N_{Ex\_buildings\_T} \times MS_{(T,S)}) \right] \right] \times EF\_N_2O\_storage\_S \times \frac{44}{28}$$

<caption>Equation 6. 21: Direct N₂O Emissions from Storage</caption>

### Where:

<table><tr><td>E_N₂O_storage</td><td>Direct N₂O emission from stored manure of animal category T, kg/head/d</td></tr><tr><td>N<sub>Ex</sub>_buildings_T</td><td>Total Nitrogen excreted inside buildings from animal category T and managed under manure system S, kg/head/d, obtained from N-model.</td></tr><tr><td>MS<sub>(T,S)</sub></td><td>Fraction of total annual nitrogen excretion for animal category T that is managed under manure management system S, dimensionless (Refer Equation 5. 6.</td></tr><tr><td>EF_N₂O_storage_S</td><td>Emission Factor of manure stored under management system (Refer Table (App.) 6. 6), kg N₂O-N/kg N</td></tr><tr><td>T</td><td>Animal category Type of manure stored</td></tr><tr><td>S</td><td>Manure System of management (both in liquid and solid)</td></tr><tr><td>i</td><td>Manure stored under management S (</td></tr><tr><td>44/28</td><td>Conversion factor from N_N2O to N2O</td></tr></table>

#### 6.5.2.2 Direct N₂O emissions - Yards

$$E\_N_2O\_yard = (N\_ex\_yard \times EF\_N_2O\_yard) \times \frac{44}{28}$$

<caption>Equation 6. 22: Direct N₂O emissions from Excreta in the yards</caption>

### Where:

<table><tr><td>E_N₂O_yard</td><td>Total N₂O Emission in the yard, kg/head/d</td></tr><tr><td>N_ex_yard</td><td>Nitrogen excreted in the yard, kg/head/d, N-model</td></tr><tr><td>EF_N₂O_yard</td><td>Emission Factor of manure excreted in the yard (Table (App.) 6. 6), kg N₂O-N/kg N</td></tr></table>

#### 6.3.2.3 Indirect N₂O emissions – Storage

Using IPCC 2006 Tier 1 calculation has two steps: first to calculate Nitrogen volatilization and then calculate the emissions by using emission factor values for pigs is used to calculate N volatilization in forms of NH₃ and NOₓ from manure management systems by multiplying amount of nitrogen excreted and stored under each manure management system, as well as from excretions in yards. Therefore the calculation has two steps.

#### 6.3.2.4 Nitrogen volatilization
$$N_{\text{Volatilization\_MMS}} = \sum_{S} \left[ \sum_{T} (N_{\text{Ex\_buildings\_T}} \times \text{MS}_{(T,S)}) \times \left( \frac{\text{Frac}_{\text{GasMS}}}{100} \right)_{(T,S)} \right]$$

Equation 6. 23: N volatilization

Where:

N<sub>Volatilization_MMS</sub> Amount of manure nitrogen that is lost due to volatilization of NH<sub>3</sub> and NO<sub>x</sub>, kg N per year

Frac<sub>GasMS</sub> Percent of managed manure nitrogen for livestock category T that volatilizes as NH<sub>3</sub> and NO<sub>x</sub> in the manure management system S, %

Indirect emission due to volatilization of N from manure management

$$E\_N_2O\_G\_(mm)\_storage = (N_{\text{Volatilization\_MMS}} \times EF\_N_2O\_pigs) \times \frac{44}{28}$$

Equation 6. 24: Indirect N2O emissions from storage

Where:

E_N<sub>2</sub>O_G<sub>(mm)</sub> Indirect N<sub>2</sub>O emissions due to volatilization of N from manure management S, kg N<sub>2</sub>O per head per year

EF_N<sub>2</sub>O_pigs Emission factor for N<sub>2</sub>O emissions from atmospheric depositions of nitrogen on spoils and water surfaces, kg N<sub>2</sub>O-N (NH<sub>3</sub>-N + NO<sub>x</sub>-N volatilized)<sup>-1</sup>, Default value = 0.01 kg N<sub>2</sub>O-N (NH<sub>3</sub>-N + NO<sub>x</sub>-N volatilized)<sup>-1</sup>

6.3.2.5 Indirect N<sub>2</sub>O emissions – yards

Because of excretions in the yard, N<sub>2</sub>O is emitted to atmosphere.

$$N_{\text{Volatilization\_MMS}} = (N_{\text{Ex\_yard\_T}}) \times \left( \frac{\text{Frac}_{\text{GasMS}}}{100} \right)_{(T,S)}$$

Equation 6. 25: Indirect N volatilization from the yards

And indirect N<sub>2</sub>O emission is given by

$$E\_N_2O\_G\_(mm)\_yards = (N_{\text{Volatilization\_MMS\_yards}} \times EF\_N_2O\_pigs) \times \frac{44}{28}$$

Equation 6. 26: Indirect N<sub>2</sub>O emission from yards

Frac<sub>GasMS</sub> in both cases (storage and yards) is calculated by summing NH<sub>3</sub> and NO<sub>x</sub> emissions obtained using NH<sub>3</sub> and NO<sub>x</sub> models in the storage place and yards.

Model Summary

<table><thead><tr><th>Inventory</th><th>Sources</th></tr></thead><tbody><tr><td>1 Input from inventory</td><td></td></tr><tr><td>Technical performance</td><td>Animal Category Section 5.2.2</td></tr><tr><td>Excretion Management</td><td>Average Annual Number of Animals present Table (App.) 5. 10</td></tr><tr><td></td><td>Type of manure stored Equation 5. 4</td></tr><tr><td></td><td>Type of manure management system</td></tr><tr><td></td><td>Qty\_manure stored under different management</td></tr></tbody></table>
<table><tbody><tr><td></td><td>systems</td><td></td></tr><tr><td>2</td><td>Inputs from N-model</td><td>Equation 6. 12</td></tr><tr><td></td><td>N<sub>EX</sub>_building</td><td>Equation 6. 13</td></tr><tr><td></td><td>N<sub>EX</sub>_yards</td><td></td></tr><tr><td>3</td><td>Inputs from NH3-model</td><td></td></tr><tr><td></td><td>NH<sub>3</sub> emitted in the yards</td><td></td></tr><tr><td>4</td><td>Inputs from NO-model</td><td></td></tr><tr><td></td><td>NO emitted in the yards (calculated as a special input to NO model)</td><td></td></tr><tr><td>5</td><td>Parameter Inputs</td><td></td></tr><tr><td></td><td>EF_N<sub>2</sub>O_storage</td><td>Table (App.) 6. 7</td></tr><tr><td></td><td>EF_N<sub>2</sub>O_yard</td><td></td></tr><tr><td></td><td>EF_N<sub>2</sub>O_pigs (default value)</td><td>0.01 kg N<sub>2</sub>O-N (NH<sub>3</sub>-N + NOx-N volatilized)-1</td></tr><tr><td>6</td><td>Model _Output</td><td></td></tr><tr><td></td><td>Direct</td><td>Direct N<sub>2</sub>O emission from storage (solid and liquid) Equation 6. 21</td></tr><tr><td></td><td></td><td>Direct N<sub>2</sub>O emission in yards Equation 6. 22</td></tr><tr><td></td><td>Indirect</td><td>Indirect N<sub>2</sub>O emission from storage Equation 6. 24</td></tr><tr><td></td><td></td><td>Indirect N<sub>2</sub>O emission in yard Equation 6. 26</td></tr></tbody></table>

## 6.6 Model - Methane (CH₄)

IPCC 2006 Tier 1 and Tier 2 guidelines are used to quantify methane emission sources from enteric fermentation and manure management respectively (IPCC, 2006; Koch & Salou, 2015; CITEPA, 2014; Rigolot, et al., 2010(II)).

### General concepts of methane Gas emissions

Methane originates from an anaerobic degradation of organic matter by bacteria inside the animal digestive tract and the release from waste (IPCC, 2006; CITEPA, 2014). The level of enteric fermentation methane gas production capacity is related to type and amount of feed consumed, source and solubility of dietary fiber content. Once manure is excreted, it is also exposed to further chemical changes that release CH₄ to air.

#### 6.6.1 Methane (CH₄) – model Flow Diagram

Methane gas emitted comes from enteric fermentation and from manure management systems both in the building and outside doors. Methane that is emitted from manure management mainly comes from the urine excretion

<figure><img src="image_29.png" /><figcaption>Figure 6. 6: Methane emission model flow diagram</figcaption></figure>
### 6.6.2 Factors that affect methane emissions

Methane is sourced from the fermentation process within the animal body and biological degradation of manure excreta (IPCC, 2006). It is gas emitted as part of the difference between digestible energy (DE) and metabolisable energy (ME) and energy of urine (Rigolot, et al., 2010(I); Jorgensen, 2011). The dietary characteristic (fiber content) and amount, age and physical activities, digestion and fermentation in the intestine are the major factors for the production of methane gas (Jorgensen, 2011). Other factors that influence methane emissions are temperature, manure type and management system and manure storage duration (Rigolot, et al., 2010(II)).

### 6.6.3 Models Selected and Equations

#### 6.6.3.1 Enteric Fermentation

IPCC 2006 Tier 1 is used for calculation of CH₄ emission from enteric fermentation. On average, produce 1.5 kg CH₄/head/year. And depending on the number of animals, the total amount is obtained.

$$E_{\_CH_4\_enteric} = \text{Average\_annual\_Number\_animals}_i \times \text{EF\_CH}_4 - \text{enteric} * \frac{D}{365,25}$$

<table><caption>Equation 6.27: CH4 emission from enteric fermentation</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Where:</td><td></td></tr><tr><td>E_CH₄enteric</td><td>Total annual CH₄ emission from enteric fermentation process of pigs, kg/year</td></tr><tr><td>Average_annual_Number_animalsᵢ</td><td>Average annual number of animals present in category T (breeding sows, weaners and fattening pigs),</td></tr><tr><td>EF_CH₄enteric</td><td>Annual enteric emission factor for pigs =1.5 kg CH₄/head/year (IPCC, 2006)</td></tr><tr><td>D</td><td>Duration of the presence of animal category in the farm in a year</td></tr></tbody></table>

#### 6.6.3.2 Manure Management

IPCC 2006 Tier 2 is selected for calculation of emissions from manure wastes.

$$E_{\_CH_4\_manure} = \text{Average\_annual\_Number\_animals}_i \times \text{EF\_manure}$$

<table><caption>Equation 6.28: CH4 emissions from manure wastes</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>E_CH₄manure</td><td>Total annual CH₄ emission from manure wastes, kg/year</td></tr><tr><td>Average_annual_Number_animalsᵢ</td><td>Average annual number of animals present in category i (breeding sows, weaners and fattening pigs),</td></tr><tr><td>EF_CH₄manure</td><td>Emission factor calculated using Tier 2, kg CH₄/head/year (IPCC, 2006)</td></tr></tbody></table>

$$EF_{\_CH_4\_manure_{T}} = (VS_{T} \times 365.25) \times \left[ B_{o(T)} \times 0.67 \, \text{kg/m}^{3} \times \sum_{S,K} \frac{MCF_{S,K}}{100} [GF_{(T,S,K)}] \right]$$

<table><caption>Equation 6.29: CH4 Emission Factor for Tier 2</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>EF_CH₄manure</td><td>Emission Factor for category T in kg/head/year</td></tr><tr><td>VS<sub>(T)</sub></td><td>The volatile solids excreted daily by category T in kg DM/head/day</td></tr></tbody></table>
<table>
   <tr>
    <td>
     365.25
    </td>
    <td>
     Conversion of VS production to per year
    </td>
   </tr>
   <tr>
    <td>
     B
     <sub>
      o(T)
     </sub>
    </td>
    <td>
     Maximum CH
     <sub>
      4
     </sub>
     production capacity for the manure produced by the category animal T, m
     <sup>
      3
     </sup>
     CH
     <sub>
      4
     </sub>
     /kg VS excreted
    </td>
   </tr>
   <tr>
    <td>
     0.67 kg/m
     <sub>
      3
     </sub>
    </td>
    <td>
     Conversion factor of m
     <sup>
      3
     </sup>
     CH
     <sub>
      4
     </sub>
     to kilograms CH
     <sub>
      4
     </sub>
    </td>
   </tr>
   <tr>
    <td>
     MCF
     <sub>
      (S,K)
     </sub>
    </td>
    <td>
     Methane (CH
     <sub>
      4
     </sub>
     ) conversion factor for the management system S for each climatic region K, in %
    </td>
   </tr>
   <tr>
    <td>
     GF
     <sub>
      (T,S,K)
     </sub>
    </td>
    <td>
     Fraction of the category T manure processed(treated) using the management systems S in the climatic region K
    </td>
   </tr>
  </table>

$$
SV = \left[ GE \times \left( 1 - \frac{DF\%}{100} \right) + (UE \times GE) \right] \times \left[ \left( \frac{1 - Ash}{18.45} \right) \right]
$$

Equation 6. 30: Quantity of daily volatile solids

Where:

<table>
   <tr>
    <td>
     VS
    </td>
    <td>
     Volatile solids excreted daily on the basis of dry matter, in kg DM/head/day
    </td>
   </tr>
   <tr>
    <td>
     GE
    </td>
    <td>
     Gross Energy Intake, MJ/day
    </td>
   </tr>
   <tr>
    <td>
     DF%
    </td>
    <td>
     Digestibility of feed
    </td>
   </tr>
   <tr>
    <td>
     (UE x GE)
    </td>
    <td>
     Urinary energy expressed as a fraction of the G, UE = 0.02 (for pigs)
    </td>
   </tr>
   <tr>
    <td>
     Ash
    </td>
    <td>
     Ash content of the ration, %
    </td>
   </tr>
   <tr>
    <td>
     18.45
    </td>
    <td>
     Conversion factor for the EC of the dietary of dry matter MJ/kg DM
    </td>
   </tr>
  </table>

Model Parameters

6.6.3.3 Calculation Procedures

i. Calculation of Total Energy Content (EC)

Gross energy intake value obtained from each animal feed consumed is summed to calculate total digestible energy provided to the animal. To perform such calculations, we need to obtain EC values for each feed ingredient (i) contained in the feed stuff. From the inventory tables (section 4.3.2), EC% values are generated from internal database of the software and gives the sum for each feed type (j). If animals in a given category consume “m” types of feed with feed that contains n ingredients, the total sum is calculated by summing the values obtained from each ingredient and feed consumed as it is given below:

$$
\text{Total\_GE}_{\text{feed(T)}} (\text{MJ/d}) = \sum_{j=1}^{m} \left( \sum_{i=1}^{n} (\text{GE}(\text{kcal})_i \times \text{DM}_i \times \text{\%Composition}_i \times \text{feed\_consumed}_j) \right) \times 0.004184
$$

Equation 6. 31: Calculation of Digestible Gross Energy, kcal/kg

The over-all feed digestibility is calculated by using proportional values of each feed
consumed. The value is expressed in %. Formula:

$$
\text{Total\_DF}_{\text{feed(T)}} (\%) = \frac{\sum_{j=1}^{m} \left( \sum_{i=1}^{n} (\text{DF}(\%)_i \times \%\text{Composition}_i \times \text{feed\_consumed}_j \right)}{\sum_{j=1}^{m} \left( \sum_{i=1}^{n} (\%\text{Composition}_i \times \text{feed\_consumed}_j \right)}
$$

Equation 6. 32: Total Digestible feed, DF, %

ii. Calculation of Ash content feed consumed (Ash, %)

The over-all ash content (%) of the feed consumed is calculated by taking into account ash content of each feed ingredient (%Ash are given in Annex 3). The total ash content is proportional to the composition of each ingredient in each animal feed and the total feed consumed by the animals in a given category T. Its value is determined by:
$$ Ash\_conet_{feed(T)}(\%) = \frac{\sum_{j=1}^{m} \left( \sum_{i=1}^{n} (Ash(\%)_i \times DM_i \times Composition_i \times feed\_consumed_j ) \right)}{\sum_{j=1}^{m} \left( \sum_{i=1}^{n} (Composition_i \times feed\_consumed_j ) \right)} $$

Equation 6. 33: Ash content of feed, %

**Where:**

<table><tr><td>Total_EC<sub>feed(T)</sub></td><td>Energy consumed, MJ/day by animal category T</td></tr><tr><td>GE</td><td>Gross Energy Intake from feed ingredient i of feed type j, kcal</td></tr><tr><td>%DM</td><td>Dry matter content of feed ingredient i, %</td></tr><tr><td>%Composition</td><td>Percentage of feed ingredient composition in each feed consumed</td></tr><tr><td>Feed consumed</td><td>Amount of feed consumed in a given period of time, kg/d</td></tr><tr><td>0.004184</td><td>Conversion ratio of kcal into MJ/d</td></tr><tr><td>Total DF<sub>feed(T)</sub></td><td>Over-all Feed Digestibility of animal feed consumed by animal category T, %</td></tr><tr><td>Total Ash<sub>feed(T)</sub></td><td>Over-all Ash content of animal feed consumed by animal category T, %</td></tr><tr><td>T</td><td>Animal category</td></tr><tr><td>i</td><td>Feed ingredient</td></tr><tr><td>j</td><td>Feed type</td></tr></table>

### 6.6.3.2 Emission Location

Methane emission is divided between buildings and yards depend on the amount of time spent in each location. This is simply done by proportioning the emission between buildings and yards by multiplying the percentage of time spent (given by in each location as follows:

$$ Total\_E\_CH_4 = E\_CH_4\_enteric + E\_CH_4\_manure $$

Equation 6. 34: Total Methane Emissions

$$ E\_CH_4\_enteric\_Building = Average\_annual\_Number\_animal\_x \times EF\_enteric $$

Equation 6. 35: Enteric Emissions in Buildings

$$ E\_CH_4\_manure\_Building = Average\_annual\_Number\_animal\_x \times EF\_manure $$

Equation 6. 36: Methane emissions from Storage

### 6.6.3.3 Model Parameters

CH₄ model requires a number of input parameters. Some are constants given directly (B₀, UE, as given in Table (App.) 6. 8), and others require some manipulations (MCF, Table (App.) 6. 10). Other inputs are obtained from inventory platform directly (such as, %_Composition and feed consumption rate) and some from properties of feed ingredients stored in internal data base of the system (%DM, EC, %DF, %Ash, Annex 3).

IPCC (2006) recommends MCF values to be based on the climatic regions of the area under study and values that depend on manure management system and average annual outside temperature as given in Table (App.) 6. 9. On the other hand, AGRIBALYSE® has taken medium MCF values that depend on the type of manure and system of management when they calculate emissions, considering a mean annual temperature. For the development of platform MEANS values used by AGRIBALYSE® recommendations are used to simplify parameter inputs.

### 6.6.4 Model-Summary

item Inventory

Inventory
<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>1</td><td>Input from inventory</td><td>Animal Category</td><td>Input Source</td></tr><tr><td></td><td>A. Technical performance</td><td>Average Annual Number of Animals present</td><td>Section 5.2.2</td></tr><tr><td></td><td>B. Animal Feed</td><td>Number of animals in each class Feed consumption rate</td><td>Table 5.4</td></tr><tr><td></td><td>C. Manure management</td><td>%Composition Feed ingredients (properties) %time passed inside buildings</td><td>Table (App.) 5. 10</td></tr><tr><td></td><td></td><td>Type of Excreta</td><td>Table (App.) 6. 8</td></tr><tr><td></td><td></td><td>Type of system of management</td><td>Table (App.) 6. 8</td></tr><tr><td></td><td></td><td><strong>E_CH4_enteric</strong></td><td></td></tr><tr><td></td><td></td><td>B<sub>o</sub></td><td></td></tr><tr><td></td><td></td><td>UE</td><td></td></tr><tr><td></td><td></td><td>MCF</td><td>Table (App.) 6. 10</td></tr><tr><td></td><td></td><td>t<sub>buildings</sub></td><td>Equation 6. 11</td></tr><tr><td></td><td></td><td>EC, MJ/d</td><td>Equation 6. 31</td></tr><tr><td></td><td></td><td>DF%</td><td>Equation 6. 32</td></tr><tr><td></td><td></td><td>%Ash</td><td>Equation 6. 33</td></tr><tr><td></td><td></td><td>SV</td><td>Equation 6. 30</td></tr><tr><td></td><td></td><td>EF_CH4_manure</td><td>Table (App.) 6. 8</td></tr><tr><td>2</td><td>Parameter Inputs</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model Out-put</strong></td><td></td></tr><tr><td></td><td></td><td>E_CH4_buildings</td><td>Equation 6. 35</td></tr><tr><td></td><td></td><td>E_CH4_manure</td><td>Equation 6. 36</td></tr><tr><td></td><td></td><td>E_CH4_yards</td><td></td></tr><tr><td></td><td></td><td>Total methane emissions</td><td>Equation 6. 34</td></tr></tbody></table>

## 6.7 Summary of models retained

<table><caption>Table 6. 5: Models incorporated of emission inventory from pig production systems</caption><thead><tr><th>Item</th><th>Substance</th><th>Emission to</th><th>Emission/Excretion location</th><th>Emission Models Retained</th></tr></thead><tbody><tr><td>1</td><td>Nitrogen</td><td></td><td>Excretion</td><td>CORPEN 2003</td></tr><tr><td></td><td></td><td></td><td>Emission in the Building</td><td>EMEP/EEP 2013 Tier 2</td></tr><tr><td></td><td></td><td></td><td></td><td>EMEP/EEP 2013 Tier 3</td></tr><tr><td>2</td><td>NH<sub>3</sub></td><td>Air</td><td>Emission from manure storage structures</td><td>EMEP/EEP 2013 Tier 2</td></tr><tr><td></td><td></td><td></td><td>Emission in Out-door runs</td><td>EMEP/EEP 2013 Tier 2</td></tr><tr><td></td><td></td><td></td><td></td><td>EMEP/EEP 2013 Tier 3</td></tr><tr><td>3</td><td>N<sub>2</sub>O direct</td><td>Air</td><td>Emission from manure storage structures</td><td>IPCC 2006b Tier 2</td></tr><tr><td></td><td></td><td></td><td>Emission in Out-door runs</td><td>IPCC 2006b Tier 2</td></tr><tr><td></td><td></td><td></td><td>Emission in the Building</td><td>IPCC 2006b Tier 2</td></tr><tr><td>4</td><td>N<sub>2</sub>O Indirect</td><td>Air</td><td>Emission in Out-door runs</td><td>IPCC 2006b Tier 2</td></tr><tr><td></td><td></td><td></td><td>Emission in the Building</td><td>EMEP/EEP 2013 Tier 1</td></tr><tr><td>5</td><td>NO</td><td>Air</td><td>Emission from manure storage structures</td><td>EMEP/EEP 2013 Tier 1</td></tr><tr><td></td><td></td><td></td><td>Emission from manure storage structures</td><td>EMEP/EEP 2013 Tier 2</td></tr><tr><td>6</td><td>NO<sub>3</sub><sup>-</sup></td><td>Eau</td><td>Emission in Out-door runs</td><td>Basset-Mens et al. 2007</td></tr><tr><td></td><td></td><td></td><td>Enteric emissions</td><td>IPCC 2006b Tier 1</td></tr><tr><td></td><td></td><td></td><td>Emission in the Building</td><td>IPCC 2006b Tier 2</td></tr><tr><td>7</td><td>CH<sub>4</sub></td><td>Air</td><td>Emission from manure storage structures</td><td>IPCC 2006b Tier 2</td></tr></tbody></table>

<footnote>a - Nitrogen is not by itself an emission but is the material excreted by the animals and becomes the main source of emission</footnote>
# 7. Allocations and Impact Assessment Indicators and Correspondence to Simapro

When a system produces more than one input, a question raise in LCA modeling: how to share the impact of the system between the products and co-products? Allocation is a way of sharing the impacts between the co-products. There are several allocation methods (based on the mass of products and co-products, on their economic value...). In MEANS platform, according to AGRIBALYSE (Koch and Salou, 2015), the biophysical method has been chosen.

## 7.1 Allocation Rule Applied for pig production systems

For developing the InOut form for impact allocation, biophysical allocation system is implemented. During design, one stage (animal category) is considered at a time. For the stage selected, the main interest output from the stage is treated as a responsible agent for the impacts to the environment caused because of the production activity. Here the output of interests is the main products and co-products from each stage. As it is illustrated in Figure 7.1, from breeding stage, piglets and culled sows leaving the stage are the products and co-products respectively. From post-weaners and fattening stage the products are pork and there is no co-products co-product.

<figure><img src="image_30.png" /><figcaption>Figure 7.1: Production Systems, environmental impacts and allocation rule</figcaption></figure>

### 7.1.1 Determination of Biophysical Allocation Factor

To assign the allocation for stages with product and co-product, first allocation factor is calculated by proportioning the amount of feed consumed by each animal class in a given category. Allocation factor is a function of the type of animal class in a given category, quantity of feed consumed, duration of feeding and the allocation rule applied for the product/co-product produced from each category. The equation is given by

$$A_{(P,T)} = \frac{\sum_{i=1}^{n} (\% F_{C-i} \times Qty\_feed_{(C-i,T)})}{\sum_{i=1}^{n} (Qty\_feed_{C-i,T})}$$

<caption>Equation 7.1: Calculation of Allocation based on Biophysical Allocation method for pigs</caption>

The animal in a given class is considered as supplied with several feeds and with different duration of feeding throughout the presence period (see chapter 5 section 0). Then at category level, a proportional average value of feed consumption rate is taken for calculating the feed percentage share as given in equation below: