# Jannick H Schmidt

Department of Development and Planning Aalborg University 

# Life cycle assessment of rapeseed oil and

Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil 

# palm oil

## Title: Life assessment of rapeseed oil and palm oil. Ph.D. th esis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Author: Jannick Hoejrup Schmidt

Published by: Department of Development a nd Planning, Aalborg University

Distribution: http://vbn.aau.dk

Date: 15-jun-2007 (version for the assessment committe)

Cover: Fotos by:

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8 1) Jannick H Schmidt, Rapeseed field near Aalborg, May 2007 2) Calle Schmidt, Rapeseed field in Northern Jutland, May 2007 3) Picture obtained from: http://jyndevad.okologgen.dk (Accessed May 2007) 4) Jannick H Schmidt, Rapeseed oil, June 2007 5) Jannick H Schmidt, Palm oil, May 2007 6) Jannick H Schmidt, United Plantations Berhad, Malaysia, November 2006 7) Jannick H Schmidt, United Plantations Berhad, Malaysia, November 2006 8) Jannick H Schmidt, United Plantations Berhad, Malaysia, August 2005

# Preface
This report is published as part of the Ph.D. thesis: Li fe cycle assessment of rapeseed oil and palm oil. The thesis consists of three parts: 1. Part 1: Summary report The summary report describes the overall problem of the Ph.D. project, the re search outline, summaries of the research and perspectives and recommendations. 2. Part 2: Article collection (6 scientific articles) The article collection presents the core of the scientific output of the Ph.D. project. 3. Part 3: Life cycle inventory of rapeseed oil and palm oil This life cycle inventory report provides and documen ts the background material for the scientific article: Comparative life cycle assessment of rapeseed o il and palm oil (published in part two of the thesis). This includes definition of system boundaries, the collected data, the modelling of the investigatedsystem, sensitivity analyses and an evaluation of sensitivity, completeness and consistency. The inventory report has character of an appendix report to the life cycle assessment

The Ph.D. thesis was carried out at the Department of Development and Planning at Aalborg University. The project was initiated under the framework of the DUCED - I&UA project which is a Danish university consortium for environment and development. The thesis was supervised by professor Per Christensen and associate professor Eskild Holm Nielsen, Department of Development and Planning, Aalborg University.

Many people, companies and institutions have provi ded essential data, insights and great hospitality during meetings and longer stays which have made this study possible. Therefore, a special thanks goes to:

Carl Bek-Nielsen (Executive director of United Plantation), Dr. Gurmit Singh (Research director), Dr. Xaviar Arulandoo (Research controller), Dr. Mukesh Sharma (Research manager) and the rest of the Research Department at United Plantations Berhad, Teluk Intan, Malaysia Jesper Korning (Quality manager), Lars Kronborg (Quality engineer), Anders Krom and Hansen (Engineer) and Anders Dahl (Trading Manager), AarhusKa rlshamn, Aarhus, Denmark Vijaya Subramaniam (Research officer), E ngineering & Processing Division, Energy and Environment Unit, Malaysian Palm Oil Board (MPOB), Kajang, Malaysia Prof. Dr. P Agamuthu (Professor) and the students at the Solid Wast e Lab, Institute of Biological Science, University of Malaya
, Kuala Lumpur, Malaysia Randolph Jeremiah (Project officer), MUCED, Malaysian University Consortium for E nvironment and Development, University of Malaya, Kuala Lumpur, Malaysia Sumiani Yusoff (Researcher), Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia
Peter Shonfield, LCA Team Lead er, Chemistry & Environmental Protection Department, Safety and Environmental Assurance Centre, Unilever



## Jannick Hoejrup Schmidt, June 2007

# Contents

1 Introduction................................................................................................................................................................9 1.1 Purpose of the lif e cycle i nventory.............................................................................................................................9 1.2 Functional unit............................................................................................................ ................................................9 1.3 Method for system delimitation............................................................................................. .....................................9 1.4 Method applied for Life Cy cle Impact Asse ssment (LCIA)..................................................................... ................12 1.5 Data collection............................................................................................................ ..............................................16 1.6 Structure of the report.................................................................................................... ...........................................16 2 System boundaries of inventoried systems....................................................................................... ......................19 2.1 Marginal suppliers of affected crops ....................................................................................... .................................19 2.2 System expansio n – oil mill stage .......................................................................................... ..................................20 2.3 System expansion – agricultural stage...................................................................................... ................................23 2.4 Scenarois related to system expansion ...................................................................................... ...............................28 2.5 Inventoried system – summary............................................................................................... ..................................30 3 Energy......................................................................................................................... ..............................................33 3.1 Electricity from the grid in Denmark ....................................................................................... ................................33 3.2 Electricity from the grid in Malaysia and Indon esia ........................................................................ ........................37 3.3 Electricity from the grid in Brazil ........................................................................................ ....................................38 3.4 Electricity from the grid in Canada ........................................................................................ ..................................39 3.5 Electricity in differe nt countries, summary ..............................................................................................................39 3.6 District hea ting in Denmark ................................................................................................ .....................................40 4 Transport..................................................................................................................................................................43 4.1 Transport with lorry ....................................................................................................... ..........................................43 4.2 Transport with ocean tanker ................................................................................................ .....................................44 4.3 Traction ....................................................................................................................................................................45 4.4 Transport with passenger car............................................................................................... .....................................46 5 Agricultural stage: Rapeseed ................................................................................................... ...............................47 5.1 Product flow in agricultura l stage......................................................................................... ....................................48 5.2 Omitted inventory data in agricultu ral stage ............................................................................... .............................51 5.3 Energy use................................................................................................................................................................52 5.4 Materials.................................................................................................................. .................................................58 5.5 Co-products ................................................................................................................ ..............................................65 5.6 Emissions .................................................................................................................................................................66 5.7 Overhead in agricultural stage............................................................................................. .....................................77 5.8 Capital goods in agricultural stage ........................................................................................ ...................................77 5.9 Transport of material s in agricultu ral stage............................................................................... ...............................80 5.10 LCI of rapeseed agri cultural stage, summary............................................................................... ..........................81 6 Agricultural stage: Oil palm , fresh fruit b unches (FFB) ........................................................................ ..............83 6.1 Product flow in agricultura l stage......................................................................................... ....................................87 6.2 Omitted inventory data in agricultu ral stage ............................................................................... .............................88 6.3 Energy use................................................................................................................................................................88 6.4 Other processes ............................................................................................................ ............................................90 6.5 Materials.................................................................................................................. .................................................90 6.6 Co-products ................................................................................................................ ..............................................93 6.7 Emissions .................................................................................................................................................................93 6.8 Overhead in agricultural stage............................................................................................. ...................................111 6.9 Capital goods in agricultural stage ........................................................................................ .................................111 6.10 Transport of material s in agricultu ral stage.............................................................................. ............................112 6.11 LCI of oil palm ag ricultural stag e, summary............................................................................... .........................114 7 Agricultural st age: Soybean.................................................................................................... ..............................115 7.1 Product flow in agricultura l stage......................................................................................... ..................................115 7.2 Omitted inventory data in agricultu ral stage ............................................................................... ...........................116 7.3 Energy use................................................................................................................. .............................................117 7.4 Materials.................................................................................................................. ...............................................117 7.5 Emissions .................................................................................................................. .............................................118 7.6 Overhead in agricultural stage............................................................................................. ...................................122 7.7 Capital goods in agricultural stage ........................................................................................ .................................122 7.8 Transport of material s in agricultu ral stage............................................................................... .............................123 7.9 LCI of soybean agri cultural stag e, summary................................................................................. .........................123 8 Agricultural st age: Barley..................................................................................................... ................................127 8.1 Product flow in agricultura l stage......................................................................................... ..................................127

## 8.2 Omitted inventory data in agricultu ral stage............................................................................... ............................129 8.3 Energy use ................................................................................................................. .............................................130 8.4 Materials .................................................................................................................. ...............................................130 8.5 Co-products ................................................................................................................ ............................................131 8.6 Emissions.................................................................................................................. ..............................................132 8.7 Overhead in agricultural stage ............................................................................................. ...................................139 8.8 Capital goods in agricultural stage........................................................................................ ..................................139 8.9 Transport of material s in agricultu ral stage ............................................................................... .............................139 8.10 LCI of barley agri cultural stage, summary ................................................................................. ..........................141 9 Oil mill stage: Rapeseed oil................................................................................................... .................................143 9.1 Rapeseed oil mill product flow............................................................................................. ..................................143 9.2 Omitted inventory data in rapeseed o il mill st age.......................................................................... .........................146 9.3 Power central .............................................................................................................. ............................................146 9.4 Rapeseed oil mill processi ng: Pressing an d extraction ...................................................................... .....................147 9.5 Waste to treatment ......................................................................................................... .........................................149 9.6 Overhead................................................................................................................... ..............................................150 9.7 Capita l goods .............................................................................................................. ............................................151 9.8 Transport of raw ma terials and ancillaries to rapeseed oil mill ............................................................ ..................153 9.9 LCI of rapeseed oil mill, summary .......................................................................................... ...............................153 10 Oil mill stage: Palm oil....................................................................................................... ....................................155 10.1 Palm oil mi ll product flow................................................................................................ ....................................155 10.2 Omitted inventory data in palm oil mill stage............................................................................. ..........................157 10.3 Power central ............................................................................................................. ...........................................157 10.4 Palm oil mill processi ng: Pressing, drying etc............................................................................ ..........................161 10.5 POME treatment ............................................................................................................ .......................................162 10.6 Co-products/was te to treatment ............................................................................................ ................................164 10.7 Overhead.................................................................................................................. .............................................165 10.8 Cap ital go ods ............................................................................................................. ...........................................165 10.9 Transport of raw materials an d ancillaries to palm o il m ill ..................................................................................166 10.10 LCI of palm oil mill, summary ............................................................................................ ...............................167 11 Oil mill stage: Pa lm kernel oil ................................................................................................ ...............................169 11.1 Oil mill product flow ............................................................................................................................................169 11.2 Omitted inventory data in palm kernel o il mill st age...................................................................... ......................169 11.3 Ener gy use ................................................................................................................ ............................................170 11.4 Materi al use .............................................................................................................. ............................................170 11.5 Emissions................................................................................................................. .............................................170 11.6 Waste to treatment ........................................................................................................ ........................................170 11.7 Overhead.................................................................................................................. .............................................171 11.8 Cap ital go ods ............................................................................................................. ...........................................171 11.9 Transport of raw materials and an cillaries to palm kernel o il m ill ........................................................ ...............171 11.10 LCI of palm kernel oil mill, summary ..................................................................................... ...........................171 12 Oil mill stage: Soybean meal .................................................................................................................................173 12.1 Oil mill product flow ............................................................................................................................................173 12.2 Omitted inventory data in soybean oil mill stage .......................................................................... .......................173 12.3 Ener gy use ................................................................................................................ ............................................173 12.4 Materi al use .............................................................................................................. ............................................174 12.5 Emissions................................................................................................................. .............................................174 12.6 Waste to treatment ........................................................................................................ ........................................174 12.7 Overhead.................................................................................................................. .............................................174 12.8 Cap ital go ods ............................................................................................................. ...........................................174 12.9 Transport of raw materials an d ancillaries to soybean oil mill ............................................................ .................175 12.10 LCI of soybean oil mill, summary ......................................................................................... .............................176 13 Refinery stage: Rapeseed oil................................................................................................... ...............................177 13.1 Materi al use .............................................................................................................. ............................................178 13.2 Ener gy use ................................................................................................................ ............................................182 13.3 Emissions................................................................................................................. .............................................182 13.4 Waste to treat ment/co-products ............................................................................................................................182 13.5 Overhead.................................................................................................................. .............................................184 13.6 Cap ital go ods ............................................................................................................. ...........................................185 13.7 Transport of raw material and ancillaries to refinery..................................................................... .......................185 13.8 LCI of rapeseed oil refinery, summary ..................................................................................... ............................187 14 Refinery stage: Palm oil....................................................................................................... ..................................189 14.1 Materi al use .............................................................................................................. ............................................189

## 14.2 Ener gy use................................................................................................................ ............................................190 14.3 Emissions ................................................................................................................. ............................................190 14.4 Waste to treat ment/co-products............................................................................................................................190 14.5 Overhead .................................................................................................................. ............................................191 14.6 Cap ital go ods............................................................................................................. ...........................................191 14.7 Transport of raw material and ancillaries to refinery ..................................................................... ......................191 14.8 LCI of palm o il refinery, summary......................................................................................... ..............................192 15 Refinery stage: Palm kernel oil................................................................................................ .............................195 15.1 LCI of palm kernel oil refinery, summary.................................................................................. ..........................195 16 Refinery stage: Soybean oil .................................................................................................... ...............................197 17 Transport stage ................................................................................................................ ......................................199 18 Emissions from intens ified cult ivation .................................................................................................................201 18.1 Identification of met hods of increasing yields............................................................................ ..........................201 18.2 Determination of changed parameters....................................................................................... ...........................201 18.3 LCI of intensified cultivation, summar ies ............................................................................................................203 19 Emissions from transformation of land and from non-cultivated land............................................................. 211 19.1 Emissions from tr ansformation processes ................................................................................... .........................212 19.2 Avoided emissions from occu pation of non-cu ltivated land .................................................................. ..............216 20 Life cycle impact assessment (LCIA) ............................................................................................ .......................219 21 Sensitivity analyses.................................................................................................................................................221 21.1 No. 1: LCIA-methods....................................................................................................... ....................................223 21.2 No. 2: System delimitati on, marginal su pplier of crops .................................................................... ...................225 21.3 No. 3: System delimita tion, type of land transfor med into agricultural land............................................... .........228 21.4 No. 4: Energy, margin al source of electri city.......................................................................................................229 21.5 No. 5: Energy, representativiness of data on district he ating in Denmark.................................................. ..........230 21.6 No. 6: Agricultural cultiv ation, energy for trac tion..............................................................................................231 21.7 No. 7: Rapeseed cultivation, soil type .................................................................................... ..............................232 21.8 No. 8: Rapeseed cultivativation, N-fertiliser pr oduced using best availabl e techniques (BAT) ..........................232 21.9 No. 9: Agricultural cultiva tion, N changes in soil matter................................................................. ....................233 21.10 No. 10: Agricultural cultivation, heavy metal contents in fertilisers.................................................... ..............234 21.11 No. 11: Agricultural cultivation, in itial compartment of pesticide emissions ....................................................234 21.12 No. 12: Oil palm cultivation, yields ..................................................................................... ..............................235 21.13 No. 13: Oil palm cultivation, soil type .................................................................................. .............................235 21.14 No. 14: Oil palm cultivation, uncertainties of CO 2 

## -emissions from peat soil.....................................................236 21.15 No. 15: Barley cultiva tion, omission of use and emissions of pesticides .................................................. .........236 21.16 No. 16: Rapeseed oil mill, so lvent extraction ve rsus full press.......................................................... ................237 21.17 No. 17: Oil m ills, capital goods..........................................................................................................................238 21.18 No. 18: Palm oil mill, POME treatment .............................................................................................................239 21.19 No. 19: Palm oil mill, steam requirement................................................................................. ..........................240 21.20 No. 20: Palm oil mill, altern ative management options for EFB............................................................ ............242 21.21 No. 21: Insensified cultivation, yield-response s to additiona l fertiliser ................................................ .............243 21.22 Summary of se nsitivity an alyses ........................................................................................................................245 22 Identification of improvement options.......................................................................................... .......................249 23 Sensitivity, completeness and consiten cy checks ................................................................................ .................251 23.1 Sensitiv ity ch eck ..................................................................................................................................................251 23.2 Complete ness check ........................................................................................................ .....................................252 23.3 Consis tency check ................................................................................................................................................252 24 References...............................................................................................................................................................255 Appendix 1: Data on fuels...................................................................................................... ........................................267 Appendix 2: Rapeseed produc tion in Euro pe in 2004 .............................................................................. ...................269 Appendix 3: Soil types and adju stments to di fferent sources .................................................................... .................271 Appendix 4: Characterisation factors for land use in Brazil and Canada......................................................... ........273 Abbrevia tions..................................................................................................................................................................275

## 1 Introduction 9



# 1 Introduction
This document is a complete life cycle inventory (LCI) for a comparative life cycle assessment of refined rapeseed oil from Denmark and palm oil from Malaysia and In donesia. The inventory has been carried out following the reuirements in ISO 14040 (2006) and ISO 14044 (2 006). The purpose of this report is to provide the basis for a comparative LCA of rapeseed oil and palm oil. Therefore, all four phases 1 

## of a life cycle assessment (LCA) is not fully included in this report. Figure 1.4 (page 17 ) shows how the different sections in the document relate to the four phases.



# 1.1 Purpose of the life cycle inventory
The inventory provides the basis for a comparative life cycle assessment (LCA) of refined rapeseed oil and palm oil. This includes definition of system boundaries, data collection, the modelling of the investigated system, sensitivity analyses, identification of improvement options and an evaluation of sensitivity, completeness and consistency. The comparative LCA of rapeseed oil and palm oil is presented in Schmidt (2007a) 2 

## . According to Schmidt (2007a) the main purpose of the LCA is to assess the environmental impacts related to increasing supply of vegetable oils to the EU. The two main o ils used in the EU are rapeseed oil and palm oil. The LCA analyses rapeseed oil from Denmarks as representa tive for the supply of rapeseed oil and palm oil from Malaysia and Indonesia as representative for the supply of palm oil. For futher details, see Schmidt (2007a).



# 1.2 Functional unit
The functional unit is defined as one tonne refined vege table oil suitable for the most important food purposes delivered in central Europe (Amsterdam). The most importa nt uses of vegetable oils and fats are in margarine, shortening and frying and salad oils (Bockisch, 1998). But si nce different oils and fats have different properties in terms of crystallization, stability to heat, health et c. care should be taken when determining the reference flow. In order to compare one tonne rapeseed oil with one tonne palm oil the oils have to be substitutable.



# 1.3 Method for system delimitation

Consequential system delimitation The consequential approach to system delimitation has b een applied, i.e. the system inventoried reflects the actually affected processes (Weidema, 2003). The core differences between consequential LCAs and so called attributional or traditional LCAs are summarised in Table 1.1

.

1 

## Phase 1: Goal and scope defi nition, Phase 2: Life cycle inventory, Phase 3: Life cycle impact assessment and Phase 4: Life cycle interpretation (ISO 14040 2006)

2 

## Schmidt (2007a) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

## 10 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Feature Consequential modelling Attributional modelling Nature Attempts to predict to responses to a change in demand Describes how existing production is taking place

Included processes/suppliers Marginal Average Co-product allocation Co-product allocation is avoided by system expansion Co-product allocation is treated by using allocation factors 

## Table 1.1: Main characteristics of and differences between conseq uential and attributional modelling in life cycle inventory (based on Weidema 2003; Schmidt and Weidema 2007 3 

## ).



## A marginal technology or supplier is defined as the one actually affected by a change in demand, and it is identified as the one most sensitive to changes in demand. A supplier or technology must be within the relevant market segment and among those which are flexible, i.e. not constrained by legal, physical or market conditions. When the relevant suppliers or technologies are identified, the marginal one can be identified as the most competitive in situations with an increasing or consta nt market trend and reversely the least competitive insituations with a decreasing market trend. The most or least competitive supplier or technology can be determined on the basis of the price relations between the technologies. Alternatively it can be assumed that the most competitive suppliers are those which are increasing with the highest rate.

The main argument for applying the consequential approach is that only the actual affected processes are included (Weidema, 2003). Technologies that are not likely to respond to a change in demand should not be included in an LCA since this will not reflect the actual change in environmental impact.

In order to keep the opportunity to compare results with other LCAs on vegetbale oils, results achived using attributional modelling are presented paralle l to the other scenarios investigated.


# System delimitation in the agricultural stage According to Schmidt (2007b) 4 

## no agricultural LCAs which include the following three important aspects have been identified (i) the identification of the actually aff ected crops and regions, (ii) the identification of how increased demand for an agricultural product is met and (iii) when land under natural vegetation is transformed into agricultural land there are obvious some induced em issions from fertiliser inputs etc. But the emissions from the land under natural vegetation are also avoided. Re lating to (i), increased demand for e.g. rapeseed in Denmark may lead to either increased import or incr eased cultivation or a combination. If cultivation is increased, it is important to clarify if this affects the ar ea cultivated with other crops in the region. E.g. in Denmark where the total agricultural area has been declining the last decades it is likely that increased cultivationof rapeseed will cause less area available for other crops. Thus, the marginal crop will be displaced as a consequence of increased rapeseed cultivation. If it is assumed that increased production of rapeseed does not affect the overall food security in the world, the displaced cr op will be compensated for in the region representing the marginal supplier of that crop. Relating to (ii) it is re levant to clarify if increased agricultural production is met by increased yield or by in creased area, i.e. transformation of non-p roductive land into agricultural land. According to Schmidt (2007b) the differences between these two strategies is significant; increased cultivated area is associated with land use e ffects while increased yields may lead to significant effects relating to global warming and eutrophication. Relating to (iii), it is well known that even pristine nature causes undesirable emissions, though the level is commonly significant lower than the emission level from agricultural land. Thus,

3 

## Schmidt and Weidema (2007) is part of the Ph.D. thesis: ‘L ife assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil an d palm oil. Ph.D. thesis, Part 2: Article collection’.

4 

## Schmidt (2007b) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

## 1 Introduction 11



## the interventions from cultivation of crops should be represented by the difference between the actual interventions from the agricultural land and th e interventions from the form the a lternative land use, i.e. commonly land under natural vegetation.

Changes in agricultural production of a certain crop can pr incipally be achieved by affecting one of or a combination of three different systems; 1) changes in agri cultural productivity per unit of area (e.g. fertiliser, pesticides, organic/conventional), 2) transformation of land between agricultural land and non-cultivated land or 3) the change in cultivated area of the desired cr op affect the area cultivated with other crops.

System 1) Changes by yield: When agricultural production is increased by a change in yield, there are no effects on land-use. Only the interventions per unit of ar ea are affected. The interventions are calculated as the intervations from 1 hectare of intensified cultivation mi nus the interventions from 1 hectare of ‘business as usual’ cultivation. The principle is illustrated in Figure 1.1

.

1 ha y: Intesified cultivation Change by yield

1 ha y: 'business as usual' cultivation minus



## Figure 1.1: Principle in modelling of changes in agricultural production by a change in yield. The crop output from this system is equal to the difference in yields of the two processes. The abbreviation ha y refers to 1 hectare in one year.



## System 2) Changes by area: When agricultural production is increased by a change in the cultivated area there are two different sources of interventions: Firstly, there are interventions rela ted to cultivation of the transformed land. This includes occupation processes of the land and and emissions corresponding to ‘business as usual’ cultivation. Secondly, ther e are interventions related to the tr ansformation process itself, i.e. transformation processes of land and the associated changes in the standing stocks of carbon and nitrogen. The principle is illustrated in Figure 1.2

.

1 ha y: 'business as usual' cultivation Change by area

1 ha y: avoided emissions from noncultivated land minus

1 ha: interventions from transformation of

non-cultivated land to agricultural land plus



## Figure 1.2: Principle in modelling of changes in agricultural production by a change in area. The crop output from this system is equal to the annual yield of the relevant crop . The abbreviation ha y refers to 1 hectare in one year.



## In Figure 1.2 it is easy to relate the interventions from cultivation and the avoided interventions from noncultivated land to the functional unit, because the inte rventions are proportional with the annual yield. However, the interventions from transformation of 1 ha non-cu ltivated land into 1 ha agricultural land is difficult to relate to the functional unit, because it is difficult to predict how many functional units the land-transformation will support. Therefore, the interventions related to th e transformation process itself are treated separately in the LCI and LCIA.

## 12 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## System 3) Displacement of other crops: When agricultural production of a crop x is changed at the expense of cultivation of another crop y, the displaced crop y must be produced somewhere else, i.e. in the region that represents the marginal supplier of crop y. The amount ofdisplaced crop y per ha cultivation of crop x is determined as: (yield crop y 

## / yield crop x 

## ). The changes in production of crop y then have to be distributed on changes by area and changes by yield. The principle is illustrated in Figure 1.3

.

1 ha y: crop x Change displacement of other crops

1 ha y: crop y minus

Cultivation of 1 t * (yield crop y, region a / yield crop x, region a )region aregion b plus

Change by area Change by yield 

## Figure 1.3: Principle in modelling of changes in agricultural production of crop a by displacement of crop b.


# System delimitation in the oil mill stage and the refinery stage There are two main product outputs from the oil mill stage; vegetable oil and the meal which is used for animal fodder. The meal has two functions as animal fodder; pr otein source and energy source. Thus, when inventorying the interventions relating to the oil, it must be ta ken into account that the meal substitutes the marginal sources of fodder protein and fodder energy. A method for avoiding co-product allocation by system expansion take these factors into account is pr esented in Schmidt and Weidema (2007).

In addition to the co-products from the oil mill there are also co-products from the refinery, i.e. vegetable oil and free fatty acids which are used as fodder energy. The sy stem expansion related to that is also dealt with using the method presented in Schmidt and Weidema (2007).



# 1.4 Method applied for Life Cycl e Impact Assessment (LCIA)
Though, this report is only a life cycle inventory, LCIA methods are used for some purposes. Firstly, when comparying different database LCI data for energy, tran sport, material or process inputs to the system, an LCIA method is used for selection of relevant compar able indicators (e.g. global warming), and for comparison. In the selection of relevant comparable indicators the weighted results using a LCIA method are used. In most cases global warming, eutrophication and toxicity tu rn out to be the three most relevant indicators to compare, i.e. these are the most significant potential e nvironmental impacts related to the products/services compared. When the indicators have been selected the characterised results are compared. Hereby the most significant differences between the considered database LCI data are monitored. Secondly, LCIA methods are used when performing sensititity anal yses of the significance of different assumptions and data uncertainties on the overall result. Hereby, the assumption’s or selected data’s contribution to the total potential environmental impact within some selected impact categories is analysed.

It is chosen to use the Danish EDIP97-method (Wen zel et al. 1997 and Hauschild and Wenzel 1998) as the default LCIA method in this study. The LCIA-methods Impact 2002+ and EcoIndicator are used as a sensitivity analysis on the level of LCIA methods (see section 21.1 ).

## 1 Introduction 13



## The EDIP97-method as available in SimaPro 7.0 is not a pplied directly in this study. The following modifications have been implemented in the EDIP97-method in SimaPro:

Modification 1) Updates: The original EDIP97-method has been upda ted continouosly since the release in 1996. The newest version of the updated EDIP97-met hod which includes addition of new substances and updates of characterisation factors as well as normalisati on and weighting factors is available in (LCA-center 2007). The updates are implemented in the EDIP97-method in SimaPro. However, the updated version is not available in a format compatible with SimaPro and the work load for entering all updated factors is too big. Therefore, the EDIP97-method in SimaPro has only been partly updated focussing only on the most significant interventions. The partly update has been conducted in the following way: 1. Substances of pesticides and their character isation factors have been included, see Table 1.3 2. The final LCI entered in SimaPro is analysed usi ng the original version of EDIP97. The characterisation factors of the top-10 most significant substan ces are checked for updates (analysing rapeseed oil and palm oil in scenario 1). At the same time, in or der to check for addition of new substances in the updated version of EDIP97, the top- 10 list of substances obtained when using EcoIndicator and Impact 2002+ are also chcked for updates. See Table 1.2 3. The normalisation and weighting factors are updated

The characterisation factors for the added ac tive ingredients of pesticides are given in Table 1.3 . Besides, the pesticides given in Table 1.3 , characterisation factors for the following substances have also been updated in accordance to LCA-center (2007):

## 14 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

EDIP97 (version in SimaPro) (ecotoxicity, water chronical/

ecotoxicity, water acute/ ecotoxicity, soil chronical) Impact2002+ (aquatic ecotoxicity/ terrestrial ecotoxicity) EcoIndicator 99 (H) (ecotoxicity) Total list of emissions checked for updates

Acetone (air) Aluminium (air) Cadmium (soil) Acetone (air) Arsenic (water) Aluminium (soil) Chromium (air) Aluminium (air) Benzene (air) Aluminium (water) Chromium (soil) Aluminium (soil) Cadmium (air) Arsenic (soil) Copper (air) Aluminium (water) Cadmium, ion (water) Cadmium (soil) Copper (soil) Arsenic (soil) Chromium VI (water) Chromium (soil) Lead (air) Arsenic (water) Cobalt (water) Copper (air) Nickel (air) Benzene (air) Copper (air) Copper (soil) Nickel (soil) Cadmium (air) Copper, ion (water) Copper, ion (water) Zinc (air) Cadmium (soil) Cyanid (air) Cypermethrin (air) Zinc (soil) Cadmium, ion (water) Cyanid (water) Cypermethrin (water) - Chromium (air) Formaldehyd (air) Glyphosate (soil) - Chromium (soil) Hexane (air) Mercury (water) - Chromium VI (water) Hydrogen sulphide (water) Nickel (air) - Cobalt (water) Iron (soil) Nickel (soil) - Copper (air) Iron (water) Selenium (soil) - Copper (soil) Lead (water) Zinc (air) - Copper, ion (water) Magenese (soil) Zinc (soil) - Cyanid (air) Manganese (water) Zinc, ion (water) - Cyanid (water) Molybdenum (soil) - - Cypermethrin (air) Nickel (water) - - Cypermethrin (water) Selenium (soil) - - Formaldehyd (air) Strontium (water) - - Glyphosate (soil) Titanium, ion (water - - Hexane (air) Zinc, ion (water) - - Hydrogen sulphide (water)

- - - Iron (soil)

- - - Iron (water)

- - - Lead (air)

- - - Lead (water)

- - - Magenese (soil)

- - - Manganese (water)

- - - Mercury (water)

- - - Molybdenum (soil)

- - - Nickel (air)

- - - Nickel (soil)

- - - Nickel (water)

- - - Selenium (soil)

- - - Strontium (water)

- - - Titanium, ion (water

- - - Zinc (air)

- - - Zinc (soil)

- - - Zinc, ion (water) 

## Table 1.2: Lists of the 10 most significant emissions contributing to (eco)toxicity using different LCIA-methods when analysing the inventories of scenario 1 described in section 2.5 . The right column shows the total list of emissions that are checked for updates in the EDIP-method.

## 1 Introduction 15

Active ingredient (a.i.) Compartment ETWC (m 3 /g) ETWA (m 3 /g) ETSC (m 3 /g) air 0 0 0 water 14.5 17.1 0 Herbicide, Clomazone

soil 0 0 1.51 air 0 0 0 water 255 13.2 0 Herbicide, Propyzamid

soil 0 0 40 air 7.25 0 18.1 water 14.5 1.45 0 Herbicide, Clopyralid

soil 0 0 36.2 air 0 0 0 water 62.6 7.69 0 Herbicide, Glyphosate

soil 0 0 155 air 625 0 3.00E3 water 1.25E3 125 3.00E3 Herbicide, Tribenuron-methyl

soil 0 0 0 air 154 0 188 water 769 76.9 0 Herbicide, 2,4-D

soil 0 0 235 air water Herbicide, imazethapyr

soil No data, omitted from LCIA

air 0 0 0 water 5.81E7 2.13E6 0 Insecticide, Cypermethrin

soil 0 0 2.63E4 air 0 0 0 water 3.33E5 1.00E5 0 Insecticide, Alpha-cypermethrin

soil 0 0 244 air 0 0 0 water 1.00E5 1.00E4 0 Insecticide, Tau-fluvalinat

soil 0 0 4.94 air 0 0 0 water 1.79E+08 5.00E+06 0 Insecticide, Chlorpyrifos

soil 0 0 8.75E+05 air water Insecticide, Deltamethrin

soil No data, omitted from LCIA

air water Rodenticide, Warfarin

soil No data, omitted from LCIA



## Table 1.3: Characterisation factors for pesticides applied to the EDIP97-method in SimaPro. The characterisation factors are obtained from LCA-center (2007).



## Modification 2) Inclusion of land-use (biodiversity) as an impact category: The EDIP97-method does not include land-use. Therefore, this has been included. Si nce no sufficient methods for land-use impacts exist, a method has been developed for this reason: Schmidt (2007c) 5 

## . The existing methods are either too coarse (i.e. no distiguishing between crops, extensive/intensive etc.) or they are only covering a smaller part of the world, e.g. Europe.

Modification 3) Exclusion of char acterisation factors for biogenic CO 2 

## emission and CO 2 

## -uptake in biomass: In the EDIP97-method in SimaPro 7.0 the following two emissions are included as contributers to global warming: ‘ Carbon dioxide, biogenic ’ (emission to air, characterisation factor 1 g CO 2 

## -eq./g CO 2 

## ) and

‘ Carbon dioxide, in air ’ (raw material, characterisation factor 1 g CO 2 

## -eq./g CO 2 

## ). These emissions correspond to the emissions included in ecoinve nt-processes in the case of oxydation (burning) of biomass and in the case of agricultural cultivation and forestry. However, since it is almost impossibl e to ensure that the uptake of car-

5 

## Schmidt (2007c) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

## 16 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## bon in agriculture and forestry is ba lanced with carbon emitted as biogenic CO 2 

## , the characterisation factors
are set to zero.



# 1.5 Data collection
The data collection for rapeseed cultivation in Denmar k is among others based on cultivation guidelines for rapeseed oil (Dansk Landbrugsrådgivning 2005), the LC Afood database and background material (Nielsen et al. 2005; Dalgaard 2007). The life cycle inventory for the oil mill and refinery stages for rapeseed oil takes its point of departure in a detailed data collection at Aa rhusKarlshamns in Aarhus (Korning 2006; Kronborg L; Hansen 2006; Aarhus United 2005a; Aarhus United 2005b).

The data collection for oil palm cultivation as well as th e oil mill and refinery stages takes its point of departure in a detailed data collection for palm oil at Unite d Plantations in Malaysia (Bek-Nielsen 2006; Singh 2006; UPRD 2004). Other important data sources regarding pa lm oil are the Malaysian Palm Oil Board, MPOB (Subramaniam 2006a; Subramaniam 2006b; Subramaniam et al. 2005; Subramaniam et al. 2004) and various oil palm research (Corley and Tinker 2003).

The co-product from the oil mill pr ocess, the oil meal affects soyb ean meal and barley (see section 2 ). The data collection related to these commodities is based on Dalg aard et al. (2007) for soybean meal and Dansk Landbrugsrådgivning (2005), Nielsen et al. (2005) and Dalgaard (2007) for barley.

Inventory data for background processes, i.e. for energy production, fertiliser production, transportation, construction, maintenance and disposal of buildings etc. are largely based on the ecoinvent database (ecoinvent 2004). The latest available version of ecoinvent, available in the LCA software SimaPro, is used, i.e. ecoinvent v1.3 (Frischknecht et al. 2006). When data from databases ar e used, they modified in order to comply with the consequential approach to system delimitation in LCA.



# 1.6 Structure of the report
The structure of the report is summarised in Figure 1.4

.
It appears from section 1.3 that changes in agricultural production can be achieved either by increasing the cultivated area or by increasing the yield. Changes in the production from oil milling and oil refineries are more simple, since changes in production here is achieve d simply be scaling the whole product system of these life cycle stages. Figure 1.4 shows how agricultural production, oil m illing and refining can be changed and in which sections in the report the single affected elements are described.

## 1 Introduction 17

Definition of inventoried systems

Change by area

Change by yield Cultivation of crop

Transformation of land

Emissions from transformed land

Intensified cultivation of crop

Cultivation of crop Cultivation of rapeseed Cultivation of oil palm Cultivation of soybean Cultivation of barley

Intensified cultivation of rapeseed Intensified cultivation of oil palm Intensified cultivation of soybean Intensified cultivation of barley Section 5 Section 6 Section 7 Section 8 Emissions from transformation and from transformed land Section 19

Section 18

Cultivation of rapeseed Cultivation of oil palm Cultivation of soybean Cultivation of barley Section 5 Section 6 Section 7 Section 8 plus

minus

minus

Oil mill stage
Change in prod. Oil milling Rapeseed oil mill Palm oil mill Palm kernel oil mill Soybean oil mill Section 9 Section 10 Section 11 Section 12
Refinery stage
Change in prod. Refining Rapeseed oil refinery Palm oil refinery Palm kernel oil refinery Soybean oil refinery Section 13 Section 14 Section 15 Section 16
Transport stage
Transport Transport of oil to Central Europe Transport of rapeseed oil and palm oil Section 17 General inventory data
Energy Different sources of energy Electricity and district heat Section 3 Transport Different means of transportation Lorry, ocean tanker and traction Section 4 System delimitation
Scoping System delimitation Section 2

Agricultural stage

Life cycle impact assessment (LCIA)

Results

Assessment of significanse of various assumptions, methods and data Section 20 Structure of the report

Sensitivity analyses and improvement options
Sensitivity Section 21

Sensitivity, completeness and consitency checks

Confidence Section 23 Evaluation of the confidence in LCA results Very brief presentation of characterised results of scenarios Definition of goal and scope

Phase 1

Phase 2

Life Cycle Inventory analysis (LCI)

Phase 3

Phase 4 Life Cycle Impact

Assessment (LCIA)

Interpretation of results ISO 14040/44

Identification of improvement options in the product chains Improvements Section 22



## Figure 1.4: Structure of the report. The right colu mn shows how the different sections relate to the four phases of an LCA in accordance to the ISO 140 40 and 14044 standards.

## 18 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 2 System boundaries of inventoried systems 19



# 2 System boundaries of inventoried systems
The production of rapeseed oil and palm oil are divided into four stages; ag ricultural stage, oil mill stage, refinery stage and transport stage. The transport stage only includes transport of oil from the refinery to final use which is assumed to be in Mid-europe represented by Amsterdam. Other transport processes are included in the other life cycle stages. The LCA includes direct a ffected processes, overhead (operation of buildings, administration, marketing etc.) and capital goods (building, machinery and means of transportation). The determination of the system boundaries relating the oil mill st age and refinery stage is based on the methodology presented in Schmidt and Weidema ( 2007) and the determination of the system boundaries relating the agricultural stage is based on the methodology presented in Schmidt (2007b)



# 2.1 Marginal suppliers of affected crops
The system expansions requi red as described in section 2.2 implies that more crops than rapeseed and fresh fruit bunches (FFB) from oil palm s are affected. In section 2.2 these secondary affected crops are identified tobe barley and soybean. When the demand for any crop changes, the consequential approach to system delimitation prescribes that the included supplier should be the ma rginal one. Therefore, the marginal suppliers of the affected crops are identified. According to FAOSTAT (2006) the market trend for all these crops has been increasing (rapeseed, FFB and soybean) or relatively consta nt (barley) from 1995 to 2005 and FAPRI (2006) predict that the market trend will increase for all crops from 2005 to 2015. Thus, according to Weidema (2003) the marginal suppliers should be identified as the most competitive among those who are flexible. It is difficult to identify the most competitive supplier using price rela tions since the market prices for the crops mainly are determined by the world supply and demand and therefore are equal for all suppliers. For this reason, the marginal suppliers are identified as the ones who are predic ted to face the largest annual increase in production in the near future (2005 to 2015) (see Table 2.1 ). For comparison, the average an nual increase the last ten years (1995-2000 and 2000-2005) are also shown in Table 2.1

.

Annual change

1995-2000 (FAOSTAT 2006) Annual change 2000-2005 (FAOSTAT 2006) Annual change 2005/06-2015/16

(FAPRI 2006) Crop Supplier

1000 t 1000 t 1000 t EU25* 636 916 417 China 273 422 151 Rapeseed Canada 466 477 145 Indonesia* 449 1,251 965 Malaysia* 598 840 552 Palm oil Nigeria 17 57 No data Brazil* 1,819 4,037 3,535 Argentina 2,021 3,115 1,514 Soybean US 2,947 1,586 149 Canada* -198 182 229

EU25 430 -443 181 Barley Russia -1,015 19 162 

## Table 2.1: Identification of marginal suppliers of crops: Histor ical and predicted annual average increases among the leading suppliers, i.e. those who are predicted to face the larg est annual increase in the future in FA PRI (2006). The suppliers that are assumed to repr esent the marginal suppliers are marked with *. No outlook data have been available for oil palm fruit (FFB). For this reason palm oil production is show n instead. However, palm oil production is to a large extent proportional to FFB and there is no intern ational trade with FFB, thus production of palm oil always takes place in the same country as production of FFB.



## The reason for choosing the marked (*) marginal supplie rs of rapeseed, FFB and soybean appear to be quite obvious because these regions/countries are predicted to in crease their production significantly more than the

## 20 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## other suppliers. For barley the picture is not so clear . Nevertheless, looking at the development from 1995 to2005 Canada seems to increase its production while the EU25 decreases its production. Therefore, it is regarded as more likely that Canada will represent the marg inal supplier than the EU25. The uncertainties related to the identification of marginal suppliers of barley a nd palm oil are assessed in a sensitivity analysis in section 21.2

.


# 2.2 System expansion – oil mill stage

As described in section 1.3 the consequential approach to LCA implie s that system expansion has to be considered in the agricultural as well as the oil milling stage. Firstly, here in section 2.2 , system expansion in the oil mill stage is considered and secondly, in section 2.3 , system expansion in the agricultural stage is described.

From the processing of rapeseed as well as palm kernels 6 

## in the oil mills there are outputs of rapeseed oil meal and palm kernel cake (PKC) respectively which are used for fodder purposes. Thus, a change in demand for rapeseed oil or palm oil will affect the pr oduction of oil meals/cakes. According to section 1.1 the purpose of the inventory is to model the interventions related to increased supply of vegetable oils to the EU, i.e. to model

a change in demand. It is assumed that changed production ofoil meals, as a consequence of a change in demand for vegetable oils, does not affect the production of meat. Hence, the demand for animal fodder is not assumed to be affected as a consequence of a change in demand for vegetable oils. Therefore, increased production of oil meals will substitute the marginal animal fodder in the market. According to Schmidt and Weidema (2007) fodder mainly consists of two components, i.e. fodder energy and fodder protein. Dalgaard et al. (2007) and Nielsen et al. (2005) identify the marginal sources of protein and fodder energy as soybean meal and barley respectively. The marginal suppliers of the affected crops are identified in section 2.1

.
Since soybean meal is co-produced wi th soybean oil and since soybean meal as well as barley contain proteins as well as fodder energy, the determination of the amounts of affected crops related to 1 tonne rapeseed oil or palm oil is more complex than just simple product s ubstitutions. In addition, displacement of soybean meal causes a change in the supply of vegetable oil to the ma rket. This change will then affect the marginal supplier of vegetable oil. According to Schmidt and Weidema (2007) the marginal vegetable oil is palm oil. Hence, three parameters must be balanced, i.e. oil, fodder protein and fodder energy. Balancing the affected commodities relating to a change in demand for 1 tonne vegetable oil the corresponding change in supply of fodder protein and fodder energy should be zero. In this respect , the relevant properties for of vegetable oils, oil meals and barley are given in Table 2.2

.
In addition to the co-products from the oil mill stage (v egetable oil, fodder protein and fodder energy), there are also co-products from the refinery stage that affect the same systems. From the neutralisation process in the refining, there is an output of free fatty acids (FFA). According to Weidema and Wesnæs (2006) the FFA is e.g. used for animal feed, in rubber processing, in th e flavour industry, for production of candles and cosmetics. Further, Weidema and Wesnæs (2006) regard it as lik ely that the use for animal feed is the marginal use where the FFA acts as energy source. Therefore, it is assumed that the FFA is used as fodder fat and that it displaces the marginal source of fodder energy, i.e. barl ey. Corresponding to the vegetable oils, oil meals and barley, the relevant properties of fodder fat are also given in Table 2.2

.

6 

## Palm kernels is a co-product from the processing of oil palm fruit (fresh fruit bunches, FFB) in the palm oil mill, see section 10.

## 2 System boundaries of inventoried systems 21

Paramter Vegetable oil, per kg oil Rapeseed meal, per kg meal Soybean meal, per kg meal Palm kernel cake, per kg meal Barley, per kg barley Fodder fat, per kg fodder fat Oil 1 kg oil 0 kg oil 0 kg oil 0 kg oil 0 kg oil 0 kg oil Protein 0 g protein 340 g protein 436 g protein 149 g protein 91.8 g protein 0 g protein Fodder energy* 0 SFU** 0.954 SFU 1.20 SFU 0.791 SFU 0.952 SFU 2.31 SFU 

## Table 2.2: Relevant properties of affected commodities related to the product system of rapeseed oil and palm oil. *Fodder energy is measured in SFU (Scandinavian Fodder Units ). **The reason why there is no fodder energy in vegetable oil is that the aim of this LCA is to assess the environmen tal impacts from vegetable oils used for food purposes (some times vegetable oils are used as animal fodder).



## In order to balance the product system it is necessary to know the ratio between the co-products from the involved processed. The product outputs from the rapeseed, palm oil, palm kernel oil and soybean oil mills are given in Figure 9.2
, Figure 10.1
, Figure 11.1 and Figure 12.1 respectively. In addition, the losses in refining of vegetable oils and the output of the co-product, fo dder fat, must be taken into account. 1 tonne crude palm oil is not directly substitutable with 1 tonne crude rapeseed or soybean oil because of different losses in the refining process. The losses and outputs of fodder fat from re fining of rapeseed oil, palm oil, palm kernel oil and soybean oil are given in Figure 13.1
, Figure 14.1
, Figure 15.1 and section 16 respectively. The land-use requrements are calculated from the yields given in Figure 5.4
, Figur 6.13
, Figure 7.1 and Figure 8.2 . Based on these informations, it is possible to draw the overa ll product systems for the affected commodities, see Figure 2.1 and Figure 2.2 . In the bottom of the figures the produc t outputs from the systems are transformed into contents of oil, fodder protein and fodder ener gy. This is done by applying the figures provided in Table 2.2 to the vegetable oil, meals/cakes, barley and fodder fat. The bottom line of the figures corresponds to the relevant outputs of co-products when considering syst em expansion and product displacement, see next section.

Rapeseed oil (RSO)

Palm kernel oil mill Palm oil mill

0.111 t crude PKO

0.129 t PKC 1.000 t NBD PO+PKO 4.656 t FFB Oil palm plantation 0.247 ha y Malaysia and Indonesia

Rapeseed oil mill

1.000 t NBD RSO 1.369 t meal 2.428 t rapeseed

Rapeseed field

0.751 ha y Denmark

465 kg protein 1,334 SFU energy 19.2 kg protein 102 SFU energy Refinery

Refinery Refinery 1.017 t crude RSO 0.930 t CPO 0.248 t kernels

0.890 t NBD PO

0.110 t NBD PKO 12 kg fodder fat

0 kg protein 28 SFU energy 38 kg fodder fat 0 kg protein 89 SFU energy 37 kg ffa

1 kg ffa Palm oil (PO)



## Figure 2.1: Product systems and their co-produ cts for the directly affected syst ems (rapeseed oil and palm oil).

## 22 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Soybean mill

1.000 t SM 0.244 t NBD SO 1.294 t soybean Soybean field 0.403 ha y

1.000 t BL Barley field 0.357 ha y Brazil Canada

436 kg protein 1,200 SFU energy 91.8 kg protein 952 SFU energy Refinery 0.249 t crude SO

3 kg fodder fat 0 kg protein 7 SFU energy Soybean meal (SM) Barley (BL)



## Figure 2.2: Product systems and their co-products for the indirectly affected systems in system expansion (soybean meal and barley).


# Rapeseed oil When performing system expansion in the product system of rapeseed oil and other related (affected) systems, the equation system described in Schmidt a nd Weidema (2007) is applied. By solving Equation (1) the amounts of rapeseed oil, palm oil, soybean meal and barl ey are balanced in order to provide a net output of 1 tonne oil, 0 kg protein and 0 SFU (right side of the e quation). The left side of the equation represents the amount of the affected crops multiplied by vectors of co -products. Here the amount of rapeseed oil is 1 tonne and the other three commodities (palm oil, soybean meal and barley) are independent variables, each representing the marginal sources of vegetable oil, fodder potein and fodder energy respectively.

0.157 BLt 1.045 SMt 0.255POt 1.000 RSOt SFU0 prot.kg0

oilt1

BLSFU/t952 BLprot./tkg91.8 BLoil/tt0
BLt SMSFU/t1,207 SMprot./tkg436 SMoil/tt0.244
SMt
POSFU/t191 POprot./tkg19.2 POoil/tt1
POt
RSOSFU/t 1,362 RSOprot./t kg465 RSOoil/tt 1
RSO t 1

−= −= = = ⇓ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

= ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅



## (1)

It appears that a change in demand for rapeseed from Denmark at 1 tonne causes increased production of rapeseed oil in Denmark at 1 t, increased production of palm oil (PO+PKO) in Malaysia and Indonesia at 0.255 t, displacement of 1.045 tonne soybean meal in Brazil a nd displacement of 0.157 tonne barley in Canada. The reason, that a change in demand for rapeseed oil causes changed production of palm oil is that the displacement of soybean meal also causes displacement of soybean oil. This ‘missing’ vegetable oil then has to be compensated for by a change in production of the marginal oil which is palm oil.



# Palm oil Corresponding to the calculation of affected commodities related to a change in demand for rapeseed oil from Denmark, the calculation is done for a change in demand for palm oil, see Equation (2)

.

0.198 BLt 0.00245 SMt 1.001POt SFU0 prot.kg0

oilt1

BLSFU/t952 BLprot./tkg91.8 BLoil/tt0
BLt SMSFU/t1,207 SMprot./tkg436 SMoil/tt0.244
SMt
POSFU/t191 POprot./tkg19.2 POoil/tt1
POt

−= −= = ⇓ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

= ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅


## (2)

## 2 System boundaries of inventoried systems 23



## It appears from Equation (2) that a change in demand for palm oil at 1 tonne causes increased production of palm oil in Malaysia and Indonesia at 1.001 t, displ acement of 0.00245 tonne soybean meal in Brazil and displacement of 0.198 tonne barley in Canada. The reason that production of more pa lm oil than demanded is required is that the displacement of soybean meal also causes displacement of soybean oil. Then, as explained in the case of rapeseed oil above, this missing oil has to be compensated for by a change in production of the marginal vegetable oil, i.e. palm oil.


# System expansion in the oil mill stage - summary

Table 2.3 summarises the amount of agricultural crop inputs to the system and the corresponding land occupations. These inputs are determined from the product flows given in Figure 2.1 and the results of Equation (1) and (2) . The data in Table 2.3 only represent the immediate affected amounts of crops and land occupations. First when system expansion in the agricultural stag e has been carried out, the actual amounts of crops and land occupations can be identified. The land occupations given in Table 2.3 correspond to the traditional way of determining land use in the life cy cle inventory phase, i.e. the land unde r the crop of interest determined from present crop yields (Schmidt 2007b).

1 t rapeseed oil in Denmark 1 t palm oil in Malaysia and Indonesia Affected crops Amount Land occupation Amount Land occupation Rapeseed in Denmark 2,428 t 0,751 ha y 0 t 0 ha y Oil palm fruit (FFB) in Malaysia and Indonesia 1,187 t 0,063 ha y 4,661 t 0,247 ha y Soybean in Brazil -1,352 t -0,421 ha y -0,0032 t -0,0010 ha y Barley in Canada -0,157 t -0,056 ha y -0,198 t -0,071 ha y Total immediate land occupation - 0.337 ha y - 0.175 ha y 

## Table 2.3: Affected commodities and suppliers related to a change in demand of 1 t rapeseed oil and 1 t palm oil.



# 2.3 System expansion – agricultural stage
The amounts of crops given in Table 2.3 represent the immediate change in demand for crops related to demand for 1 tonne rapeseed oil from Denmark and 1 tonne palm oil from Malaysia/Indonesia. In the following the changes in demand for each of the crops in Table 2.3 will be described regarding system expansion in the agricultural stage, i.e. the identifi cation of how increased production is ach ieved (yield or area) and the identification of land constraints and its c onsequences for the actual affected cr ops. The description is based on the methodology presented in Schmidt (2007b).


# Rapeseed in Denmark The change in demand for rapeseed from Denmark can be met either by increasing the yield or by increasing the area cultivated or a combination. If the area cultiva ted is increased, there are arguments against as well as for that this will be likely to affect to total cultivated area in Denmark. An argument that supports that the total cultivated area will increase is related to the EU st rategy on biofuel (The European Commission 2006a). The European Commission (2006a) specifies that set-aside areas can be grown with energy crops in order to help further facilitating energy crops. According to the European Commission (2006b) 8,500 km 2 

## set-aside area in the European Union has already been used for growing oilseeds for ener gy purposes. For comparison the total cultivated area with rapeseed in 2005 in EU25 is 47,600 km 2 

## (FAOSTAT 2006). On the other hand, an argument against that increased demand for rapeseed will affect the total cultivat ed area is, that the total agricultural area in Denmark has been slightly decreasing the l ast decades (Danmarks Statistik 2006) and in addition, a main goal in the Danish forestry action plan, is to d ouble the forested area within the next 80 year (The Danish Government 2002). This will increase the total fo rested area in Denmark from 12% (FAO 2006) to 20-25% (The Danish Government 2002). For comparison the set-aside area in 2005 covered only 4% of the total area in Denmark (Danmarks Statistik 2006). Thus , even when including set-aside areas in agricultural production, the

## 24 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## agricultural area in Denmark is likely to continue its decrease. Increased area cultivated with rapeseed in Denmark will therefore be likely to displace the marginal crop in Denmark. Schmidt (2007b) has identified the marginal crop in Denmark to be spring barley. The marg inal supplier of barley is previously identified as Canada. Thus, the displaced barley in Denmark will most likely be compensated for by increased production in Canada. From 2005/06 to 2015/16 the average annual increase in rapeseed yield in Europe is predicted to be 1.01% (FAPRI 2006). The average annual increase in the ra peseed area in Europe is predicted to be 1.52% (FAPRI 2006). Hence, 40% of the future increase in produc tion is predicted to be met by yield. It is assumedthat the data for Europe in FAPRI are representative for Denmark. Thus, 40% of the increase in Danish rapeseed production is met by locally increasing the yields while the remaining 60% is met by increasing the area which displaces spring barley. The yield of Danish sp ring barley in 2005 (calculated by regression from 1990 to 2005 and substracted seed) is 5.120 t/ha (see Figure 8.3 ). Since the yield of rapeseed in Denmark is 3.231 t/ha and since 1.457 tonne rapeseed (60% of the desired 2.428 tonne in Table 2.3 ) is met by increased area, it can be calculated that 2.308 tonne spring barley is disp laced in Denmark. Since Cana da is regarded as the marginal supplier of barley, it is assumed that Canada will increase its barley production with 2.308 tonne.

If the purpose of the LCA was to compare rapeseed oil fro m the EU instead of Danish rapeseed oil with palm oil, it could be argued that the increased demand for ra peseed oil produced in the EU would be met by yield and area increases within the EU, i.e. no displacement of other crops in the EU. The reason for this is, that the total cultivated area in the EU is predicted to increase slightly (based on FAPRI 2006) and that set-aside areas may be included in cultivation of ra peseed. In addition EU is the region in the world that is predicted to facethe largest annual increase in rapeseed production from 2005/06 to 2015/16 (FAPRI 2006). Therefore, a scenario where increased demand for rapeseed is met by 40% increased yield and 60% increased area in the EU is included. Since the aim of this scenario is to assess the differences between local expansion and abroad expansion of the agricultural area, it is in practise defined as the increases in yield and area took place in Denmark, i.e. data for Danish yield and Danish technology is applied.

It appears from the previous that wh en rapeseed cultivation is increased by area in Denmark, the corresponding changes in land use will not take place in Denmark si nce increased cultivation tak es place at the expense of spring barley. Therefore, the changes in land use will mo st likely take place in Canada. In the case of the scenario describing increased rapeseed cu ltivation by area in the EU, the corresponding land use changes will be from set-aside area to rapeseed fields.


# FFB in Malaysia and Indonesia The change in demand for FFB in Malaysia and Indonesi a is assumed to take place by 4% increased yield and 96% increased area. It has not been possible to identify any data on predicted yields and cultivated area for the future. Therefore, the distribution between yield and area is based on historical data from FAOSTAT (2006) from 1995 to 2005.

When oil palm cultivation is increased by area it is assume d that no displacements of other crops take place. It is often stated that a considerable share of the oil pa lm expansion has and is taking place on land released from other crops (Henson 2004, Corley and Tinker 2003 p 480 and Teoh 2000). According to Henson (2004) oil palm in Malaysia has largely been planted on land rel eased from rubber, coconut a nd cocoa. This is in good accordince with data obtained from FAOSTAT (2006) fo r Malaysia where the planted area of rubber, cocoa and coconuts have been decreasing from around the year 1990 to the year 2005 while the planted area of oil palm has been increasing during the same period of time. However, looking at Malaysia and Indonesia as one marginal supplier of palm oil, there is a general incr ease in the cultivated area of rubber, coconut and cocoa,

see Figure 2.3 . Only an insignificant decrease in the cultivated area of cocoa is identified from 1994 to 1999.

## 2 System boundaries of inventoried systems 25



## Thus, no displacements between crops are considered when identifying how oil palm cultivation is increased by area.

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,00019611963196519671969197119731975197719791981198319851987198919911993199519971999200120032005km2 Oil palm

Rubber

Cocoa Coconuts Malaysia and Indonesia



## Figure 2.3: Planted area of oil palm, natural rubber, coconuts and cocoa in Malaysia and Indonesia 1961-2005. Based on FAOSTAT (2006).



## Since no crop displacement is taking place when considering Malaysia and Indonesia together, it is presumed that a change in demand for oil palm affects the cultivation with 4% change in yield and 96% transformation of non-agricultural land into oil palm cultivation.

For changes achived by land transformation the question is then what kind of land that is transformed. Some NGO’s refer to land transformation related to oil palm as clearing of primary forest, e.g. see Frese et al. (2006), Casson (2003) and Wakker (2004). However, oil palm is almost always est ablished on already disturbed land (Glastra et al. 2002, ProForest 2003, Bek-Nielsen 2006).

Disturbed land may be either abandoned agricultural land, cleared forest (grassland/alang-alang land) or secondary forest. If oil palm is planted directly on transf ormed primary forest, the transformation from primary to degraded forest should be ascibed to logging since chang es in demand for timber is the main driving force of logging. Thus, land use changes related to oil palm in this study is represented by either transformation of secondary/degraded forest to oil palm or transformation of grassland/alang-alang land to oil palm.

From 1993 to 2003 the agricultural area in Malaysia and Indonesia has been increasing in average 5,600 km 2 

## annually (FAOSTAT 2006). In the same period the area plan ted with oil palm has been increasing in average 3,400 km 2 

## annually. These data represents the newst data available for a ten years period.

For comparison the annual change in forest cover in Malaysia and Indonesia from 1990 to 2000 has been 19,500 km 2 

## (FAO 2006), 96% in Indonesia and 4% in Malaysia. This has only changed slightly looking at 2000 to 2005 where the annual deforestation rate in Malaysia and Indonesia together was 20,110 km 2 

## . For comparison Pagiola (2000) specifies an annual deforestation rate in Indonesia at 16,420 km 2 

## from 1987 to 1997. In Malaysia the change in the extent of primar y forest has not changed from 1990 to 2005 while the annual degradation of primary forest at 14,478 km 2 

## in Indonesia has been the same in 1990 to 2000 and 2000 to 2005. It appears that the annual deforestation in Malaysia and Indonesia is significan t larger than the increase

## 26 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## in agricultural area, also when looking at degradation of primary forest only. This underpins that it is not likely that oil palm is not the driving force of the logging of primary forest.

According to Garrity et al. (1997) and Corley (2006) large areas of grassland (also called alang-alang or imperata) is available for expanding the agricultural area in Indonesia. Garrity et al. (1997, p 20) estimate the area of alang-alang grass land in Indonesia as 75,000–130,300 km 2 

## , i.e. 4-7% of the total area, while the area of grassland available for agricultural expa nsion in Malaysia is only 1,000–5,000 km 2 

## , i.e. 0.3–1.5% of the total area. A suggested specific target in the WWF initiated Round Table on Sustainable Palm Oil (RSPO) is that at least 75% of the new oil palm plantings should be on degraded or abandoned, deforested land, rather than new clearings (Corley 2006; RSPO 2006)

From the figures presented above it is not possible to estimate the composition of land use types transformed into oil palm. It is assumed that 50% of oil palm e xpansions take place by transformation of grassland and the other 50% take place by transformation of degraded/ secondary forest. However, this assumption associated with large uncertainties. Therefore, the uncertainties rela ted to the determination of the affected land use types (grassland, degraded/secondary forest and primary for est) are assessed in a sens itivity analysis in section 21.3

. 

# Soybean in Brazil The change in demand for soybean in Brazil is presum ed to take place by 19% in creased yield and 81% increased area. This presumption is based on predicti ons from 2005/06 to 2015/16 in FAPRI (2006). According to Dros (2004) expansion of soybean cultivation in Brazil mainly takes place in the Cerrados and the Amazon forests. Dros (2004) specifies the annual expansion ra tes from 1995/96 to 2003/04 in the Cerrado as approximately 7,500 km 2 

## /year, while the rate in the Amazon states in the same period is around 369 km 2 

## /year. This is in good accordance with FAOSTAT (2006) from which an average increase in th e soybean cultivated area from 1993 to 2003 can be determined as 7,812 km 2 

## /year. Based on the figures provided by Dros (2004), it is assumed that changed soybean production by expanding the agricultural area in Brazil is achived by 95% transformation of the Cerrado savannah and 5% transforma tion of the Amazon rain forest. As in the case of oil palm in Malaysia and Indonesia, degr adation of primary forest into degraded/secondary forest is ascribed to timer logging while transformation of degraded forest/sec ondary forest into soybean fields is ascribed to soybean cultivation. The transformation of the Cerrado savanna h into soybean fields is fully ascribed to soybean cultivation.

From 1993 to 2003 the arable land in Brazil has been increasing in average 6,740 km 2 

## annually (FAOSTAT 2006). Thus, the increase in the area cultivated with soybean is larger than the total increase in arable land. An immidiate explanation of that could be that the incr ease in soybean has been taking place at the expense of displacement of other crops. However, that is not the case because the sum of change in the cultivated area of all crops in FAOSTAT (2006) shows a total increase at 10,951 km 2 

## /year. The reason why this figure is larger than the total increase of arable land is because of in creased cultivation of cropping systems with more than one crop anually. FAOSTAT (2006) distinguishes between ‘c ultivated area’ and ‘arabl e land’. The ‘cultivated area’ is equal to the harvested area. And since some fiel ds are harvested more than once a year, the ‘cultivated area’ is larger than the area of the ‘arable land’. Accordi ng to Schnepf et al. (2001) three crops per year are in principple possible in some areas of Brazil. Double-cropppi ng in Argentina and Brazil is often put forward as an important mean of increasing the cultivated area (t he “virtually” area) (Jales et al. 2006 and USDA 2006).

Double-croppping in Argentina is refereed to as 37% of the soybean cultivated area in USDA (1997) and 25% in Dalgaard et al. (2007). No information on double-croppi ng in Brazil has been identified. Therefore, it is assumed that 25% of the soybean in Bazil is cultivated in a double-cropping system. The consequence of this

## 2 System boundaries of inventoried systems 27



## is, that the yield of soybean obtained from FAOSTAT statistics should be multiplied by a factor of 1.25 (=0.25

⋅ 2 + 0.75) in order to reflect the annual yield of one hectare of arable land.


# Barley in Canada From predictions in FAPRI (2006) it can be found that 31% of the future increase in production of barley in Canada is met by yield while the remaining 69% is met by increased area. However, since the total agricultural area in Canada has been decreasing in the period from 1993 to 2003 (FAOSTAT 2006) it is not assumed to be likely that 69% of future changes in demand for barley w ill be met by transformation of nature to agriculture. According to FAOSTAT (2006) there have been major in creases in the cultivation of peas, rapeseed, soybeans, barley, oats, linseed, maize and lentils while there has been a corresponding decrease in the area cultivated with wheat. It could then be argued that the increased production of barley in Canada is achived by displacement of wheat. However, identifying the marginal supp lier of wheat and then perform system expansion again would not add much acuracy to prediction of affected crops . The reason for that is that there is a considerable substitutability between different cereals (wheat, barley, oa t, rye and to some extent maize and rice). It could then be asked if the marginal supplier of barley should be the same as the marginal supplier of cereals. In order to assess the uncertainties related to th e identification of the marginal supplier of barley, a sensitivity analysis in section 21.2 assesses the effect on the results if other suppliers of barley are are marginal.

According to FAOSTAT (2006) the average annual in creases in cereals production from 1990 to 2005 have taken place as 1.48% increase in yield and 0.36% decr ease in cultivated area. Similar figures showing that yield increases are the dominant way of increasing cereal production can be found in FAPRI (2006). In FAPRI, the predicted increased production of cereals is achived by 1.02% increase in yield and only 0.12% increase in cultivated area. Based on that, an incr ease in the production of cereals is as sumed to be achived by increase in yields only. However, in some of the included scenario s increases in production of barley are assumed to be achieved by increase in the cultivated area. In these cases it is assumed that the transformed land is prairie grassland.


# System expansion in the agr icultural stage - summary Figure 2.4 and Figure 2.5 summarises the product flows, and the a ffected crops and suppliers when performing system expansion in the agricultural stage. The yiel ds of rapeseed and spring barley in Denmark, FFB in Malaysia and Indonesia, soybean in Brazil and barley in Canada are obtained from sections 5.1
, 6.1
, 7.1 and 8.1

.

## 28 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

1.000 t rapeseed DK: rapeseed (3.23 t/ha) increased area
0.600 t rapeseed

0.186 ha y DK: barley (5.12 t/ha) reduced area 0.951 t barley

DK: rapeseed (3.23+ Δ Y t/ha) increased yield
0.400 t rapeseed CAN: barley (2.80+ Δ Y t/ha) increased yield

1.000 t rapeseed DK: rapeseed (3.23 t/ha) increased area
0.600 t rapeseed

0.186 ha y

DK: rapeseed (3.23+ Δ Y t/ha) increased yield
0.400 t rapeseed 0 ha y Rapeseed in DK (constrained agr. area) Rapeseed in DK (local expansion of agr. area) Oil palm fruit (FFB) in Malaysia and Indonesia

1.000 t FFB

MY&IN: FFB (18.87 t/ha) increased area

0.960 t FFB 0.051 ha y

MY&IN: FFB (18.87+ Δ Y t/ha) increased yield

0.040 t FFB 0 ha y 0 ha y 0 ha y



## Figure 2.4: Expanded agricultural product systems related to 1 t rapeseed and 1 t FFB. Abbreviations: Canada (CAN), Denmark (DK) and Malaysia and Indonesia (MY&IN). Δ Y represents the desired increase in yields when increased production is achieved that way.

1.000 t soybean BR: Soybean (3.21 t/ha) increased area
0.810 t soybean

0.252 ha y

BR: Soybean (3.21+ Δ Y t/ha) increased yield
0.190 t soybean Soybean in Brazil

1.000 t barley 0 ha y Barley in Canada

CAN: barley (2.80+ Δ Y t/ha) increased yield 0 ha y



## Figure 2.5: Expanded agricultural product systems related to 1 t soybean and 1 t barley. Abbreviations: Canada (CAN) and Brazil (BR). Δ Y represents the desired increase in yields wh en increased production is achieved that way.



# 2.4 Scenarois related to system expansion
The system delimitation in the main scenario is descr ibed in the previous sections. However, most LCAs on products from oil crops are performed us ing attributional modelling in systemdelimitation and LCI, i.e. allocation is typically done by economic value (or by energy content/mass/100 percent/0 percent) and system expansion is not taken into account (Defra 2005; Beer et al. 2002; Mehlin et al. 2003; Wightman et al. 1999; McManus et al. 2004; Yusoff and Hansen 2005; Koch 2003; Althaus et al. 2003; Zah and Hischier 2003). Some LCAs on oil crop products have adopted consequential modelling in the oil mill stage (Dalgaard et al. 2007; Nielsen et al. 2005). However, no existing LCAs wh ich include system expansion in the agricultural stage have been identified. Though, progress in that field is ongoing. A model including a case study on wheat is presented in Schmidt (2007b) and work is ongoing by Kløverpris (2007). See Kløverpris (2006) for a description of the work.

In order to make the results obtained from the present LCI transparent and comparable with other LCIs, the results are shown on all three levels of LCI-modelling: 1) Attributional modelling, i.e. traditional LCA with no system expansion, 2) semi-consequential modelling, i.e. only taking system expansion in the oil mill stage into account and 3) consequential modelling in the oil mill stage as well as in the agricultural stage.

## 2 System boundaries of inventoried systems 29



## Attributional modelling: The economic allocation factors applied in the attributional modelling are based on the product flows shown in Figure 2.1 and the average prices on oils and meals from 1996/97 to 2003/04 provided in Oil World (2005). The allocation factors ascrib ing shares of the input ofrapeseed to the two coproducts from rapeseed milling are 73% for the oil and 37% for the meal. Two co-products are also considered from the palm oil mill together with the palm kernel oil mill (see Figure 2.1 ), i.e. palm oil (PO+PKO) and palm kernel meal. The allocation factors ascribing shares of the input of FFB to the co-products are 98% for the oil and 2% for the meal. From 1996/97 to 2003/04 the relative world market prices between oils and meals have been changing so that the allocation factor for palm oil would vary from 97.5% to 98.7% and the allocation factor for rapeseed oil would vary from 66% to 78%. Thus, relative variations between prices on oil and meals are not considered as a significant source of uncertainty.

Attributional modelling in the agricultural stage means th at only the area under the immediate affected crops is affected. Attributional modelling describes how existing pr oduction takes place. Contrary to that, consequential modelling attempts to predict responses to changes in demand. Therefore, attributional modelling in this this study does not consider changes in yield as well as constraints on area available for agricultural cultivation are not considered.

Semi-consequential modelling (system expansion in oil mill stage only): The system defined by system expansion in the oil mill stage is presented in Table 2.3 . Attributional modelling is applied in the agricultural stage, see above.

Consequential modelling (system expansion in oil mill stage as well as agricultural stage): The system inventoried is described as the system in Table 2.3 (system expansion in oil mill stage) combined with the system in Figure 2.4 and Figure 2.5 (system expansion in agricultural stage). Since the determination of the share of changes in agricultural production achieved by area and yield is related to some uncertainty, three scenarios are assessed within th e consequential modelling, i.e. i) cha nges in production achieved by a combination of area and yield (as described in section 2.3 ), ii) changes in production achieved by changes in area only, and iii) changes in production achieved by changes in yield only.

Hence, in total five scenarios are included: three con sequential, one semi-consequen tial and one attributional. Additional to that, for the two conse quential scenarios which include chang es in the cultivated area, two alternative ways of increasing rapeseed production in Denmar k are included, i.e. a) increased area cultivated with rapeseed causes displacement of the marginal crop in Denmark, spring barley, and b) increased area cultivated with rapeseed causes local transformation of set-aside ar ea. These two alternative ways of increasing rapeseed production in Denmark are described in ‘ Rapeseed in Denmark ’ in section 2.3

. Table 2.4 provides an overview of the included five different scenarios relating to the system delimitation.

## 30 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Description of product system Scenarios: compositions of product systems 1 t rapeseed oil in Denmark 1 t palm oil in Malaysia and Indonesia Sc 1) Consequential modelling in oil mill and agr. stages. Marginal increases are assumed to be achived by a combination of increase in agr. area and yields a) RSO (constrained area) and b) RSO (flexible area) PO+PKO

Sc 2) Consequential modelling (area only) in oil mill and agr. stages. Marginal increases are assumed to be achived by increase in agr. area only a) RSO (constrained area) and b) RSO (flexible area) PO+PKO

Sc 3) Consequential modelling (yield only) in oil mill and agr. stages. Marginal increases are assumed to be achived by increase in agr. yields only RSO PO+PKO

Sc 4) Semi-consequential modelling

, system expansion in oil mill stage and attributional modelling in agr. stage RSO PO+PKO

Sc 5) Attributional modelling, i.e. economic allocation and no system expansion RSO (73% allocated to oil) PO+PKO (98% allocated to oil) 

## Table 2.4: Overview of the included scenarios relating to system delimitation.



# 2.5 Inventoried system – summary

Agricultural stage

Table 2.5 summarises the product flow in the agricultural stage in the included scenarios. The values are calculated on the basis of the descriptions in section 2.4 and the data presented in Table 2.3 

## (system expansion in oil mill stage) and Figure 2.4 and Figure 2.5 (system expansion in agricultural stage).

## 2 System boundaries of inventoried systems 31

Affected crops, t

Scenario 1 Ref. flow Increase Rapeseed (DK) FFB (MY&IN) Soybean (BR) Barley (CAN) Barley (DK) Area 1.457 t 1.140 t -1.096 t 0 t -2.308 t 1 t RSO (con- strained area) Yield 0.971 t 0.0475 t -0.257 t 2.151 t 0 t Area 1.457 t 1.140 t -1.096 t 0 t 0 t 1 t RSO (local

expansion) Yield 0.971 t 0.0475 t -0.257 t -0.157 t 0 t Area 0 t 4.475 t -0.00257 t 0 t 0 tSc 1) Consequential modelling in oil mill and agr. stages.Marginal increases are assumed to be combined increase in agr. Area and yields 1 t PO+PKO Yield 0 t 0.186 t -0.000602 t -0.198 t 0 t

Scenario 2 Ref. flow Increase Rapeseed (DK) FFB (MY&IN) Soybean (BR) Barley (CAN) Barley (DK)1 t RSO (constrained area) Area 2.428 t 1.187 t -1.352 t 3.690 t -3.847 t
1 t RSO (local

expansion) Area 2.428 t 1.187 t -1.352 t -0.157 t 0 tSc 2) Consequential modelling (area) in oil mill and agr. stages. Marginal increases are assumed to be increase in agr. area only 1 t PO+PKO Area 0 t 4.661 t -0.00317 t -0.198 t 0 t

Scenario 3 Ref. flow Increase Rapeseed (DK) FFB (MY&IN) Soybean (BR) Barley (CAN) Barley (DK)

1 t RSO Yield 2.428 t 1.187 t -1.352 t -0.157 t 0 tSc 3) Consequential modelling (yield) in oil mill and agr. stages. Marginal increases are assumed to be increase in agr. yields only 1 t PO+PKO Yield 0 t 4.661 t -0.00317 t -0.198 t 0 t

Scenario 4 Ref. flow Increase Rapeseed (DK) FFB (MY&IN) Soybean (BR) Barley (CAN) Barley (DK)

1 t RSO Area 2.428 t 1.187 t -1.352 t -0.157 t 0 t Sc 4) Semi-consequential modelling , system expansionin oil mill stage and attributional modelling in agr. stage 1 t PO+PKO Area 0 t 4.661 t -0.00317 t -0.198 t 0 t

Scenario 5 Ref. flow Increase Rapeseed (DK) FFB (MY&IN) Soybean (BR) Barley (CAN) Barley (DK)

1 t RSO Area 1.772 t 0 t 0 t 0 t 0 tSc 5) Attributional modelling, i.e. economic allocation and no system expansion
1 t PO+PKO Area 0 t 4.568 t 0 t 0 t 0 t



## Table 2.5: Overview of the affected crops in th e different scenarios relating to the functional unit of 1 t vegetable oil. Each line represents the affected crops relating to the functional unit.



## In addition to the affected crops in Table 2.5 the changes achived by increased cultivated area will have correspondingly effects on transformation of non-cultivated area into agricultural land. Based on Figure 2.4
, Figure 2.5 and Table 2.5 the transformation processes related to 1 tonne rapeseed oil and 1 tonne palm oil are calculated, see Table 2.6 . It must be stressed that the transformations given in Table 2.6 are for the first year of harvesting. E.g. a crop yielding 1 t/ha per year will have a correspondingly transformation process at 1 ha/t crop. However, the transformed area will continue yielding crops after the first year. Therefore, the figures given in Table 2.6 are not directly related to the functional unit as in Table 2.5

. Table 2.5 represents processes proportional to the functional unit while Table 2.6 represents processes that only occur one time, and the cultivation on the same area after that will not be associated with any transformation processes.

## 32 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

DK MY&IN BR CAN Transformation from... Set-aside Forest Grassland Savannah Forest Grassland Transformation to... Rapeseed field Oil palm plantation Soybean field Barley field Sc 1) Consequential modelling in oil mill and agr. stages. Marginal increases are assumed to be combined increase in agr. area and yields 1 t RSO (constrained area) 0 ha 0.0302 ha 0.0302 ha -0.324 ha -0.0171 ha 0 ha 1 t RSO (local expansion) 0.451 ha 0.0302 ha 0.0302 ha -0.324 ha -0.0171 ha 0 ha 1 t PO+PKO 0 ha 0.119 ha 0.119 ha -0.000760 ha -0.0000400 ha 0 ha Sc 2) Consequential modelling (area) in oil mill and agr. stages. Marginal increases are assumed to be increase in agr. area only 1 t RSO (constrained area) 0 ha 0.0315 ha 0.0315 ha -0.400 ha -0.0211 ha 1.318 ha 1 t RSO (local expansion) 0.751 ha 0.0315 ha 0.0315 ha -0.400 ha -0.0211 ha -0.0561 ha 1 t PO+PKO 0 ha 0.124 ha 0.124 ha -0.000938 ha -0.0000494 ha -0.0707 ha Sc 3) Consequential modelling (yield) in oil mill and agr. stages. Marginal increases are assumed to be increase in agr. yields only 1 t RSO 0 ha 0 ha 0 ha 0 ha 0 ha 0 ha 1 t PO+PKO 0 ha 0 ha 0 ha 0 ha 0 ha 0 ha Sc 4) Semi-consequential modelling , system expansion in oil mill stage and attributional modelling in agr. Stage 1 t RSO 0.751 ha 0.0315 ha 0.0315 ha -0.400 ha -0.0211 ha -0.0561 ha 1 t PO+PKO 0 ha 0.124 ha 0.124 ha -0.000938 ha -0.0000494 ha -0.0707 ha Sc 5) Attributional modelling, i.e. economic allocation and no system expansion 1 t RSO 0.549 ha 0 ha 0 ha 0 ha 0 ha 0 ha 1 t PO+PKO 0 ha 0.121 ha 0.121 ha 0 ha 0 ha 0 ha 

## Table 2.6: Overview of the transformed area (ha) in the different scenarios relating to the functional unit of 1 t vegetable oil. Each line represents the affected crops relating to the functional unit.



## Since the figures in Table 2.5 and Table 2.6 are not related to the functional unit the same way, the LCIA results from transformation processes are treated seperately.


# Oil mill and refinery stages

Based on Figure 2.1 and the results of Equation (1) and Equation (2) , the affected processes in the oil mill and refinery stages are calculated, see Table 2.7

.

Rapeseed oil Palm oil Palm kernel oil Soybean oil Scenario Oil mill (oil) Refinery (oil) Oil mill (oil) Refinery (oil) Oil mill (oil) Refinery (oil) Oil mill (meal) Refinery (oil) Sc 1), 2), 3) and 4) Consequential modelling in oil mill and refinery stage 1 t RSO 1.017 t 1.000 t 0.237 t 0.227 t 0.0283 t 0.0280 t -1.045 t -0.255 t 1 t PO+PKO 0 t 0 t 0.931 t 0.891 t 0.111 t 0.110 t -0.00245 t -0.000599 t Sc 5) Attributional in oil mill and refinery stage 1 t RSO 0.742 t 1.000 t 0 t 0 t 0 t 0 t 0 t 0 t 1 t PO+PKO 0 t 0 t 0.913 t 0.891 t 0.109 t 0.110 t 0 t 0 t 

## Table 2.7: Overview of the affected oil mill and refinery processe s in the different scenarios relating to the functional unit of 1 t vegetable oil. Each line represents the a ffected processes relating to the functional unit.

## 3 Energy 33



# 3 Energy
Energy is used in many of the incl uded processes in the life cycle of rapeseed oil and palm oil. Electricity supply is delivered from the grid which is supplied from a range of different power plants connected to the European grid. Heat is supplied either through the district heating system or through own production in a furnace. This section describes the used life cycle inventory data fo r the different types of energy used in the life cycle of rapeseed oil and palm oil. The amount of energy used in the different unit process es is described in the distinct sections which inventory the single stages in th e life cycle of rapeseed oil and palm oil; sections 5 to 17

.


# 3.1 Electricity from the grid in Denmark
Electricity supply to the grid in Denmark comes from a variety of different technologies. These mainly comprise centralized coal and natural gas power plants, wind mills, waste incineration plants, decentralized combined heat and power plants based on natural gas, coal or biomass. When the demand for electricity is changed only the marginal suppliers are affected. Hence, there is a range of suppliers that are not affected when the demand for electricity is changed. These technologies in clude; wind mills which are constrained by the wind available and capacity determined by energy policy, decentralized combined heat and power plants whose production is determined by the local demand for heat, el ectricity production in waste incineration plants is determined by the amount of waste available. Thus, the marginal source of electricity is either coal, gas or both.


# Marginal energy source of elec tricity: Coal or natural gas? According to Weidema (2003) the most competitive technolog y is coal followed by natural gas, heavy fuel oil and biomass in the mentioned order. Since the consumption of electricity is increasing, it is the most competitive suppliers that are affected. Thus, coal based tec hnology seem to be the marginal technology. LCA-Center (2006) also suggests that coal based power production is mo st likely to be affected; even when hour by hour fluctuations are taken into account.

However, all new power plants projected in the Nord ic countries are natural gas fired (Weidema 2003). Coal based technology is likely to cover the demand within existing capacity the next 10 years (Weidema 2003). Concerning central Europe, Weidema identifies a range of properties of gas based technology that indicate that this technology could be the marginal in the longer term ; installation of natural gas requires lower costs than coal, political goals on reduction of SO 2 

## -, NO x 

## - and CO 2 

## -emissions are easier reached using natural gas, and finally it is possible to regulate the power output of natu ral gas fired plants on a minute basis. Hence, the marginal technology tend to be coal based in the shor t term and natural gas based in the longer term.

Nevertheless, it may be ques tioned if gas can be marginal in the l onger term since the re serves in Denmark as well as Europe are getting scarce. According to Energist yrelsen (2005) the outlook prediction on possible Danish gas production the next 20 years shows a decline in annual production at 83% from approximately 23 mio

m 3 

## in 2004 to approximately 4 mio m 3 

## in 2024. According to Bentley (2002) Europe has little way to go before its peak in production is reached. On the other hand, EIA (2005) expects gas to be the fastest growing fuel in European until 2025. The increased demand is expected to be met by import; thus the reliance on import is predicted to increase from ~33% in 2002 to ~50% in 2025 (EIA 2005).

If natural gas should be considered as the marginal source of electricity in the longer te rm it must be flexible (i.e. not constrained). There are some indications on that it may be constrained by available gas resources in

## 34 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## the future. EIA (2005, p 39) estimates the global reserves- to-production ratio to be 67 years while the ratio in the former Soviet Union from where EU imports its na tural gas is 77 years. However, the Kyoto mechanisms may speed up the consumption and prices of natural gas which may lead to constraints in the future.

Different studies suggest different marginal technologies fo r electricity and choosing either coal or gas as the marginal will be related to some uncertainty. It is cho sen to apply coal based electricity as the marginal since this is regarded as the marginal in the short term. As indicated it can be questioned if the gas resources are adequate (not constrained) to be th e marginal technology in the longer term. Therefore, coal is regarded more likely to be the marginal in the shor t term as well as in the longer term. However, since the identification of marginal electricity suppliers is related to some uncer tainties, gas based technology is applied in a sensitivity analysis in section 21.4

. 

# Marginal technology for electricity : Co-production of heat or not? Many centralized power plants in Denmark are producing both heat and electricity and they are able to regulate the ratio by operating between condensation mode and back pressure mode. According to Weidema (2003) cogenerating technology is constrained by local demand for heat. Thus, marginal electricity is produced without co-generation of heat.


# Inventory data for electricity Loss in the grid also has to be considered. When the pow er plant delivers its electricity, it is sent into the high voltage grid (150-400 kV). This grid connects the power plants with the distribution grid, also denoted as the low voltage grid (<60 kV). According to Energi E2 et al . (2000) the loss in the high voltage grid is 1% of the supply to the grid. In the transformation to <60 kV and di stribution in the low voltage grid to the end-users the loss is 3.8% of the supply to the high voltage grid. Figure 3.1 shows a flowchart of electricity per 1 MJ delivered.

## 3 Energy 35Power plant

Electricity, at plant1.048 MJ1.038 MJLow voltage1 MJ Electricity 150-400 kVHigh voltage

Electricity 150-400 kV Electricity 150-400 kV

Electricity <60 kV 

### System boundaryLoss: 0.038 MJLoss: 0.01 MJ



## Figure 3.1: Flowchart of electricity from power plant to end-user.



## Several life cycle inventories for coal and natural gas based electricity production have been identified. However, many of these are based on data for co-generation of heat and power and in these studies this is dealt with by co-product allocation based on either energy conten t or energy quality. These inventories, which among other include a detailed LCA of Danish electricity; (Energ i E2 et al. 2000), are not considered in the following because they are not consistent with the used system delimitation described above.

Analysing existing inventories of electricity based on co al and gas available in SimaPro and using the EDIP97- method for LCIA, it appears that global warming, acidification and toxicity are the most significant impact categories. In Table 3.1 , four relevant data sets are compared within these categories.

## 36 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Coal: LCI-data for electricity CO2-eq. SO2-eq. ETWC, m3 water Electricity efficiency Description of data

‘Electricity coal power plant in NL’, ETH-ESU database (Frischknecht et al., 1996) 274 g 0.79 g 105 m 3 No data

‘Electricity coal power plant in UCPTE’, ETH-ESU database (Frischknecht et al., 1996) 302 g 1.45 g 119 m 3 No data Time: Data from 1990-94 Geography: The Netherlands/UCPTE 7 Technology: Average gas fired plants Co-product allocation : Not relevant Capital goods: Machinery, buildings

‘Electricity, hard coal at power plant/UCTE’, ecoinvent database (Emmenegger et al. 2003) 298 g 1.38 g 37.3 m 3 36%

‘Electricity, hard coal at powerplant/NORDEL’, ecoinvent database (Emmenegger et al. 2003) 267 g 0.57 g 17.7 m 3 42% Time: Data from 2000 Geography: UCTE 7 / NORDEL 8 Technology: Average gas fired plants Co-product allocation : Not relevant Capital goods: Machinery, buildings

Natural gas: LCI-data for electricity CO 2 -eq. SO 2 -eq. ETWC,

m 3 water Electricity efficiency Description of data

‘Electricity gas power plant in NL’, ETH-ESU database (Frischknecht et al., 1996) 182 g 0.18 g 2.8 m 3 No data

‘Electricity gas power plant in UCPTE’, ETH-ESU database (Frischknecht et al., 1996) 256 g 0.35 g 11.5 m 3 No data Time: Data from 1990-94 Geography: The Netherlands/ UCPTE 7 Technology: Average gas fired plants Co-product allocation : Not relevant Capital goods: Machinery, buildings

‘Electricity, natural gas, at power plant/UCTE’, ecoinvent database (Emmenegger et al. 2003) 166 g 0.20 g 6.1 m 3 38%

‘Electricity, natural gas, at powerplant/NORDEL’, ecoinvent database (Emmenegger et al. 2003) 152 g 0.19 g 5.1 m 3 41% Time: Data from 2000 Geography: UCTE 7 / NORDEL 8 Technology: Average gas fired plants Co-product allocation : Not relevant Capital goods: Machinery, buildings



## Table 3.1: Comparison of LCIs of 1 MJ electricity based on coal and natural gas technology. The comparison is shown as characterised results using the EDIP97-method for LCIA. The applied data are marked with a black dotted frame. The marked natural gas inventory is applied in a sensitivity analysis in section 21.4 while the coal inventory is applied in the baseline scenario.



## Table 3.1 compares different inventories for coal as well as gas based electricity. In Table 3.1 a data set is shown for Dutch plants because this is used in the LC Afood database (Nielsen et al. 2005) where it is assumed to represent Danish plants. Comparing of the data sets in Table 3.1 , it appears that the environmental impacts from plants from the Netherlands and NORDEL seem to be smaller than from plants attached to the UCPTE/UCTE grid. Furthermore, it appears that the cont ributions to global warming from the power plants in ecoinvent are smaller than the ones from ETH-ESU. The reason for the smaller contributions to global warming in the ecoinvent data is probably that th ese data represent 6-10 years newer power plants.

It is chosen to use the data for NORDEL from ecoinve nt because these data best represent the technology in Denmark and because these data are the most updated. In case of electricity consumption outside Denmark ecoinvent data for UCPTE are used. The data in ecoinve nt includes particle removal, DeNOx, DeSOx, waste water treatment, disposal/recycling of ash, transport of fuels and ancillaries and capital goods such as production maintenance and disposal of the power plant 9 

## (Röder 2003).

7 

## UCPTE/UCTE includes: Austria, Belgium, Germany, Spain, Luxembourg, Italy, the Netherlands, Greece and Portugal.

8 

## NORDEL: Denmark, Finland, Norway and Sweden.

9 

## Interventions related to production, maintenance and disposal of the power plant has due to an error in ecoinvent not been included in the original database entry (Bauer 2006). Therefore, this module has been manually applied to the data set in SimaPro.

## 3 Energy 37



## The data described above only comprises environmental interventions related to electricity ex plant. Therefore, loss in the grid and production, maintenance and disposal of the grid also have to be taken into account. Data on loss in the grid is described above. Only one life cy cle inventory on production, maintenance and disposal of the grid have been identified. The data are related to transmission and distribution of 1 kWh and are available in SimaPro in the ecoinvent database and they are described in Emmenegger et al. (2003). The data from ecoinvent distinguishes between high voltage grid (>150 kV), medium voltage grid (50-60 kV) and low voltage grid (<1 kV). As described in the above this study only distinguishes between high and low voltage grids. Therefore the medium voltage grid in ecoinvent is include d in the low voltage grid in this study. The ecoinvent data are based on transmission and distribution of electr icity in Switzerland and loss in the grid is 13%. Since loss in the Danish grid is 4.8% this value is used instead. The used data are shown in Table 3.2

.

Grid Database Loss High voltage grid (150-400 kV) ‘Electricity, high voltage, at grid/DK’ (ecoinvent 2004) 1,0% of electricity at plant Low voltage grid (<60 kV) ‘Electricity, medium voltage, at grid/DK’ (ecoinvent 2004) plus ‘Electricity, low voltage, at grid/DK’ (ecoinvent 2004) 3,8% of electricity at plant



## Table 3.2: Used LCI data on production, maintenance and disposal of transmission and distribution grid in Denmark. The losses in the grid in the ecoinvent data are deleted and the given values in the table are used instead.



## The applied inventory data for marginal electric ity in all relevant countries are summarised in Table 3.3

.


# 3.2 Electricity from the grid in Malaysia and Indonesia

Malaysia The electricity mix in Malaysia is constituted by 28 % coal, 3% oil, 62% natural gas and 7% hydro (IEA 2007). The marginal source of electricity in Malaysia is regarded as coal. The reason for that is that the share of coal based electricity in Malaysia has increased rapidly the last few years and will continue to increase in the future (see below) and that coal based el ctricity is the cheapest and thereb y the most competitive (Mohammed and Lee 2006). The share of coal based electricity has in creased from ~7% in 2000 to 28% in 2004 (IEA 2007). According to Mohammed and Lee (2006) the share is e xpected to increase futher to around 40-45%. At the same time the share of natural gas is expected to decrease to less than 50% (Mohammed and Lee).

According to IEA (2007) the Malaysian coal based electri city production in 2004 was 83.3 PJ while the use of coal in the coal power plants was 197.9 PJ. This corresponds to an average efficience of coal power plants in Malaysia at 42%. The use of coal in Malaysia’s coal power plants in 2004 was 8,819 kt (IEA 2007). Comparing use of coal in terms of mass with the use of coal in terms of calorific units gi ven above, this corresponds to energy content of coal at 22.4 MJ/kg.

The efficiency at 42% is relatively high. According to Table 3.1 the efficiency of coal power plants (condensing mode) in Central Europe and Scandinavia are 36% and 42% respectively which is lower than Malaysian average. The reason for this is that the coal fired plan ts in Malaysia are newer than the ones in Europe and Scandinavia. Since the share of coal fired plant in Mala ysia has faced a drastic increase the last few years, the plants in use are relatively new. According to Weid ema (2003) no new planned power plants in Scandinavia have been based on coal since the mid 1990ies. Also in Central Europe, not many new coal fired plants have been buildt 10 

## . Therefore, the coal power plants in Central Eu roepe and Scandinavia are older than the ones in

10 

## This statement is based on the fact that the coal based shar e of electricity production in Central and Eastern Europe has been almost constant from around mid 1990ies to 2004 (IEA 2007).

## 38 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Malaysia, and consequential it is not unlikely that the Malaysian coal power plants have a higher efficiency than European plants.

Since the efficiency of Malaysian and Scandinavian plants is the same, it is chosen to apply the same inventory data for electricity from the grid in Malaysia as in Denm ark. Inventory data for electricity in Denmark are described in section 3.1
, Table 3.1

.
In addition to the inventory data for production of electricity loss in the grid and the production, maintenance and disposal of the transmission grid also are to be in cluded. According to IEA (2007) the loss in the grid in Malaysia in 2004 was 4.9% of the produced electricity.

The same inventory data on production, maintence and disp osal of the grid as for Danish electricity is applied,

see Table 3.2

.
The applied inventory data for marginal electric ity in all relevant countries are summarised in Table 3.3

.


# Indonesia The electricity mix in Indonesia is constituted by 40% co al, 30% oil, 16% natural gas, 6% geothermal and 8% hydro (IEA 2007). According to APERC (2006, p 36) 54% of the new capacity requirements for electricity generation is predicted to be coal wh ile natural gas accounts for 40%. This combined with the fact that coal based electricity is the cheapest source of electricity lead to the assumption that coal is the marginal source of electricity in Indonesia.

According to IEA (2007) the Indonesian electricity pr oduction based on coal in 200 4 was 173.6 PJ while the use of coal in the coal power plants was 589.2 PJ. This corresponds to an average efficience of coal power plants in Indonesia at 29%. The use of coal in Indonesia’s coal power plants in 2004 was 22,882 kt (IEA 2007). Comparing use of coal in terms of mass with the use of coal in terms of calorific units given above, this corresponds to energy content of coal at 25.7 MJ/kg.

Losses in the grid are 13% (IEA 2007)

The inventory data for coal based electricity in Denmark are adjusted in order to reflext electricity in Indonesia, i.e. adjusting the input of coal burned to fit an efficiency at 29%. The same inventory data on production, maintence and disposal of the grid as for Danish electricity is applied, see Table 3.2

.
The applied inventory data for marginal electric ity in all relevant countries are summarised in Table 3.3

. 

# Malaysia and Indonesia When electricity is used in Malaysia and Indonesia rega rded as one region, the marginal source of electricity is assumed to be represented as the average of the mar gnal sources of electricity in the two countries.



# 3.3 Electricity from the grid in Brazil
The electricity mix in Brazil in 2004 is constituted by 3% coal, 3% oil, 5% natural gas, 3% biomass, 83% hydro (IEA 2007). The marginal source of electricity in Brazil is regarded as natural gas. The reason for that is that the share of the most important source of electric ity, hydro, is predicted to decrease from 2002 to 2030 to the benefit of natural gas (EIA 2004, p 224).

## 3 Energy 39



## According to IEA (2007) the Brazilian electricity pr oduction based on natural gas in 2004 was 59.4 PJ while the use of natural gas in the gas fired power plants w as 180.9 PJ. This corresponds to an average efficience of gas fired power plants in Brazil at 33%.

Distribution losses in the grid in Brazil in 2004 were 17% (IEA 2007).

The inventory data for gas based electricity in Denmark are adjusted in order to reflext electricity in Brazil, i.e. adjuting the input of coal burned to fit an efficiency at 33%. The same inventory da ta on production, maintence and disposal of the grid as for Danish electricity is applied, see Table 3.2

.
The applied inventory data for marginal electric ity in all relevant countries are summarised in Table 3.3

.


# 3.4 Electricity from the grid in Canada
The electricity mix in Canada in 2004 is constituted by 17% coal, 4% oil, 5% natural gas, 1% biomass, 15% nuclear, 57% hydro (IEA 2007). According to APERC (2006, p 12) the share of coal, nuclear and hydro will decrease from 2002 to 2030 while the share of natural gas w ill increase. Therefore, natu ral gas is regarded as the marginal source of electricity in Canada.

Since a considerable share of the gas fired power plants in Canada are combined heat and power plants, it is not possible calculating the efficiency from statistics in IEA (2007).

The distribution losses in the grid in Canada were 6.6% in 2004 (IEA 2007).

Due to lack on data on the efficiency of gas fired power plants in Canada, it is chosen to apply inventory data for Canadian gas based electricity as the same as Danish gas fired power plants, see section 3.1 , i.e. applying an efficiency at 41%. The same inventory data on produc tion, maintence and disposal of the grid as for Danish electricity is applied, see Table 3.2

.
The applied inventory data for marginal electric ity in all relevant countries are summarised in Table 3.3

. 

# 3.5 Electricity in different countries, summary
Based on the efficiencies and losses in the gr id described in section the previos sections, Table 3.3 summarises the applied inventory data for marginal electricity in Denmark (DK), Malaysia (MY), Indonesia (IN), Brazil (BR) and Canada (CAN).

## 40 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

1 MJ electricity, coal/gas, at plant DK MY IN BR CAN Applied LCI data Coal burned in power plant 2.38 MJ 2.38 MJ 3.45 MJ - - Hard coal, burned in power plant/NORDEL Gas burned in power plant - - - 3.03 MJ 2.44 Natural gas, burned in power plant/NORDEL 1 MJ electricity, coal/gas, at user DK MY IN BR CAN Applied LCI data Electricity, coal, at plant 1.048 MJ 1.049 MJ 1.130 MJ - - see ‘Electricity, coal, at plant’ above Electricity, gas, at plant - - - 1.170 MJ 1.066 MJ see ‘Electricity, gas, at plant’ above High voltage grid (capital goods) 1 MJ 1 MJ 1 MJ 1 MJ 1 MJ Modified version of ‘Electricity, high voltage, at grid/DK’ , (ecoinvent 2004), see Table 3.2 1 MJ 1 MJ 1 MJ 1 MJ 1 MJ Modified version of ‘Electricity,medium voltage, at grid/DK’ (ecoinvent 2004), see Table 3.2 Low voltage grid (capital goods)

1 MJ 1 MJ 1 MJ 1 MJ 1 MJ Modified version of ‘Electricity, low voltage, at grid/DK’ (ecoinvent 2004),

see Table 3.2 

## Table 3.3: Summary of the applied inventory data related to 1 MJ marginal electricity at user in different countries.



# 3.6 District heating in Denmark
In both the agricultural, oil mill and refinery stages heat is used and produced. The main part of this heat is produced on-site on the farm when drying crops, in the bi omass plant that burnes straw, and in the power centrals of the oil mill and refinery. The emissions from on-site production of heat are dealt with in the respective processes. In addition to on-site heat production sometimes di strict heating is used or displaced. This is mainly for heating of administration buildings and laboratories in the rapeseed oil mill and refinery, and processes that displace district heat; energy production from straw in agriculture substitutes district heating. Interventions from marginal district heating in Denmark vary from regi on to region or even from town to town since different heat plants are used and a variety of local circum stances determine which supplier that is the marginal. Therefore point of departure is taken in district heati ng in Aarhus where the oil mill AarhusKarlshamn is situated. However, this may not be repr esentative for substituted district hea ting from straw in agriculture. Therefore, a sensitivity analysis of this assumption is presented in section 21.5

.
The supply of heat (hot water) to the district heating system in Aarhus is distributed on supply from the central power plant, Studstrupværket (~86%), waste incinerati on (~10%), excess heat from industry (~2%), peak load oil boilers (<1%) and other minor suppliers (Århus Kommunale Værker 2005). According to Århus Kommunale Værker (2005) changes in demand for heat is regulated through the heat production at Studstrupværket. Heat from waste incineration is constrained by waste generation, the minor supply from industry is constrained by its production and the boile r plants are only used in a fraction of the time in situations with peak load or in cases of failure of supply from other sour ces. Thus, Studstrupværket is considered as the marginal supplier of heat to the district heating system in Aarhus. Studstrupværket regulates the ratio in which it produces electricity and heat by continuously switching be tween condensation pressure and back pressure. In pure condensation pressure mode the energy efficiency is ~ 40% (only electricity) and in pure back pressure mode its ~80% (Andersen 2005), see Figure 3.2 . In back pressure mode the 80% efficiency is distributed on ~52% points heat and ~28% points electricity, see Figure 3.2

.

## 3 Energy 41

40% 80%

52% points

28% points

Condensating

pressure Backpressure Heat

Electricity Overall efficiency



## Figure 3.2: Relation between heat and electricity efficiencies and pressure mode for a combined heat and power plant that can continuously switch between condensation pressure and back pressure.



## If demand for heat increases 1 MJ, this can be met in two ways: 1) the position on the scale between condensation pressure and back pressure is mowed towards back pressure, or 2) by increasing the power input of coal. Meeting the demand by 1), the total energy input at St udstrupværket is unchanged, the electricity output decreases and the heat output increases – but the heat production increases more than the electricity production decreases. The other way around, meeting the demand by 2), both the total energy input at Studstrupværket and the output of heat and electricity increase. The margin al energy input in order to produce 1 MJ heat is calculated assuming a situation combining 1) and 2) keepi ng the production of electricity constant. Since the two product outputs (heat and electricity) at Studstrupværket can be varied independently, the interventions from the product of interest should be found by including the consequences of changing the output of this coproduct while keeping the other output constant (Weide ma, 2003, p 87, 90). The calculation takes its point of departure in two different positions on the scale between condensation pressure and back pressure. According to Andersen (2005) the output of electricity decrease s approximately linear by increased heat production, see Figure 3.2 . Since the heat and electricity efficiencies are linear with the position on the scale, it does not matter where on the scale the change takes place. Therefore, the calculation takes its point of departure in heat production at 100% back pressure. Electricity production in back pressure mode is 0.54 MJ per 1 MJ heat. Keeping the total output of electricity constant, the inte rventions from marginal production of 0.54 MJ electricity must be subtracted from the intervention related to co -production of 1 MJ heat and 0.54 MJ electricity. In section 3.1 , marginal electricity is identifie d as coal based electricity produ ced in condensation pressure with approximately 40% efficiency, which is the same as at Studstrupværket. If the marginal technology for electricity production is the same as the technology at Studs trupværket (coal), it makes no difference if this electricity is produced at Studstrupværket or another marginal electricity supplier. Thus, the energy input related to production of 1 MJ marginal heat at Studstrupværket can be determined by the difference between (1) the energy input for producing 1 MJ heat and 0.54 MJ electric ity in back pressure mode at Studstrupværket and (2) the energy input for producing 0.54 MJ electricity in condensation pressure mode at a marginal power plant. Using the energy efficiencies shown in Figure 3.2 , (1) can be determined as 1.93 MJ and (2) as 1.35 MJ. Thus, the marginal energy input related to production of 1 MJ heat is 0.58 MJ. However, in order to keep the inventory open for sensitivity analysis with other technologies as marginal, the inventory shown in Table 3.4 is based on 1.93 MJ burned coal at Studstrupværket and displacement of 0.54 MJ marginal electricity.

## 42 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## According to Andersen (2005) there is not excess heat fro m waste incineration and industry at any time at the year. Thus, the calculated energy input related to producti on of 1 MJ marginal heat is valid as well in summer as winter seasons.

Studstrupværket is fired with ~92% coal, ~7% straw and 1% fuel oil (Elsam 2005). According to Elsam (2005) the amount of straw used is constrained by capacity at th e plant. Thus coal is the marginal source of fuel to Studstrupværket. In section 3.1 inventory data on marginal electricity b ased on coal is described; ‘Hard coal, burned in power plant/NORDEL’ (ecoi nvent 2004). However, the ecoinvent database also contains inventories on coal burned in power plants in other countries, but this is of minor interest since power plants connected to the NORDEL grid is most representative for Studstrupværket.

Furthermore 5.8 kJ electricity per MJ delivered heat is used. The inventory data for 1 MJ district heating in Aarhus is summarised in Table 3.4 . Water consumption for compensation of loss of water in the grid and production, maintenance and disposal of the district heating grid is not included.

LCI-data for district heating in Aarhus Amount LCI data used in this study Coal burned at Studstrupværket 1.93 MJ ‘ Hard coal, burned in power plant/NORDEL ’ (ecoinvent 2004) Displaced electricity (electricity at plant) -0.54 MJ Electricity, see Table 3.3 Electricity (electricity at user) 0.0058 MJ Electricity, see Table 3.3 

## Table 3.4: LCI of 1 MJ district heating in Aarhus.

## 4 Transport 43



# 4 Transport
Transport processes are used in several processes in the agricultural, oil m ill, refinery and transport stages. This chapter describes inventory data for transport, eith er per tkm (transportation of 1 tonne at a distance of 1 kilometre) or per MJ diesel burned. Transport distan ces are described together with the processes that use transport, i.e. transport of the mate rials used in the relevant process. Transport types included are transport by truck, by oceanic freighter and burning of diesel in agricultural machinery (traction).



# 4.1 Transport with lorry
Several inventories are available for transport with lorry . There are also inventories for different sizes of lorry available. In this study three different lorri es are applied; 10-16t, 25-28t and 40-48t trucks.

Different data sources operate with different assumptions on load factor, i.e. the effective transported load over one kilometre. Two factors determine the load factor; share of a round-trip where the truck is empty and the actual working load compared to load capacity. In a literature review in Spielmann et al. (2004) load factors in the interval 0.25 – 0.62 have been identified. Load factors exceeding 0.5 means that cargo is transported on both the outward journey and on the return trip.

The load factors applied in the ecoinvent database (Spi elmann et al. 2004) and the EDIP database (EDIP 2000) are shown in Table 4.1

.

Load factor Lorry Ecoinvent (Spielmann et al. 2004) EDIP (2000) Lorry 10-16 t (load capacity 5-8 t) 0.42 0.48 Lorry 17-28 t (load capacity 10-17 t) 0.47 0.48 Lorry 29-48 t (load capacity 18-32 t) 0.46 0.70 

## Table 4.1: Load factors in the ecoinvent and EDIP databases. (Spielmann et al., 2004 and EDIP 2000)



## Regarding transport of oil seeds, vegetable oils and anc illary materials it is assumed that load factors above 0.5 are unrealistic. This is because this study concerns products (oil crops to o il mill, oil to market) and ancillary material to agriculture and oil mills where it is assume d that no cargo is sent back from the place of delivery. Thus, it is assumed that the lorries make the return trip empty. Therefore, the load factors from ecoinvent are applied in this study.

Analysing existing inventories for transport with lorry from the EDIP and ecoinvent databases in SimaPro and using the EDIP97-method for LCIA it appears that global warming, acidifi cation and ecotoxicity are the most significant impact categories. However, the contributi ons to ecotoxicity, which mainly originates from strontium emissions from discharged water from crude oil produc tion, are considered as either very uncertain or as related to an error in the LCIA-method; EDIP97 (Wen zel et al., 1997 and Hauschild and Wenzel 1998). Therefore, contributions to ecotoxicity when analysing the di fferent inventories are not included in a comparison of the inventories. Since the contribution to global warming and acidification is mainly related to the amount of diesel burned, this is used for comparison of the invent ories. Also in order to assess the comprehensiveness of the inventories, the number of included emissions from ope ration of the vehicles is included in the comparison. In Table 4.2 inventories for 29-48 tonne lorries are compared from three different sources. The figures for different inventories of 10-16 tonne and 17-28 tonne lorries vary somehow in the same ratio as for 29-48 tonne lorries. Therefore, these data are not shown.

## 44 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

LCI-data for transport Number of included emissions to air Diesel consumption Description of data

Truck 40-48t EU2 70%, kgkm, mixedDK (EDIP 2000) 14 336 g/km Time: Data from late 1990ies Geography: Denmark Technology: Average; 80% motorway, 15% highway and 5% city, EURO class 2 engine Co-product allocation : Not relevant Capital goods: Not included Truck 40-48t EU2 70%, kgkm, motorway (EDIP 2000) 14 319 g/km Time: Data from late 1990ies Geography: Denmark Technology: Average; 100% motorway, EURO class 2 engine Co-product allocation : Not relevant Capital goods: Not included Truck 40t (ETH-ESU, 1996) 13 - Time: Data from early 1990ies Geography: USA, Switzerland and Germany Technology: Average, not specified Co-product allocation : Not relevant Capital goods: Construction, maintenance and disposal of trucks and roads are included Transport, lorry 40t (ecoinvent 2004) 21 348 g/km Time: Data from early 2000s Geography: Switzerland Technology: Average, fleet composition according to EURO classes Co-product allocation : Not relevant Capital goods: Construction, maintenance and disposal of trucks and roads are included 

## Table 4.2: Comparison of LCIs of transport with truck 29-48 tonne. The inventories are compared within the number of included emissions from operation and diesel consumption per kilometre. The applied data are marked with a black dotted frame.



## It appears from Table 4.2 that the difference in fuel consumption is not significant; it varies from 319 g/km to 348 g/km. The compared data sets represent different geographical regions. EDIP represent Denmark, ecoinvent represent Switzerland and ETH-ESU represent USA, Switzerland and Germany. The most comprehensive data are the ones from ecoinvent; they include 21 emissi ons to air from operation of vehicle and include inventories on production, maintenance and disposal of vehicles as well as roads. It is chosen to apply transport inventories from ecoinvent. The applied inventories for transport with lorries from ecoinvent comprises three different lorry sizes: 16t, 28t and 40t. The applied data are shown in Table 4.3 for different transport distances and for different amounts of annual goods to the consid ered production unit. The distribution of lorry sizes as a function of distance and annual transpor ted goods is based on rough estimates.

Distance/transported goods 0 – 9 tonne/year 10 – 99 tonne/year ≥ 100 tonne/year 0 - 99 km Transport, lorry 16t Transport, lorry 28t Transport, lorry 28t 100 – 999 km Transport, lorry 16t Transport, lorry 28t Transport, lorry 40t
≥ 1000 km Transport, lorry 28t Transport, lorry 28t Transport, lorry 40t 

## Table 4.3: Applied inventories for transport with lorry from ecoinvent (2004). The lorry sizes applied vary dependent on the distance and annual transported goods.



# 4.2 Transport with ocean tanker
The only processes in this study that include transport with ship are transportation of palm oil from Malaysia to Europe and transportation of agricultural chemicals (fertiliser s and pesticides). It is presumed that this is done in large tankers. Two inventories for oceanic ship tran sport with tanker have been identified. These are compared in Table 4.4 within diesel consumption and number of included emissions for operation.

## 4 Transport 45

LCI-data for transport Number of included emissions to air Diesel consumption Description of data

Tanker oceanic ETH U (ETH-ESU) 24 1.8 g/tkm Time: Data from early 1990ies Geography: Not relevant Technology: Average, ship size not specified. Bilge oil waste and anti fouling agents are included. Co-product allocation : Not relevant Capital goods: Construction, maintenance and disposal of ship are included. Harbour not included.Transport, transoceanic tanker/OCE (ecoinvent 2004) 25 1.3 g/tkm Time: Data from early 2000s Geography: Not relevant Technology: Average, 150,000 t capacity. Bilge oil waste and anti fouling agents are included. Co-product allocation : Not relevant Capital goods: Construction, maintenance and disposal of ship and harbour are included. Harbour operations are also included. 

## Table 4.4: Comparison of LCIs of tran sport with tanker ship. The inventories are compared within the number of included emissions from operation and fuel consumption per tkm. The applied data are marked with a black dotted frame.



## It appears from the table that the difference between the two inventories is not significant within the compared factors. It is chosen to apply the inventory from ecoi nvent since this data set is the newest, most well documented and most comprehensive; it includes ha rbour operations and harb our related capital goods.



# 4.3 Traction
The term traction covers burning of diesel in agricultu ral machinery and it is measured in MJ. Traction includes production, transport and burning of diesel and use of lubricants. Life cycle inventories for traction are identified in the ecoinvent database (Nemecek et al. 2003) and the LCAfood database (Nielsen et al. 2005). Table 4.5 compares the two inventories.

Since, no of the two inventories include lubricants, this has been added. According to Dalgaard et al. (2001) the use of lubricants is proportional with the use of diesel. Dalgaard et al. (2001) specifies the use of lubricants as 3.6 MJ/litre diesel burned in tractor. Assuming same dens ity and calorific value for lubricant as for diesel (see Appendix 1: Data on fuels ), the use of lubricants can be calculated as 0.099 kg lubricant/kg diesel, or 0.0024 kg lubricant/MJ diesel. The inventory on production of lubricants ‘ Lubricating oil, at plant/RER ’ (ecoinvent 2004) has been applied. The ecoinvent data on lubricants do not include emissions related to the fate of the lubricant after it is used. It is assumed that lubricants end as emissions to air (when burned or heated up in the motor) and soil (when dripping). In both cases the oil will degrade into mainly CO 2 

## and water. Traces of heavy metals, SO 2 

## , NO x 

## , NMVOC and other additives in the oil will also be emitted. However due to lack of data and uncertainties in determining the fate, it is chosen only to account for CO 2 

## -emissions. According to Meyer et al. (1994) the CO 2 

## emission from burning of diesel is 74 g/MJ (This is also in accordance with the ecoinvent data in Table 4.5 ). 74 g CO 2 

## /MJ lubricant corresponds to 3.1 kg CO 2 

## /kg. Hence, this is added to the ecoinvent data on lubricants.

## 46 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Amount (g/MJ diesel) Interventions for traction Ecoinvent (Nemecek et al. 2003, p 56-59) LCAfood (Nielsen et al. 2005) LCAfood compared to Ecoinvent, factor

Fuel consumption Diesel 23.1 28.0 Diesel Emission Carbon dioxide (CO 2 ) 74.5 87 1.2 Sulphur dioxide (SO 2 ) 2.41E-02 2.50E-02 1.0 Methane (CH 4 ) 3.08E-03 4.10E-03 1.3 Benzene 1.74E-04 - - Cadmium (Cd) 2.39E-07 - - Chromium (Cr) 1.19E-06 - - Copper (Cu) 4.06E-05 - - Dinitrogen monoxide (N 2 O) 2.86E-03 9.10E-03 3.2 Nickel (Ni) 1.67E-06 - - Zink (Zn) 2.39E-05 - - Benzo(a)pyrene 7.16E-07 - - Ammonia (NH 3 ) 4.77E-04 - - Selenium (Se) 2.39E-07 - - PAH (poly cyclic hydrocarbons) 7.85E-05 - - Hydro carbons (HC, as NMVOC) 6.80E-02 ( 11 ) 1.17E-01 1.7 Nitrogen oxides (NO x ) 1.06 ( 11 ) 1.10 1.0 Carbon monoxide (CO) 1.50E-01 ( 11 ) 2.80E-01 1.9 Particulates (<2.5 μ m) 1.07E-01 ( 11 ) 7.10E-02 0.7 

## Table 4.5: Comparison of life cycle inventory data for 1 MJ traction in ecoinvent and LCAfood.



## It appears from Table 4.5 that ecoinvent includes more emissions th an LCAfood and for most interventions the amount in LCAfood is higher than in ecoinvent. Most of this difference can be ascribed to difference in the applied calorific values in the two data sources. Compari ng with other data sources, the calorific value at 43.3 MJ/kg diesel in ecoinvent seems more correct than the 35.7 MJ/kg which is used in LCAfood (see also Appendix 1: Data on fuels ', where a calorific value at 41.9 MJ/kg is given).

Since the data in ecoinvent, shown in Table 4.5 are the most comprehensive regarding the number of emissions included and uses the most normally applied calorific value for diesel, it is chosen to apply these data in this study. The use of lubricant at 0.0024 kg lubricant/MJ diesel and an emission of 3.1 kg CO 2 

## /kg lubricant are added to the ecoinvent data. The applied inventory data for production of diesel is: ‘Diesel, at regional storage/RER’ (ecoinvent 2004).



# 4.4 Transport with passenger car
The use of transport with passenger car in this study is insignificant compared to the other means of transportation. The applied inventory data is ‘Transport, passe nger car/RER’ (ecoinvent 2004). Beside burning and production of petrol, the following processes are also include d in the data set: construction, maintenance and disposal of cars and roads. Since this dataset is in the unit of personkm, it is transformed into the unit of km. According to ecoinvent (Spielmann et al. 2004), 1 personkm corresponds to 0.629 km. Thus, the ecoinvent data are transformed into the unit of km by multiplying with 0.629 km/personkm.

11 

## Emission of HC, NO x 

## , CO and particles in ecoinvent are not related to kg or MJ diesel burned. Instead it is related to engine speed, engine power and operation time. Anyhow, in order to estimate the emissions, they are calculated as theaverage per amount of diesel from the five most significant field work processes which are: Combine harvesting, ploughing, fertilising (by broadcaster), harr owing (tine harrow) and rolling (see Table 5.10 ). The emissions for different field work processes are given in Nemecek et al. (2003).

## 5 Agricultural stage: Rapeseed 47



# 5 Agricultural stage: Rapeseed
The rapeseed plant (Brassica napus) is an annual crop, it grows to a height of approximately 140 cm and the rapeseeds contain around 44% oil and 23% protein (Mø ller et al. 2000; Dansk Landbrugsrådgivning 2007).

The agricultural stage includes activiti es related to cultivation of rapeseed. The inventory takes its point of departure in cultivation of 1 hectare of rapeseed in Denmark. Most data are from 2002 - 2005. Two types ofrapeseed exist; spring and winter rapeseed. The inventory is based on average production of conventional rapeseed in Denmark in 2004 where spring rapeseed constitutes approximately 1% of the rapeseed cultivated area. In 2004 an area of 1,218 km 2 

## was planted with rapeseed in Denm ark (FAOSTAT 2006). That corresponds to 3% and 5% of the total land area and th e arable land in Denmark respectively.

The following description of rapeseed cultivation is mainly based on Dansk Landbrugsrådgivning (2005a and 2005b).

The production target for rapeseed is a high yield of seed with a high content of oil and protein and a low content of glucosinolate and euricic fatty acids. Rapeseed can be grown on sandy as well as clay soils. In order to avoid fungus attack there should be at least four year s between two rapeseed crops (and other crops with hollow stem). Normally 50 – 100 plants per m 2 

## are appropriate. Before sowing the soil should be sprayed with herbicides to control weeds, ploughed and compacted. The seeds are normally treated in order to avoid pest attack during seed germination. Wint er rapeseed is sown between the 10 th 

## and 25 th 

## of August and spring rapeseed is sown in the beginning of April or later if there is a risk of night frost. The need for nitrogen fertilisers depends on the crop (winter/spring rapeseed), soil type an d previously crop and application of manure. Other fertilisers include phosphorus (P), potassium (K), manga nese (Mg), sulphur (S), and possibly boron (B) on sandy soils. Weeding of the rows betw een the rapeseed plants (inter-row tillage) are typically done twice during autumn for winter rapeseed and twice for spring rap eseed during spring. In addition weed and pest control can be done applying several different herbicides and insecticides. Harvesting typically takes place between 2 nd 

## of July and 5 th 

## of August for winter rapeseed and between 5 th 

## and 20 th 

## of August for spring rapeseed. Harvesting can be done either by laying in swaths or by a conventional harvester. Both methods are used. When harvesting by laying in swaths the harvested crop is drying and possible ripening in the field and is picked up later. This method is appropriate if ripeness is not the same all over the field (Dansk Landbrugsrådgivning, 2005a). By conventional harvesting the rapeseed and possi ble straw are picked up from the field immediately. This method is appropriate if ripe ness is equal all over the field.

Figure 5.1
, Figure 5.2 and Figure 5.3 

## show a flowering rapeseed field, harvesting of rapeseed and harvested rapeseed respectively.

## 48 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Figure 5.1: Flowering rapeseed field. Picture taken by Jannick H Schmidt near Aalborg in 2007. Figure 5.2: Harvesting of rapeseed. Picture obtained from: http://jyndevad.okologgen.dk (May 2007)

Figure 5.3: Harvested rapeseed. Picture taken by JannickH Schmidt in 2007. Samples provided by Nordic Folkecenter for Renewable Energy.



## According to Dansk Landbrug (2004) rapeseed from Danish agriculture must maintain the specifications given in Table 5.1

.

Specifications for rapeseed Content Oil 40% Water 8-9% Pure commodity 100% Pure seed 98% Glucosinolate 25 micro mol/mol Euricic fatty acids <2% Free fatty acids <2% 

## Table 5.1: Specifications for rapeseed from Danish agriculture. If the seed do not maintain the specifications the transfer price is adjusted. (Dansk Landbrug 2004). The water content in Dansk Landbrug (2004) (9%) vary slightly from the water content given in Møller et al. (2000) which is 8%.



# 5.1 Product flow in agricultural stage
The inventory of the agricultural stage is divide d into the unit processes shown as shaded boxes in Figure 5.4

. The product flow is determined per hectare from st atistical data on Denmark’s production of rapeseed from Statistics Denmark (Danmarks Statistik 2006), see descriptions below the figure.

## 5 Agricultural stage: Rapeseed 49Capital goods 

### Farm - buildings

Tractor

Harvester1 hectare rapeseed field 

### Rapeseed: 5 kg

Sowing

Fertilizer applying Ploughing/compacting

Pesticide spraying

Possibly row clearing

Harvesting

Straw: 2930 kg

Rapeseed: 3240 kg Drying

Rapeseed: 3231 kg Seed processing

Pesticide spraying

Rapeseed: 3340 kg

Water: 100 kg Other agr. machinery

Straw to biomass plant: 381 kg Straw left in field: 2549 kg



## Figure 5.4: Product flow related to cultivation of 1 ha rapeseed field in 1 year. The grey shaded boxes represent the unit processes in the agricultural stage

.


## Seed input: According to Dansk Landbrugsrådgi vning (2005a) 4-6 kg seed/ha is needed in order to achieve the desired plant density. 5 kg seed/ha is applied in this study.

Rapeseed output for seed production: According to section 5.4 the land use related to production of 5 kg seed corresponds to the lands use related to production of 9 kg conventional rapeseed.

Water loos in drying: According to section 5.3 , 100 kg of water is lost in the drying process per ha.

Yield: The rapeseed yield is applied as the yield in 2005 calculated from linear regression of yields from 1990 to 2005, see Table 5.2 . Yields are obtained from FAOSTAT (2006). Th e expected yield in 2005 can be calculated as 3.24 t/ha using the equation in Figure 5.5 . For comparison with yields determined using other methods

see Table 5.2

.

yield = 0.0403*(year - 1989) + 2.59 R 2 

### = 0.23

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.51990199119921993199419951996199719981999200020012002200320042005Yield (t/ha) 

### Rapeseed in Denmark



## Figure 5.5: Rapeseed yields in Denmark 1990 to 2005. The linear regression line and its corresponding equation and R 2 

## are also shown. The yields are obtain ed from Danmarks Statistik (2006).

## 50 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Region Yield 2005 (based on regression

1990-2005) Average yield 1990- 2005 Average yield 2000- 2005 Yield 2003 Yield 2004 Yield 2005

Denmark 3.24 t/ha 2.93 t/ha 3.16 t/ha 3.38 t/ha 3.86 t/ha 3.10 t/ha 

## Table 5.2: Rapeseed yield in Denmark determined using different methods. The applied yield is marked with a dotted line.



## In order to clarify calculations and to perform sensitivity analysis for cultivation of different soils the yield isalso determined for sand and clay soils. This is based on averages of yields for four soil types in Plantedirektoratet (2005a) which specify the expected yield. The calculation of averages is clarified and explained in Table 5.14 and Table 5.16

. Table 5.3 shows the applied yields in this study.

The distribution of agricultural soil types in Denmark is determined to be 41% sand and 59% clay, see Table 5.13 . Applying these numbers to the yields given in Plan tedirektoratet (2005a) it appears that the calculated yields for average soil is in surpri singly good accordance with the yield in Table 5.2 . Sine only 1% of the rapeseed grown in Denmark is spring rapeseed, Table 5.3 only shows data for winter rapeseed. As it appears in in Figure 5.5 there are great yield variations from year to year. Thus, the good accordance may be a coincidence. Therefore, the yields from Plantedirektoratet for sand and clay soils are adjusted to meet the level estimated in Danmarks Statistik (2006), see the lower part of Table 5.3

.

Average soil (41% sand / 59% clay) Sand Clay Calculated yields based on Plante- direktoratet (2005a) (tonne/ha) (Plantedirektoratet, 2005a) Winter rape 3.30 2.85 3.62 Average soil (41% sand / 59% clay) Sand Clay Applied yield in this study (tonne/ha) (Danmarks Statistik 2006) Winter rape 3.24 2.79 3.55 

## Table 5.3: Yields based on Plantedirektoratet (2005a) and Danmarks Statistik (2006).



## Comparing to yields in the major rapeseed producing countries shown in Table 5.4 , it appears that the yield in Denmark is similar to the yield in other major rape seed producing countries; Germany, France and the UK. However, the yields in Poland and Czech Republic seem to be lower.

Country Yield: 2002-2004 (tonne/ha) Average applied artificial N-fertiliser (kg N/ha) Germany 3.3 153 France 3.3 156 Poland 2.4 100 United Kingdom 3.2 112 Czech Republic 2.5 91 Denmark 3.2 119 

## Table 5.4: Average yields of rapeseed during 2002 to 2004 in the six major rapeseed producing countries in Europe. Data on yield based on Eurostat (2006), see Table A2.2 in Appendix 2: Rapeseed production in Europe in 2004 . Data on fertiliser is based on Eurostat (2006) (for co nsumption of artificial N-fe rtiliser) and FAOSTAT (2006) (for agricultural land).



## There may be several reasons for varying yield in the different countries. Climate, fertiliser application and weed control are considered as important factors. It appears from Table 5.3 that the input of artificial fertiliser in Poland and Czech Republic is lower than in the ot her countries. The yields in Germany, France, UK and Denmark are almost equal even though the input of artif icial fertilizer in UK and Denmark is lower than in Germany and France. The reason for that may be due to difference in input of manure. Figure 5.6 illustrates the effect of manue application. It appears from the figur e, that application of manue one year has effect on the

## 5 Agricultural stage: Rapeseed 51



## yield the following year. Above explanation can only be used for illustration because several factors influence the yield.

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

0 50 100 150 200 250 kg N/haton rapeseed/ha



### No manure Manure applied the year before



## Figure 5.6: Correlation between fertiliser input (kg N/ha) and yield. The numbers are shown for fields with and withoutapplication of manure the previous season. The difference between the two graphs shows that application of manure provides nutrients available for crops the following season. Based on Pedersen (2005, p 162).



## Straw: Rapeseed straw is co-produced with rapeseed. Diffe rent values for straw production have been identified. Jensen et al. (2005) specify a straw to crop ratio at 0.83 for high yield winter rapeseed cultivation on clay (3,600 kg rapeseed/ha) soils and 1.04 for low yield cu ltivation on sandy soils (2,300 kg rapeseed/ha). Brandt (2003) specifies a straw to crop ratio at 2.2 which represent production in Canada. A lower yield in Canada may explain some of the high ratio for production in Cana da compared to Denmark (see data from Jensen et al 2005 above). According to FAOSTAT (2006) the average yiel d for rapeseed cultivation in Canada during 2003 to 2005 is only 1,550 kg /ha. Danmarks Statistik (2006) also uses a straw to crop ration in the annual statistic on straw production in Denmark. During 1997 to 2004 the applied straw to crop ratio for rapeseed has been within the interval between 0.85 and 0.94. The average during this period is 0.89.

It is chosen to apply the data from Jensen et al. (200 5). The reason for not using data from Danmarks Statistik is that this source applies an average straw to crop ratio and thus, it does not take into account that the straw to crop ratio decreases as the yield increase. Table 5.16 in section 5.4 specifies the soils cultiv ated with rapeseed in Denmark as 41% sand and 59% clay. Table 5.3 specifies a yield at 2.79 tonne/ha on sandy soils and 3.55 tonne/ha on clay soils. Thus the straw production on average soils can be determined as 2.80 tonne straw per ha. The produced straw is shown in Table 5.5

.

Winter rapeseed Soil type Share of Denmark’s Straw to crop ratio agricultural soil Yield norm, t/ha Straw production, t/ha Sand 41% 1.04 2.79 2.90 Clay 59% 0.83 3.55 2.95 Total 100% 2.93 

## Table 5.5: Straw production for different yields (soil types) and winter/spring rapeseed. The applied data are marked with a dotted black frame.



## According to section 5.5 , 13% of the straw is utilised for energy purposes and 87% is left in the field.



# 5.2 Omitted inventory data in agricultural stage
Magnesium (Mg), Sulphur (S) and Boron fertilisers and dust emissions and soil erosion have not been taken into account. These interventions are regarded as insignificant.

## 52 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 5.3 Energy use
It appears from Figure 5.4 that several different field work pro cesses are included in rapeseed production. In this section the energy use for each of these field work pr ocesses is determined. In addition to the field work processes there are two other energy comsuming processes, i.e. drying of harvested rapeseed and miscellaneus transport (typically in person car), e.g. inspection of field.


# Drying of seed According to Jensen et al. (2005) the water content in rap eseed is dried 3 percent points. Nemecek et al. (2003, p 121) and Dalgaard et al. (2001) assume 2 percent points for unspecified grains. This study applies 3 percent point since the data from Jensen et al. (2005) concerns ra peseed and the other two data sets relate to drying of unspecified grains. Table 5.6 below shows the energy consumption re lated to drying from two different identified data sets. The functional unit for drying is selected as 10 g water dried equalling 1 percent point water of 1 kg crop.

Drying of 1 % point of 1 kg crop/ Drying of 10 g water Nemecek et al. (2003) Dalgaard et al. (2001)

Electricity 36 KJ - Heat (fuel oil) (50-70 KJ in source; assuming 6 KJ) 60 KJ - Unspecified - 50 KJ 

## Table 5.6: Comparison energy consumption related to drying of 1% point of 1 kg crop equalling drying of 10 g water from two data sources.



## The inventory from ecoinvent described in Nemece k et al. (2003) is applied in this study; ‘ Grain drying, low temperature/CH ‘. However, the electricity in the inventory is displaced by marginal electricity as described in Table 3.3 . This inventory also includes construction, mainte nance and disposal of machinery and buildings for the drying process.

The yield from one hectare is 3,240 kg dried rapeseed. Sin ce the rapeseed is dried 3 percent points, the undried yield is 3,340 kg. Thus, 100 kg water is dried per hectar e. This correspons to an energy use at 3.6 MJ electricity/ha and 6.0 MJ heat (fuel oil)/ha.


# Miscellaneaus transport (person car) Energy for miscellaneaus transport, e.g. inspection of field, is 6.1 litre diesel/ha (Dalgaard 2007). This is the applied energy use for miscellaneaus transpor t in LCAfood. Applying the data on fuels in Appendix 1: Data on fuels , 6.1 litre diesel corresponds to 222 MJ diesel/ha. The applied inventory data for inspection of field is:

‘ Transport, passenger car/RER ’ (ecoinvent 2004). This data set includ es burning of petrol, production maintenance and disposal of car and construction of road. In the data from ecoinvent there is an energy input of 0.00465 kg diesel and 0. 0615 kg petrol per km. Applying the data in Appendix 1: Data on fuels , this corresponds to 2.82 MJ/km. Thus, miscella neaus transport is 79 km/ha. The LCAfood assumes that all miscellaneus transport is in diesel cars. In this study the distri bution given in ecoinvent ( 2004) is applied instead.


# Specific diesel consumption for different field work processes Two data sources on diesel consumption for different fiel d work processes have been identified; applied data in ecoinvent (Nemecek et al. 2003) and applied data in LCAfood (Dalgaard et al., 2006 and Dalgaard et al. 2001).

Table 5.7 shows fuel consumption for different field wo rk processes obtained from ecoinvent and LCAfood. Data from Nemecek et al. (2003) represents Swiss agri culture and are based on m easurements as well as litera-

## 5 Agricultural stage: Rapeseed 53



## ture studies. Data from the LCAfood project represent Da nish agriculture and are also based on measurements as well as literature studies. The specific diesel consumpti ons for different field work processes applied in the LCAfood project are given in Dalgaard et al. (2001). Howe ver an aggregation of these data to the overall agricultural sector in Denmark, described in Dalgaard et al. (2006), showed that the calculated diesel consumption was 18% lower than the total diesel consumption in the Danish agricultural sector. Therefore, Dalgaard et al. (2006) suggests to add 22% extra diesel to the data given in Dalgaard et al (2001). The data shown in Table 5.7 from the LCAfood project include 22% extra diesel compared to Dalgaard et al. (2001). These data are referred to as LCAfood.

There are two methods for harvesting; combine harvesting or laying by swathes. It has not been possible to identify appropriate data on fuel cons umption for laying in swathes. Firstly, it requires a swath operation with rotary windrower. This requires approxi mately 68 MJ/ha (Dalgaard et al., 2 001 and Dalgaard et al. 2006). Next it requires combine harvesting with rough estimated en ergy consumption 90% of conventional combine harvesting; 560 MJ/ha (90% of the energy consumption obtained from Dalgaard et al., 2001 and Dalgaard et al. 2006). The total very rough estimated energy use for layi ng by swathes is 628 MJ/ha. The energy use for combine harvesting obtained from Dalgaard et al. (2001) and Dalgaard et al. (2006) is 622 MJ/ha which is much similar to the estimated energy consumption for laying in swathes. Therefore, it is presumed suitable to apply the energy use related to combine harvesting for all rapeseed harvesting.

LCAfood Specific diesel consumption fo r different field work proc-

ess Unit Ecoinvent Sandy soil Clay soil Ploughing MJ/ha 1,086 919 1,124 Harrowing, by tine harrow/disc harrow MJ/ha 185 279 342 Packing MJ/ha 81 98 Rolling MJ/ha 132 81 98 Seed bed harrowing MJ/ha 160 195 Seed bed harrowing, heavy MJ/ha 240 293 Inter-row tillage/weeding MJ/ha 67 133 133 Tillage

Straw chopping 12 MJ/ha 0 0 0 Sowing Sowing MJ/ha 159 120 146 Fertilising, by broadcaster MJ/ha 220 89 89 Fertilising, lime application MJ/ha 67 67 Slurry spreading MJ/tonne 9 13 13 Fertilising

Solid manure loading and spreading MJ/tonne 22 24 24 Pesticide Application of plant protection products, by field sprayer MJ/ha 73 67 67

Harvesting Combine harvesting MJ/ha 1,386 622 622 Transport, tractor and trailer (including empty return trip) MJ/tkm 1.7 8.9 8.9 Transport

Machine transport MJ/km 1.8 1.8 

## Table 5.7: Specific fuel consumption for differe nt field work processes in the eco invent (Nemecek et al., 2003, p 181- 182) and LCAfood (Dalgaard et al., 2001 and Dalgaard et al. 2006). Ecoinvent data are based on a yield at 3.15 tonne/ha (Nemecek et al., 2003, p 247). Conversion from litre and kg diesel is based on energy content given in Appendix 1: Data on fuels

.


## It appears from Table 5.7 that the differences between data from ec oinvent and LCAfood are relatively small. However, significant differences > factor 2 are identified for three processes: 1) transport with tractor and trailer (difference = factor 5.2), 2) fertiliser applicati on (difference = factor 2.5), and 3) harvesting (difference = factor 2.2).

12 

## It is assumed that straw chopping is done with combine harvesting (Jensen et al 2005)

## 54 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil


# Field work operations: which and how many times? In the following fuel consumption for rapeseed cultivation for different data sets are described. Firstly, the number of different field work processes are estimated, see Table 5.8 . Next, the number of field work processes for rapeseed cultivation for different existing data sets are described, see Table 5.9

. Table 5.10 summarises the diesel use for all investig ated data sets. The data set from LCAfood represents normal practice for rapeseed cultivation in Denmark.

Inventorying the fuel consumption it is important to be aware of differences betw een LCIs conducted applying the attributional versus the conseque ntial approach for system delimitation. In Denmark most plant production takes place at farms which also produces animals. Therefor e, a significant share of the fertilizer applied is manure. The data on plant production in ecoinvent, which is conducted applying the a ttributional approach, is based on average shares of artificial fertiliser and manure. On the other hand, data in LCAfood, which is conducted applying the consequential appr oach, only account for artificial fertiliser. This is because manure is presumed constrained by the production of animals and not production of plants. Therefore, all interventions related to slurry spreading (including traction for spr eading and emissions of N from manure), have been disregarded. The need for nutrients is purely met by artific ial fertilisers, because a change in crop production will affect the marginal supply of fertiliser which is artificial fertiliser and consequentia l traction for spreading and emissions.

In this study the consequential approach is applied. Therefore, the need for fertiliser is met by artificial fertilisers, traction and spreading is for artificial fertilisers an d also the nutrient loss from the field is calculated based on a nutrient balance using artificial fertiliser. Thus, in Table 5.8 , there are no operations related to spreading of slurry.

## 5 Agricultural stage: Rapeseed 55

Estimated number

of operations Field work process

High Low References

Ploughing 1 1 Dansk Landbrugsrådgivning (2005a), Jensen et al. (2005) Harrowing, by tine harrow/disc harrow 2 1 Several harrowing is recommended in order to reduce the number of snails in the soil (Dansk Landbrugsrådgivning, 2005a). Jensen et al. (2005) specify one treatment Packing 1 0 Packing after ploughing is recommended in Dansk Landbrugsrådgivning (2005a) No packing in Jensen et al. (2005) Rolling 1 1 Rolling after ploughing is recommended in Dansk Landbrugsrådgivning (2005a). Jensen et al. (2005) also specify one treatment. Seed bed harrowing 2 0 Seed bed harrowing, heavy 1 0 Packing and seed bed harrowing is recommended in (Dansk Landbrugsrådgivning, 2005a). Two light seed bed harrowing followed by one heavy seed bed harrowing is used in LCAfood. Jensen et al. (2005) do not include this process. Inter-row tillage/weeding 2 2 2 treatments recommended in Dansk Landbrugsrådgivning (2005a). Jensen et al. (2005) do not include this process. Tillage

Straw chopping 1 0 If the straw is not removed from the field it should be chopped inorder to improve germination of lost seeds (Dansk Landbrugsrådgivning, 2005a). Jensen et al. (2005) include this process with combine harvesting. Sowing Sowing 1 1 Dansk Landbrugsrådgivning (2005a), Jensen et al. (2005) Fertilising, by broadcaster 2 2 This is specified in LCAfood, ecoinvent and Jensen et al. (2005) Fertilising, lime application 1 0 This is used in LCAfood. . Jensen et al. (2005) do not include this
process. Slurry spreading 0 0 Fertilising

Solid manure loading and spreading 0 0 Since manure and slurry is constrained to animal production, only artificial fertiliser is ascribed to plant production

Pesticide Pesticide, by field sprayer 4 3 In LCAfood the number of pesticide spraying operations is 5. According to Kristensen (2006) two inter-row tillage/weeding operations substitute one spraying in the autumn, thus 4. Jensen et al. (2005) specify 3 treatments. Harvesting Combine harvesting 1 1 The energy consumption in combine harvesting and laying by swathes is estimated to be similar. Therefore, combine harvesting isapplied for all rapeseed. ). Jensen et al. (2005) include straw chopping with combine harvesting. Transport, tractor and trailer (including empty return trip) 3.2 tkm 3.2 tkm Transport of the harvested rapeseed (yield at 3.1 tonne/ha and dried 3 percent points). Dalgaard et al. (2001) estimates an average distance at 1 km between the field and the farm. Transport

Machine transport 40 km 24 km This is calculated as the sum of field work processes multiplied with the distance to the field (round-trip). The distance is 1 km each way; see transport, tractor and trailer. 

## Table 5.8: Description and documentation of estimated number of field work operations applied in this study.



## Table 5.9 summarises the number of field work operations in different studies. The diesel consumption related to the estimate shown in Table 5.8 is calculated using specific diesel consumption from ecoinvent as well as LCAfood. However, in the case of ecoinvent there are no data for seed bed harrowing. Therefore, two operations of tillage, rolling are applied instead of the one given in Table 5.8 . Also there is no data for ‘tillage, packing’. Therefore, there is applied an extra operation of ‘tillage, rolling’ instead. ‘Tillage, straw chopping’ has been left out since there is no data for that in ecoinve nt. Furthermore, there is no field work process for lime application in ecoinvent. Therefore, this is applied as an extra ‘fertiliser, by broadcasting’ operation instead.

## 56 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Adjusted to available processes in; ecoinvent LCAfood Estimate; ecoinvent Own high Own low LCAfood Own high Own low Ploughing 1 1 1 1 1 1 Harrowing, by tine harrow/disc harrow 2 2 1 2 2 1 Packing 1 1 Rolling 3 2 1 1 1 Seed bed harrowing 2 2 Seed bed harrowing, heavy 1 1 Inter-row tillage/weeding 2 2 2 Tillage

Straw chopping 1 1 Sowing Sowing 1 1 1 1 1 1 Fertilising, by broadcaster 3 3 2 2 2 2 Fertilising, lime application 1 1 Slurry spreading 8.55 t Fertilising

Solid manure loading and spreading 5.55 t Pesticide Pesticide, by field sprayer 3.5 4 3 5 4 3 Harvesting Combine harvesting 1 1 1 1 1 1 Transport, tractor and trailer (including empty return trip) 3.2 tkm 3.2 tkm 3.2 tkm 3.2 tkm Transport

Machine transport - 34 km 22 km 48 km 40 km 24 km 

## Table 5.9: Number of field work processes in different calculations of the diesel consumption in rapeseed cultivation. The number of field work operations for different processes in the LCAfood project is obtained from Dalgaard (2007).


# Diesel consumption related to cultivation of rapeseed The diesel consumption related to cu ltivation of rapeseed is shown in Table 5.10 . The numbers are calculated from data in Table 5.7 and Table 5.9

.

Specific diesel consumption based on; Ecoinvent LCAfood Number of field work processes based on; Ecoinvent Own, high Own, low LCAfood Own, high Own, low Own, high Own, low Soil type; avg. avg. avg. sand clay sand sand clay clay Ploughing 1,086 1,086 1,086 919 1,124 919 919 1,124 1,124 Harrowing, by tine harrow/disc harrow 370 370 185 559 683 559 559 683 683

Packing 0 0 0 81 98 81 0 98 0 Rolling 0 396 264 81 98 81 81 98 98 Seed bed harrowing 0 0 0 320 390 320 0 390 0 Seed bed harrowing, heavy 0 0 0 240 293 240 0 293 0 Inter-row tillage/weeding 0 134 0 0 0 266 266 266 266 Tillage

Straw chopping 0 0 0 178 178 178 0 178 0 Sowing Sowing 159 159 159 120 146 120 120 146 146 Fertilising, by broadcaster 660 660 440 178 178 178 178 178 178 Fertilising, lime application 0 0 0 67 67 67 0 67 0 Slurry spreading 77 0 0 0 0 0 0 0 0 Fertilising

Solid manure loading and spreading 122 0 0 0 0 0 0 0 0

Pesticide Pesticide, by field sprayer 256 292 219 336 336 268 201 268 201 Harvesting Combine harvesting 1,386 1,386 1,386 622 622 622 622 622 622 Transport, tractor and trailer (including empty return trip) 0 5 5 0 0 28 28 28 28 Transport

Machine transport 0 0 0 88 88 73 48 73 48 Total 4,116 4,488 3,744 3,787 4,301 4,000 2,738 4,513 3,049 

## Table 5.10: Diesel consumption (MJ) in agricultural field work pr ocesses for 1 ha rapeseed cultivation based on different data.



## It appears from Table 5.10 that the diesel consumption varies be tween 2,738 MJ/ha and 4,513 MJ/ha depending on the assumptions applied and the data used. In orde r to provide a better overview of the influence of the different assumptions and data, Table 5.11 shows different averages based on Table 5.10

.

## 5 Agricultural stage: Rapeseed 57

Specific diesel consumption Average Number of field work processes Average Soil type Average 1

Total diesel consumption 3,860 Specific diesel consumption Ecoinvent LCAfood Number of field work processes Average Soil type Average 2

Total diesel consumption 4,116 3,731 Specific diesel consumption Ecoinvent LCAfood Number of field work processes Ecoinvent Own, high Own, low LCAfood Own, high Own, low

Soil type Average 3

Total diesel consumption 4,116 4,488 3,744 4.044 4,256 2,894 Specific diesel consumption Ecoinvent LCAfood Number of field work processes Ecoinvent Own, avg. LCAfood Own, avg.

Soil type Average sand clay sand clay 4

Total diesel consumption 4,116 4,116 3,787 4,301 3,369 3,781 Specific diesel consumption Ecoinvent LCAfood Number of field work processes Ecoinvent Own, high Own, low LCAfood Own, High Own, low Own, high Own, low Soil type Average sand clay sand sand clay clay 5

Total diesel consumption 4,116 4,488 3,744 3,787 4,301 4,000 2,738 4,513 3,049 

## Table 5.11: Five different averages of fuel consumption (MJ) in ag ricultural field work processes for 1 ha rapeseed cultivation based on different data. For each of the five averages, th e parameters that are kept co nstant (average) are italic and underlined. The applied data are marked with a black dotted frame; the other data are used in a sensitivity analysis in section 21.6

.


## According to Table 5.13 Danish agricultural land is distributed on 41% sand and 59% clay. Thus the average diesel consumption for traction can be calculated as 3,612 MJ.

The uncertainty related to the difference in energy u se determined from data in ecoinvent and from data in LCAfood (average 4 in the table) is asse ssed in a sensitivity analysis, see section 21.6

.
Difference in diesel consumption related to number of field work operations: It appears from Table 5.11 (average 3 in the table) that the difference between the total diesel consumption based on standard number of field work processes in ecoinvent and LCAfood compared to the fuel consumption based on own estimate are between high and low of own estimates. However, the aver age of own estimates (avera ge 4 in the table) differ from the ecoinvent data with 0% and for the LCAfood data the difference is 9-11%.

Difference in diesel consumption related to use of sp ecific diesel consumption based on either ecoinvent or LCAfood: It appears from Table 5.11 (average 2) that diesel consumption based on specific fuel consumption data from ecoinvent is 10% highe r than if based on LCAfood. The main reason for this difference is higher specific diesel consumption for harvesting and fertiliser application.

Overall there is good coherence between the different da ta sources on diesel consumption related to cultivation of rapeseed. It is chosen to base the fuel consump tion on specific fuel consumption data from LCAfood. The reason for this is that these data are based on Danish agriculture. Furthermore it is chosen to base the number of field work operation on an average of ow n high and low estimates, see dotted frame in Table 5.11

.

## 58 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Inventory data for burning of diesel in tractor and production, maintenance and disposal of tractor and is described in section 4.3

. 

# 5.4 Materials
The materials used for rapeseed growing include seed, artificial fertiliser and pesticides.



# Seed According to Figure 5.4 there is an input of 5 kg seed per ha. The interventions related to the agricultural stage in production of seed are accounted for by subtracting an amount of seed from the yield. According to Nemecek et al. (2003, p 103) the yield of seed production is 43% less than for conven tional cultivation of rapeseed. Thus, it requires 75% more land to produce seed than c onventional rapeseed. Therefore, the use of 5 kg seed requires the interventions as cultivation of 8.75 kg ~ 9 kg conventional rapeseed.

Only one life cycle inventory of seed has been identified; ‘ Rape seed IP, at regional storehouse/CH ’ (ecoinvent 2004). This inventory is shown in Table 5.12 below. The inventory has been modified applying interventions for electricity as described in section 3.1 instead of average Swiss electricity as in ecoinvent.

LCI-data for production of 1 kg seed Amount LCI data used in this study Rapeseed from agriculture 1.75 kg This is accounted for by subtracting 1.75 kg from the yield at 3.24 tonne/ha per kg seed used, see Figure 5.4

. Electricity 0.21 MJ Electricity, see Table 3.3 Building for storage 2.0

⋅ 10 -5 m 3 Building, multi-storey/RER (ecoinvent 2004) Transport from farm to warehouse 0.13 tkm Transport, lorry 28t/CH (ecoinvent 2004) 

## Table 5.12: Inventory data for production of 1 kg seed for rapeseed cultivation (ecoinvent 2004).



# Fertilisers Fertilisers used for rapeseed cu ltivation encompass nitrogen fertili sers, phosphorus, potassium, manganese, sulphur and possibly boron on sandy soils. The need for n itrogen fertilisers is described in Plantedirektoratet (2005a) and depends on the crop (winter/spring rapeseed), so il type, irrigation, previously crop and application of manure.

Life cycle inventories have been identified for strai ght fertilisers (N, P and K respectively) as well as for mixes. All the applied inventories are for straight fer tilisers since these are considered as the marginal affected. The mixes often consist of mixes of the straight fertilisers.

Nitrogen (N): When determining the soil types on which rapeseed are grown, it is assumed that rapeseed is equally distributed on the soil types in Denmark. The argument for this assumption is that rapeseed does not require any special physical conditions on so il quality (Dansk Landbrugsrådgivning, 2005a).

The distribution of soil types in Denmark is shown in Table 5.13 , also see Appendix 3: Soil types and adjustments to different sources . However, the soil types in Plantedirektor atet (2005a) which is used for determination of the fertiliser input do not fit with the soil types in Table 5.13 . In addition Plantedirektoratet distinguishes whether the sandy soils are irrigated or not. No information on share of irrigated rapeseed on sandy soils has been identified. It is assumed that 50% is irriga ted. In order to overcome differences in soil type terminology in different sources used, a conversion of the soil types has been carried out, see Appendix 3: Soil types and adjustments to different sources

.

## 5 Agricultural stage: Rapeseed 59

Soil type Area, km 2 Share of agricultural soils Sand/clay Coarse sandy soil 10,548 26% Sand Grinding sand 4,233 10% Sand Sandy soil with clay 11,523 28% Clay Clay soil with sand 9,817 24% Clay Clay soil 2,390 6% Clay Heavy clay soil 303 1% Clay Humus soil 2,090 5% Sand Calcareous soil 87 0% Sand City 1,704 - - Total 42,694 100% Sand (41%)/clay (59%) 

## Table 5.13: Distribution of soil types in agricultural soils in Denmark. See Appendix 3: Soil types and adjustments to different sources

.



## Table 5.14 shows the N-norm for winter rapeseed on different soils. Furthermore, the share of Denmark’s agricultural land is given for the diffe rent soil types. This is based on Table 5.13

.

Winter rapeseed Soil type Share of Denmark’s agricultural soil Yield norm, tonne/ha N-norm, kg N/ha Average of: Non-irrigated coarse sandy soil (JB 1+3) and Irrigated sandy soil (JB 1-4) 26% 2.7 159

Average of : Non-irrigated grinding sand (JB 2+4 and 10-12) and Irrigated sandy soil (JB 1-4) 16% 3.1 164

Mixed sand and clay soil (JB 5-6) 52% 3.6 171

Clay soil (JB 7-9) 7% 3.8 174

Total 100% 3.3 169 

## Table 5.14: Norms for yield and application of N-fertiliser for di fferent soil types (Plantedir ektoratet, 2005a, p 47).



## When determining the application of N ascribed to rap eseed, its effect on N application on the next crop the following year should be credited for (Hvid et al., 2004, p 25). Also the available N from the previous crop should be accounted for. Rapeseed cultivation always en ters into a crop rotation scheme including other crops. Since these crops have different ‘previous crop’ va lues it is necessary to estimate a typical crop rotation scheme from where the typical ‘previous crop’ value for cr ops before rapeseed can be determined. In Jacobsen et al., (2002) the most common crop rotation scheme for plant cultivation/pig production farms have been determined. The crop rotation scheme and ‘previous crop’ values are shown in Table 5.15 . It is stressed that the ‘previous crop’ values given in Table 5.15 do not necessarily reflect the actual effect on the nitrogen cycle. The ‘previous crop’ values given are those used in regul ation of Danish agriculture which is not purely based on natural science, i.e. the values are politically agreed.

## 60 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Field no Crop rotation ‘Previous crop’ value, kg N/ha 1 Spring barley, with second crop 17-25 2 Spring barley, with second crop 17-25 3 Set-aside 0 4 Winter wheat 0 5 Peas 23 6 Winter wheat 0 7 Winter rye 0 8 Winter barley 0 9 Winter rapeseed 27 10 Winter wheat 0 

## Table 5.15: ‘Most common’ crop rotation scheme for plant cultivation/pig production farms (Jacobsen et al., 2002, p 95). The ‘previous crop’ values are from (Plantedirektoratet, 2005a)



## It appears from Table 5.15 that the ‘previous crop’ value for crops be fore rapeseed typically is 0 and the ‘previous crop’ value for rapeseed is 27 kg N/ha. Thus, no N from crops before rapeseed has to be considered, and the application of N fertiliser on the crop after rapeseed should be 27 kg/ha less than prescribed by the norm (Plantedirektoratet, 2005a, p 13). Therefore, the 27 kg N/ha is subtracted from the N-norm when determining the application of N on rapeseed.

In order to simplify the distinction between sandy soil and clay soil, Table 5.14 is divided into two soil types; sandy soils (weighted average of the three first rows) a nd clay soils (weighted average of the two last rows).

The applied N-fertiliser on sandy, clay and average soils is shown in Table 5.16 , which also take into account credit from the ‘previous crop’-value for rapeseed.

Winter rapeseed Soil type Share of Denmark’s agricultural soil Yield norm, tonne/ha N-application, kg N/ha Sand 41% 2.85 134 Clay 59% 3.62 144 Total 100% 3.30 140 

## Table 5.16: Applied N-fertiliser in the st udy. The numbers are based on Table 5.14 and N-application is subtracted a ‘previous crop’-value at 27 kg N/ha. The applied data are marked with a dotted black frame

.


## It appears from Table 5.16 that 140 kg N/ha is applied to rapeseed. In reality 140 + 27 (‘ previous-crop’-value) = 167 Kg N/ha is applied, but 27 kg N/ ha can be saved for the crop after rapeseed. Therefore, the 27 kg N/ha is credited to rapeseed.

According to Nielsen et al. (2005) and Weidema (2003), the consumption of N-fertiliser in Europe is decreasing. This is also supported in IFA (2007a), where the consumption of N-fertiliser in Western Europe, Central Europe, Eastern Europe and Central Asia has been d ecreasing from around 27 million tonne in 1986/87 to 14 million tonne in 2002/03. According to IFA (2007a) the same decreasing tendency can be identified for P- and K-fertilisers. The affected supplier in a deceasing mark et is, according to Weidema (2003) the least competitive supplier. Nielsen et al. (2005) have identified the aff ected supplier to be less efficient ammonium plants in Eastern Europe. Thus, Nielsen et al. (2005) assume that the market is geographical limited to Europe and that the market trend is decreasing.

But is the geographical market limited to Europa? Acco rding to IFA (2007a) the import share of the supply to EU25 in 2005 was 20% for ammonia, 25% for ammonia nitrate and 39% for calcium ammonia nitrate. The import share of triple super phosphate was almost 1 00% and the import share of potash was 44% in 2005 (IFA

## 5 Agricultural stage: Rapeseed 61



## 2007a). Based on these figures, it is assumed that the ge ographical markets for nitrogen, phosphate and potash fertilisers are global.

The World market for nitrogen fertilisers have been constant or slightly increasing from 1993/04 to 2005/06 (IFA 2007a). The increasing world market for nitrogen fer tilisers is expected to continue in a 30 year outlook (IFA 2007b). According to IFA (2007a) the same increasing tendency can be identified for P- and K-fertilisers on the world market. Thus, the market trend on the global market is increasing.

Since the market trend then is increasing, the margin al suppliers are then, according to Weidema (2003), the most competitive. All identified LCI-data on production on fertilisers represents average suppliers to the European market. It is assumed that these da ta represents the marginal suppliers.

According to EFMA (2004) the most frequently used stra ight N-fertilisers in Europe are; Calcium ammonium nitrate (23% of all inorganic N applied in Europe), Ammonium nitrate (21%), Urea (16%) and Urea ammonium nitrate (12%). N-fertilisers in mixed fertilisers (a lso containing P and K) cons titute 22% of the inorganic nitrogen applied in Europe. It is assumed that the aff ected type of N-fertiliser is the most commonly used straight N-fertiliser; calcium ammonium nitrate.

Several life cycle inventories for nitrogen fertilisers have been identified. Analysing the data sets in SimaProand using the EDIP97 for LCIA, it appears that global warming, acidification and toxicity are the most significant impact categories. In Table 5.17 inventories for the most widely app lied N-fertilisers are compared within these categories.

LCI-data for N-fertiliser CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘ Fertiliser (N) ’, LCAfood database (Nielsen et al. 2005) 9.62 kg 32 g 0.9 m 3 Time: Data from early 1990ies Geography: Former West Germany Technology: Average, SNG marginal electricity Co-product allocation : Not relevant Capital goods: No ‘Calcium a mmonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) 9.05 kg 33 g 2,480 m 3

‘ Ammonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) 8.95 kg 28 g 2,300 m 3

‘ Ammonium sulphate, as N, at regional storehouse/RER ’ (ecoinvent 2004) 2.68 kg 8.3 g 1,340 m 3

‘ Urea, as N, at regional storehouse/RER ’, (ecoinvent 2004) 3.29 kg 13 g 1,670 m 3 Time: Data from mid to late 1990ies Geography: Western Europe Technology: Average, European average electricity Co-product allocation : Not relevant Capital goods: Machinery, buildings



## Table 5.17: Comparison of LCIs of N-fertiliser. The comparison is shown as characterised results using the EDIP97- method for LCIA. All the life cycle inventories are for 1 kg fe rtiliser, as N. The applied data are marked with a black dotted frame.



## It appears from Table 5.17 that the difference in contributions to global warming and acidification is not significant among ammonium nitrate fertilisers. The differen ce in contribution to ecotoxicity is mainly due to emissions of strontium related to diesel for transport in ecoinvent and copper related to disposal of metals in chemical plant. These emissions are not included in th e inventory in LCAfood. The contribution to global warming, acidification and ecotoxicity is smaller for am monia sulphate and urea. Electricity from the grid for N-fertiliser production is relative small (Nemecek et al. 200 3). Therefore, it has almost no effect on the results that the data in ecoinvent uses data for average electr icity from grid in Europe and not marginal electricity.

## 62 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Since the data from ecoinvent include capital goods and t hus are more complete, it is chosen to apply these data in the study. It is chosen to apply data for calcium ammonium nitrate since this fertiliser type is the most frequently applied N-fertiliser.

The European Commission (2006d) presents a range of different technologies to be considered in order to reduce N 2 

## O emissions from production of nitric acid which is used in the production of ammonium nitrate fertilisers. Examples of technologies are alternative oxidation catalysts, extension of reactor chamber, catalytic N 2 

## O decomposition in the oxidation reactor, combined NO x 

## and N 2 

## O abatement in tail gases and non-selective catalytic reduction of NO x 

## and N 2 

## O in tail gases. The presented technologies reduce the N 2 

## O emission from 30- 50% up to 98-99%. Section 21.8 presents a sensitivity analysis where BA T has been applied in the production of nitric acid.

Phosphorus (P): The application of P-fertiliser is determin ed from the soil’s P-value (Dansk Landbrugsrådgivning, 2005a). However, the application of P-fertiliser in this study is based on average numbers given in Dansk Landbrugsrådgivning (2005a and 2005b). This is shown in Table 5.18 . In Dansk Landbrugsrådgivning (2005a and 2005b) the use of P-fertiliser is given for two types of sandy soil; ‘ non irrigated sand, JB 1+3 ’ and

‘ Grinding sand, JB 2+4 ’. The use of P-fertiliser on sandy soil is regarded as the average of these two.

Soil type Share of Denmark’s agricultural soil Winter rapeseed kg P/ha Sand 41% 21 Clay 59% 28 Total 100% 25 

## Table 5.18: Applied P-fertiliser in the study. The applied data ar e marked with a black dotted frame. 25 kg P corresponds to 57 kg P 2 

## O 5 

## .


## In the section where N-fertiliser is described it appears that the market for P- and K-fertilisers is global and increasing. Thus, the affected suppliers are those who are most competitive. All identified LCI-data on production on fertilisers represents average suppliers to the Eur opean market. It is assumed that these data represents the marginal suppliers.

According to EFMA (2006) the most frequently applied stra ight P-fertilisers are; triple super phosphate (8% of all applied inorganic P in the EU) and double/single super phosphate (3%) . P-fertilisers in mixed fertilisers (also containing N and K) constitute 86 % of the inorganic phosphate applied in the EU. It is assumed that the affected type of P-fertiliser is the most commonly used straight P-fertiliser; triple super phosphate.

Three life cycle inventories for straight phosphorus fer tilisers have been identified. Analysing the inventories on P-fertiliser available in Simapro and using the EDIP 97 for LCIA, it appears that global warming, acidification and toxicity are the most significant impact categories. In Table 5.19 the inventories are compared within these categories. The electricity consumption for produc tion of triple super phosphate is relative higher than for production of N-fertiliser (Nemecek et al. 2003). Therefore, it has some effect on the emission levels that the data in ecoinvent uses data for average electricity in Europe and not marginal electricity. Thus, a modified version of triple super phosphate where electricity produ ced from coal instead of average electricity in Europe has been applied to the LCI in ecoinve nt is analysed. This is shown in Table 5.19

.

## 5 Agricultural stage: Rapeseed 63

LCI-data for P-fertiliser CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘ Fertiliser (P 2 O 5 ) ’, (triple super phosphate), LCAfood database (Nielsen et al. 2005) 1.19 kg 18 g 0.9 m 3 Time: Data from early 1990ies Geography: Former West Germany Technology: Average, European average electricity Co-product allocation : Not relevant Capital goods: No

‘ Single superphosphate, as P

2 O 5 , at regional storehouse/RER’ , ecoinvent database (Nemecek et al. 2003) 2.64 kg 42 g 4,170 m 3

‘Triple superphosphate, as P 2 O 5 , at regional storehouse/RER ’, ecoinvent database (2004) (Nemecek et al. 2003) 2.02 kg 34 g 5,110 m 3 Time: Data from mid to late 1990ies Geography: Western Europe Technology: Average, European average electricity Co-product allocation : Not relevant Capital goods: Machinery, buildings

Modified version of: ‘Triple superphosphate, as P 2 O 5 , at regional storehouse/RER ’, ecoinvent database (2004) (Nemecek et al. 2003) 2.46 kg 35 g 5,040 m 3 Time: Data from mid to late 1990ies Geography: Western Europe Technology: Average. Electricity; marginal (coal) Co-product allocation : Not relevant Capital goods: Machinery, buildings 

## Table 5.19: Comparison of LCIs of P-fertiliser. The comparison is shown as characterised results using the EDIP97- method for LCIA. All the life cycle inventories are for 1 kg fertiliser, as P 2 

## O 5 

## . For conversion to kg P; 1 kg P corresponds to 2.29 kg P 2 

## O 5 

## . The applied data are marked with a black dotted frame.



## It appears from Table 5.19 that contribution to global warming and aci dification differs with a factor 2.2 to 2.3. The difference between the two data sets for trip le super phosphate is not as significant as the difference between single super phosphate and triple super phosphate – they only vary with a factor 1.6 to 1.8. As in the case of N-fertilisers in the ecoinvent database the difference in contributi on to ecotoxicity is mainly due to emissions of cadmium from phosphoric acid production, stro ntium related to diesel for transport in ecoinvent and copper related to disposal of metals in chemical pl ant. These emissions are not included in the inventory in LCAfood. Since the data from ecoinvent include capital goods and thus are more complete, it is chosen to apply these data in the study. It is chosen to apply data for triple super phosphate sinc e this fertiliser type the most widely used P-fertiliser type in EU. Furthermore it is chosen to apply the modified version of the ecoinvent data on triple super phosphate where average electricity has been replaced by marginal electricity produced from coal.

Potassium (K): The application of K-fertiliser is determ ined from the soil’s K-value (Dansk Landbrugsrådgivning, 2005a). However, the application of K-fertiliser in this study is based on average numbers given in Dansk Landbrugsrådgivning (2005a and 2005b). This is shown in Table 5.20 . In Dansk Landbrugsrådgivning (2005a and 2005b) the use of K-fertiliser is given for two types of sandy soil; ‘ non irrigated sand, JB 1+3 ’ and

‘ Grinding sand, JB 2+4 ’. The use of K-fertiliser on sandy soil is regarded as the average of these two.

Soil type Share of Denmark’s agricultural soil Winter rapeseed kg K/ha Sand 41% 68 Clay 59% 91 Total 100% 82 

## Table 5.20: Applied K-fertiliser in the study. The applied data ar e marked with a black dotted frame. 82 kg K corresponds to 99 kg K 2 

## O.



## In the section where N-fertiliser is described it appears that the market for P- and K-fertilisers is global and increasing. Thus, the affected suppliers are those who are most competitive. All identified LCI-data on production on fertilisers represents average suppliers to the Eur opean market. It is assumed that these data represents the marginal suppliers.

## 64 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## According to EFMA (2006) the most frequently applied straight K-fertilisers are; potassium magnesium sulphate (51% of all applied inorganic K in the EU), po tassium chloride (27%) and potassium sulphate (6%). Kfertilisers in mixed fertilisers (also containing N and P) constitute 16% of the inorga nic potassium applied in the EU. It is assumed that the affected type of K-ferti liser is the most commonly used straight K-fertiliser; potassium magnesium sulphate. However, no inventory data have been identified for that type of K-fertiliser. Therefore, the type of K-fertiliser app lied in this study is potassium chloride.

Three life cycle inventories on straight potassium fertiliser have been identified. On e unspecified type in the LCAfood database (Nielsen et al. 2005) and two in th e ecoinvent database (Nemecek et al. 2003); potassium chloride and potassium sulphate. The data sets are compared in Table 5.21

.

LCI-data for K-fertiliser CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘ Fertiliser (K 2 O) ’, LCAfood database (Nielsen et al. 2005) 0.67 kg 1.1 g 0.3 m 3 Time: Data from early 1990ies Geography: Former West Germany Technology: Average, European average electricity Co-product allocation : Not relevant Capital goods: No

‘ Potassium chloride, as K 2 O, at regional storehouse/RER’

, ecoinvent database (Nemecek et al. 2003) 0.50 kg 1.9 g 402 m 3

‘Potassium sulphate, as K 2 O, at regional storehouse/RER ’, ecoinvent database (Nemecek et al. 2003) 1.42 kg 22.1 g 1,630 m 3 Time: Data from mid to late 1990ies Geography: Western Europe Technology: Average, European average electricity Co-product allocation : Not relevant Capital goods: Machinery, buildings



## Table 5.21: Comparison of LCIs of K-fertiliser. The comparison is shown as characterised results using the EDIP97- method for LCIA. All the life cycle inve ntories are for 1 kg fertiliser, as K 2 

## O. For conversion to kg K; 1 kg K corresponds to 1.21 kg K 2 

## O. The applied data are marked with a black dotted frame.



## The comparison in Table 5.21 shows that there is only little differ ence between data from the LCAfood database and ‘ Potassium chloride ’ in the ecoinvent database regarding gl obal warming and acidification. The contribution to ecotoxicity is significant higher in the inventories from ecoinvent. The difference in contribution to ecotoxicity is mainly due to emissions of strontium re lated to diesel for transport in ecoinvent and copper related to disposal of metals in chemical plant. These emissions are not included in the inventory in LCAfood. The electricity use for K-fertiliser production is produced by co-production of heat and electricity on the factory’s own cogeneration units (Nemecek et al. 2003). Therefore, it h as almost no effect on the results that the data in ecoinvent uses data for average electricity from the grid in Europe and not marginal electricity. Since the data from ecoinvent include capital goods and thus are more complete, it is chosen to apply these data in the study. It is chosen to apply data for potassium chloride since this fertiliser is the most frequently applied in the EU of the fertiliser types for which LCI data are available.

Magnesium (Mg), Sulphur (S) and Boron fertilisers: The average consumption of Mg, S and B fertilisers is shown in Table 5.22 . No life cycle inventories have been identified for these fertilisers. Therefore, they are not included in the study. The total amount of N, P and K fertiliser equals 235 kg while the total amount of Mg, S and B fertilisers equals around 36 kg. Thus, the not incl uded Mg, S and B fertilisers constitutes approximately 13% of the total application of fertilisers.

## 5 Agricultural stage: Rapeseed 65

Fertiliser Fertiliser application, kg/ha Magnesium 8-9 Sulphur 20-30 Boron (sandy soils) 0-5 

## Table 5.22: Applied other fertilisers in rapeseed cultivation. These fe rtilisers are not included in the study because of lack of inventory data.



# Pesticides Data on pesticide use in rapeseed cultiv ation is based on data provided in Dalgaard et al. (2007) and Dalgaard (2007). The data in Dalgaard et al. (2007) are based on the total national sales and the distribution of crops. The use of pesticides per hectare is given in Table 5.23 and reflects averages of cultivation in Denmark.

Pesticide kg/ha kg a.i./ha Herbicide (clomazone) 0.14 0.050 Herbicide (propyzamid) 0.36 0.18 Herbicide (clopyralid) 0.20 0.020 Insecticide (Pyrethroid, cypermethrin) 0.070 0.0070 Insecticide (Pyrethroid, alpha-cypermethrin) 0.040 0.0020 Insecticide (Pyrethroid, tau-fluvalinat) 0.030 0.0072 Total 0.84 0.27 

## Table 5.23: Use of pesticides per hectare per year in rapeseed cu ltivation in Denmark. The amount and type of pesticides used are based on data provided in Da lgaard et al. (2007) and Dalgaard (2007).



## Inventory data for production of the pesticides in Table 5.23 have only been identified for pyrethroid: ‘Pyretroid-compounds, at regional storehouse/RER’ (ecoinvent 2004). The data set includes energy use, transport to regional storehouse and capital goods, i.e. machinery a nd buildings. Analysing the data in Simapro and using the EDIP97 method use of electricity appears to be si gnificant within several imp act categories. Therefore, European average electricity is repl aced by marginal electricity based on coal technology. Production of the other pesticides has been applied as ‘P esticide unspecified, at regional storehouse/RER’ (ecoinvent 2004). Also in this case, European average elect ricity is replaced by marginal electricity based on coal technology.



# 5.5 Co-products
Rapeseed is co-produced with straw from field. The utilisation of straw from winter rapeseed in Denmark is given in Table 5.24 below.

Straw uses 1997 1998 1999 2000 2001 2002 2003 2004 Straw for energy 10% 7% 9% 9% 9% 15% 13% 10% Straw for fodder 0% 0% 0% 0% 0% 0% 0% 0% Straw for bedding etc. 3% 5% 2% 3% 4% 4% 3% 3% Straw left in the field 87% 88% 89% 88% 87% 81% 84% 87% Straw, total 100% 100% 100% 100% 100% 100% 100% 100% 

## Table 5.24: Production and uses of winther rapeseed straw in Denmark. (Danmarks Statistik 2006)



## In average 87% of the straw is left in the field, 10% is used for energy purposes and 3% is used for bedding. In order to avoid co-product allocation the marginal applica tion of straw is identified. The demand for bedding is determined by animal production. Hence, this cannot be th e marginal use. It is assumed that the marginal use of straw is distributed on 87% left in the field and 13% for energy purposes. A ccording to Energistyrelsen (2006) 29% of the potential for straw for energy purposes was utilised in 2002. This may increase in the future, but no clear tendency can be identified in Table 5.24 . In this study 87% left in the field and 13% used for energy purposes is applied. The interventions related to the 87% straw left in the field are dealt with in section 5.6

.

## 66 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## According to Danish Energy Authority et al. (2004), a typical small 5-15 MW modern straw fired combined heat and electricity power plant in Denmark has an overa ll efficiency at 88-90% and electricity efficiency at 29-30%. Thus it is assumed that straw used for energy purposes are fired in a co-generating heat and power plant with overall energy efficiency at 89% and electric ity efficiency at 29%. Straw has a calorific value at 13.5 MJ/kg. It is assumed that the produced energy disp lace marginal electricity and heat. Inventory data for marginal electricity and heat are described in section 3.1 and 3.6

.
No inventory data on burning of straw has been identifie d. Instead burning of softwood is assumed to represent the emissions that occur from burning of straw. The inventory: ‘ Wood chips, from forest, softwood, burned in furnace 1000kW/CH ’ (ecoinvent 2004) has been applied for this purpose. This inventory includes the emissions, production of wood, transport of wood, disposal of slag and ashes and production, maintenance and disposal of the plant. Since the interventions related to production of the straw are included in the rapeseed cultivation, the corresponding production of wood is left out from the data. In addition the use of electricity in the ecoinvent process has been displaced by marginal Danish electricity (see Table 3.3 ). The inventory for 1 kg straw used for energy purposes is shown in Table 5.25

.

LCI-data for 1 kg straw used for energy purposes Amount LCI data used in this study

Wood chips burned in furnace 13.5 MJ ‘ Wood chips, from forest, softwood, burned in furnace 1000kW/CH ’(ecoinvent 2004). Modified: Production of wood is left out and marginal electricity has been applied instead of average electricity. Displaced electricity -3.92 MJ Electricity, see Table 3.3 Displaced heat -8.10 MJ Heat, see section 3.6 

## Table 5.25: LCI of 1 kg straw used for energy purposes.



# 5.6 Emissions
Emissions from cultivating rapeseed include emissions to air, water and soil from the field. Emissions related to the energy use are described section 4.3 and 5.3 . Emissions to water from agricultural soils are determined as substances that leave the root zone of the plants. Th ereby, the topsoil is regarded as a part of the technosphere. E.g. nutrients are added to the soil and most of it is assimilated and harvested by the crops. Emissions are only related to the surplus, i.e. the differen ce between inputs to and re movals from the field.

All emissions are inventoried for average, sand and clay soil. According to Table 5.13 average soil consist of 41% sand and 59% clay. Data for average soil are applie d in the baseline scenario while inventories for sand and clay are applied as sensitivity analyses described in section 21.7

.


# Emissions related to N-balance Calculating N related emissions, the point of departure is a N-balance on the field scale. Known inputs and outputs related to seed, fertiliser, changes in N in soil ma tter and harvested crop and straw are balanced in order to determine the N surplus. This surplus is then distributed on different emissions. The balance is mainly based on the data given in Table 5.26 . The data in Table 5.26 are described under the table. The N-balance is shown in Table 5.27

.

## 5 Agricultural stage: Rapeseed 67

Parameter Average soil Sand Clay Yield 3,240 kg/ha 2,790 kg/ha 3,550 kg/ha Crop residue 6,480 kg/ha 5,580 kg/ha 7,100 kg/ha Straw removed (13%) 381 kg/ha 377 kg/ha 383 kg/ha Crop residue left in field 6,099 kg/ha 5,203 kg/ha 6,717 kg/ha N-content in rapeseed 0.0293 kg/kg N-content in crop residue 0.0053 kg/kg 

## Table 5.26: Parameters for rapeseed and rapeseed cultivation used when establishing N-balance on the field level. Ncontent in rapeseed and crop residue is given as kg N per kg plant material fresh weight matter: Rapeseed (92% DS) and crop residue (85% DS).



## Yields for rapeseed cultivation are given in Table 5.3 . The amount of crop residue is assumed to be 2 which is the default value suggested in IPCC (2000, p 4.58). Howe ver this is related to some uncertainty. Other identified data used for rapeseed vary be tween 1.2 and 3. The factor 1.2 is applied in the LCAfood database where it is assumed to be of same value as winter barley, data de livered by Dalgaard (2007). The factor 3 is used in the Finnish national greenhouse gas emission account to the EU (Statistics Finland 2006). Straw removed from the field is calculated as 13% of the produced straw. Straw production is given in Table 5.5 and the share of straw production that is removed from the field (13%) is given in section 5.5 . N-content in rapeseed is based on Møller et al. (2000) and N-content in crop residue is from Clausen (2002).

N-deposition on land areas in Denmark were 10-20 kg N/ ha in 2004 (Ellermann et al. 2005). According to Ellermann et al. (2005) there are not observed any rema rkable changes in the level of deposition from 2003 to 2004. The value for deposition used in this study is assumed to be represented by the average deposition which is 15 kg/ha. The seed input in Table 5.27 is determined in section 5.4 . The fertiliser input is given in Table 5.16

.
N changes in soil matter: The organic nitrogen pool in the soil matter is considered as consisting of two types of nitrogen; 1) easy available nitrogen which is dealt with as the ‘previous crop’ value determining the amount of N applied with fertiliser; and 2) more difficulty a ccessible nitrogen bound in organic matter (Petersen and Berntsen 2002). There is also a third type of organi c nitrogen which is bound in humus with turnover times at thousands of years (Petersen and Berntsen 2002). This is considered as constant and thus is not accessible. As mentioned, the fluxes of the easily available nitrogen ar e accounted for when adjusting the fertiliser input for the ‘previous crop’ value, see description of fertilisers in section 5.4 . The more difficult accessible organic bound nitrogen may be building up or decomposed in the soil matter depending on the cultivation practice (Wiggers et al. 2005). The nitrogen pool in the soil matter is a product of several years of cultivation practice. Application of large amounts of organic material (s traw, ‘second crop’ and animal manure) contributes to accumulation of organic bound nitrogen (Wiggers et al. 20 05). According to Wiggers et al. (2005) a change in cultivation practice will have significant importance on the leaching of nitrogen the first years, but after a period of years (20-100 years) the changes in nitrogen c ontent will be of minor importance. Petersen and Berntsen (2002) have modelled the changes in N in the soil pool for three different simplified farms; a cattle farm, a pig farm and a plant farm having normal crop rotations. Figure 5.7 shows the changes in N in the soil when establishing an average pig farm and an average plant farm on a hypothetic old cattle farm. The model calculation in Petersen and Berntsen (2002) is based on soil type JB4 (8% clay).

## 68 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Cattle

Pig

Plant

Year Equilibrium cattle

Equilibrium pig

Equilibrium plant



## Figure 5.7: Model calculation of changes in organic N in the soil for three farm types all established on hypothetic old cattle farm soil (Petersen and Berntsen, 2002, p 16).



## Figure 5.7 It appears from that cattle farms have the highest level of organic N in soil. Plant farms have the lowest level. The equilibrium for cattle farms and pl ant farms is 12,700 kg N/ha and 7,900 kg N/ha respectively. After a 100 year period, the organic N in soil unde r a plant farm established on a hypothetic old cattle farm will have only reached a level at 9,000 kg N/ha. If the cattle and the plant farm are assumed to represent the extremes the difference is 4,800 kg N/ha. If equilibrium is achieved in 200 years (this can not be seen in the figure), the difference at 4,800 kg N/ha corresponds to 24 kg N/ha per year.

Changes in rapeseed cultivation in the EU will take pl ace as a shift between crops or as changes in the setaside area or nature.

It is assumed that the change of N in the soil pool is zer o. The reason for that is: 1) No data on N in the soil under nature and set-aside areas have be en identified. 2) shifting from one crop to another crop is assumed to have neglicible effect on the N content in the soil p ool, 3) any change will have a maximum effect at 24 kg N/ha per year (assuming that plant farms and cattle fa rms represent the extremes and that equilibrium is achieved in 200 years) and 4) According to Petersen a nd Berntsen (2002) the main reason for changes in the soil N in agricultural soil is due to manure from anim al production – rapeseed cultivation does not affect the production of animal and con sequential manure spreading.

In the case of transformation of other land use types into agricultural cultivation the effect on the soil N may be positive or negative. Some land use types such as n itrogen poor ecosystems (heath land and commons) possibly have less N in the soil pool than agricultural soils and some meadows and forests may have more N in the soil pool. Set-aside areas may be somewhere in between. No data have been identified on that. Therefore, changes in the soil pool as a consequence of la nd use transformation are assumed to be zero.

In order to evaluate the effect of N changes in soil matter, a sensitivity analyses is carried out where it is assumed that N decomposition in soil matter is 24 kg/ha per year, see section 21.8

.

N-balance: The N-balance is summarised in Table 5.27

.

## 5 Agricultural stage: Rapeseed 69

Input, kg N/ha Average soil Sand Clay Deposition 15 15 15 N in seed 0.2 0.2 0.2 N-fertiliser 140 134 144 N from changes in N in soil matter 0 0 0 Total 155.2 149.0 159.5 Output, kg N/ha N in harvested rapeseed 94.8 81.6 103.8 N in removed straw 2.0 2.0 2.0 Total 96.8 83.6 105.9 Balance N surplus (input – output) 58.4 65.4 53.6 

## Table 5.27: N-balance for 1 hectare rapeseed field; sandy soil clay soil and average soil (41% sand and 59% clay).



## The N surplus is distributed on different emissions. The determination of each emission is described in the following.

Ammonia from crop: There are two sources to ammonia emission; crops and fertiliser application. According to Andersen et al. (2001, p 34) the generalised ammonia emission from crops is 3 kg N/ha for grass and 5 kg N/ha for other crops. Thus, the ammoni a emission from rapeseed is 5 kg NH 3 

## -N/ha.

Ammonia from fertiliser application: The ammonia emission from fertiliser depends on the fertiliser applied. According to Andersen et al. (2001, p 35) th e ammonia emission from fer tilisers based on ammonia is 2% of the N content in the fertiliser. Thus the ammonia emission as NH 3 

## -N can be calculated as 2% of the applied fertiliser in Table 5.16

.
Denitrification (total): The total denitrification (gaseous N oxides and molecular N 2 

## ) is calculated using the model; SimDen (Vinther and Hansen 2004) . The reason why the total denitrification is calculated is that this is the only way of estimating N-loss as N 2 

## . N 2 

## is determined as the total denitrification minus N 2 

## O and NO. The model also calculates the N 2 

## O-emission.

The model is developed for Danish soils. The model cal culates denitrification as the sum of background denitrification, denitrification from application of artificial fertiliser, denitrification rela ted to application of manure and denitrification from fixed nitrogen. Since marginal plant production does not affect manure and since rapeseed does not fix nitrogen these two sources to denitrifica tion is not considered. The background denitrification depends on the past history of organic matter content in the soil and the soil type (Vinther and Hansen, 2004, p 30). The model includes calculations for eight different soil types and three levels of organic content. Average organic content in soil is assumed. The actual soil types are accounted for. Denitrifi cation from application of artificial fertiliser depends on the amount of N applied with fertiliser, a factor describing the ratio between applied N and N 2 

## O-emission and the N 2 

## /N 2 

## O-ratio (Vinther and Hansen, 2004, p 31). The fertiliser application for different soil types is described in Table 5.14 . The soil type terminology in SimDen does not fit into the soil types for which data for Danish agriculture are available (see Table 5.13 ). Therefore the soil types in SimDen are adjusted in order to fit with the available data. This is described in Appendix 3: Soil types and adjustments to different sources

.
The total denitrification is calculated as 14.1 kg N/ha for average soil, 6.0 kg N/ha for sand and 19.8 kg N/ha for clay.

Direct N 2 

## O: The direct N 2 

## O emission is calculated using a model described in IPCC (2000). The calculated emissions are then compared with the results using two other models described in FAO and IFA (2001) and

## 70 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Vinther and Hansen (2004). The results obtained using these models are due to consistency not applied in this study. The model described in Vinther and Hansen ( 2004) is developed for Danish conditions and may therefore not be applicable under conditions in Malaysia, Indonesia, Brazil and Canada which are affected regions in this life cycle inventory. The model in FAO and IFA (2001) does not include peat soils which is relevant in Malaysia and Indonesia.

IPCC (2000): According to IPCC (2000, p 4.54) the direct N 2 

## O emission is calculated as:

N 2 

## O Direct 

## -N = [(F SN 

## + F AM 

## + F BN 

## + F CR 

## ) ⋅ EF 1 

## ] + (F OS 

## ⋅ EF 2 

## ) (3)



## The parameter values used in Equation (3) 

## are described in Table 5.28

.

Parameter Description Parameter value F SN Annual amount of synthetic fertiliser nitrogen applied to soils adjusted to account for the amount that volatilises as NH3 and NOx Fertiliser application, see Table 5.16 . 2% volatises

F AM Annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx No manure

F BN Amount of nitrogen fixed by N-fixing crops cultivated annually No N-fixing F CR Amount of nitrogen in crop residues returned to soils annually 32 kg N/ha (aver. Soil), 27 kg N/ha (sandy soil), 35 kg N/ha (clay soil), calculated using the informations in in Table 5.26 F OS Area of organic soils cultivated annually (ha) 0%, according to Table 5.13 this should be 5%. But since most organic soils in Denmark are bogs and meadows it is assumed that rapeseed is not cultivated here EF 1 Emission factor for emissions from N inputs (kg N2O-N/kg N input) 1.25% (IPCC 2000, p 4.60)

EF 2 Emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr) 8 (IPCC 2000, p 4.60)



## Table 5.28: Parameters in the equation calculating direct N 2 

## O-emissions in IPCC (2000, p 4.54)



## FAO and IFA (2001): According to FAO and IFA (2001, p 34) the direct N 2 

## O emission is calculated as:

ln (kg N 2 

## O-N/ha) = -0.414 + (F

⋅ N-app.) + Cr + S + C + D + pH + Cl + LM + FM (4)



## The parameter values used in Equation (4) are described in Table 5.29

.

Parameter Description Parameter value F Type of fertiliser 0.0037, Fertiliser type is calcium ammonia nitrate N-app. Applied N, kg/ha See Table 5.16 Cr Crop type 0.000, Crop type is ‘other’ S Soil texture -0.008 (coarse for sandy soil) and 0.000 (fine for clay soil),average soil is the average of -0.008 (coarse), -0.472 (medium) and 0.000 (fine) C Soil organic C content 0.140, 1-3% C in normal soil types, based on Berntsen and Petersen (2007) D Soil drainage -0.420, good drainage. Good drainage is needed in order to have suitable conditions for agriculture in Denmark pH Soil pH 0.109, Soil pH 5.5 - 7.3 Cl Climate 0.000, Temp. climate LM Length of measurement period (the model is constructed to fitwith literature measurements. Thus, to model emissions obtained from literature the method of measurement in literature should also be considered since this affects the measured emission) 0.825 Length of measurement period is >300 days, i.e. the longest period available in the model (chosen as the most precise)

FM Frequency of measurement (see comment above) 0.000 Frequency of measurement is >1measure/day, i.e. the highest frequency available in the model (chosen as the most precise) 

## Table 5.29: Parameters in the equation calculating direct N 2 

## O-emissions in FAO and IFA (2001, p 34-35)

## 5 Agricultural stage: Rapeseed 71



## Vinther and Hansen (2004): The model calculates the direct N 2 

## O emission for eight different soil types. The variable parameters are fertiliser a pplication, applied manure, N input to pastures (N-deposit during grazing and N 2 

## fixing by clover) and N 2 

## fixing by legumes crops. All parameters except from fertiliser input is zero. The fertiliser input is given in Table 5.16

.

The calculated N 2 

## O emissions using the three models are summarised in Table 5.30

.

Soil type IPCC (2000), kg N2O-N/ha FAO and IFA (2001), kg N2O-N/ha Vinther and Hansen (2004), kg N2O-N/ha Aver. soil 2.1 2.1 2.3 Sand soil 2.0 2.3 1. 7 Clay soil 2.2 2.5 2.8 

## Table 5.30: Calculated N 2 

## O emissions (kg N 2 

## O-N/ha) for sand, clay and average so ils. The applied values are marked with a dotted line.



## It appears from Table 5.30 

## that the deviations between the three models are relatively small. Thus, the determination of the applied N 2 

## O emissions is regarded as relativy certain.

Direct NO: The emission of NO is calculated using a mode l described in FAO and IFA (2001). According to FAO and IFA (2001, p 35) the direct NO emission is calculated as:

ln (kg NO-N/ha) = -1.527 + (F

⋅ N-app.) + C + D (5)



## The parameter values used in Equation (5) are described in Table 5.31

.

Parameter Description Parameter value F Type of fertiliser 0.0062, Fertiliser type is calcium ammonium nitrate N-app. Applied N, kg/ha See Table 5.16 C Soil organic C content 0.000, <3.0% C in normal soil types, based on Berntsen and Petersen (2007) D Soil drainage 0.946, good drainage. Good drainage is needed in order to have suitable conditions for agriculture in Denmark 

## Table 5.31: Parameters in the equation calculating direct NO-emissions in FAO and IFA (2001, p 35)



## The only parameters that are changed are the application of N-fertiliser.

The calculated NO emission is summarised in Table 5.32

.

Soil type kg NO-N/ha Aver. soil 1.3 Sand soil 1.3 Clay soil 1.4 

## Table 5.32: Calculated NO emissions (kg NO-N/ha) using the model described in FAO and IFA (2001). The applied values are marked with a dotted line.



## Nitrate: The nitrate emission is calculated as the residual or rest; i.e. the surplus-N from the N-balance minus the other calculated emissions described above.

Since the nitrate emission is calculated as residual of the N-surplus it may be related to some uncertainty. Therefore, the nitrate emission has also been calcula ted using a model described in Østergaard (2000). The variable parameters are crop, fertiliser application, manure application, soil type and net precipitation. The calculation is performed for winter rapeseed fertilised in accordance with the norms and net precipitation at

## 72 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## 300 mm/year. An average net precipitation at 300 mm/y ear in Denmark is estimated from Miljøstyrelsen (2002, appendix 3, map 20). The calculation for average soil is obtained as the average of sand soil, clay mixed sand soil and sand mixed clay soil. The calculated n itrate emission using the two approaches is shown in Table 5.33

.

Soil type Nitrate calculated as residual Østergaard (2000)

Aver. Soil 36.5 97 Sand soil 51.8 132 Clay soil 26.0 72 

## Table 5.33: Calculated nitrate emissions (kg NO 3 - 

## -N/ha) using the the two approaches described. The applied values are marked with a dotted line.



## It appears from Table 5.33 that the nitrate emissions are very sensitiv e to the model used for determination of that. Despite the fact that Østergaard (2000) in principl e should be more precise that the residual approach, it is chosen to apply the residual approach. Firstly, this is chosen because the high figur es calculated with the model described in Østergaard (2000) may be due to inclusion of changes in the soil pool (e.g . effect of high input of organic material/manue previous years) in the model. According to Figure 5.7 the annual changes in the N soil pool may be as high as 100 kg N/ha the first years after a shift of farm type from cattle to plant. Secondly, the residual approach is chosen because the inputs and outputs of N must be in balance. And when determining the components in the N-balance models for determining nitr ate emissions is regarded as the most uncertain compared to determination of the other N -related emissions. Therefore, it is chosen to determine nitrate emissions be the residual approach instead of model calculations.

N 2 

## O, indirect from NH 3 

## and nitrate: Besides the direct N 2 

## O emissions from the field which is calculated in the previous, there are indirect emissions from the em itted ammonia and nitrate. The emission of ammonia and its subsequent deposition as NO x 

## and NH 4 

## and nitrate leached from the field increase the amount of N available for denitrification and nitrification (IPCC, 2000, p 4.67). The N 2 

## O-N emission produced from deposited NH 4 

## - N and NO x 

## -N (originating from NH 3 

## -emission) is 0.01 kg N 2 

## O-N/kg N. In this respect it is assumed that all emitted ammonia will end as deposited NO x 

## or NH 4 

## . The N 2 

## O-N emission produced from leached nitrate is 0.025 kg N 2 

## O-N/kg NO 3 

## -N. The indirect N 2 

## O emissions are not assumed to affect the calculated emissions ofammonia and nitrate since the denitrification may take place long time after the emissions took place. Therefore, the ammonia and nitrate emissions may alr eady have had an effect on the environment.

NO, indirect: This is not included.

Summary of emissions related to N-balance: Distribution of the N-surplus is summarised in Table 5.34

.

Emission and source Average soil Sand Clay Ammonia from crop (kg NH 3 -N/ha) 2.8 2.7 2.9 Ammonia from fertiliser application (kg NH 3 -N/ha) 5.0 5.0 5.0 Denitrification (kg N/ha) 14.1 6.0 19.8 - N 2 O part of denitrification (kg N 2 O-N/ha) 2.1 2.0 2.2 - NO part of denitrification (kg NO-N/ha) 1.3 1.3 1.4 - N 2 part of denitrification (kg N/ha) 10.6 2.7 16.2 Nitrate (kg NO 3 -N/ha) 36.5 51.8 26.0 N-surplus (kg N/ha) 58.4 65.4 53.6 N 2 O, indirect from NH 3 (kg N 2 O-N/ha) 0.1 0.1 0.1 N 2 O, indirect from nitrate (kg N 2 O-N/ha) 0.9 1.3 0.6 

## Table 5.34: Distribution of the N-surplus from the field N-balance on different emissions and sources. All numbers are given in kg N/ha.

## 5 Agricultural stage: Rapeseed 73



## Table 5.35 summarises the emissions in Table 5.34 and converts them into kg emission per ha instead of kg N/ha.

Emission as kg N/ha Average soil (41% sand and 59% clay) Sand soil Clay soil

Ammonia to air (kg NH 3 -N/ha) 7.8 7.7 7.9 N 2 O to air (kg N 2 O-N/ha) 3.1 3.4 2.9 NO to air (kg NO-N/ha) 1.3 1.3 1.4 Nitrate to water (kg NO 3 -N/ha) 37 52 26 Emission as kg emission/ha Average soil (41% sand and 59% clay) Sand soil Clay soil

Ammonia to air (kg NH 3 /ha) 9.5 9.3 9.6 N 2 O to air (kg N 2 O/ha) 4.9 5.3 4.6 NO to air (kg NO/ha) 2.9 2.7 2.9 Nitrate to water (kg NO 3 /ha) 162 229 115 

## Table 5.35: Emissions related to N-balance.



# Emissions related to P-balance The phosphorus cycle is somehow simpler than the N-cycl e. It includes no emissions to air. The emission of P as phosphate is calculated from a simple field balance. The inputs include seed and P-fertiliser and the outputs are harvested rapeseed, straw and accumulation in the soil matter. Accumulation in the soil matter is more constant than for nitrogen since the phosphorous is fixed by strong bindings to the soil particles. Therefore, theleaching of phosphate can be calculated as a fraction of the P surplus in the field. Dalgaard et al. (2007) specifies phosphate leaching as 2.9% of the surplus of P. The remaining is accumulated in the soil matter.

The content of P in harvested crop is 6.2 g per kg rap eseed and 0.77 g per kg straw (fresh weight basis) (Møller et al. 2000). No data on P content in rapeseed straw have been identified. Therefore, the 0.77 g per kg straw is based on straw from barley and wheat.

Applying the yields and straw production for different soil types specified in Table 5.26 , assuming 13% removal of straw, seed input of 5 kg/ha and application of P fertilisers for winter rapeseed as specified in Table 5.18 , the P-surplus can be calculated. This is shown in Table 5.36 for different soil types.

Input (kg P/ha) Average soil Sand Clay Seed 0.03 0.03 0.03 P-fertiliser 25.0 21.0 28.0 Total P input 25.0 21.0 28.0 Output (kg P/ha) Harvested rapeseed 20.0 17.2 21.9 Removed straw 0.3 0.3 0.3 Total P output 20.3 17.5 22.2 Balance (kg P/ha) P surplus 4.8 3.6 5.9 P leaching (2.9%) 0.14 0.10 0.17 

## Table 5.36: P balance for different soil types.



# Emissions related to C-balance Mass balances for carbon are not as detailed as for nitroge n. This is because emission of carbon dioxide from biotic origin is not included. Only CO 2 

## from net changes of carbon in the soil and from land transformation are included.

It is assumed that the C/N-ratio in soils is relatively cons tant. Since the change in N in the soil pool is assumed to be zero there will be no change in the soil C pool neither.

## 74 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil


# Emissions of heavy metal Fertilisers contain contaminants of heavy metals. The em issions from this input of heavy metals are calculated on basis of a simple field balance. Th e inputs are heavy metals in seed a nd N, P and K fertilisers and the outputs are heavy metals in harvested ra peseed and in removed straw. Depositi on of heavy metals from air is not taken into account in the field balance. In addition to th e field balance, the final compartments (water and soil) of the outputs of heavy metals have been estimated.

It is assumed that the heavy metal is distributed on emi ssions to soil and emissions to water. The emission of heavy metals to soil is calculated as th e total input with fertiliser and seeds mi nus the share that ends in water. This means that heavy metals in leaching and runoff have been assumed to be zero, i.e. inputs of heavy metals to the field minus harvested heavy metals ends as emissions to soil. It is assumed that one eighth, i.e. 12.5%, of the heavy metals harvested with crop ends as emission to water and the other 87.5% ends as emission to soil. The underlying assumptions behind these shares are associated with some uncertainty and are described in the following. The rapeseed send to oil m ill is processed into oil and meal. There are no data available on how heavy metals are distributed in the oil fraction and in th e meal fraction. Therefore, it is assumed that 25% of the heavy metals go with the oil and the other 75% with the meal. The oil is refined where heavy metals are almost completely separated out and end in waste water from the oil mill. It is assumed that half of the heavy metals that go with waste water to waste water treatment end in an aquatic recipient while the rest ends in sludge which is spread out on agricultural soil. The heavy metals contained in the meal will primarily end as manure from pig and cattle which is spread out on agricu ltural soil, and subsequently as emissions to soil. The heavy metals contained in removed straw primary end as emissions to air and as heavy metals in deposited ashes (spread out on agricultural soil) from biofuel power plan ts. However, it is assumed that all straw is left in the field. This assumption has little effect because a consid erable share of the heavy metals from burned straw anyway would end as emissions to soil.

Heavy metal content in fertiliser: Table 5.37
, Table 5.38 and Table 5.39 show the content of heavy metals in calcium ammonium nitrate, triple super phosphate and potassium fertiliser from different data sources.

Calcium ammonium nitrate CAN 21+ CAN-17 17-0-0 Heavy metals in N fertiliser; mg/kg N (Nemecek, et al. 2003) (WSDA 2006) (WSDA 2006) Calcium ammonium nitrete, applied in this study Arsenic (As) - 2.4 <0.6 1.5 Cadmium (Cd) 0.2 0.5 <0.6 0.4 Chromium (Cr) 3.0 - - 3.0 Cobalt (Co) - 0.5 <1.2 0.8 Copper (Cu) 8.5 - - 8.5 Mercury (Hg) - 0.2 <0.1 0.1 Molybdenum (Mo) - 6.7 <0.6 3.6 Nickel (Ni) 12.6 0.5 <29.4 14.2 Lead (Pb) 5.9 5.7 <0.6 4.1 Selenium (Se) - 26.2 <0.6 13.4 Zink (Zn) 100 1.9 <58.8 53.6 

## Table 5.37: Heavy metal content (mg/kg N) in calcium ammonia n itrate (CAN) fertilisers fr om different sources. The applied data are the average of the identified da ta; they are marked with a black dotted frame.



## The European Parliament and the Council (2003, annex III) specify a limit at 10 mg copper per kg N for ammonium nitrate fertilisers. There are no limits specified fo r other heavy metals. It appears that the applied copper content in N fertiliser in Table 5.37 is not exceeding the limit specified in European Parliament and the Council (2003, annex III).

## 5 Agricultural stage: Rapeseed 75

Triple superphosphate SSP 0-45-0 PHOS 0-30-0 Heavy metals in P fertiliser; mg/kg P 2 O 5 (Nemecek et al. 2003) (WSDA 2006) (WSDA 2006) Triple super phosphate, applied in this study

Arsenic (As) - 15.5 <1.7 8.6 Cadmium (Cd) 113 253 0.5 5.7* Chromium (Cr) 567 567 Cobalt (Co) - 2.7 0.2 1.5 Copper (Cu) 97.8 97.8 Mercury (Hg) - 0.03 <0.01 0.02 Molybdenum (Mo) - 22.1 0.3 11.2 Nickel (Ni) 95.7 331 0.2 142 Lead (Pb) 7.6 <5.6 <0.3 4.5 Selenium (Se) - <11.1 <0.7 5.9 Zink (Zn) 650 2333 1.0 995 

## Table 5.38: Heavy metal content (mg/kg P 2 

## O 5 

## ) in triple super phosphate (TSP) fer tilisers from different sources. The two P-fertilisers from WSDA (2006) are not TSP fertilisers, see text below table. The ap plied data are the average of the identified data; they are marked with a black dotted frame. * See explanation in text below table.



## The data from WSDA (2006) are not for TSP fertilisers but for single super phosphate and for unspefified phosphate respectively. It has not been possible to obtain other data on heavy metal content in TSP.

The European Commission is planning to introduce limit s for cadmium contaminants in phosphate fertilisers. According to a draft proposal (The European Commission 2006c) the following limits are suggested:

• 60 mg Cd/kg P 2 

## O 5 

## (five years after entry into force)

• 40 mg Cd/kg P 2 

## O 5 

## (10 years after entry into force)

• 20 mg Cd/kg P 2 

## O 5 

## (15 years after entry into force)

In 1989 Denmark introduced a limit value at 110 mg Cd per kg P for phosphate fer tilisers corresponding to 48 mg Cd per kg P 2 

## O 5 

## . (Ministeriet for Fødevarer, Landbrug og Fi skeri 2004). According to Plantedirektoratet (2004), the average cadmium content in P fertiliser a pplied in Denmark has been decreasing from approximately 40 mg/kg P 2 

## O 5 

## in the late 80ies to 5.7 mg/kg P 2 

## O 5 

## in 2004. Therefore, the cadmium content is set to 5.7 mg/kg P 2 

## O 5 

## instead of the average value from the three data sets.

Potassium chloride POTASH SOLUTION 0-0-13 POTASH SOLUTION 0-0-15 Heavy metals in K fertiliser; mg/kg K 2 O (Nemecek, et al. 2003) (WSDA 2006) (WSDA 2006) Applied in this study

Arsenic (As) - 5.5 4.0 4.7 Cadmium (Cd) 0.1 <0.02 <0.01 0.04 Chromium (Cr) 3.3 - - 3.3 Cobalt (Co) - 1.1 0.3 0.7 Copper (Cu) 8.3 - - 8.3 Mercury (Hg) - <0.2 <0.1 0.1 Molybdenum (Mo) - <0.2 <0.1 0.1 Nickel (Ni) 3.5 20.2 5.3 9.7 Lead (Pb) 9.2 <2.3 <2.0 4.5 Selenium (Se) - 2.1 <0.7 1.4 Zink (Zn) 76.7 296 90.7 155 

## Table 5.39: Heavy metal content (mg/kg K 2 

## O) in potassium chloride fertilisers from different sources. The applied data are the average of the iden tified data; they are marked with a black dotted frame.



## Heavy metal content in harvested rapeseed and straw: Table 5.40 show the content of heavy metals in the harvested crop; seed and straw. The applied data are based on two different sources. It has not been possible to identify data on the heavy metal content in rapeseed stra w. Instead it is assumed that the heavy metal content in straw can be estimated as the av erage of barley and wheat straw.

## 76 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Rapeseed Straw (barley) Applied in this study Heavy metals in harvested crop; mg/kg crop (Nemecek, et al. 2003, p 154) (Møller et al. 2000) (Nemecek, et al. 2003, p 154) (Møller et al. 2000) Rapeseed Straw

Arsenic (As) - - - - - - Cadmium (Cd) 1.47 - 0.16 - 1.5 0.16 Chromium (Cr) 0.46 - 0.36 - 0.46 0.36 Cobalt (Co) - 0.11 - 0.12 0.11 0.12 Copper (Cu) 3.04 2 4.23 3 2.4 3.5 Mercury (Hg) 0.09 - 0.07 - 0.09 0.07 Molybdenum (Mo) - - - - - - Nickel (Ni) 2.39 - 0.35 - 2.4 0.35 Lead (Pb) 4.83 - 2.67 - 4.8 2.7 Selenium (Se) - 0.04 - 0.05 0.04 0.05 Zink (Zn) 44.16 31 11.50 89 38 50 

## Table 5.40: Heavy metal content (mg/kg crop) in harvested rapeseed and straw. The applied data are marked with a black dotted frame.



## Heavy metal emissions: Applying the yields for different soil types specified in Table 5.26 , assuming a seed input of 5 kg/ha and application of N, P and K fert ilisers for winter rapeseed as specified in section 5.4 , the heavy metal emissions to soil and water can be calculated. This is shown in Table 5.41 . Since there are no data on arsenic and molybdenum content in harvested crop and straw it is assumed that the total input to the field of these heavy metals ends as emissions to soil.

Average soil Sand Clay Heavy metal emis- sions Soil (g/ha) Water (g/ha) Soil (g/ha) Water (g/ha) Soil (g/ha) Water (g/ha) Arsenic (As) 1.2 - 1.0 - 1.3 - Cadmium (Cd) -0.21 0.61 -0.18 0.52 -0.23 0.67 Chromium (Cr) 33 0.19 28 0.16 37 0.20 Cobalt (Co) 0.22 0.045 0.20 0.038 0.24 0.049 Copper (Cu) 6.7 1.0 5.7 0.84 7.4 1.1 Mercury (Hg) -0.011 0.036 -0.0084 0.031 -0.013 0.040 Molybdenum (Mo) 1.2 - 1.0 - 1.2 - Nickel (Ni) 10 1.0 8.7 0.8 11 1.1 Lead (Pb) -0.64 1.9 -0.52 1.7 -0.73 2.1 Selenium (Se) 2.3 0.016 2.2 0.014 2.4 0.018 Zink (Zn) 65 15 55 13 72 17 

## Table 5.41: Heavy metal emissions (g/ha) to soil and water. Emissions to soil are calculated as the input of heavy metals minus 12.5% of the content in harvested rapeseed. Emissions to water are calculated as 12.5 % of the heavy metal content in harvested rapeseed. The applied data in this study are marked with a dotted black frame.



## A negative emission means that some substance is rem oved from the soil (with harvested rapeseed). However, the net emission of heavy metals is positive for all su bstances. The negative numbers only reflect a redistribution of the fate of the net input of heavy metals; i.e. some of the heavy metals in the harvested rapeseed are sorted out in the refinery process and are subsequently discharged to water.

It must be stressed that the numbers on heavy metal c ontent in fertilisers and in harvested crop are taken from different sources of different ages and different regions. Therefore, the amount as well as the comparetment of heavy metal emissions given in this section is related to significant uncertainties. Therefore, the environmental significans of emissions of heavy metals is analysed in a sensitivity analysis in section 21.10

. 

# Emissions of pesticides The compartments that receive the applied active ingredie nts in the pesticides are assumed to be 33% soil, 33% water and 33% air. This is a very rough estimate which is tested in a sensitivity analysis in section 21.11

.

## 5 Agricultural stage: Rapeseed 77



# 5.7 Overhead in agricultural stage
Overhead includes electricity and heat for administration. Administration related to rapeseed cultivation is not included since it is assumed that this takes place in the farmer’s home.



# 5.8 Capital goods in agricultural stage
Capital goods include means of production, i.e. buildings and machinery. Only one inventory of agricultural buildings and machinery has b een identified; ecoinvent (2004). Since it is not possible to obtain detailed data on building materials, foundations, machinery etc. from farms in Denmark, the inve ntory of capital goods will be rather roughly and based on ecoinve nt (2004). The data in ecoinvent ar e described in detail in Nemecek et al. (2003) where capital goods are divided into two categor ies: 1) Agricultural buildings and 2) Agricultural machinery. These are described in the following two sections.


# Agricultural buildings The relevant buildings for rapeseed cultivation are:

• Shed, for machinery (barn building for shelter)

• Buildings for drying

The inventory of shed is described in Nemecek et al. (2003) and includes construction, maintenance and disposal of the materials used. The use of shed for machiner y in ecoinvent is inventoried per hectare of field work processes and it is calculated as surface occupied by the tractor and machin ery multiplied with the field work time for one hectare divided with the lifetime of the shed and with the annual employment of the machine (Nemecek et al., 2003, p 55). The included different field work processes and number of operations per hectare for rapeseed cultivati on are described in Table 5.8 . However, not all included processes are included in the ecoinvent database. This involves; soil packing, seed bed harrowing and straw chopping. Therefore, the use of shed for these field work processes are assumed to be equal to harrowing, by tine harrow. Table 5.42 shows the required shed per hectare operation and the number of different field work operations per hectare given in Table 5.8

.

## 78 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Use of shed for machinery Shed, m 2 per ha per operation Number of field work operation per ha Shed, m 2 per ha total

Estimate; - High Low High Low Ploughing 0.00801 1 1 0.00801 0.00801 Harrowing, by tine harrow/disc harrow 0.00584 2 1 0.01168 0.00584 Packing* 0.00584 1 0.00584 0 Rolling 0.00534 1 1 0.00584 0.00584 Seed bed harrowing* 0.00584 2 0.01168 0 Seed bed harrowing, heavy* 0.00584 1 0.00584 0 Inter-row tillage/weeding 0.00349 2 2 0.00698 0.00698 Tillage

Straw chopping* 0.00584 1 0.00584 0 Sowing Sowing 0.00546 1 1 0.00546 0.00546 Fertilising, by broadcaster 0.00171 2 2 0.00342 0.00342 Fertilising Fertilising, lime application 0.00171 1 0.00171 0 Pesticide Pesticide, by field sprayer 0.00198 4 3 0.00792 0.00594 Harvesting Combine harvesting 0.00858 1 1 0.00858 0.00858 Transport, tractor and trailer (including empty return trip) 8.13E-05 m 2 /tkm 3.2 tkm 3.2 tkm 0.00026 0.00026 Transport

Machine transport 0 40 km 24 km 0 0 Total 0.0891 0.0503 Total, average 0.070 

## Table 5.42: Calculation of use of shed per hectare rapeseed cultivation. The calculations are based on field work operations given in Table 5.9 and shed use per ha per operation in Nemecek et al. (2003).*Assumed to have same utilisation of shed as harrowing by spring tine harrow. The applied data are marked with a black dotted frame.



## In Table 5.42 the average use of 0.0697 m 2 

## shed corresponds to 3.5 m 2 

## shed during the assumed 50 years lifetime of a shed.

The applied inventory data for shed is; ‘ Shed/CH/I ’ (ecoinvent 2004).

Building hall for drying is included in the invent ory data applied for dr ying described in section 5.3 : ‘ Drying of seed ’. Administration buildings related to rapeseed cultivatio n is not included since it is assumed that this work is done in the farmer’s home.


# Agricultural machinery The relevant machinery for rapeseed cultivation is show n below. The machinery are categorised as in ecoinvent. Ecoinvent contains an inventory for each category.

• Tractor

• Combine harvester

• Agricultural machinery, tillage: Plough, spring tine harrow, weeder, roller

• Agricultural machinery, general: Seeder, broadcaster for fertilising, trailer and field sprayer for pesticides

The inventory of agricultural machinery is described in Nemecek et al. (2003) and includes production, maintenance and disposal of the materials used. The use of machinery in ecoinvent is inventoried per hectare of field work processes and it is calculated as the weight of the machine multiplied with the operation time per hectare divided with the life time of the machine (Nem ecek et al., 2003, p 49). The included different field work processes and number of operations per hect are for rapeseed cultiva tion are described in Table 5.8

. However, not all included processes are included in the eco invent database. This involves; soil packing, seed bed harrowing and straw chopping. Therefore, these machin es are assumed to be equal to a spring tine harrow. Table 5.43 shows the required machinery per ha per operation.

## 5 Agricultural stage: Rapeseed 79

Use of machinery, per ha per operation Machinery, kg per ha per operation Category of machinery Tractor Combine harvester Tillage General

Ploughing 1.55 - 2.16 - Harrowing, by tine harrow/disc harrow 0.41 - 1.00 - Packing* 0.41 - 1.00 - Rolling 0.41 - 1.82 Seed bed harrowing* 0.41 - 1.00 - Seed bed harrowing, heavy* 0.41 - 1.00 - Inter-row tillage/weeding 0.23 - 0.63 Tillage

Straw chopping* 0.41 - 1.00 - Sowing Sowing 0.60 - - 0.97 Fertilising, by broadcaster 0.69 - - 0.24 Fertilising Fertilising, lime application 0.69 - - 0.24 Pesticide Pesticide, by field sprayer 0.32 - - 0.53 Harvesting Combine harvesting - 6.30 - - Transport, tractor and trailer (including empty return trip) 0.0037 kg/tkm - - 0.024 kg/tkm Transport

Machine transport Included in other field work processes 

## Table 5.43: Required machinery per ha per operation distributed on the four categories of agricultural machinery in ecoinvent (2004). *Assumed to have same amount of required machinery as harrowing by spring tine harrow.



## Table 5.44 shows the total required amount of machinery. Th is is calculated using the number of operations given in Table 5.8

.

Use of machinery, per ha total Machinery, kg per ha total Category of machinery Tractor Combine harvester Tillage General

Estimate; High Low High Low High Low High Low Ploughing 1.55 1.55 0 0 2.16 2.16 0 0 Harrowing, by tine harrow/disc harrow 0.82 0.41 0 0 2.00 1.00 0 0 Packing 0.41 0 0 0 1.00 0 0 0 Rolling 0.41 0.41 0 0 1.82 1.82 0 0 Seed bed harrowing 0.82 0 0 0 2.00 0 0 0 Seed bed harrowing, heavy 0.41 0 0 0 1.00 0 0 0 Inter-row tillage/weeding 0.46 0.46 0 0 1.26 1.26 0 0 Tillage

Straw chopping 0.41 0 0 0 1 0 0 0 Sowing Sowing 0.60 0.60 0 0 0 0 0.97 0.97 Fertilising, by broadcaster 1.38 1.38 0 0 0 0 0.48 0.48 Fertilising Fertilising, lime application 0.69 0 0 0 0 0 0.24 0 Pesticide Pesticide, by field sprayer 1.28 0.96 0 0 0 0 2.12 1.59 Harvesting Combine harvesting 0 0 6.3 6.3 0 0 0 0 Transport, tractor and trailer (including empty return trip) 0.01 0.01 0 0 0 0 0.08 0.08 Transport

Machine transport Included in other field work processes Total 9.25 5.78 6.30 6.30 12.24 6.24 3.89 3.12 Total, average 7.5 6.3 9.2 3.5 

## Table 5.44: Required machinery in kg per ha distributed on the four categories of agricultural machinery in ecoinvent (2004). Calculations are based on Table 5.43 and number of operations given in Table 5.8 . The applied data are marked with a black dotted frame.



## The applied inventories for the four cat egories of agricultural machinery in Table 5.44 are:

• Tractor: ‘ Tractor, production/CH/I ’ (ecoinvent 2004)

• Combine harvester: ‘ Harvester, production/CH/I ’ (ecoinvent 2004)

• Agricultural machinery, tillage: ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004)

• Agricultural machinery, general: ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004)

## 80 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 5.9 Transport of materials in agricultural stage
Raw materials are transported with lorry to the rapeseed cultivating farms. Inventory data per tkm transport by lorry are described in section 4.1 . The transport distances are estimated in this section. Since there are several suppliers and since there is a general lack of data on the specific marginal affected supplier, all transport distances are based on rough estimates. Determination of size of lorries is based on Table 4.3 and very rough estimates on the total amount of goods transported.

Determination of the amount of transported fertiliser product is shown in Table 5.45 . The contents of nutrients in fertiliser products are estimated from a re view of various fertilisers in WSDA (2006).

Fertiliser Content of nutrient in fertiliser Applied nutrient Applied fertiliser product
Calcium ammonium nitrate 19% N 140 kg N/ha 737 kg/ha Triple superphosphate 38% P 2 O 5 (16% P) 25 kg P/ha 156 kg/ha Potassium chloride 14% K 2 O (12% K) 82 kg K/ha 683 kg/ha Magnesium 10% Mg 8.5 kg Mg/ha 85 kg/ha Sulphur 15% S 25 kg S/ha 167 kg/ha Boron 10% B 2.5 kg B/ha 25 kg/ha Total 1,853 kg/ha 

## Table 5.45: Determination of amount of transported fertiliser product. The applied nutrients are obtained from Table 5.16
, Table 5.18
, Table 5.20 and Table 5.22

.



## Table 5.46 shows the transported amounts, the route and the distances.

Material Amount per ha From To Distance Lorry size Seed 5 kg Seed traders, Denmark Farms in DK 100 km 16t Rapeseed 3,240 kg Farms in DK Seed traders 100 km 28t Rapeseed straw to utilisation 381 kg Farms in DK Biomass plant 100 km 28t N, P, K, Mg, S and B fertilisers 1,853 kg Abroad chemical plant Farms in DK 1000 km 40t Pesticides 0.84 kg Abroad chemical plant Farms in DK 1000 km 28t 

## Table 5.46: Transport of goods related to the agricultural stage. The return trip is included in the inventory data.



## The estimated transport in Table 5.46 is summarized per type of lorry in Table 5.47

.

Type of lorry Transport 16t 0.5 tkm 28t 363 tkm 40t 1,853 tkm 

## Table 5.47: Summary of transport in the agricultural stage.

## 5 Agricultural stage: Rapeseed 81



# 5.10 LCI of rapeseed agricult ural stage, summary

Table 5.48 summarises the inventory data relating to 1 ha cultivated in 1 year with rapeseed.

Denmark: 1 ha y rapeseed field Interventions Amount Applied LCI data Product output Rapeseed (8-9% DS) 3.131 t Product of interest Straw removed from field 381 kg Co-product allocation between rapeseed and straw is avoided by system expansion, see below System expansion Burning of straw in biomass plant 381 kg See Table 5.25 Energy use Traction, burned diesel 3,612 MJ See Table 4.5 Drying of rapeseed (evaporated water) 100 kg Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Miscellaneaus transport (passenger car) 79 km see section 4.4 Material use Seed 5 kg See Table 5.12 N-fertiliser (as N) 140 kg ‘Calcium a mmonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 57 kg Modified version of: ‘ Triple superphosphate, as P 2 O 5 , at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 K-fertiliser (as K 2 O) 99 kg ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Herbicide (clomazone) 0.050 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Herbicide (propyzamid) 0.18 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Herbicide (clopyralid) 0.020 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, cypermethrin) 0.0070 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, alpha-cypermethrin) 0.0020 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, tau-fluvalinat) 0.0072 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Capital goods Agricultural buildings 0.070 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 7.5 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 6.3 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 9.2 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.5 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 16t lorry 0.5 tkm ‘ Transport, lorry 16t ’ (ecoinvent 2004) 28t lorry 363 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 40t lorry 1,853 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) ... table continued on the next page... 

## Table 5.48: Interventions per ha y soil cultivated with rapeseed. Table continued on the next page...

## 82 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

... continued from previous page... Emissions Air Water Soil ... continued from previous page... Ammonia (NH 3 ) 9.5 kg - - Dinitrogen oxide (N 2 O) 4.9 kg - - Nitric oxide (NO) 2.9 kg Nitrate (NO 3 ) - 162 kg - Phosphorus (P) - 0.14 kg - Clomazone 0.017 kg 0.017 kg 0.017 kg Propyzamid 0.060 kg 0.060 kg 0.060 kg Clopyralid 0.0067 kg 0.0067 kg 0.0067 kg Cypermethrin 0.0023 kg 0.0023 kg 0.0023 kg Alpha-cypermethrin 0.0067 kg 0.0067 kg 0.0067 kg Tau-fluvalinat 0.0024 kg 0.0024 kg 0.0024 kg Arsenic (As) - - 1.2 g Cadmium (Cd) - 0.61 g -0.21 g Chromium (Cr) - 0.19 g 33 g Cobalt (Co) - 0.045 g 0.22 g Copper (Cu) - 1.0 g 6.7 g Mercury (Hg) - 0.036 g -0.011 g Molybdenum (Mo) - - 1.2 g Nickel (Ni) - 1.0 g 10 g Lead (Pb) - 1.9 g -0.64 g Selenium (Se) - 0.016 g 2.3 g Zink (Zn) - 15 g 65 g 

## Table 5.48 : Interventions per ha y soil cultivated with rapeseed.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 83



# 6 Agricultural stage: Oil palm , fresh fruit bunches (FFB)
The oil palm (Elaeis guineensis) is a perennial crop a nd it grows to a height of approximately 10 m before replantet after around 25 years. The oil palm fruits are a ttached to bunches (FFB – fresh fruit bunches) weighting around 25 kg and each carrying 1500-2000 single fruits (for oil palms 10-15 years old). The harvested FFB contain around 20% oil, 25% nuts (5% kernels, 13% fibre and 7% shell) and 23% empty fruit bunches. The kernels contain around 55% oil and 8% protei n (Corley and Tinker 2003; Møller et al. 2000).

The agricultural stage for oil palm in cludes activities related to cultivation of oil palm. The reference for the inventory is 1 hectare oil palm plantation in Malaysia and Indonesia. The plante d area includes mature and immature palms. Most data are based on collected data on cultivation practices in Malaysia. The yield of fresh fruit bunches (FFB) is applied as the average yield in Malaysia and Indonesia. Most data are from 2004 to 2006.

Since oil palm is a perennial three different stages ar e considered: Nursery, immature plantation and mature plantation. The interventions from oil palm cultivation ar e applied as a weighted aver age of the three stages.

In 2003 an area of 32,600 km 2 

## and 30,400 km 2 

## was planted with oil palm in Malaysia and Indonesia respectively (FAOSTAT 2006). That corresponds to 10% and 41% of the total land area and the agricultural land in Malaysia respectively and 2% and 7% of the total land area and the agricultural land in Indonesia respectively

There are different influential factors on the interven tions and thereby the environmental impact related to production of oil palm fruit (FFB). Relevant factors to ta ke into account are: 1) type of agricultural practice (estate or small holder) as well as region – both factors aff ect the yield, 2) type of cultivated soil (mineral soil versus peat soil). These factors will be taken into account in sensitivity analyses in section 21

.
Before the oil palms are planted in the plantation they are grown in poly bags in the nursery. The nursery consists of two main stages. In the pre-nursery that last fro m the sowing of the seed in a small poly bags until the seedling is approximately three to four months. The se edlings in the small poly bag are kept under a cover protecting them for direct sunlight. After that in the main-nursery, the seedling is planted in a larger poly bag in which it is cultivated without a protecting cover until it is 12 to 13 months old and ready for planting in the plantation (Singh 2006 and Corley and Tinker 2003, p 271). The pre-nursery is illustrated in Figure 6.1 and Figure 6.2 while the main-nursery is shown in Figure 6.3 and Figure 6.4

.

## 84 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Figure 6.1: Pre-nursery, the seeds are sown in small poly bags where the seedlings are cultivated until they are three to four month. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006. Figure 6.2: Pre-nursery, different stages. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006.

Figure 6.4: Main-nursery, palms ready for planting in plantation. Picture taken Jannick H Schmidt in United Plantation Berhad 2006. Figure 6.3: Main-nursery, early st age, the seedlings are cultivated in large poly bags when they are 3-5 months until they are 12-13 months and ready for planting in the plantation. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006.



## Oil palms from the nursery are planted when they are a pproximately 12-13 month old. The palms are planted with a density of 136 - 148 palms per ha on mineral soils (assumed average at 142 palms per ha) and approximately 160 palms on peat soils (Xavier 2006). Before the pa lms are planted, the soil is sprayed with pesticides, ploughed, compacted and legume cover is sown, typically pueraria. The cover crop prevents erosion and fixes nitrogen from the atmosphere especially when the pa lms are young (Corley and Tinker 2003, p 265). Around each palm a circle with no vegetation is established, i.e. the palm circle. The purpose of having the palm circle for young palms is to prevent competitors to the palm (Corley and Tinker 2003, p 295). Later for the mature palm, the purpose is to allow access for harvesting and to be able to see how many fruits from the ripeningfresh fruit bunch which drop down. The number of loose fruits is used as a guideline for when to harvest (Arulandoo 2006). The palm circle is kept free from weeds by application of herbicides.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 85



## The oil palms are harvested first time when they are thr ee years old, i.e. two years after planting in the plantation (Singh 2006 and Corley and Tinker 2003, p 271). The palms are mature from two years after planting to 20-30 years after planting. An average age of palms when replanting at 26, i.e. 25 years after planting, is estimated from Corley and Tinker (2003, p 318). Yusoff and Hansen (2005) estimate the age of palms when replanting at 25-30 years. Figure 6.5 Figure 6.6 Figure 6.7 and shows immature palms and shows mature palms.

Figure 6.5: Immature oil palms, newly planted palms from the nursery. Shredded biomass from the old stands can be seen between the rows of new palms. The planted palms from the nursery are i mmature until two years after planting, i.e. they are three years old. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006. Figure 6.6: Immature oil palms, ground cover established. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006.

Figure 6.7: Mature oil palms, the palms are harvested from two years to approximately 25 after planting. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006.

Figure 6.8: Harvesting of fresh fruit bunches (FFB). Picture taken by Jannick H Schmidt in United Plantation Berhad 2005.



## The palms are harvested approximately every second weekyear round, each time one FFB is harvested. Harvesting is done manually with a knife attached on a shaft, see Figure 6.8 . Firstly, the frond beneath the fruit

## 86 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## bunch is pruned. Hereby the FFB is accessible for being cut of. The pruned fronds are placed in the field between the palms for mulching, see Figure 6.9 . The harvested FFB consist of two main components; the fruits and the stalks they are attached to, also referred to empty fruit bunch (EFB). The mean weight of one FFB from young palms (3 years) is less 5 kg and more than 25 kg for older palms (10-15 y ears) (Corley and Tinker 2003, p 47). Figure 6.10 and Figure 6.11 show a harvested FFB and two single fruits from the FFB respectively. The palm’s requirements for nutrients are me t by artificial fertilisers as well as mulching of pruned fronds, EFB from the palm oil mill and land applicati on of anaerobically treated POME and solid sludge. Weeds are treated with application of herbicides in the palm circle and in additio n insecticides, fungicides and rodenticide are applied in order to control insect, fungus and rats. Often, the use of insecticides and rodenticide is reduced by an integrated pest management programme . Usually that includes planting of beneficial flowering plants which attract parasites and predators of th e common pests of the oil palm (Arulandoo 2006; Fee and Sharma 1999). Another serious pest is rats which damage the seedlings in the nursery, immature palms and eat the fruits (Fee and Sharma 1999). In a integrated pest management programme, the number of rats are reduced by barn owls. Barn owls are attrac ted by setting up nesting boxes. Another pa rt of integrated pest management is setting up of rhinoceros beetle traps (Sharma 2005).

Figure 6.9: Pruned fronds left between the oil palm rows for mulching. Picture taken by Jannick H Schmidt in United Plantation Berhad 2006. Figure 6.10: Harvested fresh fruit bunch (FFB). Picture taken by Jannick H Schmidt in United Plantation Berhad 2005.

Figure 6.11: Two oil single oil palm fruits. Picture taken by Jannick H Schmidt 2007. Samples provided by United Plantation Berhad.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 87



## When the palms are getting too unproductive, the palms are poisoned and felled and the land is totally cleared with bulldozers. The specific reason for replanting may be decreasing productivity due to difficulties of harvesting (too high), too many dead palms or the FFB yield is simply decreasing with age. Years ago the biomass after felling was burned. However, due to undesirable emissions and uncontrollable forest fires open burning was prohibited in Malaysia in 1989 (Henson 2004, p 13). Henson (2004) assumes 90% compliance in 1998. Open burning is also prohibited in Indonesia. Therefor e, the common practice today is shredding of the trunks from the clearing of the field. Hereafter the trunks are placed in the field as mulch, see Figure 6.5

.


# 6.1 Product flow in agricultural stage
The reference for the inventory is one hecatare in one y ear, i.e. the average of the life cycle of one generation of oil palms, see Figur 6.13 . The agricultural stage of oil palm is divi ded into three stages. The first two stages are the immature palms and the mature palms. The immatu re stage is regarded as the first two years after planting the palms from the nursery. After that it is assumed th at the palms are yielding FFB in 23 years. It is assumed that the age of the oil palms in Malaysia’s and Indonesia’s oil palm plantations are evenly distributed. Thus, one average hectare consists of 8% immature palm s and 92% mature palms. The third stage is clearing and replanting. It is assumed that this stage occur in stantaneously. The FFB yield is applied as the yield in 2005 calculated from linear regression of yields from 1990 to 2005, see Figure 6.12 . The same method as for determining the yield for rapeseed has been used, see section 5.1 . Yields are obtained from FAOSTAT (2006). The expected yield in 2005 can be calcula ted as 18.87 t/ha when using the equation in Figure 6.12 . For comparison with yields determined when using other methods see Table 6.1

.

yield = 0.0919*(year - 1989) + 17.40 R 2 

### = 0.22

15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.01990199119921993199419951996199719981999200020012002200320042005Yield (t/ha) 

### FFB in Malaysia and Indonesia



## Figure 6.12: FFB yields in Malaysia and Indonesia 1990 to 2005. Th e linear regression line an d its corresponding equation and R 2 

## are also shown. The yields ar e obtained from FAOSTAT (2006).

Region Yield 2005 (based on regression

1990-2005) Average yield 1990- 2005 Average yield 2000- 2005 Yield 2003 Yield 2004 Yield 2005

Malaysia 19.84 t/ha 18.74 t/ha 19.29 t/ha 20.48 t/ha 20.49 t/ha 20.90 t/ha Indonesia 17.95 t/ha 17.40 t/ha 17.73 t/ha 17.30 t/ha 18.20 t/ha 17.85 t/ha Malaysia and Indonesia 18.87 t/ha 18.19 t/ha 18.53 t/ha 18.95 t/ha 19.36 t/ha 19.38 t/ha 

## Table 6.1: FFB yields in Malaysia and Indonesia determined using different methods. The applied yield is marked with a dotted line.

## 88 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oilCapital goods 

### Builings (adm., R&D, nursery, shed for machinery)

Tractors

Agr. machinery1 ha immature palms(2 years)



### Establishment of cover crop

Maintenance (fertiliser, pesticide etc.) Planting of seedlings

Land preparation

FFB: 18,870 kg per year1 ha mature palms(23 years) 

### Harvesting Maintenance (fertiliser, pesticide etc.)1 ha clearing after25 years


### Land preparation for next generation Clearing of palms Transport to the oil mill



## Figur 6.13: Product flow related to cultivation of 1 ha oil palm in 25 year. The grey shaded boxes represent the unit processes in the agricultural stage.



# 6.2 Omitted inventory data in agricultural stage
The production of seeds has been omitted. This is not consistent with the i nventory of the agri cultural stage of rapeseed production. However, the life time of oil palms are 26 years compared to one year for rapeseed. Therefore, the interventions from seed production have b een regarded as insignificant for oil palm cultivation.

Several pesticides are used in oil palm cultivation. However, only inventory data production of glyphosate has been identified. Therefore, production of cypermethrin , warfarin and several different fungicides has been omitted. On a mass basis glyphosate comprises 88% of the ac tive ingredients in the u sed pesticides in mature oil palm plantations, see Table 6.7 . On the emission side the EDIP97-method does not include characterisation factors for the active substances in pesticides. However, in the LCAfood database characterisation factors for glyphosate and cypermethrin have been determined w ithin the framework of the EDIP97-method. No characterisation factors have been found fo r warfarin and fungicides. The active ingredients in warfarin and fungicides only comprise 0.02% of the total applied active ingredients in mature oil palm plantations, see Table 6.7

. This share is assumed to be insignificant and therefore no characterisation factors have been calculated for warfarin and fungicides.

It has not been possible to identify any data on methan e emissions from the oil palm plantation nor the nature that is converted into oil palm plantation. Therefore, this aspect is not included.



# 6.3 Energy use
As in the case of rapeseed cultiva tion in Denmark described in section 5.3 oil palm cultivation implies several field work processes using fossil fuel such as planting ofnew palms, sowing of crop cover, fertiliser application, pesticide application, harvesting and finally after 25 years clearing and preparing the field for replanting.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 89


# Field work operations: which and how many times? It has not been possible to determin e the specific diesel consumption per single field work process per hectare. Data has only been available as the total diesel consump tion per year on the estate level. However, though not used, the number of the included field wo rk operations has been estimated, see Table 6.2

.

Field work process Number of
processes per year References and comments

Clearing of old stands 1/28 (Xavier 2006) Chopping of trunks 1/28 (Xavier 2006) Placement of chopped trunks 1/28 (Xavier 2006) Planting of palms 1/28 (Xavier 2006) Placement of chopped trunks 1/28 (Xavier 2006) Replanting

Sowing of crop cover (Pueraria) 1/28 (Xavier 2006) N-fertiliser (urea or ammonia nitrate) 3

Rock phosphate 1 Potash 3 Magnesium 1 Fertilising

Boron 1 Numbers apply to palm > 3-5 years. After 5 years fertiliser application is done by mechanical spreader attached to tractor (Xavier 2006)

Pesticide ? Harvesting 26 (Xavier 2006) Transport Transport of FFB to mill ~18.9 t x 5 km Esti mated per hectare from average yield in Malaysia and Indonesia and estimated distance in 50 km 2 estate 

## Table 6.2: Description and documentation of estimated number of field work processes. These data are only for information purposes, the data is not used as inventory data.



# Diesel useAs mentioned in the previous section it has not been possible to establish a relation between field work processes and the energy consumption with the given data ma terial. Therefore, diesel consumption in machinery in the plantation is determined as a total value including all field work processes per ha per year. Different data sources have been identified, see Table 6.3 . The data in Table 6.3 represent average of oil palm plantation including an area covered by immature palms.

Fuel United Plantations (Singh 2006) Yusoff and Hansen (2005) Unilever (1990) Zah and Hischier (2003) Applied

Diesel 1,611 MJ 2,366 MJ 2,038 MJ 9,857 MJ 2,118 MJ Petrol 340 MJ - - - - Total 1,951 MJ 2,366 MJ 2,038 MJ 9,857 MJ 2,118 MJ 

## Table 6.3: Consumption of diesel and petrol per ha of oil palm plantation. The applied data are marked with a dotted line.



## In Table 6.3 the energy consumption in United Plantations, Yusoff and Hansen (2005) and Unilever (1990) are given in litre. This has been converted into mega jo ules by using calorific values for diesel at 36.4 MJ/litre given in Appendix 1: Data on fuels

.
It appears from Table 6.3 that the diesel consumption given in Zah and Hischier (2003) is 4 to 5 times higher than the figures given in the other three sources. The data in Zah and Hischier (2003) are based on Hirsinger et al. (1995). As described in section 11.3 the energy figures in Hirsinger et al. (1995) are not regarded as consistent. Therefore, the energy consumption in Zah and Hi schier (2003) is not regarded as representative for Malaysian oil palm plantations. The energy use in United Plan tations may be slightly lower than in average plantations because they use rail based transport of the harv ested FFB to the oil mill wh ich requires less energy than truck transportation. Not many plan tations have that technology because it is only applicable in flat terrain and investments costs are high. The applied energy u se is the average of United Plantations, Yusoff and Han-

## 90 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## sen (2005) and Unilever (1990). For simplification reasons all fuel used in oil palm plantations is regarded as diesel.

The uncertainty related to the difference between energy uses determined from the data sources in Table 6.3 is assessed in a sensitivity analysis, see section 21.6



# 6.4 

# Other processes
Other processes include those processes which are not rela ted to the field. This includes the nursery where the seeds are sown and grown for one year before the seedlings are planted in the plantation. According to section 6.2 the nursery is omitted from the inventory.



# 6.5 

# Materials

Seeds According to section 6.2 production of oil palm seeds have been omitted from the inventory.



# Fertilisers

Several data on the use of fertilisers in o il palm plantations have been identified see Table 6.4 . Data for mature and immature oil palms have been identified. Thus, distinction between these two stages is done.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 91

Fertiliser N (kg N/ha) P (kg P 2 O 5 /ha) K (kg K 2 O/ha) Mg (kg MgO/ha) S B Source

Applied fertiliser in oil palm plantations 1) United Plantations 2005 136 77 297 42 - - (United Plantations Berhad 2006, p 110, 123, 129) 2) Malaysia, average 96 28 172 48 - - (Yusoff and Hansen 2005) 3) Malaysia, costal soils* 124* 128* 256* 19* 16* - (Subranamiam 2006a) 4) Malaysia, average 2001 100 45 205 - - - (IFA et al. 2002, p 13) 5) Malaysia, average 2002 76 86 119 - - - (FAO 2004) 6) Malaysia, immature 90 35 140 29 (Henson 2004, p 33) Applied value (mature) 106 73 210 36 16 - Average of 1, 2, 3, 4 and 5 Applied value (immature) 90 35 140 29 - - The value given in 6 Theoretical figures Recommended application (by MPOB) 128 144 200 - - - (FAO 2004)

Nutrient demand, 10 year old palms 114 32 180 53 - - (Corley and Tinker 2003, p 332)

Nutrient demand, 15 year old palms 182 56 315 95 - - (Corley and Tinker 2003, p 332)



## Table 6.4: Input of nutrients to the plantation. The applied fertiliser uses in this study are marked with a dotted line. * The data in Subranamiam (2006a) are provided as total fertiliser product per palm. Th is is converted to fertiliser component per ha be assuming 142 palms/ha (see description of oil palm cultivation in the beginning of section 6 ) and 35% N in ammonia nitrate, 30% P 2 

## O 5 

## in china rock phosphate, 60% K 2 

## O in muriate of potash and 27% MgO and 23% S in kieserite (Corley and Tinker 2003, p 385).



## The fertiliser uses applied in this study are shown in Table 6.4 . The adopted figures are the average of fivedifferent sources on the fertiliser use in mature oil palm plantations in Malaysia and one data source for immature oil palms in Malaysia. The nut rient demand for oil palm given in Table 6.4 is the total demand that may be met by inputs of artificial fertilisers, biomass re siduals (pruned fronds, EFB and POME), decomposition from the atmosphere and possible decr ease in the soil nitrogen pool. In a ddition losses as leaching, run-off and evaporation appear. Therefore, the nutrient demand cannot be used as a stand alone guideline for application of artificial fertiliser.

The total amounts of applied nutrients in fertiliser in o il palm plantations are calcu lated as the average of 2 years immature and 23 mature palms. Thus, th e average uses are 105 kg N/ha, 31 kg P/ha (70 kg P 2 

## O 5 

## ), 170 kg K/ha (204 kg K 2 

## O/ha), 21 kg Mg/ha (35 kg MgO/ha) and 16 kg S/ha.

According to Corley and Tinker (2003, p 385) more than ha lf of the fertiliser use in Malaysia is used in the palm oil industry. Therefore, national statistics on types of N, P and K fertilisers used are assumed to be representative for oil palm. However, only statistical data on import and export have been identified (FAO 2004). However, according to FAO (2004) most of the artificial fertiliser used in Malaysia is imported. Therefore, the import of artificial fertilisers is assumed to represent the use of fertiliser in oil palm plantations. The distribution between different types of imported N, P and K fertilisers in Malaysia is shown in Table 6.5

.

Nitrogen Share Phosphate Share Potash Share Ammonium sulphate 70% Phosphate rock 64% KCl 83% Urea 26% Ammonium phosphate 6% K 2 SO 4 4% Other 4% Other 30% Other 13% 

## Table 6.5: Distribution between different types of N, P and K fertilisers imported in Malaysia in 2001 (FAO 2004).



## Nitrogen: According to Subranamiam (2006a) urea is typically applied on costal soils while ammonium sulphate is applied on inland soils. The re ason for that may be that costal soils are moister. Hereby, the urea hydrolyses faster and volatilisation of ammonia is reduced (based on discussions on United Plantations Berhad

## 92 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## with Singh 2006). According to Corley and Tinker (2003, p 367) 27-48% of the applied N with urea volatises. However, if the urea is applied on moister costal so ils volatilisation is estimated to only 10-15% (based on discussions on United Plantations Berhad with Singh 20 06). It is assumed that the marginal source of Nfertiliser is 73% ammonium sulphate and 27% urea. Mo re sophisticated assumptions could have been done, e.g. that increased production by expanding the cultivat ed area takes place on inland soils while increases in yields are more likely to take place in the well establishe d plantations in the costal areas. Thus, the marginal fertiliser for increases by area should be ammonium sulphate (inland) while the margin al fertiliser for increases by yield should be urea (coastal areas). However, it has not been possible to find any data that document that. Therefore, the same fertiliser mix of 73% ammonia sulpha te and 27% urea is regarded as the marginal fertiliser for increased production by area as well as yield.

Inventory data for ammonia sulphate and urea are described in section 5.4 and Table 5.17

.
Phosphate: The marginal source of phosphate fertiliser is assumed to be phosphate rock. In Europe, the marginal phosphate fertiliser is assumed to be triple super phosphate, see section 5.4 . The reason why plain phosphate rock can beneficially be applied in South East Asia is that the soils are acidic (Corley and Tinker 2003, p 387). That helps dissolving the phosphate. In addition, ther e is more time for the crops to absorb the phosphate because oil palms are perennials (Corley and Tinker 2003, p 387).

Two inventories of phosphate rock have been identifie d, one for phosphate rock from Morocco and one for phosphate rock from U.S. Analysing the data sets in Si mapro and using the EDIP97 for LCIA, it appears that global warming, acidification and toxicity are the most significant impact categories. In Table 5.17 the two inventories are compared within these categories.

LCI-data for N-fertiliser CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘ Phosphate rock, as P2O5, beneficiated, dry, at plant/MA’ (ecoinvent 2004) 217 g 1.0 g 151 m 3 Time: Data from around 2000 Geography: Morocco Technology:US technology for dry rock or dried wet rock processing Co-product allocation : Not relevant Capital goods: Machinery, buildings

‘ Phosphate rock, as P2O5, beneficiated, wet, at plant/US’ (ecoinvent 2004) 194 g 1.2 g 187 m 3 Time: Data from around 2000 Geography: US Technology: US technology for wet rock processing Co-product allocation : Not relevant Capital goods: Machinery, buildings 

## Table 6.6: Comparison of LCIs of phosphate rock. The comparison is shown as characterised results using the EDIP97- method for LCIA. All the life cycle inve ntories are for 1 kg fertiliser, as P 2 

## O 5 

## .


## It appears from Table 5.17 that the differences between the two data sets for phosphate rock are not significant. It is chosen to apply the averag e of the two. Analysing the data sets in SimaPro it appears that the use of electricity is significant regarding the impact categories shown in Table 5.17 . Therefore, is chosen to apply a modified version of the ecoinvent data on phosphate ro ck where average electricity has been replaced by marginal electricity assumed to be produced from coal.

Potassium (K): The marginal source of potash is assumed to be KCl, see Table 6.5 . Inventory data for potassium chloride are described in section section 5.4 and Table 5.21

.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 93



# Pesticides The use of pesticides is determined from data provi ded by United Plantations Research Department (Singh 2006). The use of pesticides per hectare is given in Table 6.7

.

Mature Immature Nursery Pesticide kg/ha kg a.i./ha kg/ha kg a.i./ha kg/ha kg a.i./ha Herbicide (typically glyphosate) 4.3 2.3 11 3.6 49 8.2 Insecticide (typically cypermethrin) 0.82 0.30 5.3 0.45 5.6 0.28 Fungicide (several different fungicides) 0.00059 0.00018 0.21 0.16 12 6.8 Rodenticide (typically warfarin) 0.60 0.00022 0.19 0.000092 - - Total 5.7 2.6 16.7 4.2 66.6 15.3 

## Table 6.7: Use of pesticides per hectare per year. The amount and type of pesticides used are based on figures from United Plantations in 2005 (Singh 2006).



## Inventory data for production of the pesticides in Table 6.7 have only been identified for glyphosate: ‘Glyphosate, at regional storehouse/RER’ (ecoinvent 2004). The data set includes energy use, transport to regional storehouse and capital goods, i.e. machinery and buildin gs. Analysing the data in Simapro and using the EDIP97 method use of electricity appears to be significan t within several impact categories. Therefore, European average electricity is replaced by marginal electricity based on coal technology. Production of the other pesticides has been applied as ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004). Also in this case, European average electricity is replaced by ma rginal electricity based on coal technology. The applied a.i. of pesticides are the aver age of 2 years immature and 23 years ma ture oil palm, i.e. annual applicationof 2.4 kg a.i. glyphosate/ha, 0.31 kg a.i. cypermethrin/ha, 0.013 kg a.i. fungicides/ha and 0.00021 kg a.i. warfarin/ha.



# 6.6 Co-products
There are no co-products in the agricultural stage of palm oil production. The pr uned fronds and felled trunks at replanting, however, could be regarded as a co-produc ts displacing artificial fertiliser. But since mulching of is a common practice and since the amounts of pruned fr onds and trunks at replanti ng are proportional with the cultivated area, changes in oil palm cultivation do not affect the nutrientbalances and the consequential emissions and fertiliser use per unit of area. Only if changes in the use of pruned fronds are affected, the effects should be included. The co-products EFB and POME are co-products from th e oil mill stage, but they appear as inputs to the agricultural stage. Normally, application of EFB and POME is consta nt with the plantation area as in the case of pruned fronds. Thus, a change in demand for palm oil will not affect the application of EFB and POME per unit of area and the consequential emissions and fertiliser use per unit of area.



# 6.7 Emissions
Emissions from the plantation are determined from mass ba lances of the relevant subs tances, i.e. N, P and C, during the oil palms life cycle. However, since most of the net fluxes of C during the life cycle of one oil palm generation are zero (CO 2 

## uptake equals CO 2 

## release), a detailed balance has not been conducted. Mass balances are established per ha on an annual basis. Firstly a ll known inputs and outputs are summarised. The residuals are then distributed on various emissions calculated from different models. Oil palm is not an annual crop like rapeseed. Oil palm is a perennial. During the life cycl e of one generation oil palm plantation the input and uptake of nutrients, harvesting of FFB and decomposition ofbiomass residues varies. Therefore, it is not desirable to establish a simple annual nutrient balance as for rapeseed described in section 5.6 . The 25 years from planting to replanting is divided into several stages representing different inputs and outputs of nutrients. The following four components of the balance are considered : 1) net inputs, 2) release from decomposition of biomass, 3) uptake of nutrients from the soil in the standing biomass and 4) harvested biomass. The residual is

## 94 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## then the field surplus of N and P from which emissions are calculated. During the life cycle of one oil palm generation 2) and 3) are equal, but within the singe st ages they may differ. Comparing the nutrient balance for oil palm with rapeseed, 2) and 3) are not necessary fo r rapeseed because the balance is ‘reset’ each year. The principle for establishing a field balance of nutrients in each stage during the life cycle of one generation of oil palm is illustrated in Figure 6.14 . The four components are described in the following.

1) Net input

Plantation 2) Release from decom position of biomass

4) Harvested nutrients 3) Uptake: Changes in stored nutrients 5) Surplus Inputs:

Outputs: 

## Figure 6.14: Principle for establishing a field nutrient balance. The surplus of from which different emissions are calculated is grey shaded.



## All emissions are inventoried for average, peat and mineral (i.e. non-peat) soils. According to Henson (2004), an area of 1,360 km 2 

## was planted with oil palm on peat in Malays ia in 2003/2004. The total planted area with oil palm in Malaysia was 32,600 km 2 

## in 2003 and 34,100 km 2 

## in 2004. Thus, 4.0 - 4.2% of the area planted with oil palm in Malaysia is on peat. The same figures ar e assumed to be valid for oil palm in Indonesia. Data for average soil (4.1% peat) is applie d in the baseline scenario while invent ories for peat and mineral soils are applied as sensitivity analyses described in section 21.13 . According to Corley and Tinker (2003, p 81) much of the open land remaing land for expanding the oil palm cultivation in SE-Asia is peat soils. According to Wetland International (2007), more than 50% of the pl anned new plantations in Malaysia and Indonesia are on peat soils.


# Inputs: Net inputs of nutrients Inputs of nutrients include artificial N- and P-fer tiliser, N-deposition from the atmosphere, N-fixation from legume cover crops (typical pueraria), planting of palm s and application of EFB and POME as organic fertiliser.

Artificial fertiliser: The inputs of artificial fertilisers are described in Table 6.4 . The amount of applied P 2 

## O 5 

## is converted into P by use of molar masses.

N-deposition: Three figures on atmospheric N-deposition in Malaysia have been identified; 17 kg N/ha (Corley and Tinker 2003, p 358), 21 kg N/ha (Corley and Tinker 2003, p 292) and 14.6 kg N/ha (Chew 1999, p 64). It is chosen to apply the average of these three values as representa tive for N-deposition in Malaysia, i.e. 17.5 kg N/ha.

N-fixation: According to Chew et al. (1999, p 64) legumes c over crop has a negligible effect on addition of nitrogen in a nitrogen balance for oil palm. However, it may be questioned if the legumes cover crop has only a negligible effect on the nitrogen balance, at least fo r nitrogen balances the first five years after planting.Hauser (2006) provides experimental results for pueraria and mucuna legumes grown in maize fields in Cameroon. These results show that the average annual nitr ogen fixation over a period of four years are 35.3 kg N/ha for mucuna and 76.0 kg N/ha for pueraria. Swar t and Diest (1987, p 145) found a higher value for pueraria at 292 kg N/ha during a growing season at 51⁄2 mont h. Haque and Jutzi (1984) provides average nitrogen fixation rates for 16 forage legumes (not including pue raria and mucuna) in Sub-Sahara Africa. The means ranges from 62 to 290 kg/ha on an annual basis. Arula ndoo (2006) estimates the total contribution of the leguminous cover crop in young oil palm plantations around 20 0 kg N/ha. It appears that very different figures

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 95



## exist. It is certain that fixation takes place. It is assume d that fixation the first year is 200 kg N/ha and after that it gradually decreases to zero the sixth year.

Planting of palms: No data on the weight of palm seedlings have been identified. Therefore, it assumed that each palm has a dry weight at 10 kg corresponding to 1.42 t/ha with a palm density at 142 palms/ha. Four different data sources for N and P contents in oil palms have been identified, see Table 6.8 . Three of the data sources are cited in Henson (2004).

Nutrients content in oil palm kg N/t biomass kg P/t biomass 1) Henson (2004, p 33) 6.3 0.62 2) Henson (2004, p 33) 5.8 0.54 3) Henson (2004, p 33) 6.4 0.57 4) Corley and Tinker (2003, p 328) 5.4 0.78 Applied value 6.0 0.63 

## Table 6.8: Contents of N and P in oil palm total standing biomass (dry weight basis). The applied values are marked with a dotted line.



## EFB: The production of EFB amounts 22.5% of the FFB (see Table 10.2 ). It is assumed that the EFB from the palm oil mill are distributed evenly through the plantation on immature as well as mature palms. Thus, the production of EFB is 22.5% of the FFB yield of 18.87 t/h a, i.e. 4.25 t/ha. Henson (2004, p 24) estimates the decay/decomposition time for EFB as one year. Th e content of N and P in EFB is given in Table 6.9

.

Nutrients content in EFB kg N/t EFB kg P/t EFB Dry weight 8.0 0.96 Fresh weight (60% water) 3.2 0.38 

## Table 6.9: Contents of N and P in empty fruit bunches (EFB). (Singh et al. 1999, p 172)



## POME: The production of POME is 672.5 kg/t crude palm oil (see Figure 10.1 ). Applying the oil extraction rate at 19.98% (see Figure 10.1 ) and the yield at 18.87 t FFB/ha, the amount of POME can be calculated as 2.54 t POME/ha. As for EFB it is assumed that POME is applied evenly distributed in the plantation on immature as well as mature palms. This does not represent what actually happens, since only a minor part of the plantation receives POME. However, it is assumed that unequally distribution of nutrients because of POME application is equalised through differe ntiated application of artificial fer tiliser. Henson (2004, p 24) estimates the decay/decomposition time for POME as one year. Th e content of N and P in POME is given in Table 6.10

.

Nutrients content in POME kg N/t POME kg P/t POME Dry weight 19 3.0 Fresh weight (95% water) 0.95 0.15 

## Table 6.10: Contents of N and P in palm oil mill effluent (POME). Nutrients content (Department of environment 1999) and moisture content (Singh 1999).



## The net inputs of N and P are summarised in Table 6.11

.

## 96 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Input of nitrogen, kg N/ha per year year 1 year 2 year 3 year 4 year 5 year 6-25 N-fertiliser 90 90 106 106 106 106 N-deposition from the atmosphere 17.5 17.5 17.5 17.5 17.5 17.5 N-fixation by legumes 200 160 120 80 40 0 Planted palm seedlings 8.5 0 0 0 0 0 EFB 13 13 13 13 13 13 POME 2.4 2.4 2.4 2.4 2.4 2.4 Total net input of N 332 283 259 219 179 139 Input of phosphorus, kg P/ha per year year 1 year 2 year 3 year 4 year 5 year 6-25 P-fertiliser 15 15 32 32 32 32 Planted palm seedlings 0.9 0 0 0 0 0 EFB 1.6 1.6 1.6 1.6 1.6 1.6 POME 0.4 0.4 0.4 0.4 0.4 0.4 Total net input of P 18 17 34 34 34 34 

## Table 6.11: Summary of annual net inputs of N and P to the plantation.


# Inputs: Release of nutrients fr om decomposition of biomass Decomposition of biomass residues includes decompositi on of fronds, trunks, cover crop, EFB and POME Table 6.12 summarises the biomass residues from the agricultural stage left in the field during the life cycle of one generation of an oil palm plantation. The prune d fronds are left in the field as a biomass resource.

Field source At replanting Biomass residues per ha Immature: year 1 and 2 Mature: year 3-25 Pruned fronds 0 t/ha 10.70 t/ha 0 t/ha Biomass at felling 0 t/ha 0 t/ha 94.9 t/ha 

## Table 6.12: Biomass residues as annual output and from replanting as a single output in the end of the palm’s economical life. Data on annual pruned fronds and biom ass at felling are obtained as the average of data from two different references cited in Weng (1999, p 43)



## The data for pruned fronds in Table 6.12 are slightly higher than values identified in Henson (2004, p 10): 8-9 t/ha and in Corley and Tinker (2003, p 293): 9 t/ha. Wh en comparing the data for biomass at felling values found in Henson (2004, p 100 t/ha) are in good accordance w ith the used values. However, Corley and Tinker (2003, p 92) specify biomass at felling as around 62 and 68 t/ha for 17.5 and 27.5 years old palms respectively, which seem to be underestimated compared to We ng (1999) and Henson (2004). The data in Weng (1999) seem to be more consistent and comparable than the data provided in Corley and Tinker (2003) and Henson (2004). Therefore, it is chosen to apply these data in this study.

As it appears from Table 6.12 the output of biomass from the field varies significant in the three stages. Therefore the three different stages have to be considered when establishing nutrient ba lances and in addition the decomposition time of biomass residues left in the fiel d must also be taken into account. The decomposition time is assumed to be 2 years for fronds and shredded tr unks. 2 years are the same decomposition time as for unburned shells (Henson 2004, p 24). Figure 6.15 shows production and decompos ition of biomass residues during the life cycle of one ha oil palm plantation.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 97

time 10.70 t/ha



### 1 2 3 4 5 0 Replanting decomposition, previous generation production
decomposition, this generationFrom field:Pruned fronds 10.70 t/ha

2.68 t/ha 8.03 t/ha

2.68 t/ha 8.03 t/ha 10.70 t/ha

10.70 t/ha

0 t/ha 0 t/ha 0 t/ha



### 25 Replanting

Immature Mature Production

Deconposition time 1 2 3 4 5 0 Replanting 25 Replanting decomposition

production

47.45 t/ha 47.45 t/ha 94.90 t/haFrom replanting:Fronds and trunks



## Figure 6.15: Production and decomposition of fronds and trunk during the life cycle of one ha of oil palm. The figure is divided into two parts where the one in the top refers to clearing and replanting and the one in the bottom refers to annual production and decomposition of biotic residues.



## Table 6.12 In addition to the biotic residues from the agricultural stage given in legume cover crop also constitute inputs of nutrients to the plantation. According to Chew et al. (1999, p 64) legumes play an important role in immobilising nitrogen from decomposing trunks a nd fronds during the immature period. The immobilised nitrogen is released as the cover crop dies back when it is shaded out at about 3-5 years after planting (Chew et al., p 72). Thus, after 12 month the N accumulated in cover crop is around 289 kg/ha, whiles this is reduced to 227 kg after 20 month (Chew et al., p 73). Based on above figures, the net N-uptake in cover crops the first five years after planting is estimated, see Table 6.13 

## . It is assumed that there is no decomposition of legume cover the first year. The decomposition in year 2 to 5 is assumed to be 72.3 kg each years adding up to the 289 kg. The 289 kg N/ha is regarded as uptake and is included in that component of the nitrogen balance. Uptake in and decomposition of legume cover crops only include the pa rt of nitrogen that is taken up from the soil. In addition the cover crop fixes nitrogen from the atmosphere. This is regarded as an input which is released the same year as it is fixed in the plant. The effect of le gume cover crop is included for the nitrogen balance only. According to Corley and Tinker (2003, p 387) there are so far no data on the effect of cover crop on the phosphate balance.

## 98 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Net N-decomposition in cover crop year 1 year 2 year 3 year 4 year 5 N-decomposition (kg N/ha) 0 72.3 kg 72.3 kg 72.3 kg 72.3 kg 

## Table 6.13: Net uptake by cover crops under oil palm the first five years after planting. Due to shading, the presence of cover crops is assumed to insignificant after five years.



## Table 6.14 summarises the amount of biomass residu es that decompose in each stage.

Decomposition of biotic residues, t/ha (dry weight) year 1 year 2 year 3 year 4 year 5 year 6-25

Pruned fronds, present generation - - 2.68 t/ha 8.03 t/ha 10.70 t/ha 10.70 t/ha Fronds, previous generation 8.03 t/ha 2.68 t/ha - - - - Biomass from replanting 47.45 t/ha 47.45 t/ha - - - - Legume cover crop 0 kg N/ha 72.3 kg N/ha 72.3 kg N/ha 72.3 kg N/ha 72.3 kg N/ha - 

## Table 6.14: Decomposition of biotic residues during different stages of the life cycle of one generation of oil palm.



## In order to establish a nutrient balance it is also necessary to knowing the contents of N and P in the fronds and felled biomass at replanting, i.e. whole palms. This is given in Table 6.15

.

Component kg N/t dry weight kg P/t dry weight Reference

Fronds 12.2 0.9 Fronds consists of 65% rachis and 35% leaflet (Tinker and Smilde 1963, p 354). Nutrient contents of rachis and leaflets are obtained from Khalid et al. (2000, p 50) Whole palm 6.0 0.63 Aver. of values in Henson (2004, p 33) and Corley and Tinker (2003, p 328) FFB 5.5 0.8 Corley and Tinker (2003, p 331) 

## Table 6.15: Nutrient contents in oil palm components.



## Based on Table 6.14 

## and Table 6.15 the inputs of nutrients to the soil from release of decomposing biomass are determined, see Table 6.16



## .

Release of nitrogen from decomposition, kg N/ha year 1 year 2 year 3 year 4 year 5 year 6-25

Release from pruned fronds, present generation 0 0 33 98 131 131

Release from pruned fronds, previous generation 98 33 0 0 0 0

Release from felled biomass from replanting 285 285 0 0 0 0

Release from dying back of legume cover
crop 0 72 72 72 72 0

Total input of nitrogen from decomposition of biomass 383 390 105 170 203 131

Release of phosphorus from decomposition, kg P/ha year 1 year 2 year 3 year 4 year 5 year 6-25

Release from pruned fronds, present generation 0,0 0,0 2,3 6,8 9,1 9,1

Release from pruned fronds, previous generation 6,8 2,3 0,0 0,0 0,0 0,0

Release from felled biomass from replanting 29,9 29,9 0,0 0,0 0,0 0,0

Total input of nitrogen from decomposition of biomass 36,7 32,2 2,3 6,8 9,1 9,1



## Table 6.16: Summary of release of N and P from decomposition of biomass residues.


# Outputs: Uptake, stored and harvested nutrients Uptake includes harvested nutrients an d increases in nutrients in stored bi omass, i.e. annual accumulated nutrients in standing biomass. Stored nutrients in the biomass are considered as an output because it immobilises nutrients until they are released when the biomass dies back. A considerable share of the nutrient inputs are

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 99



## accumulated in the living biomass until it dies back. The release of nutrients from decomposition of biomass is described in the previous section.

Increase in standing biomass, oil palms: It is assumed that the stored bi omass in the oil palms increases linearly from estimated 1.42 t/ha (see nutrients in planted palms described previously) to 94.9 t/ha at felling (see Figure 6.15 ). Thus, during the 25 years of one oil palm generation the annual accumulation in standing biomass is 3.74 t/ha (dry weight). The content of N and P in oil palms is given in Table 6.15

.
Increase in standing biomass, cover crops: According to section 6.7 : “ Inputs: Release of nutrients from decomposition of biomass ” there is only a net uptake of N in cover crops the first year. The uptake is 289 kg N/ha.

Harvested FFB: The annual yield of FFB from mature and immatu re oil palm plantations is 18.87. Two of the 25 years of the life cycle of one generation of oil palm are immature. Thus the average yield from mature oil palms can be determined as 20.51 t/ha. The yield va ries during the mature period from around 10-20 t/ha the first years after planting to 25-34 t/ha when the yield reaches its maximum (Corley and Tinker 2003, p 239, 321). Based on the yield profiles provided in Corley a nd Tinker (2003, p 239, 321) an average profile is estimated, see Table 6.17



## .

Year 1-2 3 4 5 6 7 8-10 11-16 17 18-19 20 21 22 23 24 25 Yield, as given 0 15 18 21 24 26 27 27 27 26 26 25 24 23 22 21 Yield, adjusted 0 13 15 17 20 22 22 23 22 22 21 21 20 19 18 18 

## Table 6.17: Yield profile (t FFB/year, fresh weig ht) as a function of palm age. The va lues in the top line are obtained as the average of six yield profiles given in Corley and Tinker (2003, p 293, 321). The figures in the bottom lin e are adjusted values of the top line so that they fit the total average yield in the mature period at 20.51 t FFB/ha.



## The harvested N and P in FFB can then be de termined from the adjusted yield profile in Table 6.17 

## and the content of N and P in FFB given in Table 6.15

.

Pruned fronds: According to Weng (1999, p 43) 10.70 t/ha 13 

## (dry weight) pruned fronds are harvested each year. However, this is only from mature oil palms. No fr onds are harvested the two first, immature, years. As a comparison United Plantations has 9.56 t pruned fronds/h a (dry weight) in 2005 (United Plantations Berhad 2006, p 123 and 129). The reason for deviation from th e 10.70 t/ha obtained from Weng (1999) may be due to different distribution between immature and mature ar eas. Since the data from Weng (1999) represents averages of a larger area, the 10.70 t/ha is applied. It is assumed that the amount of pruned fronds is constant through the entire mature period that last from 2 to 25 y ears after planting. This may be a simplification of the actual situation, but no data on changes with palm age have been identified. The content of N and P in fronds is given in Table 6.15

.

The increase in stored N and P in the field are summarised in Table 6.18

.

13 

## This is adopted as the average of two different values given in Weng (1999).

## 100 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Year Uptake, stored and har- vested nitrogen, kg N/ha 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Uptake in oil palms 23 23 23 23 23 23 23 23 23 23 23 23 23 23 Uptake in cover crop 289 0 0 0 0 0 0 0 0 0 0 0 0 0 Harvested FFB 0 0 36 43 50 57 63 65 65 65 65 65 65 65 Pruned fronds 0 0 131 131 131 131 131 131 131 131 131 131 131 131 Total nitrogen 312 23 190 197 204 211 216 218 218 218 219 219 219 219 Year Uptake, stored and har- vested phosphorus, kg P/ha 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Uptake in oil palms 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Uptake in cover crop 0 0 5 6 7 9 9 10 10 10 10 10 10 10 Harvested FFB 0 0 9 9 9 9 9 9 9 9 9 9 9 9 Total phosphorus 2 2 17 18 19 20 21 21 21 21 21 21 21 21

Year Uptake, stored and har- vested nitrogen, kg N/ha 15 16 17 18 19 20 21 22 23 24 25 Uptake in oil palms 23 23 23 23 23 23 23 23 23 23 23 Uptake in cover crop 0 0 0 0 0 0 0 0 0 0 0 Harvested FFB 65 65 64 64 62 61 59 57 55 53 52 Pruned fronds 131 131 131 131 131 131 131 131 131 131 131 Total nitrogen 219 219 218 217 216 215 213 211 209 207 205 Year Uptake, stored and har- vested phosphorus, kg P/ha 15 16 17 18 19 20 21 22 23 24 25 Uptake in oil palms 2 2 2 2 2 2 2 2 2 2 2 Uptake in cover crop 10 10 10 9 9 9 9 9 8 8 8 Harvested FFB 9 9 9 9 9 9 9 9 9 9 9 Total phosphorus 21 21 21 21 21 21 20 20 20 19 19 

## Table 6.18: Summary of release of N and P per ha per year from decomposition of biomass residues.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 101



# Emissions related to N-balance Point of departure when calculating N-emissions is a N- balance on the field scale. The N-balance is shown in
Table 6.19 

## below.

Year Inputs: Net inputs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 N-fertiliser 90 90 106 106 106 106 106 106 106 106 106 106 106 106 N-deposition from the atmosphere 18 18 18 18 18 18 18 18 18 18 18 18 18 18 N-fixation by legumes 200 160 120 80 40 0 0 0 0 0 0 0 0 0 Planted palm seedlings 9 0 0 0 0 0 0 0 0 0 0 0 0 0 EFB 14 14 14 14 14 14 14 14 14 14 14 14 14 14 POME 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Total 332 283 259 219 179 139 139 139 139 139 139 139 139 139 Inputs: Release from decomposition of biomass Pruned fronds, present generation 0 0 33 98 131 131 131 131 131 131 131 131 131 131 Pruned fronds, previous generation 98 33 0 0 0 0 0 0 0 0 0 0 0 0 Felled biomass from replanting 285 285 0 0 0 0 0 0 0 0 0 0 0 0 Dying back of legume cover crop 0 72 72 72 72 0 0 0 0 0 0 0 0 0 Total 383 390 105 170 203 131 131 131 131 131 131 131 131 131 Output: Increase in standing biomass (uptake that stays in the field) Uptake in oil palms 23 23 23 23 23 23 23 23 23 23 23 23 23 23 Uptake in cover crop 289 0 0 0 0 0 0 0 0 0 0 0 0 0 Pruned fronds 0 0 131 131 131 131 131 131 131 131 131 131 131 131 Total 312 23 154 154 154 154 154 154 154 154 154 154 154 154 Output: Harvested FFB (uptake that is brought out of the field) Harvested FFB 0 0 36 43 50 57 63 65 65 65 65 65 65 65 Total 0 0 36 43 50 57 63 65 65 65 65 65 65 65 Balance N surplus (input – output) 403 650 175 193 179 59 54 52 52 52 52 52 52 51

Year Inputs: Net inputs 15 16 17 18 19 20 21 22 23 24 25 N-fertiliser 106 106 106 106 106 106 106 106 106 106 106 N-deposition from the atmosphere 18 18 18 18 18 18 18 18 18 18 18 N-fixation by legumes 0 0 0 0 0 0 0 0 0 0 0 Planted palm seedlings 0 0 0 0 0 0 0 0 0 0 0 EFB 14 14 14 14 14 14 14 14 14 14 14 POME 2 2 2 2 2 2 2 2 2 2 2 Total 139 139 139 139 139 139 139 139 139 139 139 Inputs: Release from decomposition of biomass Pruned fronds, present generation 131 131 131 131 131 131 131 131 131 131 131 Pruned fronds, previous generation 0 0 0 0 0 0 0 0 0 0 0 Felled biomass from replanting 0 0 0 0 0 0 0 0 0 0 0 Dying back of legume cover crop 0 0 0 0 0 0 0 0 0 0 0 Total 131 131 131 131 131 131 131 131 131 131 131 Output: Increase in standing biomass (uptake that stays in the field) Uptake in oil palms 23 23 23 23 23 23 23 23 23 23 23 Uptake in cover crop 0 0 0 0 0 0 0 0 0 0 0 Pruned fronds 131 131 131 131 131 131 131 131 131 131 131 Total 154 154 154 154 154 154 154 154 154 154 154 Output: Harvested FFB (uptake that is brought out of the field) Harvested FFB 65 65 64 64 62 61 59 57 55 53 52 Total 65 65 64 64 62 61 59 57 55 53 52 Balance N surplus (input – output) 52 52 52 53 54 55 57 59 61 63 65 

## Table 6.19: N-balance for 1 hectare oil palm plantation.



## The N surplus is distributed on different emissions. Dete rmination of each emission is described in the following. The same approach as determination of emissi ons from rapeseed fields has been used, see section 5.6

.

## 102 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Ammonia from crop: No data on ammonia emission from oil palm have been identified. Therefore the same value as for rapeseed has been applied, i.e. 5 kg NH 3 

## -N/ha.

Ammonia from fertiliser application: The ammonia emission from fertiliser depends on the type of fertiliser applied. According to Andersen et al. (2001, p 35) the ammonia emission from fertilisers based on ammonia is 2% of the N content in the fertiliser. As described in section 6.5 the ammonia volatilisation from urea is as high as 27-48% of the applied N (Corley and Tinker 2003, p 367). However, if the urea is applied on moister costal soils volatilisation is estimated to only 10-15% (based on discussions on United Plantations Berhad with Singh 2006). It is assumed that ammonia volatilisati on from urea averages at 30%. According to section 6.5 the applied N-fertiliser consists of 73% ammonium su lphate and 27% urea. Thus , 9.6% of the applied Nfertiliser volatises as ammonia.

Denitrification (total): The total denitrification (gaseous N oxides and molecular N 2 

## ) is calculated using the model; SimDen (Vinther and Hansen 2004) . The reason why the total denitrification is calculated is that this is the only way of estimating N-loss as N 2 

## . N 2 

## is determined as the total denitrification minus N 2 

## O and NO.

The model is developed for Danish soils. Thus, the calcu lated total denitrification may be related to some uncertainty. The model calculates denitrification as the sum of background denitrificati on, denitrification from application of artificial fertiliser, denitrification relate d to application of manure and denitrification from fixed nitrogen. No manure is applied in oil palm plantati ons. The background denitrifi cation depends on the past history of organic matter content in the soil and the soil type (Vinther and Hansen, 2004, p 30). The model includes calculations for eight different soil types and thr ee levels of organic content. Average organic content for mineral soil and high organic content for peat soil are assumed. Denitrification from application of artificial fertiliser depends on the amount of N applied with fer tiliser, a factor describing the ratio between applied N and N 2 

## O-emission and the N 2 

## /N 2 

## O-ratio (Vinther and Hansen, 2004, p 31) . Because of lack of information of soil types in Malaysia, it is chosen to apply a value re presenting the average of all ei ght soil types included in the model as representative for mineral soil. The model does not include peat soil. Since denitrification increases with higher C content in the soil (IPCC 2000 a nd FAO and IFA 2001) and since denitrification increases with higher clay content (Vinther and Hansen 200 4) it is assumed that clay soils containing >25% clay is representative for peat soil. One of the eight so il types included in the model contains >25% clay.

The two parameters; N-fertiliser and N-fixation vary during the first six years of cultivation. There are no variations in the period 6-25 years after planting. Thus, the total denitrification is calculated for the first six years after planting, see Table 6.20



## .

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 103

Parameter year 1 year 2 year 3 year 4 year 5 year 6-25 N-fertiliser, kg N/ha 90 90 106 106 106 106 N-fixation, kg N/ha 200 160 120 80 40 0 Total calculated denitrification, kg N/ha year 1 year 2 year 3 year 4 year 5 year 6-25 Mineral soil 21 19 18 15 13 11 Peat soil 52 49 46 42 38 35 Average soil (95.1% mineral soil and 4.1% peat soil) 22 20 19 16 14 12 

## Table 6.20: Total denitrification from oil palm cultivation. Calc ulated using the model SimDen (Vinter and Hansen 2004)



## Direct N 2 

## O: The direct N 2 

## O emission is calculated using a model described in IPCC (2000). The calculated emissions are then compared with the results using another model described in FAO and IFA (2001). The results obtained from FAO and IFA (2001) are not applied in this study because the model does not include peat soils.

IPCC (2000): According to IPCC (2000, p 4.54) the direct N 2 

## O emission is calculated as shown in Equation (3 ) in section 5.6 . The parameter values used in Equation (3) are described in Table 6.21

.

Parameter Description Parameter value F SN Annual amount of synthetic fertiliser nitrogen applied to soils adjusted to account for the amount that volatilises as NH3 and NOx Fertiliser application, see Table 6.19 . 9.6% volatises

F AM Annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx No manure

F BN Amount of nitrogen fixed by N-fixing crops cultivated annually See Table 6.19 F CR Amount of nitrogen in crop residues returned to soils annually EFB, POME and decomposition of biomass in Table 6.19 F OS Area of organic soils cultivated annually (ha) 0% for mineral soil,100% for peat soil and 4.1% of the area for average soil EF 1 Emission factor for emissions from N inputs (kg N2O-N/kg N input) 1.25% (IPCC 2000, p 4.60)

EF 2 Emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr) 16 (IPCC 2000, p 4.60)



## Table 6.21: Parameters in the equation calculating direct N2O-emissions in IPCC (2000, p 4.54)



## FAO and IFA (2001): According to FAO and IFA (2001, p 34) the direct N 2 

## O emission is calculated as shown in Equation (4) in section 5.6 . The parameter values used in Equation (4) are described in Table 6.22

.

## 104 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Parameter Description Parameter value F Type of fertiliser 0.0042, Fertiliser type is a combination of organic and mineral N-app. Applied N, kg/ha See Table 6.19 Cr Crop type 0.000, Crop type is ‘other’ S Soil texture -0.008, -0.472 and 0.000,The N2O emission is calculated as the average of coarse, medium and fine soil C Soil organic C content 0.140, 1-3% C in mineral soil, based on Corley and Tinker 2003, p 84) D Soil drainage -0.420, good drainage. Good drainage is needed in order to have suitable conditions for cultivating oil palms (Corley and Tinker 2003, p 75-77) pH Soil pH 0.000, Soil pH <5.5 (based on Corley and Tinker 2003, p 84) Cl Climate 0.824, Tropical climate LM Length of measurement period (the model is constructed to fitwith literature measurements. Thus, to model emissions obtained from literature the method of measurement in literature should also be considered since this affects the measured emission) 0.825 Length of measurement period is >300 days, i.e. the longest period available in the model (chosen as the most precise)

FM Frequency of measurement (see comment above) 0.000 Frequency of measurement is >1measure/day, i.e. the highest frequency available in the model (chosen as the most precise) 

## Table 6.22: Parameters in the equation calculating direct N 2 

## O-emissions in FAO and IFA (2001, p 34-35)



## The calculated N 2 

## O emissions using the two models are summarised in Table 6.23

.

N 2 O Mineral soil Peat soil Average soil

Year IPCC (2000), kg N2O-N/ha FAO and IFA (2001), kg N2O-N/ha IPCC (2000), kg N2O-N/ha IPCC (2000), kg N2O-N/ha 1 8.5 3.7 24.5 9.2 2 8.1 3.7 24.1 8.7 3 4.2 4.0 20.2 4.9 4 4.5 4.0 20.5 5.2 5 4.4 4.0 20.4 5.1 6-25 3.0 4.0 19.0 3.7 

## Table 6.23: Calculated N 2 

## O emissions (kg N 2 

## O-N/ha) for mineral soil, peat soil and average soil. For mineral soil the calculated emission is compared with emissions calculate d with the model: FAO and IFA (2001). The annual emission from the years 6-25 are equal.



## It appears from Table 6.23 

## that the deviation between the two models is relatively small from 6-25 years after planting. The larger deviations during the first six years are mainly due to high decomposition of biomasswhich is included as an influential factor in the IPCC model and not in the FAO and IFA model. Decomposition of biomass causes release of fixed nitrogen. Some of this nitrogen ends as N 2 

## O emissions from denitrification in the soil. The IPCC model also take into account that different amounts of N are fixed in the cover crop the first six years. The reason why the emission level of N 2 

## O from peat is significant higher than from mineral soil is that there is a frequent presence of anaerobi c pockets in which denitrification takes place.

Direct NO: The emission of NO is calculated using a mode l described in FAO and IFA (2001). According to FAO and IFA (2001, p 35) the direct NO emission is calculated shown in Equation (5) 

## in section 5.6 . The parameter values used in Equation (5) 

## are described in Table 6.24



## .

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 105

Parameter Description Parameter value F Type of fertiliser 0.0055, Fertiliser type is a combination of organic and mineral N-app. Applied N, kg/ha See Table 6.19 C Soil organic C content 0.000, <3% C in mineral soil, based on Corley and Tinker 2003, p 84) and 2.571, >3% C in peat soil D Soil drainage 0.946, good drainage. Good drainage is needed in order to have suitable conditions for cultivating oil palms (Corley and Tinker 2003, p 75-77) 

## Table 6.24: Parameters in the equation calculating direct NO-emissions in FAO and IFA (2001, p 35)



## The FAO and IFA model is relatively coarse. Therefore, th e emission of NO is only calculated in four different situations: Immature (year 1 and 2) a nd Mature (year 3-25) palms and for mineral soils (95.9% of the area) and peat soils (4.1% of the area). The only parameters that are changed are the application of N-fertiliser which varies from immature to mature and the content of so il C which varies from mineral soils to peat soils. However, the calculation for peat is done with only soil C at >3% because the model does not include organic soils.

The calculated NO emission is summarised in Table 6.25

.

Mature/immature Mineral soils Peat soils Average (95.9 mineral and 4.1% peat) Immature (year 1 and 2) 0.9 12.0 1.4 Mature (year 3-25) 1.0 13.1 1.5 

## Table 6.25: Calculated NO emissions (kg NO-N/ha) using the model described in FAO and IFA (2001). The applied values are marked with a dotted line.



## Nitrate: The nitrate emission is calculated as the residual or rest; i.e. the surplus-N from the N-balance minus the other calculated emissions described above. The aver age nitrate emission for the 25 years is 80.7 kg NO 3 

## - N/ha. However, there are great variations with the hi ghest values the first five years at 141-618 kg N/ha and more moderate figures the rest of the period at 25-39 kg N/ha.

Since the nitrate emission is calculated as residual of the N-surplus it may be related to some uncertainty. Corley and Tinker (2003, p 358) specify annual N losses (leaching, runoff and erosion) from mature oil palms at 21 kg N/ha. This is in relatively good accordance w ith the calculated nitrate emission at 25-39 kg/ha from the 6 th 

## to the 25 th 

## years after planting.

N 2 

## O, indirect from NH 3 

## and nitrate: Besides the direct N 2 

## O emissions from the field which is calculated in the previous, there are indirect emissions from the em itted ammonia and nitrate. The emission of ammonia and its subsequent deposition as NO x 

## and NH 4 

## and nitrate leached from the field increase the amount of N available for denitrification and nitrification (IPCC, 2000, p 4.67). The N 2 

## O-N emission produced from deposited NH 4 

## - N and NO x 

## -N (originating from NH 3 

## -emission) is 0.01 kg N 2 

## O-N/kg N (IPCC 2000, p 4.73). In this respect it is assumed that all emitted ammonia will end as deposited NO x 

## or NH 4 

## . The N 2 

## O-N emission produced from leached nitrate is 0.025 kg N 2 

## O-N/kg NO 3 

## -N (IPCC 2000, p 4.73). The indirect N 2 

## O emissions are not assumed to affect the calculated emissions of ammonia a nd nitrate since the denitrification may take place long time after the emissions took place. Therefore, the amm onia and nitrate emissions may already have had an effect on the environment.

NO, indirect: This is not included.

N changes in soil matter: In the sub-section “ Emissions related to C-balance ” later here in section 6.7 it is determined that there are no changes in soil carbon - neither in relation to transformation of forest/other three crops into oil palm nor in relation to continuous cultivation of oil palm. Assuming a relatively constant C/N-

## 106 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## ration in the soil, there are also no changes of N conten t in the soil matter. This corresponds to the assumtions done in the case of dtermination of emissions re lated to N-balance for rapeseed, see section 5.6

.
Summary of emissions related to N-balance: The distribution of the N-surplus is summarised in Table 6.26



## .

Year Emission and source 1 2 3 4 5 6 7 8 9 10 11 12 13 Ammonia from crop (kg NH 3 -N/ha) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Ammonia from fertiliser application (kg NH 3 -N/ha) 8.6 8.6 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2

Denitrification (kg N/ha) 22 20 19 16 14 12 12.0 12.0 12.0 12.0 12.0 12.0 12.0 - N 2 O part of denitrification (kg N 2 O-N/ha) 9.2 8.7 4.9 5.2 5.1 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7

- NO part of denitrification (kg NO-N/ha) 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 - N 2 part of denitrification (kg N/ha) 11.7 10.1 12.8 9.4 7.4 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 Nitrate (kg NO 3 -N/ha) 367 616 140 162 149 32.3 26.9 25.1 25.0 24.9 24.5 24.5 24.4 N-surplus (kg N/ha) 403 650 175 193 179 59.4 54.0 52.2 52.1 52.0 51.6 51.6 51.5 N 2 O, indirect from NH 3 (kg N 2 O-N/ha) 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 N 2 O, indirect from nitrate (kg N 2 O-N/ha) 9.2 15.4 3.5 4.0 3.7 0.8 0.7 0.6 0.6 0.6 0.6 0.6 0.6

Year Emission and source 14 15 16 17 18 19 20 21 22 23 24 25 Ammonia from crop (kg NH 3 -N/ha) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Ammonia from fertiliser application (kg NH 3 -N/ha) 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2

Denitrification (kg N/ha) 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 - N 2 O part of denitrification (kg N 2 O-N/ha) 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7

- NO part of denitrification (kg NO-N/ha) 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 - N 2 part of denitrification (kg N/ha) 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 Nitrate (kg NO 3 -N/ha) 24.3 24.4 24.5 25.3 26.1 27.2 28.3 30.2 32.1 34.2 36.3 38.0 N-surplus (kg N/ha) 51.4 51.5 51.6 52.4 53.2 54.3 55.4 57.3 59.2 61.3 63.4 65.1 N 2 O, indirect from NH 3 (kg N 2 O-N/ha) 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 N 2 O, indirect from nitrate (kg N 2 O-N/ha) 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.9 0.9 0.9 

## Table 6.26: N-related emissions from 1 hectare oil palm plantation, average soil (96.9% mineral soil and 4.1% peat soil). All values are given in kg N/ha.



## The summary of N-related emissions from mineral soil and peat soil are not specified each of the 25 years, only the total annual average is shown, see Table 6.27

.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 107

Emission as kg N/ha Average soil (95.9% mineral soil and 4.1% peat soil) Mineral soil Peat soil
Ammonia to air (kg NH 3 -N/ha) 15.1 15.1 15.1 N 2 O to air (kg N 2 O-N/ha) 6.4 5.8 21.2 NO to air (kg NO-N/ha) 1.5 1.0 13.0 Nitrate to water (kg NO 3 -N/ha) 79.7 80.7 55.9 Emission as kg emission/ha Average soil (95.9% mineral soil and 4.1% peat soils) Mineral soil Peat soil
Ammonia to air (kg NH 3 /ha) 18.3 18.3 18.3 N 2 O to air (kg N 2 O/ha) 10.1 9.1 33.3 NO to air (kg NO/ha) 3.2 2.1 27.9 Nitrate to water (kg NO 3 /ha) 353 358 248 

## Table 6.27: Emissions related to N-balance.



# Emissions related to P-balance Emissions of P are simpler to determine than emissions re lated to N. Only one substance is emitted, i.e. P as phosphate to water/soil. As described in the case of emissions from rapeseed cultivation (see section 5.6 ) accumulation of phosphorus in the soil is relatively constant due to strong binding to the soil. Therefore, the emission of P is calculated as a fraction of the field surplus. According to section 5.6 : ‘ Emissions related to Pbalance ’, 2.9% of the P surplus is emitted to water as leaching of phosphate. The rema ining is accumulated in the soil matter.

The P-balance for one generation of oil palm is presented in Table 6.28 

## . P-balance is based on the description of inputs to and outputs from the field previous in this section.

## 108 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Year Inputs: Net inputs 1 2 3 4 5 6 7 8 9 10 11 12 13 P-fertiliser 15.3 15.3 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 Planted palm seedlings 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 EFB 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 POME 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Total 18.2 17.3 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 Inputs: Release from decomposition of biomass Pruned fronds, present generation 0.0 0.0 2.3 6.8 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Pruned fronds, previous generation 6.8 2.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Felled biomass from replanting 29.9 29.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Total 36.7 32.2 2.3 6.8 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Output: Increase in standing biomass (uptake that stays in the field) Uptake in oil palms 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 Pruned fronds 0.0 0.0 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Total 2.4 2.4 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 Output: Harvested FFB (uptake that is brought out of the field) Harvested FFB 0.0 0.0 5.4 6.4 7.5 8.5 9.3 9.6 9.6 9.6 9.7 9.7 9.7 Total 0.0 0.0 5.4 6.4 7.5 8.5 9.3 9.6 9.6 9.6 9.7 9.7 9.7 Balance P surplus (input – output) 52.5 47.1 19.3 22.8 24.0 23.0 22.2 21.9 21.9 21.9 21.8 21.8 21.8 P leaching (2.9%) 1.52 1.37 0.56 0.66 0.70 0.67 0.64 0.63 0.63 0.63 0.63 0.63 0.63

Year Inputs: Net inputs 14 15 16 17 18 19 20 21 22 23 24 25 P-fertiliser 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 Planted palm seedlings 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 EFB 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 POME 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Total 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 Inputs: Release from decomposition of biomass Pruned fronds, present generation 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Pruned fronds, previous generation 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Felled biomass from replanting 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Total 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Output: Increase in standing biomass (uptake that stays in the field) Uptake in oil palms 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 Pruned fronds 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 Total 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 Output: Harvested FFB (uptake that is brought out of the field) Harvested FFB 9.7 9.7 9.7 9.6 9.4 9.3 9.1 8.8 8.6 8.2 7.9 7.7 Total 9.7 9.7 9.7 9.6 9.4 9.3 9.1 8.8 8.6 8.2 7.9 7.7 Balance P surplus (input – output) 21.8 21.8 21.8 21.9 22.0 22.2 22.4 22.7 22.9 23.3 23.6 23.8 P leaching (2.9%) 0.63 0.63 0.63 0.64 0.64 0.64 0.65 0.66 0.67 0.67 0.68 0.69 

## Table 6.28: P-balance for 1 hectare oil palm plantation.



## The average annual emission of phosphate duri ng the 25 years oil palm life is 0.71 kg PO 4 

## -P/ha. This is in good accordance with P losses in runoff reported in Corley and Tinker (2003, p 380) at 0.7-1.1 kg P/ha per
year.

Since large amounts of P are accumulated in the soil, th e emission may be larger in the case of erosion. Erosion depends on the slope of the field, terracing, rainfa ll, vegetation cover, residue management (mulching of pruned fronds and EFB) and oil palm ag e (Corley and Tinker 2003, p 236, 3 80). Thus, it is relatively difficult to determine soil loss from erosion. However, Corley and Tinker (2003, p 381) report P lost in eroded sediments as 0.5-1.3 kg P/ha per year (measures from a plantation within normal rates of erosion). The average value at 0.9 kg P/ha per year is applied in this stud y. Hence, the total annual emission of P is 1.61 kg P/ha.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 109



## No data on differences between soil types have been identified. Thus, there is no distinguishing between Prelated emissions from mineral and peat soils.



# Emissions related to C-balance Mass balances for carbon are not as detailed as for nitroge n. This is because emission of carbon dioxide from biotic origin is not included. Only CO 2 

## from net changes of carbon in the soil and from land transformation are included. Carbon emissions that arise fr om transformation of alang-alang and tropical rain forest into oil palm plantations are described in section 19.1

.
Henson (2004, p 15-17) summarises different surveys on changes in soil carbon under oil palm. In general there is lack of good data on soil carbon changes under oil palm in the long term (Henson 2004). Henson (2004) identifies studies that show increasing, no change and decreasing carbon in soil under oil palm. Thus, Henson (2004) concludes that the best assumption is to presume that there are no changes in soil carbon from continuous cultivation of oil palm. Hence, it is chosen to apply the assumption suggested by Henson (2004): soil carbon in mineral soils, i.e. not peat so ils, does not change over time under oil palm.

Peat soils which contain almost 100% organic material ar e a special case. Wh en peat soils are drained aeration is increased. The subsequent oxidation causes release of carbon dioxide (Henson 2 004, p 28). Henson (2004) has calculated a mean annual emission from peat soils at 7.5 t C/ha. This mean value take into account that carbon emissions gradually decreases over time as subsid ence is taking place. According to Henson (2004) carbon emissions as high as 12 to 18 t C/ha have been measured. Reijnders and Huijbregts (2006) report 10-15 t C/ha from oil palm cultivated on peat and IPCC (2003, p 3.79) specify 20 t C/ha per year for cultivation on tropical peat soils (average tillage and drainage). It is chosen to apply 10 t C/ha from oil palm on peat. 10 t C/ha corresponds to ~37 t CO 2 

## /ha. The value at 10 t C/ha is significan tly smaller than the value specified by IPCC. However, oil palm plantations have a very low le vel of tillage (once every 25 years). Therefore, a lower value than specified by IPCC is assumed. Applying a FFB yield at 18.87 t/ha and an oil extraction rate in the oil mill at 0.2 t oil/t FFB the 37 t CO 2 

## /ha corresponds to ~10 t CO 2 

## /t crude palm oil. This is a significant contribution to global warming compared to contributio ns from other stages and processes, e.g. methane from POME treatment in the palm oil mill contributes with only ~0.2 t CO 2 

## -eq 14 

## /t crude palm oil (see Table 10.10 ) and CO 2 

## from burning of diesel in machinery in the oil palm plantation contribut es with only ~40 kg CO 2 

## /t crude palm oil 15 

## . Therefore, a sensitivity analysis applying othe r levels of carbon emission from peat soils is presented in section 21.14 . Section 21.13 presents a sensitivity analysis that compares cultivation on peat with cultivation on mineral soils.

The area planted on peat soil in Malaysia in 2003/2004 acc ounted for 4.1% of the total planted area. The same figures are assumed to be valid for oil palm in Indonesia. Thus, the CO 2 

## -emission from degradation under average oil palm in Indonesia and Malaysia can be calculated as 4.1% of 37 t CO 2 

## , i.e. 1.5 t CO 2 

## /ha.


# Emissions of heavy metal Fertilisers contain contaminants of heavy metals. The em ission from the input of heavy metals is regarded as emissions to soil. The crude palm oil and kernels are br ought out of the field, but no data on the contents of heavy metals have been identified. Therefore, the entire input is regarded as emissi ons to soil. The heavy metal content in the crude palm oil is sorted out in the refi ning process. Since a considerable share of the heavy met14 

## GWP at 25 g CO 2 

## -eq/g CH 4 

## has been applied.

15 

## The diesel consumption and inventory data for burning diesel in machinery as described in section 6.3 and an oil extraction rate in the oil mill at 0.2 t oil/t FFB have been applied.

## 110 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## als will be in the sludge from waste water treatment, some of the heavy metals in the CPO will end as emissions to soil from landfilling of the sludge. The same is the case for palm kernel oil. The palm kernel cake which is used as animal fodder also ends as emissions to soil with the droppings fro m the animals. Therefore, the assumption that the entire input of heavy metals with fertiliser ends as emissions to soil is regarded as relatively certain.

The content of heavy metals in ammonia sul phate, urea and phosphate rock are shown in Table 6.29 to Table 6.31 . The heavy metal content in potassium chloride is shown in Table 5.39

.

Ammonium sulphate AMMONIUM SULFATE 21-0-0 AMMONIUM SULFATE 21-0-0Heavy metals in N fertiliser, Ammonia Sulphate; mg/kg N (Nemecek, et al. 2003) (WSDA 2006) (WSDA 2006) Ammonia sulphate, applied in this study

Arsenic (As) - <0.3 <0.1 0.2 Cadmium (Cd) 0.2 <0.03 <0.2 0.1 Chromium (Cr) 10.0 - - 10.0 Cobalt (Co) - <0.04 <1.1 0.6 Copper (Cu) 19.0 - - 19.0 Mercury (Hg) - <0.01 <0.004 0.01 Molybdenum (Mo) - <0.1 <0.1 0.1 Nickel (Ni) 8.6 <0.1 <0.5 3.1 Lead (Pb) 5.2 <0.3 <1.1 2.2 Selenium (Se) - <0.3 <0.1 0.2 Zink (Zn) 143 <0.1 <1.1 48.1 

## Table 6.29: Heavy metal content (mg/kg N) in ammonia sulphate fertilisers from different sources. The applied data are the average of the three data sets; they are marked with a black dotted frame.

Urea UREA 46% UREA 46-0-0 Heavy metals in N fertil- iser, UREA; mg/kg N (Nemecek, et al. 2003) (WSDA 2006) (WSDA 2006) Urea, applied in this study

Arsenic (As) - <0.2 <0.5 0.4 Cadmium (Cd) 0.1 <0.5 <0.5 0.4 Chromium (Cr) 4.4 - - 4.4 Cobalt (Co) - <2.3 <0.5 1.4 Copper (Cu) 13.0 - - 13.0 Mercury (Hg) - <0.01 <0.3 0.16 Molybdenum (Mo) - <2.3 <0.5 1.4 Nickel (Ni) 4.4 <1.4 <0.5 2.1 Lead (Pb) 2.4 <0.05 <0.5 1.0 Selenium (Se) - <0.2 <0.5 0.4 Zink (Zn) 95.7 <46.0 <0.5 47.4 

## Table 6.30: Heavy metal content (mg/kg N) in Urea fertilisers from di fferent sources. The applied data are the average of the three data sets; they are ma rked with a black dotted frame.

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 111

Rock Phosphate Pelletized rock phosphate 0-3-0Heavy metals in P fertiliser, Phosphate Rock; mg/kg P 2 O 5 (Nemecek, et al. 2003) (WSDA 2006) Phosphate rock, applied in this study

Arsenic (As) - <0.6 0.6 Cadmium (Cd) 50 2.6 26.3 Chromium (Cr) 612 - 612 Cobalt (Co) - 0.09 0.09 Copper (Cu) 115 - 115 Mercury (Hg) - 0.01 0.01 Molybdenum (Mo) - 0.6 0.6 Nickel (Ni) 76.9 5.1 41.0 Lead (Pb) 23.8 0.3 12.1 Selenium (Se) - <1.5 1.5 Zink (Zn) 915 0.04 458 

## Table 6.31: Heavy metal content (mg/kg P 2 

## O 5 

## ) in Urea fertilisers from different sour ces. The applied data are the average of the three data sets; they are marked with a black dotted frame.



## Based on the fertiliser input shown in Table 6.4 and the contents of heavy metals in fertilisers in Table 6.29 to Table 6.31 and Table Table 5.39 , the emissions of heavy metals from fe rtiliser can be calculated. The distribution between ammonia sulphate and urea as N fertiliser is 73% to 27%. The results are shown in Table 6.32

.

Heavy metal emissions Emission, g/ha Arsenic (As) 1.0 Cadmium (Cd) 1.9 Chromium (Cr) 44.4 Cobalt (Co) 0.2 Copper (Cu) 11.6 Mercury (Hg) 0.03 Molybdenum (Mo) 0.1 Nickel (Ni) 5.1 Lead (Pb) 2.0 Selenium (Se) 0.4 Zink (Zn) 68.7 

## Table 6.32: Heavy metal emissions from oil palm cultivation, g/ha.



# Emissions of pesticides The compartments that receive the applied active ingredie nts in the pesticides are assumed to be 33% soil, 33% water and 33% air. This is a very rough estimate which is tested in a sensitivity analysis in section 21.11

.


# 6.8 Overhead in agricultural stage
No data on electricity use in administration, research, laboratory and nursery buildings related to oil palm cultivation have been identified. Therefore it is very ro ughly assumed that overhead for the agricultural stage is equal to the palm oil mill at 1 MJ/t FFB, see section 10.7 . Applying a yield at 18.87 t FFB/ha, this corresponds to 0.053 MJ/ha per year. Buildings are not heated in Malaysia.



# 6.9 Capital goods in agricultural stage
Capital goods in the agricultural stage for rapeseed are described in section 5.8 . It includes means of production, i.e. buildings and machinery. Only one inventory of agricultural buildings a nd machinery has been identified; ecoinvent (2004). This has been used for descri bing capital goods in rapeseed cultivation, see section 5.8

. The data in ecoinvent (2004) covers capital goods in European agricultu re. Since no other data have been identified, it is chosen to use these data for production, main tenance and disposal of the relevant capital goods for oil palm cultivation in Malaysia and Indonesia.

## 112 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## No data on the amounts of used buildings (shed and buildings for administration, research and laboratories/nursery) and machinery (tractors, agr. machinery-tilla ge and agr.machinery-general) have been identified. Thus, this is determined from modified figures of th e capital goods used in rapeseed cultivation. It is assumed that the amount of tractors, agricultural machinery-genera l and shed are equivalent with the diesel consumption per hectare. The diesel consumption in ra peseed cultivation is 3,612 MJ/ha (see section 5.3 ) and the diesel consumption in oil palm cultivation is 2,118 MJ/ha (see Table 6.3 ). Thus, use of tractors, shed and agricultural machinery per ha oil palm plantation is 59% of the use per ha rapeseed field. Since the oil palm field is only cleared once every 25 years, the use of agricultural machinery-tillage is determined as 1/25 of the use in rapeseed cultivation. The use of buildings for administrati on, research and laboratories/nursery is assumed to be equal to the use of buildings for administration in the palm oil mill at 0.0262 m 3 

## ‘ Building, multi-storey ’ per t FFB, see section 10.8 . Applying a yield at 18.87 t FFB/ha, this corresponds to 0.0014 m 3 

## /ha per year.

Use of capital goods, kg/ha per year
Capital goods Rapeseed ( Table 5.44 ) Oil palm

Tractor 7.5 kg 4.4 kg Agricultural machinery, general 3.5 kg 2.1 kg Agricultural machinery, tillage 9.2 kg 0.4 kg Shed 0.070 m 2 0.041 m 2Administration, research and laboratories/nursery buildings 0 m3 0.0014 m 3



## Table 6.33: Use of capital goods in oil palm cultivation.



# 6.10 Transport of materials in agricultural stage
Raw materials are transported with lorry to the oil palm plantations. Inventory data per tkm transport by lorry are described in section 4.1 . Since there are several suppliers and sinc e there is a general lack of data on the specific marginal affected supplier, all transport distances are based on rough estimates. Determination of size of lorries is based on Table 4.3 and very rough estimates on the total amount of goods transported.

Determination of the amount of transported fertiliser is shown in Table 6.34 . The contents of nutrients in fertilisers are estimated from a commonly used fertilisers in oi l palm cultivation in Corley and Tinker (2003, p 385) and a review of various fertilisers in WSDA (2006).

## 6 Agricultural stage: Oil palm, fresh fruit bunches (FFB) 113

Fertiliser Content of nutrient in fertiliser Applied nutrients (see section 6.5 ) Applied fertiliser product

Ammonium sulphate 21% N 76 kg N/ha 362 kg/ha Urea 35% N 28 kg N/ha 80 kg/ha Phosphate rock 30% P 2 O 5 (13% P) 31 kg P/ha 238 kg/ha Potassium chloride 14% K 2 O (12% K) 170 kg K/ha 1,417 kg/ha Magnesium 27% MgO 35 kg MgO/ha 130 kg/ha Sulphur 23% S 16 kg S/ha 70 kg/ha Boron 10% B - kg B/ha - kg/ha Total 2,297 kg/ha 

## Table 6.34: Determination of amount of transported fertiliser product. The applied nutrients are obtained from Table 6.4

.


## The amount of pesticide products used is adopted as th e average of 2 years immature and 23 years mature given in Table 6.7 , i.e. 6.6 kg pesticide product/ha per year.

Table 6.35 shows the transported amounts, the route and the distances.

Material Amount per ha From To Distance Means of transportation Seed insignificant - - - - FFB 18,870 kg Plantation Palm oil mill EFB 4,150 kg Palm oil mill Plantation Transport is included in energy use in the plantation Fertilisers 2,297 kg Abroad chemical plant Port in Malaysia/Indonesia 5000 km Transoceanic tanker Fertilisers 2,297 kg Port in Malaysia/Indonesia Oil palm plantation 500 km 40t lorry Pesticides 6.6 kg Abroad chemical plant Port in Malaysia/Indonesia 5000 km Transoceanic tanker Pesticides 6.6 kg Port in Malaysia/Indonesia Oil palm plantation 500 km 40t lorry 

## Table 6.35: Transport of goods related to the agricultural stage. The return trip is included in the inventory data.



## The estimated transport in Table 6.35 is summarized per type of lorry in Table 6.36

.

Means of transportation Transport 40t lorry 1,152 tkm Oceanic tanker 11,520 tkm 

## Table 6.36: Summary of transport in the agricultural stage.

## 114 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 6.11 LCI of oil palm agricu ltural stage, summary

Table 6.37 summarises the inventory data relating to 1 ha soil cultivated in 1 year with oil palm.

Malaysia and Indonesia: 1 ha y oil palm plantation Interventions Amount Applied LCI data Product output FFB 18.87 t Product of interest Energy use Traction, burned diesel 2,118 MJ See section 4.3 Electricity for overhead 0.053 MJ See Table 3.3 Material use N-fertiliser, ammonium sulphate (as N) 76 kg ‘ Ammonium sulphate, as N, at regional storehouse/RER ’ (ecoinvent 2004) N-fertiliser, urea (as N) 28 kg ‘ Urea, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 70 kg Phosphate rock, see section 6.5 : Fertilisers K-fertiliser (as K 2 O) 204 kg Potassium chloride, as K 2 O, at regional storehouse/RER’ , ecoinvent (2004) Herbicide, typically glyphosate 2.4 kg ‘Glyphosate, at regional storehouse/RER’ (ecoinvent 2004) Insecticide, typically cypermethrin 0.31 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Fungicides, various different 0.013 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Rodenticide, typically warfarin 0.00021 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings, shed 0.041 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Agricultural buildings, administration etc. 0.00139 m 3 ‘ Building, multi-storey ’ (ecoinvent 2004) Machinery, tractor 4.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 0.4 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 2.1 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,152 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Oceanic tanker 11,520 tkm ‘ Transport, transoceanic tanker/OCE ’ (ecoinvent 2004) Emissions Air Water Soil Carbon dioxide (CO 2 ) 1,500 kg Ammonia (NH 3 ) 18.3 kg - - Dinitrogen oxide (N 2 O) 10.1 kg - - Nitric oxide (NO) 3.2 kg - - Nitrate (NO 3 ) - 353 kg - Phosphorus (P) - 1.6 kg - Glyphosate 0.80 kg 0.80 kg 0.80 kg Cypermethrin 0.10 kg 0.10 kg 0.10 kg Fungicides, various different Not included, no characterisation data exist in LCIA methods Warfarin Not included, no characterisation data exist in LCIA methods Arsenic (As) - - 1.0 g Cadmium (Cd) - - 1.9 g Chromium (Cr) - - 44 g Cobalt (Co) - - 0.23 g Copper (Cu) - - 12 g Mercury (Hg) - - 0.026 g Molybdenum (Mo) - - 0.11 g Nickel (Ni) - - 5.1 g Lead (Pb) - - 2.0 g Selenium (Se) - - 0.42 g Zink (Zn) - - 69 g 

## Table 6.37: Interventions per ha y oil palm plantation.

## 7 Agricultural stage: Soybean 115



# 7 Agricultural stage: Soybean
The soybean plant (Glycine max.) is an annual leguminous plant which grows to a height of 120-180 cm, and the soybeans contain around 35% protein and 18% oil (Dalgaard et al. 2007).

This section provides data for a change in production of so ybean in Brazil. Some of the inventory data for soybean are directly applied from Dalgaard et al. (2007) a nd other are modified in orde r to maintain consistency with the methodologies for inventorying rapeseed and oil palm cultivation. The modified data are the emissions related to the N-, P- and C-bala nces and the inventory data for tracti on and fertilisers. In addition to the modifications, the inventory presented here includes more processes than included in Dalgaard et al. (2007). These are; Production and emissions of pesticides, dryi ng of soybeans, the use of and processing of soybean seed, capital goods and transport of materials in agricultural stage.

Reference for the data in Dalgaard et al. (2007) is one crop rotation on one hectare. Since 25% of the area is double-cropped, this is not the same as one hectare in one year. This study applies a reference for the inventory in the agricultural stage of one hectare in one year. Thus, the uses of energy and materials in Dalgaard et al. (2007) have to be multiplied with (0.25

⋅ 2 + 0.75) = 1.25 in order to represent cultivation of one hectare in one
year.



# 7.1 Product flow in agricultural stage
The inventory of the agricultural stage is divide d into the unit processes shown as shaded boxes in Figure 7.1

. The product flow is determined per hectare per year, see descriptions below the figure.Capital goods Farm - buildings

Tractor

Harvester1 hectare soybean field Soybean seed: 138 kg

Field operations: Pesticide, sowing, fertiliser, possible tillage, harvesting

Soybeans: 3,350 kg Drying

Soybeans: 3,212 kg Seed processing

Water: 68 kg Other agr. machinery Field operations: Pesticide, sowing, fertiliser, possible tillage, harvesting 25% double cropping 75% single-cropping

Seed: 82.5 kg Seed: 27.5 kg

Field operations: Pesticide, sowing, fertiliser, possible tillage, harvesting Seed: 27.5 kg

Soybeans: 684 kg Soybeans: 684 kg Soybeans: 2,051 kg

Soybeans: 3,418 kg



## Figure 7.1: Product flow related to cultivation of 1 ha soybean field in 1 year. The grey shaded boxes represent the unit processes in the agricultural stage

.

## 116 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Yield: The soybean yield is applied as the yield in 2005 calculated from linear regression of yields from 1990 to 2005, see Figure 7.2 . The same method as for determining the yiel d for rapeseed has been used, see section 5.1 . Yields are obtained from FAOSTAT (2006). The expected yield in 2005 can be calculated as 2.68 t/ha t/ha using the equation in Figure 7.2 . For comparison with yields determined using other methods see Table 7.1

. However, the yield calculated from the formula in Figure 7.2 has to be adjusted in order to take into account that 25% of the soybean cultivated area in Brazil is by double-cropping (see section 2.3 ). Thus, since 25% of the area has the double output, the adjusted yield is calculated by multiplying with (0.25

⋅ 2 + 0.75) = 1.25. Table 7.1 shows the adjusted as well as the non-adjusted yields.

yield = 0.0548*(year - 1989) + 1.80 R 2 

### = 0.63

0.0 0.5 1.0 1.5 2.0 2.5 3.01990199119921993199419951996199719981999200020012002200320042005Yield (t/ha) 

### Soybean in Brazil



## Figure 7.2: Soybean yields in Brazil 1990 to 2005. The linear regression line and its corresponding equation and R 2 

## are also shown. The yields are obtained from FAOSTAT (2006).

Region Yield 2005 (based on regression

1990-2005) Average yield 1990- 2005 Average yield 2000- 2005 Yield 2003 Yield 2004 Yield 2005

Brazil (not adjusted for double-cropping) 2.68 t/ha 2.27 t/ha 2.54 t/ha 2.82 t/ha 2.31 t/ha 2.30 t/ha

Brazil (adjusted for double-cropping) 3.35 t/ha 2.84 t/ha 3.18 t/ha 3.52 t/ha 2.89 t/ha 2.88 t/ha



## Table 7.1: Soybean yield in Brazil determined using different methods. The applied yield is marked with a dotted line.



## Seed input: According to Nemecek et al. (2003, p 125) the use of seed is 110 kg/ha. These data are for cultivation without double cropping. Thus, the use of seed when 25% of the area is double-cropped is 138 kg.

Water loos in drying: According to Nemecek et al. (2003, p 121) a nd Dalgaard et al. (2001) grains (unspecified grains) are typically dried 2 percent points. No speci fic data on drying of soybeans have been identified. Therefore, the two percent points are applied. Since th e dried yield is 3,350 kg soybeans per ha, the water dried is 68 kg.



# 7.2 Omitted inventory data in agricultural stage
Magnesium (Mg), Sulphur (S) and Boron fertilisers and dust emissions and soil erosion have not been taken into account. These interventions are regarded as insignificant.

## 7 Agricultural stage: Soybean 117



# 7.3 Energy use

Traction According to Dalgaard et al. (2007) the energy use as bur ning of diesel in agricultural machinery is 42 litre/ha. Adjusting for double cropping this corresponds to 52.5 litre/ha. Applyi ng calorific values as given in Appendix 1: Data on fuels , the energy use is 1,911 MJ/ha.

The inventory data for traction are described in section 4.3

. 

# Drying of seed According to Figure 7.1 , 68 kg of water is dried out of the harvest ed soybeans per ha. The inventory data for drying of seed are described in section 5.3 : ‘ Drying of seed ’. However, the use of electricity in the inventory is adjusted to Brazilian marginal electricity, see Table 3.3

.


# 7.4 Materials

Seed The interventions related to the agricultural stage in production of seed ar e accounted for by subtracting an amount of seed from the yield. According to Nemecek et al. (2003, p 103) the yield of seed production is the same as for conventional cultivation of soybean.

No inventory data for production of so ybean seed have been identified. Howe ver, in ecoinvent the data in an inventory of pea seed are used as representative for pr oduction of soybean seed. It is chosen to apply a modyfied version of these data in this study. The modifi cations corresponds to the modifications done for rapeseed production, see section 5.4 : ‘ Seed ’. The inventory of soybean seed production is shown in Table 7.2

.

LCI-data for production of 1 kg seed Amount LCI data used in this study

Soybean from agriculture 1 kg This is accounted for by subtracting 1 kg from the yield at 3.35 tonne/ha per kg seed used, see figure Figure 7.1

. Pesticide 0.0016 kg ‘Pesticide unspecified, at regional storehouse/CH’ (ecoinvent 2004) Electricity 0.21 MJ Electricity, see Table 3.3 Building for storage 2.0

⋅ 10 -5 m 3 Building, multi-storey/RER (ecoinvent 2004) Transport from farm to warehouse 0.76 tkm Transport, lorry 28t/CH (ecoinvent 2004) 

## Table 7.2: Inventory data for production of 1 kg soybean seed (ecoinvent 2004).



# Fertiliser No N and K fertiliser are applied in soybean cultivation (Dalgaard et al. 2007). The amount of applied P fertiliser in Dalgaard et al. (2007) is 16 kg P per hectar e. Adjusting for double-cropping and transforming to P as P 2 

## O 5 

## this corresponds to 46 kg P 2 

## O 5 

## /ha.

According to IFA (2007), the most widely used P-fertiliser in Latin America is phosphate rock. Therefore, it is assumed that this represents the marginal source of P-fer tiliser in Brazil. Inventory data for phosphate rock are described in section 6.5 : Fertilisers

.


# Pesticides The use of pesticides in soybean cultivation is given in Dalgaard et al. (2007). However, Dalgaard et al. (2007) do not include pesticides as interventions in the LCIA a nd consequentially no inventory data are applied for the

## 118 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## production of pesticides. According to Dalgaard et al . (2007) the following pesticides are used: glyphosate (herbicide), 2-4 D (herbicide), imazethapyr (herbicide), cypermethrin (insecticide), chlorpyrifos (insecticide). However, for 2-4 D and imazethapyr the application of ac tive ingredients (a.i.) is not specified. Therefore, because of lack of data, the use and consequential emission of these pesticides has been omitted from the study. Inventory data have only been identified for glyphosate and cypermethrin (as pyretroid-compounds). These data are from the ecoinvent database (ecoinvent 2004) . Inventory data for the remaining pesticides are applied as ‘Pesticide unspecified’, also obtained from the ecoinvent database. The use of pesticides per hectare is given in Table 7.3 . In Table 7.3 the data obtained from Dalgaard et al. (2007) are adjusted for doublecropping

Dalgaard et al (2007) Applied in this study Pesticide kg/ha kg a.i./ha kg/ha kg a.i./ha Herbicide, glyphosate 5 1.8 6.3 2.3 Herbicide, 2-4 D 0.35 no data 0.44 no data Herbicide, imazethapyr 1 no data 1.3 no data Insecticide, pyrethroid, cypermethrin 0.1 0.01 0.13 0.013 Insecticide, Chlorpyrifos 0.8 0.16 1.0 0.20 Total 7.3 2.0 9.1 2.5 

## Table 7.3: Use of pesticides per hectare per year. The amount and type of pesticides used are based on figures from Dalgaard et al. (2007) and Dalgaard (2007).



# 7.5 Emissions
N- and P balances are established for soybean cultivation. The relevant data in this respect are given in Table 7.4

.

Parameter Amount Reference Annual soybean yield 3,350 kg/ha See Table 7.1 Seed input 138 kg/ha Nemecek et al (2003, p 125) Soybean dry matter 87% Møller et al. (2000, p 17) Soybean straw solid matter 87% Møller et al. (2000, p 43). Assumed to be the same as pea straw Straw removed from field 0% Nemecek et al (2003, p 125) Residue to crop ratio 2.1 IPCC (2000, p 4.58) Soybean N-content 0.065 kg N/kg DS Møller et al. (2000, p 17), N content is protein content divided by 6.25 Residue N-content 0.012 kg N/kg DS Møller et al. (2000, p 43). Assumed to be the same as pea straw, N content is protein content divided by 6.25 Soybean P-content 0.063 kg P/kg DS Møller et al. (2000, p 17) Residue P-content 0.002 kg P/kg DS Møller et al. (2000, p 43). Assumed to be the same as pea straw Atmospheric N-deposition 8.0 kg/ha y Trebs et al. (2004) N-fixing 165 kg N/ha y Dalgaard et al. (2007) specifies 132 kg N/ha per crop rotation. Adjusting for 25% double-croppping, N-fixing is 165 kg N/ha per year. 

## Table 7.4: Relevant data in order to establish field balances of N and P for soybean cultivation.



## The N- and P-balances are shown in Table 7.5

.

## 7 Agricultural stage: Soybean 119

Inputs N P Deposition 8.0 kg N/ha 0 kg P/ha Seed 9.0 kg N/ha 0.76 kg P/ha Fertiliser 0 kg N/ha 20.0 kg P/ha N-fixing 165 kg N/ha - Changes in soil matter 0 kg N/ha - Total 182.0 kg N/ha 20.8 kg P/ha Outputs Harvested soybeans 189.4 kg N/ha 18.4 kg P/ha Removed straw 0 kg N/ha 0 kg P/ha Total 189.4 kg N/ha 18.4 kg P/ha Balance surplus (input – output) -7.5 kg N/ha 2.4 kg P/ha 

## Table 7.5: Annual N- and P-balances fo r 1 hectare soybean field.



## It appears that there is a negative N- surplus from soybean cultivation. This means that more N is removed with the harvested soybeans than the input of N from deposition, N-fixing a nd soybean seed. A negative N-surplus is consistent with Dalgaard et al . (2007) and Austin et al. (2006).



# Emissions related to the N-balance Since there is a negative N-surplus in the N-balan ce, it is assumed that there are no emissions of NH 3 

## from crop and no nitrate leaching. This is in accordance with Da lgaard et al. (2007). Since there is no surplus of N, it is not necessary to calculate the total denitrif ication. Neither indirect emissions of N 2 

## O from NH 3 

## and nitrate are relevant since there are no emissions of NH 3 

## and nitrate. In the following the direct emissions of N 2 

## O and NO are determined.

Direct N 2 

## O: The direct N 2 

## O emission is calculated using a model described in IPCC (2000). The calculated emissions are then compared with the results using another model described in FAO and IFA (2001). The results obtained from FAO and IFA (2001) are not applied in this study because the model does not include peat soils which is relevant for palm oil – thus, it would not be consistent to apply calculated emissions from different emissions for the desired crops.

IPCC (2000): According to IPCC (2000, p 4.54) the direct N 2 

## O emission is calculated as shown in Equation (3 ) in section 5.6 . The parameter values used in Equation (3) are described in Table 7.6

.

## 120 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Parameter Description Parameter value F SN Annual amount of synthetic fertiliser nitrogen applied to soils adjusted to account for the amount that volatilises as NH3 and NOx No N-fertiliser application

F AM Annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx No manure

F BN Amount of nitrogen fixed by N-fixing crops cultivated annually 151 kg N/ha, see Table 7.5 F CR Amount of nitrogen in crop residues returned to soils annually 67.1 kg N/ha, calculated using the informations in in Table 7.5 F OS Area of organic soils cultivated annually (ha) 0% The total area of peat soil in Brazil is 15,000 km 2 (Andriesse 1988). Comparing with the total area of Brazil at 8.46 mio km 2 , this corresponds to 0.2% of Brazil which is regarded as insignificant. EF 1 Emission factor for emissions from N inputs (kg N2O-N/kg N input) 1.25% (IPCC 2000, p 4.60)

EF 2 Emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr) 16 (IPCC 2000, p 4.60)



## Table 7.6: Parameters in the equation calculating direct N2O-emissions in IPCC (2000, p 4.54)



## FAO and IFA (2001): According to FAO and IFA (2001, p 34) the direct N 2 

## O emission is calculated as shown in Equation (4) in section 5.6 . The parameter values used in Equation (4) are described in Table 7.7

.

Parameter Description Parameter value F Type of fertiliser Not relevant, no N-fertiliser application N-app. Applied N, kg/ha No N-fertiliser application Cr Crop type -0.023, Crop type is ‘legume’ S Soil texture -0.008, -0.472 and 0.000,The N2O emission is calculated as the average of coarse, medium and fine soil C Soil organic C content 0.140, 1-3% C in mineral soil. Assumed to be the same as in Malaysia, see Table 6.22 D Soil drainage -0.420, good drainage. It is assumed that good drainage is needed in order to have suitable conditions for cultivating soybeans pH Soil pH 0.000, Soil pH <5.5, Assumed to be the same as in Malaysia,

see Table 6.22 Cl Climate 0.824, Tropical climate LM Length of measurement period (the model is constructed to fitwith literature measurements. Thus, to model emissions obtained from literature the method of measurement in literature should also be considered since this affects the measured emission) 0.825 Length of measurement period is >300 days, i.e. the longest period available in the model (chosen as the most precise)

FM Frequency of measurement (see comment above) 0.000 Frequency of measurement is >1measure/day, i.e. the highest frequency available in the model (chosen as the most precise) 

## Table 7.7: Parameters in the equation calculating direct N 2 

## O-emissions in FAO and IFA (2001, p 34-35)



## The calculated N 2 

## O emissions using the two models are summarised in Table 7.8

.

IPCC (2000) FAO and IFA (2001) 3.0 kg N2O-N 2.5 kg N2O-N 

## Table 7.8: Calculated N 2 

## O emissions (kg N 2 

## O-N/ha) for soybean cultivation in Braz il using the two models. The applied N 2 

## O-emission is marked with a dotted line.



## It appears from Table 7.8 that the deviation between the two m odels is relatively small, only ~17%.

Direct NO: The emission of NO is calculated using a mode l described in FAO and IFA (2001). According to FAO and IFA (2001, p 35) the direct NO emission is calculated shown in Equation (5) 

## in section 5.6 . The parameter values used in Equation (5) are described in Table 7.9

.

## 7 Agricultural stage: Soybean 121

Parameter Description Parameter value F Type of fertiliser Not relevant, no N-fertiliser application N-app. Applied N, kg/ha No N-fertiliser application C Soil organic C content 0.000, <3% C, see Table 7.7 D Soil drainage 0.946, good drainage, see Table 7.7 

## Table 7.9: Parameters in the equation calculating direct NO-emissions in FAO and IFA (2001, p 35)



## The calculated NO emission is 0.6 kg NO-N/ha.

In summary, the annual N-related emissi ons from soybean cultivation are 4.7 kg N 2 

## O/ha and 1.2 kg NO/ha.



# Emissions related to the P-balance

As described in the case of emissions from rapeseed cultivation (see section 5.6 ) accumulation of phosphorus in the soil is relatively constant due to strong binding to the soil. Therefore, the emission of P is calculated as a fraction of the field surplus. According to section 5.6 : ‘ Emissions related to P-balance ’, 2.9% of the P surplus is emitted to water as leaching of ph osphate. The remaining is accumulated in the soil matter. Thus the emission of P is 2.9% of the surplus at 2.4 kg P/ha given in Table 7.5 , i.e. 0.07 kg P/ha.



# Emissions related to the C-balance As in the case for rap eseed cultivation (section 5.6 ) and oil palm cultivation (section 6.7 ) it is assumed that continuous cultivation of soybean does not affect the soil content of carbon.

Carbon emissions that arise from transformation of th e Cerrado and Amazon forest into soybean fields are described in section 19

. 

# Emissions of heavy metal The emissions of heavy metals from input of fertiliser are calculated using the same method as for rapeseed cultivation, see section 5.6 : ‘ Emissions of heavy metal ’. Hence, it is assumed that the heavy metal input from P-fertiliser is distributed on emissions to soil and emissi ons to water. The emission of heavy metals to soil is calculated as the total input with fertiliser and seeds minus the share that ends in water. This means that heavy metals in leaching and runoff have been assumed to be zer o, i.e. inputs of heavy metals to the field minus harvested heavy metals ends as emissions to soil. It is assumed that one eighth, i.e. 12.5%, of the heavy metals harvested with crop ends as emission to water and the other 87.5% ends as emission to soil.

The content of heavy metals in phosphate rock is shown in Table 6.31 and the content in soybeans is shown in Table 7.10

.

## 122 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Soybeans Heavy metals in harvested crop; mg/kg crop (Nemecek, et al. 2003, p 154) (Møller et al. 2000, p 17) Applied in this study

Arsenic (As) - - - Cadmium (Cd) 0.052 - 0.052 Chromium (Cr) 0.45 - 0.45 Cobalt (Co) - 0.070 0.070 Copper (Cu) 13.1 13.9 13.5 Mercury (Hg) 0 - 0 Molybdenum (Mo) - - - Nickel (Ni) 4.63 - 4.63 Lead (Pb) 0.070 - 0.070 Selenium (Se) - 0.087 0.087 Zink (Zn) 41.5 39.2 40.4 

## Table 7.10: Heavy metal content (mg/kg crop) in harvested soybeans. The applied data are marked with a dotted frame.



## Based on the fertiliser input at 46 kg P 2 

## O 5 

## , the content of heavy metals in P-fertiliser in Table 6.31 , and the content of heavy metals in harvested crop in Table 7.10 , the emissions of heavy metals from fertiliser can be calculated. The results are shown in Table 7.11

.

Heavy metal emissions Soil (g/ha Water (g/ha) Arsenic (As) - - Cadmium (Cd) 1.2 0.0 Chromium (Cr) 27.9 0.2 Cobalt (Co) 0.0 0.0 Copper (Cu) 1.5 5.7 Mercury (Hg) 0.0 0.0 Molybdenum (Mo) - - Nickel (Ni) 0.6 1.9 Lead (Pb) 0.5 0.0 Selenium (Se) 0.0 0.0 Zink (Zn) 9.6 16.9 

## Table 7.11: Heavy metal emissions to soil and water from soybean cultivation, g/ha.


# Emissions of pesticides The compartments that receive the applied active ingredie nts in the pesticides are assumed to be 33% soil, 33% water and 33% air. This is a very rough estimate which is tested in a sensitivity analysis in section 21.11

.


# 7.6 Overhead in agricultural stage
The amount of electricity used in administration buildings etc. is assumed to be insignificant.



# 7.7 Capital goods in agricultural stage
Corresponding to capital goods in oil palm cultivation, see section 6.9 , the use of capital goods is determined from modified figures of the capital goods used in rapeseed cultivation. It is assumed that the amount of harvesters, tractors, agricultural machin ery-tillage, agricultural machinery-gene ral and shed are equivalent with the diesel consumption per hectare. The diesel consump tion in rapeseed cultivation is 3,612 MJ/ha (see section 5.3 ) and the diesel consumption in soybean cultivation is 1,911 MJ/ha (see section 7.2 ). Thus, use of capital goods per ha soybean field is 53% of the use per ha rap eseed field. The use of bu ildings for administration, research and laboratories is assumed to be zero corresponding to rapeseed cultivation.

## 7 Agricultural stage: Soybean 123

Use of capital goods per ha per year
Capital goods Rapeseed ( Table 5.44 ) Soybean

Tractor 7.5 kg 4.0 kg Harvester 6.3 kg 3.3 kg Agricultural machinery, tillage 9.2 kg 4.8 kg Agricultural machinery, general 3.5 kg 1.9 kg Shed 0.070 m 2 0.037 m 2 Administration, research and laboratories buildings 0 m 3 0 m 3



## Table 7.12: Use of capital goods in soybean cultivation.



# 7.8 Transport of materials in agricultural stage
Raw materials are transported with lorry to the soybean farms. Inventory data per tkm transport by lorry are described in section 4.1 . Since there are several suppliers and since there is a general lack of data on the specific marginal affected supplier, all transport distan ces are based on rough estimates. Determination of size of lorries is based on Table 4.3 and very rough estimates on the to tal amount of goods transported.

The amounts of used seed, fertiliser and pesticides are described in section 7.1 and 7.4 . The contents of P 2 

## O 5 

## in rock phosphate is 30% (see Table 6.34 ) and the use of P 2 

## O 5 

## is 46 kg/ha. Thus the use of P-fertiliser solution is 153 kg/ha.

Table 7.13 shows the transported amounts, the route and the distances.

Material Amount per ha From To Distance Means of transportation Seed 138 kg Seed trader Soybean farm 100 km 40t lorry Soybean 3,212 kg Soybean farm Soybean trader 500 km 40t lorry Fertilisers 153 kg Abroad chemical plant Port in Brazil 5000 km Transoceanic tanker Fertilisers 153 kg Port in Brazil Soybean farm 500 km 40t lorry Pesticides 9.1 kg Abroad chemical plant Port in Brazil 5000 km Transoceanic tanker Pesticides 9.1 kg Port in Brazil Soybean farm 500 km 40t lorry 

## Table 7.13: Transport of goods related to the agricultural stage. The return trip is included in the inventory data.



## The estimated transport in Table 7.13 is summarized per type of lorry in Table 7.14

.

Means of transportation Transport 40t lorry 1,701 tkm Oceanic tanker 811 tkm 

## Table 7.14: Summary of transport in the agricultural stage.



# 7.9 LCI of soybean agricult ural stage, summary

Table 7.16 summarises the inventory data relating to 1 ha soybean field in 1 year. Since the LCI in this study are highly based on the LCI of soybean described in Dalgaard et al. (2007), the applied data are compared with these data in Table 7.15

.

## 124 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Dalgaard et al (2007) This study

Interventions Per crop rotation Ajusted to annual values (multiplied with 1.25) Per year

Product output Yield 2.63 t/ha 3.29 t/ha 3.212 kg/ha Energy use Traction, burned diesel 42 litre ~ 1,529 MJ 52.5 litre ~ 1,911 MJ 1,911 MJ Drying of soybenas (evaporated water) Not included Not included 68 kg Materials Seed Not included Not included 138 kg N-fertiliser (as N) 0 kg 0 kg 0 kg P-fertiliser (as P 2 O 5 ) 37 kg 46 kg 46 kg K-fertiliser (as K 2 O) 0 kg 0 kg 0 kg Pesticides Not included Not included Included Capital goods Agr. machines, buildings Not included Not included Included Transport Lorry and ship Not included Not included Included Emissions Ammonia (NH3), to air 0 kg/ha 0 kg/ha 0 kg/ha Dinitrogen oxide (N2O), to air 4.7 kg/ha 5.9 kg/ha 4.7 kg/ha Nitric oxide (NO), to air Not included Not included 1.2 kg/ha Nitrate (NO3), to water 0 kg/ha 0 kg/ha 0 kg/ha Phosphorus (P), to water 0 kg/ha 0 kg/ha 0069 kg/ha Pesticides, to air, water and soil Not included Not included Included Heavy metals, to air, water and soil Not included Not included Included 

## Table 7.15: Comparison of LCI data in Dalgaard et al. (2007) and this study

## 7 Agricultural stage: Soybean 125

Brazil: 1 ha y soybean field Interventions Amount Applied LCI data Product output Soybean 3.212 t Product of interest Energy use Traction, burned diesel 1,911 MJ See Table 4.5 Drying of soybeans (evaporated water) 68 kg Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 138 kg See Table 7.2 P-fertiliser (as P 2 O 5 ) 46 kg Phosphate rock, see section 6.5 : Fertilisers Herbicide, glyphosate 2.3 kg ‘Glyphosate, at regional storehouse/RER’ (ecoinvent 2004) Herbicide, 2-4 D

- - Herbicide, imazethapyr

- - Insecticide, pyrethroid, cypermethrin 0.013 kg ‘Pyretroid-compounds, at regional storehouse/RER’ (ecoinvent 2004) Insecticide, Chlorpyrifos 0.20 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.037 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 4.0 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 3.3 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 4.8 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 1.9 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,701 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Oceanic tanker 811 tkm ‘ Transport, transoceanic tanker/OCE ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 0 kg - - Dinitrogen oxide (N2O) 4.7 kg - - Nitric oxide (NO) 1.2 kg - - Nitrate (NO3) - 0 kg - Phosphorus (P) - 0.069 kg - Glyphosate 0.77 kg 0.77 kg 0.77 kg 2-4 D No LCI data on a.i. Imazethapyr No LCI data on a.i., and no characterisation data exist in LCIA methods Cypermethrin 0.0043 kg 0.0043 kg 0.0043 kg Chlorpyrifos 0.067 kg 0.067 kg 0.067 kg Arsenic (As) - - - Cadmium (Cd) - 0.022 g 1.2 g Chromium (Cr) - 0.19 g 28 g Cobalt (Co) - 0.029 g -0.016 g Copper (Cu) - 5.7 g 1.5 g Mercury (Hg) - 0 g 0.00046 g Molybdenum (Mo) - - - Nickel (Ni) - 1.9 g 0.58 g Lead (Pb) - 0.029 g 0.53 g Selenium (Se) - 0.036 g 0.044 g Zink (Zn) - 17 g 9.6 g 

## Table 7.16: Interventions per ha y soybean field.

## 126 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 8 Agricultural stage: Barley 127



# 8 Agricultural stage: Barley
The barley plant (Hordeum vulgare) is an annual crop which grows to a height of 70-100 cm (Dansk Landbrugsrådgivning 2007).

This section provides data for a change in production ofBarley in Denmark and Canada. Some of the inventory data for barley are directly applied from Nielsen et al. (2005) and other are modified in order to maintain consistency with the methodologies for inventorying ra peseed, oil palm and soybean cultivation. The modified data are the same as for soybean cultivation, i.e. the emissions related to the N-, P- and C-balances and the inventory data for traction and fertiliser s. In addition to the modifications, the inventory in this study includes more processes. These are; Production and emissions of p esticides, drying of barley, the use of and processing of barley seed, capital goods and transpor t of materials in agricultural stage.



# 8.1 Product flow in agricultural stage
Figure 8.1 The inventory of the agricultural stage is divide d into the unit processes shown as shaded boxes in and Figure 8.2 . The product flows are determined per hectar e per year, see descriptions below the figure.Capital goods 

### Farm - buildings

Tractor

Harvester1 hectare barley field 

### Barley seed: 110 kg

Barley: 5,230 kg Drying

Barley: 5,120 kg Seed processing

Water: 105 kg Other agr. machinery Field operations: Pesticide, sowing, fertiliser, tillage, harvesting

Barley: 5,335 kg Barley in Denmark

Straw: 2,981 kg

Straw to biomass plant: 2,176 kg Straw left in the

field: 805 kg



## Figure 8.1: Product flow related to cultivation of 1 ha spring barley field in 1 year in Denmark. The grey shaded boxes represent the unit processes in the agricultural stage

.

## 128 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oilCapital goods 

### Farm - buildings

Tractor

Harvester1 hectare barley field 

### Barley seed: 110 kg

Barley: 2,910 kg Drying

Barley: 2,800 kg Seed processing

Water: 58 kg Other agr. machinery Field operations: Pesticide, sowing, fertiliser, tillage, harvesting

Barley: 2,968 kg Barley in Canada

Straw: 1,620 kg

Straw to biomass plant: 810 kg Straw left in the

field: 810 kg



## Figure 8.2: Product flow related to cultivation of 1 ha barley field in 1 year in Canada. The grey shaded boxes represent the unit processes in the agricultural stage

.



## Yields: The barley yields are applied as the yields in 2005 calculated from linear regression of yields from 1990 to 2005, see Figure 8.3 and Figure 8.4 . The same method as for determin ing the yield for rapeseed has been used, see section 5.1 . Yields are obtained from Danmarks st atistik for Denmark FAOSTAT (2006) for Canada. The expected yields in 2005 can be calculate d as 5.23 t/ha and 2.91 t/ha for DEnmark and Canada respectively using the equations in Figure 8.3 and Figure 8.4 . For comparison with yields determined using other methods see Table 8.1

.

yield = 0.0374*(year - 1989) + 4.63 R 2 

### = 0.094

0.0 1.0 2.0 3.0 4.0 5.0 6.01990199119921993199419951996199719981999200020012002200320042005Yield (t/ha) 

### Barley in Denmark



## Figure 8.3: Spring barley yields in Denmark 1990 to 2005. The linear regression line and its corresponding equation and R 2 

## are also shown. The yields are obta ined from Danmarks Statistik (2006).



## Since changed production of rapeseed in Denmark in some scenarios will affect the ar ea cultivated with spring barley, it is also necessary to know the emissions from spring barley cultivation on different soil types. Based on specified expected yields of spring barley on sa nd and clay soils given in Dansk Landbrugsrådgivning (2006) and the average yield at 5.23 t/ha, the yields on sand and clay have been determined as 4.23 t/ha on sand and 5.94 t/ha on clay. Due to lack of data and mode ls to fit Canadian environments, it has not been possible to distinguish between soil types in Canada.

## 8 Agricultural stage: Barley 129

yield = -0.003*(year - 1989) + 2.95 R 2 

### = 0.0031

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.51990199119921993199419951996199719981999200020012002200320042005Yield (t/ha) 

### Barley in Canada



## Figure 8.4: Barley yields in Canada 1990 to 2005. The linear regression line and its corresponding equation and R 2 

## are also shown. The yields are obtained from FAOSTAT (2006).

Region Yield 2005 (based on regression

1990-2005) Average yield 1990- 2005 Average yield 2000- 2005 Yield 2003 Yield 2004 Yield 2005

Denmark 5.23 4.95 5.14 5.19 4.97 5.23 Canada 2.91 2.93 2.83 2.77 3.26 3.13 

## Table 8.1: Barley yields in Denmark and Canada determined using different methods. The applied yields are marked with dotted lines.



## Based on predicted yields on different soils given in Plantedirektoratet (2005a) and adjustments in accordance to the method used for rapeseed yiel ds on sandy and clay soils in Table 5.3 , the yeilds of spring barley on sandy and clay soils can be calculated as 4.29 t/ha and 6.02 t/ha respectively.

Seed input: According to Nemecek et al. (2003, p 125) the use of barley seed is 110 kg/ha.

Water loos in drying: According to Nemecek et al. (2003, p 121) a nd Dalgaard et al. (2001) grains (unspecified grains) are typically dried 2 percent points. No sp ecific data on drying of barley have been identified. Therefore, the two percent points are applied. Since th e dried yields in Denmark and Canada are 5,230 kg/ha and 2,910 kg/ha respectively, the water dried is 105 kg/ha in Denmark and 58 kg/ha for barley in Canada.

Straw: According to Table 8.6 the straw to crop ratio is 0.57, and the removed straw from the field is 73% in Denmark and 50% in Canada.



# 8.2 Omitted inventory data in agricultural stage
The use of and emissions of pesticides have been omitted because of lack of data.

Magnesium (Mg), Sulphur (S) and Boron fertilisers and dust emissions and soil erosion have not been taken into account. These interventions are regarded as insignificant.

## 130 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 8.3 Energy use

Traction

It is assumed that there is no difference btween the en ergy use for traction in Denmark and Canada. According to Nielsen et al. (2005) the energy use as burning of diesel in agricultural m achinery is 4,029 MJ/ha.

Based on the percentual differences for traction in rapeseed cultivation on sand, clay and average soils in section 5.3 : ‘ Diesel consumption related to cultivation of rapeseed ’, the energy for traction on sand and clay for spring barley cultivation can be calculated as 3, 758 MJ/ha for sand and 4,217 MJ/ha for clay.

The inventory data for traction are described in section 4.3

. 

# Drying of seed According to Figure 8.1 and Figure 8.2 , 105 kg and 58 kg of water is dried out of the harvested barley per ha in Denmark and Canada respectively. The inventor y data for drying of seed are described in section 5.3 :

‘ Drying of seed ’. However, the use of electricity in the inventor y is adjusted to Danish and Canadian marginal electricity, see Table 3.3

.


# 8.4 Materials

Seed The interventions related to the agricultural stage in production of seed ar e accounted for by subtracting an amount of seed from the yield. According to Nemecek et al. (2003, p 102) the yield of seed production is the same as for conventional cultivation of cereals.

Only one life cycle inventory of seed has been identified; ‘ Barley seed IP, at regional storehouse/CH ’ (ecoinvent 2004). This inventory is shown in Table 8.2 below. The inventory has been modified applying interventions for electricity as shown in Table 3.3 instead of average Swiss electricity as in ecoinvent.

LCI-data for production of 1 kg seed Amount LCI data used in this study Barley from agriculture 1 kg This is accounted for by subtracting 1 kg from the product outputs per kg seed used, see Figure 8.1 and Figure 8.2 Electricity 0.086 MJ Electricity, see Table 3.3 Pesticide 0.088 g ‘Cyclic N-compounds, at regional storehouse/CH’ (ecoinvent 2004)

Building for storage 2.0

⋅ 10 -5 m 3 Building, multi-storey/RER (ecoinvent 2004) Transport from farm to warehouse 0.13 tkm Transport, lorry 28t/CH (ecoinvent 2004) 

## Table 8.2: Inventory data for production of 1 kg seed for barley cultivation (ecoinvent 2004).



# Fertiliser Fertiliser applications of N, P and K in Danish cultivati on of spring barley are obtained from Plantedirektoratet (2005a) and Dansk Landbrugsrådgivning ( 2006), and fertiliser application in Canada is obtained from IFA et al. (2002). This is shown in Table 8.3

.

Fertiliser Spring barley in Denmark Barley in Canada N 121 kg N/ha 67 kg N/ha P 2 O 5 46 kg P 2 O 5 /ha 26 kg P 2 O 5 /ha K 2 O 66 kg K 2 O/ha 10 kg K 2 O/ha 

## Table 8.3: RFertiliser application on average soils in Denmark and Canada for barley cultivation.

## 8 Agricultural stage: Barley 131



## Since changed production of rapeseed in Denmark in some scenarios will affect the ar ea cultivated with spring barley, it is also necessary to know the emissions from spring barley cultivation on different soil types. The soil types in Denmark are described in section 5.4 : ‘ Fertilisers ’. The fertiliser application on different soils in Danish spring barley cultivation is shown in Table 8.4

.

Fertiliser Spring barley in Denmark: Average soil (59% clay and 41% sand) Spring barley in Denmark: Sand Spring barley in Denmark: Clay N 121 kg N/ha 117 kg N/ha 123 kg N/ha P 2 O 5 46 kg P 2 O 5 /ha 39 kg P 2 O 5 /ha 50 kg P 2 O 5 /ha K 2 O 66 kg K 2 O/ha 55 kg K 2 O/ha 74 kg K 2 O/ha 

## Table 8.4: Fertiliser application on different soils in Danish spring barley cultivation.



## The types of N, P and K fertilisers used in Denmark and corresponding LCI data are described in section 5.4 :

‘ Fertilisers ’.

According to IFA (2007), the most widely used N-fertili ser in Northern America is ammonia. However, since no inventory data on straight ammonia have been identidied, it is assu med that the marginal source of N is ammonia nitrate which is the second most widely used N -fertiliser in Northern America. Inventory data for ammonia nitrate are specified in Table 5.17 . The most widely used P-fertiliser in Northern America is phosphate rock (IFA 2007). Therefore, it is assumed that phosphate rock represents the marginal source of Pfertiliser in Canada. Inventory data for phosphate rock are described in section 6.5 : Fertilisers . No data on the use of different types of K-fertilisers in Canada have been identified. Thus, it is assumed that potassuim chloride is the marginal source of K-fertiliser, i.e. the sam e marginal source of K-fertiliser as in Denmark and Malaysia/Indonesia. Inventory data for KCl are described in Table 5.21

.


# Pesticides No good and representative data on the use of pesticides in barley cultivation in Denmark and Canada have been identified. Therefore, this is omitted from the study. In section 21.15 the significanse of this omission is analysed.



# 8.5 Co-products

Denmark: Barley is co-produced with straw from field. The utilisation of straw from spring barley in Denmark is given in Table 8.5 below.

## 132 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Straw uses 1997 1998 1999 2000 2001 2002 2003 2004 Straw for energy 8% 6% 8% 9% 10% 15% 17% 15% Straw for fodder 56% 59% 52% 50% 43% 33% 33% 42% Straw for bedding etc. 13% 14% 17% 17% 18% 29% 28% 16% Straw left in the field 22% 21% 23% 24% 29% 24% 23% 27% Straw, total 100% 100% 100% 100% 100% 100% 100% 100% 

## Table 8.5: Production and uses of spring barley straw in Denmark. (Danmarks Statistik 2006)



## In average 24% of the straw is left in the field, 11% is used for energy purposes, 46% is used for fodder and 19% is used for bedding. In order to avoid co-product a llocation the marginal application of straw is identified. The demand for bedding is determined by animal productio n. It is also assumed that the demand for straw for fodder purposes is determined by animal production, and that the use of straw for fodder does not substitude grains. Hence, bedding and fodder is not considered as the marginal use. It is assumed that the marginal use of straw is distributed on 24% left in the field and the re maining 76% is used for energy purposes. According to Energistyrelsen (2006) 29% of the potential for straw for energy purposes was utilised in 2002. This may increase in the future. In this study 24% left in the fiel d and 76% used for energy purposes is applied. The interventions related to the straw left in the field are dealt with in section 8.6

.
Inventory data for utilisation of straw for energy purposes in biomass plants are described in section 5.5

.

Canada: The amount of straw left in the field in Canada is assumed to be 100%. According to Islam et al. (2004), straw is not used for energy purposes in Canada and only an insignificant fraction is used for bioethanol purposes and according to Wood and Layzell (2003) r esidues may be removed when yields exceeds 4.0 t/ha which is significant higher than the present 2.91 t/ha. Wood and Layzell (2003) also specify that between 750 and 1,500 kg crop residue per ha on prairie is essential to preven t wind erosion. Based on these considerations it is regarded as a good assumption that no resi dues are removed from barley production in Canada.



# 8.6 Emissions
N- and P balances are established for barley cultivation in Denmark and Canada. The re levant data in this respect are given in Table 8.6

.

## 8 Agricultural stage: Barley 133

Parameter Denmark: Spring barley Canada: Barley Reference

Annual barley yields 5,230 kg/ha 2,910 kg/ha See Table 8.1 Seed input 110 kg/ha Nemecek et al (2003, p 125) Barley dry matter 85% Møller et al. (2000, p 16) Straw solid matter 85% Møller et al. (2000, p 43). Assumed to be the same as pea straw Straw removed from field 73% 0% Straw removal in Denmark is obtained for Denmark in 2004 from Danmarks Statistik (2006). Residue to crop ratio 1.2 IPCC (2000, p 4.58) Straw to crop ratio 0.57 Jensen et al. (2005) Barley N-content 0.0173 kg N/kg DS Møller et al. (2000, p 16), N content is protein content divided by 6.25 Residue N-content 0.0064 kg N/kg DS Møller et al. (2000, p 43) Barley P-content 0.0035 kg P/kg DS Møller et al. (2000, p 16) Residue P-content 0.0009 kg P/kg DS Møller et al. (2000, p 43) Atmospheric N-deposition 15.0 kg/ha y 3.3 kg/ha y Denmark: Ellermann et al. (2005) and Canada: Bergström and Jansson (2006) N-fixing 0 kg N/ha y - 

## Table 8.6: Relevant data in order to establish field balances of N and P for barley cultivation.



## The N- and P-balances are shown in Table 8.7

.

Denmark Canada Inputs N P N P Deposition 15 kg N/ha - 3.3 kg N/ha - Seed 1.9 kg N/ha 0.01 kg P/ha 1.9 kg N/ha 0.01 kg P/ha Fertiliser 121 kg N/ha 20.0 kg P/ha 67 kg N/ha 11.4 kg P/ha N-fixing 0 kg N/ha - 0 kg N/ha - Changes in soil matter 0 kg N/ha - 0 kg N/ha - Total 137.6 kg N/ha 20.0 kg P/ha 72.2 kg N/ha 11.4 kg P/ha Outputs Harvested barley 76.9 kg/ha 15.6 kg P/ha 42.8 kg N/ha 8.7 kg P/ha Removed straw 11.8 kg/ha 1.7 kg P/ha 0 kg N/ha 0 kg P/ha Total 88.7 kg/ha 17.3 kg P/ha 42.8 kg N/ha 8.7 kg P/ha Balance surplus (input – output) 48.9 kg N/ha 2.7 kg P/ha 29.4 kg N/ha 2.7 kg P/ha 

## Table 8.7: Annual N- and P-balances for 1 hectare barley field in Denmark and Canada.



# Emissions related to the N-balance The N-surplus in Table 8.7 is distributed on different emissions fo llowing the same methods as for rapeseed, see section 5.6 : ‘ Emissions related to N-balance ’.

Ammonia from crop: The ammonia emission from crops 5 kg N/ha, see section 5.6 : ‘ Emissions related to Nbalance ’.

Ammonia from fertiliser application: The ammonia emission from fertiliser depends on the fertiliser applied. According to Andersen et al. (2001, p 35) th e ammonia emission from fer tilisers based on ammonia is 2% of the N content in the fertiliser. Thus the ammonia emission as NH 3 

## -N can be calculated as 2% of the applied fertiliser in Table 8.3

.
Denitrification (total): The total denitrification (gaseous N oxides and molecular N 2 

## ) is calculated using the model; SimDen (Vinther and Hansen 2004) . The reason why the total denitrification is calculated is that this is the only way of estimating N-loss as N 2 

## . N 2 

## is determined as the total denitrification minus N 2 

## O and NO. The model also calculates the N 2 

## O-emission.

## 134 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## The model is described in section 5.6 : ‘ Emissions relate d to N-balance ’. In Denmark the actual soil types are accounted for, while average of all so il types has been applied in Canada. The fertiliser application in Canada is given in Table 8.3 and the fertiliser application on diffe rent soils in Denmark is given in Table 8.4

The total denitrification in Denmark is calculated as 13. 2 kg N/ha for average soil, 5.5 kg N/ha for sand and 18.6 kg N/ha for clay. The total denitrifica tion in Canada is calculated as 11.8 kg N/ha

Direct N 2 

## O: The direct N 2 

## O emission is calculated using a model described in IPCC (2000). The calculated emissions are then compared with the results using two other models described in FAO and IFA (2001) and Vinther and Hansen (2004). The results obtained using these models are due to consistency not applied in this study. The model described in Vinther and Hansen ( 2004) is developed for Danish conditions and may therefore not be applicable under conditions in Malaysia, Indonesia, Brazil and Canada which are affected regions in this life cycle inventory. The model in FAO and IFA (2001) does not include peat soils which is relevant in Malaysia and Indonesia.

IPCC (2000): According to IPCC (2000, p 4.54) the direct N 2 

## O emission is calculated as shown in Equation (3 ) in section 5.6 . The parameter values used in Equation (3) are described in Table 8.8

.

Parameter Description Parameter value F SN Annual amount of synthetic fertiliser nitrogen applied to soils adjusted to account for the amount that volatilises as NH3 and NOx Fertiliser application, see Table 8.3 and Table 8.4 . 2% volatises

F AM Annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx No manure

F BN Amount of nitrogen fixed by N-fixing crops cultivated annually No N-fixing F CR Amount of nitrogen in crop residues returned to soils annually Denmark: 22 kg N/ha (aver. Soil), 16 kg N/ha (sandy soil), 26 kg N/ha (clay soil) Canada: 19 kg N/ha (the figures are calculated using the informations in in Table 8.6 ) F OS Area of organic soils cultivated annually (ha) Denmark: 0% (see in Table 5.28 ) Canada: 0% (The total area of peat soil in Canada is 1,500,000 km 2 (Andriesse 1988). Comparing with the total area of Canada at 9.09 mio km 2 , this corresponds to 16% of Canada. However, it is assumed that most of the peat soils are non-cultivated soils in the North) EF 1 Emission factor for emissions from N inputs (kg N2O-N/kg N input) 1.25% (IPCC 2000, p 4.60)

EF 2 Emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr) 8 (IPCC 2000, p 4.60)



## Table 8.8: Parameters in the equation calculating direct N 2 

## O-emissions in IPCC (2000, p 4.54)



## FAO and IFA (2001): According to FAO and IFA (2001, p 34) the direct N 2 

## O emission is calculated as shown in Equation (4) in section 5.6 . The parameter values used in Equation (4) are described in Table 8.9

.

## 8 Agricultural stage: Barley 135

Parameter Description Parameter value F Type of fertiliser Denmark: 0.0037, Fertiliser type is calcium ammonia nitrate Canada: 0.0061, Fertiliser type is ammonia nitrate N-app. Applied N, kg/ha See Table 8.3 and Table 8.4 Cr Crop type 0.000, Crop type is ‘other’ S Soil texture -0.008 (coarse for sandy soil) and 0.000 (fine for clay soil),average soil is the average of -0.008 (coarse), -0.472 (medium) and 0.000 (fine) C Soil organic C content 0.140, 1-3% C in mineral soil types, based on Berntsen and Petersen (2007) D Soil drainage -0.420, good drainage. Good drainage is needed in order to have suitable conditions for agriculture pH Soil pH 0.109, Soil pH 5.5 - 7.3 Cl Climate 0.000, Temp. climate LM Length of measurement period (the model is constructed to fitwith literature measurements. Thus, to model emissions obtained from literature the method of measurement in literature should also be considered since this affects the measured emission) 0.825 Length of measurement period is >300 days, i.e. the longest period available in the model (chosen as the most precise)

FM Frequency of measurement (see comment above) 0.000 Frequency of measurement is >1measure/day, i.e. the highest frequency available in the model (chosen as the most precise) 

## Table 8.9: Parameters in the equation calculating direct N 2 

## O-emissions in FAO and IFA (2001, p 34-35)



## Vinther and Hansen (2004): The model calculates the direct N 2 

## O emission for eight different soil types. The variable parameters are fertiliser a pplication, applied manure, N input to pastures (N-deposit during grazing and N 2 

## fixing by clover) and N 2 

## fixing by legumes crops. All parameters except from fertiliser input is zero. The fertiliser input is given in Table 8.3 and Table 8.4

.

The calculated N 2 

## O emissions using the three models are summarised in Table 8.10

.

Soil type IPCC (2000), kg N2O-N/ha FAO and IFA (2001), kg N2O-N/ha Vinther and Hansen (2004), kg N2O-N/ha Denmark Aver. soil 2.54 1.96 2.16 Sand soil 2.95 2.21 1.53 Clay soil 2.27 2.27 2.60 Canada Aver. soil 1.40 1.41 1.85 

## Table 8.10: Calculated N 2 

## O emissions (kg N 2 

## O-N/ha) for sand, clay and average so ils. The applied values are marked with dotted lines.



## The results of the FAO and IFA model and the Vinter a nd Hansen model deviate from the results of the IPCC model in Table 8.10 from around <1% to 48%.

Direct NO: The emission of NO is calculated using a mode l described in FAO and IFA (2001). According to FAO and IFA (2001, p 35) the direct NO emission is calculated shown in Equation (5) 

## in section 5.6 . The parameter values used in Equation (5) are described in Table 8.11

.

## 136 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Parameter Description Parameter value F Type of fertiliser Denmark: 0.0062, Fertiliser type is calcium ammonium nitrate Canada: 0.0040, Fertiliser type is ammonium nitrate N-app. Applied N, kg/ha See Table 8.3 and Table 8.4 C Soil organic C content 0.000, <3.0% C in mineral soil types, based on Berntsen and Petersen (2007) D Soil drainage 0.946, good drainage. Good drainage is needed in order to have suitable conditions for agriculture 

## Table 8.11: Parameters in the equation calculating direct NO-emissions in FAO and IFA (2001, p 35)



## The only parameters that are changed are the application of N-fertiliser.

The calculated NO emission is summarised in Table 8.12

.

Soil type kg NO-N/ha Denmark Aver. soil 1.2 Sand soil 1.2 Clay soil 1.2 Canada Aver. soil 0.7 

## Table 8.12: Calculated NO emissions (kg NO-N/ha) using the model described in FAO and IFA (2001). The applied values are marked with dotted lines.



## Nitrate: The nitrate emission is calculated as the residual or rest; i.e. the surplus-N from the N-balance minus the other calculated emissions described above. The calculated nitrate emission is shown in Table 8.13

.

Soil type kg NO3-N/ha Denmark Aver. soil 28.3 Sand soil 49.1 Clay soil 14.7 Canada Aver. soil 11.3 

## Table 8.13: Calculated nitrate emissions (kg NO 3 - 

## -N/ha). The applied values ar e marked with dotted lines.



## N 2 

## O, indirect from NH 3 

## and nitrate: These calculations are described in section 5.6 : ‘ Emissions related to Nbalance ’.

NO, indirect: This is not included.

Summary of emissions related to N-balance: Distribution of the N-surplus is summarised in Table 8.14

.

## 8 Agricultural stage: Barley 137

Denmark Canada Emission and source Average soil Sand Clay Average soil Ammonia from crop (kg NH 3 -N/ha) 2.4 2.3 2.5 1.3 Ammonia from fertiliser application (kg NH 3 -N/ha) 5.0 5.0 5.0 5.0 Denitrification (kg N/ha) 13.2 5.5 18.6 11.8 - N 2 O part of denitrification (kg N 2 O-N/ha) 1.8 1.6 1.8 1.1 - NO part of denitrification (kg NO-N/ha) 1.2 1.2 1.2 0.7 - N 2 part of denitrification (kg N/ha) 10.2 2.7 15.5 10.0 Nitrate (kg NO 3 -N/ha) 28.3 49.1 14.7 11.3 N-surplus (kg N/ha) 48.9 61.9 40.7 29.4 N 2 O, indirect from NH 3 (kg N 2 O-N/ha) 0.1 0.1 0.1 0.1 N 2 O, indirect from nitrate (kg N 2 O-N/ha) 0.7 1.2 0.4 0.3 

## Table 8.14: Distribution of the N-surplus from the field N-balance on different emissions and sources. All numbers are given in kg N/ha.



## Table 8.15 summarises the emissions in Table 8.14 and converts them into kg emission per ha instead of kg N/ha.

Denmark Canada
Emission as kg N/ha Average soil (41% sand and 59% clay) Sand Clay Average soil
Ammonia to air (kg NH 3 -N/ha) 7.4 7.3 7.5 6.3 N 2 O to air (kg N 2 O-N/ha) 2.5 2.9 2.3 1.4 NO to air (kg NO-N/ha) 1.2 1.2 1.2 0.7 Nitrate to water (kg NO 3 -N/ha) 28.3 49.1 14.7 11.3 Emission as kg emission/ha Average soil (41% sand and 59% clay) Sand Clay Average soil
Ammonia to air (kg NH 3 /ha) 9.0 8.9 9.1 7.7 N 2 O to air (kg N 2 O/ha) 4.0 4.6 3.6 2.2 NO to air (kg NO/ha) 2.5 2.5 2.6 1.6 Nitrate to water (kg NO 3 /ha) 125 217 65 50 

## Table 8.15: Emissions related to N-balance.



# Emissions related to the P-balance

As described in the case of emissions from rapeseed cultivation (see section 5.6 ) accumulation of phosphorus in the soil is relatively constant due to strong binding to the soil. Therefore, the emission of P is calculated as a fraction of the field surplus. According to section 5.6 : ‘ Emissions related to P-balance ’, 2.9% of the P surplus is emitted to water as leaching of ph osphate. The remaining is accumulated in the soil matter. Thus the emission of P is 2.9% of the surplus in the P-balances given in Table 8.7 . P emissions for sand and clay soils in Denmark are also calculated. This is done by first establishing P-balances as in Table 8.7 using the data in Table 8.4 and Table 8.6 . The P emissions are given in Table 8.16

.

Soil type kg P/ha Denmark Aver. soil 0.078 Sand soil 0.084 Clay soil 0.075 Canada Aver. soil 0.078 

## Table 8.16: Calculated P emissions (kg P/ha). The applied values are marked with dotted lines.



# Emissions related to the C-balance As in the case for rapeseed, oil palm and soybean cultiv ation it is assumed that continuous cultivation of barley does not affect the soil content of carbon.

## 138 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## Carbon emissions that arise from transformation of land (pra irie) in Canada into barley fields are described in section 19.1

. 

# Emissions of heavy metal The emissions of heavy metals from input of fertiliser are calculated using the same method as for rapeseed cultivation, see section 5.6 : ‘ Emissions of heavy metal ’. Hence, it is assumed that the heavy metal input from fertilisers is distributed on emissions to soil and emissi ons to water. The emission of heavy metals to soil is calculated as the total input with fertiliser and seeds minus th e share that ends in water. One eighth, i.e. 12.5%, of the heavy metals harvested with crop ends as emi ssion to water and the other 87.5% ends as emission to soil.

The contents of heavy metals in fertilisers used in Denmark 16 

## are described in section 5.6 : ‘ Emissions of heavy metal ’ and the contents of fertilisers used in Canada 17 

## are described in section 5.6 : (N and K) and section 6.7 :‘ Emissions of heavy metal ’ (P) and the contents in barley and straw are shown in Table 8.17

.

Barley Heavy metals in harvested crop; mg/kg crop (Nemecek, et al. 2003, p 154) (Møller et al. 2000, p 17) Applied in this study

Arsenic (As) - - - Cadmium (Cd) 0.068 - 0.068 Chromium (Cr) 0.31 - 0.31 Cobalt (Co) - 0.0085 0.0085 Copper (Cu) 5.10 2.55 3.83 Mercury (Hg) 0.051 - 0.051 Molybdenum (Mo) - - - Nickel (Ni) 0.33 - 0.33 Lead (Pb) 0.35 - 0.35 Selenium (Se) - 0.034 0.034 Zink (Zn) 37.8 26.4 32.1 Barley straw Heavy metals in harvested straw; mg/kg crop (Nemecek, et al. 2003, p 154) (Møller et al. 2000, p 43) Applied in this study

Arsenic (As) - - - Cadmium (Cd) 0.11 - 0.11 Chromium (Cr) 0.36 - 0.36 Cobalt (Co) - 0.16 0.16 Copper (Cu) 4.34 2.55 3.45 Mercury (Hg) 0.085 - 0.085 Molybdenum (Mo) - - - Nickel (Ni) 0.35 - 0.35 Lead (Pb) 1.70 - 1.70 Selenium (Se) - 0043 0043 Zink (Zn) 11.1 125.0 68.1 

## Table 8.17: Heavy metal content (mg/kg crop) in harvested barley and barley straw. The applied data are marked with dotted frames.



## Based on the fertiliser inputs given in Table 8.3 and Table 8.4 , the contents of heavy metals in fertilisers, and the contents of heavy metals in harvested barley and straw in Table 8.17 , the emissions of heavy metals from fertiliser can be calculated. The results are shown in Table 8.18

.

16 

## Fertilisers in Denmark; N: Calcium ammonia nitrate, P: Triple super phosphate, K: Pottasium chloride.

17 

## Fertilisers in Canada; N: Ammonia nitrate, P: Rock phosphate, K: Pottasium chloride.

## 8 Agricultural stage: Barley 139

Denmark Canada

Aver. soil Sand Clay Aver. soil

Emission Soil (g/ha) Water (g/ha) Soil (g/ha) Water (g/ha) Soil (g/ha) Water (g/ha) Soil (g/ha) Water (g/ha) Arsenic (As) 0.9 - 0.8 - 1.0 - 0.2 - Cadmium (Cd) 0.3 0.0 0.2 0.0 0.3 0.1 0.7 0.03 Chromium (Cr) 26 0.2 22 0.2 29 0.2 16 0.1 Cobalt (Co) 0.2 0.01 0.2 0.00 0.2 0.01 0.06 0.003 Copper (Cu) 4.0 2.5 3.7 2.0 4.2 2.8 2.7 1.4 Mercury (Hg) -0.01 0.03 0.00 0.03 -0.01 0.04 -0.005 0.02 Molybdenum (Mo) 1.0 - 0.9 - 1.0 - 0.3 - Nickel (Ni) 9 0.2 7.6 0.2 9 0.2 2.0 0.1 Lead (Pb) 0.8 0.2 0.8 0.2 0.8 0.3 0.6 0.1 Selenium (Se) 2.0 0.02 1.9 0.02 2.0 0.03 1.0 0.01 Zink (Zn) 45 21 40 17 48 24 8.9 12 

## Table 8.18: Heavy metal emissions to soil and water from barley cultivation in Denmark and Canada.


# Emissions of pesticides

As described in section 8.4 the use of pesticides in barley culti vation has been omitted from the study.



# 8.7 Overhead in agricultural stage
As in the case of rapeseed cultivation, the amount of el ectricity used in administration buildings etc. is assumed to be insignificant.



# 8.8 Capital goods in agricultural stage
Corresponding to capital goods in oil palm and soybean cultivation, (e.g. see section 7.7 ), the use of capital goods is determined from modified figures of the capital goods used in rapeseed cultivation. It is assumed that the amount of harvesters, tractors, agricultural machin ery-tillage, agricultural machinery-general and shed are equivalent with the diesel consumption per hectare. Th e diesel consumption in rapeseed cultivation is 3,612 MJ/ha (see section 5.3 ) and the diesel consumption in barley cultivation is 4,029 MJ/ha (see section 8.2 ). Thus, use of capital goods per ha barley field is 112% of the u se per ha rapeseed field. The use of buildings for administration, research and laboratories is assumed to be zero corresponding to rapeseed cultivation.

Use of capital goods per ha per year

Capital goods Rapeseed ( Table 5.44 ) Barley in Denmark and Canada Tractor 7.5 kg 8.4 kg Harvester 6.3 kg 7.0 kg Agricultural machinery, tillage 9.2 kg 10.3 kg Agricultural machinery, general 3.5 kg 3.9 kg Shed 0.070 m 2 0.078 m 2 Administration, research and laboratories 0 m 3 0 m 3 

## Table 8.19: Use of capital goods in barley cultivation.



# 8.9 Transport of materials in agricultural stage
Raw materials are transported with lorry to the cereal fa rms. Inventory data per tkm transport by lorry are described in section 4.1 . Since there are several suppliers and since there is a general lack of data on the specific marginal affected supplier, all transport distances are b ased on rough estimates. Determination of size of lorries is based on Table 4.3 and very rough estimates on the to tal amount of goods transported.

## 140 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## The amounts of used seed, fertiliser an d pesticides are described in section 8.1 and 8.4 . The contents of N, P 2 

## O 5 

## and K 2 

## O in the used fertilisers are described in Table 5.45 and Table 6.34 (however, the content of N in ammonia nitrate is found in WSDA (2006)) and the use of fertilisers is descibed in Table 8.3

.

Fertiliser Content of nutrient in fertiliser Applied nutrients (see Table 8.3 ) Applied fertiliser product

Denmark
Calcium ammonium nitrate 19% N 121 kg N/ha 637 kg/ha Triple superphosphate 38% P 2 O 5 (16% P) 46 kg P 2 O 5 /ha 121 kg/ha Potassium chloride 14% K 2 O (12% K) 66 kg K 2 O/ha 471 kg/ha Total 1,229 kg/ha Canada Ammonia nitrate 20% N 67 kg N/ha 335 kg/ha Phosphate rock 30% P 2 O 5 (13% P) 26 kg P 2 O 5 /ha 87 kg/ha Potassium chloride 14% K 2 O (12% K) 10 kg K 2 O /ha 71 kg/ha Total 493 kg/ha 

## Table 8.20: Determination of amount of tr ansported fertiliser product.



## Table 8.21 shows the transported amounts, the route and the distances.

Denmark Material Amount per ha From To Distance Means of transportation Seed 110 kg Seed trader, DK Barley farm, DK 100 km 40t lorry Barley 5,230 kg Barley farm, DK Cereal trader, DK 100 km 40t lorry Straw to utilisation 2,176 kg Barley farm, DK Biomass plant, DK 100 km 40t lorry Fertilisers, N, P and K 1,229 kg Abroad chemical plant Barley farm, DK 1000 km 40t lorry Pesticides Not included Abroad chemical plant Barley farm, DK 1000 km 40t lorry Canada Material Amount per ha From To Distance Means of transportation Seed 110 kg Seed trader, CAN Barley farm, CAN 250 km 40t lorry Barley 2,910 kg Barley farm, CAN Cereal trader, CAN 250 km 40t lorry Straw to utilisation 810 kg Barley farm, CAN Biomass plant, CAN 250 km 40t lorry Fertilisers, N, P and K 493 kg Abroad chemical plant Barley farm, CAN 1000 km 40t lorry Pesticides Not included Abroad chemical plant Barley farm, CAN 1000 km 40t lorry 

## Table 8.21: Transport of goods related to the agricultural stage. The return trip is included in the inventory data.



## The estimated transport in Table 8.21 is summarized in Table 8.22

.

Means of transportation Transport, 40t lorry Barley in Denmark 1,981 tkm Barley in Canada 1,451 tkm 

## Table 8.22: Summary of transport in the agricultural stage.

## 8 Agricultural stage: Barley 141



# 8.10 LCI of barley agricult ural stage, summary

Table 8.23 summarises the inventory data relating to 1 ha spri ng barley cultivated in 1 year in Denmark and Table 8.24 summrises the inventory for barley in Canada.

Denmark: 1 ha y spring barley field Interventions Amount Applied LCI data Product output Spring barley (Denmark) 5.120 t Product of interest Straw removed from field 2,176 kg Co-product allocation between barley and straw is avoided by system expansion, see below System expansion Burning of straw in biomass plant 2,176 kg See Table 5.25 Energy use Traction, burned diesel 4,029 MJ See Table 4.5 Drying of barley (evaporated water) 105 kg Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 110 kg See Table 8.2 N-fertiliser (as N) 121 kg ‘Calcium a mmonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 46 kg Modified version of: ‘ Triple superphosphate, as P 2 O 5 , at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 K-fertiliser (as K 2 O) 66 kg ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.078 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 8.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 7.0 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 10.3 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.9 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,981 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 9.0 kg - - Dinitrogen oxide (N2O) 4.4 kg - - Nitric oxide (NO) 2.5 kg - - Nitrate (NO3) - 125 kg - Phosphorus (P) - 0.078 kg - Arsenic (As) - - 0.88 g Cadmium (Cd) - 0.044 g 0.27 g Chromium (Cr) - 0.20 g 26 g Cobalt (Co) - 0.0056 g 0.21 g Copper (Cu) - 2.5 g 4.0 g Mercury (Hg) - 0.033 g -0.0081 g Molybdenum (Mo) - - 1.0 g Nickel (Ni) - 0.22 g 8.7 g Lead (Pb) - 0.23 g 0.81 g Selenium (Se) - 0.022 g 2.0 g Zink (Zn) - 21 g 45 g 

## Table 8.23: Interventions per ha y spring barley field in Denmark.

## 142 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Canada: 1 ha y barley field Interventions Amount Applied LCI data Product output Barley (Canada) 2.800 t Product of interest Straw removed from field 0 kg - System expansion Burning of straw in biomass plant 0 kg - Energy use Traction, burned diesel 4,029 MJ See Table 4.5 Drying of barley (evaporated water) 58 kg Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 110 kg See Table 8.2 N-fertiliser (as N) 67 kg ‘Ammonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 26 kg Phosphate rock, see section 6.5 : Fertilisers K-fertiliser (as K 2 O) 10 kg ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.078 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 8.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 7.0 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 10.3 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.9 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,451 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 7.7 kg - - Dinitrogen oxide (N2O) 2.2 kg - - Nitric oxide (NO) 1.6 kg - - Nitrate (NO3) - 50 kg - Phosphorus (P) - 0.078 kg - Arsenic (As) - - 0.16 g Cadmium (Cd) - 0.025 g 0.69 g Chromium (Cr) - 0.11 g 16 g Cobalt (Co) - 0.0031 g 0.061 g Copper (Cu) - 1.4 g 2.7 g Mercury (Hg) - 0.019 g -0.0050 g Molybdenum (Mo) - - 0.26 g Nickel (Ni) - 0.12 g 2.0 g Lead (Pb) - 0.13 g 0.55 g Selenium (Se) - 0.012 g 0.94 g Zink (Zn) - 12 g 8.9 g 

## Table 8.24: Interventions per ha y barley field in Canada.

## 9 Oil mill stage: Rapeseed oil 143



# 9 Oil mill stage: Rapeseed oil
The LCI for the oil mill stage takes its point of departur e in production of rapeseed oil at AarhusKarlshamn in Aarhus, Denmark. In order to have a representative se t of data, data from both 2003 and 2004 are used where possible.

Two different types of oil mill technologies exist; solven t extraction and full press. The difference between the two technolies is that the solvent extraction technology uses hexane as a solvent in order to achieve a higher oil extraction rate. The solvent extraction which is used at AarhusKarlshamn is the most dominant technology in the rapeseed oil industry (Kronborg 2006). As sensitivity analysis, data are also collected for a full press mill; Scanola in Aarhus, Denmark, see section 21.16

.
The inventory of the oil mill stage is mainly based AarhusKarlshamn’s energy report (2005b) and environmental report (Aarhus United, 2005a). Since AarhusKarl shamn produces other products than rapeseed oil and since their activities include milling and refining as well as modification of oils such as interestification, fractionation and hardening, overall envi ronmental data from environmental accounts and energy accounts are not directly applicable. Therefore use of energy, use of ancillary materials, emissions and waste have to be based on more detailed investigations – mainly based on personal communication with Korning (2006), Kronborg (2006) and Hansen (2006). However, there are interventi ons which are not directly connected to any of the production lines. In these cases allocation has been carried out. This has been necessary in the case of heat and power supply for buildings (administration and research and development). Allocation is carried out between products and between process stages for rapeseed oil (milling, refining and modifying).

Figure 9.1 

## shows the output of rapeseed meal from the oil mill.

Figure 9.1: Rapeseed meal. Picture taken by Jannick H Schmidt 2007. Samples provided by Nordic Folkecenter for Renewable Energy..



# 9.1 Rapeseed oil mill product flow
The inventory of the oil mill stage is divided into the unit processes shown in Figure 9.2 Figure 9.2

. also shows the product flows through the oil mill. The product flow is determined from data on production of crude rapeseed oil at AarhusKarlshamn in 2004 (A arhus United 2005b; Kronborg 2006 and Hansen 2006).

## 144 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oilPressingExtraction Rapeseed: 2.387 t

Pre-heating

Rolling

Solvent extraction Pressing

Cooling

Oil: 0.422 t Clarification

Extraction cake Miscella

Desolventization Heating

Solvent removal Drying Drying Rolling

Clarification

Centrifugation

Drying Lecithin

Oil: 0.578 t Raw material reception and storage

ScreeningOverhead Heat

Electricity

Meal: 1.346 t Conditioning (heat treatment)

Waste to biogas: 0.010 t Crude rapeseed oil: 1.000 tPower central Boiler

Turbine Steam

Electricity Steam Accumulator SteamOil mill processing



## Figure 9.2: Product flow related to production of 1 t crude rapeseed oil in the rapeseed oil mill. The numbers are obtained from Aarhus United in 2004. The grey shaded boxes repr esent the unit processes in the rapeseed oil mill stage.

2003 2004 Milling and extraction Rape Shea Rape Shea Inputs Seed and nuts 75,481 50,006 61,841 86,533 Outputs Oil from pressing 13,587 11,001 11,131 19.037 Oil from extraction 17,314 13,437 15,231 23,401 Meal 44,581 25,568 35,479 44,094 Total 75,482 50,006 61,841 86,532 

## Table 9.1: Mass balance for milling and extraction at AarhusKarlshamn 2003 and 2004. All numbers are given in tonne. (Aarhus United, 2004b and 2005b)



## The numbers shown in Table 9.1 from AarhusKarlshamn only cover produc tion of crude oils at AarhusKarlshamn and they do not take into account that there is a minor loss of water and residuals from screening of the incoming rapeseed. It is the numbers on amount of produced crude oils that are the correct ones. The loss of residual and water per kg crude oil is given in Figure 9.3

.

## 9 Oil mill stage: Rapeseed oil 145PressingExtraction Rapeseed: 2.387 kg DS: 2.187 kg Water: 0.200 kg

Pre-heating

Rolling

Solvent extraction Pressing

Cooling

Oil: 0.422 kg Clarification

Extraction cake Miscella

Desolventization Heating

Solvent removal Drying Drying Rolling

Clarification

Centrifugation

Drying Lecithin

Oil: 0.578 kg Raw material reception and storage

Screening

Conditioning (heat treatment)

Residual: 0.010 kg

DS: 0.009 kg Water: 0.001 kg Water: 0.010 kg

Water: 0.010 kg

Water: 0.010 kg

Crude oil: 1.000 kg DS: 1.000 kg Water: 0.000 kg Meal: 1.346 kg

DS: 1.178 Water: 0.168 kg 

## Figure 9.3: Losses of water and residual in the screening process related to pressing and extraction of 1 kg crude rapeseed oil. The calculation of losses is described in the following.



## Loss; residual from screening: According to Kronborg (2006) the amount of residual from screening of the incoming rapeseed is approximately 0.4%. The water conten t in residual is assumed to be the same as in rapeseed which is 8-9%, see Table 5.1

.

Loss; water: It has not been possible to obtain data on lo ss of water in the rapeseed through the processes at AarhusKarlshamn. Therefore, point of departure is take n in the overall loss in ra peseed oil mills in the EU which was 1.7% in 2003 and 2004 (Based on Oil World 2005). Applying the overall loss of incoming rapeseed at 1.7% in the EU and the ratio between oil and meal as in Figure 9.2 , the total input of rapeseed per tonne crude oil is 2.387 tonne.

The water content of the meal is estimated as 12.5%. Th is based on rapeseed meal containing 4% fat in Møller et al. (2000). Møller et al. (2000) provide data on differe nt types of rapeseed meals; meals containing 4%, 10% and 13% fat. Meals containing 10% an d 13% fat are meals from oil mills without solvent extraction. Based on the numbers given in Figure 9.2 and a content of 199 g protein and 442 g fat per kilo rapeseed (Møller et al. 2000) and an overall loss of inputs to oil to rapeseed oil m ills in the EU at 1.7%, it can be calculated that the

## 146 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## meal produced at AarhusKarlshamn c ontains approximately 4.1% fat and 35.3% protein. A standard rapeseed meal containing 4% fat in Møller et al. (2000) contains 34% protein, which relatively close to the numbers calculated.

The overall loss at 1.7% of the input of rapeseed is di stributed on 0.4% residual from screening of rapeseed; ~0.0095 t, and the remaining 1.3% is water; 0.031 tonne. Since the content of water in the meal is 12.5%, the amount of water in the meal is 0.168 tonne. The water cont ent of the residual is assume d to be the same as in rapeseed ~8-9%; ~0.001 tonne. The water content in th e incoming rapeseed is calculated from the dry matter content in the outputs; the meal (1.178 t), the crude oil (1.000 t) and the residual (~0.009 t) totalling 2.187 tonne dry solid matter. The total input of rapeseed was determined to be 2.387 tonne. Comparing the dry solid matter and the total input, the water content can be cal culated as 8.4% (0.200 t). This is consistent with Table 5.1 which specify water content at 8-9%. Thus, the loss ofwater in the different drying processes can be calculated as the residual water in excess; water input (0.200 t) minus water output (0.168 t + 0.001 t) = 0.031 t. It is assumed that this water loss is equally dist ributed on the three drying processes given in Figure 9.2 . Thus, the water loss is 0.010 t in each drying process.



# 9.2 Omitted inventory data in rapeseed oil mill stage

There are some interventions related to the oil mill st age that are not included. The reason for omitting these interventions is: i) the interventions account for an insigni ficant share of the interventions related to the oil mill stage, ii) inclusion would involve great uncertainties, iii) data collection would be qu ite time consuming. Relating to ii) one reason for uncertainties is that there is no known direct relationship between production of refined rapeseed oil at AarhusKarlshamn and the interventi ons. Therefore, the added value to the inventory is estimated to be very low compared to the work load needed. All omitted interventions accounts for less than 0.6 g per kg produced oil at AarhusKarlsham n. The omitted interventions are shown in Table 9.2

.

Material flows Total at AarhusKarlshamn in 2004 Amount per kg product at AarhusKarlshamn Products 312,000 tonne 1 kg Omitted material use Lubricating oil 3 tonne 0.01 g Cleaning agents 13.4 tonne 0.04 g Omitted waste Paper and card board to recycling 42 tonne 0.1 g Plastic to recycling 8 tonne 0.03 g Lubricating oil to recycling 3 tonne 0.01 g Metals to recycling 180 tonne 0.6 g Waste to landfill 80 tonne 0.3 g 

## Table 9.2: Omitted inventory data. Data from Aarhus United (2005a).



## In addition to the omitted material uses and waste streams in Table 9.2 also consumption of varies minor products such as tools, paper, computers, pencils, mi scellaneous equipment etc. for process management and administration are omitted.



# 9.3 Power central
The energy supply to the oil mill includes electricity and steam. At AarhusKarlshamn steam is co-produced with electricity on the company’s own combined heat and power plant (CHP) and deficiency of electricity is purchased from the grid. The determining product is steam and the dependent product is electricity.

The energy efficiency in term of steam and electri city production for AarhusKarl shamn’s CHP is shown in Table 9.3 . The numbers are based on the total input of fuel oil and the net output of steam and electricity.

## 9 Oil mill stage: Rapeseed oil 147

Energy distribution 2003 2004 Average Heat 90.1% 84.6% 87.3% Electricity 4.9% 5.2% 5.0% Loss 5.1% 10.1% 7.6% Total 100.1% 99.9% 99.9 

## Table 9.3: Energy efficiency at Aa rhusKarlshamn’s CHP. The small differences in total are due to rounding. (Aarhus United, 2004b and 2005b)



## It can be seen from Table 9.3 that the CHP has an overall average efficien cy of 92.3 percent. This is consistent with Energistyrelsen (1995) where effi ciency of oil boilers in Denmark is assessed to be 90-92%. According to Aarhus United (2005a) the use of fuel in 2004 was distri buted on 98.3% fuel oil and 1.7% oil waste from AarhusKarlshamn. 20% of the oil waste comes from degummi ng of shea nut oil and 80% comes from different oil waste of vegetable oils. In 2004 29% of the crude oil production was rapeseed oil and 71% was shea nut oil (Aarhus United, 2005b). Thus, 29% of 80% of the 1.7% oil waste, i.e. 0.4% ~ 0% of the oil waste can be ascribed to the production of rapeseed oil. Hence, the amount of oil waste from rapeseed oil can be neglected. Comparing the water use for energy production in Aarhus United (2005a) the production of steam in Aarhus United (2005b) the use of water can be found as 0.12 litres per MJ heat produced.

Interventions related to production, transportation and burning of fuel o il are found using data from ecoinvent (2004): Light fuel oil, burned in boiler 100kW, non-modulating . The data set includes infrastructure and machinery for production and transportation of the oil.

The produced electricity is sold to the grid, where marginal supply of electricity is displaced. Table 9.4 gives an overview of the interventions rela ted to production of 1 MJ at the power central at AarhusKarlshamn.

1 MJ heat (steam) Amount Applied LCI data Fuel oil burned in boiler 1.145 MJ ‘Light fuel oil, burned in boiler 100kW, non-modulating’, ecoinvent (2004) Electricity (sold to the grid) -0.057 MJ See Table 3.3 Water, tap 0.12 litre See Table 13.5 

## Table 9.4: Inventory data for 1 MJ heat produced at AarhusKarlshamn’s power central.



# 9.4 Rapeseed oil mill processi ng: Pressing and extraction
This process includes raw material reception, storage, screening, pre-heating, rolling and conditioning. After rolling the flakes from the press process are sent to solv ent extraction. There are two outputs from the solvent extraction; extraction cake and miscella. The solvent is removed from the cake and after drying the meal is finished. Miscella is a mix of oil approximately 10-30% oil and 70-90% solvent. The solvent is removed andreused and the oil is clarified and centrifuged. The residual from the centrifugation is lecithin which at AarhusKarlshamn is fed into the meal fraction. Some mills sell the lecithin which can be used as emulsifier. The segregated lecithin comprises around 2% of the desolventi zed miscella (Korning 2006). It is presumed that the lecithin in general at modern European oil mills is fe d into the meal. Thus, it is not treated as a co-product. Interventions in the pressing and extraction stage are calc ulated per kg extracted rapeseed oil. The product flow per kilo pressed and extracted oil is shown in Figure 9.2

.


# Material use There is no material use besides the input of rapeseed to the pressing process. The interventions related to rapeseed production are described in section 0 . In the extraction process solvent is used. The solvent used at AarhusKarlshamn is hexane. According to Bockisch ( 1998) hexane is the most dominant solvent used. Other minor solvents are benzenes, carbon disulphide and trichloroethylene.

## 148 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## According to Kronborg (2006) consump tion of hexane per tonne pressed a nd extracted rapeseed oil is 1.188 kg.

It has not been possible to identify any inventory data on production of hexane. However, two inventories of solvents representing hexane have been identified. The first one is an inventory used to describe hexane for extraction of rapeseed oil in an LCA of rapeseed oil (Nielsen et al. 2005). The data used is an inventory of ‘Chemicals inorganic’ from the ETH-database (Frischknecht et al., 1996). The second data set is data used to describe hexane for soybean oil extraction in an LCA of soybean oil (Althaus et al. 2003). The data set is an inventory of production of pentane in the ecoinvent data base (Hischier 2003). In Althaus et al. (2003, p 695) it is argued that the use of data for pentane should not affect the result of the LCA of soybean oil because the manufacturing processes of hexane and pentane are similar. Analysing the two data sets in Simapro and using the EDIP97 for LCIA, it appears that global warming, acidi fication and toxicity are the most significant impact categories. In Table 9.5 the two data sets are compared within these categories.

Representative LCI-data for hexane CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘Chemicals, inorganic’, ETHESU database (Frischknecht et al., 1996) 0.65 kg 8.8 g 530 m 3 Time: Data from 1990-94 Geography: Western Europe Technology: Average Co-product allocation : Not relevant Capital goods: Included for sub processes but not for the production of chemicals. ‘Pentane, at plant’, ecoinvent database (Hischier 2003) 1,2 kg 9.7 g 32 m 3 Time: Data from 1990 and 1996 Geography: France, Germany and UK Technology: Average Co-product allocation : Mass-allocation between co-products in the steps before pentane production (refining of oil etc) Capital goods: Machinery, buildings. 

## Table 9.5: Comparison of two LCIs representing production of 1 kg hexane. The comparison is shown as characterised results using the EDIP97-method for LCIA. The applied data are marked with a black dotted frame.



## It appears from Table 9.5 that the two data sets are significantly di fferent within the compared impact categories. The difference in terms of global warming varies w ith a factor 2 and ecotoxicity with a factor 17. The main reason for the differences is probably that the two LC Is have different scopes; ‘chemicals, inorganic’ and ‘pentane, at plant’. The validity of this argument is underpinned in the following. Besides pentane, there are inventories of 15 other different solvents in Hischier (2003). In comparison to Table 9.5 these data varies from 0.62 kg CO 2 

## -eq to 3.3 kg CO 2 

## -eq and 36 m 3 

## to 1,000 m 3 

## water ecotoxicity. It can be seen that the span of contributions from different solvents in Hischier (2003) embrace the data from Frischknecht et al. (1996). Since the data from Hischier (2003) are the newest, the most precise concerning scope and the most documented of the two data sets, it is chosen to a pply these in the LCA of rapeseed oil.


# Energy use According to Kronborg (2006) energy consumption rela ted to both pressing and extraction is 1,586 MJ heat and 419 MJ electricity per tonne of rapeseed oil. B ased on Kronborg (2006) and Aar hus United (2005b) it is determined that pressing comprises 22% of the heat consumption and 64% of the electricity use. These energy uses are per tonne of produced oil, see Table 9.6

.

Energy use per kg pressed rapeseed oil Pressing Solvent extraction Total Electricity 268 MJ 151 MJ 419 MJ Heat (steam) 349 MJ 1,237 MJ 1,586 MJ 

## Table 9.6: Energy consumption per t rapeseed oil produced in 2004 at AarhusKarlshamn. (Kronborg, 2006 and Aarhus United 2005b)

## 9 Oil mill stage: Rapeseed oil 149



## The interventions from production of electri city and heat are described in section 3.1 and 9.3

.


# Emissions There are no emissions from the press except from dust. This originates from the handling of rapeseed and meal. However, according to Kronborg (2006) the dust emission related to rapeseed is insignificant.

From the solvent extraction process there is emission of hexane. There are three sources of hexane emission related to production of crude rapeseed oil; extractio n, processing of rapeseed meal and from storage tanks.

Hexane emission from extraction: At AarhusKarlshamn the exhaust gas from the extraction is sent to the power central where it is used as input air to the burning of fuel oil. Hereby, the hexa ne is converted into water and carbon dioxide. Since the amount of hexane sent to the power central is insignificant compared to the amount of fuel oil used per kg extracted rapeseed oil, it is presumed that there are no emissions related to hexane sent through the power central. According to Aar hus United (2005a) the emission of hexane from extraction was 10.9 tonne. And according to Kronborg (2006) the emis sion of hexane can be estimated to be constant with operating hours. Since rapeseed oil was extracted 25% of the time in the extractor, the emission related to the production of 26,362 tonne cr ude rapeseed oil in 2004 (see Table 9.1 ) can be calculated as 0.10 kg per tonne rapeseed oil. However, the emission in Aarhus Un ited (2005a) is given as the output from the extractor. Thus, it is not taken into account that approximately 95% of the time the exhaust gas from the extractor is sent to the power central. Hence, the emission of he xane is 0.0052 kg per tonne crude rapeseed oil.

Hexane emission from processing of rapeseed meal: A considerable share of the input of hexane goes with the meal to final processing. The total emission of hexane from meal processing is 37.1 tonne. This has to be allocated between 35,479 tonne rapeseed meal and 44,094 tonne shea meal (see Table 9.1 ). Thus, the emission of hexane from meal processing is 16.5 tonne in 2004.

The hexane emission related to the production of 26,362 tonne crude rapeseed oil in 2004 (see Table 9.1 ) can then be calculated as 0.63 kg per tonne rapeseed oil.

Hexane emission from storage tanks: This emission comes from the storage of hexane. The emission related to rapeseed oil can be calculated as the use of hexane used for extracting rapeseed oil multiplied with the emission of hexane from storage tanks per kg total used hexa ne. From Aarhus United (2005a) it can be derived that there is a hexane emission from storage tanks at 46 g per kg hexane used. The c onsumption of hexane per tonne rapeseed oil is 1.188 kg (see Material use , p 147 ). Thus, the emission from storage tanks can be found as 0.055 kg per tonne rapeseed oil.

The total emission of hexane from extraction is 0.69 kg per tonne crude rapeseed oil.



# 9.5 Waste to treatment

There are two waste streams that are sent to treatment. According to Kr onborg (2006) the amount of residual from screening of the incoming rapeseed is approximately 0.4% equalling ~10 kg residual per tonne rapeseed oil, also see Figure 9.2 . The residual is sent to biogas. For invent ory data for residual sent to biogas, see section 13.4 , where inventory data for bleaching earth sent to biogas are described as well.

According to Kronborg (2006) there were 1,500 m 3 

## waste water from the extraction process in 2004. Relating to the 26,362 tonne crude rapeseed oil in 2004 (see Table 9.1 ) this corresponds to 57 litres per tonne rapeseed

## 150 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## oil. According to Aarhus United (2005a) the content of COD in the waste water is 3,578 mg/l. Hence, 57 litres corresponds to 0.204 kg COD per tonne rapeseed oil.

When waste water is sent to a waste water treatment plan t it is treated in order to meet a certain emission limitvalue defined by environmental legislation. Different properties of the waste water, i.e. quantity and contaminants, affect the interventions at the plant in differentways. Kromann (1996, p 112) has investigated how different properties of the waste water affects the energy use, material use and waste generation (sludge). The findings of that are described in Table 9.7 and Table 9.8

.

Waste water Amount Waste water 6,850 m3/day COD 7,800 kg/day N 650 kg/day P 190 kg/day 

## Table 9.7: Overview of composition of waste water to a Danish waste water treatment plant. (Kromann, 1996, p 112)

Waste/co-product Amount Determining property Electricity for pumping 1,094 kWh/day Determined by the amount of waste water Electricity for stirring 1,328 kWh/day Independent Electricity for air mixing 6,561 kWh/day Determined by amount of organic compounds, i.e. COD (and N) Electricity for sludge dewatering 234 kWh/day Determined by amount of organic compounds, i.e. COD (and P) Other 156 kWh/day Independent Iron sulphate for removal of P 3.6 – 5.4 g/g P Determined by the amount of P (It is presumed that removal of phosphorus is done by combined biological removal and precipitation with iron sulphate) Polymers for sludge dewatering 1.5 g/kg COD Determined by amount of organic compounds, i.e. COD (and P) 

## Table 9.8: Overview of energy use, material use and waste generation from different properties of waste water. (Kromann, 1996, p 112).



## Based on Table 9.7 and Table 9.8 and the assumption that air mixing and amount of sludge is dependant on the amount of COD, the interventions per m 3 

## waste water, per kg P and per kg COD can be found, see Table 9.9 . It is assumed that N in waste water is equivalent w ith COD. Thus, the interventions related to removal of N in waste water are included in removal of COD. Si nce the discharge of P is dependant on emission limit values and not the input of P to the sewage purifying plant, a change in P to the plant will not affect the emission of P. Thus, the marginal emission is 0. The same is the case for COD. Furthermore, the independent interventions at the plant are not affected by the quantity or contaminants of the waste water.

Interventions per m 3 waste water Amount Applied LCI-data Electricity, pumping 0.57 MJ See Table 3.3 Interventions per kg P content Amount Applied LCI-data Iron sulphate 4.5 kg ‘Iron sulphate, at plant/RER’, ecoinvent (2004) Interventions per kg COD Amount Applied LCI-data Electricity for air mixing 3.0 MJ See section 3.1 Electricity for sludge dewatering 0.1 MJ See section 3.1 Polymers for sludge dewatering 1.5 g No inventory data have been identified 

## Table 9.9: Life cycle inventories for the quantity and contaminants of waste water.



# 9.6 Overhead
Overhead includes electricity and heat for administration and research and development. It is not possible to establish a direct relationship between interventions from overhead and the production of refined rapeseed oil and rapeseed meal. This is because there are no di rect linkages between production processes and overhead activities and because intermediate products are both purchased at and delivered from different stages in the oil mill, see Figure 9.4 below.

## 9 Oil mill stage: Rapeseed oil 151

Milling Rapeseed and shea nuts

Crude oils

Semirefined oils Neutralisation and bleaching Deodorisation

Interestification Hardening Fractionation Crude oils and oil meals

Semi refined oils

Semirefined oils

Chemical/technical oil products Purchased raw materials/products Delivered products



## Figure 9.4: Simplified overview of processes and flow of products at AarhusKarlshamn.

Production of refined rapeseed oil includes milling, neutralisation, bleaching and deodorisation in Figure 9.4

. Since it is not possible to establish a direct relations hip between interventions from overhead and the production of refined rapeseed oil a presumed proportion of the interventions from overhead activities is used. This proportion could be estimated based on tu rnover, mass or other properties. It has not been possible to obtain data on turnover distributed on all AarhusKarlshamn’s products. Thus, allocation c ould be based on mass of delivered products. For milling, rapeseed oil and meal constitute approximately 40% of the product flow. For the other processing stages it is difficult to find these num bers because the input of crude oils and semi refined oils in some cases are mixtures of different oils. Howe ver, mass allocation do reflect a causal in a wrong way; most work in laboratories, administration, marketing etc. is related to highly modified speciality oils. Therefore instead it is chosen to derive allocation factors ba sed on the number of processing stages and products produced at AarhusKarlshamn. It is very roughly estimated that 10% of the interventions from administration, marketing, laboratories etc. can be ascribed to milling, 10% to refining and 80% to modification of oils. Three oils constitute the main oils used at AarhusKarlshamn; rapeseed oil, shea oil and palm oil. However, only two oil seeds are milled, i.e. rapeseed and shea. The outputs ar e then two different oils and two different meals. Thus, crude rapeseed oil’s proportion of the overhead activ ities is determined as 25% of the 10%. Hence, the proportion related to rapeseed activities can be estimated as 2.5%.

According to Aarhus United (2005b) electricity consumption for administration and laboratories was approximately 1220 MWh in 2004. Using above determined a llocation factor, the electricity consumption for overhead related to rapeseed oil can be estimated as 60 MW h in 2004. Relating abovementioned electricity use to the annual production of rapeseed oil in 2004 at 26,362 tonne shown in Table 9.1 , the specific electricity use can be found as 4 MJ/t oil. This amount s approximately 1% of the total el ectricity consumption for production of crude rapeseed oil at AarhusKarlshamn, see Table 9.13

.
Heat consumption for administration and laboratori es was approximately 550 MWh in 2004 (Aarhus United, 2005b). Using abovementioned allo cation factor and values in Table 9.1 the heat consumption can be found as 2 MJ/kg rapeseed oil. Administration buildings and laborat ories are heated with municipal district heat. Interventions from district heating in Aarhus are described in section 3.6

.


# 9.7 Capital goods
Capital goods include means of production, i.e. buildings and machinery. According to Althaus et al. (2003) the environmental burden from capita l goods for production of chemicals are of minor importance compared to other processes. Analysing capital goods’ share of the to tal burden from different oil mills inventoried in ecoinvent (2004), the contributions from capita l goods do not seem to be insignificant, see Table 9.10

.

## 152 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Chemical plant’s share of environmental burden from oil mill Soybean oil mill Palm oil mill Palm kernel oil mill Coconut oil mill

Global warming 7% 5% 3% 8% Acidification 13% 13% 6% 20% ETWC, m 3 water 19% 31% 13% 49% 

## Table 9.10: The environmental burden from capital goods compared to the total environmental burden from activities at different oil mills. Production of oil seeds, fruits and nuts is not included. The results are found analysing the inventory in Simapro and using the EDIP97 method for LCIA. LCI databases from ecoinvent (2004) are used.



## However, the great contributions to toxicity are infect ed with significant uncertainties and errors. A major part of the contributions originates from long term emissions of especially copper from disposal of electronics used in control units in the plant. These emissions are calcula ted in a time frame of several thousands years. There is no consensus in the LCA community on the time frame to be used for long term emissions. Hansen et al (2004) suggest that effects from long term emissions should be dealt with as separate impact categories. Another important contribution to toxicity is emission of iron to air. This originates from production of hard coal coke used as fuel for production of machinery a nd buildings. However, the emission of iron is approximately 10 kg per 1 kg coal. According to personal communi cation with Bauer (2005) this is due to an error in ecoinvent by six orders of magnitude.

Not many life cycle inventories of buildings and mach inery exist, only one has been identified; ecoinvent (2004). Since it is not possible to obtai n detailed data on building material s, foundations, machinery etc. from oil mills in Denmark, the inventory of capital goods w ill be rather roughly and based on ecoinvent (2004). The data in ecoinvent are described in detail in Althaus et al. (2003) where capital goods are divided into three categories: 1) Building, hall, 2) Building, multi story a nd 3) Facilities, chemical production. The inventories of buildings are described in Kellenberger et al. (2003) and facilities in Althaus et al. (2003). The amount of building, hall used is measured in m 2 

## . Building, multi story is measured in m 3 

## and facilities are measured as average composition of chemical production facilities per kg of the facility. In Althaus et al (2003) facilitiesmainly consist of distillation units and minor amounts of pipes and control units (electronic equipment). Disposal of buildings and facilities is included in the used inventories.

Using the life cycle inventories for capital goods from eco invent (2004) it is only necessary to collect data on the area and volume for building halls and multi story build ings respectively and weight of the facilities used for pressing, extraction and refining. The used data sets in ecoinvent are: ‘ Building, hall, steel construction ’,

‘ Building, multi-storey ’ and ‘ Facilities, chemical production ’.


# Oil mill buildings The area covered by buildings is estimated from the muni cipal district plan for the current district (Århus Kommune 2004).

Pressing and extraction: The buildings for pressing and extr action cover approximately 2000 m 2 

## and silos for storage of nuts and seeds cover approximately 300 m 2 

## . Thus, the buildings for storage of raw material and pressing and extraction are estimated as 2,300 m 2 

## building hall. An average life time of building halls is estimated to 50 years. The annual amount of extracted oil at AarhusKarlshamn is approximately 50,000 tonne. Hence the building hall required per kg of pressed and ex tracted oil can be determined as approximately 9.2

⋅ 10 -4 

## m 2 

## building hall/t oil.

Overhead: Administration buildings cover an area of approximately 2,500 m 2 

## . In average these buildings are four storey buildings with an estimated height of 15 m. Thus, administration buildings are estimated to be at 37,500 m 3 

## multi story building. Applying the allocation f actor at 0.22 for overhead described in section 9.6 the

## 9 Oil mill stage: Rapeseed oil 153



## buildings for overhead related to rap eseed oil can be estimated as 8,300 m 3 

## multi story building. As the other buildings the life time is estimated to be 50 years. Relating the 8,300 m 3 

## multi story building to the annual production of rapeseed oil at approximately 50,000 t onne, the specific requirement of buildings for overhead can be found as 3.3

⋅ 10 -3 

## m 3 

## multi story building/t oil. However, since all rapeseed oil produced is also refined the 3.3

⋅ 10 -3 

## m 3 

## multi story building/t oil is related to the oil m ill as well as the refinery. Therefore, half of this is ascribed to the oil mill and the othe r half to the refinery. Thus, the area of administration buildings related to the oil mill process for rapeseed oil at Aar husKarlshamn has been estimated as 3.3

⋅ 10 -3 

## m 3 

## multi story building/t oil divided by 2, i.e. 1.7

⋅ 10 -3 

## m 3 

## multi story building/t oil.


# Oil mill machinery (facilities) The weight of machinery is very roughly estimated from personal communication with Kronborg (2006). The relevant numbers are given in Table 9.11

.

Process Weight of machinery incl. pipes Estimated life time Annual production Machinery (kg) per kg oil Press and extraction 100 tonne 10 years 50,000 tonne 0.20 kg/t oil 

## Table 9.11: Required machinery (kg) per t oil produced at AarhusKarlshamn. Numbers are based on very rough estimates.



## Determination of capital goods is regarded as very uncerta in. Therefore a sensitivity analysis is carried out in section 21.17

.


# 9.8 Transport of raw mat erials and ancillaries to rapeseed oil mill
Rapeseed and ancillary materials are transported with lo rry to AarhusKarlshamn. Inventory data per tkm transport by lorry is described in section 4.1 . The transport distances are estimated in this section. Since there are several suppliers and since there is a general lack of da ta on the specific marginal affected supplier, all transport distances are based on rough estimates. Determination of size of lorries is based on Table 4.3

.

Material Amount to AarhusKarlshamn Amount per t crude rapeseed oil From To Distance Lorry size

Rapeseed ~60,000 t/year 2.387 t Seed traders, Denmark Aarhus 100 km 40t Light fuel oil ~36,000 t/year 44.7 kg Fuel oil supplier, Denmark Aarhus 10 km 28t Hexane ~158 t/year 1.19 kg Abroad chemical plant Aarhus 1000 km 40t Rapeseed residual ~260 t/year 10 kg Aarhus Biogas plant 30 km 28t Rapeseed meal ~34,000 t/year 1.346 t Aarhus Meal trader 10 km 28t 

## Table 9.12: Transport distances of the used raw materials and ancillaries in the oil mill stage. The return trip is included in the inventory data.



## Transport of the rapeseed oil to the refinery is included in the refinery stage in section 13

.
The amount of rapeseed in the column ‘Amount to AarhusKarlshamn’ in Table 9.12 is based on Table 9.1

. The amount of light fuel oil is estimated from a fuel use at 409,748 MWh at AarhusKarlshamn in 2004 (Aauhus United 2005b). With a calorific value at 40.6 MJ/kg ( Appendix 1: Data on fuels ) this corresponds to 36,000 tonne fuel oil. The amount of hexane is given in Aarhus United (2005a) and the amount of rapeseed residual is calculated from Table 9.1 and Table 9.13 . The amounts given in the column ‘Amount per t crude rapeseed oil’ are calculated from Table 9.13

.


# 9.9 LCI of rapeseed oil mill, summary

Table 9.13 summarises the inventory data relating to 1 kg crude rapeseed oil produced at AarhusKarlshamn.

## 154 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Denmark: 1.000 t crude rapeseed oil from oil mill Interventions Power central Pressing and extraction Overhead Total Applied LCI data

Product output Crude rapeseed oil - 1.000 t - 1.000 t Product of interest Rapeseed meal - 1.346 t - 1.346 t Co-product allocation is avoided by system expansion, see Table 2.3 Material use Rapeseed - 2.387 t - 2.387 t See Table 5.48 Hexane - 1.188 kg - 1.188 kg ‘Pentane, at plant’ (ecoinvent 2004) Energy use Electricity - 419 MJ 4 MJ 423 MJ See Table 3.3 Heat (steam) - 1,586 MJ - 1,586 MJ See Table 9.4 Heat (district heat) - - 2 MJ 2 MJ See section 3.6 Emissions to air Hexane - 0.69 kg - 0.69 kg Emission to air Waste to treatment Waste water, quantity - 57 ltr. - 57 ltr. See Table 9.9 Waste water, COD - 0.204 kg - 0.204 kg See Table 9.9 Rapeseed residual to biogas 10 kg - - 10 kg See Table 13.8

Capital goods Building halls - 9.2

⋅ 10 -4 m 2 - 9.2

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’ (ecoinvent 2004) Building, multi story - - 1.7

⋅ 10 -3 m 3 1.7

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery - 0.20 kg - 0.20 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to oil mill Rapeseed, 40t lorry - 238.7 tkm - 238.7 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Fuel oil 0.45 tkm - - 0.45 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Hexane - 1.19 tkm - 1.19 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Rapeseed residual - 0.3 tkm - 0.3 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Rapeseed meal - 13.5 tkm - 13.5 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 9.13: Interventions per t crude rapeseed oil.

## 10 Oil mill stage: Palm oil 155



# 10 Oil mill stage: Palm oil
The LCI for the palm oil mill stage represents palm o il milling in Malaysia 2003 to 2005. For some processes alternative technologies and practices exist.

There are different influential factors on the interventi ons and thereby the environmental impact related to the production of palm oil in the palm oil mill. Some of th ese factors are related to uncertainties in data and some are related to improvement options. The considered fact ors are: 1) technology for treating palm oil mill effluent (POME), 2) required steam consumption in the palm oil mill and 3) the utilisation/waste treatment of empty fruit bunches (EFB). Regarding 1) the most common technology for treating POME is open anearobic and aerobic ponds. Therefore this is applied as the tec hnology in the baseline scenario. The alternative technology which is installing of digester tanks for biogas capturing and subsequent utilisation of biogas is considered as an improvement option (see section 21.18 ). Relating to 2) different studies show different amounts of required steam in the palm oil mill process. Steam cons umption at 0.65 tonne is applied in the baseline scenario in this study while 0.5 tonne is applied in a sensitivity analysis in order to analyse the effect of uncertainty in data (see section 21.19 ). Concerning 3) the three main treatments of EFB are: application as mulch in the plantation, landfilling near the palm oil mill and utilisation as biofuel for electricity production (UNFCCC 2007). In the past open burning was applied as treatment of EFB, but this management option is now banned (UNFCCC 2007). Appilcation as mulch is the most likely management option and is therefore used in the baseline scenario (Corley and Tinker 2003, p 376). The effects of the two other options are analysed in a sensitivity analysis (see section 21.20 ). The alternative treatments of EFB are regarded as potential improvement options.

The inventory of the oi l mill stage is mainly based on data collect ed with United Plantation Berhad’s Research Department (Singh 2006), data provided by MPOB (S ubranamiam 2006a) and general literature on oil palm processing; Singh et al. (1999) and Department of Environment (1999).



# 10.1 Palm oil mill product flow
Figure 10.1 shows the product flows through the oil mill. The oil mill stage is divided into the four unit processes shown as grey shaded boxes in Figure 10.1

.

## 156 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oilOil mill processing FFB: 1,000 kg

Pressing

Clarification Sterilisation

StrippingOverhead Electricity

Nut and fibre separation

Nut cracking Decanter press cake

Nuts

CPO: 199.8 kg Fibre: 130.0 kg

N:P:K eq.: 0:0:0 kg POME: 672.5 kgPOME treatment
Anarobic pond

Anaerobically digested POME

Land application of POME Shell: 70.0 kg

Kernel: 53.2 kg 130.0 kg 70.0 kgPower central Boiler

Turbine Steam

Electricity:

104 MJ Steam: 1,691 MJ

Steam release: 16 MJ waste water

Drying

Shell (fuel): 0 kg Fibre (fuel): 0 kg EFB to mulching: 225.0 kg Digester

Accumulator Steam

Electricity:

32 MJ 

## Figure 10.1: Product flow related to processing of 1 tonne FFB in the palm oil mill. The grey shaded boxes represent the unit processes in the palm oil mill stage.



## Figure 10.1 It appears from that the palm oil mill has several product outputs. The production of crude palm oil (CPO) and kernel per tonne of FFB is determined as the Malays ian average in 2003 to 2005 given in MPOB (2005) and MPOB (2006), see Table 10.1 . Malaysian national figures on the product flows of mesocarp fibre, shell, EFB and POME per tonne of processed FFB in 1996 and 2002 are shown in Table 10.2
, where the applied numbers are the average of 1996 and 2002 figures.

Oil mill products 2003 2004 2005 Applied CPO 197.5 kg/t FFB 200.3 kg/t FFB 201.5 kg/t FFB 199.8 kg/t FFB Kernel 53.6 kg/t FFB 52.5 kg/t FFB 53.4 kg/t FFB 53.2 kg/t FFB 

## Table 10.1: Crude palm oil (CPO) and kernel production per tonne of processed FFB in 2003 and 2004 (MPOB 2005) and 2005 (MPOB 2006). The numbers represent Malaysian national figures.

Oil mill co-product 1996 2002 Applied Fibre 120.0 kg/t FFB 140.0 kg/t FFB 130.0 kg/t FFB Shell 70.0 kg/t FFB 70.0 kg/t FFB 70.0 kg/t FFB EFB 220.0 kg/t FFB 230.0 kg/t FFB 225.0 kg/t FFB POME 670.0 kg/t FFB 675.0 kg/t FFB 672.5 kg/t FFB 

## Table 10.2: Mesocarp fibre, shell, EFB and POME per t FFB in 1996 (Singh 1999) and in 2002 (Ma et al. 2004). The numbers represent Malaysian national figures.



## The oil mill has its own power and steam supply. The power central is fuelled with some of the fibre and shell from the processing. Subranamiam et al. (2005) present the amount of fibre and shell used as fuel in the mill’s power central for six palm oil mills per tonne of FFB. According to Subranamiam et al. (2005) an average 82%

## 10 Oil mill stage: Palm oil 157



## of the fibre and 55% of the shells are fed into the boile r, while Weng (1999) suggests that 90% of the fibre and 80% of the shells are used as boiler fuel. According to Subranamiam et al. (2005) excess of fibre and shell are sold out as fuel. The energy production and related em issions from burning of fibre and shell in the power central is described in section 10.3

.
According to Singh and Thorairaj (2006) and Subranamiam et al. (2005) th e steam requirement for processing of 1 tonne FFB is 0.65 tonne at 3 bar equalling 1,691 MJ.

The figures provided in Subranamiam et al. (2005) seem to be too low since the burning of 82% of the fibre and 55% of the shell only amount to an energy input of 1,919 MJ/t FFB 18 

## . This would require steam to fuel input ratio (efficiency) at 88%, which exceeds likely levels: Husain et al. (2003) specifies steam to fuel input ratio for co-generating heat and power plants in palm oil mills ranging from 50.7% to 72.7%, averaging 61.8%. Also, the boiler fuel input provided by Weng (1999) seems to be too low. Applying the boiler fuel input given by Weng (1999) the energy input is 2,359 MJ. This woul d require steam to fuel input ratio at 72% which is also slightly higher than a likely level. The reason why the fuel inputs suggested by Subranamiam et al. (2005) and Weng (1999) are too low is probably that the steam re quirement at 0.65 t per t FFB is high compared to average palm oil mills. Hence, Chavalparit et al. (2006) report steam consumption at 0.4 t/t FFB and according to Singh and Thorairaj (2006) the steam consumption in some mills may be as low as 0.45 – 0.50 t steam/t FFB. The 0.65 tonne provided by Singh and Thorairaj (2006) includes steam for drying of nuts/kernels and less steam demanding drying decanted effluent (processed into organic fertiliser), which are not done in most mills. Still, the steam requirement of 0.65 t per t FFB is applied in this study. This is b ecause nut/kernel drying has to be done elsewhere if not done in the oil mill. The overall result is then the same no matter if drying is done in the oil mill using more fibre and shell for fuel, or elsewhere using other fuel for drying while the excess fibre and shell also displace other fuels. In order to assess uncertainties relating the the steam reqiremet, a sensitivity analysis with steam requirement at 0.5 t st eam (1,301 MJ) has been carried out, see section 21.19

.
With available fuel energy of fibre and shell at 2,763 MJ and a steam to fuel input ratio at 61.8%, the produced steam amounts to 1,708 MJ (0.66 t steam at 3 bar). This is very close to the required 0.65 t steam. Therefore it is assumed that all of the fibre and shell is used as boiler fuel.



# 10.2 Omitted inventory data in palm oil mill stage

Minor amounts of chemicals are used in POME treatme nt. No information has been identified on these chemicals. Therefore they are not included. Since lubricating o il is not included in the inventory of the rapeseed oil mill, it has also been excluded from the inventory of th e palm oil mill. Also consumption of varies minor products such as tools, paper, computers, pencils, misce llaneous equipment etc. for process management and administration are omitted.



# 10.3 Power central
The energy supply to the oil mill includes electricity and steam. Most, if not all, palm oil mills are self sufficient of electricity and heat (Henson 2004, p 30). Norm ally, fibre and shells are burned for energy purposes (Henson 2004; Department of Environment 1999; Subranamiam 2005). According to section 10.1 the utilisation of fibre and shell for boiler fuel are assumed to be 100%. Thus, 130.0 kg fibre and 70.0 kg shell are burned per tonne of FFB processed. Fibre and shell have calorific values 19.1 MJ/kg DS and 20.1 MJ/kg (dry matter basis) respectively (Subranamiam et al. 2004). The moisture content of fibre and shell are 40% and 10% respectively (average of values given in Singh 1999, Yosoff 2006, Ma et al. 2004 and Yosof and Weng 2004).

18 

## The applied calorific values (dry matter basis) and moisture contents are given in section 10.3.

## 158 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## The calorific value of the fuel composition of 65% fi bre and 35% shell can be determined as 13.8 MJ/kg. Hence, the total energy input per tonne of FFB processed is 2,763 MJ.

Husain et al. (2003) have surveyed seven palm oil mills where utilisation factors 19 

## range between 55.0% and 76.6%, averaging at 65.6%. The average heat to power ratio of the seven palm oil mills is 17.9. Thus, the total heat and power production per t FFB is 1,811 MJ or 0.70 t steam at 3 bars 20 

## (65.6% of 2.763 MJ) distributed on 1,708 MJ steam and 104 MJ electricity. Above calcula ted production of 0.70 t steam per t FFB and 104 MJ electricity per t FFB are in good accordance with figures on steam and electricity production per t FFB at United Plantation Berhad. These figures show 0.72 t stea m/t FFB and 97 MJ electricity (Singh and Thorairaj 2006). At United Plantations Berhad all fibre and she ll are burned corresponding to the assumptions in this study.

It is common that excess steam is released to the atmosphere (Subranamiam 2006a; Kandiah et al. 1992). Therefore, the difference between the required stea m (1,691 MJ) and the produced steam (1,708 MJ) is assumed to be released to the atmosphere, i.e. 16 MJ per t FFB.

The electricity recovered from the turbine, i.e. 104 MJ/t FFB or 28.9 kWh/t FFB, exceeds the requirement for processing the FFB. The required electricity for processi ng 1 tonne of FFB varies between 14.5 kWh (Chavalparit et al. 2006) through 17.7 KWh (Yusoff and Ha nsen 2005) to 18-22 kWh (Singh and Thorairaj 2006) and 20 kWh (Ma et al. 2004). The average requirement is assumed to be 20 kWh per t FFB. Thus, there is approximately 30% electricity in excess, i.e. 32 MJ/t FFB. In addition to that additi onally 1 MJ/t FFB is assumed to be used in administration, research, laboratories etc. buildings, see section 10.7 . Since palm oil mills are not connected to the national grid, the excess electricity is normally used locally on the estate in administration buildings, residence buildings for the workers and someti mes in a refinery if the estate has its own refinery plant. But since these buildings are co nnected to the national grid or to local generators, the excess electricityis assumed to displace electricity delivered from the grid. Interventions related to electricity production in Malaysia are described in section 3.2

.
In addition to the input of 130 kg fibre and 70 kg shell per t FFB the power central uses fossil fuel for start-ups of the boiler. According to Subranamiam et al. (2005) oil mills uses 0.37 litre of diesel per t FFB (average of six palm oil mills). It is assumed that this is used in the power central. According to ‘ Appendix 1: Data on fuels ’, 0.37 litres of diesel per t FFB corresponds to 14 MJ/t FFB. The emissions from burning of 14 MJ diesel per t FFB are obtained from ecoinvent: ‘ Diesel, burned in building machine ’, documented in Kellenberger et al. (2003). Since production, maintenance and dispo sal of the power central is included in section 10.8 , the data set ‘ Diesel, burned in building machine ’ is modified so that it only includes the production of diesel and related emissions from burning it and thereby not the bu ilding machine itself and its maintenance. It is assumed that the steam produced during start-up is insigni ficant because of low boiler e fficiency at low temperatures and because the energy input of diesel is negligible compared to energy from fibre and shell. Therefore, the produced steam from burning of diesel for start-up is not accounted for.

The water consumption for steam produc tion is assumed to be equivalent to the steam produced, i.e. 0.65 t/t FFB. For water, it is assumed that the interventions relate d to water in Malaysia are the same as in Denmark.

19 

## Utilisation factor of co-generating system is the total output of power en delivered heat divided by the total fuel energy input.

20 

## Ma et al. (2004) specify energy content at 2.604 MJ/kg steam at 3 bars.

## 10 Oil mill stage: Palm oil 159



## The energy and material use pr. m 3 

## water are very low; 1.3 MJ electricity/m 3 

## and 4.5 g chemicals/m 3 

## (see Table 13.5 ). Thus, this assumption may only cause insignificant impacts on the result.



# Emissions related to the bu rning of fibre and shell There exist only few inventory data on the burning of fi bre and shell. Subranamiam et al. (2005) is the only existing inventory on boiler emissions from palm oil mills identified. Yusoff and Hansen (2005) regard emissions from the burning of wood in small boilers as representa tive for the burning of fibre and shell. In addition to the two existing data sets on stack emissions from palm oil mills emissions are calculated from figures given in an environmental audit report fro m United Plantations Berhad (2005).

The emissions in United Plantations Berhad (2005) ar e all given as concentrations in the flue gas, see Table 10.3 . The desired data format is emissions per kg fuel (fibre and shell). This conve rsion is done based on the carbon content in the boiler fuel and the carbon in CO 2 

## concentration in the stack flue gas. The carbon content of the boiler fuel on dry matter basis is 41% and 52% of fibre and shell respectively (Henson 2004, p 22). The moisture content of fibre and shell are given previously in this section; 40% for fibre and 10% for shell. The distribution between fibre and shell used as boiler fuel is given in Figure 10.1 as 65% fibre and 35% shell. From these figures it can be calculated that 1 kg boiler fu el (fibre and shell) contains 0.325 kg C. Relating to the CO 2 

## concentration of the flue gas given in Table 10.3 , assuming that all the carbon in the fuel is converted to CO 2 

## and applying the ratio 3.67 kg CO 2 

## per kg C burned (based on molar masses and chemical reaction of carbon with oxygen), it can be calculated that 1 kg boiler fuel gives 33.1 kg flue gas. Then it is easy to convert the concentration of substances in the stack flue gas into absolute numbers per kg boiler fuel. The values are given for the relevant substances in the right column in Table 10.3

.

Emissions Concentration (w/w%) Emission (g/kg boiler fuel) CO 2 3.6% 1,190.000 g SO 2 0.00005% 0.017 g NO 2 0.00005% 0.017 g NO 0.0101% 3.339 g CO 0.0154% 5.092 g N 2 80.2% - O 2 16.2% - Total 100% - 

## Table 10.3: Stack flue gas emissions from Ulu Bernam Palm Oil Mill. The measures of concentration of substances are converted into emissions per kg boiler fuel (fibre and shell). Concentrations of substances in stack flue gas are obtained from United Plantations Berhad (2005, p 10).



## The calculated emissions based on measured stack flue gas emissions in Ulu Bernam Palm Oil Mill, United Plantations Berhad are compared w ith stack emissions obtained from Subranamiam et al. (2005) and Yusoff and Hansen (2005) in Table 10.4 . Since the identified inventories of palm oil mill stack emissions only include NO x 

## , SO 2 

## and CO, the remaining emissions are estimated fro m an inventory of burning of wood chips burned in 50 kW furnaces in Europe. Data for burning of wood ch ips are obtained from the ecoinvent database (Bauer 2003) and the data are treated in SimaPro 7.0.

## 160 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Emissions from burning of 1 kg boiler fuel (g/kg fuel) Based burning of fibre and shell Based on burning of wood Emissions

Calculated ( Table 10.3 ) Subranamiam et al. (2005) Yusoff and Hansen (2005) Burning of wood chips (Bauer 2003) Applied

NO x 3.356 0.175 0.552 1.050 1.361 SO 2 0.017 0.002 0.017 0.024 0.012 CO 5.092 0.104 4.862 1.120 3.353 Particulates < 2.5 um - - 1.190 0.324 0.757 Acetaldehyde - - - 5.810E-04 5.810E-04 Ammonia - - - 0.017 0.017 Arsenic - - - 9.520E-06 9.520E-06 Benzene - - - 8.670E-03 8.670E-03 Benzene, ethyl- - - - 2.860E-04 2.860E-04 Benzene, hexachloro- - - - 6.860E-11 6.860E-11 Benzo(a)pyrene - - - 4.760E-06 4.760E-06 Cadmium - - - 6.670E-06 6.670E-06 Chlorine - - - 1.710E-03 1.710E-03 Chromium - - - 3.770E-05 3.770E-05 Chromium VI - - - 3.810E-07 3.810E-07 Copper - - - 2.100E-04 2.100E-04 Dinitrogen monoxide - - - 0.029 0.029 Dioxins, measured as 2,3,7,8- tetrachlorodibenzo-p-dioxin - - - 2.950E-10 2.950E-10

Formaldehyde - - - 1.240E-03 1.240E-03 Hydrocarbons, aliphatic, alkanes - - - 8.670E-03 8.670E-03

Hydrocarbons, aliphatic - - - 0.030 0.030 Lead - - - 2.380E-04 2.380E-04 m-Xylene - - - 1.140E-03 1.140E-03 Manganese - - - 1.620E-03 1.620E-03 Mercury - - - 2.860E-06 2.860E-06 Methane - - - 6.670E-03 6.670E-03 Nickel - - - 5.710E-05 5.710E-05 NMVOC - - - 8.570E-03 8.570E-03 PAH - - - 1.060E-04 1.060E-04 Phenol, pentachloro- - - - 7.710E-08 7.710E-08 Phosphorus - - - 2.860E-03 2.860E-03 Toluene - - - 2.860E-03 2.860E-03 Zinc - - - 2.860E-03 2.860E-03 

## Table 10.4: Comparison of stack flue gas emissions from palm oil mills from different sources . The first two columns are based on measurements in palm oil mills, while the figures obtained from Yusoff and Hansen (2005) and Bauer (2003) are based on burning of wood chips. The applie d data are marked with a black dotted frame.



## The applied data in Table 10.4 are the average of the three inventories on palm oil mill stack emissions while the remaining emissions are obtained from the inventory of burning of wood chips.

## 10 Oil mill stage: Palm oil 161



# Waste to treatment/co-prod ucts from power central Ash from burning of fibre and shell in the boiler is norma lly used as road material in the plantation. The ash contents of fibre and shell are 6.1% and 3.0% dry matter basis respectively (Ma et al. 2004). Applying the moisture contents at 40% for fibre and 10% for shell, th e ash contents on fresh weight basis are 3.7% for fibre and 2.7% for shell. Thus, based on the product flows given in Figure 10.1 , each kg fibre and shell burned generates 0.34 kg ash. It is assumed that the ash used for road material displaces a corresponding alternative road material, i.e. sand. The interventions from sand and gravel production are obtained from ecoinvent: ‘ Sand, at mine ’, documented in Kellenberger et al. (2003).



# 10.4 Palm oil mill processing : Pressing, drying etc.
The main processes in the oil mill process (see Figure 10.1 ) include sterilisation, stripping, digestion, pressing, clarification of the oil and water mix, decantering of th e waste water, nut treatment and drying processes. The inputs to and outputs from the system from the oil mill process include steam and electricity from the power central, diesel for vehicles, lubricating oil and electricity from the grid. The interventions related to treatment of POME are described in section 10.5 . In addition to the desired product from the palm oil process, crude palm oil, there are various co-products, see Figure 10.1 . The product substitutions from these co-products are described in section 10.6

.


# Material use According to Chavalparit (2006) and UPRD (2004) the total water consumption per t FFB is 1.26 t and 1.47 t 21 

## respectively. The average at 1.37 t/t FFB is assumed to be representative. The water consumption in the oil mill processing stage is then 1.37 t/t FFB minus the water c onsumption in the power central at 0.65 t, i.e. 0.72 t/t FFB. As described in section 10.3 it is assumed that the interventions related to water in Malaysia are the same as in Denmark, see Table 13.5

.
According to section 10.2 lubricating oil is excluded from the inventory of the palm oil mill. According to Singh, the use of lubricating oil at United Plantations six palm oil mills in 2005 were 0.053 litre/t FFB. Assuming a density at 0.9 kg/litre this corresponds to 0.048 kg/t FFB. Applying an oil extraction rate of 19.98% the use of lubricant oil amounts 10 g/t CPO which is regarded as insignificant.


# Energy use The energy uses in the palm oil mill are shown in Table 10.5

.

21 

## Applying OER at 21% for 2004 in accordance with United Plantation Berhad (2006).

## 162 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Energy Amount/t FFB Source Applied inventory data Steam from power central 1,691 MJ (Singh and Thorairaj; Subranamiam et al. 2005) Emissions are included in the unit process: ‘Power central’, see section 10.3

Electricity from power central 72 MJ See section 10.3 Emissions are included in the unit process: ‘Power central’, see section 10.3 Diesel for vehicles 7.6 MJ Average of six palm oil mills (Subranamiam et al. 2005) ‘ Diesel, burned in building machine (ecoinvent 2004)’

Electricity from the grid 0.8 MJ Average of six palm oil mills (Subranamiam et al. 2005) See section 3.2



## Table 10.5: Energy use per t FFB in the unit process: Oil mill processing.



# 10.5 POME treatment
There are great differences of how palm oil mill effluent (POME) is treated in different palm oil mills. Before 1978 palm oil mills in Malaysia discharged their effluent untreated directly into nearby water courses. After introduction of the Environmental Quality Act (EQA 2005) in 1975 and the related sp ecific regulations on palm oil mill effluent (EQ crude palm-oil regulations 2005) the emission limit value of BOD has gradually been reduced from 25,000 mg/litre in the 70ies to 100 mg/litre after 1984 (Chun and Jaafar 2005; EQ crude palm-oil regulations 2005, p 59). Because of the high content of nutrients in POME, focus on utilisation as land application has been increasing (Lim et al. 1999). But also high emission levels of the green house gas methane from the anaerobic treatment have been in fo cus. Several CDM projects within the Kyoto framework have been carried out implementing a digester tank with subsequent utilisation of the captured biogas instead of anaerobic lagoons. The effect of this technology is assessed in a sensitivity analysis presented in section 21.18

.
There are three main sources of POME in the palm oil mill: steriliser condensate (36% of total POME), clarification waste water (60% of total POME) and hydro cycl one waste water from nut and fibre separation (4% of total POME) (Department of Environment 1999). Some oil mills extract a considerable share of the solids of POME with a decanter prior to POME treatment, see Figure 10.1 . This decanter cake can be mixed with inorganic fertilisers and sold or it can be used as animal feed (Singh and San 2002). However, it is not common practice to derive sludge solids with a decanter.

The composition of POME is given in Table 10.6

.

Component Steriliser condensate Clarification waste water Hydro cyclone waste water Mixed POME Dissolved solids (g/kg) 34 22 100 40.5 N (g/kg) 0.500 1.200 0.100 0.950 P (g/kg) - - - 0.150 K (g/kg) - - - 1.960 BOD (3 day, 30 ° C), (g/kg) 23.000 29.000 5.000 25.000 Cu (g/kg) - - - 0.0009 Zn (g/kg) - - - 0.0023 

## Table 10.6: Composition of POME from the three most important s ources in the oil mill (D epartment of Environment 1999, p 26-28). The given numbers in Department of Environment (1999) are in units of mg/litre. Since the moisture content of POME is 95% (Singh 1999) it is a ssumed that the density of POME is 1 kg/litre.



## After recovering residual oil in the waste water using o il traps etc., the POME is treated in an anaerobic pond,

see Figure 10.2 . After that, the treated POME meets the regulatory requirements of a maximum BOD at 5,000

## 10 Oil mill stage: Palm oil 163

22 

## mg/litre for land application (EQ crude palm-oil regulations 2005, p 55). Figure 10.3 shows a land application system.

Figure 10.2: Pond for anaerobic treat ment of POME (Picture taken in United Plantation Berhad 2006). Figure 10.3: Land application of treated POME (Picture taken in United Plantation Berhad 2006).



## Table 10.7 shows POME characteristics before and after anaer obic digestion and the table is used for estimating the removal of nitrogen during treatment. However, a minor part of the removed nitrogen is fixed in the sludge solids from digested POME. Sludge solids are rem oved in the desludging operation to avoid sludge to build-up in the ponds and are used locally as fertiliser . No figures on the amount of sludge solids per kg of POME have been identified. However, the amount of N in the sludge is regarded as insignificant because a great share will removed by denitrification due to lo ng storage time on the bottom of the pond under anaerobic conditions. Therefore, it is assumed that the amount of nitrogen fixed in the accumulative build-up of slurry in the bottom of the pond is negligible. Since content of phosphorus and potassium is not changed chemically during the process, the remaining P and K is assumed to be in the bottom slurry, see Table 10.7

.

Component Raw POME Anaerobically di- Assumed figures for gested POME bottom slurry 25.000 1.300 - BOD (3 day, 30 ° C), (kg/t POME) N (kg/t POME) 0.950 0.900 0 P (kg/t POME) 0.150 0.120 0.030 K (kg/t POME) 1.960 1.800 0.160 

## Table 10.7: POME characteristics before and after anaerobic digestion (Department of Environment 1999, p 28).



## Table 10.7 It appears from that 0.05 kg N per t POME is removed. The removed N should be distributed on NH 3 

## and denitrified N: N 2 

## O and N 2 

## . No inventories on N-related emissions from POME treatment have been identified. Thus, the removed N is estimated from figures on storage of manure.

According to IPCC (2000, p 4.43) 0.1% of the N in manure stored in anaerobic lagoons denitrifies as N 2 

## O. This corresponds to 0.1% of 0.950 kg N/t POME = 0.00095 kg N 2 

## O-N/t POME. 0.00095 kg N 2 

## O-N/t POME corresponds to 0.0015 kg N2O/t POME. It appears that 0.00095 kg N 2 

## O-N/t POME is insignificant (1.9%) compared to the total removed N at 0.05 kg N/t POME. Based on that, the amount of denitrified N 2 

## is also assumed to be insignificant and therefore, the total amount of N removed by denitrification is assumed to be 3.8% with equal shares of N 2 

## O-N and N 2 

## -N.

22 

## BOD (3 day, 30 ° C)

## 164 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## According to Mikkelsen et al. (2005, p 36) 9% of stored N from pigs and 6% of stored N from cattle volatizes as NH 3 

## during storage in open uncovered ma nure tanks. Applying these figures to the N content of POME at 0.95 kg N/t POME gives volatilization of 0.057 – 0.086 kg N/t POME. This is more than the removed N at 0.05 kh N per t POME (according to Table 10.7 ). Therefore, N removed as NH 3 

## volatilization is assumed to be the total removed of N at 0.05 kg N/t POME minus the 3. 8% that is moved by denitrification, i.e. 0.048 kg NH 3 

## -N/t POME. 0.048 kg NH 3 

## -N/t POME corresponds to 0.058 kg NH 3 

## /t POME.

The emissions of N-related substances are given in Table 10.8

.
The decomposition of organic matter in the POME causes biogas. The generation of biogas in the ponds is 28

m 3 

## per t POME (Ma et al. 2004). With methane content of biogas at 60-70% averaging 65% of the biogas (Ma et al. 2004) the methane emission is 18.2 m 3 

## per t POME. With a density of methane at 0.717 g/litre (Andersen et al. 1981, p 119), the CH 4 

## emission is 13.0 kg per t POME. Yacob et al. (2006) have measured the methane emission from a pond system over a period of 12 months . The average methane emission reported from that study is 13.1 kg CH 4 

## /t POME, which is in good accordance with th e figures provided in Ma et al. (2004).

Ma (1999a, p 120) reports a hydrogen su lphide content of biogas at less than 2%. Assuming a content of H 2 

## S at 0.2% and applying the density of H 2 

## S at 1.539 g/litre (Andersen et al. 1981, p 119), the H 2 

## S emission is 86.2 g/t POME.

Emissions from anaerobic POME treatment Compartment Amount CH 4 (kg/t POME) Air 13.0 H 2 S (kg/t POME) Air 0.0862 NH 3 (kg/t POME) Air 0.058 N 2 O (kg/t POME) Air 0.0015 Nitrate Water 

## Table 10.8: Emissions from anaerobic digestion of 1 t POME.



## The anaerobically digested POME and the sludge obtaine d from desludging are applied as fertiliser in the plantation. The emissions associated with that are dealt with in section 6.7 . Since there is no discharge of waste water, there are no emissions to water from POME treatment.



# 10.6 Co-products/waste to treatment
According to Figure 10.1 there are several co-products from the palm oil mills stage. Table 10.9 provides an overview of how the co-products are deal t with in the life cycle inventory.

## 10 Oil mill stage: Palm oil 165

Co-products Description of how the co-product is dealt with in the LCI CPO (Crude Palm Oil) Together with processed kernels (palm kernel oil), CPO is the product of interest Kernel Together with CPO, the processed kernels (palm kernel oil) is the product of interest. The LCI of the palm kernel oil mill is described in section 11 Land application of POME This is dealt with as inputs to nutrient balances in the agricultural stage of oil palm, section 6.7 N:P:K eq. This co-product is processed decanter cake sold as organic fertiliser. However, it is not normal to have a decanter attached to the clarification process in the palm oil mill. Therefore, there is no output of this co-product in Figure 10.1 Shell (fuel) Fibre (fuel) According to Figure 10.1 fibre and shell sold as fuel is zero. However, this isbased on steam consumption at 0.65 t/t FFB which may be too high. Therefore, a sensitivity analysis where the steam consumption is 0.5 t/t FFB is described in 21.19 . In the sensitivity analysis with steam consumption at 0.5 t/t FFB there is excess of fibre and shell and therefore some of it is sold as fuel Electricity According to section 10.3 there is 30% excess of electricity from the power central. This displaces electricity from the grid in Malaysia which is described in section 3.2 Steam Steam release The produced steam is either used internally in the palm oil mill or released to the atmosphere. Thus, steam as a co-product does not displace anything EFB to mulching This is dealt with as inputs to nutrient balances in the agricultural stage of oil palm, section 6.7 . A sensitivity analysis presented in section 21.20 analyses two alternative management options, i.e. utilisation for energy purposes in a biomass plant and disposal at landfill site 

## Table 10.9: Co-products from the palm oil mill stage and implications for LCI.



# 10.7 Overhead
No data on electricity use in administration, research a nd laboratory buildings have been identified. Therefore it is assumed that electricity use from rapeseed oil milling is representativ e for the palm oil mill. These numbers are very uncertain. However, the electricity co nsumption for overhead from rapeseed oil milling at AarhusKarlshamn only accounts for 1% of the total electricity use. Therefore, the uncer tainties in the assumption that electricity use for overhead is the same for rape seed oil milling and palm oil milling will only affect the results insignificantly. The electricity consumption fo r overhead is then assumed to be 4 MJ/t CPO corresponding to 1 MJ/t FFB. Buildings are not heated in Malaysia.



# 10.8 Capital goods
The interventions related to capital goods in the vegetable oil industry are described in section 9.7 . There are three types of capital goods considered in the vegetable oil industry: ‘ Building, hall, steel construction ’,

‘ Building, multi-storey ’ and ‘ Facilities, chemical production ’.

It is assumed that the capital goods estimated per tonne rapeseed oil for the rapeseed oil mill are representative for the palm oil mill per tonne of palm oil. Thus, the capital goods can be calculated as:

• 1.8

⋅ 10 -4 

## m 2 

## ‘ Building, hall, steel construction ’ per t FFB

• 0.34

⋅ 10 -3 

## m 3 

## ‘ Building, multi-storey ’ per t FFB

• 0.040 kg ‘ Facilities, chemical production ’ per t FFB

In the ecoinvent database capital goods for the palm oil mill is also estimated, in the data set: ‘ Crude palm oil, at plant/MY ’ (ecoinvent 2004). However, that data set is pe r kg crude palm oil, and mass based co-product allocation between CPO, kernels and shell has been done . Weidema and Wesnæs (2006) have ‘translated’ the ecoinvent data set on palm oil into consequential modelling. Therefore, according to Weidema and Wesnæs (2006) the data set ‘ Crude palm oil, at plant/MY ’ should be multiplied with 1.1 86 in order to include the FFB

## 166 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## needed for generation of kernel and shell. Thus, the u se of buildings and machinery per tonne FFB can be calculated as:

• 10

⋅ 10 -4 

## m 2 

## ‘ Building, hall, steel construction ’ per t FFB

• 15

⋅ 10 -3 

## m 3 

## ‘ Building, multi-storey ’ per t FFB

• 1.2 kg ‘ Facilities, chemical production ’ per t FFB

It appears from a comparison between the applied da ta on capital goods and the figures determined from theecoinvent data set, that there are significant differences. None of the data on capital goods are regarded as certain. Therefore a sensitivity analysis is carried out in section 21.17

.


# 10.9 Transport of raw materials an d ancillaries to palm oil mill
Transport of FFB to the oil mill is included in the agricu ltural stage of oil palm since the oil mill is situated in or very near to the plantation. Thus, all transport of FFB is taking place in the plantation. The only material transported to the palm oil mill is then diesel to the power central and diesel for vehicles. According to section 10.3 diesel to the power central is 0.37 litre of diesel per t FFB corresponding to 0.32 kg per t FFB. Diesel used for vehicles is 7.6 MJ diesel/t FFB (see Table 10.5 ) corresponding to 0.18 kg diesel. Thus, the total use of diesel is 0.50 kg/t FFB. It is very roughly estimated that the diesel is transported in a 28 t lorry 200 km from a regional store house. Interventions related to transport with lorry are described in section 4.1

.

## 10 Oil mill stage: Palm oil 167



# 10.10 LCI of palm oil mill, summary

Table 10.10 

## summarises the inventory data relating to 1 tonne FFB.

Malaysia and Indonesia: processing of 1.000 t FFB in oil mill Interventions Power central Oil mill POME treatment Overhead Total Applied LCI data

Product output Crude palm oil - 199.8 kg - - 199.8 kg Together with processed kernels (palm kernel oil), CPO is the product of interest Kernels - 53.2 kg - - 53.2 kg Together with CPO, the processed kernels (palm kernel oil) is the product of interest. The LCI of the palm kernel oil mill is described in section 11

. Fibre (fuel) - 0 kg - - 0 kg Co-product Shell (fuel) - 0 kg - - 0 kg Co-product Electricity 104 MJ -72 MJ - -1 MJ 31 MJ Co-product, see displaced products below N:P:K eq. - 0 kg - - 0 kg Co-product Displaced products Electricity from the grid 31 MJ - - - 31 MJ See section 3.2

Sand 68 kg - - - 68 kg ‘Sand, at mine ’ (ecoinvent 2004) Material use FFB - 1000 kg - - 1000 kg The interventions related to production of FFB are included in the plantation stage Water 0.65 t 0.72 t - - 1.37 t See Table 13.5 Energy use Steam from power central - 1,691 MJ - - 1,691 MJ The emissions to air from burning of 130 kg fibre and 70 kg shell are given in this table Electricity from power central - 72 MJ - 1 MJ 73 MJ The emissions to air from burning of 130 kg fibre and 70 kg shell are given in this table Electricity from the grid - 0.8 MJ - - 0.8 MJ See Table 3.3

Diesel for startups in power central 14 MJ - - 14 MJ Modified version of ‘ Diesel, burned in building machine ’ (ecoinvent 2004), see section 10.3 Diesel for vehicles 7.6 MJ 7.6 MJ ‘ Diesel, burned in building machine ’ (ecoinvent 2004) Waste to treatmentAnaerobically digested POME andsludge from desludging of POME - - 672.5 kg - 672.5 kg POME is applied in the plantation. Interventions related to that are described in section 6.7

EFB - 225 kg - - 225 kg EFB are applied as mulch in the plantation. Interventions related to that are described in section 6.7 Capital goods Building halls - 4.6

⋅ 10 -3 m 2 - - 4.6

⋅ 10 -3 m 2 ‘Building, hall, steel construction ’, (ecoinvent 2004) Building, multi story - - - 8.5

⋅ 10 -3 m 3 8.5

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery - 1.0 kg - - 1.0 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to the oil mill Diesel 0.064 tkm 0.036 tkm - - 0.10 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 10.10: Interventions per t FFB processed. Table continued on the next page...

## 168 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Interventions Power central Oil mill POME treatment Overhead Total Applied LCI data

Emissions to air Methane 1.33 g - 8,743 g - 8,744 g Emission to air NO x 272 g - - - 272 g Emission to air SO 2 2.40 g - - - 2.40 g Emission to air CO 671 g - - - 671 g Emission to air Particulates < 2.5 um 151 g - - - 151 g Emission to air Acetaldehyde 1.16E-01 g - - - 1.16E-01 g Emission to air Ammonia 3.40 g - 39.0 g - 42.4 g Emission to air Arsenic 1.90E-03 g - - - 1.90E-03 g Emission to air Benzene 1.734 g - - - 1.734 g Emission to air Benzene, ethyl- 5.72E-02 g - - - 5.72E-02 g Emission to air Benzene, hexachloro- 1.37E-08 g - - - 1.37E-08 g Emission to air Benzo(a)pyrene 9.52E-04 g - - - 9.52E-04 g Emission to air Cadmium 1.33E-03 g - - - 1.33E-03 g Emission to air Chlorine 3.42E-01 g - - - 3.42E-01 g Emission to air Chromium 7.54E-03 g - - - 7.54E-03 g Emission to air Chromium VI 7.62E-05 g - - - 7.62E-05 g Emission to air Copper 4.20E-02 g - - - 4.20E-02 g Emission to air Dinitrogen monoxide 5.80 g - 1.01 g - 6.81 g Emission to air Dioxins, measured as 2,3,7,8- tetrachlorodibenzo-p-dioxin 5.90E-08 g - - - 5.90E-08 g Emission to air

Formaldehyde 2.48E-01 g - - - 2.48E-01 g Emission to air Hydrocarbons, aliphatic, alkanes 1.73 g - - - 1.73 g Emission to air Hydrocarbons, aliphatic 6.00 g - - - 6.00 g Emission to air Hydrogen sulphide - - 58.0 g - 58.0 g Emission to air Lead 4.76E-02 g - - - 4.76E-02 g Emission to air m-Xylene 2.28E-01 g - - - 2.28E-01 g Emission to air Manganese 3.24E-01 g - - - 3.24E-01 g Emission to air Mercury 5.72E-04 g - - - 5.72E-04 g Emission to air Nickel 1.14E-02 g - - - 1.14E-02 g Emission to air NMVOC 1.71 g - - - 1.71 g Emission to air PAH 2.12E-02 g - - - 2.12E-02 g Emission to air Phenol, pentachloro- 1.54E-05 g - - - 1.54E-05 g Emission to air Phosphorus 5.72E-01 g - - - 5.72E-01 g Emission to air Toluene 5.72E-01 g - - - 5.72E-01 g Emission to air Zinc 5.72E-01 g - - - 5.72E-01 g Emission to air 

## Table 10.10 : ... Continued from previous page. Interventions per t FFB processed.

## 11 Oil mill stage: Palm kernel oil 169



# 11 Oil mill stage: Palm kernel oil
The LCI for the palm kernel oil mill stage represents average palm kernel oil mills in Malaysia in 2005 and 2006. Palm kernel oil and palm kernel cake are extracte d from the kernels in a mechanical pressing process. It is also possible to extract the oil using solvent extracti on. However, only one out of Malaysia’s 41 palm kernel oil mills were using that technol ogy in 2005 (Singh 2006 and MPOB 2006). Therefore, the inventory in this study assumes that all palm kernel oil extraction tak es place using mechanical extraction. According toSubranamiam (2006a) mechanical pressing in Malaysia is typically done using a double pressing method without pre-heating.

The inventory is mainly based on a survey of five pa lm kernel oil mills in Malaysia provided by MPOB (Subranamiam 2006a and Subranamiam 2006b). The palm ke rnel oil mill processes th e kernels from the palm oil mill into palm kernel oil (PKO) and palm kernel m eal. Not many data on interventions related to palm kernel oil milling exist. Therefore some figures are determined as estimates based on data for palm oil mills described in section 10

.


# 11.1 Oil mill product flow
The product flow in the palm kernel oil mills is shown in Figure 11.1 . The product flow of palm kernel oil (PKO), palm kernel cake (PKC) and processed kernels is based on figures from 2002/03 and 2003/04 given in Oil World (2005), see Table 11.1 . The flow chart is based on Bockisch (1998).

Kernel: 2.228 t

Structuring

Double pressing

Clarification

Drying Raw material reception and storageOverhead ElectricityOil mill processing

Crude PKO: 1.000 t PKC: 1.161 t 

## Figure 11.1: Product flow related to production of 1 t crude PKO in the palm kernel oil mill. The grey shaded boxes represent the unit processes in the palm kernel oil mill stage

.

Product 2002/03 2003/04 Average per t PKO Kernels processed 3,634,000 t 3,544,000 t 2.228 t Palm kernel oil (PKO) 1,628,000 t 1,594,000 t 1.000 t Palm kernel cake (PKC) 1,896,000 t 1,844,000 t 1.161 t Loss (difference between input and output) 110,000 t 106,000 t 0.067 t 

## Table 11.1: Processing of kernels and production of PKO and PKC in Malaysia 2002/03 and 2003/04. (Oil World 2005)



# 11.2 Omitted inventory data in pa lm kernel oil mill stage
As in the LCI of the rapeseed oil mill and the palm o il mill lubricating oil is omitted. Also consumption of varies minor products such as tools, paper, computers, pencils, miscellaneous equipment etc. for process man-

## 170 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## agement and administration are omitted. Because of no data on water consumption and that it is regarded as insignificant, the use of water has been omitted.



# 11.3 Energy use
Several sources on energy use in the palm kernel oil milling process have been identified, see Table 11.2

. Table 11.2 shows the energy use associated with production of1 tonne PKO and 1.161 tonne PKC, i.e. processing of 2.228 tonne kernels. Energy use MJ per t PKO Energy 5 PKO mills in Malaysia (Subranamiam 2006b) PKO mills >500 t/year in Nigeria (Bamgboye and Jekayinfa 2006) PKO mills in Malaysia (Zah and Hischier2003) and corrected in accordance with (Weidema and Wesnæs 2006) PKO mills in Malaysia (Schmidt 2004) based

on (Unilever 1990) and (Shonfield 2004) Applied data

Electricity from the grid 962 MJ 91 MJ 228 MJ 540 MJ 962 MJ

Fuel burned for steam production - 252 MJ 6,217 MJ 740 MJ 0 MJ

Diesel for machinery/transportation - - - 72 MJ 0 MJ



## Table 11.2: Energy use per t PKO and attendant PKC, i.e. 1 t PKO and 1.161 t PKC. The applied data are marked with a dotted line.



## It appears from Table 11.2 that the different sources on energy use pe r tonne PKO vary si gnificant. According to Subranamiam (2006a) the main difference is whether th e oil mills use a 100% mechanical pressing or if it is combined with heating with steam. Ba mgboye and Jekayinfa (2006) applies to rather small PKO mills in Nigeria (500 t/year and above) which may operate under different circumstances that in Malaysia and the electricity use seems to be unrealistic low. Zah and Hischier (2003) are based in figures in Hirsinger et al. (1995) which do not provide consistent energy figures. One example is th at no reference for energy uses is given, i.e. it is not specified if the energy uses are per kg PKO or kernels or per kg or tonne product (Hirsinger et al. 1995, table3). It is also difficult to see if energy for production of material inputs is included for the different figures provided. The use of fuel oil for steam in Zah and Hisch ier (2003) seems to be too high compared to steam consumption at 1,586 MJ/t rapeseed oil, see Table 9.6 

## . Schmidt (2004) applies to oil mills that are using preheating before pressing which is not the most common technology in Malaysia. Therefore, the energy use given in Subranamiam (2006b) is regarded as the most representa tive for present energy use in Malaysian palm kernel oil mills.

Inventory data for electricity from the grid in Malaysia are described in Table 3.3

.


# 11.4 Material use
Water is the only material used in the palm kernel o il mill. However, no data on water consumption in palm kernel oil mills have been identified. Since no water is needed for steam production or other processing, the water consumption is regarded as insignificant and is omitted.



# 11.5 Emissions
The only emissions from the palm kernel oil mill are emissions to air from the power central and contaminants in the discharged effluent. These emissions are described in section 11.3 and 11.6 respectively.



# 11.6 Waste to treatment

No data on the amount of waste water from the PKO mill as well as the applied technology for waste water treatment have been identified. However, as described in section 11.4 the consumption of water is regarded as

## 11 Oil mill stage: Palm kernel oil 171



## insignificant. Therefore, the amount of waste water is al so regarded as low. It is assumed that the waste water is treated in an aerobic pond. The COD content can be estimated to be of the same magnitude as for the effluent from the rapeseed oil mill, i.e. 3,578 mg/l, see section 9.5 . This is not enough for anaerobically digestion. In an aerobic pond the content of COD (organic matter) reacts with oxygen and forms CO 2 

## . However, this is of biotic origin and therefore, it has no impacts on global warming. There may be minor emissions of methane from small anaerobic pockets in the aerobic ponds. However, this is regarded as insignificant. The content of nitrogen in the effluent is assumed to be same per kg o il as for rapeseed oil mill effluent, i.e. 0.7 kg N (based on the amount of effluent per kg rapeseed oil, i.e. 57 kg and Aarhus United 2005a). The amount of phosphorus and other contaminants in the effluent is assumed to be insignificant. Further it is assumed that all of the content of N in the effluent sent to treatment e nds as emissions of nitrate to water, i.e. 3 g NO 3 - 

## to water per tonne PKO.



# 11.7 Overhead
As in the case for palm oil mills no data on electricity use in administration, research and laboratory buildings have been identified. Therefore it is assumed, as for the palm oil mill, that electricity use in rapeseed oil milling is representative for the palm kernel oil mill, i.e. 4 MJ/t PKO.



# 11.8 Capital goods
The interventions related to capital goods in the vegetable oil industry are described in section 9.7 . There are three types of capital goods considered in the vegetable oil industry: ‘ Building, hall, steel construction ’,

‘ Building, multi-storey ’ and ‘ Facilities, chemical production ’.

It is assumed that the capital goods estimated per tonne i nput of rapeseed for the rapeseed oil mill are representative for the palm kernel oil mill per tonne of input of kernels. Thus, the capital goods can be calculated as:

• 8.6

⋅ 10 -4 

## m 2 

## ‘ Building, hall, steel construction ’ per t PKO

• 1.6

⋅ 10 -3 

## m 3 

## ‘ Building, multi-storey ’ per t PKO

• 0.19 kg ‘ Facilities, chemical production ’ per t PKO

Determination of capital goods is regarded as very uncerta in. Therefore a sensitivity analysis is carried out in section 21.17

.


# 11.9 Transport of raw mat erials and ancillaries to palm kernel oil mill
According to Subranamiam (2006b) the average distan ce from palm oil mills, where the kernels are produced, to the palm kernel oil mill is 79 km. It is assumed that the 2,228 kg kernels per tonne PKO are transported in a 28 t lorry.

The distance from the palm kernel oil mill to a meal trad er is assumed to be 200 km. The palm kernel cake is assumed to be transported in a 28 t lorry.

Transportation of the palm kernel oil to the refi nery is included in the refinery stage in section 15

.


# 11.10 LCI of palm kernel oil mill, summary

Table 11.3 summarises the inventory data relating to 1 tonne crude palm kernel oil.

## 172 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Malaysia and Indonesia: 1.000 t crude palm kernel oil from oil mill Interventions Palm kernel oil mill Overhead Total Applied LCI data Palm kernel oil 1000 kg - 1000 kg Together with CPO from the palm oil mill PKO is the product of interest Palm kernel cake 1,161 kg - 1,161 kg Co-product allocation is avoided by system expansion,

see Table 2.3 Material use Kernels 2,228 kg - 2,228 kg The interventions related to production of kernels are included in the plantation stage and the palm oil mill stage Water 400 kg - 400 kg See Table 13.5 Energy use Electricity from the grid 751 MJ 4 MJ 755 MJ See Table 3.3 Emissions Nitrate to water 3 g - 3 g Emission to water Waste to treatment No interventions - - - - Capital goods Building halls 8.6

⋅ 10 -4 m 2 - 8.6

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’, (ecoinvent 2004) Building, multi story - 1.6

⋅ 10 -3 m 3 1.6

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery 0.19 kg - 0.19 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to the oil mill Kernels 176 tkm - 176 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Palm kernel cake 46 tkm - 46 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 11.3: Interventions per t palm kernel oil

## 12 Oil mill stage: Soybean meal 173



# 12 Oil mill stage: Soybean meal
The LCI for the soybean mill stage represents average soybean mills in around 2000-2005. Soybean oil and soybean meal are extracted from the soybeans in a solvent extraction pro cess similar to rapeseed milling. The inventory data for soybean meal milling are mainly obtained from Dalgaard et al. (2007). Since the LCI of soybean mills in Dalgaard et al (2007) is not as detailed as in this study, the missing data are represented with the inventory data for rapeseed milling. These data in clude; electricity for overhead, capital goods and transport of raw materials to the soybean mill.



# 12.1 Oil mill product flow
The product flow in the soybean oil mill is shown in Figure 12.1 . The figure is based on Bockisch (1998). The product flow of soybean oil (SO), soybean meal (S M) and processed soybeans is based on figures from 2002/03 and 2003/04 given in Oil World (2005), see Table 12.1

.

Soybeans: 1.294 t

Structuring

Pre pressing

Clarification

Drying Raw material reception and storageOverhead ElectricityOil mill processing

Soybean meal: 1.000 t Crude SO: 0.249 t Solvent extraction

Solvent removal Desolventisation



## Figure 12.1: Product flow related to processing of 1 t soybeans in the soybean mill. The grey shaded boxes represent the unit processes in the palm kernel oil mill stage

.

Product 2002/03 2003/04 Average per t soybean meal Soybeans crushed 27,718,000 t 29,298,000 t 1.294 t Soybeanl oil 5,325,000 t 5,629,000 t 0.249 t Soybean meal 21,534,000 t 22,520,000 t 1.000 t Loss (difference between input and output) 859,000 t 1,149,000 t 0.046 t 

## Table 12.1: Processing of soybeans and production of soybean oil and soybean meal in Brazil 2002/03 and 2003/04. (Oil World 2005)



# 12.2 Omitted inventory data in soybean oil mill stage
As in the LCI of the rapeseed oil mill and the palm o il mill lubricating oil is omitted. Also consumption ofvaries minor products such as tools, paper, computers, pencils, miscellaneous equipment etc. for process management and administration are omitted. Because of lack of data on water consumption it has been omitted.



# 12.3 Energy use
The energy use for processing of the soybeans is obtained from Dalgaard et al. (2007), see Table 12.2

.

## 174 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Source Energy use Electricity from the grid 56 MJ Fuel oil 188 MJ Natural gas 365 MJ 

## Table 12.2: Energy use per t soybean meal and attendant soybean oil, i.e. 1 t SM and 0.249 t SO.



## Inventory data for electricity from the grid in Brazil are described in Table 3.3 . Inventory data for burning of fuel oil are specified in Table 9.4 : ‘Light fuel oil, burned in boiler 100kW, non-modulating’, ecoinvent (2004). The applied inventory data for burning of natural g as is: ‘Natural gas, burned in industrial furnace >100kW/RER’ (ecoinvent 2004). The inventory data for ener gy use in the soybean oil mill all include production, maintenance and disposal of capital goods.



# 12.4 Material use
Hexane for solvent extraction and water are the only materials used in the soybean oil mill. According to Dalgaard et al. (2007) the use of hexane is 0.52 kg per t soybean meal. The inventory da ta applied for hexane are described in Table 9.5

.

Because of lack of data interventions rela ted to water consumption have been omitted.



# 12.5 Emissions
The only emissions from the soybean oil mill are emissi ons to air of hexane and emissions from the power central, and contaminants in the discharged effluent. A ccording to Dalgaartd et al. (2007) the emissions of hexane and nitrate in waste water are 0.26 kg/t soybean meal and 0.005 g/t soybean meal respectively. The emissions from the power central are included in the inve ntory data on burning of fuel oil and natural gas described in section 12.3

.


# 12.6 Waste to treatment
There are no significant wastes sent to treatment from the soybean oil mill.



# 12.7 Overhead
As in the case for palm oil mills and palm kernel oil m ills no data on electricity use in administration, research and laboratory buildings have been identified. Therefore it is assumed that electricity use in rapeseed oil milling per tonne input is representative for the soybean o il mill per tonne processed soybeans, i.e. 1.3 MJ/t soybean meal.



# 12.8 Capital goods
The interventions related to capital goods in the vegetable oil industry are described in section 9.7 . There are three types of capital goods considered in the vegetable oil industry: ‘ Building, hall, steel construction ’,

‘ Building, multi-storey ’ and ‘ Facilities, chemical production ’.

It is assumed that capital goods in rapeseed oil milling per tonne input rapeseed is representative for the soybean oil mill per tonne input of soybeans. T hus, the capital goods can be determined as:

• 5.0

⋅ 10 -4 

## m 2 

## ‘ Building, hall, steel construction ’ per t soybean meal

• 9.2

⋅ 10 -4 

## m 3 

## ‘ Building, multi-storey ’ per t soybean meal

• 0.11 kg ‘ Facilities, chemical production ’ per t soybean meal

## 12 Oil mill stage: Soybean meal 175



## Determination of capital goods is regarded as very uncerta in. Therefore a sensitivity analysis is carried out in section 21.17

.


# 12.9 Transport of raw mat erials and ancillaries to soybean oil mill
Soybeans and ancillary materials are transported with lorr y to the oil mill. Since ther e are several suppliers and since there is a general lack of data on the specific marg inal affected supplier, all transport distances are based on rough estimates. Determination of size of lorries is roughly based on Table 4.3

.

Material Amount per t soybeam meal From To Distance Lorry size

Soybeans 1.294 t Seed trader Oil mill 200 km 40t Fuel oil 4.6 kg Fuel oil supplier Oil mill 200 km 28t Hexane 0.52 kg Abroad chemical plant Oil mill 1000 km 40t Soybean meal 1.000 t Oil mill Meal trader 300 km 40t 

## Table 12.3: Transport distances of the used raw materials and ancillaries in the oil mill stage. The return trip is included in the inventory data.



## The amount of fuel oil is calculated from the u se of 188 MJ fuel oil and data on fuel oil in Appendix 1: Data on fuels . The transport of soybean oil to the refinery is included in the refinery stage.

Inventory data per tkm transport by lorry are described in section 4.1

.

## 176 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 12.10 LCI of soybean oil mill, summary

Table 11.3 summarises the inventory data relating to 1 tonne soybean meal.

Brazil: 1.000 t soybean meal from oil mill Interventions Soybean oil mill Overhead Total Applied LCI data Soybean meal 1000 kg - 1000 kg Product of interest Crude soybean oil 0,249 kg - 1,161 kg Co-product allocation is avoided by system expansion, see Table 2.3 Material use Soybeans 1,294 kg - 1,294 kg The interventions related to production of soybeans are included in the agricultural stage:Soybean, see section 7 Hexane 0.52 kg - 0.52 kg Energy use Electricity from the grid 56 MJ 1.3 MJ 57 MJ See Table 3.3 Fuel oil 188 MJ 188 MJ ‘Light fuel oil, burned in boiler 100kW, non-modulating’, ecoinvent (2004) Natural gas 365 MJ 365 MJ ‘Natural gas, burned in industrial furnace >100kW/RER’ (ecoinvent 2004) Emissions Hexane to air 0.26 kg 0.26 kg Emission to air Nitrate to water 0.005 g - 0.005 g Emission to water Waste to treatment No interventions - - - Capital goods Building halls 5.0

⋅ 10 -4 m 2 - 5.0

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’, (ecoinvent 2004) Building, multi story - 9.2

⋅ 10 -4 m 3 9.2

⋅ 10 -4 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery 0.11 kg - 0.11 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to the oil mill Soybeans 259 tkm - 259 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Fuel oil 0.9 tkm - 0.9 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Hexane 0.5 tkm - 0.5 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Soybean meal 300 tkm - 300 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) 

## Table 12.4: Interventions per t soybean meal.

## 13 Refinery stage: Rapeseed oil 177



# 13 Refinery stage: Rapeseed oil
The refining process includes neutralisation, bleaching a nd deodorisation of the oil. The output from the refinery is then NBD rapeseed oil (neutr alised, bleached and deodorised). Principally, there are two different ways of refining; chemical refining and physical refining (B ockisch 1998). The most widespread method for refining is chemical refining which is also used at AarhusKarlshamn. However, according to Hansen (2006) the tendency goes towards more and more physical refining of oil for food purposes. The inventory in this section takes point of departure in chemical refining. The description of the refining process is based on Hansen (2006) and Bockisch (1998).

The purpose of neutralisation, which includes degumming a nd neutralisation, is to remove lecithin and free fatty acids. Firstly, the lecithin is removed by applyi ng phosphoric acid in the de gumming process. After that the content of free fatty acids are removed by applying sodium hydroxide. When the sodium hydroxide reacts with the free fatty acids the outcome is soap and water. Next, the mix of oil, soap and water is centrifuged in order to separate out the soap. The so ap is sent through the soap stock splitting process, where sulphuric acid is applied. The soap and acid react and the outcome are free fatty acids which are used as fodder and sodium sulphate which is sent with waste water to the munici pal sewage water system (Hansen 2006). Loss in the neutralisation process includes the separated free fatty acids (1% of the oil) and loss of oil to the soap fraction (0.1%) (Hansen 2006). Thus the total loss of the crude oil input to the process is 1.1%.

The bleaching process is applied in order to remove undesi red coloured particles and substances. In the bleaching process the oil is brought in contact with surface- active substances which absorb the undesired particles. Bleaching earth (bentonite) is the most common used agent for filtering the oil. Sometimes filter aid is added to the bleaching earth. This helps building up a filter stru cture and avoiding the filter to get clogged. Filter aid consists of e.g. cellulose fibres. However, at AarhusKa rlshamn no filter aid is used. The oil content in used bleaching earth is 30-40% (Hansen 2006). In Table 13.1 it appears that the use of bleaching earth is 9.0 g per kg NBD oil. Assuming oil content at 35% in used bleach ing earth, loss of oil to the bleaching earth can be determined as 4.8 g oil per kg NBD oil.

Finally, the oil is sent through the deodorisation process. The purpose of deodorisation is to remove undesired odoriferous or flavouring compounds. In the deodorisati on process minor amounts of different ancillaries are applied, e.g. citric acid, BHT, ascorbyl palmitate a nd A and D vitamins. Since these ancillaries constitute insignificant amounts (0.19 – 20 g per tons NBD oil) and since there is a lack of life cycle inventory data on these ancillaries, they are omitted from the study. 0.1% of the oil is lost in the deodorisation process (Hansen 2006). The loss goes with the separated compounds to the free fatty acids which are used as fodder.

The product flow related to 1 tonn e NBD rapeseed oil is shown in Figure 13.1

.

## 178 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oilRefining Neutralization

Bleaching

Deodorisation Soap stock splitting

Oil: 1.006 t

Oil: 1.001 t

Oil in fodder: 12 kg Crude rapeseed oil: 1.017 t

Oil in bleaching earth: 5 kg NBD rapeseed oil: 1.000 t 11 kg 1 kgOverhead Heat

Electricity



## Figure 13.1: Product flow related to production of 1 t NBD rapeseed oil. Based on Hansen (2006)

. 

# 13.1 Material use

Table 13.1 shows the material use per tonne NBD rapeseed o il. All numbers are based on refining of rapeseed oil at AarhusKarlshamn in 2004 and da ta are delivered by Hansen (2006).

The consumption of phosphoric acid in the degumming process amounts 0.08% of the input of oil. Since the density of phosphoric acid is 1.58 g/cm 3 

## the consumption is 1.3 g per kg NBD oil. The consumption of sodium hydroxide in the neutralisation process is 9.1 g (14% so lution). Since sodium hydroxi de is purchased in 50% solution, the 9.1 g is equivalent to 2.6 g (50% solution). The soap from the neutralisation process is sent to soap stock splitting where sulphuric acid is applied. The consumption of sulphuric acid equals 0.19 kg H 2 

## SO 4 

## per kg free fatty acids (Hansen 2006) 23 

## . Since the content of free fatty acids is 10 g per kg NBD oil, the consumption of sulphuric acid is 1.9 g per kg NBD oil.

The water consumption in the neutralisation process amounts 2% of the input of oil, i.e. 20.3 g per kg NBD oil. Further, there is an input of water from the sodium hydroxide solution. At AarhusKarlshamn the purchased 50% sodium hydroxide solution is diluted to 14% by a dding 7.0 g water per kg NBD oil. Thus the water consumption in the neutralisation process is 27.3 g per kg NBD oil.

In the bleaching process the consumption of bleaching earth amounts ~0.9% of the input of oil, i.e. 9.0 g. There is no use of water in the bleaching and deodorisation processes.

Ancillaries Neutralisation Bleaching Deodorisation Phosphoric acid 0.8 kg

- - Sodium hydroxide (NaOH in 50% water) 2.1 kg

- - Sulphuric acid (100%) 1.9 kg

- - Bleaching earth

- 9.0 kg - Tap water 27.3 kg

- - 

## Table 13.1: Material uses in the refining stage. All numbers are related 1 t NBD oil. (Hansen 2006)



## The following describes the used inventory data relating to the materials given in Table 13.1

.
Phosphoric acid: Two life cycle inventories have been identifie d for production of phosphoric acid. Analysing the data sets in Simapro and using the EDIP97 for LC IA, it appears that global warming, acidification and toxicity are the most significant impact categories. In Table 13.2 the data sets are compared within these categories.

23 

## This is calculated using the reaction equation below. A surplus of 10% of H 2 

## SO 4 

## is used. 2C 17 

## H 33 

## COONa (soap) + H 2 

## SO 4 

## (sulphuric acid) → 2C 17 

## H 33 

## COOH (fatty acid) + Na 2 

## SO 4 

## (Sodium sulphate)

## 13 Refinery stage: Rapeseed oil 179

LCI-data for phosphoric acid CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘Phosphoric acid’, ETH-ESU database (Frischknecht et al., 1996) 1.17 kg 22 g 695 m 3 Time: Data from before 1980 Geography: Western Europe Technology: Average Co-product allocation : Not described Capital goods: Included for sub processes but not for the production of phosphoric acid ‘Phosphoric acid, industrial grade, 85% in H2O, at plant’, ecoinvent database (Althaus et al. 2003) 1.31 kg 34 g 2430 m 3 Time: Data from 1995-2000 Geography:Weighted average of production in US and Morocco Technology: Average Co-product allocation : Mass-allocation between phosphoric acid and fluosilicic acid Capital goods: Machinery, buildings 

## Table 13.2: Comparison of LCIs of phosphoric acid. The comparison is shown as characterised results using the EDIP97- method for LCIA. Both life cycle inventories are for 1 kg H 3 

## PO 4 

## in water solution. The applied data are marked with a black dotted frame.



## According to Table 13.2 there is no significant difference between phosphoric acid from Frischknecht (1996) and Althaus et al. (2003) except from t oxicity. Since the data from Althaus et al. are the newest and since they include capital goods it is chosen to use this data set.

Sodium hydroxide (NaOH): Several life cycle inventories on sodium hydroxide have been identified. As in the case of phosphoric acid an analysis of the data se ts in Simapro and using the EDIP97-method for LCIA shows, that global warming, acidification and toxici ty are the most significant impact categories. In Table 13.3 the data sets are compared within these categories.

## 180 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

LCI-data for NaOH CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘NaOH’, BUWAL database (BUWAL 250, 1996) 1.20 kg 15 g 69 m 3 Time: Data from 1990-94 Geography: Western Europe Technology: Average Co-product allocation : Mass-allocation between sodium hydroxide and chlorine Capital goods: No ‘NaOH’, ETH-ESU database (Frischknecht et al., 1996) 0.88 kg 6 g 336 m 3 Time: Data from 1990-94 Geography: Western Europe Technology: Average Co-product allocation : Mass-allocation between sodium hydroxide and chlorine Capital goods: Included for sub processes but not for the production of NaOH. ‘Sodium hydroxide, diaphragmcell, at plant’, ecoinvent database (Althaus et al. 2003) 1.06 kg 6 g 1,020 m 3

‘Sodium hydroxide, membranecell, at plant’, ecoinvent database (Althaus et al. 2003) 0.87 kg 5 g 866 m 3

‘Sodium hydroxide, mercury cell, at plant’, ecoinvent database (Althaus et al. 2003) 0.94 kg 5 g 928 m 3

‘Sodium hydroxide, productionmix, at plant’, ecoinvent database (Althaus et al. 2003) 0.95 kg 5 g 937 m 3 Time: Data from 2000 Geography: Europe Technology: Present state of technology for diaphragm cells Co-product allocation : Mass-allocation between sodium hydroxide, chlorine and hydrogen Capital goods: Machinery, buildings



## Table 13.3: Comparison of LCIs of sodium hydroxide. The comparison is shown as characterised results using the EDIP97-method for LCIA. All the life cycle inventories are for 1 kg NaOH in 50% water solution. The applied data are marked with a black dotted frame.



## It appears that the differences in environmental burden from the different data sets are relatively small concerning global warming and acidification. Within ecotoxicity the data from BUWAL 250 (1996) and Frischknecht et al. (1996) shows significant lower values than the data from Althaus et al. (2003). It has not been possible to identify the reason fo r this difference, but the two first mentioned data sets are based on other data for both the production processes for NaOH and for sub pr ocesses. It is chosen to use data from Althaus et al. (2003) since these data include capital goods and they are 7-10 years newer than the data in BUWAL 250 (1996) and Frischknecht et al. (1996). Further it is chosen to use the data for production mix of the three technologies; diaphragm cell, membrane ce ll and mercury cell. Since there is no significant difference between the three data sets and since the use of sodium hydroxide in the oil mill stage is very small this choice will not have any substantial effects on the result of the LCA.

Sulphuric acid: Three life cycle inventories have been identifie d for production of sulphuric acid. As in the case for sodium hydroxide, analysis of the data sets in Simapro and using the EDIP97 for LCIA shows that global warming, acidification and toxicity are the most significant impact categories. In Table 13.4 the data sets are compared within these categories.

## 13 Refinery stage: Rapeseed oil 181

LCI-data for sulphuric acid CO 2 -eq. SO 2 -eq. ETWC, m 3
water Description of data

‘Sulphuric acid’, BUWAL database (BUWAL 250 1996) 92 g 26 g 2.1 m 3 Time: Data from before 1995 Geography: Europe Technology: Not specified Co-product allocation : Not relevant Capital goods: Not included ‘Sulphuric acid, liquid, at plant’, ecoinvent database (Althaus et al. 2003) 111 g 13 g 241 m 3 Time: Data from 1994-2003 Geography: Europe Technology:Data based on sources on averages as well as stateof-art technologies Co-product allocation : Not relevant Capital goods: Machinery, buildings ‘Svovlsyre (H2SO4)’ EDIP database (EDIP 1996) 160 g 43 g 0.009 m 3 Time: Data from before 1995 Geography: Europe Technology: Not specified Co-product allocation : Not relevant Capital goods: Not included 

## Table 13.4: Comparison of LCIs of sulphuric acid. The comparison is shown as characterised results using the EDIP97- method for LCIA. All the life cycle inventories refer to 1 kg 100% H 2 

## SO 4 

## . The applied data are marked with a black dotted frame.



## According to Table 13.4 there is some difference between the contributions to global warming and acidification. The contribution to toxicity varies with a factor 27,000 from the lowest value to the highest value. Since the data from Althaus et al. are the newest and since they include capital goods it is c hosen to use this data set.

Bleaching earth: Only one life cycle inventory of production of bleaching earth has been identified; Bentonite, at processing/DE (Kellenberger et al. 2003). This inventory is part of the ecoinvent database and it is available in SimaPro. The data represents a mix of alka line activated bentonite (61%), acid activated bentonite (38%) and catalytic converters (1%). Only the acid activated bentonite is used in oil mills. Some of the most important uses of the other bentonites are for casting m oulds, as a cement product in building industry, wine clarification and for removal of printing ink in the recy cling process of waste paper. The data set covers data for 1998 for one German company which comprises 70% of the total German production in 1998 (Kellenberger et al. 2003). The data includes mining and processing of the bentonite, transport and production, maintenance and breaking down of capital goods.

Tap water: Three life cycle inventories for tap water have been identified; ‘Water, tap’ (Nielsen et al. 2005), ‘Vandværksvand, dansk’ (EDIP, 1996) and ‘Tap water, at user/RER’ (Althaus et al. 2003). It is chosen to use a modified version of the data from Nielsen et al. (2005). The data in Nielsen et al. (2005) describes tap water in Copenhagen in 1999. Capital goods are not included. The modifications include adjustment of LCI data for electricity to be consistent with section 3.1 and clarification of LCI data on auxiliaries. The two other data sets for tap water are omitted because the data from EDIP ( 1996) only include energy consumption and the data from Althaus et al. (2003) cover water supply in Germany and Switzerland. The modifica tions of the data from Nielsen et al. (2005) are shown in Table 13.5

.

## 182 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

LCI-data for tap water Amount Original LCI used in Nielsen et al. (2005) LCI data used in this study

Ground water 1.05 kg resource resource Electricity 1.3 kJ ‘Electricity gas power plant in NL’, ETHESU database (Frischknecht et al., 1996) See Table 3.3

Sodium hypochlorite 4 mg ‘Sodium hypochlorite, 15% in H2O, at plant/RER’, ecoinvent (2004) Ammonium sulphate 0.46 mg ‘Ammonium sulphate, as N, at regional storehouse/RER’, ecoinvent (2004) Sodium hydroxide 0.06 mg ‘Chemicals organic’, ETH-ESU database (Frischknecht et al., 1996)

‘Sodium hydroxide, production mix, at plant’, ecoinvent (2004) 

## Table 13.5: Modification of LCI of 1 kg tap water delivered to consumer in Nielsen et al. (2005).



# 13.2 Energy use

Table 13.6 shows the energy use per tonne NBD rapeseed o il. All numbers are based on refining of rapeseed oil at AarhusKarlshamn in 2004 and data are provided by Hansen (2006).

Energy Neutralisation Bleaching Deodorisation Total Electricity 20 MJ 84 MJ 104 MJ Heat 41 MJ 41 MJ 144 MJ 226 MJ 

## Table 13.6: Energy use in the refining stage relating to 1 t NBD rapeseed oil. (Hansen 2006)



## The interventions related to production of electricity are described in section 3.1 . The heat is produced at the power central at AarhusKarlshamn, see interventions in Table 9.4



# 13.3 Emissions
There are no direct emissions from refining.



# 13.4 Waste to treatment/co-products

Table 13.7 shows the waste/co-products identified in Figure 13.1 and the section ‘Material use’. Also, Table 13.7 specifies the further treatment or application of the flows.

## 13 Refinery stage: Rapeseed oil 183

Waste/co-product Amount Treatment Free fatty acids from soap splitting 10 kg 9.1 g is sold as fodder and 0.09 is lost with waste water, see ‘Waste water’ below Oil loss from neutralisation 1 kg Sold as fodder Oil loss from deodorisation 1 kg Sold as fodder Phosphoric acid (H 3 PO 4 ) (100%) 0. 8 kg This is neutralised with NaOH in the neutralisation process. The P is discharged with the waste water from soap stock splitting to the municipal sewage system, see ‘Waste water’ below Sodium hydroxide (NaOH) (100%) 1.05 kg The NaOH reacts with the free fatty acids in the neutralisation process and forms soap (C 17 H 33 COONa) Sulphuric acid (H 2 SO 4 ) (100%) 1.9 kg The H 2 SO 4 reacts with the soap in the soap stock splitting process and forms sodium sulphate (Na 2 SO 4 ) and free fatty acids (which is used as fodder, see above). Na 2 SO 4 is a salt and is discharged with the waste water from soap stock splitting to the municipal sewage system, see ‘Waste water’ below Bleaching earth 9 kg Oil loss from bleaching: oil in bleaching earth 5 kg The used bleaching earth is sent to biogas. The oil content in the bleaching earth is 35%.

Waste water 79 litre The 79 litre waste water has the following content: COD: 0.54 kg (Aarhus United, 2005a) Fat: 0.09 kg (Aarhus United, 2005a) P: 0.26 kg (Calculated from the consumption of phosphoric acid) Na 2 SO 4 : 2.5 kg (Determined from stoichiometric calculation from reaction between soap and H 2 SO 4 to FFA and Na 2 SO 4 The waste water is sent the municipal sewage system where it is lead to the municipal waste water treatment plant 

## Table 13.7: Overview of waste and co-product flows and their treatment/application. All numbers refer to 1 t NBD oil. Based on (Hansen 2006).



## The different co-products/wastes to treatment from the re finery stage include: 1) Fodder fat, 2) Waste water and 3) Bleaching earth sent to biogas. The interventions related to these three outputs of co-products/wastes are described in the following.



# Fodder fat This co-product is treated in the LCI by system expansion, see section 2.2

. 

# Waste water, COD and P

Interventions related to waste water and its contents of COD and P are described in Table 9.9

. 

# Biogas (residual from rape seed and bleaching earth) In the biogas plant the residual from rapeseed screening a nd oil containing bleaching earth is digested. According to Kromann (1996) the production of biogas can be estimated from the content of fat, protein, cellulose (difficult digestible carbon) and glucose (easy digestib le carbon) in the biomass. The only digestible component in bleaching earth is fat and the residual from rap eseed screening is presumed to mainly consist of cellulose. According to Kromann (1996, p 37) biogas production (methane) can be expected as 0.81 Nm 3 

## per kg fat and 0.12 Nm 3 

## per kg cellulose. The energy content in biogas is 36.1 MJ/Nm 3 

## (methane) and the density is 0.72 kg/m 3 

## (Kromann, 1996, p 36 and Kromann et al. 2004).

The following data for the biogas plant, i.e. ener gy production and emissions ar e based on Kromann et al. (2004). Before the biomass is sent to digestion it is hy gienized, i.e. heated from approximately 15C to 75C, requiring 281 kWh per tonne DS. Assuming water content at ~10% in residual and bleaching earth, the energy for hygienization is 312 kWh per tonne. The produced g as (methane) is burned at the biogas plant with coproduction of electricity and heat in a gas motor with 37% electrical efficiency and 48% heat efficiency. The electricity use at the biogas plant is 9 kWh per tonne biomass. Emission of methane is estimated to 1.5% of the input of gas to the motor. Other emission from the gas motor include 0.018 g SO 2 

## /MJ input of energy in biogas

## 184 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## and 0.200 g NO x 

## /MJ input of energy in biogas. In an LCA of organic waste from catering sent to biogas, the emission of ammonia constitutes the most significant c ontribution to eutrophication and acidification (Kromann et al. 2004). In Kromann et al. (2004) the emission of ammonia from storing of the biomass material before the digesting process is estimated as 4% of the total N content. The emission of ammonia from slurry spreading after digestion is estimated as 10-15% of the N content. The content of N in riddle from rapeseed and bleaching earth would be contained in the protein. It is presumed that the content of protein in the riddle is very little. Furthermore, there is no protein in the fat in bleaching earth. Thus, emission of ammonia is assumed as 0. Since the content of N is estimated as 0 the emission of N 2 

## O from slurry spreading can also be set to 0. Furthermore, since the content of N is estimated as 0, displacement of fertilizers from slurry spreading is also estimated as insignificant. Another significant emission in Kromann et al. (2004) is methane from storing the biomass before digestion. However, the riddle from rap eseed and bleaching earth are stored with a low content of water. Thus anaerobic conditions, which are required for biogas production, will not be likely to appear. In Kromann (1996, p 41) transport of the digested biomass to field application is estimated to 50 MJ/tonne DS of biomass input to the plant including transport with truck, slurry spreading and ploughing. The 50 MJ/tonne is based on 20% weight reduction in the digester. Assumi ng of water content of riddle from rapeseed and bleaching earth at 10%, the energy use for transport is 56 MJ/tonne. There is assumed no emissions to soil/water from the digested riddle and bleaching earth. Buildings an d machinery is not included for the biogas plant.

The inventory data for 1 kg rapeseed residual a nd 1 kg bleaching earth (35% fat) is shown in Table 13.8

.

1 kg residual from rapeseed screening Amount Applied LCI data

Produced gas 0.12 Nm 3 /4.3 MJ The gas is incinerated and electricity and heat are produced, see below. Electricity (from energy production at the plant) -1.6 MJ See Table 3.3

District heating (from energy production at the plant) -2.1 MJ See section 3.6

Heat for hygienization 1.1 MJ This is taken from own production of heat, see above Electricity, own consumption 0.03 MJ See Table 3.3 Emission of methane (CH 4 ) 1.3 g Emission to air Emission of SO 2 0.078 g Emission to air Emission of NO x 0.87 g Emission to air Transport of digested biomass to field application 56 KJ ‘Diesel, burned in building machine/GLO’, ecoinvent (2004)

1 kg bleaching earth (35% fat) Amount Applied LCI data Produced gas 0.28 Nm 3 /10.2 MJ The gas is incinerated and electricity and heat are produced, see below. Electricity (from energy production at the plant) -3.7 MJ See Table 3.3

District heating (from energy production at the plant) -4.9 MJ See section 3.6

Heat for hygienization 1.1 MJ This is taken from own production of heat, see above Electricity, own consumption 0.03 MJ See Table 3.3 Emission of methane (CH 4 ) 3.0 g Emission to air Emission of SO 2 0.18 g Emission to air Emission of NO x 2.0 g Emission to air Transport of digested biomass to field application 56 KJ ‘Diesel, burned in building machine/GLO’, ecoinvent (2004)



## Table 13.8: Inventory data for 1 kg rapeseed riddle and 1 kg bleaching earth sent to biogas.



# 13.5 Overhead
The energy consumption related to overhead at AarhusKarlshamn is described in section 9.6 . 10% of the total energy consumption from overhead, i.e. administration, labo ratories, marketing etc., is allocated to refining. Since three oils constitute the main o ils used at AarhusKarlshamn; rapeseed oil, shea oil and palm oil, refinery of rapeseed oil constitute one third of the 10%. Hence, assuming that one third of the refined oils was rapeseed oil the proportion related to rapeseed activities can be estimated as 3.3%.

## 13 Refinery stage: Rapeseed oil 185



## According to Aarhus United (2005b) electricity consumption for administration and laboratories was approximately 1220 MWh in 2004. In 2004 the production of refined oils at AarhusKarlshamn was 200,000 tonne (Aarhus United 2005b). Thus, the electricity consumption for overhead relate d to refining of rapeseed oil can be estimated as 0.7 MJ/t NBD rapeseed oil. This amounts 0.5% of the total electricity consumption for production of refined rapeseed oil at AarhusKarlshamn, see Table 13.11

.
Heat consumption for administration and laboratori es was approximately 550 MWh in 2004 (Aarhus United, 2005b). Using abovementioned allocati on factor and the annual production ofrefined oils, the heat consumption can be found as 0.3 MJ/t NBD rapeseed oil. Administration buildings and laboratories are heated with municipal district heat. Interventions from distri ct heating in Aarhus are described in section 3.6

.


# 13.6 Capital goods
The interventions related to capital goods in the vegetable oil industry are described in section 9.7 . There are three types of capital goods considered in the vegetable oil industry: ‘ Building, hall, steel construction ’,

‘ Building, multi-storey ’ and ‘ Facilities, chemical production ’.

The area covered by buildings is estimated from the muni cipal district plan for the current district (Århus Kommune 2004).


# Refinery buildings Refining and storage of finished products: The buildings for refini ng cover approximately 4000 m 2 

## and silos for storage of oil are estimated to cover approximately 300 m 2 

## . Thus, the buildings for refining and storage of finished products are estimated as 4,300 m 2 

## building hall. An average life time of building halls is estimated to 50 years. The annual amount of NBD oil at AarhusKa rlshamn is approximately 200,000 tonne. Hence the building hall required pe r kg of pressed and extracted oil can be determined as approximately 4.3

⋅ 10 -4 

## m 2 

## building hall/t oil.

Overhead: In section 9.7 the area of administration buildings per tonne NBD rapeseed oil has been estimated. Half this is ascribed to the oil mill and the other half to the refinery. Thus, the area of administration buildings related to refining of rapeseed oil at Aa rhusKarlshamn has been estimated as 3.3

⋅ 10 -3 

## m 3 

## multi story building/t oil divided by 2, i.e. 1.7

⋅ 10 -3 

## m 3 

## multi story building/t oil.


# Refinery machinery (facilities) The weight of machinery is very roughly estimated from personal communication with Kronborg (2006). The relevant numbers are given in Table 13.9

.

Process Weight of machinery incl. pipes Estimated life time Annual production Machinery (kg) per kg oil Refining 300 tonne 10 years 200,000 tonne 0.15 kg/t oil 

## Table 13.9: Required machinery (kg) per t oil produced at AarhusKarlshamn. Numbers are based on very rough estimates.



# 13.7 Transport of raw material and ancillaries to refinery

It is assumed that refineries are attached to oil mills. Thus there is no transport of crude rapeseed oil to the refinery. This is also the case at AarhusKarlshamn. Table 13.10 provides an overview of the ancillaries transported to AarhusKarlshamn, both in terms of total annual amounts and amounts per tonne NBD oil. Since there

## 186 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## are several suppliers and since there is a general lack of data on the specific marginal affected suppliers, all transport distances are based on rough estimates. Determination of size of lorries is based on Table 4.3

.

Material Amount to AarhusKarlshamn Amount per t NBD oil From To Distance Lorry size

Phosphoric acid ~21 tonne/year 0.8 kg Abroad chemical plant Aarhus 1000 km 28t NaOH ~55 tonne/year 2.1 kg Abroad chemical plant Aarhus 1000 km 28t Sulphuric acid ~49 tonne/year 1.9 kg Abroad chemical plant Aarhus 1000 km 28t Bleaching earth ~230 tonne/year 9.0 kg Abroad chemical plant Aarhus 1000 km 40t Light fuel oil ~36,000 t/year 6.4 kg Fuel oil supplier Aarhus 200 km 28t Fodder fat ~3,700 t/year 12 kg Refinery Fodder trader 10 km 28t 

## Table 13.10: Transport distances of the used raw materials and ancillari es in the refinery stage. The return trip is included in the inventory data.



## The amounts in the column ‘Amount to Aar husKarlshamn’ are calcu lated from figures in Table 9.1 and Table 13.11 , the amount of fuel oil is given in Table 9.12 . The amounts in the column ‘Amount per t NBD oil’ are obtained from figures in Table 13.11

.

## 13 Refinery stage: Rapeseed oil 187



# 13.8 LCI of rapeseed oil refinery, summary

Table 13.11 summarises the inventory data relating to 1 kg NBD rapeseed oil.

Denmark: 1.000 t NBD rapeseed oil from refinery Interventions Refining Overhead Total Applied LCI data Product output NBD rapeseed oil 1.000 t - 1.000 t Product of interest Fodder fat 12 kg - 12 kg Co-product allocation is avoided by system expansion,

see Table 2.3 Material use Crude rapeseed oil 1.017 t - 1.017 t The interventions related to production of rapeseed oil are included in the oil mill stage stage, see section 9 Phosphoric acid 0.8 kg - 0.8 kg ‘ Phosphoric acid, industrial grade, 85% in H2O, at plant ’ (ecoinvent 2004) NaOH 2.1 kg - 2.1 kg ‘ Sodium hydroxide, production mix, at plant ’ (ecoinvent 2003) Sulphuric acid 1.9 kg - 1.9 kg ‘ Sulphuric acid, liquid, at plant ’ (ecoinvent 2004) Bleaching earth 9.0 kg - 9.0 kg ’ Bentonite, at processing/DE ’ (ecoinvent 2004) Tap water 27.3 kg - 27.3 kg See Table 13.5 Energy use Electricity 104 MJ 0.7 MJ 105 MJ See Table 3.3 Heat (steam) 226 MJ - 226 MJ See Table 9.4 Heat (district heat) - 0.3 MJ 0.3 MJ See section 3.6 Waste to treatment Waste water, quantity 79 ltr. - 79 ltr. see Table 9.9 Waste water, P 0.26 kg - 0.26 kg see Table 9.9 Waste water, COD 0.54 kg - 0.54 kg see Table 9.9 Bleaching earth (35% veg. oil) to biogas 14 kg - 14 kg See Table 13.8

Capital goods Building halls 4.3

⋅ 10 -4 m 2 - 4.3

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’ (ecoinvent 2004) Building, multi story - 1.7

⋅ 10 -3 m 3 1.7

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery 0.15 kg - 0.15 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to oil mill Phosphoric acid 0.8 tkm - 0.8 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) NaOH 2.1 tkm - 2.1 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Sulphuric acid 1.9 tkm - 1.9 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Bleaching earth 9.0 tkm - 9.0 tkm ‘ Transport, lorry 40t ’ (ecoinvent 2004) Light fuel oil 1.3 tkm - 1.3 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Fodder fat 0.1 tkm - 0.1 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 13.11: Interventions per t NBD rapeseed oil.

## 188 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 14 Refinery stage: Palm oil 189



# 14 Refinery stage: Palm oil
Refining of palm oil is principally si milar to refining of rapeseed oil. However, the interventions may differ due to different practices in the refinery. Also the loss of oil during neutralisation differs because of different content of free fatty acids (FFA) to be removed. The FFA content of crude palm oil produced at United Plantations Berhad is maximum 2.5% while the maximum limit for traded crude palm oil is 5% (Singh 2006). According to Kang (2006) CPO normally has a content of free fatty acid at 3 to 5%. Thus, it is assumed that CPO sent to refining has FFA content at 4%.

Losses of oil take place in the deodorisation, bleaching and neutralisation processes. The loss in the deodorisation process is assumed to be the same as in rapeseed refi ning, i.e. 0.1%. In the bleaching process oil is lost due to oil content of approximately 30% in the spent bleach ing earth (Singh 2006). Since the use of bleaching earth is 4.53 kg/t NBD palm oil (UPRD 2004) the loss of oil in the bleaching process can be calculated as 1.94 kg palm oil. The loss in the neutralisation process can be cal culated from the content of FFA at 4%. The loss of oil from deodorisation is assumed to be discharged with e ffluent. The product flow re lated to production of 1 tonne NBD palm oil is shown in Figure 14.1

.Refining Neutralization

Bleaching

Deodorisation Oil: 1.003 t

Oil: 1.001 t

FFA: 42 kg Crude palm oil: 1.045 t

Oil in bleaching earth: 2 kg NBD palm oil: 1.000 t 1 kg

Oil in waste water: 1 kgOverhead Electricity



## Figure 14.1: Product flow related to produ ction of 1 t NBD palm oil.



# 14.1 Material use

Table 14.1 shows the material use per tonne NBD palm oil. The consumption of sodium hydroxide in the neutralisation process is 10.4 kg per t, see Table 14.1 , (assumed 14% solution as in AarhusKarlshamn). Since sodium hydroxide is purchased in 50% solution, the 10.4 kg/t NBD oil is equivalent to 2.9 kg/t (50% solution). The soap from the neutralisation process is sold to soap manufacturing. It is assumed that there is excess of soap from oils and fat refineries. Thus, the soap does not displace any other soap material in the market.

Ancillaries Neutralisation Bleaching Deodorisation Phosphoric acid 0.25 kg

- - Sodium hydroxide (NaOH in 50% water) 2.9 kg

- - Bleaching earth

- 4.53 kg - Water 700 kg

- - 

## Table 14.1: Material uses in the refining stage. A ll numbers are related 1 t NBD oil. (UPRD 2004)



## The used inventory data for phosphoric acid, sodium hydroxide, bleaching earth and water are described in section 13.1 . For water, it is assumed that the interventions re lated to water in Malaysia are the same as in Denmark. The energy and material use pr. m 3 

## water are very low; 1.3 MJ electricity/m 3 

## and 4.5 g chemicals/m 3 

## (see Table 13.5 ). Thus, this assumption may only cause insignificant impacts on the result.

## 190 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 14.2 Energy use

Table 13.6 shows the energy use per tonne NBD palm oil

Energy Neutralisation, bleaching and deodorisation Electricity from the grid 126 MJ Heat (burning of diesel) 328 MJ 

## Table 14.2: Energy use in the refining stage relating to 1 t NBD palm oil. The use of heat ener gy is based on diesel consumption at 9 litres/t NBD palm oil and calorific value given in Appendix 1: Data on fuels

.


## The interventions related to production of electricity are described in section 3.2 . Interventions related to burning of diesel in boiler are obtained from ecoinvent: ‘ Light fuel oil, burned in boiler 100kW, nonmodulating/CH ’ (ecoinvent 2004). This data set includes air em issions from combustion, production of fuel oil and capital goods (boiler, chimney and storage of fuel oil).



# 14.3 Emissions
There are no direct emissions from refining.



# 14.4 Waste to treatment/co-products

Table 14.3 shows the waste to treatment and co-products from the refining of palm oil. The table is based on Table 13.7 (describing the waste to treatment and co-products from the refining of rapeseed oil) and the material inputs identified in section 14.1

.

Waste/co-product Amount Treatment FFA 42 kg It is assumed that the distillate formed from the FFA displaces the marginal source of fodder energy, i.e. barley. Oil loss from deodorisation 1 kg Discharged with effluent Phosphoric acid (H 3 PO 4 ) (100%) 0. 25 kg This is neutralised with NaOH in the neutralisation process. The P is discharged with the waste water Sodium hydroxide (NaOH) (100%) 1.5 kg The NaOH reacts with the free fatty acids in the neutralisation process and forms soap (C 17 H 33 COONa) Bleaching earth 4.5 kg Oil loss from bleaching: oil in bleaching earth 1.9 kg The 6.4 kg used bleaching earth is landfilled. The oil content in the bleaching earth is 30%.

Waste water 700 litre The 700 litre waste water has the following content: COD: 630 g (900 mg/litre), (Ma 1999b) Fat: 1 kg P: 0.08 kg (Calculated from the consumption of phosphoric acid) The waste water is sent the municipal sewage system where it is lead to the municipal waste water treatment plant 

## Table 14.3: Overview of waste and co-product flows and their treatment/application relating to 1 t NBD oil.



## Interventions from disposal of the waste streams given in Table 14.3 are described in the following.


# Waste water, COD and P The waste water is treated in an aerobic lagoon, see Figure 14.2 

## . The amount of N in the effluent is assumed to be insignificant because clean vegetable oil does not c ontain N. The content of COD (organic matter) reacts with oxygen and form CO 2 

## . However, this is of biotic origin. Ther efore, it has no impacts. There may be minor emissions of methane from small anaerobic pockets in the aerobic ponds. However, this is regarded as insignificant. The content of P will be discharged with the treat ed effluent to a water stream, i.e. 0.08 kg P/t NBD palm oil.

## 14 Refinery stage: Palm oil 191

Figure 14.2: Pond for aerobic treatment of palm oil refinery effluent (Picture taken in United Plantation Berhad 2006).


# Used bleaching earth to landfill The used bleaching earth is normally disposed of to landfill locally. Over time the oil content will be decomposed into CO 2 

## and water. Since the CO 2 

## is of biotic origin, it does not contribute to any impacts. There may be forming of methane in anaerobic pockets in the landfill. However, this is regarded as insignificant. Bleaching earth mainly consists of silicon dioxide, alumin ium oxide, ferric oxide, magnesium oxide and calcium oxide (AVL 2006). None of these substances cause environm ental effects of significance. Thus, no interventions are included for landfilling of used bleaching earth.



# FFA used for animal fodder This co-product is treated in the LCI by system expansion, see section 2.2

. 

# 14.5 Overhead
No data on overhead in palm oil refining are available. Therefore, the same data as for refining of rapeseed oil are applied. However, no heating of administration bu ildings are applied since refining takes place in Malaysia. According to section 13.5 the electricity use per tonne NBD rapeseed oil is 0.7 MJ.



# 14.6 Capital goods
No data on capital goods in palm oil refineries have been identified. Thus, it is assumed that data for capital goods for a rapeseed oil refinery are representative for a palm oil refinery. The data are described in section 13.6

.


# 14.7 Transport of raw material and ancillaries to refinery

It is assumed that refineries are attached to oil mills. T hus there is no transport of crude palm oil to the refinery. This is also the case at United Plantation Berhad. Table 14.4 provides an overview of the ancillaries transported to the refinery. Since there are several suppliers a nd since there is a general lack of data on the specific marginal affected suppliers, all transport distances are based on very rough estimates. Lorry sizes in Malaysia are all assumed to be 28t

## 192 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Material Amount per t NBD oil From To Distance Lorry size

Phosphoric acid 0.25 kg Abroad chemical plant Refinery 1000 km 28t NaOH 2.9 kg Abroad chemical plant Refinery 1000 km 28t Bleaching earth 4.5 kg Abroad chemical plant Refinery 1000 km 28t Diesel 7.8 kg Fuel oil supplier, Malaysia Refinery 200 km 28t Fodder fat 42 kg Refinery Fodder trader 200 km 28t 

## Table 14.4: Transport distances of the used raw materials and ancillaries in the refinery stage. The return trip is included in the inventory data.



## The amounts in the column ‘Amount per t NBD oil’ are obtained from the figures in Table 14.5 

## , and for diesel also the calorific value given in ‘ Appendix 1: Data on fuels ’ has been used.



# 14.8 LCI of palm oil refinery, summary

Table 14.5 

## summarises the inventory data relating to 1 kg NBD palm oil.

## 14 Refinery stage: Palm oil 193

Malaysia and Indonesia: 1.000 t NBD palm oil from refinery Interventions Refining Overhead Total Applied LCI data Product output NBD palm oil 1.000 t - 1.000 t Product of interest FFA 42 kg - 42 kg Co-product allocation is avoided by system expansion,

see Table 2.3 Material use Crude palm oil 1.045 t - 1.045 t The interventions related to production of palm oil are included in the oil mill stage stage, see section 10 Phosphoric acid 0.25 kg - 0.25 kg ‘ Phosphoric acid, industrial grade, 85% in H2O, at plant ’ (ecoinvent 2004) NaOH 2.9 kg - 2.9 kg ‘ Sodium hydroxide, production mix, at plant ’ (ecoinvent 2003) Bleaching earth 4.5 kg - 4.5 kg ’ Bentonite, at processing/DE ’ (ecoinvent 2004) Water 700 kg - 700 kg See Table 13.5 Energy use Electricity 126 MJ 0.7 MJ 127 MJ See Table 3.3 Heat (steam) 328 MJ - 328 MJ ‘ Light fuel oil, burned in boiler 100kW, nonmodulating/CH ’ (ecoinvent 2004) Emissions to water Phosphorus 0.08 kg - 0.08 kg Emission to water (from waste water) Waste to treatment Waste water 700 kg - 700 kg Emission of P, see above Bleaching earth to landfill 6.4 kg - 6.4 kg No interventions

Capital goods Building halls 4.3

⋅ 10 -4 m 2 - 4.3

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’ (ecoinvent 2004) Building, multi story - 1.7

⋅ 10 -3 m 3 1.7

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery 0.15 kg - 0.15 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to oil mill Phosphoric acid 0.25 tkm - 0.25 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) NaOH 2.9 tkm - 2.9 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Bleaching earth 4.5 tkm - 4.5 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Diesel 1.6 tkm - 1.6 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Fodder fat 8.4 kg - 8.4 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 14.5: Interventions per t NBD palm oil.

## 194 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 15 Refinery stage: Palm kernel oil 195



# 15 Refinery stage: Palm kernel oil
Refining of palm kernel oil is principally similar to re fining of palm oil and rapeseed oil. However, the interventions may differ due to different practices in the re finery. Also the loss of oil during neutralisation differs because of different content of free fatty acids (FFA) to be removed.

No data on refining of palm kernel oil have been identif ied. Therefore, it is assume d that refining of palm kernel oil is similar to refining of palm oil. The only diff erence is the content of FFA in crude PKO which is lower than in crude palm oil. It is assumed that the FFA content is 1% as in crude rapeseed oil, see section 13 . The product flow chart including this modification of the refining process of palm oil is shown in Figure 15.1

.Refining Neutralization

Bleaching

Deodorisation Oil: 1.003 t

Oil: 1.001 t

FFA: 10 kg Crude palm kernel oil: 1.013 t

Oil in bleaching earth: 2 kg NBD palm kernel oil: 1.000 t 1 kg

Oil in waste water: 1 kgOverhead Electricity



## Figure 15.1: Product flow related to production of 1 t NBD palm kernel oil.



## The interventions related to refining of palm kernel oil are described in section 14 . The only difference is the amount of the co-product, 10 kg FFA sold as fodder fat, which displaces palm oil, barley and soybean meal. The system expansion is described in section 2.2

. 

# 15.1 LCI of palm kernel oil refinery, summary

Table 15.1 summarises the inventory data relating to 1 tonne NBD palm kernel oil.

## 196 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Malaysia and Indonesia: 1.000 t NBD palm kernel oil from refinery Interventions Refining Overhead Total Applied LCI data Product output NBD palm kernel oil 1.000 t - 1.000 t Product of interest FFA 10 kg - 10 kg Co-product allocation is avoided by system expansion,

see Table 2.3 Material use Crude palm kernel oil 1.013 t - 1.013 t The interventions related to production of palm kernel oil are included in the oil mill stage stage, see section 11 Phosphoric acid 0.25 kg - 0.25 kg ‘ Phosphoric acid, industrial grade, 85% in H2O, at plant ’ (ecoinvent 2004) NaOH 2.9 kg - 2.9 kg ‘ Sodium hydroxide, production mix, at plant ’ (ecoinvent 2003) Bleaching earth 4.5 kg - 4.5 kg ’ Bentonite, at processing/DE ’ (ecoinvent 2004) Water 700 kg - 700 kg See Table 13.5 Energy use Electricity 126 MJ 0.7 MJ 127 MJ See Table 3.3 Heat (steam) 328 MJ - 328 MJ ‘ Light fuel oil, burned in boiler 100kW, nonmodulating/CH ’ (ecoinvent 2004) Emissions to water Phosphorus 0.08 kg - 0.08 kg Emission to water (from waste water) Waste to treatment Waste water 700 kg - 700 kg Emission of P, see above Bleaching earth to landfill 6.4 kg - 6.4 kg No interventions

Capital goods Building halls 4.3

⋅ 10 -4 m 2 - 4.3

⋅ 10 -4 m 2 ‘Building, hall, steel construction ’ (ecoinvent 2004) Building, multi story - 1.7

⋅ 10 -3 m 3 1.7

⋅ 10 -3 m 3 ’ Building, multi-storey’ (ecoinvent 2004) Machinery 0.15 kg - 0.15 kg ‘ Facilities, chemical production ’, (ecoinvent 2004) Transport of raw materials and ancillaries to oil mill Phosphoric acid 0.25 tkm - 0.25 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) NaOH 2.9 tkm - 2.9 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Bleaching earth 4.5 tkm - 4.5 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Diesel 1.6 tkm - 1.6 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Fodder fat 2.0 tkm - 2.0 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) 

## Table 15.1: Interventions per t NBD palm kernel oil.

## 16 Refinery stage: Soybean oil 197



# 16 Refinery stage: Soybean oil
No data on refining of soybean oil have been identified. Therefore it is a ssumed that the interventions related to 1 tonne NBD rapeseed oil is the same as 1 tonne NB D soybean oil. The inventory data for refining rapeseed oil are summarised in Table 13.11

.
However a few adjustments to the inve ntory for rapeseed oil refining are done:

• Electricity from the grid: Marginal electricity in Brazil is applied, see See Table 3.3

• District heat: No district heating is used

• Bleaching earth to biogas: This is not sent to biog as in Brazil, disposal is assumed to be landfilling without any interventions

• Transport distance of fodder fat from refinery to fo dder trader is assumed to be 300 km instead of 10 km as in Denmark

## 198 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 17 Transport stage 199



# 17 Transport stage
The transport stage includes transportation of the finished refined rapeseed oil and palm oil to final consumption which is assumed to be in central Europe represented by Amsterdam.

The refined rapeseed oil is transported in a 40 t lorry from AarhusKarlshamn in Aarhus in Denmark to Amsterdam in the Netherlands, i.e. 791 km (Krak 2006).

The refined palm oil produced in Malaysia and Indon esia is regarded as being transported 200 km in a 28 t lorry to a port. From there it is transported in an o ceanic tanker 8,117 nautical miles corresponding to 15,033 km 24 

## (Distances 2006). The distance is represented by the di stance from Port Kelang in Malaysia to the port in Amsterdam, the Netherlands.

The interventions related to transportation of the refined oil to final consumption are summarised in Table 17.1 . The interventions related to each tonne kilometre (tkm) are described in section 4

.

Transport of NBD oil Rapeseed oil Palm oil Applied LCI data Lorry, 28 t - 200 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004) Lorry, 40 t 791 tkm - ‘ Transport, lorry 40t ’ (ecoinvent 2004) Ocean tanker - 15,033 tkm ‘ Transport, transoceanic tanker/OCE ’ (ecoinvent 2004) 

## Table 17.1: Interventions per t NBD vegetable oil in the transport stage.

24 

## 1 nautical mile corresponds to 1.852 km.

## 200 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 18 Emissions from intensified cultivation 201



# 18 Emissions from intensified cultivation
The emissions from intensified production are mainly determined from the business as usual cultivation of crops described in sections 5
, 6
, 7 and 8 . Firstly the marginal technologies of increasing yields are determined. After that, the parameters representing the identified mean s of changing the yields are changed in the business as usual cultivation of crops in sections 5
, 6
, 7 and 8

.


# 18.1 Identification of metho ds of increasing yields
Based on Schmidt (2007b) Table 18.1 provides an overview of the most common means of achieving increased productivity in agriculture.

Increased agricultural inputs Alternative technology Management Fertiliser Agricultural machinery (man, ox or tractor) Scheduling of agricultural inputs Irrigation GMO Integrated pest management (weed control) Pesticides (weed control) Variety selection Double-cropping (more crop rotations per year)

- Seed improvement -

- Drainage - 

## Table 18.1: Overview of most important means of inccreasing yields.



## Identifying the marginal method of increasing productivity in agriculture is very difficult. Some improvementstake place regardless of changes in demand for the crop of interest. Thus, most farmers seek to maximise economical benefit of their farm which often imply cha nges in agricultural inputs, technology and management. The marginal changes are those which are directly related to increased demand for the desired crop.

According to Weidema (2003) the marginal method of in creasing yields in Europe is by additional nitrogen fertiliser input. Based on discussions with United Planta tions Research Department (Singh 2006) nitrogen fertiliser is also considered as the marginal source of increasing yields of oil palm. Soybean is only fertilised with phosphate, thus it is assumed that there are no significant yield responses to input of N-fertiliser. According toJales et al. (2006) and USDA (2006) the most important method used for increasing yields of soybean in Argentina and Brazil has been increased double-croppping. The use of biot echnology has resulted in a shorthercrop cycle of soybean which has made increased double-cropping possible (USDA 2006). Therefore, the marginal method for increasing yields in Brazil is assu med to be increased double-croppping. According to Table 8.1 the yield of barley in Canada is considerable lower than in Denmark and according to IFA et al. (2002) the use of fertilisers in Canadian barley cultivation is al so significant lower than in Denmark. Therefore, corresponding to the EU, it is assumed that N-fertiliser is al so the marginal method for increasing the yields in Canada.



# 18.2 Determination of ch anged parameters
In the previous section the marginal methods for increasi ng yields of rapeseed, oil palm, soybean and barley have been identified. In order to calculate the emissi ons from intensified cultivati on, the changed parameters firstly have to be determined.



# Level of intensification Rapeseed, oil palm and barley: For increased yields of rapeseed, oil palm and barley which are achived by additional N-fertiliser, intensified cu ltivation is assumed to be with an additional fertiliser input of 5%.

## 202 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## The level of intensification at 5% increase is chosen abitrary since there is no information on how much fertiliser application is increased in order to increase yields. However, since the N- and P-related emissions that will be affected by additional fertiliser are proportional with N and P input this assumption will not affect the results.

Soybean: Intensified soybean cultivation is assumed to take place with 5% points increas e in the area which is double-cropped, i.e. the double-cropped area is inreased from 25% to 30%.


# Yield responses to fertiliser (rapeseed, oil palm, barley)

In Table 18.2 , which is directly obtained from Schmidt ( 2007b), yield responses to additional N-fertiliser are given for different levels of N-application.

Δ yield (kg/ha) / Δ N-rate (kg N/ha) Crop and region 0-50 kg N/ha 50-100 kg N/ha 100-150 kg N/ha 150-200 kg N/ha Maize (Nebraska, USA) 56 19 7 4 Maize (Oklahoma, USA) 24 22 12 8 Winter wheat (Oklahoma, USA) 15 6 - - Winter wheat (Sweden) 40 17 12 4 Rapeseed (Denmark) 13 15 9 4 Rapeseed (Germany) 10 12 10 4 Soybean (Argentina) 0 0 0 0 Fresh fruit bunches from oil palm (Malaysia) 149 87 62 48 

## Table 18.2: Yield responses to increased N-fe rtiliser input. (Schmidt 2007b).



## It appears from Table 18.2 that the yield response is very sensitive to the desired region/study (e.g. compare winter wheat in USA and Sweden) and the level of N- application (e.g. compare maize responses to additional fertiliser in Nebraska for low and high levels of N-app lication). Therefore, determin ation of emissions related to increased production by increased yields is associated with considerable uncertainties. These uncertainties are assessed in a sensitivity analysis in section 21.20

.
The levels of N-application are obtained from section 5.4
, 6.5 and 8.4 . In Table 18.2 the data for rapeseed in Sweden are applied for rapeseed cultivat ion, the data for winter wheat in Sweden are applied for barley cultivation and the data for FFB in Malays ia are applied for oil palm cultivati on. The yield responses to additional application of N-fertiliser applied in this study are summarised in Table 18.3

.

Crop and region Level of Napplication N-application interval in Table 18.2 Present yield, t/ha Yield response, kg crop/ha per kg N/ha Rapeseed, Denmark 167 kg N/ha 150-200 3.24 4 Oil palm, Malaysia and Indonesia 105 kg N/ha 100-150 18.87 62 Barley, Denmark 121 kg N/ha 100-150 5.23 12 Barley, Canada 67 kg N/ha 50-100 2.91 17 

## Table 18.3: Yield responses applied in this study.



## Based on the present levels of N-application and the yield responses to additional N-fertiliser in Table 18.3 and the increase in N-fertiliser at 5%, the N-application and yields in intensified cultivation are summarised in Table 18.4

.

## 18 Emissions from intensified cultivation 203

Crop and region Additional N-application, Δ N (5% increase) New level of Napplication Yield response, Δ Y kg crop/ha Yield, kg/ha

Rapeseed, Denmark 7.0 kg N/ha 147.0 kg N/ha* 28 kg/ha 3,268 (3,240+28) Oil palm, Malaysia and Indonesia 5.3 kg N/ha 110.3 kg N/ha 329 kg/ha 19,199 (18,870+329) Barley, Denmark 6.1 kg N/ha 127.1 kg N/ha 73 kg/ha 5,303 (5,230+73) Barley, Canada 3.4 kg N/ha 70.4 kg N/ha 58 kg/ha 2,968 (2,910+58) 

## Table 18.4: N-application and yields in intensified cultivation. * The reason why N-application in rapeseed cultivation is less than specified in Table 18.3 is that the value in Table 18.3 is inclusive a ‘previous crop’ value at 27 kg N/ha.



## When determining the emissions from intensified cultivati on of rapeseed, oil palm and barley only the input of N-fertiliser and the yield is changed. In order to maintain balance in the application of different nutrients to the fields, P and K fertilisers are also increased by 5%.

The changes in yield affect energy for drying of seed a nd transport of crops and co-products to traders. The changes in application of fertilisers affect transportati on of fertilisers to farm a nd N and P related emissions.The N and P related emissions are calculated establishing new N and P balances, and the emissions are calculated using the same method as described in sections 5.6
, 6.7 and 8.6

. 

# Yield responses to increased double-cropping (soybean) According to Table 7.1 the yield for single-cropping is 2.68 t/ha and for 25% double-croppping the yield is 3.35 t/ha. If the area that is double-cropped is 30% the yield yield for single-cropping must be multiplied by a factor of 1.3 (=0.30

⋅ 2 + 0.70). Thus, the yield when 30% of the area is double-cropped is 3.484 t/ha.

When determining the emissions from intensified cultiva tion of soybean almost all interventions are proportional with the increase in yield. Thus these are simply multiplied with a facto of 3. 484/3.350 = 1.04. However, the N-related emissions are not directly proportional with increased in yield. This is because the input of N from atmospheric deposition is not changed as a conse quence of double-cropping. Therefore, a new N-balance is established and the N-related emissions are calculated using the same method as described in section 7.5

.


# 18.3 LCI of intensified cultivation, summaries
In this section the summary tables of the inventories of intensified cultivation of rapeseed (Denmark), FFB (Malaysia and Indonesia), soybean (Brazil), barley (Denma rk) and barley (Canada) are presented. The inventories are based on data and methods described in sections 5
, 6
, 7 and 8 and the changed para meters presented in previous section (section 18.2 ).

## 204 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# LCI of intensified rapeseed cu ltivation (Denmark), summary

Table 18.5 summarises the inventory data relating to 1 ha y rapeseed field.

Denmark: 1 ha y intensified rapeseed field Interventions Amount Applied LCI data Product output Rapeseed 3.259 t* Product of interest, 3,259 kg (=3,268 kg minus 9 kg for seed production) Straw removed from field 381 kg Co-product allocation between rapeseed and straw is avoided by system expansion, see below System expansion Burning of straw in biomass plant 381 kg See Table 5.25 Energy use Traction, burned diesel 3,612 MJ See Table 4.5 Drying of rapeseed (evaporated water) 101 kg* Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Miscellaneaus transport (passenger car) 79 km ‘ Transport, passenger car/RER ’ (ecoinvent 2004) Material use Seed 5 kg See Table 5.12 N-fertiliser (as N) 147 kg* ‘Calcium a mmonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 60 kg* Modified version of: ‘ Triple superphosphate, as P 2 O 5 , at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 K-fertiliser (as K 2 O) 104 kg* ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Herbicide (clomazone) 0.050 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Herbicide (propyzamid) 0.18 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Herbicide (clopyralid) 0.020 kg Modified version of: ‘ Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, cypermethrin) 0.0070 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, alpha-cypermethrin) 0.0020 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Insecticide (Pyrethroid, tau-fluvalinat) 0.0072 kg Modified version of: ‘ Pyretroid-compounds, at regional storehouse/RER ’ (ecoinvent 2004), see section 5.4 Capital goods Agricultural buildings 0.070 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 7.5 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 6.3 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 9.2 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.5 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 16t lorry 0.5 tkm ‘ Transport, lorry 16t ’ (ecoinvent 2004) 28t lorry 366 tkm* ‘ Transport, lorry 28t ’ (ecoinvent 2004) 40t lorry 1,942 tkm* ‘ Transport, lorry 40t ’ (ecoinvent 2004) ... table continued on the next page... 

## Table 18.5: Interventions per ha y intesified rapeseed field in Denmark. Parameters which are changed compared to the inventory in section 5 are marked with a *. Table continued on the next page...

## 18 Emissions from intensified cultivation 205

... continued from previous page... Emissions Air Water Soil Ammonia (NH 3 ) 9.6 kg* - - Dinitrogen oxide (N 2 O) 5.3 kg* - - Nitric oxide (NO) 3.0 kg* - - Nitrate (NO 3 ) - 188 kg* - Phosphorus (P) - 0.17 kg* - Clomazone 0.017 kg 0.017 kg 0.017 kg Propyzamid 0.060 kg 0.060 kg 0.060 kg Clopyralid 0.0067 kg 0.0067 kg 0.0067 kg Cypermethrin 0.0023 kg 0.0023 kg 0.0023 kg Alpha-cypermethrin 0.0067 kg 0.0067 kg 0.0067 kg Tau-fluvalinat 0.0024 kg 0.0024 kg 0.0024 kg Arsenic (As) - - 1.2 g* Cadmium (Cd) - 0.61 g* -0.20 g* Chromium (Cr) - 0.19 g* 35 g* Cobalt (Co) - 0.045 g* 0.24 g* Copper (Cu) - 1.0 g* 7.0 g* Mercury (Hg) - 0.037 g* -0.010 g* Molybdenum (Mo) - - 1.2 g* Nickel (Ni) - 1.0 g* 11 g* Lead (Pb) - 2.0 g* -0.60 g* Selenium (Se) - 0.016 g* 2.5 g* Zink (Zn) - 16 g* 68 g* 

## Table 18.5 : Interventions per ha y intesified rapeseed field in Denmark. Parameters which are changed compared to the inventory in section 5 are marked with a *.

## 206 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# LCI of intensified oil palm cultivat ion (Indonesia and Malaysia), summary
Table 18.6 summarises the inventory data relating to 1 ha y cultivated with oil palm.

Malaysia and Indonesia: 1 ha y intensified oil palm plantation Interventions Amount Applied LCI data Product output FFB 19,199 kg* Product of interest Energy use Traction, burned diesel 2,118 MJ See section 4.3 Electricity for overhead 0.053 MJ See Table 3.3 Material use N-fertiliser, ammonium sulphate (as N) 80.3 kg* ‘ Ammonium sulphate, as N, at regional storehouse/RER ’ (ecoinvent 2004) N-fertiliser, urea (as N) 29.7 kg* ‘ Urea, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 73 kg* Phosphate rock, see section 6.5 : Fertilisers K-fertiliser (as K 2 O) 215 kg* Potassium chloride, as K 2 O, at regional storehouse/RER’ , ecoinvent (2004) Herbicide, typically glyphosate 2.4 kg ‘Glyphosate, at regional storehouse/RER’ (ecoinvent 2004) Insecticide, typically cypermethrin 0.31 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Fungicides, various different 0.013 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Rodenticide, typically warfarin 0.00021 kg ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings, shed 0.041 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Agricultural buildings, administration etc. 0.00139 m 3 ‘ Building, multi-storey ’ (ecoinvent 2004) Machinery, tractor 4.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 0.4 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 2.1 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,208 tkm* ‘ Transport, lorry 40t ’ (ecoinvent 2004) Oceanic tanker 12,080 tkm* ‘ Transport, transoceanic tanker/OCE ’ (ecoinvent 2004) Emissions Air Water Soil Carbon dioxide (CO 2 ) 1,500 kg Ammonia (NH 3 ) 18.9 kg* - - Dinitrogen oxide (N 2 O) 10.4 kg* - - Nitric oxide (NO) 3.3 kg* - - Nitrate (NO 3 ) - 375 kg* - Phosphorus (P) - 1.7 kg* - Glyphosate 0.80 kg 0.80 kg 0.80 kg Cypermethrin 0.10 kg 0.10 kg 0.10 kg Fungicides, various different Not included, no characterisation data exist in LCIA methods Warfarin Not included, no characterisation data exist in LCIA methods Arsenic (As) - - 1.1 g* Cadmium (Cd) - - 2.0 g* Chromium (Cr) - - 47 g* Cobalt (Co) - - 0.25 g* Copper (Cu) - - 12 g* Mercury (Hg) - - 0.028 g* Molybdenum (Mo) - - 0.12 g* Nickel (Ni) - - 5.4 g* Lead (Pb) - - 2.1 g* Selenium (Se) - - 0.44 g* Zink (Zn) - - 72 g* 

## Table 18.6: Interventions per ha y intensified oil palm plantati on in Malaysia and Indonesia. Parameters which are changed compared to the inventory in section 6 are marked with a *.

## 18 Emissions from intensified cultivation 207



# LCI of intensified soybean cu ltivation (Brazil), summary

Table 18.7 summarises the inventory data relating to 1 ha y soybean field.

Brazil: 1 ha y intensified soybean field Interventions Amount Applied LCI data Product output Soybean 3.341 t* Product of interest, 3,341 kg (=3,484 kg minus 143 kg for seed production) Energy use Traction, burned diesel 1,987 MJ* See Table 4.5 Drying of soybeans (evaporated water) 71 kg* Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 143 kg* See Table 7.2 P-fertiliser (as P 2 O 5 ) 48 kg* Phosphate rock, see section 6.5 : Fertilisers Herbicide, glyphosate 2.4 kg* ‘Glyphosate, at regional storehouse/RER’ (ecoinvent 2004) Herbicide, 2-4 D

- - Herbicide, imazethapyr

- - Insecticide, pyrethroid, cypermethrin 0.014 kg* ‘Pyretroid-compounds, at regional storehouse/RER’ (ecoinvent 2004) Insecticide, Chlorpyrifos 0.21 kg* ‘Pesticide unspecified, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.038 m 2 * ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 4.2 kg* ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 3.4 kg* ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 5.0 kg* ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 2.0 kg* ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,769 tkm* ‘ Transport, lorry 40t ’ (ecoinvent 2004) Oceanic tanker 843 tkm* ‘ Transport, transoceanic tanker/OCE ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 0 kg - - Dinitrogen oxide (N2O) 4.9 kg * - - Nitric oxide (NO) 1.2 kg* - - Nitrate (NO3) - 0 kg - Phosphorus (P) - 0.072 kg* - Glyphosate 0.80 kg* 0.80 kg* 0.80 kg* 2-4 D No LCI data on a.i., and no characterisation data exist in LCIA methods Imazethapyr No LCI data on a.i., and no characterisation data exist in LCIA methods Cypermethrin 0.0045 kg* 0.0045 kg* 0.0045 kg* Chlorpyrifos 0.070 kg* 0.070 kg* 0.070 kg* Arsenic (As) - - - Cadmium (Cd) - 0.023 g* 1.2 g* Chromium (Cr) - 0.20 g* 29 g* Cobalt (Co) - 0.030 g* -0.016 g* Copper (Cu) - 5.9 g* 1.5 g* Mercury (Hg) - 0 g* 0.00048 g* Molybdenum (Mo) - - - Nickel (Ni) - 2.0 g* 0.60 g* Lead (Pb) - 0.030 g* 0.56 g* Selenium (Se) - 0.038 g* 0.046 g* Zink (Zn) - 18 g* 10 g* 

## Table 18.7: Interventions per ha y intensified soybean field in Brazil. Parameters which are changed compared to the inventory in section 7 are marked with a *.

## 208 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# LCI of intensified barley cu ltivation (Denmark), summary

Table 18.8 summarises the inventory data relating to 1 ha y spring barley field in Denmark.

Denmark: 1 ha y intensified spring barley field Interventions Amount Applied LCI data Product output Spring barley (Denmark) 5.193 t* Product of interest, 5,193 kg (=5,303 kg minus 110 kg for seed production) Straw removed from field 2,207 kg* Co-product allocation between barley and straw is avoided by system expansion, see below System expansion Burning of straw in biomass plant 2,207 kg* See Table 5.25 Energy use Traction, burned diesel 4,029 MJ See Table 4.5 Drying of barley (evaporated water) 108 kg* Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 110 kg See Table 8.2 N-fertiliser (as N) 127.1 kg* ‘Calcium a mmonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 48 kg* Modified version of: ‘ Triple superphosphate, as P 2 O 5 , at regional storehouse/RER’ (ecoinvent 2004), see section 5.4 K-fertiliser (as K 2 O) 69 kg* ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.078 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 8.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 7.0 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 10.3 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.9 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 2,050 tkm* ‘ Transport, lorry 40t ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 9.2 kg* - - Dinitrogen oxide (N2O) 4.3 kg* - - Nitric oxide (NO) 2.6 kg* - - Nitrate (NO3) - 147 kg* - Phosphorus (P) - 0.10 kg* - Arsenic (As) - - 0.93 g* Cadmium (Cd) - 0.045 g* 0.29 g* Chromium (Cr) - 0.21 g* 28 g* Cobalt (Co) - 0.0056 g* 0.22 g* Copper (Cu) - 2.5 g* 4.2 g* Mercury (Hg) - 0.034 g* -0.0076 g* Molybdenum (Mo) - - 1.0 g* Nickel (Ni) - 0.22 g* 9.1 g* Lead (Pb) - 0.23 g* 0.86 g* Selenium (Se) - 0.023 g* 2.1 g* Zink (Zn) - 21 g* 48 g* 

## Table 18.8: Interventions per ha y intensified barley field in Denmark. Parameters which are changed compared to the inventory in section 8 are marked with a *.

## 18 Emissions from intensified cultivation 209



# LCI of intensified barley cu ltivation (Canada), summary

Table 18.9 summarises the inventory data relati ng to 1 ha y barley in Canada.

Canada: 1 ha y intensified barley field Interventions Amount Applied LCI data Product output Barley (Canada) 2.858 t* Product of interest, 2,858 kg (=2,968 kg minus 110 kg for seed production) Straw removed from field 0 - System expansion Burning of straw in biomass plant 0 - Energy use Traction, burned diesel 4,029 MJ See Table 4.5 Drying of barley (evaporated water) 61 kg* Modified version of: ‘ Grain drying, low temperature/CH ‘ (ecoinvent 2004), see section 5.3 Material use Seed 110 kg See Table 8.2 N-fertiliser (as N) 70.4 kg* ‘Ammonium nitrate, as N, at regional storehouse/RER ’, (ecoinvent 2004) P-fertiliser (as P 2 O 5 ) 27 kg* Phosphate rock, see section 6.5 : Fertilisers K-fertiliser (as K 2 O) 11 kg* ‘ Potassium chloride, as K 2 O, at regional storehouse/RER’ (ecoinvent 2004) Capital goods Agricultural buildings 0.078 m 2 ‘ Shed/CH/I ’ (ecoinvent 2004) Machinery, tractor 8.4 kg ‘ Tractor, production/CH/I ’ (ecoinvent 2004) Machinery, combine harvester 7.0 kg ‘ Harvester, production/CH/I ’ (ecoinvent 2004) Machinery, tillage 10.3 kg ‘ Agricultural machinery, tillage, production/CH/I ’ (ecoinvent 2004) Machinery, general/miscellaneous 3.9 kg ‘ Agricultural machinery, general, production/CH/I ’ (ecoinvent 2004) Transport 40t lorry 1,502 tkm* ‘ Transport, lorry 40t ’ (ecoinvent 2004) Emissions Air Water Soil Ammonia (NH3) 7.8 kg* - - Dinitrogen oxide (N2O) 2.4 kg* - - Nitric oxide (NO) 1.6 kg* - - Nitrate (NO3) - 60 kg* - Phosphorus (P) - 0.090 kg* - Arsenic (As) - - 0.17 g* Cadmium (Cd) - 0.025 g* 0.73 g* Chromium (Cr) - 0.12 g* 17 g* Cobalt (Co) - 0.0032 g* 0.064 g* Copper (Cu) - 1.4 g* 2.8 g* Mercury (Hg) - 0.019 g* -0.0049 g* Molybdenum (Mo) - - 0.27 g* Nickel (Ni) - 0.12 g* 2.1 g* Lead (Pb) - 0.13 g* 0.57 g* Selenium (Se) - 0.013 g* 1.0 g* Zink (Zn) - 12 g* 9.5 g* 

## Table 18.9: Interventions per ha y intensified barley field in Canada. Parameters which are changed compared to the inventory in section 8 are marked with a *.

## 210 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 19 Emissions from transformation of land and from non-cultivated land 211



# 19 Emissions from transformati on of land and from noncultivated land
Increases in agricultural production are often achived by expanding the cultiv ated area. Emissions arise when non-cultivated land (e.g. set-aside land, forest, savannah, grassland) is transformed into agricultural land and the emissions from the non-cultivated land are displaced by the emissions from the new agricultural land.

In this section the emissions related to transformation of land are inventoried. As indicated above, this includes two sources of emissions: Firstly, there are emissions ar ised from the transformation itself, i.e. emissions related to difference in stading stock of carbon, nitrogen and other substances, and secondly, when noncultivated land is transformed into ag ricultural land the contineous emissi ons from this non-cultivated land are avoided and subsequently displaced by the emissions from the new agricultural land.

The two sources of emissions are treated seperately because the emissions from the transformation itself are hard to relate to the functional unit, while the avoi ded contineous emissions from non-cultivated land are proportional with the annual yields and thereby easy to relate to the functional unit. The emissions from the transformation itself are referred to as emissions from tr ansformation processes and the contineous emissions are referred to as avoided emissions from occupation of non-cultivated land. These term s are in accordance with the terms used i relation to land use in LCA and are d escribed more in detail in Schmidt (2007c). The emissions from transformation processes are defined as all emissi ons that are related to a change in equilibrium of the standing stock of living and dead biomass. The a nnual change in the soil organic carbon and nitrogen due to land transformation years ago could be included as emissions related to occupation processes. This is actually the case in Nielsen et al. (2005). However, annual changes of carbon and nitrogen in the soil matter are not constant with time, and consequentily it is difficult to relate the changes to the functional unit. According to Petersen and Berntsen (2002), the duration before a new e quilibrium state is reached may take up to more than 200 years.

In section 2.3 , the affected land use types related to transfoma tion of 1 ha non-cultivated land into agriculture are specified, see Table 19.1

.

Region Transformation from... Transformation to... Denmark Set-aside area Cropland, rapeseed Indonesia and Malaysia Secondary/degraded tropical rainforest (50%) Alang-alang grass land (50%) Perennial cropland, oil palm plantation

Brazil Secondary/degraded tropical rainforest (5%) Cerrado savannah (95%) Cropland, soybean

Canada Prairie grass land Cropland, barley 

## Table 19.1: Affected land use types when 1 ha non-cultivated land is transformed into agriculture in the relevant regions. The data are obtained from section 2.3

.


## For the land use types in Table 19.1 , the following two sections describe the emissions that arise from transformation processes (section 19.1 ) and the avoided emissions from occ upation of non-cultivated land (section 19.2 ).

## 212 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 19.1 Emissions from trans formation processes
The relevant emissions related to transformation process es are those which are related to the C- and N-cycles. The emissions related to the C-cycle are CO 2 

## , CO and CH 4 

## . Because of lack of data the emissions of CO and CH 4 

## have been omitted. In the IPCC guidelines (IPCC 2003) emissions of CO and CH 4 

## are only considered in the case of burning of residues and therefore these em issions are considered as insignificant when claering forests wiothout burning. The emissions related to the N-cycle are N 2 

## O, NO, N 2 

## and nitrate. The emissions of N 2 

## O are calculated based on the change in the stock of nitr ogen. Because of lack of data it has not been possible to calculate the emission of NO related to transformation processes. Therefore, this emission has been omitted. The emission of N 2 

## is based on generalised figures on the ration between N 2 

## O and N 2 

## and the emission of nitrate is calculated as the residual from the N-balance.

There may also be emissions of P and heavy metals because the land transformation processes may contribute to a release of these substances. However, this is omitted from this study.

Table 19.2 and Table 19.3 specify the carbon stocks in the different affected land use ty pes. The carbon stock is constituted by soil organic carbon and carbon in biom ass. In some cases, for carbon in biomass, data have only been available for above ground biomass. In these cases the total carbon in bi omass have been calculated using the root-to-shoot ratios given in IPCC (2003, p 3.110).

Applied data Crop and region Carbon stock Reference Determining parameters for calculation/estimation of carbon stock Rapeseed, Denmark Soil organic carbon 23 t C/ha (IPCC 2003, p 3.75- 3.77) Cultivation: full tillage, low input of organic matter Conditions: cold temperate, dry, average of clay and sandy soils Carbon in biomass 0 t C/ha (IPCC 2003, p 3.84) Since the dominant vegetation is removed allmost entirely every year the stock is assumed to be zero Total 23 t C/ha - - Plantation, Malaysia and Indonesia Soil organic carbon 50 t C/ha Estimated from Henson (2004, p 17) -

C in biomass 26 t C/ha (Henson 2004, p 7) Average value for 25 years replanting cycle in Malaysia C in ground vegetation 1.3 t C/ha (Henson 2004, p 10) Average value for 25 years replanting cycle in Malaysia

C in frond piles 2.1 t C/ha (Henson 2004, p 12) Average value for 25 years replanting cycle in Malaysia

C in felled non-burned oil palm material 2.5 t C/ha (Henson 2004, p 12) Average value for 25 years replanting cycle in Malaysia

Total 82 t C/ha - - Soybean, Brazil Soil organic carbon 26 t C/ha (IPCC 2003, p 3.75- 3.77) Cultivation: reduced tillage, medium input of organic matter Conditions: tropical, moist, average of clay and sandy soils Carbon in biomass 0 t C/ha (IPCC 2003, p 3.84) Since the dominant vegetation is removed allmost entirely every year the stock is assumed to be zero Total 26 t C/ha - - Barley, Canada Soil organic carbon 25 t C/ha (IPCC 2003, p 3.75- 3.77) Cultivation: full tillage, medium input of organic matter Conditions: cold temerpate, dry, average of clay and sandy soils Carbon in biomass 0 t C/ha (IPCC 2003, p 3.84) Since the dominant vegetation is removed allmost entirely every year the stock is assumed to be zero Total 25 t C/ha - - 

## Table 19.2: Determination of carbon stock per hectare in the fields of the affected crops.

## 19 Emissions from transformation of land and from non-cultivated land 213

Data Land-use type and region Carbon stock Reference Determining parameters for calculation/estimation of carbon stock Set-aside land, DenmarkCarbon stock in setaside 47 t C/ha (IPCC 2003, p 3.75- 3.77) Carbon in soil is calculated using (IPCC 2003, p 3.75-3.77) and Cultivation: 20 year set-aside, no tillage, high input of organic matter, Conditions: cold temerpate, dry, average of clay and sandy soils, i.e. 38 t C/haCarbon in biomass is assumed to be the same as grassland in cold, temperate, dry climate (IPCC 2003, p 3.109-3.110), i.e. 9 t C/ha. Applied data 47 t C/ha - - Secondary/degraded forest, Indonesia and MalaysiaCarbon stock in tropical forest 195 t C/ha (IPCC 2003) Tropical forest, Asia, wet:: 57 t C/ha in soil (PCC 2003, p 3.43) and 138 t C/ha in biomass stock (IPCC 2003, 3.157) Applied data 195 t C/ha - - Alang-alang grassland, Malaysia and IndonesiaCarbon stock in grassland 30 t C/ha (Tian et al. 2000) Grassland (Amazon)

Carbon stock in grassland 66 t C/ha (IPCC 2003) Grassland, tropical, moist, average of clay and sandy soils: 50 t C/ha in soil (IPCC 2003, p 3.117) and 16 t C/ha in biomass stock (IPCC 2003, pp 3.109-3.110)Carbon stock in grassland 73 t C/ha (IPCC 2003) Grassland, tropical, wet, average of clay and sandy soils: 57 t C/ha in soil (IPCC 2003, p 3.117) and 16 t C/ha in biomass stock (IPCC 2003, pp 3.109-3.110) Applied data 73 t C/ha - The figures from IPCC for wet tropical grassland are applied. The reason for chosing wet grassland is that oil palm requires >2000 mm precipitation annually (Corley and Tinker 2003, p 67) Cerrado savannah, BrazilCarbon stock in savannah 204 t C/ha (Chen et al. 2003) Tropical savannah, Australia

Carbon stock in savannah 102 t C/ha (Tian et al. 2000) Savannah (Amazon)

Carbon stock in grassland 59 t C/ha (IPCC 2003) Grassland/savannah, tropical, moist, average of clay and sandy soils: 50 t C/ha in soil (IPCC 2003, p 3.117) and 9 t C/ha in biomass stock (IPCC 2003, pp 3.109-3.110) Applied data 102 t C/ha - Singificant differences between compared data. The actual modelling of savannah in the Amazon at 102 t C/ha is close to the average of the three references. This is applied Secondary/degraded forest, BrazilCarbon stock in tropical forest 226 t C/ha (Tian et al. 2000) Tropical evergreen forest (Amazon)

Carbon stock in tropical forest 243 t C/ha (Tian et al. 2000) Other forest/woodland (Amazon)

Carbon stock in tropical forest 231 t C/ha (IPCC 2003) Tropical forest, America, wet:: 57 t C/ha in soil (IPCC 2003, p 3.43) and 174 t C/ha in biomass stock (IPCC 2003, 3.157) Applied data 231 t C/ha - The difference between the compared data is small. The data from IPCC are applied Prairie, grassland, CanadaCarbon stock in grassland 48 t C/ha (IPCC 2003, p 3.117) Grassland, cold temperate, dry, average of clay and sandy soils: 39 t C/ha in soil (IPCC 2003, p 3.117) and 9 t C/ha in biomass stock (IPCC 2003, pp 3.109-3.110) Applied data 48 t C/ha - - 

## Table 19.3: Determination of carbon stock per hectare in the affect ed non-cultivated land use types that are transformed into agriculture.



## In addition to the changes in the stock of carbon rela ted to land transformation, there are also associated changes in the stock of nitrogen. IPCC (2003, p 3.94) sugg est a C:N-ratio in soils at 15. Billore et al. (1995) suggest C:N-ratios for biomass of grassland and evergr een forests at 65 and 32 respectively. These ratios are applied to the carbon stocks given in Table 19.2 and Table 19.3 . The N in soil matter for the affected land use types is specified in Table 19.4 and Table 19.5

.

## 214 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Crop and region Carbon stock C:N-ration Nitrogen stock Rapeseed, Denmark Soil 23 t C/ha 15 1.5 t N/ha Biomass 0 t C/ha - - Total 23 t C/ha - 1.5 t N/ha Plantation, Malaysia and Indonesia Soil 50 t C/ha 15 3.3 t N/ha Biomass 32 t C/ha 32 1.0 t N/ha Total 82 t C/ha - 4.3 t N/ha Soybean, Brazil Soil 26 t C/ha 15 1.7 t N/ha Biomass 0 t C/ha - - Total 26 t C/ha - 1.7 t N/ha Barley, Canada Soil 25 t C/ha 15 1.7 t N/ha Biomass 0 t C/ha - - Total 25 t C/ha - 1.7 t N/ha 

## Table 19.4: Determination of nitrogen stocks per hectare in the fields of the affected crops.

Land use type and region Carbon stock C:N-ration Nitrogen stock

Set-aside, Denmark Soil 38 t C/ha 15 2.5 t N/ha Biomass 9 t C/ha 65 0.1 t N/ha Total 47 t C/ha - 2.6 t N/ha Secondary/degraded forest, Indonesia and Malaysia Soil 57 t C/ha 15 3.8 t N/ha Biomass 138 t C/ha 32 4.3 t N/ha Total 195 t C/ha - 8.1 t N/ha Alang-alang grassland, Malaysia and Indonesia Soil 57 t C/ha 15 3.8 t N/ha Biomass 16 t C/ha 65 0.2 t N/ha Total 73 t C/ha - 4.0 t N/ha Cerrado savannah, Brazil Soil 86 t C/ha 15 5.7 t N/ha Biomass 16 t C/ha 65 0.2 t N/ha Total 102 t C/ha - 5.9 t N/ha Secondary/degraded forest, Brazil Soil 57 t C/ha 15 3.8 t N/ha Biomass 174 t C/ha 32 5.4 t N/ha Total 231 t C/ha - 9.2 t N/ha Prairie, grassland, Canada Soil 39 t C/ha 15 2.6 t N/ha Biomass 9 t C/ha 65 0.1 t N/ha Total 48 t C/ha - 2.7 t N/ha 

## Table 19.5: Determination of nitrogen stocks per hectare in the affected non-cultivated land use types that are transformed into agriculture.



## Table 19.6 summarises the changes in carbon and nitrogen stocks related to land use transformation.

## 19 Emissions from transformation of land and from non-cultivated land 215

From set-aside to rapeseed, Denmark Rapeseed Carbon release Nitrogen release Soil 15 t C/ha 1.0 t N/ha Biomass 9 t C/ha 0.1 t N/ha Total 24 t C/ha 1.1 t N/ha From secondary/degraded forest to oil palm, Malaysia and Indonesia Oil palm Carbon release Nitrogen release Soil 7 t C/ha 0.5 t N/ha Biomass 106 t C/ha 3.3 t N/ha Total 113 t C/ha 3.8 t N/ha From alang-alang grassland to oil palm, Malaysia and Indonesia Oil palm Carbon release Nitrogen release Soil 7 t C/ha 0.5 t N/ha Biomass -16 t C/ha -0.8 t N/ha Total -9 t C/ha -0.3 t N/ha From cerrado savannah to soybean, Brazil Soybean Carbon release Nitrogen release Soil 60 t C/ha 4.0 t N/ha Biomass 16 t C/ha 0.2 t N/ha Total 76 t C/ha 4.2 t N/ha From secondary/degraded forest to soybean, Brazil Soybean Carbon release Nitrogen release Soil 31 t C/ha 2.1 t N/ha Biomass 174 t C/ha 5.4 t N/ha Total 205 t C/ha 7.5 t N/ha From prairie grassland to barley, Canada Barley Carbon release Nitrogen release Soil 14 t C/ha 0.9 t N/ha Biomass 9 t C/ha 0.1 t N/ha Total 23 t C/ha 1.0 t N/ha 

## Table 19.6: Summary of the changes in carbon and nitrogen stocks related to land use transformation.



## Since open burning when clearing land in Indonesia and Mala ysia is prohibited, it is assumed that clearing for oil palm is done without burning. Hereby, all the ch ange in carbon is assumed to oxide to carbon dioxide and the change in nitrogen is assumed to be distributed between N 2 

## O, N 2 

## and nitrate. According to IPCC (2003, p 3.94) the emission factor for N 2 

## O related to changes in soil N is 0.0125 kg N 2 

## O-N/kg N. Since degradation of biomass will end as nitrate in the soil, it is assumed that the emission factor provided by IPCC applies to the total change in the nitrogen stock. The emission of N 2 

## is based on a N 2 

## /N 2 

## O-ration at approximately 3 for mixed sand and clay soils in Denmark (Vinther and Hansen 2004). Thus it can be calculated that the relase of nitrogen is distributed on 1.25% N 2 

## O-N, 3.75% N 2 

## -N and 95% NO 3 

## -N. However, when there is a net up-take of N as in the case of transformation of alang-alang gr assland into oil palm in Ma laysia and Indonesia, no emissions related to N are accounted for. This is because it is assumed, that the plant’s need for nitrogen is met by N-fertiliser, i.e. no removal of N in soil matter. Table 19.7 summarises the emissions that arise from transformation. The amounts of emissions per kg C or kg N are calculated using the molar proporties: 3.67 kg CO 2 

## /kg C, 1.57 kg N 2 

## O/kg N and 4.43 kg nitrate/kg N.

## 216 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Rapeseed From set-aside to rapeseed, Denmark CO 2 88 t CO 2 /ha N 2 O 0.022 t N 2 O/ha Nitrate 4.6 t NO 3 /ha Oil palm From secondary/degraded forest to oil palm, Malaysia and Indonesia CO 2 415 t CO 2 /ha N 2 O 0.075 t N 2 O/ha Nitrate 16.0 t NO 3 /ha Oil palm From alang-alang grassland to oil palm, Malaysia and Indonesia CO 2 -33 t CO 2 /ha N 2 O 0 t N 2 O/ha Nitrate 0 t NO 3 /ha Soybean From cerrado savannah to soybean, Brazil CO 2 279 t CO 2 /ha N 2 O 0.082 t N 2 O/ha Nitrate 17.7 t NO 3 /ha Soybean From secondary/degraded forest to soybean, Brazil CO 2 752 t CO 2 /ha N 2 O 0.147 t N 2 O/ha Nitrate 31.6 t NO 3 /ha Barley From prairie grassland to barley, Canada CO 2 84 t CO 2 /ha N 2 O 0.020 t N 2 O/ha Nitrate 4.2 t NO 3 /ha 

## Table 19.7: Summary of the changes in carbon and nitrogen stocks related to land use transformation.



# 19.2 Avoided emissions from occupat ion of non-cultivated land

This section describes emissions from non-cultivated land. The only relevant emissions from non-cultivated land are assumed to be N-related emi ssions. The only input is atmospheric deposition and this is distributed on the following emissions:

• N 2 

## O and NO: These are determined from literature studies

• N 2 

## : This is calculated assuming a N 2 

## /N 2 

## O-ratio at 3 correspondingly to determination of N 2 

## in section 19.1

• Nitrate: The emission of nitrate is regarded as the residual

Since non-cultivated land is assumed to be in an equilibr ium state, there will be no changes in the soil matter of C and N.



# N 2 

# O- and NO-emissions

Set-aside area: Ruser et al. (2001) have measured the annual em ission of dinitrogen oxide from set-aside areas in Southern Germany as 0.29 kg N 2 

## O-N/ha. This is also applied as N 2 

## O-emissions from set-aside areas in Denmark. No data on NO-emissions from set-aside areas have been identified. Therefore, it is assumed that the ratio between N 2 

## O and NO emissions from set-aside areas is the same as for natural grassland described below. Thus, the emission of NO is 1.1 kg NO-N/ha.

Grassland: N 2 

## O emissions from grassland, i.e. alang-alang gr assland in Malaysia and Indonesia and prairie grassland in Canada, are obtained from Stehfest and Bouwman (2006). Stehfest and Bouwman (2006) compare the N 2 

## O and NO emissions from soils under natural vegetation on the global scale calculated with two models. It is chosen to apply the average of the two emissions levels given in Stehfest and Bouwman (2006). Thus the emission of N 2 

## O from grassland is 0.32 kg N 2 

## O-N/ha. The NO-emission from grassland is 0.89 kg NO-N/ha.

## 19 Emissions from transformation of land and from non-cultivated land 217



## Savannah: N 2 

## O-emissions from the Cerrado savannah in Brazil are obtained from the same source as emissions from grassland described above. The N 2 

## O-emission from savannah/open tropical forest in Stehfest and Bouwman (2006) is in average 0.59 kg N 2 

## O-N/ha. The emission of NO is 1.8 kg NO-N/ha.

Tropical forests: Tropical forests in Indonesia and Malysia and to a lesser extent Brazil are affected in this study. According to Kiese and Butterbach-Bahl (2003) there are great variations of N 2 

## O emissions from tropical forests from year to year. Thus, they show a difference with a factor of 6-7 from one year to the following year. Kiese and Butterbach-Bahl (2003) specify an emission at 0.97 kg N 2 

## O-N/ha while Kiese and ButterbachBahl (2002) specify an emission at 6.2 kg N 2 

## O-N/ha. Stehfest and Bouwman (2006) specify a N 2 

## O-emission at 1.37 kg N 2 

## O-N/ha. Werner et al. (2006) summarises varies studies of N 2 

## O emissions from primary and secondary forests in South Easy Asia. The average N 2 

## O emission from primary forests in Werner et al. (2006) is 0.34 kg N 2 

## O-N/ha and from secondary forests it is 0.63 kg N 2 

## O-N/ha. These figures are significant lower than those given in Kiese and Butterbach-Bahl (2002), Ki ese and Butterbach-Bahl (2003) and Stehfest and Bouwman (2006). The variations may be due to differec es in soil types, precipiation, and methods of measurements and modelling. However, since Werner et al. (2006) include a summary of several real measurements and since these measurements are in South East Asia, it is chosen to apply the average value of primary and secondary forest, i.e. 0.49 kg N 2 

## O-N/ha.

The only identified data on NO-emissions from tropical forests are found in Stehfest and Bouwman (2006) where the average of the given figures is 0.53 kg NO-N/ha. This is applied in this study.



# N 2 

# -emissions

The N 2 

## -emissions are determined using a N 2 

## /N 2 

## O-ration at 3.



# Nitrate The emissions of nitrate are calculated as the input of N which is assumed to be the atmospheric N-deposition minus the emissions of N 2 

## O, NO and N 2 

## . The N-deposition is the affected areas are descibed in sections 5.6
, 6.7
, 7.5 and 8.6 . The atmospheric N-depositions are Denmark (15 kg N/ha), Indonesia and Malaysia (17.5 kg N/ha), Brazil (8 kg N/ha) and Canada (3.3 kg N/ha). The nitrate emissions are shown in Table 19.8

. 

# Summary of avoided emissi ons from occupation of non-cultivated land

Table 19.8 summarises the N-balance for non-cultivated land.

Region DK MY&IN BR CAN Land-use type Set-aside Forest Grassland Savannah Forest Grassland Input: N-deposition 15.0 kg N/ha 17.5 kg N/ha 17.5 kg N/ha 8.0 kg N/ha 8.0 kg N/ha 3.3 kg N/ha Output Denitrification: N 2 O 0.29 kg N/ha 0.49 kg N/ha 0.32 kg N/ha 0.59 kg N/ha 0.49 kg N/ha 0.32 kg N/ha Denitrification: NO 1.1 kg N/ha 0.53 kg N/ha 0.89 kg N/ha 1.8 kg N/ha 0.53 kg N/ha 0.89 kg N/ha Denitrification: N 2 0.87 kg N/ha 1.47 kg N/ha 0.96 kg N/ha 1.77 kg N/ha 1.47 kg N/ha 0.96 kg N/ha Total denitrification 2.3 kg N/ha 2.5 kg N/ha 2.2 kg N/ha 4.2 kg N/ha 2.5 kg N/ha 2.2 kg N/ha Surplus: Nitrate 12.7 kg N/ha 15.0 kg N/ha 15.3 kg N/ha 3.8 kg N/ha 5.5 kg N/ha 1.1 kg N/ha 

## Table 19.8: N-balance for non-cultivated land. All values are given in kg N/ha.

Table 19.9 

## Summarises the avoided emissions fro m occupation of non-cultivated land.

## 218 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Region DK MY&IN BR CAN Land-use type Set-aside Forest Grassland Savannah Forest Grassland N 2 O 0.46 kg N 2 O/ha 0.77 kg N 2 O/ha 0.50 kg N 2 O/ha 0.93 kg N 2 O/ha 0.77 kg N 2 O/ha 0.50 kg N 2 O/ha NO 2.4 kg NO/ha 1.1 kg NO/ha 1.9 kg NO/ha 3.9 kg NO/ha 1.1 kg NO/ha 1.9 kg NO/ha Nitrate 56 kg NO 3 /ha 66 kg NO 3 /ha 68 kg NO 3 /ha 17 kg NO 3 /ha 24 kg NO 3 /ha 5 kg NO 3 /ha 

## Table 19.9: Summary of the avoided emissions from non-cultivated land.

## 20 Life cycle impact assessment (LCIA) 219



# 20 Life cycle impact assessment (LCIA)
The purpose of this report is to provide life cycle in ventory data on the product systems of rapeseed oil and palm oil, not to carry out an LCIA. However, in order to carry out sensitivity analyses (see section 21 ), the characterised results for the inventoried scenarios are presented here but without comments and interpretation.

The scenarios are described in section 2.4

.

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Impact category RSOa RSOb PO RSOa RSOb PO RSO PO RSO PO RSO PO Global warming (t CO 2 ) 12.0 8.19 2.16 5.15 2.40 2.32 17.1 2.60 2.39 2.36 2.22 2.47 Ozone depletion (mg CFC11) 366 304 43.8 210 147 44.6 549 77.8 147 44.6 163 54.5

Acidification (kg SO 2 ) 52.2 47.9 13.8 39.2 25.8 13.0 82.4 23.5 26.0 13.3 20.2 14.8 Eutrophication (t NO 3 ) 1549 1159 80.6 201 172 102 2733 337 211 119 140 124 Photochemical smog (kg ethene) 1.36 1.30 0.526 1.22 0.869 0.509 1.95 0.617 0.869 0.509 0.887 0.551

ETWC (mio m 3 water) -4857 -4857 1354 -4685 -4685 1423 -5748 -13.4 -4685 1423 75.2 1407 ETWA (mio m 3 water) -133 -133 49.8 -127 -127 52.3 -161 -0.362 -127 52.3 3.12 51.6 ETSC (mio m 3 soil) -25.1 -25.1 0.538 -24.5 -24.5 0.625 -27.6 -0.0602 -24.5 0.625 0.0473 0.670 Land use (ha y) -0.281 0.170 0.235 0.960 0.337 0.175 0 0 0.337 0.175 0.548 0.242 Biodiversity (wS100) 1.11 6.97 6.64 2.36 10.8 6.84 0 0 10.8 6.84 7.13 6.78 

## Table 20.1: Characterised results of the inventoried scenarios. Th e results are obtained using the updated EDIP97-method for LCIA (see section 1.4 ). Biodiversity is obtained using the method of Schmidt (2007c)

## 220 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 21 Sensitivity analyses 221



# 21 Sensitivity analyses
Throughout the scope definition in section 2 and the LCI described in sections 3 to 19 , several improvement options and uncertainties in assumptions and data have b een identified. Most of these influencing factors are evaluated in this section. The sensitiv ity analyses presented in this section t ests how sensitive the results of the inventory are to various assumptions, uncertainties of data and alternative cultivation practices/technologies (improvement options). In order to ha ve consistency and to maintain comp arability of the different sensitivity analyses, each sensitivity analysis is carried out using the same functional unit (1 tonne vegetable oil) and the same scenario (scenario 4). Scenario 4 is chosen because this scenario is not related to uncertainties regarding system expansion in the agricultural stage where espec ially modelling of increased yield is regarded as uncertain. These uncertainties are analysed in separate sensitivity analyses using other scenarios than scenario 4.

Table 21.1 provides an overview of the included sensitivity analyses. Table 21.1 also provides references to the sections where the uncertainti es of interest are described.

## 222 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

No. Reference Aim Description of sensitivity analyses 1 Section 1.4 Uncertainty LCIA-methods: EDIP97 is applied as default LCIA-method. This sensitivity analysis compares the results when using the LCIA-methods; Impact2002+ and Eco-indicator 99 (H) 2 Section 2.1 Uncertainty System delimitation, marg inal supplier of crops: The identification of marginal suppliers is related to uncertainty, especially the marginal supplier of barley (Canada). This sensitivity analysis shows results when the marginal suppliers of barley are EU25 and Russia, and when the marginal suppliers of FFB are either Indonesia or Malaysia alone. The identification of Brazil as the marginal supplier of soybean is less uncertain, and cultivation of soybean in Argentina is much similar to Brazil 3 Section 2.3 and 19.1 Uncertainty &Improvement option System delimitation, type of land transformed into agricultural land: Uncertainties in identifying the affected land types are present. The uncertainties regarding land for oil palm in Indonesia/Malaysia and land for barley in Canada are regarded as the most significant. This sensitivity analysis compares the results when transformed land in Indonasia/Malaysia is alang-alang grassland, secondary forest and primary forest, and when transformed land in Canada is natural grassland (prairie) and degraded land (similar to EU set-aside land) 4 Section 3.1 Uncertainty Energy, marginal source of electricity: This sensitivity analysis compares the results when marginal electricity is coal based and natural gas based 5 Section 3.6 Uncertainty Energy, representativiness of data on district heating in Denmark: This sensitivity analysis assesses the uncertainties in the obtained results due to uncertainties in the applied data for district heating in Denmark 6 Section 5.6

, 6.7

, 7.5
, 8.6 Uncertainty Agricultural cultivation, energy for traction: Uncertainties in the determination of energy use for traction for the affected crops are assessed in this sensitivity analysis 7 Section 5.6 Uncertainty &Improvement option Rapeseed cultivation, soil type: This sensitivity analysis compares the results when cultivating rapeseed on average soil, clay soils and sandy soils

8 Section 5.4 Improvement option Rapeseed cultivativation, N-fertiliser produced using best available techniques (BAT): The tail gas from the production of nitric acid, which is used in the production of calcium ammonium nitrate, is treated using best available techniques. Hereby the N 2 O in the tail gas is reduced by 85% 9 Section 5.6

, 6.7

, 7.5
, 8.6 Uncertainty Agricultural cultivation, N changes in soil matter: The changes in N in soil matter in this study are assumed to be zero for continuous cultivation and all the changes are ascribed to transformation of land use. This sensitivity analysis compares the obtained results with a situation when changes in N in soil matter for rapeseed, soybean and barley cultivation are included 10 Section 5.6

, 6.7

, 7.5 Uncertainty Agricultural cultivation, heavy metal contents in fertilisers: The heavy metal content in fertilisers vary significantly in the used data sources. This sensitivity analysis assesses the uncertainties related to that 11 Section 5.6

, 6.7

, 7.5 Uncertainty Agricultural cultivation, initial compartment of pesticide emissions: This sensitivity assess the uncertainties in the obtained results related to the assumption that the initial compartment of pesticide emissions are 33% air, 33% water and 33% soil 12 Section 6 Uncertainty Oil palm cultivation, yields: The yield of oil palm is dependant on cultivation practices which again aredepandant on the region (Malaysia and Indonesia) and the ownership plantation (smallholder, stateowned FELDA or private estate). This sensitivity analysis assesses the uncertainty in the obtained results relating the determination of the yield 13 Section 6 and 6.7 Uncertainty &Improvement option Oil palm cultivation, soil type: This sensitivity analysis compares the results when cultivating oil palm on mineral soils versus peat soils

14 Section 6.7 Uncertainty Oil palm cultivation, uncertainties in CO 2 -emissions from peat soil: This sensitivity analysis assesses the uncertainties in the obtained results for palm oil relating to the determination of CO 2 - emissions from peat soils 15 Section 8.4 Uncertainty Barley cultivation, omission of use and emissions of pesticides: This sensitivity analysis assesses the uncertaines relating to the omission of pesticides in barley cultivation Table continued on the next page... 

## Table 21.1: Sensitivity analyses. The numbers refer to the sensitivity analyses presented in the following sections. Table continued on the next page...

## 21 Sensitivity analyses 223

Table continued from the previous page... No. Reference Aim Description of sensitivity analyses 16 Section 9 Uncertainty &Improvement option Rapeseed oil mill, solvent extraction versus full press: This sensitivity analysis compares the results when using the solvent extraction technology with a situation when using the full press technology

17 Section 9.7

, 10.8

, 11.8

, 12.8 Uncertainty Oil mills, capital goods: The amount of capital goods used in the oil mill stage is related to significant uncertainties. Therefore, this sensitivity analysis assesses the significans of these uncertainties

18 Section 10

, 10.5 Uncertainty &Improvement option Palm oil mill, POME-treatment: Normally the treatment of palm oil mill effluent is anaerobic and aerobic tretamtn in ponds. This sensitivity analysis applies a situation where digester tanks are used instead of anaerobic ponds and the collected biogas is utilised for electricity generation

19 Section 10

, 10.1

, 10.6 Uncertainty Palm oil mill, steam requirement: This sensitivity analysis applies a steam requirement at 0.5 t instead of the 0.65 t which is applied as default in the study 20 Section 10 Improvement options Palm oil mill, alternative management options for EFB: Empty fruit bunches (EFB) from the palm oil mill are applied as mulch in the plantation in the baseline scenario. This sensitivity analysis analyses theeffect of two alternative management options for EFB: 1) Disposal in landfill and 2) Utilisation in a biomass plant with electricity production 21 Section 18.2 Uncertainty &Improvement option Insensified cultivation, yield-responses to additional fertiliser: Great differences in yield-responsesto additional fertiliser input have been identified. Therefore, this sensitivity analysis assesses the uncertainties related to that. In Malaysia and Indonesia there are great differences in the level of fertiliser application. Therefore, there may be potentials for improvements by additional fertiliser application in extensively cultivated areas 

## Table 21.1 : Sensitivity analyses. The numbers refer to the sensitivity analyses presented in the following sections.



# 21.1 No. 1: LCIA-methods

The uncertainties in the obtained results related to the use of the EDIP97 LCIA-m ethod are assessed in thissensitivity analysis. The following impact categories are assessed; global warming, acidification, eutrophication, photochemical smog and ecotocicity. For each of the impact categories the top-five of the most significant emissions is identified using the LCIA-methods: EDIP97 (see section 1.4 ), Impact2002+ (Jolliet et al. 2003) and Eco-indicator (Goedkoop and Spriensma 2001). For each impact category the intersection of sets is counted, i.e. the number of emissions that appears in a ll three compared methods. If the intersection of sets is 5, there is a high level of consistency among the compar ed methods while if the intersection of sets is zero, there is a low level of consistency.

EDIP97: Global warming Impact2002+ Eco-indicator 99 (H) Emission Contribution Emission Contribution Emission Contribution CH 4 40% CO 2 53% CH 4 36% CO 2 31% N 2 O 26% CO 2 31% N 2 O 29% CH 4 21% N 2 O 32% CO 0,13% CO 0,050% CO 0.10% NMVOC 0,04% Sulfur hexaflouride 0.023% Sulfur hexaflouride 0.010% Total 2,358 kg CO 2 -eq Total 1,371 kg CO 2 -eq Total 4.92E-4 DALY 

## Table 21.2: Global warming, 1 t palm oil (scenario 4).



## The intersection of sets is four. The difference between EDIP97 and Eco-indicator 99 is insignificant. But it appears that there are differences in the total kg CO 2 

## -eq. between EDIP97 and Impact2002+. His is somehow surprising because characterisation models for global warm ing are normally regarded as relatively certain. The reason for the difference is primarily because EDIP97 applies a 100 years time horizon while Impact2002+ has applied a 500 years time horizon. This implies that the characterisation factors in Impact2002+ for methane is only one third of EDIP97 and for dinitr ogenmonoxide it is only half of EDIP97.

## 224 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

EDIP97: Acidification Impact2002+: Terrestrial acid/nutri Eco-indicator 99 (H): Acidification/ Eutrophication Emission Contribution Emission Contribution Emission Contribution NH 3 59% NH 3 73% NH 3 73% SO 2 17% NO x 18% NO x 18% NO x 15% NO 6.6% NO 6.6% NO 5.4% SO 2 2.6% SO 2 2.6% H 2 S 3.8% - - SO 4 -0.0068% Total 13.3 kg SO 2 -eq Total 85.9 kg SO 2 -eq Total 89.4 pdf*m2yr 

## Table 21.3: Acidification, 1 t palm oil (scenario 4).



## The intersection of sets is four. In general the consiste ncy between the methods is re garded as relatively high. However, comparing the total contribution using th e EDIP97-method and the Impact2002+ method the difference is significant. This is primarily because the cate gory indicator of acidification in Impact2002+ covers acidification as well as eutrophication. Thus, the tota l of the results is not directly comparable.

EDIP97: Eutrophication Impact2002+: Terrestrial acid/nutri Eco-indicator 99 (H): Acidification/ Eutrophication Emission Contribution Emission Contribution Emission Contribution NO 3 71% NH 3 73% NH 3 73% NH 3 13% NO x 18% NO x 18% P 13% NO 6.6% NO 6.6% NO x 3.2% SO 2 2.6% SO 2 2.6% NO 1.2% - - NO 3 -0.0068% Total 119 kg NO 3 -eq Total 85.9 kg SO 2 -eq Total 89.4 pdf*m2yr 

## Table 21.4: Eutrophication, 1 t palm oil (scenario 4).



## The intersection of sets is three different emissions. It appears that nitrate which is a major contribor using EDIP97 is insignificant when using Impact 2002+ and Ec o-indicator. Also phosphorus is not present as a contributor in Impact 2002+ and Eco-indicator. The reas on why nitrate is insignificant in Impact2002+ and Ecoindicator is that these methods only concerns terrestrial eutrophication while EDIP97 covers aquatic as well as terrestrial eutrophication.

EDIP97: Photochemical smog Impact2002+: Respiratory organics Eco-indicator 99 (H): : Respiratory organics Emission Contribution Emission Contribution Emission Contribution CH 4 57% CH 4 54% CH 4 51% NMVOC 25% NMVOC 41% NMVOC 39% CO 8.8% HC, aliphatic, alkanes 0.74% HC, aliphatic, unsaturated 5.6%

HC, aliphatic, unsaturated 4.9% Toluene 0.59% HC, aliphatic, alkanes 0.69%

HC, aliphatic, alkanes 0.8% Pentane 0.59% Toluene 0.55%

Total 509 g ethene-eq Total 456 g ethylene-eq Total 1.04E-6 DALY 

## Table 21.5: Photochemical smog, 1 t palm oil (scenario 4).



## The intersection of sets is three different emissions wh ich indicates a relatively good consistency. However, the top-two which account for 82-95% of the total contri bution is the same in all three methods, which indicates a very good consistency.

## 21 Sensitivity analyses 225

EDIP97: Ecotoxicity, water, chonic Impact2002+: Aquatic ecotoxicity Eco-indicator 99 (H): Ecotoxicity Emission Contribution Emission Contribution Emission Contribution Cypermethrin to water 101% Cypermethrin to water 80% Zinc to soil 40%

Chlorpyrifos to water -0.83% Copper to soil 11% Chromium to soil 32% Strontium to water 0.0013% Zinc to soil 4.3% Zinc to air 6.9% Copper ion to water 0.00094% Aluminium to soil 1.4% Nickel to soil 6.5% Glyphosate to water 0.00086% Chromium to soil 0.79% Nickel to air 4.3% Total 1.4E9 m 3 water Total 5.51E5 kg TEG water Total 1,270 PAF*m2yr



## Table 21.6: Ecotoxicity (aquatic), 1 t palm oil (scenario 4).



## The intersection of sets is zero and the total number of di fferent emissions identified is 12 out of 15 possible. This indicates a very low level of consistency among the compared methods. Since the number of included pesticides in the three methods vary, the comparison is also carried out when ex cluding the pesticides, see Table 21.7

.

EDIP97: Ecotoxicity, water, chonic Impact2002+ Eco-indicator 99 (H) Emission Contribution Emission Contribution Emission Contribution Strontium to water 31% Copper to soil 55% Zinc to soil 40% Copper ion to water 24% Zinc to soil 22% Chromium to soil 32% Aluminium to water 13% Aluminium to soil 7.1% Zinc to air 6.9% Iron ion to water 9.0% Chromium to soil 4.1% Nickel to soil 6.5% Zinc ion to water 8.8% Aluminium to air 2.6% Nickel to air 4.3% Total 5.7E4 m 3 water Total 1.08E5 kg TEG water Total 1,270 PAF*m2yr



## Table 21.7: Ecotoxicity (aquatic) excluding pesticides, 1 t palm oil (scenario 4).



## Excluding the pesticides from the comparison does not affect the consistency among the compared LCIAmethods. The intersection of sets is still zero and the to tal number of different emis sions identified is 13. Because of the very significant differences between the results and contributors in the LCIA-methods, the results obtained for ecotoxicity are regarded as extremely uncertain and almost useless.

It appears from the sensitivity analysis presented in this section that the LCIA-methods seem to be relatively consistent regarding global warming, acidification, eutrophication and photochemical smog. Thus, the results within these impact categories are not affected significantly by uncertainties in LCIA-methods. But when it comes to ecotoxicity the uncertainties are significant, and the value added by characterising the inventory results is regarded as very limited.



# 21.2 No. 2: System delimitation , marginal supplier of crops
When identifying the marginal suppliers of crops in section 2.1 , the main uncertainties associated with that are regarded as identification of Canada as the marginal supplier of barley and identification of Indonesia and Malaysia together as the marginal supplier of palm oil. The identification of the marginal supplier of rapeseed (the EU) and soybean (Brazil) is regarded as more certain as well as the identification of the margional crop in the EU is regarded as relatively certain. In this secti on the following sensitivity analyses are carried out, see Table 21.8

.

## 226 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Most uncertain identifications of marginal suppliers Description of sensitivity analyses

Marginal supplier of barley Two sensitivity analyses are carried out: One where Russia is applied as the marginal supplier of barley and one where EU25 is applied as the marginal supplier of barley Marginal supplier of palm oil Two sensibility analyses are carried out: One where Malaysia is applied as the marginal supplier and one where Indonesia is applied as the marginal supplier 

## Table 21.8: Description of sensitivity analyses carried out in this section.



## The presumptions for each sensibility an alysis are described in the followi ng. These data are the only ones that are changed in the inventory. Based on that new N- and P-balances and calculations of emissions are carried out following the methods described in section 6.7 for oil palm and section 8.6 for barley.

Russia as the marginal supplier of barley: Carrying out the sensitivity analys is where Russia is regarded as the marginal supplier of barley the following paramete rs are changed: The yield is changed from 2.91 t/ha to 1.85 t/ha (calculated for 2005 by regression from 1992 to 2005 based on FAOSTAT 2006) and the use of fertiliser is changed from 67 kg N, 26 kg P 2 

## O 5 

## and 10 kg K 2 

## O to 47 kg N, 20 kg P 2 

## O 5 

## and 69 kg K 2 

## O respectively (IFA et al. 2002). It is assumed that 0% of the stra w is removed and utilised for energy purposes. The N deposition is assumed to be the same as in Canada.

EU25 as the marginal supplier of barley: Here the yield is changed from 2. 91 t/ha to 4.38 t/ha (calculated for 2005 by regression from 1993 to 2005 based on FAOSTAT 2006). The same fertiliser input as in Denmark is assumed, i.e. 121 kg N/ha, 46 kg/ha P 2 

## O 5 

## and 66 kg K 2 

## O/ha. It is assumed that 50% of the straw is removed and utilised for energy purposes. The N depos ition is assumed to the same as in Denmark.

The emissions related to N- and P-balances from barley cu ltivation in the default situ ation (Canada) and in the two sensitivity analyses are summarised in Table 21.9

.

Emission Barley, Canada Barley, Russia Barley, EU25 Ammonia (NH 3 ) to air 7.7 kg/ha 7.2 kg/ha 9.0 kg/ha Dinitrogen oxide (N 2 O) to air 2.2 kg/ha 1.6 kg/ha 4.6 kg/ha Nitrix oxide (NO) to air 1.6 kg/ha 1.4 kg/ha 1.9 kg/ha Nitrate (NO 3 ) to water 50 kg/ha 36 kg/ha 199 kg/ha Phosphorus (P) to water 0.078 kg/ha 0.094 kg/ha 0.176 kg/ha 

## Table 21.9: Emissions related to the N- and P-balances fo r barley cultivation in the considered regions.



## Indonesia as the marginal supplier of FFB: The yield is changed from 18.87 t/ha to 17.95 t/ha (calculated for 2005 by regression from 1990 to 2005 based on F AOSTAT 2006). The same fertiliser input as in Malaysia/Indonesia is assumed.

Malaysia as the marginal supplier of FFB: The yield is changed from 18.87 t/ha to 19.84 t/ha (calculated for2005 by regression from 1990 to 2005 based on FAOSTAT 2006). The same fertiliser input as in Malaysia/Indonesia is assumed.

The emissions related to N- and P-balances from oil pa lm cultivation in the default situation (Malaysia and Indonesia tougether) and in the two sen sitivity analyses are summarised in Table 21.10

.

## 21 Sensitivity analyses 227

Emission FFB, MY&IN FFB, MY FFB, IN Ammonia (NH 3 ) to air 18.3 18.3 18.3 Dinitrogen oxide (N 2 O) to air 10.1 10.0 10.1 Nitrix oxide (NO) to air 3.19 3.19 3.19 Nitrate (NO 3 ) to water 353 344 361 Phosphorus (P) to water 1.61 1.60 1.62 

## Table 21.10: Emissions related to the N- and P-balances for oil palm cultivation in the considered regions.



## Table 21.11 and Table 21.12 shows the characterised results of the baseline and the sensitivity analyses described for rapeseed oil and palm oil respec tively. The results are all for scenario 4.

Impact category 1 t rapeseed oil (baseline) 1 t rapeseed oil (barley, RUS) 1 t rapeseed oil (barley, EU25) 1 t rapeseed oil (FFB, MY) 1 t rapeseed oil (FFB, IN) Global warming (t CO 2 ) 2.39 2.35 2.45 2.38 2.41 Ozone depletion (g CFC11) 0.147 0.143 0.149 0.147 0.148 Acidification (kg SO 2 ) 26.0 25.3 26.4 25.8 26.1 Eutrophication (t NO 3 ) 0.211 0.209 0.207 0.209 0.214 Photochemical smog (kg ethene) 0.869 0.845 0.878 0.867 0.870 Land use (m 2 y) 3.37 3.03 3.57 3.34 3.41 

## Table 21.11: Characterised results of 1 t rapeseed oil, baseline comp ared with the the sensitivity analyses were the margin suppliers of barley and FFB (oil palm fruit) are changed.



## It appears from Table 21.11 that the results of rapeseed oil are not very sensitive to the identification of marginal suppliers of barley and FFB. Fo r global warming the results vary be tween -2% and +3% compared to the baseline situation. The most sensitive impact category is land use where the changes are within -10% and +6% compared to the baseline situation. The results of rap eseed are most sensitive to the identification of the marginal supplier of barley while the marginal supp lier of FFB only affects the results -1% to +1%.

Impact category 1 t palm oil (baseline) 1 t palm oil (barley, RUS) 1 t palm oil (barley, EU25) 1 t palm oil (FFB, MY) 1 t palm oil (FFB, IN) Global warming (t CO 2 ) 2.36 2.30 2.43 2.28 2.43 Ozone depletion (g CFC11) 0.0446 0.0389 0.0463 0.0431 0.0461 Acidification (kg SO 2 ) 13.3 12.4 13.7 12.7 13.8 Eutrophication (t NO 3 ) 0.119 0.116 0.113 0.111 0.127 Photochemical smog (kg ethene) 0.509 0.480 0.520 0.503 0.515 Land use (m 2 y) 1.75 1.32 2.00 1.63 1.88 

## Table 21.12: Characterised results of 1 t palm oil, baseline compar ed with the the sensitivity analyses were the margin suppliers of barley and FFB (oil palm fruit) are changed.



## The results of palm oil are slightly more sensitive to th e identification of marginal suppliers of barley and FFB than rapeseed oil. The results of global warming vary be tween -3% to +3% compared to the baseline situation. The most sensitive impact category is land where the resu lts vary from -25% to +14%. In general the results are more sensitive to the identification of the marginal supplier of barley than the identification of the marginal supplier of FFB.

Comparing Table 21.11 and Table 21.12 it appears that the results of rap eseed oil and palm oil change in the same direction when the marginal su ppliers are displaced by alternative suppliers in the sensitivity analyses. Thus, the identification of marginal suppliers of barl ey and FFB has only little effect on the comparative results of rapeseed oil and palm oil.

## 228 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 21.3 No. 3: System delimitation, type of land transformed into agricultural land

Section 19.1 shows that the emissions from transformation of non-cultivated land into oil palm plantations in Malaysia and Indonesia and into soybean fields in Brazil vary significantly dependant on the affected type of land (grassland, savannah, forest). Also in the case of tr ansformation into barley fiel ds in Canada the results are regarded as uncertain because it is assumed that undi sturbed prairie grassland is affected. There may be other types of land available for transformation into barley fields, e.g. set-aside land as in the EU.

This sensitivity analysis tests the effect on impacts oftransformation of land from different assumptions regarding the affected land.

Affected crops Sensitivity analyses Rapeseed (DK) Oil palm (MY&IN) Soybean (BR) Barley (CAN) Baseline Set-aside 50% alang-alang grassland 50% deg. forest 95% savannah 5% deg. forest Grassland, natural prairie

MY&IN (grassland affected) Set-aside Alang-alang grassland 95% savannah 5% deg. forest Grassland, natural prairie

MY&IN (deg. forest affected) Set-aside Deg. forest 95% savannah 5% deg. forest Grassland, natural prairie

MY&IN (primary forest affected) Set-aside Primary forest 95% savannah 5% deg. forest Grassland, natural prairie

BR (savannah affected) Set-aside 50% alang-alang grassland 50% deg. forest Savannah Grassland, natural prairie

BR (deg. forest affected) Set-aside 50% alang-alang grassland 50% deg. forest Deg. forest Grassland, natural prairie

BR (primary forest affected) Set-aside 50% alang-alang grassland 50% deg. forest Primary forest Grassland, natural prairie

CAN (set-aside affected) Set-aside 50% alang-alang grassland 50% deg. forest 95% savannah 5% deg. forest Set-aside



## Table 21.13: Description of sensitivity analyses testing aff ected land transformed into agricultural land.



## The scenario analysed is scenario 2. Two different alte rnatives of scenario 2 exist for rapeseed oil; expansion of the cultivated takes place locally in Denmark or in creased cultivation of rap eseed displace the marginal crop, which is barley. This is then compensated for by increased productio n in Canada. Here the alternative when increased rapeseed cultiv ation takes place locally in Denmark is a pplied. Scenario 4 is not suitable analysing impacts from transformation because it do es not include any transformation processes.

Only two impact categories are analysed, i.e. global warming and biodiversity. Characterisation factors for biodiversity are based on Schmidt (2007c) and additional characterisation factors given in Appendix 4: Characterisation factors for land use in Brazil and Canada . Characterisation factors for transformation of primary forest in Malaysia/Indonesia are assumed to be represen tative for primary forest in Brazil, and transformation of set-aside land in Denmark is assumed to be represen tative for transformation of set-aside land in Canada. The characterised results for change d parameters in Malaysia/Indonesia, Brazil and Canada are shown in Table 21.14
, Table 21.15 and Table 21.16 respectively.

The results shown here represent the needed land to be transformed in order to increase the production of rapeseed oil and palm oil with 1 tonne. This functional unit is not directly comparable to the one used when considering contineous cultivation/production because transformation of a piece of land can support several functional units over time. The results are calculate d using the inventory data described in section 19.1

.

## 21 Sensitivity analyses 229

Baseline MY&IN => grassland MY&IN => deg. forest MY&IN => prim. forest Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) -59.4 43.4 -74.2 -14.9 -44.6 102 -44.6 102 Biodiversity (wS100) -385 -72.4 -402 -138 -368 -7.08 47.0 1628 

## Table 21.14: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were different types of land in Malaysia/Indonesia (MY&IN) are transformed into oil palm. (Scenario 2 has been used for this sensitivity analysis)



## It appears from Table 21.14 that the contributions to global warmi ng and biodiversity are very sensitive to assumptions regarding the affected land in Malaysia/I ndonesia. Though, the results are sensitive to assumptions the ranking of rapeseed oil and palm oil is not a ffected in any of the sensitiv ity analyses shown in the table. The negative impacts associated with rapeseed oil are because of avoided transformation of savannah and forest in Brazil and prairie grassland in Canada. It also appears that when forest (degraded or primary) is transformed in order to expand oil palm cultivation th e contribution to global warming is significant: more than 100 t CO 2 

## -eq. per t palm oil. Assuming a contribution to global warming at 2.4 t CO 2 

## -eq-/t palm oil from continuous cultivation/producti on the contribution at 102 t CO 2 

## -eq. from transformation corresponds to approximately 43 years of cultiovation/production of palm oil from the transformed area.

Baseline BR => savannah BR => deg. forest BR => prim. forest Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) -59.4 43.4 -49.0 43.5 -256 43.0 -256 43.0 Biodiversity (wS100) -385 -72.4 -393 -72.5 -229 -72.1 -3005 -78.6 

## Table 21.15: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were different types of land in Br azil (BR) are tr ansformed into soybean fields.



## It appears from Table 21.15 that palm oil is not sensitive to assump tions regarding affected land in Brazil while rapeseed oil is more sensitive. But still no of th e tested assumptions can affect the ranking between rapeseed oil and palm oil.

Baseline CAN => set-side Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) -59.4 43.4 -59.4 43.4 Biodiversity (wS100) -385 -72.4 -310 22 

## Table 21.16: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analysis were set-aide land in Canada (CAN) is transformed into barley fields.



## Table 21.16 shows that the contribution to biodiversity from pa lm oil is very sensitive the assumptions regarding affected land in Canada while rapeseed oil is less sensitive. The sensitivity analysis show no effects on the impact of global warming. Again, the assumptions regarding affected land in Canada does not affect the ranking of rapeseed oil and palm oil.



# 21.4 No. 4: Energy, margina l source of electricity
This sensitivity tests the effect of unc ertainties in identification of the ma rginal source of electricity in Denmark, Malaysia/Indonesia, Brazil and Canada. Table 21.17 shows the marginal sources of electricity in the baseline situation and the in cluded sensitivity analyses.

## 230 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Region Baseline Coal Natural gas Denmark Coal Coal Natural gas Malaysia/Indonesia Coal Coal Natural gas Brazil Natural gas Coal Natural gas Canada Natural gas Coal Natural gas 

## Table 21.17: Description of sensitivity analyses testing the marginal source of electricity.



## Table 21.18 show the characterised results for the baseline s ituation and when the marginal source of electricity in all regions are coal and natural gas respectively. The results are not shown for land use and biodiversity because these impact categories are not a ffected in this sensitivity analysis.

Baseline Coal Natural gas Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) 2.39 2.36 2.36 2.36 2.29 2.34 Ozone depletion (g CFC11) 0.147 0.045 0.154 0.045 0.169 0.048 Acidification (kg SO 2 ) 26.0 13.3 25.9 13.3 25.6 13.2 Eutrophication (t NO 3 ) 0.211 0.119 0.211 0.119 0.211 0.119 Photochemical smog (kg ethene) 0.869 0.509 0.873 0.509 0.880 0.511 

## Table 21.18: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were the marginal source of elect ricity is coal and natural gas respectively.



## It appears from Table 21.18 that the source of marginal electricity has only little influence on the results. The result for rapeseed oil of global warming is somehow surprising because both the sensitivity analyses with only gas electricity and coal electricity respectively show lo wer contribution than the b aseline situation. The reason why the sensitivity analysis that appli es coal shows a lower contribution than the baseline is, that the electricity in the coutries of substituted commodities; Ca nada and Brazil, contributes with more CO 2 

## while Denmark is unchanged. The reason why the sensitivity analysis that applies gas shows a lower contribution than the baseline is that the electricity in Denmark contributes with less CO 2 

## (gas instead of coal) while Canada and Brazil are unchanged.



# 21.5 No. 5: Energy, representativin ess of data on district heating in Denmark
In scenario 2 the two alternatives of rapeseed oil (local expansion versus crop displacement) show significant different results. The main reason for that is that in th e ‘constraint area scenario’ the displaced spring barley inDenmark is associated with a significant smaller environmental impacy than the required extra barley in Canada. In the ‘local expansion’ scenario the cultivation of spring barley in Denmark is not affected. The main reason that spring barley in Denmark performs good is that the straw is utilised for heat and electricity production. The data for the displaced heat are based on site specific data only valid for the city Aarhus and its surroundings. Therefore, this sensitivity applies other data for district heating production. Many district heating plants in Denmark are small decentral ised back pressure natural gas fire d combined heat and power plants. Opposite to large central heat and power plants whic h can swich between condensating mode and back pressure mode, the small decentralised produce an output of electricity and heat with fixed ratio. According to Danish Energy Authority (2004), a typical decentralised heat and power plant (40-125 MW gas turbine) have a total energy efficuency at 91% and a electricity effici ency at 36-42% (assumed 39%). Thus, the energy output per 100% input is 39% electricity and 52% heat. The pr oduction is determined from the local demand for heat and co-produced electricity is sold to the grid where itsubstitutes the marginal source of electricity. The inventory data for production of 1 MJ heat are shown in Table 21.19

.

## 21 Sensitivity analyses 231

Interventions Amount Applied LCI-data Product outputs District heat 1 MJ Product of interest Electricity 0.75 MJ Co-product, see system expansion below System expansion Avoided electricity -0.75 MJ See Table 3.3 Energy use Natural gas burned in gas turbine 1.92 MJ Natural gas, burned in power plant/NORDEL U 

## Table 21.19: Description of sensitivity analyses testing the marginal source of electricity.



## The characterised results of the baseline and the sensitivity analysis are shown in Table 21.20 . The results are not shown for land use and biodiversity because these imp act categories are not affected in this sensitivity analysis.

Baseline Decentralised district heat

Impact category 1 t rapeseed oil (constrained area) 1 t rapeseed oil (local expansion) 1 t rapeseed oil (constrained area) 1 t rapeseed oil (local expansion) Global warming (t CO 2 ) 5.15 2.40 4.53 2.54 Ozone depletion (g CFC11) 0.210 0.147 0.208 0.148 Acidification (kg SO 2 ) 39.2 25.8 37.9 26.1 Eutrophication (t NO 3 ) 0.201 0.172 0.200 0.172 Photochemical smog (kg ethene) 1.22 0.869 1.18 0.878 

## Table 21.20: Characterised results of 1 t rapeseed oil (constraine d area as well as local expansion), baseline compared with the sensitivity analyses were district heating from a centralised power plant is displaced by decentralised district heat. The results are for scenario 2.



## Table 21.20 shows that the data for district heating have some influence. If decentralised district heating is applied instead of centralised heat as in the baseline situation, the impact of the constrained area scenario are reduced while impacts of the local expansion scenario are increased. The reason why the impacts in the constrained area scenario are reduced is that the district heating in the sensitivity analysis is associated with smaller impacts than the impacts fro m centralised heat as in the baselin e situation. The constrained area scenario is associated with a considerable high use of dist rict heat. This is because Danish spring barley is displaced and spring barley displaces heat from utilisation of straw. On the other hand, the reason why the impact in the local expansion scenario are incr eased in the sensitivity analysis is that this scenario is associated with a negative net use of heat because more is displaced (utilisation of rapeseed st raw) than used (rapeseed oil mill and refinery).



# 21.6 No. 6: Agricultural cult ivation, energy for traction
According to Table 5.11 the energi use for traction per ha rapeseed field varies from 3,369 MJ to 4,488 MJ depandant on the soil type, cultivati on practice and data source. These vari ations correspond to percentual deviations from the applied energy use for traction at 3,612 MJ between -7% and +24%. The sensitivity analysis presented in this section present the baseline situation compared with two situations; one situation where the energy use for traction for all crops are reduced by 7% and one where it is increased by 24%. These variations are also in good accordance with the variations for traction in oil palm plantations (see Table 6.3 )

The characterised results of the sensitivity analyses are shown in Table 21.21 . The results are not shown for land use and biodiversity because these impact categori es are not affected in this sensitivity analysis.

## 232 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Baseline Reduced traction all crops (-7%) Increased traction all crops (+24%) Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) 2.39 2.36 2.38 2.36 2.44 2.36 Ozone depletion (g CFC11) 0.147 0.045 0.146 0.044 0.153 0.045 Acidification (kg SO 2 ) 26.0 13.3 25.9 13.3 26.4 13.3 Eutrophication (t NO 3 ) 0.211 0.119 0.211 0.119 0.212 0.119 Photochemical smog (kg ethene) 0.869 0.509 0.861 0.508 0.894 0.512 

## Table 21.21: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were the marginal source of elect ricity is coal and natural gas respectively.



## It appears from Table 21.21 that variations in the energy use for tr action affect the results insignificantly. Therefore, the uncertainties related to that are regarded as of minor importance.



# 21.7 No. 7: Rapeseed cultivation, soil type
The applied soil type on which Danish rapeseed is cultivat ed in the study is an average of 59% clay and 41% sand. The sensitivity analysis presente d in this section shows the differen ce of cultivating rapeseed on clay and sandy soils. The inventory data for cultivation on clay and sand are co mprehensively described in section 5

. The characterised results are shown in Table 21.22

.

Impact category 1 t rapeseed oil (baseline) 1 t rapeseed oil (clay soil) 1 t rapeseed oil (sandy soil) Change (clay) Change (sand) Global warming (t CO 2 ) 2.39 2.17 2.79 -9% 17% Ozone depletion (g CFC11) 0.147 0.138 0.164 -6% 11% Acidification (kg SO 2 ) 26.0 24.3 28.9 -7% 11% Eutrophication (t NO 3 ) 0.211 0.166 0.294 -21% 39% Photochemical smog (kg ethene) 0.869 0.823 0.955 -5% 10% Land use (m 2 y) 3374 2716 4590 -19% 36% Biodiversity (wS100) 10.8 9.90 12.3 -8% 15% 

## Table 21.22: Characterised results of 1 t rapeseed oil, baseline compared with rapeseed cultivated on clay and sandy soil.



## It appears from Table 21.22 that the impact categories eutrophication and land use are th e ones most sensitive to the type of soil. In general cultiv ation on clay is environmental prefer able to cultivation on sand. However, since most of Denmark is already occ upied with agriculture, cities and infrastructure it is hard to move agriculture from cultivation on sandy soils to cultivation on clay soils. However, when agricu lture is transformed into other types of land use it is environmental preferab le to locate these transformations on sandy soils.

In section 5 it is assumed that rapeseed fields are evenly dist ributed on the 59% clay soil and 41% sandy soil in Denmark. Since the difference between cultivation on sand and clay is relatively limited, the actual distribution of rapeseed fields can deviate relatively much from these figures without changing the results of the LCA in the same rate. Therefore, it is not likely that the soil type cultivated can affect the ranking of rapeseed oil and palm oil (see baseline for palm oil in Table 20.1

.


# 21.8 No. 8: Rapeseed cultivativation , N-fertiliser produced using best available techniques (BAT)
According to section 5.4 the emission of N 2 

## O from the production of nitric acid, which is used for the production of ammonium nitrate fertilisers, can be signifi cantly reduced using BAT. The European Commission (2006d) presents a range of different technologi es to be considered in order to reduce N 2 

## O emissions from production of nitric acid. Examples of technologies are alternative oxidation catalysts, extension of reactor chamber, catalytic N 2 

## O decomposition in the oxidation reactor, combined NO x 

## and N 2 

## O abatement in tail gases

## 21 Sensitivity analyses 233



## and non-selective catalytic reduction of NO x 

## and N 2 

## O in tail gases. The presented technologies reduce the N 2 

## O emission from 30-50% up to 98-99%. There are no trade o ffs in order to achieve the given reductions. This sensitivity analysis analyses the effect on the results if the emission of N 2 

## O in the tail gas from nitric acid is reduced by 85%. This is done by reducing the N 2 

## O emission from 8.39 g to 1.26 g per kg nitric acid. The change is implemented in the ecoinvent process: ‘Nitric acid, 50% in H2O, at plant/RER’ (ecoinvent 2004).

Table 21.23 shows the characterised results of the sensitivit y analysis. The contributions to land use and biodiversity are not shown because these impact categori es are not affected in this sensitivity analysis.

Impact category 1 t rapeseed oil (baseline) 1 t rapeseed oil (fertiliser produced with BAT) Change

Global warming (t CO 2 ) 2.39 1.89 -21% Ozone depletion (g CFC11) 0.147 0.147 0% Acidification (kg SO 2 ) 26.0 26.0 0% Eutrophication (t NO 3 ) 0.211 0.211 0% Photochemical smog (kg ethene) 0.869 0.869 0% 

## Table 21.23: Characterised results of 1 t rapeseed oil, baseline compared with rapese ed oil produced in an oil mill using the full press technology.



## It appears from Table 21.23 that the only affected impact category is global warming. Using fertiliser produced with BAT reduces the contribution to global warming remarkably with 21%.



# 21.9 No. 9: Agricultural cultiv ation, N changes in soil matter

In section 5.6 the changes in N soil matter is assumed to be zer o. The argument for that is that the the changes in N soil matter should be ascribed to transformation of land rather than continuous cultivation and therefore the changes in N soil matter are included in the impacts of transformation (see section 19.1 ). However, in order to monitor the effect of chosing that approach, this sensitivity analysis applies an annual decrease in N soil matter at 24 kg N/ha for rapeseed, barley and soybean. Th e 24 kg N/ha is estimated for rapeseed in section 5.6

. The same change in N soil matter is assumed for barley in Canada and soybean in Brazil. However, the change in N soil matter for palm oil is regarded as of minor importance since input of N in crop residues are higher and because the crop rotation has a duration of 25 years co mpared to only one year for the other crops. Henson (2004) also regards the change in N soil matter under oil palm to be zero.

The change in N soil matter at 24 kg/ha is fed into the N-balances in sections 5.6
, 7.5 and 8.6 as an input of N (which increases the N-surplus), and the corresponding em issions are calculated using the same methods as described in these sections. The affaected emissions are nitrate (which is calculated as the difference between the N-surplus and the other N-related emissions) and N 2 

## O (indirect from increased nitrate emissions).

The characterised results of the sensitivity analysis are shown in Table 21.24 . Only the contributions to global warming and eutrophication are shown since th ese are the only impact categories affected.

Baseline Change in N soil matter included Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO Global warming (t CO 2 ) 2.39 2.36 2.54 2.34 Eutrophication (t NO 3 ) 0.211 0.119 0.268 0.111 

## Table 21.24: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were the annual change in N soil matter is 24 kg N/ha.



## Table 21.24 shows that an annual change in N soil matter at 24 kg N/ha for soil under rapeseed increases the contribution to global warming and eutrophication 6% a nd 27% respectively. The change in N soil matter has

## 234 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## only minor influence on the results of palm oil. If changes in N soil matter are included the ranking of rapeseed oil and palm oil regarding global warm ing changes from being almost even to a situation where palm oil performs 7% better than rapeseed oil.



# 21.10 No. 10: Agricultural cultivati on, heavy metal contents in fertilisers
According to section 5.6 and 6.7 the determination of the contents of h eavy metals in fertiliser is related to uncertainties. Therefore this sensitivity analysis assesses these uncertainties’ influence on the LCIA results.

Heavy metals contribute to ecotoxicity and human toxicity . Human toxicity is not c onsidered here because the LCIA methods for that are even more uncertain than of ecotoxicity (see description of uncertainties of ecotoxicity in section 21.1 ). Applying the EDIP97-method to the baselin e situation and analysing the agricultural stage only, the contribution to ecotoxicity (chronical, water) is shown in Table 21.25

.

Contributing processes Rapeseed FFB Soybean Barley Heavy metals from fertilisers 0.065% <0.00001% 0.00061% 5.4% Pesticides 99.5% >99.9% >99.9% not included Other (e.g. production of fertiliser and transport) 0.46% 0% 0.0014% 94.6% 

## Table 21.25: Process contriobution to aquatic ecotoxicity for the a ffected crops (only the agri cultural stage is included).



## Table 21.25 shows that heavy metals constitute only a minor share of the total contribution to ecotoxicity. Thus, it is estimated that uncertainties in determination of the heavy metal content in fertilisers contribute with insignificant uncertainties in the result. The very low contribution to aquatic ecotoxicity from FFB is because it is assumed that all emissions are to soil, and most of those emissions do not contribute to aquatic ecotoxicity. If carrying out the same analysis for ecotoxicity to so il (EDIP97) for FFB the contribution from heavy metals are still insignificant.

In addition it should be mentioned that becau se of uncertainties in LCIA-methods, see section 21.1 , toxicity should be dealt with in a more qualitative ma nner than the remaining impact categories.



# 21.11 No. 11: Agricultural cultivati on, initial compartment of pesticide emissions

In sections 5.6
, 6.7 and 7.5 it is assumed that the applied pesticides ends as evenly distributed emissions to air, water and soil. This is a very rough assumption. Ther efore, this sensitivity anal ysis compares the baseline situation with situations where all app lied pesticides ends as emissions to air, water and soil respectively. Only the contribution to ecotoxicities is included. The characterised results are shown in Table 21.26

.

Baseline Compartment => air Compartment => water Compartment => soil Impact category 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO 1 t RSO 1 t PO ETWC (mio m3) -4685 1423 0.532 0.0567 -149600 3951 0.532 0.0567 ETWA (mio m3) -127 52.3 0.0560 0.0082 -4168 148 0.0560 0.0082 ETSC (mio m3) -24.5 0.625 0.0156 0.00249 0.0153 0.00249 -736 0.315 

## Table 21.26: Characterised results of 1 t rapeseed oil (RSO) and 1 t palm oil (PO), baseline compared with the sensitivity analyses were the compartment of pesticides are changed. Abbreviations: ETWC (ecotoxi city, water, chronic), ETWA (ecotoxicity, water, acute) and ETSC (ecotoxicity, soil, chronic)



## It clearly appears from Table 21.26 that the contribution to ecotoxicity is extremely sensitive to the intial compartment of pesticide emissions. E.g. the contribu tion to ETWC from rapeseed oil varies from -149600 mio m 3

## 21 Sensitivity analyses 235



## (when the initial compartment is water) to 0.532 m 3 

## (when the initial compartment is either air or soil). Similar vaiations are found for the impact categories ETWA and ETSC and for palm oil.

Therefore, the characterised results for ecotoxicity are associated with ve ry significant uncertainties and they do not provide useful information if not a more precise determination of compartment is carried out. However, referring to the uncertainties regading toxicity modelling in LCIA-methods in general as described in section 21.1 , the characterised results will still be associated w ith significant uncertainties. Hence, improved modelling of compartments of pesticide emissions will not improve the certainty of the results of toxicity notably.



# 21.12 No. 12: Oil palm cultivation, yields

As described in section 21.2 the yield of oil palm may vary dependant on the supplier, i.e. Malaysia or Indonesia. The yields are also depandant on the cultivation practices (see section 6 ). Cultivation practices vary among the actors, i.e. small holders, FELDA and private estates. Thus, there may be significant differences in yields in a small holder plantation and in a large private estate, ev en if the same agricultura l inputs are used. The differences may be due to water management (drainage), breed ing of new palms, timing of harvest and replanting and weed control. No data correlating agricultural practices and yields (regardless fertiliser input) have been identified. Therefore, the sensitivity analysis presented in this section refers to the results of the sensitivity analysis in section 21.2 where FFB yields of 17.95 t/ha, 18.87 t/ha and 19.84 t/ha are compared (these yields represent Indonesia, Malaysia/Indonesia and Malaysia r espectively). The variations above correspond to yield variations between -5% and +5%. The resu lts of the sensitivity analysis in section 21.2 show that changes of oil palm yields at ±5% cause chang es in the contribution to global warming at ±3% and changes in land use requirements at ±7%. Thus, the determination of yields and the effect of cultivation practice have a relative large effect on the obtained results. Thus, if the marginal suppliers of oil palm are represented by plantations with cultivation practice and corresponding yields well above average (20-25 t FFB/ha), the contribution to global warming and land use of palm oil may be reduced significantly, i.e. up to 20% for global warming and 45% for land use.



# 21.13 No. 13: Oil palm cultivation, soil type
It appears from section 6.7 that the levels of emissions from cultivation on peat and mineral soil vary significantly. According to Henson (2004) appr oximately 4.1% of the oil palm cultivated area in Malaysia is on peat. The same is assumed to be valid fo r Indonesia. According to Corley and Tinker (2003, p 81) much of the open land remaing land for expanding the oil palm cultivation in SE-Asia is peat soils. According to Wetland International (2007), more than 50% of the planned new plan tations in Malaysia and Indonesia are on peat soils.

Therefore, the applied 4.1% peat may not represent the marginal land cultivated with oil palm. This sensitivity analysis compares cultivation on peat soil with cultivation on mineral soil. The inventory data relating to that are described in section 6.7 . The characterised results are shown in Table 21.27 . The results are only shown for global warming, acidification and eutrophication since these impact categories are only ones affected in this sensitivity analysis.

Impact category 1 t palm oil (baseline) 1 t palm oil (peat soil) 1 t palm oil (mineral soil) Change (peat soil) Change (mineral soil) Global warming (t CO 2 ) 2.36 12.8 1.91 444% -19% Acidification (kg SO 2 ) 13.3 19.8 13.0 49% -2% Eutrophication (t NO 3 ) 0.119 0.105 0.119 -11% 1% 

## Table 21.27: Characterised results of 1 t palm oil, baseline compared with oil palm cultivated with low and high levels of CO 2 

## -emissions from peat.

## 236 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## It appears from Table 21.27 , that cultivation on peat contributes significantly more to global warming (4 to 5 times more) and to a lesser extent acidification than cultivation on average soil (of wich 4.1% is peat). The difference between average soil and mineral soil is less pronounced. The main reason why cultivation on peat contributes more to global warming is a higher level of CO 2 

## -emission, but also a higher level of N 2 

## O-emission contributes. The difference in contribution to acidification is due a higher level of nitric oxide (NO) from denitrification peat soil.

The significant high contribution to global warming from cultivation of oil palm on peat soil exceeds the contribution to global warming from rapeseed oil many fold . Though, the uncertain and probably overestimated contribution to global warming from rapeseed cultivated by increased yield (scenario 3) is not matched by oil palm cultivated on peat. However, as mentioned rapeseed o il in scenario 3 is not very likely to occur since regulations on N-fertiliser in the EU makes this way of increasing yields impossible in most regions. Anotheraspect is that increases by yield achieved by increased NPK-fertiliser will probably take place on the least fertilised soils. Thus, the crop response to additional fertiliser will probably by higher than the response modelled for rapeseed in scenario 3.

Hence, most efforts towards cleaner production and be tter environmental performa nce regarding global warming in the palm oil industry will be of little significance and will be overruled if the oil palm is cultivated on peat.



# 21.14 No. 14: Oil palm cultivation, uncertainties of CO 2 

# -emissions from peat soil
According to section 6.7 the annual carbon emission from cultivation of 1 ha with oil palm is 10 t (corresponding to 37 t CO 2 

## ). However, this is related to some uncertainties. In section 6.7 annual emission levels of carbon from cultivation of peat between 7.5 t C/ha and 20 t C/ ha have been identified. These figures correspond to 28 t CO 2 

## and 73 t CO 2 

## respectively. Thus, the sens itivity analysis presented in th is section compare the baseline scenario with above mentioned CO 2 

## emission levels. The characterised results are shown in Table 21.28 . The results are only shown for global warming since this is the only impact category affected in this sensitivity analysis.

Impact category 1 t palm oil (baseline) 1 t palm oil (low: 28 t CO 2 /ha) 1 t palm oil (high: 73 t CO 2 /ha) Change compared to baseline (low: 28 t CO 2 /ha) Change compared to baseline (high: 73 t CO 2 /ha) Global warming (t CO 2 ) 2.36 2.27 2.73 -4% 16% 

## Table 21.28: Characterised results of 1 t palm oil, baseline compared with oil palm cultivated with low and high levels of CO 2 

## -emissions from peat.



## It appeat from Table 21.28 that the uncertainties in the determination of CO 2 

## -emissions from peat have some effect on the results for global warming. However, if more than the actual 4.1% of the oil palm is cultivated on peat, the uncertainties of determination of CO2 from peat may be more significant.



# 21.15 No. 15: Barley cultivation, omi ssion of use and emissions of pesticides
The use of and the emissions of pesticides are omitte d from the inventory of barley. As shown in section Table 21.26 in section 21.10 , the emissions of pesticides contribute signif icantly to ecotoxicity. Thus, when omitting the emissions of pesticides from the inventory of barl ey, the credit from displaced barley will be zero when performing system expansion. Since more barley is disp laced in the product system of palm oil than for rape-

## 21 Sensitivity analyses 237



## seed oil, the omission of pesticides is therefore in the favour of rapeseed oil. However, as described in section 21.1 and 21.11 the uncertainties in LCIA-methods regarding toxi city and uncertainties in the determination of the receiving compartments of pesticide emissions make the results of toxicity so uncertain, that the results do not contribute with usable information.



# 21.16 No. 16: Rapeseed oil mill, so lvent extraction versus full
press In the baseline cenario, the applied technology in the ra peseed oil mill is solvent extraction. This sensitivity analysis applies the full press technology instead. The i nventory presented here is based on data provided byEmmersen (2006) for the rapeseed oil mill; Scanola in Aarhus, Denmark. This oil mill uses the full press technology. In 2005 Scanola processed 220,000 tonne rapeseed (Emmersen 2006).

When using the full press technology, no solvents are u sed for extraction. Not using solvents will affect the amount of oil in the oil meal. According to Emmersen (2006), the product flow in Scanola rapeseed oil mill in Aarhus Denmark related to 1 t crude rapeseed oil include an input of 2.597 t rapeseed and outputs of 1.558 t oil meal and 0.039 t loss (water). Based on the oil content in rapeseed given in Møller et al. (2000) it can be calculated that the oil content in the oil meal is 9.5%. The pr operties of rapeseed oil meal with 10% fat are given in Table 21.29

.

Paramter Rapeseed meal, per kg meal (10% fat) Protein 310 g protein Fodder energy* 1.059 SFU 

## Table 21.29: Relevant properties of rapeseed oil meal. * Fodder energy is measured in SFU (Scandinavian Fodder Units). (Møller et al. 2000)



## Table 21.29 Based on the figures in and the product flow in the full press rapeseed oil mill specified above, the product flow related to 1 t refine d rapeseed oil produced with full press technology is given in Figure 21.1 and Equation (6 ) . The losses in the refinery stage are the same as in Figure 2.1

.

Rapeseed oil (RSO)

Rapeseed oil mill

1.000 t NBD RSO 1.584 t meal 2.641 t rapeseed

Rapeseed field

0.817 ha y Denmark

491 kg protein 1,677 SFU energy Refinery 1.017 t crude RSO

12 kg fodder fat 0 kg protein 28 SFU energy 

## Figure 21.1: Product flow in the oil mill stage and refinery stag e related to 1 t refined rapeseed oil (full press).



## The affected palm oil, soybean meal and barley are calculated in Equation (6 )

.

## 238 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

0.545 BLt 1.022 SMt 0.249POt 1.000 RSOt SFU0 prot.kg0

oilt1

BLSFU/t952 BLprot./tkg91.8 BLoil/tt0
BLt SMSFU/t1,207 SMprot./tkg436 SMoil/tt0.244
SMt
POSFU/t191 POprot./tkg19.2 POoil/tt1
POt
RSOSFU/t 1,705 RSOprot./t kg491

RSOoil/tt 1
RSO t 1

−= −= = = ⇓ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

= ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅+ ⎥ ⎥ ⎥
⎦ ⎤

⎢ ⎢ ⎢
⎣ ⎡

⋅



## (6)

The energy use related to 1 t crude rapeseed oil at the full press rapeseed oil mill, Scanola are given in Table 21.30

.

Energy 1 t rapeseed oil Electricity 467 MJ Heat (fuel oil) 852 MJ 

## Table 21.30: Energy use in the oil mill stage related to 1 t crude rapeseed oil using full press technology.



## The remaining LCI data are similar to the solvent extraction oil mill as described in Table 9.13 except the use and emission of hexane and waste water. The applied LCI data for burning of fuel oil is: ‘Light fuel oil, burned in boiler 100kW, non-modulating’ (ecoinvent 2004).

Applying the product flow given in Equation (6 ) and the inventory data described above to scenario 4, the characterised results can be calculated, see Table 21.31

.

Impact category 1 t rapeseed oil (baseline) 1 t rapeseed oil Change (full press technology) Global warming (t CO 2 ) 2.39 2.27 -5% Ozone depletion (g CFC11) 0.147 0.129 -12% Acidification (kg SO 2 ) 26.0 24.6 -5% Eutrophication (t NO 3 ) 0.211 0.212 0% Photochemical smog (kg ethene) 0.869 0.537 -38% Land use (m 2 y) 3374 2723 -19% Biodiversity (wS100) 10.8 11.5 6% 

## Table 21.31: Characterised results of 1 t rapeseed oil, baseline compared with rapese ed oil produced in an oil mill using the full press technology.



## Table 21.31 It appears from that the oil mill technology has only limited influence on the result regarding global warming, ozone deple tion, acidification and eutrophication. The mo st significant change is the contribution to photochemical smog, which is reduced because of elimination of hexane emissions. The land occupation is reduced because of avoided barley production in Canada which is relatively larger than the increased land use in Denmark. The corresponding impact on biodi versity is increased in the full press sensitivity analysis. The reason for this is that o ccupation of land in Denmark is weighted higher than occupation of land in Canada (Schmidt 2007c and Appendix 4: Characterisation factors for land use in Brazil and Canada ).



# 21.17 No. 17: Oil mills, capital goods
The determination of the use of capital goods in the oil mill stage is related to notable uncertainties. Table 21.32 provides on overview of the assumed use of capital goods in the different affected oil mills and the associated contribution to global warming.

## 21 Sensitivity analyses 239

Capital goods Oil mill Baseline Global warming (% of the oil mill stage) Building halls 3.9

⋅ 10 -4 m 2 0.094% Building, multi story 7.1

⋅ 10 -4 m 3 0.12% Machinery 0.085 kg 0.40% Rapeseed oil mill

Total 0.61% Building halls 1.8

⋅ 10 -4 m 2 0.028% Building, multi story 3.4

⋅ 10 -4 m 3 0.035% Machinery 0.040 kg 0.12% Palm oil mill

Total 0.18% Building halls 3.9

⋅ 10 -4 m 2 0.081% Building, multi story 7.1

⋅ 10 -4 m 3 0.10% Machinery 0.085 kg 0.35% Palm kernel oil mill

Total 0.53% Building halls 3.9

⋅ 10 -4 m 2 0.10% Building, multi story 7.1

⋅ 10 -4 m 3 0.12% Machinery 0.085 kg 0.43% Soybean oil mill

Total 0.65% 

## Table 21.32: The use of capital goods in the oil mill stage (per 1 t crop processed)



## It appears from Table 21.32 that the use of capital goods accounts for an insignificant share of the total contribution from the oil mill stage. The use of capital goods coul d be of magnitiude five fold larger and still be insignificant. Thus, uncertainties in the determination of the use of capital goods in the oil mill stage have insignificant effect on the results.



# 21.18 No. 18: Palm oil mill, POME treatment
One of the hotspots from palm oil is the emission of methane from anaerobic digestion of palm oil mill effluent which contributes to global warming. This sensitivity anal ysis examines the effect of installing a digester tank and utilisation of the biogas for electricity production in a gas turbine. The emissions related to that technology is based on UNFCCC (2006) which describes the emissi ons from a CDM project at United Plantation, Jendarata Palm Oil Mill. According to UNFCCC (2006, p 25, 28) 92.2% of the methane is captured. The remaining 7.8% is emitted to air from anaerobic pockets in the subsequently aerobic pond treatment. 8.4% of the methane that enters the combustion process is emitted due to incomplete combustion in boiler and flaring system(UNFCCC 2006, p 30). It is assumed that the total generation of biogas and methane is the same in the anaerobic lagoon system and when a digester tank is inst alled, i.e. 13.0 kg methane/t POME (see section 10.5 ). According to UNFCCC (2006) it can be estimated that 85% of the methane is utilised for energy purposes and the remaining 15% is flared. Thus, 10.2 kg of the 11.99 kg captured methane is utili sed for energy purposes. The flaring efficiency is 50% (UNFCCC 2006, p 30). Thus the methane emission from incomplete flaring is 15% of 11.99 kg methane multiplied with 50%, i.e. 0.90 kg methane.

It is assumed that the methane is used for electricity production since most palm oil mills have excess of heat and that electricity can be sold to the national grid. An efficiency at 40% for the turbine is assumed and emissions related to displaced electricity in Malaysia and Indonesia are described in Table 3.3 . The emissions from burning of biogas are described in section 13.4 , i.e. emission of methane is 1.5% of the input of gas to the motor, emission of sulphur dioxide is 0.018 g SO 2 

## /MJ input of energy in biogas and the emission of nitrogen oxides is 0.200 g NO x 

## /MJ input of energy in biogas. The energy content of methane is 50.4 MJ/kg (see Appendix 1: Data on fuels ).

The emissions of hydrogen sulphide (H 2 

## S) and ammonia (NH 3 

## ) in the baseline treatment (anaerobic lagoon)are assumed to be eliminated when applying the digester tank and utilisation of biogas. The emission of Dinitrogen oxide (N 2 

## O) is assumed to be unchanged as done in UNFCCC (2006, p 10-11).

## 240 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## The inventory of treatment of 1 t POME when the anaer obic lagoon treatment is displaced with a digeter tank and ultilisation of the biogas is summarised in Table 21.33

.

1 t POME Baseline Sensitivity analysis Energy production 0 MJ 205 MJ electricity (40% of energy input at 10.2 kg CH 4 with energy content at 50.4 MJ/kg) CH 4 from treatment (lagoon/digester tank) 13.0 kg CH 4 11.99 kg CH 4 captured (92.2%) 1.01 kg CH 4 emission (7.8%) CH 4 from incomplete flaring 0 kg CH 4 0.90 kg CH 4 emission (50% of 15% of 11.99 kg) CH 4 from gas motor 0 kg CH 4 0.15 kg CH 4 (85% of 1.5% of 11.99 kg) SO 2 from gas motor 0 kg SO 2 0.0092 kg SO 2 NO x from gas motor 0 kg NO x 0.10 kg NO x H 2 S from lagoon 0.0862 kg H 2 S 0 kg H 2 S NH 3 from lagoon 0.058 kg NH 3 0 kg NH 3 N 2 O from lagoon 0.0015 kg N 2 O 0.0015 kg N 2 O 

## Table 21.33: Inventory of treat ment of 1 t POME when digester tank and utilisation is installet.



## The characterised results are shown in Table 21.34 . The contributions to land use and biodiversity are not shown because these impact categories are not affected in this sensitivity analysis.

Impact category 1 t palm oil (baseline) 1 t palm oil (biogas POME treatment) Change compared to baseline Global warming (t CO 2 ) 2.36 1.36 -42% Ozone depletion (g CFC11) 0.0446 0.0439 -2% Acidification (kg SO 2 ) 13.3 12.2 -8% Eutrophication (t NO 3 ) 0.119 0.118 0% Photochemical smog (kg ethene) 0.509 0.256 -50% 

## Table 21.34: Characterised results of 1 t palm oil, baseline comp ared with palm oil produced in an oil mill treating POME in a digester tank and with utilisa tion of biogas for electricity production.



## It appears from Table 21.34 that applying the biogas treatment of POME affects the impact categories global warming and photochemical smog signifi cantly. The reduction in the contribution to global warming is mainly due to reductions in methane emissions but also displacement of CO 2 

## from coal based electricity from the grid contributes. The reduction in the contribution to photoc hemical smog is due to the reduced methane emissions.

The reduction of the contribution to global warming is quite significant. If palm oil is produced using biogas treatment of POME instead of the normally anaerobic la goon systems palm oil will pe rform better than rapeseed oil in all scenarios.



# 21.19 No. 19: Palm oil mill, steam requirement
According to section 10.1 the steam requirement in palm oil mills per t FFB is 0.65 t steam. Data on steam requirement have been indetified between 0.4 t steam a nd 0.65 t steam which corresponds to an interval between 1040 MJ and 1690 MJ. This sensitiv ity analysis applies a situation were the steam requirement is 0.5 t (1300 MJ).

In the baseline scenario 100% of the 130 kg fibre and 70 kg shell per t FFB are burned in the palm oil mill’s boiler producing 1708 MJ steam and 104 MJ electricity . The small difference between the produced steam (1708 MJ) and the required steam (1690 MJ ) is released to the atmosphere.

In the sensitivity analysis the redu ced required steam is assumed to be produced from burning of 100% of the fibre and 50% of the shell. Applying the calorific valu es, moisture contentsm utilisation factors and heat to electricity ratio as described in section 10.3 , the generated energy is then 1315 MJ steam and 78 MJ electricity.

## 21 Sensitivity analyses 241



## The remaining 35 kg shell per t FFB are sold as fuel in the cement industry (Chavalparit et al. 2006). It is assumed that the shells substitutes coal in a ratio one to one by energy content. 35 kg shells have an energy content at 633 MJ. The avoided emissions associated with the avoided burning of 633 MJ coal are obtained from

‘ Hard coal, burned in power plant/NORDEL U ’ (ecoinvent 2004). This inventory is modified so that only the emission from the burning of coal is included. It is assu med that the shells are transportated 300 km in a 28t lorry.

The electricity consumption in the oil mill is 73 MJ/t FFB (see Table 10.5 ). In the baseline situation the electricity production is 104 MJ. Thus, 31 MJ/t FFB electric ity from the grid is displ aced. In the sensitivity the electricity production is 78 MJ. Thus the displaced electricity is only 5 MJ/t FFB.

Table 21.35 

## shows the changed parameters in the sensitivit y analysis compared to the baseline scenario.

Interventions Baseline Sensitivity Applied LCI-data Displaced products Displaced electricity from the grid 31 MJ 5 MJ See Table 3.3 Displaced burning of coal in cement industry 0 MJ 633 MJ Modyfied version of ‘ Hard coal, burned in power plant/NORDEL ’ (ecoinvent 2004) Energ use Steam from power central 1691 MJ 1300 MJ The emissions are described below

Emissions From palm oil mill power central, burning of... 130 kg fibre 70 kg shell 130 kg fibre 35 kg shell See Table 10.4

From cenment industry power central, burning of... 0 kg fibre 0 kg shell 0 kg fibre 35 kg shell See Table 10.4

Transport Shells from oil mill to cement industry 0 tkm 10.5 tkm ‘ Transport, lorry 28t ’ (ecoinvent 2004)



## Table 21.35: Changed parameters in the sensitivity analysis with low steam requirement in the palm oil mill.



## The characterised results are shown in Table 21.36 . The contributions to land use and biodiversity are not shown because these impact categories are not affected in this sensitivity analysis.

Impact category 1 t palm oil (baseline) 1 t palm oil (low steam requirement) Change compared to baseline

Global warming (t CO 2 ) 2.36 2.13 -10% Ozone depletion (g CFC11) 0.0446 0.0465 4% Acidification (kg SO 2 ) 13.3 12.8 -4% Eutrophication (t NO 3 ) 0.119 0.118 0% Photochemical smog (kg ethene) 0.509 0.504 -1% 

## Table 21.36: Characterised results of 1 t palm oil, baseline compar ed with palm oil produced in an oil mill with low steam requirement and where excess shells are used in the cement industry where it displaces coal as fuel.



## Table 21.36 shows that the steam requirement and the use of excess biomass (shells) have a relatively large influence on the results for global warming. Reducing th e steam requirement from 0.65 t steam with 23% to 0.5 t steam and utilising the excess sh ells as biofuel causes a reduction of 10% contribution to global warming equalling 230 kg CO 2 

## per t palm oil.

## 242 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



# 21.20 No. 20: Palm oil mill, altern ative management options for EFB
Different uses/waste management optio ns are available for the empty friut bunches (EFB) from the palm oil mill. Application of the EFB as mulch in the plantation is used in the baseline scenario since this is regarded as the most widespread management option.

In less well managed palm oil mills the EFB is disposed of in landfill sites while the EFB is used as biofuel in the most well managed palm oil mills. This sensitivity analysis compares the three management options for EFB.

Table 21.37 provides the relevant characteristics of EFB.

Characteristics of EFB Value Source Amount of EFB per t FFB 0.225 t EFB/t FFB Table 10.1 Calorific value (lower) (fresh weight basis) 7.52 MJ/kg EFB (Subranamiam et al. 2004) Water content 60% Table 6.9 N content (fresh weight basis) 3.2 kg N/t EFB Table 6.9 P content (fresh weight basis) 0.38 kg P/t EFB Table 6.9 K content (fresh weight basis) 9.6 kg K/t EFB (Singh 1999) 

## Table 21.37: Relevant characteristics of EFB for the se nsitivity analysis presented in this section.



## In both sensitivity analyses, it is assume d that the ‘missing’ application of N, P and K from the EFB (which is not applied as mulch) is compensated for by additional application of artificial fertiliser. The additional application of fertiliser is determined from the c ontent of N, P and K in the EFB given in Table 21.37 . Since the removal of the EFB is compensated for, the nutrient ba lances are not affected in the two sensitivity analyses.

Emissions from the burning of EFB in the biomass plant are assumed to be the same per burned MJ biomass as in the power central in the palm oil mill burning fibre and shell. Table 10.4 shows the emissions per burned kg of fibre and shell. Since the burned composition of fibre and shell has a calorific value at 13.8 MJ/kg (see section 10.3 ), the emissions per MJ can easily be determined. The production of electricity is based on a biomass plant burning EFB in Malaysia. The plant is docum ented as a CDM project in UNFCCC (2007). It appears from UNFCCC (2007) that the electricity output is 1.25 MJ/kg burned EFB (fresh weight). This corresponds to an energy efficiency of the biomass plant at 17% (output of electricity compared to input of EFB in terms of calorific value).

Only three emissions from the landfilling of the EFB have been accounted for, i.e. methane to air, leaching of nitrate and leaching of phosphorous. The emission of methane has been calculated using the the model described in UNFCCC (data of issue not given) and the model parameters pres ented in UNFCCC (2007). The methane emission from the landfilling of EFB has been calculated as 18 kg CH 4 

## /t EFB. The emissions of nitrate and phosphorous have been dete rmined assuming that all the content of N and P in the EFB will leach as nitrate and phosphorous.

Table 21.38 shows the changed parameters in the sensitivit y analysis compared to the baseline scenario.

## 21 Sensitivity analyses 243

Interventions Baseline Sensitivity (EFB isdisposed off by landfilingl) Sensitivity (EFB is utilised in a biomass plant) Applied LCI-data

Displaced products Displaced electricity from the grid 0 MJ 0 MJ 281 MJ See Table 3.3 Additional use of fertiliser in agricultural stage Additional N-fertiliser (as N), ammonium sulphate 0 kg N 0.53 kg N 0.53 kg N ‘ Ammonium sulphate, as N, at regional storehouse/RER ’ (ecoinvent 2004) Additional N-fertiliser (as N), urea 0 kg N 0.19 kg N 0.19 kg N ‘ Urea, as N, at regional storehouse/RER ’, (ecoinvent 2004) Additional P-fertiliser (as P 2 O 5 ) 0 kg P 2 O 5 0.20 kg P 2 O 5 0.20 kg P 2 O 5 Phosphate rock, see section 6.5 : Fertilisers Additional K-fertiliser (as K 2 O) 0 kg K 2 O 2.61 kg K 2 O 2.61 kg K 2 O Potassium chloride, as K 2 O, at regional storehouse/RER’, ecoinvent (2004) Emissions Emission from burning of EFB in biomass plant - - 225 kg EFB See Table 10.4

Emissions from EFB disposed off at landfill - 4.1 kg CH 4 3.2 kg NO 3 0.086 kgP - -



## Table 21.38: Changed parameters in the sensitivity analyses with alternative management options for EFB from the palm oil mill. All values are given per t of proces sed FFB (this is equivalent to 0.225 t EFB).



## The characterised results are shown in Table 21.39 . The contributions to land use and biodiversity are not shown because these impact categories are not affected in this sensitivity analysis.

Impact category 1 t palm oil (baseline) 1 t palm oil (EFB at landfill) 1 t palm oil (EFB as biomass) Change compared to baseline (EFB at landfill) Change compared to baseline (EFB as biomass) Global warming (t CO 2 ) 2.36 2.81 1.95 19% -17% Ozone depletion (g CFC11) 0.0446 0.0464 0.0451 4% 1% Acidification (kg SO 2 ) 13.3 13.3 12.9 0% -3% Eutrophication (t NO 3 ) 0.119 0.146 0.119 23% 0% Photochemical smog (kg ethene) 0.509 0.648 0.529 27% 4% 

## Table 21.39: Characterised results of 1 t palm oil, baseline co mpared with alternative management options for EFB.



## It appears from Table 21.39 that land filling of EFB increases the contributions to global warming, eutrophication and photochemical smog remarkable. The increased contributions to global warming and photochemical smog are caused by increased methane emissions from th e landfill site while the increased contribution to eutrophication is caused by leaching of nitrate and phosphorous from the landfill site.

Utilising EFB for energy purposes in a biomass plant reduces the contribution to gl obal warming remarkably. This is due to displaced electric ity production. No other impact categories are remarkably affected.



# 21.21 No. 21: Insensified cultivati on, yield-respons es to additional fertiliser
As it appears from section 18.2 and Table 18.2 the determination of yield-responses to additional fertiliser application is related to considerable uncertainties. Uncertainties in the determination of crop responses are directly proportional with the associated emissions in the agricultural stage. The yield-responses to additional fertiliser appliacation are highly dependant on the present level of fertiliser app lication that is changed. In this study the level of fertiliser application is determined as the average fertiliser application in the region of interest. However, it is more likely that additional fertiliser application will take place in fields where the highest

## 244 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## yield-response can be achieved and that is in the fields fertilised below the average level. Thus, it is likely that the scenarios that represent increased yield underestim ate the yield responses and correspondingly overestimate the environmental impact. Therefore, this sensitiv ity analysis tests the signi ficance of these uncetainties. Table 21.40 shows the applied yield responses and the inte rvals from which the yield resposnes are determined.

Crop Applied yield-response Interval Δ Y/ Δ N (kg crop/kg N) Δ Y/ Δ N (kg crop/kg N) Rapeseed 4 4-15 FFB 62 48-149 Barley, DK (represented by wheat) 12 4-40 Barley, CAN (represented by wheat) 17 4-40 

## Table 21.40: Applied yield-responses to additional fertiliser and th e interval of identified yield-responses indicating the uncertainties.



## It appears from Table 21.40 that the applied yield-responses are in th e lower end of the intervals. Because of proportionalibility between yield-responses from fertiliser application and some of the environmental impacts (global warming and eutrophication), a doubling of the yi eld-response will lead to a halving of the associated environmental impacts. Therefore, knowing the contributi on to e.g. global warming from the agricultural stage, it is earsy to determine the characterised results for scenario 3 (increased yield) for different yield resposnes. Figur 21.2 shows the characterised results of scenario 3 fo r global warming and the contribution from the different stages.

-5 0 5 10 15 20

Rapeseed oil Palm oilt CO2-eq. 

### Rapeseed cultivation Rapeseed oil m ill and refinery Oil palm cultivation Palm oil mill and refinery Sys. exp.: Soybean meal (BR) Sys. exp.: Barley (DK) Sys. exp.: Barley (CAN) Transport 17.1

2.60 Rapeseed cultivation 17.3 t CO 2 -eq. Oil palm cultivation 1.76 t CO 2 -eq.



## Figur 21.2: Characterised results for global warming of scenario 3 per 1 t oil and the contribution from the agricultural stage.

Table 21.41 

## shows the characterised results for rapeseed oil and palm oil when the yield responses for rapeseed and FFB are changed.

## 21 Sensitivity analyses 245

Vegetable oil Sensitivity analysis Yield-response Contribution to global warming Baseline 4 kg rapeseed per ha/kg N 17.1 t CO 2 /t oil Moderate yield response 8 kg rapeseed per ha/kg N 8.45 t CO 2 /t oil Rapeseed oil

High yield response 15 kg rapeseed per ha/kg N 4.41 t CO 2 /t oil Baseline 62 kg FFB per ha/kg N 2.60 t CO 2 /t oil Low yield-response 48 kg FFB per ha/kg N 3.11 t CO 2 /t oil Palm oil

High yield-response 149 kg FFB per ha/kg N 1.57 t CO 2 /t oil 

## Table 21.41: Characterised results for 1 t rapeseed oil and 1 t palm oil when different yield-responses are applied (scenario 3).



## It appears from Table 21.41 that the contribution to global warming is highly dependant on the determinationof yield-responses. For rapeseed oil the interval of yield-responses represent a factor 3.9 difference in the contribution to global warming, and for palm oil th e interval represents a factor 2.0 difference.

Since the determination of yield-responses and the identif ication of means of increasing the yields are related to considerable uncertainties and since the results are very sensitive to yield-responses, the results of the scenarios where increases are met with yield (scenarion 1 a nd 3) are considered as very uncertain. However, the sensitivity analysis shows that uncertainties in the dete rmination of yield-responses can not change the ranking of palm oil as the environmental preferable to rapeseed oil regarding global warming.

There are great differences in the leve l of fertiliser application in different managed oil palm plantations (see section 21.12 ). Well managed plantations typically apply more fertiliser than poor managed small holder plantations. The extremes of the intervals of yield responses given in Table 21.40 can be considered as the yield responses in highly intensified and very extensive cultivation. It appears that the environmental performance of increasing FFB yields is markable in extensive cu ltivated areas (see high yield response of FFB in Table 21.41 ). Thus additional fertiliser application in extensivel y cultivated oil palm cultivations is regarded as an improvement option. The level of fertiliser application on rapeseed in Denmark/the EU is considered as more uniform and closer to the N and P norms. It also app ears that the contribution to global warming from rapeseed oil produced by increased yield with high yield responses is relatively high, i.e. 4.41 t CO 2 

## -eq./t rapeseed oil. Thus additional fertiliser application in rapeseed cultiv ation is not considered as an improvement option.



# 21.22 Summary of sensitivity analyses

Table 21.42 provides an overview of the main findings of the sensitivity analyses presented in this section. The table is organised to provide information on whic h sources of uncertainties (tested in the single sensitivity analyses) that causes significant uncerta inty to the results, and which uncertainties that may change the ranking of rapeseed oil and palm oil. The significance of uncerta inties of results are classified as low (<10% changes in results), moderate (10-30% changes in results) and hi gh (>30% changes in results). If the tested sources of uncertainties cause uncertainties of high significance, they are marked with bold text. Corresponding to that the sources of uncertainties that change the ranking of rap eseed oil and palm oil are also maked with bold text.

## 246 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Significanse of the tested source of uncertainty... Sensitivity analysis ... the results ... ranking of rapeseed oil/palm oil 1: LCIA-methods High for toxicities and Low/moderate for the remaining impact categories -

2: System delimitation, marginal supplier of crops Low Not affected

3: System delimitation, type of land transformed into agricultural land High Not affected

4: Energy, marginal source of electricity Low Not affected

5: Energy, representativiness of data on district heating in Denmark Low/moderate in scenario 1a and 2a, and low for all other scenarios Not affected

6: Agricultural cultivation, energy for traction Low Not affected

7: Rapeseed cultivation, soil type Moderate Not affected 8: Rapeseed cultivativation, N-fertiliserproduced using best available techniques (BAT) Moderate for global warming and low for the remaining impact categories Global warming: The ranking is changed from even to a situation where rapeseed oil performs better when fertiliser produced with BAT is used 9: Agricultural cultivation, N changes in soil matter Moderate for eutrophication from rapeseed oil and Low for remaining impact categories and for palm oil Global warming: The ranking is changed from even to a situation where palm oil performsbetter when N changes in soil matter are included 10: Agricultural cultivation, heavy metal contents in fertilisers Low Not affected

11: Agricultural cultivation, initial compartment of pesticide emissions High for ecotoxicity. No effect on other impact categories Ecotoxicity: The ranking is affected. However, the results are very uncertain 12: Oil palm cultivation, yields High for land use if FFB yields of marginal suppliers is significant higher than the applied 18.87 t/ha and moderate for the remaining impact categories Global warming: The ranking is changed from even to a situation where palm oil performs better if FFB yields are significantly higher than the applied 18.87 t/ha 13: Oil palm cultivation, soil type High if land on peat soil is developed for oil palm cultivation Global warming: The ranking is changed from even to a situation where rapeseed oil performs

significantly better if oil palm is cultivated on peat soil 14: Oil palm cultivation, uncertainties in CO 2 -emissions from peat soil Moderate for global warming from palm oil. Global warming: The ranking is changed from even to a situation where rapeseed oil performs better if CO 2 -emissions are significantly higher than the applied 37 t CO 2 /ha per year 15: Barley cultivation, omission of use and emissions of pesticides - -

16: Rapeseed oil mill, solvent extraction versus full press High for photochemical smog from rapeseed oiland low/moderate for the remaining impact categories Not affected

17: Oil mills, capital goods Low Not affected 18: Palm oil mill, POME-treatment High for global warming and photochemical smog from palm oil and low for the remaining impact categories Global warming: The ranking is changed from even to a situation where palm oil performs significantly better if a digester tank is installed 19: Palm oil mill, steam requirement Low Global warming: The ranking is changed from even to a situation where palm oil performs better if the steam requirement is lower than the applied 0.65 t steam/t FFB 20: Palm oil mill, alternative management options for EFB Moderate Global warming: The ranking is changed.Rapeseed oil performs best if the EFB is disposed off by landfill, and palm oil performs best if the EFB is used in a biomass plant 21: Insensified cultivation, yieldresponses to additional fertiliser High for scenario 1 and 3 Not affected



## Table 21.42: Overview of the main findings in the se nsitivity analyses presented in this section.



## It appears from Table 21.42 that 8 out of the 21 tested sources of un certainty potentially affect the result significantly, i.e. at least one of the impact categories ar e affected with more than 30%. Futhermore 9 of the 21 sensitivity analyses show that the tested sources of un certainty may change the ranking of rapeseed oil and palm oil within at least one impact category.

## 21 Sensitivity analyses 247



## In general the sensitivity analyses show that the uncertainties associated w ith ecotoxicity are so significant, that these results do not provide usable information. The causes of these uncertainties are primarily related to uncertainties in LCIA-methods, but also to uncertain determination of the initial compartment of pesticide emissions. The characterised results of the remaining impact categories seem to be less associated with uncertainties relating to LCIA-methods.

Regarding the uncertainties that affects the ranking of rap eseed oil and palm oil, the only relevant impact category is global warming (when not taking ecotoxicity into account). In scenario 4 whic h is the scenario used in most sensitivity analyses the difference between rapeseed oil and palm oil regarding global warming is insignificant. The following uncertainties point in the dir ection that rapeseed performs better than palm oil:

• the ammonium nitrate fertiliser used is produced with BAT

• oil palm is cultivated on peat soil

• CO 2 

## -emissions from peat are significantly higher than the presumed 37 t CO 2 

## /ha per year

• EFB from the palm oil mill is disposed off in a la ndfill instead of being applied as mulch in the plantation

On the other side, the following uncertainties point in the direction that palm oil performs better than rapeseed oil:

• N changes in soil matter are included

• FFB yields are significantly higher than the applied 18.87 t/ha

• palm oil mill effluent (POME) is treated in a digester tank and the biogas is utilised

• the steam requirement in the palm oil mill is lower than the applied 0.65 t steam/t FFB

• EFB from the palm oil mill is utilised for energy purposes in a biomass plant instead of being applied as mulch in the plantation

The last sensitivity analysis (no. 21) shows that the modelling of increased yield is a significance source of uncertainty. Therefore, the results of scenario 1 and 3 are associated with a significantly higher level of uncertainty than the other scenarios.

## 248 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 22 Identification of improvement options 249



# 22 Identification of improvement options
In the previous section both improvement options and data uncertainties have been treated together as sources of uncertainties in results. The reason for treating them t ogether is that both the identification of the actual cultivation practice/technologies and the uncertainty in da ta may contribute to uncertainties in results. One of the purposes of this life cycle inventory is to identify improvement options. Therefore, this section presents those of the sensitivity analyses wh ich represent improvement options. Table 22.1 summarises the identified improvement options. Only the effects on global warmi ng and biodiversity have been considered. Each improvement option is compared with the baseline scenar io in terms of index number (baseline = index 100)

Ref. to sensitivity analysis Improvement option, rapeseed oil Improvement options Global warming Land use Biodiversity

Rapeseed oil Cultivation on clay 91 81 92 Baseline (59% clay, 41% sand) 100 100 100 7 When agricultural land is transformedinto other land use types, transformation of clay soils should be avoided since these soils give the highest yield Cultivation on sand 129 136 114

Fertiliser produced with BAT 79 - - 8 Using N-fertiliser produced with best available techniques (BAT) Baseline (BAT is not used) 100 - - Full press technology 95 81 106 16 Using full press technology in the oil mill stage instead of solvent extraction technology Baseline (solvent extraction) 100 100 100

Palm oil Expanding on grassland - - -191 Baseline (50% secondary forest and 50% grassland) - - -100 3 Expanding the oil palm planted area on grassland and avoiding transformation of forests Expanding in land covered with secondary forest - - -10

Cultivation on mineral soils 81 - - Baseline (96% mineral soil and 4% peat) 100 - - 13 Avoiding cultivation on peat

Cultivation on peat soils 542 - - Treating POME in digester tank with biogas capturing and utilisation of the methane for energy purposes 58 - - 18 Installation of digester tank and utilisa- tion of biogas instead of open anaerobic ponds for palm oil mill effluent (POME) treatment Baseline (treatment of POME in open anaerobic ponds) 100 - -

EFB disposed of by landfilling 119 - - Baseline (EFB applied as mulch in the plantation) 100 - - 20 Utilising EFB for energy purposes in a biomass plant instead of application as mulch in the plantation. Avoiding landfill as a magement option for EFB Utilising EFB in biomass plant with electricity production 83 - -

Extensively cultivated plantation (assumed yield response at 149 kg FFB/kg N) 60 - -

Baseline (assumed yield response at 62 kg FFB/kg N) 100 - - 21 Yield increases by additional fertiliser application in extensively cultivated

areas

Baseline (assumed yield response at 62 kg FFB/kg N) 120 - -



## Table 22.1: Overview of the effect of the considered improvement options. Only the effects on global warming and biodiversity have been considered.



## It appears from Table 22.1 

## that the greatest improvement potentials have been identified for palm oil. It should also be mentioned that the effect of the improvement options relating to the transformation of land only refer to the transformation process and not the overall en vironmental performance of the supply. Th at is because a piece of transformed land can support many functional units.

## 23 Sensitivity, completeness and consistency checks 251



# 23 Sensitivity, completene ss and consitency checks
According to ISO 14044 (2006) en evaluation in the interpretation phase includin g sensitivity, completeness and consistency check must be carri ed out in order to establish confid ence in the results of the LCA.



# 23.1 Sensitivity check
The objective of the sensitivity check is to assess the reliability of the r esults and how they are affected by uncertainties in data, assumptions and LCIA-methods (ISO 14044 2006). Sensitivity has been assessed on three levels in this study: System bounda ries, uncertainty in data and LCIA-methods.

Regarding system boundaries the definition of scenarios repr esents a sensitivity analys is. In addition the sensitivity analyses in section 21.2 and 21.3 assess the result’s sensitiv ity to uncertainties in and alternative system boundaries. The differences between scenarios show thatthe approach to system delimitation (consequential/attributional) significantly affects the results (see Table 20.1 ). The sensitivity analyses in section 21 show that the identification of marginal suppliers has only l ittle influence on the results while the impacts of transformation of different non-cultivated land use types in to agricultural land has major effects on the results.

Sensitivity regarding uncertainty in da ta is assessed during the inventory of each stage where different data sources have been compared and in section 21 where several sensitivity analyses have carried out in order to assess uncertainties in data. The results of th e sensitivity analyses are summarised in section 21.22

.

LCIA-methods and the related uncertainties are assessed in section 21.1 where the characterised results of EDIP97, Impact2002+ and Eco-indicator are compared. The sensitivity analysis shows that the results of toxicities are related to so significant un certainties that the information obtained from the characterised results is useless. The other impact categories are more consistent among the different LCIA-methods.

The main sources of uncertainties are identified as:

• The characterised results of toxiciti es do not provide usable information

• The results of the scenarios wher e increased cultivation is achiedv by increased yields are associated with a significantly high level of uncertainty, i.e. scenario 1 and 3

• The impacts from transformation of non-cultivated la nd into agricultural land are associated with considerable uncertainties due to determination of which type of land that is transformed

• The contribution to global warming from palm oil may be significantly higher if the share of peat soil of new land development for oil palm is larg er that the present applied value at 4.1%

• The contribution to global warming from palm oil may be lower of the yields are higher than the applied 18.87 t FFB/ha, if POME is treated in a dig ester tank with utilisation of biogas, and and if the steam requirement is lower than the applied 0.65 t steam/t FFB

If the uncertainty of the first three bullets should be reduced it would require a workload and further development of methodologies that exceeds the allocation of time for this study. Regarding the fourth bullets it is likely that a large part of future development of land for oil palm cultivation will take place on peat soil. According to Corley and Tinker (2003) much of the remaining land available for expansion of oil palm cultivation in South-East Asia is peat soil. Regarding the fifth bullet, it is likely that the marginal supplier of palm oil is represented by large private estates with good agricu ltural practices rather than private small-holders with poor cultivation practices. Therefore, the applied average yield at 18.87 t/ha may be too low. However, accord-

## 252 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil



## ing to MPOB (2005 and 2006) the share of private esta tes in Malaysia has increased from 59.1% in 2003 to 60.2% in 2005, and the share of small-holders has in creased from 10.2% in 2003 to 10.5% in 2005. Thus, statistics do not support that hypothesis. Regarding POME treatment, the technology of today is open anaerobic digestion in lagoons. However, several digester tanks and utilisation of biogas have been installed as CDMprojects within the Kyoto-protocol framework. Thus, di gester tanks may very well be the technology of the future. Regarding steam requirement, the applied 0.65 t st eam/t FFB is in the high end of the interval. And in addition increased focus on the value of excess shells as biofuel may be an incentive for reducing the steam consumption. Therefore, it is likely that the contributi on to global warming per t palm oil will decrease in the future.



# 23.2 Completeness check
The objective of a completeness check is to ensure that the information provided in the difference phases of the LCA are sufficient in order to inte rpretate the results (ISO 14044 2006).

In general the system boundaries and inventory data are de scribed comprehensively in this report. The point of departure for each life cycle stage is that all environm ental relevant processes are included in the inventory. Environmental relevant means relevant for the im pact categories included in the study, see section 1.4 . In the beginning of each section representing the life cycle stages included in the study, the omission of data, if any, is described. In general it assessed that all relevant info rmation is included, except from the use of pesticides in barley cultivation. However, as described in section 21.1 , the results of ecotoxicity are regarded so uncertain that the characterised results do not pr ovide usable information. Therefore, the impact categories of toxicity are not supported sufficiently by the invent ory data and LCIA-m odels available.

Though, the relevant data are present and included, some of the data are related to significantly uncertainties. Thus, the following uncertainties in data affect the interpretation of the results:

• The ratio between increased yield and increased ar ea when cultivation of a certain crop is increased

• The determination of the affected means of increasing yield (only increased yield by additional NPKfertiliser has been modelled)

• The modelling of yield-responses by additional fertiliser application

• The affected type of land use when transfor ming non-cultivated land into agricultural land

• There share of new land developed for oil palm that is peat soil

If abovementionned uncertainties should be reduced, it would require a workload and further development of methodologies that exceeds the allocation of time for this study. Therefore, these uncertainties are tested in sensitivity analyses (see section 21 ) and when conclusions are drawn they should be considered.



# 23.3 Consistency check
The objective of the consistency check is to verify that assumptions, methods and data are consistent with the goal and scope. Especially the consis tency regarding data qua lity along the product chain, regional/temporal differences, allocation rules/system bounda ries and LCIA are important (ISO 14044).

As described in section 23.2 the point of departure of the inventory fo r each life cycle stage has been to include all environmental relevant processes. Among the trad itional processes capital goods (machinery and buildings) have been included for all processes. Only very few data has been omitted from the i nventory. When data have been taken from life cycle inventory databases, the largest and most comprehensive database available, ecoin-

## 23 Sensitivity, completeness and consistency checks 253



## vent, has been used in all cases. Thus, differences betw een LCI-databases do not contribute to inconsistency in this study.

Regarding regional differences the location of the margin al supplier has been identified for all crops inventoried. Regarding temporal differences data have been collected for 2005 for crop yields and oil mills. However, most of the applied database data are of older date.

Regarding allocation and system delimitation, the consequential approach has been consistently applied to most included technologies (electricity, heat, fertiliser, o il mills) and suppliers (crops ). But the ecoinvent database uses average technology and allocate by using allo cation factors which do not comply with the consequential approach. However, the effect of using allocation factors and of applying average technology has been assessed and when significant differences were identified , the processes from ecoinvent have been modifyied in order to represent c onsequential modelling.

The updated version of EDIP97 has been applied for the impact categories; global wa rming, ozone depletion, acidification, eutrophication, photochemical smog and eco toxicity. Land use has been assessed using inventory data (occupied and transformed area in units of m 2 

## y and m 2 

## ) and the method of Schmidt (2007c). EDIP as well as the method of Schmidt are so-called bottom-up LCIA-met hods. However, the methods differ in the way that the indicator of Schmidt is representing an end-point indicator while the indicators of EDIP represent midpoint indicators. There is no overlapping between the impact categories in EDIP97 and in the method of Schmidt. Normalisation and weigh ting have not been carried out.

## 254 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## 24 References 255



# 24 References

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Schmidt J H (2007c) 27



### , Development of applicable characteri sation factors for land use in LCA . Journal of Cleaner Production (in prep.), Elsevier

25 

## Schmidt (2007a) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

26 

## Schmidt (2007b) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

27 

## Schmidt (2007c) is part of the Ph.D. thesis: ‘Life assessment of rapeseed oil and palm oil’ and it is published in the report ‘Life assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 2: Article collection’.

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## 266 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## Appendix 1: Data on fuels 267



# Appendix 1: Data on fuels

Fuel Density Energy content Fuelolie, heavy 0.95 tonne/m 3 40.6 MJ/kg 38.6 MJ/litre Diesel 0.87 tonne/m 3 41.9 MJ/kg 36.4 MJ/litre Petrol 0.72 tonne/m 3 42.7 MJ/kg 30.8 MJ/litre Methane 0.000717 kg/ m 3 

## Table A1.1: Density and lower calorific values for different fuels (Andersen et al., 1981, p 218) methane at 0.717 g/litre (Andersen et al. 1981, p 119)

## 268 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## Appendix 2: Rapeseed production in Europe in 2004 269



# Appendix 2: Rapeseed prod uction in Europe in 2004

Major rapeseed producing countries in Europe Rapeseed production in 2004 (1000 tonne) Rapeseed production 2004 (winter + spring)

(1000 tonne) Spring rapeseed’s share of total rapeseed 2004

Source (FAOSTAT 2006) (Eurostat 2006) (Eurostat 2006) Germany 5,277 5,277 0.8% France 3,969 3,995 0.5% Poland 1,633 1,633 5.3% United Kingdom 1,609 - - Czech Republic 935 - - Denmark 468 468 0.8% Hungary 287 - - Russian Fed 276 - - Slovakia 263 263 2.3% Sweden 228 211 39.0% Lithuania 205 205 68.0% Ukraine 149 - - Belarus 143 - - Austria 121 121 0.5% Latvia 104 104 59.4% Other <100,000 tonne 432 - - Total 16,096 12,275 3.6% 

## Table A2.1: Production of rapeseed in major rapeseed producing coun tries in Europe. In cases with no data available the cell is marked with a ‘-‘. When production data from FAOSTAT are compared with data from Eurostat, it appears that there is good consistency.

Yield , average 2002-2004 (tonne/ha) Major rapeseed producing countries in Europe Rapeseed Rapeseed Winter rapeseed Spring rapeseed Spring rapeseed in relation to winter rapeseed

Source (FAOSTAT 2006) (Eurostat 2006) Germany 3.32 3.32 3.35 1.97 -41% France 3.28 3.29 3.29 2.72 -17% Poland 2.35 2.35 2.43 1.69 -31% United Kingdom 3.18 3.23 - - - Czech Republic 2.47 2.47 - - - Denmark 3.25 3.25 3.29 2.39 -28% Hungary 1.96 1.98 - - - Russian Fed 1.04 - - - - Slovakia 1.99 1.96 1.96 1.63 -17% Sweden 2.43 2.59 3.07 2.09 -32% Lithuania 1.86 1.86 2.18 1.73 -20% Ukraine 1.07 - - - Belarus 0.90 - - - Austria 2.50 2.50 2.51 1.73 -31% Latvia 1.71 1.71 2.03 1.54 -24% Other <100,000 tonne 1.65 - - - - Total 2.78 2.58 2.68 1.94 -27% 

## Table A2.2: Yields (average of 2002-2004) of rapeseed (winter + sp ring), winter rapeseed and spring rapeseed in major rapeseed producing countries in Europe. In cases with no data available the cell is marked with a ‘-‘.

## 270 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

## Appendix 3: Soil types and adjustments to difference sources 271



# Appendix 3: Soil types an d adjustments to different

sources Several inventory data are related to different soil types. However, the data sources of these inventory data do not apply the same terminology for soil types. Therefore, this appendix adjusts the different terminologies to a common set, defined in Table A3.1

.
The share of different soil types under Danish agriculture is shown in Table A3.1 . The column to the right shows a rough classification of the soil types into sand and clay.

Adjustment of data from DJF (Danish Institute for Agricultural Science, 2005) DJF no. Soil type Area, km 2 Share of agricultural soils Sand/clay 1 Coarse sandy soil 10,548 26% Sand 2 Grinding sand 4,233 10% Sand 3 Sandy soil with clay 11,523 28% Clay 4 Clay soil with sand 9,817 24% Clay 5 Clay soil 2,390 6% Clay 6 Heavy clay soil 303 1% Clay 7 Humus soil 2,090 5% Sand 8 Calcareous soil 87 0% Sand 9 City 1,704 - - Total 42,694 100% Sand (41%)/clay (59%) Table A3.1: Distribution of soil types in Denmark.



## Soil types used in Plantedirektoratet (2005a) and conversion to soil types given in Table A3.1 are shown in Table A3.2 . Data from Plantedirektoratet (2005a) are used for determination of fertiliser application. Firstly, the data from Plantedirektoratet (2005a) are adjusted in order to avoid distinguishing between irrigated and non-irrigated soils. The assumption that 50% is irrigated is applied. This adjustment is shown in the third column. Secondly, the adjusted soil types from Plantedirektoratet (2005a) are converted to soil types matching the data given in Table A3.1 . This is shown in the fourth column.

Adjustment of data from PDIR (Plantedirektoratet, 2005a) PDIR no. Types Adjusted PDIR no. Conversion og soil type: DJF no. Share Sand/clay

1 Non-irrigated coarse sandy soil (JB 1+3) Average of 1 and 3 1 26% Sand

2 Non-irrigated grinding sand (JB 2+4 and 10-12) Average of 1 and 3 2+7+8 16% Sand

3 Irrigated sandy soil (JB 1-4) - - - - 4 Mixed sand and clay soil (JB 5-6) 4 3+4 52% Clay 5 Clay soil (JB 7-9) 5 5+6 7% Clay Table A3.2: Adjustment of soil types in Plantedirektoratet (2005a) to fit into the types given in Table A3.1

.


## Soil types used in Vinther and Hansen (2004) and conversion to soil types given in Table A3.1 are shown in Table A3.3 . Data from Vinther and Hansen (2004) are used for determination of denitrification. Firstly, the data from Vinther and Hansen (2004) are adjusted in order to fit into the categories defined by Table A3.1 – in some cases more than one soil in Vinther and Hansen (2004) fit into each type in Table A3.1 . This adjustment is shown in the second column. Secondly, the adjusted soil types from Vinther and Hansen (2004) are converted to soil types matching the data given in Table A3.1 . This is shown in the third column.

## 272 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Adjust of data from SimDen (Vinther and Hansen 2004) SimDen no. Adjusted SimDen no. Conversion of soil type: DJF no. Share Sand/clay

JB1 Average of JB1 and JB3 1 26% Sand JB2 Average of JB2 and JB4 2+7+8 16% Sand JB3 - - - - JB4 - 3+4 52% Clay JB5-6 JB5-6 5+6 7% Clay JB7-8 JB7-8 Table A3.3: Adjustment of soil types in Vinther and Hansen (2004) to fit into the types given in Table A3.1

.

## Appendix 4: Characterisation factors for land use in Brazil and Canada 273



# Appendix 4: Characterisation f actors for land use in Brazil and Canada
Schmidt (2007c) provides characterisation factors c overing Denmark/N-Europe and Malaysia/Indonesia only. Since this study also include cultivation in Brazil (the Cerrado savannah and the Amazon tropical forests) and Canada (prairie grassland), characterisation factors fo r these regions are developed in this appendix. The method for calculation of characterisation factors is described in detail in Schmidt (2007c).

Species richness: For Brazil, only the average species richness ofa single-sized sample plots have been available. Therefore, S 100 

## of these land use types has been calculated assuming a value for the species accumulation rate. Perelman et al. (2001) ha ve calculated z = 0.14 for the pampas in Ar gentina. Thus, this value is applied to the Cerrado as well as to soybean fields in Brazil. The data and results of these la nd use types are shown in Table A4.1 . The species richness of agriculture in Canada is as sumed to be equal to that of N-Europe and the species richness of prairie grassland is assumed to be equal to imperata grassland in SE-Asia.

Land use type S 100 Comments to calculation of S 100 in this study... Brazil 1 Arable, cereals/annuals, soybean, intensive 16 one entry: 7.15 species on 0.4 m 2 (Gomez and Gurevitch 1998) 5 Nature, forest (Amazon forest) 98 Assumed value as in SE-Asia, obtained from Schmidt (2007c) 7 Nature, grassland (Cerrado savannah) 24 one entriy: 430 species on 90,000 km 2 (Perelman et al. 2001) Canada 1 Arable, cereals/annuals, intensive 10 Assumed value as in N-Europe, obtained from Schmidt (2007c) 7 Nature, grassland 12 Assumed value as in SE-Asia, obtained from Schmidt (2007c) Table A4.1: Species richness of vascular plants in a standardised area of 100 m 2 for relevant land use types in Brazil and Canada.



## Ecosystem vulnerability: For the Brazil Cerrado, z is assumed to be represented by z = 0.14 for the Argentine Pampas given in Perelman et al. (2001), and for Canada, z is assumed to be represented by the same value as in N-Europe, i.e. z = 0.22 (Schmidt 2007c). The factors for ecosystem vulnerability are calculated in Table A4.2

.

Brazil Canada High intensity land Arable 590,000 456,600 Permanent crop 76,000 64,550 Built-up, roads, barren land 1,046,440 4,397,610 Low-intensity land Permanent pasture 1,970,000 153,900 Forest and woodland 4,776,980 4,020,850 Total land area 8,459,420 9,093,510
LI 0.80 0.46
Z 0.14 0.22 Factor for ecosystem vulnerability: z/LI 0.18 0.48 Table A4.2: Land cover by land use types in km 2 . The values are used for determining LI. LI and z are used for calculating the factor for ecosystem vulnerability. Data for all land use types except forest and woodland are from 2003 (FAOSTAT 2006). Data for forest and woodlan d are from 2005 (FAO 2006).



## Characterisation factors: Table A4.3 summarise the determining factors for calculating the characterisation factors as well as the calculated characterisation factors. The renaturalisation tim es for natural grassland and forests are all based on 500 years for the nature types in mid-Europe (Schmidt 2007c) corrected for the latitude using factors obtained from Schmidt (2007c). Characteris ation factors for occupation impacts are calculatedfor two different areas in Brazil, i.e. the Amazon forests and the Cerrado savannah. This is because the renaturalisation potential (species richn ess) differs in the two regions.

## 274 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil

Characterisation factors (wS100) Land use type S 100 z/LI S 100 ⋅ z/LI Renaturalisation time Occ. Trans. from... Trans. to... Brazil, Amazon region 1 Arable, cereals/annuals, soy, intensive 16 0.18 3 1 15 1 -1 7 Nature, forest, Amazon* 98* 0.18 4 355 0* 3,131* -3,131* Brazil, Cerrado region 1 Arable, cereals/annuals, soy, intensive 16 0.18 3 1 1 1 -1 7 Nature, grassland, Cerrado* 24* 0.18 4 430 0* 929* -929* Canada 1 Arable, cereals/annuals, intensive 10 0.48 5 1 1 2 -2 5 Nature, grassland* 12* 0.48 6 500 0* 1,440* -1,440* Table A4.3: Characterisation factors given in units of weighted species richness on a standard area of 100 m 2 , wS100. Reference states for natural relaxation in each region are marked with *.

## Abbreviations 275



# Abbreviations

A.i. Active ingredients in pesticides Alang-alang Grassland in Indonesia is of ten reffered to as alang-alang. Often covered by the grass species; imperata. BL Barley BR Brazil CAN Canada CDM projects Cleaner Developm ent project. Project within the Kyoto fram ework aiming at reducing greenhouse gas emissions. CPO Crude palm oil DK Denmark Dry climate <1000 mm annual precipitatio n, same definition as in IPCC (2003) EFB Empty fruit bunches FFA Free fatty acid FFB Fresh fruit bunches ha y Hectare years IN Indonesia LCI Life cycle inventory LCIA Life cycle impact assessment Moist climate 1000-2000 mm annual precipitation, same definition as in IPCC (2003) MPOB Malaysian Palm Oil Board MY Malaysia MY&IN Malaysia and Indonesia NBD Neutralised, Bleached and Deodorised (i.e. the processes in refining of vegetable oil) PKC Palm kernel cake PKO Palm kernel oil PO Palm oil PO+PKO Total output of vegetable oils from palm oil industry, i.e. PO from FFB and PKO from kernels POME Palm oil mill effluent RSO Rapeseed oil RSM Rapeseed meal RSPO Round Table on Sustainable Palm Oil SM Soybean meal SO Soybean oil Wet climate >2000 mm annual precipitatio n, same definition as in IPCC (2003)

## 276 Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil