SQCB Background Report - Roundtable on Sustainable Biomaterials

Transcription

Ma terials Sci ence & Technolog y
Sustainability Quick Check
for Biofuels
Background Report
Dr. Mireille Faist Emmenegger
Jürgen Reinhard
Dr. Rainer Zah
Empa, project lead
With contributions from:
Tobias Ziep
René Weichbrodt
Prof. Dr. Volker Wohlgemuth
FHTW Berlin
Anne Roches
Ruth Freiermuth Knuchel
Dr. Gérard Gaillard
Agroscope Reckenholz-Tänikon
Please cite as: Faist Emmenegger, M., Reinhard, J., Zah, R. : Sustainability Quick Check for Biofuels - intermediate background report. With contributions from T. Ziep, R. Weichbrodt, Prof. Dr. V. Wohlgemuth,
FHTW Berlin and A. Roches, R. Freiermuth Knuchel, Dr. G. Gaillard, Agroscope Reckenholz-Tänikon.
Dübendorf, 2009.
Content
I
List of figures………………………………………………………………..6
II
List of tables…………………………………………………………………7
1
Introduction............................................................................................................. 1
2
3
4
5
1.1
Context of the project .................................................................................................................. 1
1.2
Conception of the tool.................................................................................................................. 1
1.3
Goal of this manual...................................................................................................................... 2
1.4
Structure of the document ........................................................................................................... 2
Goal and scope definition ...................................................................................... 4
2.1
Biofuels in the SQCB tool ............................................................................................................ 4
2.2
Most relevant factors in production chain .................................................................................... 5
2.3
Integration of social factors ......................................................................................................... 6
2.4
Limitations of the tool and recommendations for its use ............................................................. 7
Description of questionnaire ................................................................................. 8
3.1
Basic data .................................................................................................................................... 8
3.2
Cultivation data .......................................................................................................................... 10
3.3
Processing data ......................................................................................................................... 16
3.4
Transport and fuel use............................................................................................................... 20
3.5
Social criteria ............................................................................................................................. 20
Modelling CO2 emissions from land use ............................................................ 22
4.1
Basics ........................................................................................................................................ 23
4.2
Computation .............................................................................................................................. 26
4.3
Tables ........................................................................................................................................ 28
4.4
Online helps for the inputs ......................................................................................................... 28
Modelling of ammonia emissions ....................................................................... 30
5.1
Structure of ammonia (NH3) computation ................................................................................. 30
5.2
Ammonia (NH3) computation ..................................................................................................... 30
5.3
Calculation of the saturation deficit ........................................................................................... 32
5.4
Cases......................................................................................................................................... 32
Mireille Faist Emmenegger, 27.10.2009
5.5
6
7
8
9
10
Tables ........................................................................................................................................ 33
Modelling of N2O and NOx emissions ................................................................. 35
6.1
N2O emissions ........................................................................................................................... 35
6.2
NOx in air ................................................................................................................................... 35
Modeling of phosphorus emissions ................................................................... 36
7.1
Origin of model and model structure ......................................................................................... 37
7.2
Computation .............................................................................................................................. 42
7.3
Tables ........................................................................................................................................ 48
7.4
Inputs ......................................................................................................................................... 54
7.5
Default values ............................................................................................................................ 61
7.6
Online help for the inputs .......................................................................................................... 65
Modeling of nitrate emissions ............................................................................. 71
8.1
Origin of model and model structure ......................................................................................... 71
8.2
Computation .............................................................................................................................. 73
8.3
Tables ........................................................................................................................................ 75
8.4
Inputs ......................................................................................................................................... 80
8.5
Default values ............................................................................................................................ 81
8.6
Online help for the inputs .......................................................................................................... 82
Modelling of further emissions in agriculture .................................................... 84
9.1
Pesticides emissions ................................................................................................................. 84
9.2
Heavy metal emissions.............................................................................................................. 84
9.3
Heavy metals in ground- and river-water .................................................................................. 84
Modelling of fuel production ............................................................................... 85
10.1 Computation .............................................................................................................................. 85
11
Modelling of fuel refining ..................................................................................... 89
11.1 Computation .............................................................................................................................. 89
12
Modelling of transport to storage ....................................................................... 93
13
Modelling of car use ............................................................................................. 94
13.1 Operation of car ......................................................................................................................... 94
13.2 Transportation of 1 pkm............................................................................................................. 94
14
Normalization to functional unit .......................................................................... 95
14.1 Computation of Use ................................................................................................................... 96
14.2 Computation of Operation ......................................................................................................... 96
14.3 Computation of Transport to storage......................................................................................... 96
14.4 Computation of fuel refining ...................................................................................................... 97
14.5 Computation of fuel production ................................................................................................. 97
14.6 Computation of cultivation ......................................................................................................... 97
15
Computing the environmental impacts .............................................................. 98
16
Fossil reference data ............................................................................................ 99
17
Description of technical aspects ....................................................................... 100
17.1 Framework ............................................................................................................................... 100
17.2 Components ............................................................................................................................ 100
17.3 Interaction of Components ...................................................................................................... 103
17.4 Requirements .......................................................................................................................... 103
18
Managing the database ...................................................................................... 105
18.1 Geographical Data................................................................................................................... 105
18.2 Structure data .......................................................................................................................... 107
18.3 Ecoinvent Imports .................................................................................................................... 109
19
Annex .................................................................................................................. 112
20
Literature ............................................................................................................. 120
List of figures
Figure 1.1: Concept of the Sustainability Quick Check Tool ..............................................................................2
Figure 2.1: Importance of the different steps in the evaluation of the production chain of biofuels (Zah, Böni
et al. 2007). .........................................................................................................................................................6
Figure 3.1: Structure of the data entry for the cultivation. ................................................................................10
Figure 3.2: Screen shot of the land use page. .................................................................................................11
Figure 3.3: Screen shot of the mineral fertilizer page. The bottom of the page with the detailed entry for Kfertilizer is cut off. ..............................................................................................................................................12
Figure 3.4: Screen shot of the organic fertilizer page. .....................................................................................15
Figure 3.5: Screen shot of the pesticide page. .................................................................................................16
Figure 3.6: Form for the entry of the relevant values for fuel production .........................................................17
Figure 3.7: Form for the entry of the relevant values for fuel refining ..............................................................19
Figure 4.1: The difference between direct and indirect land use change. .......................................................23
Figure 4.2: Land use changes, which could be calculated with the SQCB. The categories are taken from the
IPCC Guidlines 2006, p. 1.9. ............................................................................................................................24
Figure 4.3: Basic principles to calculate the carbon content of the relevant land use categories....................25
Figure 4.4: Calculation chart for carbon dioxide, from land transformation per provider and land use change
n. .......................................................................................................................................................................27
Figure 5.1: Structure for the computation of ammonia (NH3-) emissions.........................................................30
Figure 7.1: Map of wind erosion vulnerability on a global scale.
http://soils.usda.gov/use/worldsoils/mapindex/eroswind.jpg ............................................................................38
Figure 7.2: Structure of the erosion model and data flows. ..............................................................................41
Figure 7.3. Structure of the phosphorus model and data flows........................................................................42
Figure 8.1. Structure of the nitrate model and data flows ................................................................................72
Figure 10.1: Calculation chart for fuel production.............................................................................................85
Figure 11.1: Calculation chart for fuel refining..................................................................................................89
Figure 14.1: Life Cycle stages of RME with the calculation to normalize all inventory flows to the functional
unit of one pkm. ................................................................................................................................................95
Figure 17.1: simplified schema of master data..............................................................................................100
Figure 17.2: Structure of the questionnaire ....................................................................................................101
Figure 17.3: Calculation points .......................................................................................................................102
Figure 17.4: Interaction of the components ....................................................................................................103
Figure 18.1: relationship between structure data items .................................................................................107
Figure 18.2: relationship between ecoinvent flow data and structure data ....................................................109
List of tables
Table 2.1: Feedstocks and biofuels covered by the SQCB tool. ........................................................................4
Table 2.2: Regional allocation of reference data sets. .......................................................................................5
Table 3.1: Crops and the attributed fuel types. The processes tagged with an * are unique, i.e. they represent
a unique combination of inputs and outputs. The processes marked with a + could be applied to more than
one prior process, i.e. their inputs and outputs are always the same. ...............................................................9
Table 3.2: Main material and energy inputs in an LCA inventory of cultivation and their modelling in the
SQCB tool. ........................................................................................................................................................10
Table 3.3: Fertilizers in the SQCB tool .............................................................................................................13
Table 3.4: Composition of the multi-nutrient fertilizers (Nemecek and Kägi 2007) ........................................14
Table 5.1: Emission factor for NH3 –N (Nemecek and Kägi 2007). ..................................................................33
Table 5.2: total N in manure (Walther, Ryser et al. 2001) ................................................................................33
Table 5.3: Rough values for saturation deficit. .................................................................................................34
Table 7-1: Mean annual precipitation for each ecozone ..................................................................................49
Table 7-2: Erodibility for each USDA soil order ................................................................................................51
Table 7-3: Crop factor for each crop ................................................................................................................52
Table 7-4: Tillage factor for each tillage method ..............................................................................................52
Table 7-5: Practice factor for each anti-erosion practice ..................................................................................53
Table 7-6: Types of slurry and phosphorus content .........................................................................................60
Table 7-7: Types of manure and phosphorus content .....................................................................................60
Table 8-1: Clay content for each USDA soil order ...........................................................................................76
Table 8-2: Root depth for each crop .................................................................................................................77
Table 8-3: Unit uptake for each crop ................................................................................................................78
Table 9.1: Source for the heavy metal contents of inputs and outputs in agriculture ......................................84
Table 12.1: Relation between world region and transport processes. .............................................................93
Table 19.1: ‘Transformation from’ table as implemented within the SQCB. The ‘ from_c_stock –value’ is
expressed in t carbon. ....................................................................................................................................112
Table 19.2: ‘Transfromation_to” table as implemented within the SQCB. The ‘ from_c_stock –value’ is
expressed in t carbon. ....................................................................................................................................118
Table 19.3: Average organic carbon content (SOC) in t carbon. ...................................................................119
1
Introduction
Authors: Mireille Faist Emmenegger, Jürgen Reinhard, Rainer Zah
1.1
Context of the project
Various certification schemes and legislation on sustainability of biofuels are being developed at this time.
Many of them require besides the fulfillment of social criteria also the environmental sustainability over the
full product life cycle of the biofuels. However, the individual assessment of environmental impacts along a
full life cycle is a complex task that needs to be done by LCA experts. This obviously leads to resource demanding projects that cannot be afforded by most small and medium Enterprises (SME’s) in developing
countries. On the other hand, various production chains of biofuels have already been evaluated, and both,
results and knowledge on critical factors, are available.
In this context, the goal of the project "Sustainability Quick Check for Biofuels" (SQCB) is the development of
a tool that integrates current know-how on environmental impacts of biofuel production and allows for a
’quick check’ of the environmental profile of a biofuel under study. The SQCB tool performs an easy and lowcost streamlined life cycle assessment of a biofuel, using the requirements of the Swiss law on mineral oil tax
exemption as a benchmark. It gives first insights if a detailed request for tax exemption would be worthwhile.
The tool should facilitate the access to the Swiss market for biofuel producers in emerging countries, and
therefore contribute to a more sustainable implementation of biofuels production. In a further step, it is
planned to adapt the tool to other certification schemes like e.g. the principles of the Roundtable of Sustainable Biofuels (RSB), or the European criteria for sustainable biofuels.
1.2
Conception of the tool
The SQCB tool allows the user to enter own data for the parameters of the product life cycle that are most
relevant for the overall environmental assessment, like yield or fertilizer use. The user can also enter data
regarding to social criteria in the web-based questionnaire. Parameters which have a low impact on the
overall results or which are quite similar among different value chains, like e.g. transport of materials, are
taken as background data from the reference life cycle database, which is ecoinvent1. The SQCB tool calculates the LCA of the biofuel based on the user’s data, which are completed with background data. In a first
step, the results are benchmarked against the Swiss criteria for the tax exemption of biofuels. Furthermore,
the user can evaluate the critical factors of his value chain. If the criteria are not fulfilled, the user can change
his entry data and get more insights on the measures he should take to fulfill them. If the results are positive,
the user has a first indication that deposing a request for tax exemption is worthwhile (see Figure 1.1).
1
www.ecoinvent.org
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Implementation of additional measures
No
User
Web-based
Questionnaire
Does my
biofuel production
fulfill the sustainability
Criteria?
Quick check results
- Benchmark
- Critical factors
Quick Check Tool
Yes
Social criteria
Review by
FOEN / SECO
Life cycle criteria
Background
Criteria
Fulfilled?
Reference
life cycle
data
Requirements
of the ordinance
on mineral oil tax
Figure 1.1: Concept of the Sustainability Quick Check Tool
1.3
Goal of this manual
This document is a technical manual describing the main features of the “Sustainability Quick Check Tool”
(SQCB). It gives information on the modeling of the value chain of biofuels, on the calculation models for the
main parameters and on the calculation of environmental impacts. It therefore allows understanding the
background of the results that the tool produces.
Furthermore, this document describes the framework of the SQCB tool and its basic components as well as
the interrelation and the interaction between the main components. It gives insights on the managing possibilities of the database and their functions.
1.4
Structure of the document
Chapter 2 describes the goal and scope of the SQCB project. It points out the limitations of the tool and
gives recommendations for its use.
Chapter 3 describes and explains the questionnaire.
Chapters 4 to 9 describe the modeling of the emissions in the cultivation step, which are calculated from the
entries the user gives.
Chapters 10 to 13 describe the modeling of the processing step as well as the further steps in the life cycle
chain, which are transport of the fuel to Switzerland and the use in the car.
Chapters 14 to 16 describe the integration of the production chain and the normalization of the values entered and calculated to the functional unit as well as the calculation of the impacts and the fossil reference
value.
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Chapters 17 and 18 describe the technical aspects of the tool as well as how the database is managed.
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2
Goal and scope definition
2.1
Biofuels in the SQCB tool
In this project we focus on the biofuels and feedstock which have been studied in the Empa report (Zah, Böni
et al. 2007). Feedstocks or biofuels which are considered by the Swiss mineral oil tax ordinance as not being
able to satisfy the minimal requirements (cereals) or those for which no sustainability reporting is required
(waste, residues from agriculture) are not taken into account. In a first step wood will not be considered, as
its production chain is very different from the other feedstocks. Therefore the biofuels considered are the following (Table 2.1).
Table 2.1: Feedstocks and biofuels covered by the SQCB tool.
Feedstock
Biofuels
Countries in ecoinvent
Potatoes
Ethanol
CH
Sugar beet
Ethanol
CH
Sugar cane
Ethanol
BR
Sweet sorghum
Ethanol
CN
Rapeseed
Methylester
CH, RER
Soy beans
Methylester
(CH), US, BR
Oil palms
Methylester
MY
Not considered are methanol, which has a too small market, vegetable oil, which is used only for lorry, and
cereals (rye, corn), which are at this time heavily discussed because of ethical considerations.
2.1.1 Reference data
The calculations in the SQCB tools are based on reference data sets from the ecoinvent database so as to
simplify the data collection. As the ecoinvent database doesn’t provide data from all possible countries, the
same reference data sets are attributed to several countries (Table 2.2).
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Table 2.2: Regional allocation of reference data sets.
Country /
Region
Potato
CH
potatoes IP,
at farm CH
RER
Rapeseed
Soy bean
sugar beets
IP, at farm
CH
rape seed
IP, at farm
CH
soy beans
IP, at farm
CH
potatoes IP,
at farm CH
sugar beets
IP, at farm
CH
rape seed
conventional, at
farm DE
soy beans
IP, at farm
CH
North
America
potatoes IP,
at farm CH
sugar beets
IP, at farm
CH
rape seed,
at farm US
soybeans, at
farm US
South
America
potatoes IP,
at farm CH
sugar cane,
at farm BR
sugar beets
IP, at farm
CH
sweet sorghum
grains, at
farm CN
palm fruit
bunches, at
farm MY
rape seed,
at farm US
soybeans, at
farm BR
Asia
potatoes IP,
at farm CH
sugar cane,
at farm BR
sugar beets
IP, at farm
CH
sweet sorghum
grains, at
farm CN
palm fruit
bunches, at
farm MY
rape seed,
at farm US
soybeans, at
farm BR
India
potatoes IP,
at farm CH
sugar cane,
at farm BR
sugar beets
IP, at farm
CH
sweet sorghum
grains, at
farm CN
palm fruit
bunches, at
farm MY
rape seed,
at farm US
soybeans, at
farm BR
Africa
potatoes IP,
at farm CH
sugar cane,
at farm BR
sugar beets
IP, at farm
CH
sweet sorghum
grains, at
farm CN
palm fruit
bunches, at
farm MY
rape seed,
at farm US
soybeans, at
farm BR
2.2
Sugar cane
Sugar beet
Sweet
sorghum
Palm
Most relevant factors in production chain
The study of Zah (2007) showed that the most important steps in the production chain of biofuels are the
agriculture and the production of the biofuels. This is valid for the emissions of greenhouse gases as well as
in the evaluation of the overall environmental impacts with different methods (Figure 2.1)
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100% Rape M E CH
Biodiesel
100% Rape M E RER
100% P almo il M E M Y
100% So y M E US
100% So y M E B R
100% Recycled plant o il M E CH
100% Recycled plant o il M E FR
M ethano l fixed bed CH
M ethano l fluidized bed CH
Alcohol
Ethano l grass CH
Ethano l po tato es CH
Ethano l sugar beets CH
Ethano l whey CH
Ethano l wo o d CH
Ethano l sweet so rghum CN
Ethano l rye RER
Ethano l co rn US
Methane
Ethano l sugar cane B R
M ethane grass bio refinery
Infrastructure
Infrastructure
Cultivation
Cultivation
Production
Production
M ethane bio waste
Transport
Transport
M ethane sewage sludge
Operation
Operation
M ethane manure
M ethane manure+co substrate
M ethane manure, o ptimized
M ethane manure+co substrate, o ptimized
Fossil
M ethane wo o d
Diesel, lo w sulphur EURO3
P etro l, lo w sulphur EURO3
Natural gas, EURO3
0
100 200
300 400
500
600 700
[Pt/pkm ]
UBP UBP
[Pt/km]
800 900 1000
.00
0.02
0.04
0.06
0.08
0.10
0.12
EcoIndicator99 [Pt/pkm ]
Ecoindicator99 [Pt/km]
Figure 2.1: Importance of the different steps in the evaluation of the production chain of biofuels (Zah, Böni
et al. 2007).
The relevant parameters in the agriculture on the input side are the use of fertilizers, pesticides and land
transformation of tropical rain forest or other soils with high carbon stocks. On the emission side, dinitrogen
oxides, nitrate, phosphate and heavy metal emissions are relevant for the results of the assessment. These
emissions are to some extent dependent on the input of fertilizer but also on climate and soil conditions. In
the processing step, the results depend mostly on the energy demand of the process and the allocation factors.
The questionnaire allows therefore entering fertilizer and pesticides inputs in the cultivation step and requires
some basic data like crop yield or geographical information which is used for the calculation of emissions in
the agricultural step. In the processing step, data on energy efficiency and chemicals have to be entered.
2.3
Integration of social factors
The Swiss ordinance sets requirements on the environmental aspects of the life cycle of biofuels as well as
on social aspects (MinöStV 2008). As the assessment of the social factors follows other rules as the assessment of environmental impacts there are no results from the entries on social criteria. The questionnaire
on social criteria serves mainly for increasing the awareness of the user for this aspect.
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2.4
Limitations of the tool and recommendations for its use
This tool is designed to make a rough evaluation of the environmental impacts of biofuels. It isn’t meant to
replace a detailed LCA. In the actual version, specific data can only be entered for the most relevant inputs
of the cultivation and processing. The use of machines in the agriculture or the transport of the biofuel from
the producing country to Switzerland, e.g. are accounted for with default values. In addition, the calculations
of the nitrate and phosphate emissions in the agriculture are based on a coarse assessment of the parameters.
This tool therefore meant to allow an easy and low-cost assessment of the environmental impacts of a biofuel so as to give first insights if a detailed request for tax exemption would be worthwhile. However, the individual calculation of an LCA for the same biofuel, but on the basis of the detailed information required for the
Swiss tax redemption might lead to differing results. The SQCB doesn’t give any legal binding results and
cannot be used to apply for the tax exemption.
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3
Description of questionnaire
3.1
Basic data
In the page ‘basic data”, the user chooses the crop type which is needed for the production of the biofuel under study. On the basis of the crop, the SQCB tool chooses a production pathway (Table 3.1), at this time
biodiesel or bioethanol.
The user can then choose between different types of processing where available (e.g. cold pressing / hexane extraction for the milling step in biodiesel production).
The user must then enter overall delivery of biomass for each of his providers. The tool collects specific cultivation data for each provider, as it is assumed that farmers delivering biomass in a plant can have different
environmental profiles. The tool allows so comparing different providers.
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Table 3.1: Crops and the attributed fuel types. The processes tagged with an * are unique, i.e. they represent
a unique combination of inputs and outputs. The processes marked with a + could be applied to more than
one prior process, i.e. their inputs and outputs are always the same.
Feedstock
Countrie(s)
Processing
Fuel extraction
Fuel refining
Resulting
biofuel
Potatoes
CH
fermentation: ethanol, 95% in
H2O, from potatoes, at fermentations plant*
distillation: ethanol,
99,7% in H2O, from
biomass, at distillation+
Ethanol
Sugar beet
CH
H2O, from sugar beets, at fermentations plant*
99,7% in H2O, from
Ethanol
Sugar cane
BR
H2O, from sugar cane, at fermentations plant*
99,7% in H2O, from
Ethanol
Sweet
CN
H2O, from sweet sorghum, at
fermentations plant*
99,7% in H2O, from
Ethanol
Rapeseed
CH
oil pressing (cold pressing)+
esterfication*
X-Methylester
Rapeseed
RER
oil pressing (cold pressing)+
esterfication*
X-Methylester
sorghum
oil pressing (solvent extraction)*
Soya
US
esterfication*
X-Methylester
Soya
BR
esterfication*
X-Methylester
Palm
MY
esterfication*
X-Methylester
(jatropha)
India
oil pressing*
esterfication*
X-Methylester
A shown by Table 3.1, except for rape seed CH and RER the fuel production processes, i.e. fermentation
and oil pressing, are unique. This means, the input and output flows of a specific process represent an exclusive combination. Within the fuel refining processes the distillation of ethanol includes always the same
inputs and outputs, i.e. the process is always the same, whereas the esterfication process is always unique.
The difference between unique and not unique process is important. The unique processes have to be imported in all characteristics in order to reflect the differences in the processing between for example fermentation of sugar beets or sugar cane. All other processes only have to be imported once. For example, the distillation of ethanol is only hold once in the database.
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3.2
Cultivation data
The main material and energy inputs in an LCA inventory of cultivation are summarized in Table 3.2. The
most relevant ones with regard to the LCA results are asked for in the SQCB questionnaire. For inputs
judged of low relevance for the SQCB tool, like machine use, default data is used.
Table 3.2: Main material and energy inputs in an LCA inventory of cultivation and their modelling in the
SQCB tool.
Input type
Default
value
User’s total value
User’s
specific
value
Comment
Fertilizer
(mineral and
organic)
x
x
x
For the total values, the split of different fertilizers from reference data set
Pesticides
x
x
x
For the total values, the split of different pesticides from reference data set
Machine use
x
Irrigation
x
Transports
x
Grain drying
x
x
The data entry for the cultivation is divided in 4 steps (land use, mineral fertilizer, organic fertilizer, pesticides,
see also Figure 3.1). These pages are explained in the following chapters.
Figure 3.1: Structure of the data entry for the cultivation.
3.2.1.1 Land use
Figure 3.2 shows the structure of the land use page.
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Figure 3.2: Screen shot of the land use page.
The land use page collects data on yield of the crop. Furthermore, the user should enter the following geographical information:
-
ecozone: The ecological zones, or ecozones, are defined as zones or areas with relatively homogeneous natural vegetation formations, and coinciding roughly with the Köppen-Trewartha climatic
types ((FAO 2001)). The user finds this information by locating his production zone in the corresponding ecozone map.
-
annual rainfall: The ecozone concept is a relatively good indicator of the mean annual precipitations
in flat regions. In mountainous regions, the precipitation amount can be very different from one location to another according to the altitude, the mountainside orientation as well as other local effects.
The ecozone concept is thus too rough in such regions and cannot provide information about the
annual rainfall. The user has to provide the annual rainfall value for his production zone when it is located in a mountainous region.
-
2
USDA soil order: the user finds the correct soil order related to his production zone by identifying his
production zone in the soil order map. A soil order is the highest level of soil classification in the USDA classification system2. At this classification level, soils vary greatly within a given unit. Conse-
http://www.uwsp.edu/geo/faculty/ritter/glossary/S_U/soil_order.html
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quently, the utilization of these rough soil categories to derive other information (clay content for instance) can lead to inaccurate or wrong results. For a detailed assessment, a lower level of classification should be selected or field analyses should be carried out.
-
land use at reference date (01.01.2006)
The data of the three first points serve as a basis for the calculation of emissions in the agriculture (chapters
4 to 9).
3.2.1.2 Mineral fertilizer
Figure 3.3 shows the structure of the mineral fertilizer page.
Figure 3.3: Screen shot of the mineral fertilizer page. The bottom of the page with the detailed entry for Kfertilizer is cut off.
On this page, the user can choose between
1. no mineral fertilizer use
2. only total use
3. entering specific data of mineral fertilizer
When choosing 1., the user comes to the next page.
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When choosing 2., the user enters the total quantity of the specific nutrients. The SQCB tool uses the repartition in the specific fertilizer types of the ecoinvent reference data to calculate the inputs of specific fertilizers.
When choosing 3., the user can enter specific data and choose between 10 N-, 6 P- and 4 K-fertilizers (see
Table 3.3).
Table 3.3: Fertilizers in the SQCB tool
Fertilizers
Country
unit
nutrient
ammonium nitrate phosphate, as N
RER
kg
N
ammonium nitrate, as N
RER
kg
N
ammonium sulphate, as N
RER
kg
N
calcium ammonium nitrate, as N
RER
kg
N
calcium nitrate, as N
RER
kg
N
diammonium phosphate, as N
RER
kg
N
monoammonium phosphate, as N
RER
kg
N
potassium nitrate, as N
RER
kg
N
urea ammonium nitrate, as N
RER
kg
N
urea, as N
RER
kg
N
ammonium nitrate phosphate, as P2O5
RER
kg
P
diammonium phosphate, as P2O5
RER
kg
P
monoammonium phosphate, as P2O5
RER
kg
P
thomas meal, as P2O5
RER
kg
P
triple superphosphate, as P2O5
RER
kg
P
single superphosphate, as P2O5
RER
kg
P
potassium chloride, as K2O
RER
kg
K
potassium nitrate, as K2O
RER
kg
K
potassium sulphate, as K2O
RER
kg
K
If the specific fertilizer of the user doesn’t exist in the tool, the user is advised to choose a fertilizer with similar nutrients composition.
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Table 3.4: Composition of the multi-nutrient fertilizers (Nemecek and Kägi 2007)
Fertilizer type
N
ammonium nitrate
35%
calcium ammonium
nitrate
27%
urea
46%
urea ammonium nitrate
32%
calcium nitrate
16%
P 2 O5
single superphosphate
21%
triple superphosphate
48%
Thomas meal
17%
K2O
32%
potassium sulphate
50%
potassium chloride
60%
ammonium nitrate phosphate
8%
potassium nitrate
14%
monoammonium phosphate
11%
52%
diammonium phosphate,
18%
46%
Ca
52%
44%
3.2.1.3 Organic fertilizer
Figure 3.4 shows the structure of the organic fertilizer page.
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Figure 3.4: Screen shot of the organic fertilizer page.
1. no organic fertilizer use
2. only total use
3. entering specific data of organic fertilizer
When choosing 2., the user enters the total quantity of liquid or solid manure. The SQCB tool uses an average nutrient composition for the liquid resp. solid manure.
When choosing 3., the user can enter specific data and choose between liquid manure from pig or cattle
resp. solid manure from pig, cattle or poultry. Dilution water is entered separately.
3.2.1.4 Pesticides
Figure 3.5 shows the structure of the pesticide page.
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Figure 3.5: Screen shot of the pesticide page.
1. no pesticide use
2. only total use
3. entering specific data of pesticide
When choosing 2., the user enters the total quantity of pesticides. The SQCB tool uses the repartition in the
specific pesticides of the ecoinvent reference data to calculate the inputs of specific pesticides.
When choosing 3., the user can enter specific data and choose between about 200 pesticide types.
3.3
Processing data
On this page, the user can choose between using default values from the ecoinvent reference data set or enter specific data. By ticking the latter choice, the processing step divides then in two steps, production of the
raw fuel and fuel refining. For biodiesel, these two steps are milling and esterification, for ethanol, distillation
and dehydration.
For each step, the user can enter energy use, chemical use and raw material requirements of his production
process. He has also to enter the prices of his product and by-products as well as the relative production of
by-products so as to allow the calculation of the allocation factors.
3.3.1 Production of raw fuel
If the user chose to enter own data the following structure appears (Figure 3.6).
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CROP INPUT
e.g. 2.63
[kg/kg main product]
Fuel
production
MAIN PRODUCT (RAWFUEL)
e.g. 0.742
1
[kg main product]
Price [your currency/kg]
Grid Electricity
0.026
BY-PRODUCT 1
[kWh/kg main product]
z.B. 1.63
Fossil Heat
Pulldown
(explained below)
e.g. 0.75
z.B. 0.158
Energy from Biomass
Pulldown
(explained below)
e.g. 2.5
CHEMICALS INPUT
Pulldown
(explained below)
e.g. 0.02
Figure 3.6: Form for the entry of the relevant values for fuel production
3.3.1.1 Crop Input
The crop input has to be entered by user. The unit is defined as ‘kg crop required to produce one kg of the
main product fresh mass.
3.3.1.2 Energy Input
Grid Electricity:
The user has to determine the amount of grid electricity in kWh required to produce one kg of the main product. The respective electricity mix is chosen automatically based on the country entered by the user (within
the basic data). However, not each country can be related to an electricity mix since ecoinvent provides not
electricity mixes for all countries. In case no country specific electricity mix is available the average UCTE
electricity mix is used.
Fossil Heat:
The user has to determine the type and the amount of fossil heat. He can choose the type of fossil heat by
clicking the pull down menu. If no fossil heat is required the user can choose “not used”. When fossil heat is
used, the user can decide for one heat source and enter the amount of heat required in kg required to produce on kg of the main product. If more than one source of fossil heat is used the user can add an addition
input field by pressing the plus button.
Energy from Biomass:
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The user has to define if and to which amount renewable energy from biomass is used. By clicking the pull
down menu the user can choose if biomass is used or not used. When biomass is used the user has to enter
the amount of renewable energy (biomass) in kg per kg main product produced. If more than one source of
renewable energy from biomass is used the user can add an addition input field by pressing the plus button.
3.3.1.3 Chemical Input
The user has to choose the type and the amount of chemical input. When clicking the pull donw menu, the
user can select the chemical from the pull down and enter the respective amount required (in kg) to produce
on kg of the main product. If no chemicals are used, the user can select “not used”. If more than one source
of chemical input is used the user can add an addition input field by pressing the plus button.
3.3.1.4 Main Product
The amount of the main product is always one kg, i.e. it can not be adapted by the user. However, the user
has to enter the price which is used to determine the factor of the flows which could be attributed to the main
product. The currency entered is not important. In fact, it is absolutely necessary that the user use the same
currency for the price of both main and by-product. The reason for this is that the ratio between the price of
the main product and the price of the by-product determines the allocation factors.
3.3.1.5 By-Product
The user has to determine the amount and the price referring to the by-product. As regards the first, the unit
is defined as kg by-product produced with one kg of the main product. The price is used to determine the
fraction of the flows which could be attributed to the main product, i.e. the allocation. The formula to calculate
the allocation factor for the main product is determined in the next paragraph. Since some processes have
more than one by-product, the user can determine additional by-product by pressing the plus button.
3.3.2 Refinement of fuel
If the user chose to enter own data the following structure appears (Figure 3.7).
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RAW FUEL
e.g. 1.10
MAIN PRODUCT (REFINED FUEL)
Fuel
production
e.g. 1.5
1
[kg main product]
Grid Electricity
0.026
BY-PRODUCT 1
[kWh/kg main product]
z.B. 1.63
Fossil Heat
e.g. 0.75
Pulldown
(explained below)
z.B. 0.12
Energy from Biomass
e.g. 2.5
Pulldown
(explained below)
CHEMICALS INPUT
Pulldown
(explained below)
e.g. 0.02
Figure 3.7: Form for the entry of the relevant values for fuel refining
3.3.2.1 Raw Fuel Input
The input of the raw fuel has to be entered by user. The unit is defined as ‘kg raw fuel required to produce
one kg of refined fuel.
3.3.2.2 Energy Input
Grid Electricity:
The user has to determine the amount of grid electricity in kWh required to produce one kg of the main product. The respective electricity mix is chosen automatically based on the country entered by the user (within
the basic data). However, not each country can be related to an electricity mix since ecoinvent provides not
electricity mixes for all countries. In case no country specific electricity mix is available the average UCTE
electricity mix is used.
Fossil Heat:
The user has to determine the type and the amount of fossil heat. He can choose the type of fossil heat by
clicking the pull down menu. If no fossil heat is required the user can choose “not used”. When fossil heat is
used, the user can decide for one heat source and enter the amount of heat required in kg required to produce on kg of the main product. If more than one source of fossil heat is used the user can add an addition
input field by pressing the plus button.
Energy from Biomass:
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The user has to define if and to which amount renewable energy from biomass is used. By clicking the pull
down menu the user can choose if biomass is used or not used. When biomass is used the user has to enter
the amount of renewable energy (biomass) in kg per kg main product produced. If more than one source of
renewable energy from biomass is used the user can add an addition input field by pressing the plus button.
3.3.2.3 Chemical Input
The user has to choose the type and the amount of chemical input. When clicking the pull donw menu, the
user can select the chemical from the pull down and enter the respective amount required (in kg) to produce
on kg of the main product. If no chemicals are used, the user can select “not used”. If more than one source
of chemical input is used the user can add an addition input field by pressing the plus button.
3.3.2.4 Main Product
The amount of the refined fuel is always one kg, i.e. it can not be adapted by the user. However, the user
has to enter the price which is used to determine the factor of the flows which could be attributed to the main
product. The currency entered is not important. In fact, it is absolutely necessary that the user use the same
currency for the price of both main and by-product. The reason for this is that the ratio between the price of
the main product and the price of the by-product determines the allocation factors.
3.3.2.5 By-Product
The user has to determine the amount and the price referring to the by-product. As regards the first, the unit
is defined as kg by-product produced with one kg of the main product. The price is used to determine the
fraction of the flows which could be attributed to the main product, i.e. the allocation. The formula to calculate
the allocation factor for the main product is determined in the next paragraph. Since some processes have
more than one by-product, the user can determine additional by-product by pressing the plus button.
3.4
Transport and fuel use
No data entries of the user are possible with respect to the transportation of the fuel to the gauging station
and the use of the passenger car.
3.5
Social criteria
The aim of this page is to increase the awareness of the as regards the fact that the production of biofuels
must fulfill minimum social criteria’s. The page requires entry for the main social aspects that are covered by
the Swiss legislation, which are
- guarantee of free association and protection of the right to organize
- interdiction of forced labour
- interdiction of child work
- interdiction of discrimination
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The user can tick yes or no by these points.
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4
Modelling CO2 emissions from land use
Authors: Jürgen Reinhard, Mireille Faist Emmenegger, Rainer Zah
Land use activities in the agro forestry sector are one main source for anthropogenic greenhouse gas (GHG)
emissions. The most important GHG emissions of concern are CO2, N2O (di-nitrogen monoxide) und CH4
(methane) (IPCC 2006). Approx. 30% of all anthropogenic GHG emissions between 1989 and 1998 could be
allocated to land use activities (Carmenza and Blaser 2008). Land use changes, i.e. the transformation of
one land use type to another, is responsible for approx. 2/3 of those emissions (Carmenza and Blaser 2008).
In this context, optimization of land use activities and in particular land transformations plays a key role in reducing GHG emissions.
Current LCA studies show the importance of the consideration of land use changes with respect to the environmental performance of agro-biofuels (Wicke, Dornburg et al. ; Reinhard 2007; Zah, Böni et al. 2007;
Schmidt 2008). However, within ecoinvent, land use changes are only considered with regard to rain forest
clear-cut, e.g. for oil palm cultivation in Malaysia or for soy bean cultivation in Brazil. Nevertheless, land use
changes should be included consequently. One reason for this is that not merely rain forest but also other
land use types are subject of land transformations. When only the extreme land transformations are taken
into account important GHG emissions are excluded.
However, to date no methodological framework is available that governs the inclusion of land use changes in
LCAs. On this basis, the following framework primarily base on the IPCC guidelines 2006 (IPCC 2006). It is
to mention that it makes no claim to be complete. In fact, it should provide the basics to calculate the emissions from land use changes which (i) appeared relevant in practice and (ii) important as regards the results
of the LCA (Reinhard 2007). In this context, the following explanations represent something like a first step
as regards the inclusion of emissions from land use changes (LUC).
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4.1
Basics
4.1.1 Direct and indirect land use change
A direct land use change (LUC) is given whenever (i) the additional demand for more land is not caused by
other displacements, i.e. no leaching is in-between and (ii) the land transformed has not been used for the
intended production of feedstock’s, i.e. did not yield an annual, monetary profit. For example, the increased
cultivation of soybean in Brazil might rise at the expense of savannah or tropical rain forest in Brazil. Figure
4.1 elaborates the difference between direct and indirect LUC.
(1)
Switzerland:
Importing biofuel
Increased production of
agro-biofuels for export.
(2)
Replaces previously given
cultivation on the same acreage.
Prior cropland
60%
Cropland
(agro-biofuel)
100%
Rain forest Savannah
20%
20%
Direct land use change
Rain forest
20%
Cropland
60%
Grassland
40%
Indirect Land use change
(4)
The area somewhere else is
likely to be forest
(3)
Previous cropping is displaced
to an area somewhere else.
Figure 4.1: The difference between direct and indirect land use change.
Indirect land use changes in turn, result from the substitution of the intended cultivation of feedstock’s, i.e.
crops related to a monetary profit. Due to the inelasticity of food and feed the displacement of such crops is
expected to cause the further displacement of other crops until the amount displaced is balanced by expansion to areas not used for the intended production of feedstock’s or intensification of available feedstocks.
Direct land-use changes can be quantified by land cover data or content data from the IPCC guidelines 2006
(IPCC 2006)(tier 1). No GHG balancing scheme has – yet – taken indirect land use changes into account.
Based on that, only direct land use changes are taken into account within the first version of the SQCB-Tool.
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4.1.2 Principles
Figure 4.2 show the direct LUC covered by the SQCB. The land use categories refer to IPCC 2006 (IPCC
2006).
Natural ecosystem
1ha
Forest Land
Managed ecosystem
Agro-biofuel system
1ha
Forest Land
1ha
1ha
Grass Land
Grass Land
1ha
1ha
1ha
Cropland
Cropland
Cropland
1ha
Wetland
1ha
Wetland
1ha
1ha
Settlements
Settlements
1ha
Other Land
Included
1ha
Other Land
Excluded
Figure 4.2: Land use changes, which could be calculated with the SQCB. The categories are taken from the
IPCC Guidlines 2006, p. 1.9.
In order to calculate the CO2 emissions from direct LUC, the carbon content of the implemented agro-biofuel
system (cropland) is subtracted from the carbon content of the land use at the reference date (01.01.2006).
In this context, the SQCB tool covers only the transformation of natural ecosystem to agro-biofuel system.
Thus, all land use systems which yield a monetary profit refer to the cropland category. Cropland includes for
example rape, soybean, barley, hay, sugar cane and according to the IPCC also oil palm plantation or wood
plantation. The transformation from cropland to cropland is rated with zero. However, both oil palm and wood
plantation accumulate a considerable amount of carbon during their cultivation time (IPCC 2006). In order to
consider this both are handled in a generic category, i.e. ‘plantations’.
Figure 4.3 shows the basic principle which has been applied to calculate the carbon stocks of (i) the natural
ecosystems and (ii) the agro-biofuel system.
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Implicite assumtion:
todays application = future application
[1 ha Cropland]
[1 ha Cropland]
[1 ha ?]
01.01.2026
today
T
Accounting
period
LCA
Natural Ecosystem
[e.g. 1 ha forest]
1 Year
01.01.2006
Accounting period IPCC =
20 years until state of equilibrium is reached
1.
C_Content_REF0
3.
2.
C_Content_new
Figure 4.3: Basic principles to calculate the carbon content of the relevant land use categories.
In order to determine the difference between the carbon content of the natural and the managed ecosystem
three kind of carbon pools are taken into account:
(i)
above ground biomass (AGB),
(ii)
dead organic matter (DOM) and
(iii)
soil organic carbon (SOC).
This corresponds to the tier 1 methodology determined in the IPCC 2006 (IPCC 2006). In the first step
(Figure 4.3) the carbon content of the natural ecosystem is calculated in dependence on (i) the ecozone, (ii)
the land use category, and, when relevant (iii) the world region. In this way, the carbon content of the managed ecosystem is calculated using pessimistic assumptions as regards the applied management practice,
e.g. full tillage, intensive production, etc. In the third step, the difference between both, i.e. the carbon content of the natural and the managed ecosystem, is determined and related to the functional unit. This include,
the transformation of the calculated difference in carbon content to CO2 using the mol factor between C and
CO2 (44/12).
The SQCB already include caclulated mean carbon contents of natural and managed ecosystems. In other
words, only the third computation step is applied within the SQCB.
4.1.3 Limitations
All in all the following limitations have to be taken into account:
(i)
The IPCC guidelines are meant for the development of national GHG inventories and not for
LCAs. In other words, the method of the IPCC must be adapted in order to relate the emissions
calculated by the IPCC method to the functional unit of the LCA. This induces uncertainties.
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(ii)
The transformation from cropland to cropland is rated with zero. This is a simplification since different crops, i.e. in particular the related management practice (tillage, fertilization, etc.) influence the carbon stock of a given area. In general, the difference between cropland and cropland
is not important. However, if tree crops are displaced (or implemented) by increased agro-biofuel
production the differences should be taken into account. Thus the land use category ‘plantation’
was added.
(iii)
Dinitrogen monoxide (N2O) as well as methane (CH4) can play an important role with respect to
land use changes. However, the use of the inventory data (as provided for CO2 emissions) are
currently not available on a global scale (N2O) or would require assumptions with respect to the
handling of above ground biomass (AGB) (CH4) which would go beyond the scale of the SQCB.
In other words, only CO2 emissions are mentioned in the context of land use changes. In this
context, the full amount of carbon stored in AGB is assumed to be transformed to CO2.
(iv)
The transformation of managed ecosystems into agro-biofuel systems are not taken into account
within the SQCB. The reason for this is that (i) the land use changes for natural ecosystems appear to be the most important land transformation; (ii) the SQCB database would have required
additional tables in order to calculate the carbon stock of managed ecosystems on the basis of
natural ecosystem.
(v)
The default assumption in the IPCC guidelines is that changes in soil organic carbon (SOC) content induced by land transformation causes emissions/entries over 20 years until a new equilibrium is reached. However, the application of this default assumption in the context of a LCA induces that the land is not further transformed within 20 years after the transformation, which is a
simplification of reality.
(vi)
The standard values used to calculate the CO2 emissions from LUC are rather overestimated,
i.e. represent pessimistic assumptions. This was done in order to increase the awareness of the
user as regards CO2 emissions resulting from direct land transformation.
In the following the detailed computation is elaborated.
4.2
Computation
Figure 4.4 shows the interrelation between the data entered by the user and the tables of the SQCB in order
to emphasize the computation steps.
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Country entered by
user
Table:
Transformation_from
Peat land affected?
Table:
Sqcb_country_worldregion
Selection:
Carbon stock at
reference date
Prior land use type n
Computation:
Result:
per provider and land use
change n
Carbon dioxide, from land
transformation
[kg CO2 equiv. per kg biomass]
Selection:
Carbon stock of
new land use type
Ecozone n
Table:
Calculation:
ha required per
land use change n
Ecozone
Table:
Acreage in % of land
use type n
Transformation_to
New land use:
Plantation or cropland
Yield per ha
provider n
Overall amount
provider n
Figure 4.4: Calculation chart for carbon dioxide, from land transformation per provider and land use change
n.
The computation of the overall CO2 emissions from the land use change of all providers could be expressed
by the following equation.
Equation 4-1: Equation for the computation of the annual CO2 emissions from LUC in kg CO2 per kg crop
⎧⎪
Δ CO 2 LUC =
∑ ⎨⎪ha
n
Pn *
⎩
⎡ ⎛
⎞ ⎤ ⎪⎫
44
⎡
⎤
⎢ ∑ ⎜⎜ F i , n * ⎢ (C 0 i , n − C ( 0 − T ) i , n * ( ) * 1000 kg ⎥ + CO 2 PEAT ⎟⎟ ⎥ ⎬
12
⎣
⎦
⎠ ⎦ ⎪⎭
⎣ i ⎝
∑ ha Pn *YPn 20 years
n
ΔCO2 LUC
= annual CO2 emissions from LUC in kg CO2 per kg crop.
n
= amount of providers.
i
= specific land use change.
Fi , n
= fraction i (x%) of a specif land use type in relation to the sum of all land use types (100%)
( F LUC i =
i
∑i
) of provider n.
haPn
= area of provider n, in ha.
YPN20 years
= annual yield of provider n, in kg per ha applied to 20 years.
C 0 i ,n
= carbon stock at reference data for provider n an land use change i, in tons carbon.
C ( 0 − t ) i ,n
= carbon stock of the managed ecosystem after 20 years for provider n and land use change
i, in tons carbon.
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1000
(
= change factor for tons to kilogram.
− 44
)
12
= Transformation of carbon to CO2 based on the mol fraction.
CO 2 Peat
= annual emissions caused by the transformation of peat land, here we use the likely value
according to (Hooijer, Silvius et al. 2006), i.e. always 86.000 kg CO2 per ha/year * 20 years.
4.3
Tables
In dependence on (i) the ecozone, (ii) the land use category, and, when relevant (iii) the world region one
‘representative” value was calculated for each natural ecosystem based on the data provided by the IPCC
guidelines (IPCC 2006). For the detailed table see chapter in the annex.
4.4
Online helps for the inputs
An online help is provided for the user for each input parameter. Under each corresponding field, there is a
blue question mark. By clicking on it, the user can access to the help text. The help text is as short as possible. For most of the help texts, some words are underlined in blue and can be selected with the mouse. They
lead to a new help text describing or explaining the underlined word or giving an example.
4.4.1 General
Please mind the units indicated for each parameter. We use the International System of Units (SI) in this
tool. If you use a different units system in your workaday, please convert your data in the International System of Units before typing them in.
4.4.2 Type of land use at reference date
Please indicate the utilization of the land on the first of January 2004.
4.4.3 Acreage in percent
If different types of surfaces were changed (i.e. more than one type of land transformed), please indicate the
acreage fraction of the respective land use in percent
4.4.4 Peat land
Check the box if the land area affected by the land transformation was peat land. According to (IMCG 2008)
Peatlands are those wetland ecosystems characterized by the accumulation of organic matter (peat) derived
from dead and decaying plant material under conditions of permanent water saturation. While covering only
2% of the World’s land area, Peatlands contains as much carbon as all terrestrial biomass, twice as much as
all global forest biomass, and about the same as in the atmosphere.
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5
Modelling of ammonia emissions
5.1
Structure of ammonia (NH3) computation
Modelling follows the guidelines of Nemecek et al (Nemecek and Kägi 2007).
Mineral fertilizer N
Table: NH3
emission factor of
mineral fertilizer
Solid manure
yield
liquid manure
without water
dilution water
NH3-Emissions
Computation: NH3 emissions
from organic fertilizer
Table: Table_NH3_organic
Table: Saturation_deficit
ecozone
Figure 5.1: Structure for the computation of ammonia (NH3-) emissions.
5.2
Ammonia (NH3) computation
NH3 [kg NH3 /kg]= (NH3, organic, liquid, cattle&pigs + NH3, organic, solid, cattle&pigs + NH3 , dplh + NH3 , blh + NH3 , broilers + NH3, mineral )/yield
NH3, dplb [kg NH3 /ha]= Quantitylitter, dplb * 5 [kg NH3 /t litter]* 0.2 * 17/14
QuantityN, dplb [t /ha]: Entry quantity litter from deep pits from laying hens
Nemecek et al (Nemecek and Kägi 2007), page 28 gives an emission factor of 0.2 (N-losses in the form of
ammonia referring to the total N-content) for litter from deep pits from laying hens
NH3 /t litter from (Walther, Ryser et al. 2001).
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NH3, blb [kg NH3 /ha]= Quantitylitter, blb * 3.6 [kg NH3 /t litter]* 0.25 * 17/14
QuantityN, blb [kg NH3/ha]: Entry quantity litter from belts from laying hens
(Nemecek and Kägi 2007), page 28 gives an emission factor of 0.25 (N-losses in the form of ammonia referring to the total N-content) for litter from belts from laying hens
NH3, broilers [kg NH3 /ha]= Quantitylitter, broilers * 8 [kg NH3 /t litter] * 0.15 * 17/14
Quantitylitter, broilers [kg /ha]: Entry quantity litter from broilers
(Nemecek and Kägi 2007), page 28 gives an emission factor of 0.15 (N-losses in the form of ammonia referring to the total N-content) for litter from broilers
NH3, mineral [kg NH3 /ha]= QuantityN, mineral fertilizerX * NH3 –N fertilizerX * 17/14
QuantityN, mineral fertilizerX [kg /ha]: Entry quantity N from a specific fertilizer
NH3 –N fertilizerX [kg NH3 /kg Dünger]: emission factor from Table 5.1
NH3, organic, liquid, cattle&pigs [kg NH3 /ha]= 17/14 * (-9.5 + 19.4*TANliquid / ((Quantityliquid + Quantitywater )/ Quantityliquid )+ 1.1*SD) * (0.0214* (Quantityliquid + Quantitywater ) + 0.358) * AS
Korrigiert:
NH3, organic, liquid, cattle&pigs [kg NH3 /ha]= 17/14 * (‐9.5 + 19.4*(TAN li‐
quid,cattle* (Quantity liquid,cattle/ Quantity liquid,total)+ TAN liquid,pigs* (Quantity liquid,pigs/ Quantity liquid,total))+ 1.1*SD) * (0.0214* (Quantity total,liquid + Quantity water ) + 0.358) * AS NH3, organic, liquid, cattle&pigs [kg NH3 /ha]= 17/14 * (‐9.5 + 19.4*(TAN average)+ 1.1*SD) * (0.0214* (Quantity total,liquid + Quantity water ) + 0.358) * AS TANliquid [kg NH3 /m3]= from table 2. If the possibility ‘only total known” is used, then use the line ‘average liquid manure”
Quantityliquid [m3 /ha]= quantity of liquid manure excl. dilution water
If Quantityliquid + Quantitywater is smaller than 40 then AS = ( Quantityliquid + Quantitywater )/ 40 and Quantityliquid
+ Quantitywater is set to 40 to avoid too high values of ammonia. If Quantityliquid + Quantitywater is greater than
40 then AS = 1.
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Quantitywater [m3 /ha] = entry from user
SD= saturation deficit, calculated with the formula described in chapter 5.3
NH3, organic, solid, cattle&pigs [kg NH3 /ha]= 17/14 * (0.787 TANsolid * M + 0.757) * 0.75 * AM
TANsolid [kg NH3 /t] = from table. If the possibility ‘only total known” is used, then use the line ‘average solid
manure”
M [t /ha]= quantity of solid manure as entried
AM [%]= Fraction of area, where manure is spread. If M is smaller than 16 then AM = ( M)/ 16 and M is set to
16 to avoid too high values of ammonia. If M is greater than 16 then AS = 1.
5.3
Calculation of the saturation deficit
The saturation deficit is calculated with the formula from (Nemecek and Kägi 2007),
SDm = (1-rHm)*6.112*e((17.67Tm)/(243.5+Tm))
rHm = average relative humidity in month m (%/100) (entry from user)
Tm = average temperature in month m (°C) (entry from user)
Default values are used in case the user doesn't make any entry. The saturation deficit is then coupled with
the ecozone. However, this relation is very small and therefore these values are very coarse Table 5.3.
5.4
Cases
5.4.1 Default values
Quantityliquid = ecoinvent entry ‘slurry spreading”
Quantitywater = Quantityliquid / 40 * 60
M = ecoinvent entry ‘solid manure spreading”
TAN : resp. average value for slurry/manure
5.4.2 Only total known
Quantityliquid = entry liquid manure
Quantitywater = Quantityliquid / 40 * 60
M = entry solid manure
TAN : resp. average value resp. average value for slurry/manure
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5.4.3 Detailed values
Quantityliquid = entry liquid manure
Quantitywater = entry water
M = entry solid manure
TAN : resp. value for the specific slurry/manure
5.5
Tables
Table 5.1until Table 5.3 show the factors used fort he calculation of NH3.
Table 5.1: Emission factor for NH3 –N (Nemecek and Kägi 2007).
Type of fertiliser
Emission factor for NH3-N
ammonium nitrate phosphate, as N, at regional storehouse
ammonium nitrate, as N, at regional storehouse
ammonium sulphate, as N, at regional storehouse
4.0%
calcium ammonium nitrate, as N, at regional storehouse
calcium nitrate, as N, at regional storehouse
diammonium phosphate, as N, at regional storehouse
2.0%
monoammonium phosphate, as N, at regional storehouse
potassium nitrate, as N, at regional storehouse
urea ammonium nitrate, as N, at regional storehouse
4.0%
urea, as N, at regional storehouse
generic N
2.0%
8.0%
2.0%
4.0%
4.0%
8.5%
15.0%
4.0%
Table 5.2: total N in manure (Walther, Ryser et al. 2001)
Criteria
Total Nlös (TAN) Remark
[kg/m3]
Liquid manure
Beef and dairy cattle
Pigs
TAN liquid
2.3
3.8
average liquid manure
3
kg/t
TAN solid
1.3
2.3
1.5
Solid manure
Pigs
average solid manure
(Walther, Ryser et al. 2001)
Average from (Walther, Ryser et al.
2001)
own calculation
own calculation
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Table 5.3: Rough values for saturation deficit.
Ecozone
TAr
TAwa
TAWb
TBSh
TBWh
TM
SCf
SCs
SBSh
SBWh
SM
TeDo
TeDc
TeBSk
TeBWk
TeM
Ba
Bb
Saturation deficit
SD
6.3
7.0
9.3
11.7
16.4
2.1
6.2
12.4
12.4
14.4
2.5
4.2
1.4
7.0
9.8
2.1
6.1
6.1
Ecozone
Tropical rain forest
Tropical moist deciduous forest
Tropical dry forest
Tropical shrubland
Tropical desert
Tropical mountain systems
Subtropical humid forest
Subtropical dry forest
Subtropical steppe
Subtropical desert
Subtropical mountain systems
Temperate oceanic forest
Temperate continental forest
Temperate steppe
Temperate desert
Temperate mountain systems
Boreal coniferous forest
Boreal tundra woodland
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6
Modelling of N2O and NOx emissions
6.1
N2O emissions
The calculation of the N2O emissions is based on the formula in Nemecek (2007) and adopts the new IPCC
guidelines from 2006 (IPCC 2006).
N2 O = 44/28 ∗ (EF1 ∗ ( N av − 14 / 17 ∗ NH 3 + N cr ) + EF4 ∗ 14/17 ∗ NH3 + EF5 ∗ 14/62 ∗ NO 3- )
With:
N2O = emissions of N2O [kg N2O/ha]
EF1 = 0.01 (IPCC 2006, S. 11.11)
Nav = available nitrogen [kg N/ha]
Ncr = nitrogen contained in the crop residues [kg N/ha]
EF4 = 0.01 (IPCC 2006, S. 11.24)
NH3 = losses of nitrogen in the form of ammonia [kg NH3/ha]; calculated according to chapter 5
EF5 = 0.0075 (IPCC 2006, S. 11.24)
NO3- = losses of nitrogen in the form of nitrate [kg NO3-/ha]; calculated according to chapter 8.
6.2
NOx in air
The calculation of the NOx emissions is based on the formula in Nemecek (2007).
NOx = 0.21 * N2O
Whereas N2O is calculated according to the preceding chapter 6.1.
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7
Modeling of phosphorus emissions
Authors: Anne Roches, Gerard Gaillard, Ruth Freiermuth
Nutrients, like phosphorus and nitrogen, play key roles in the plants (oxygen transfer, protein building, etc.)
and inputs of these nutrients are essential for profitable crop agriculture. They can however have some adverse effects when they are present in high concentration in some environmental compartments. They are of
major concern in water bodies where they cause eutrophication (Sharpley 2003).
In this report, phosphorus emissions refer to the phosphorus quantities transported from soil to waters and
further causing potential eutrophication. Transport of phosphorus from soil to water bodies occurs following
several pathways, under particulate forms and under dissolved forms (Hutchins, S.G. Anthony et al. 2002).
Phosphorus losses are not easy to estimate and several methods are used across the world (Prasuhn 2006),
(Marchant, Walter et al. 2005), (Hutchins, S.G. Anthony et al. 2002). The USA authorities have conducted
important model simulations of soil loss and nutrient losses associated with crop production, taking into account a lot of physical processes and management practices, and having led to the set up of an important
database (Potter 2006). The model used was EPIC3, a continuous simulation model including a lot of components, such as weather simulation, nutrient cycling and so on. Such models have the great advantage to
be transposable, that means that they can be used everywhere requiring no or little adaptations, since they
are based on physical laws to a large extent. However since they are complex and their use is time and
money consuming, they cannot be used in a project like SQCB because of the additional efforts required.
Others models, based on statistical regression or on mechanistic equations, do exist (Prasuhn 2006), (Marchant, Walter et al. 2005), (Roy 2003) and are more practicable. These models are usually applied in
projects dealing with rapid assessment of environmental impacts, where the resources are not sufficient to
set up and operate a complex simulation model. These less complex models however have the major disadvantage of giving imprecise or even incorrect results when changing the conditions of their application (Nemecek, A. Heil et al. 2004). They are developed for specific conditions (climate, crop, soil, etc.) and should
be applied in other conditions only carefully and only as a rough assessment method. On-site investigations
and/or more accurate modeling are necessary for a more precise evaluation of the environmental impact, in
the framework of political decision making, legal processes or academic research.
The selection of a method is however not easy since, as mentioned before, no simple method seems to be
applicable on a global scale. After a literature review and a comparison between the methods, we find some
similarities between the method proposed in (Roy 2003) for the assessment of nutrient balance in Africa and
between the method used in ecoinvent (Nemecek, A. Heil et al. 2004). The method used in ecoinvent is
based on the methodology described in (Prasuhn 2006) but does not take into account some refinements
applied in (Prasuhn 2006) corresponding to site-specific conditions. The authors in (Roy 2003) assumes that
only the phosphorus losses under particulate form through the erosion process are important since the
phosphorus is strongly bounded to the soil matrix in tropical soils. In Swiss soils, we consider that phosphorus losses under dissolved forms are important too (Prasuhn 2006). The method described in (Prasuhn
2006) thus consider other pathways than only erosion: phosphate leaching, phosphate loss through run-off
and phosphate loss through drainage when the field is drained. The loss through erosion is computed in a
3
http://gcmd.nasa.gov/records/EPIC.html
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very similar way in (Prasuhn 2006) and in (Roy 2003). Furthermore the model in (Roy 2003) has been applied for a nutrient assessment in the whole sub-Saharan Africa. The similarity between the two methods and
their relatively broad application under different pedo-climatic conditions (one has been applied over the African continent and the other one is used as a reference for the LCA computations in Switzerland) has led to
their selection for this project.
7.1
Origin of model and model structure
We use the models described in (Nemecek, A. Heil et al. 2004), (Roy 2003) and (Prasuhn 2006) with some
minor adaptations. These are all mechanistic models and have similar parts. We choose to consider several
pathways for phosphorus losses and to distinguish the losses in surface water from those in groundwater:
1. Phosphorus loss in particulate form to surface water: soil erosion
2. Phosphorus loss in dissolved form (phosphates) to surface water: run-off and drainage
3. Phosphorus loss in dissolved form (phosphates) to groundwater: leaching
7.1.1 Phosphorus loss in particulate form to surface water: soil erosion
Phosphorus loss in particulate form to surface water occurs through soil erosion and are described in the
same way in (Prasuhn 2006), (Nemecek, A. Heil et al. 2004) and in (Roy 2003). The equation is quite the
same, with slightly varying coefficients but (Roy 2003) has no correction factor to express the effective loss in
the water system.
Nevertheless, both relate the lost quantity of phosphorus through erosion to:
-
The quantity of eroded soil
-
The phosphorus concentration in the soil
-
An enrichment factor, expressing the fact that the upper soil layer has a higher phosphorus content
than the other horizons, since phosphorus is strongly bounded to fine particles (clay, silts) and that
these fine particles are preferentially taken off by erosive agents (water, wind).
Furthermore, (Prasuhn 2006) and (Nemecek, A. Heil et al. 2004) consider a correction factor, expressing the
fact that only a given percentage of the eroded quantity of phosphorus will effectively reach the surface water
system.
The crucial parameter is the quantity of eroded soil. No indications about how to compute this parameter are
furnished in (Prasuhn 2006) and in (Roy 2003). An equation for computing this parameter is given in (Nemecek, A. Heil et al. 2004) and is referred to be based on a personal communication with an erosion expert.
This equation is for water erosion only, since wind erosion does not play a major role in Switzerland.
Erosion
Soil erosion is a major problem across the world (Van Lynden 1993), (Batjes 1996) and several studies have
been carried out in order to better understand the mechanisms of erosion under different conditions (Alegre
and Cassel 1996), (Engel F.L., I. Bertol et al. 2007). Erosion consists of the detachment, transport and deposition of soil particles. This phenomenon is a dynamical process evolving in time and space (Batjes 1996).
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There are two agents causing erosion: water and wind (Van Lynden 1993). According to (Batjes 1996), wind
erosion is two times less important than water erosion in term of affected area (548 million ha for wind erosion against 1094 million ha for water erosion). Wind erosion is not considered in the model and consequently in the tool. The eroded volume and the related phosphorus loss are then underestimated in some cases,
particularly when the wind erosion potential is high (see Figure 7.1).
Figure 7.1: Map of wind erosion vulnerability on a global scale.
http://soils.usda.gov/use/worldsoils/mapindex/eroswind.jpg
Several methods and models have been developed in order to assess soil losses due to water erosion: the
WEPP model (Water Erosion Prediction Project)4, the RUSLE model (Revised Universal Soil Loss Equation)5, etc. These models are quite complex and depend on databases, in order to furnish the inputs needed
by the models. These databases are unfortunately developed for the USA only and provide no help for the
rest of the world. The requirements to apply them outside the USA are very large and do not suit the time
scale of this project.
A widely used approach to assess rill and sheet erosion at the field scale is the Universal Soil Loss Equation
(USLE) (Wischmeier and Smith 1978). This equation gives an idea about the yearly long term erosion
caused by water. It is not possible to predict the erosion associated with extreme events or with individual
events. We decide to use this equation to compute soil erosion.
4
http://topsoil.nserl.purdue.edu/nserlweb/weppmain/docs/readme.htm
5
http://www.ars.usda.gov/Research/docs.htm?docid=5971
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7.1.2 Phosphorus loss in dissolved form (phosphate) to surface water: run-off and drainage
Phosphorus loss in dissolved form (phosphate) to surface water is described in (Prasuhn 2006) and in (Nemecek, A. Heil et al. 2004), but only (Prasuhn 2006) considers loss through drainage. We will consider runoff and drainage if the plot is conventionally drained with a drainage pipes system.
The relevant transport mechanisms are on one hand depending on the soil characteristics and on the other
hand on the type of fertilizer. Mineral fertilizers are quickly absorbed by the soil and manure is hardly transported in the soil because of its structure. Mineral fertilizers and manure are therefore neglected for calculating phosphate loss through drainage. They however play a role in the phosphate loss through run-off (Prasuhn 2006), since the flow concerns the nutrients on the soil surface.
Run-off
The phosphate loss through run-off in both (Nemecek, A. Heil et al. 2004) and (Prasuhn 2006) is assessed
along the same principle, but (Nemecek, A. Heil et al. 2004) neglects some site-specific correction factors
since an LCI should refer to average conditions and not to site-specific conditions. We are in the same type
of situation and do not take into account these further correction factors. The phosphate loss through run-off
is related to:
-
An average P quantity lost through run-off for a given land use type
-
A factor related to the P fertilizers applied (quantity and type)
The site-specific correction factors in (Prasuhn 2006) are set to their default value (=1).
Drainage
As mentioned before, only (Prasuhn 2006) computes phosphate loss through drainage. Like for the computation of phosphate loss through run-off, the drainage equation contains some site-specific correction factors,
which are not taken into account in the present project. These factors are set to their default value (=1). In
this case, the phosphate loss through drainage is related to:
-
An average P quantity lost through leaching for a given land use type
-
A factor related to the P fertilization through slurry application
-
A drainage coefficient
7.1.3 Phosphorus loss in dissolved form (phosphates) to groundwater: leaching
Slurry solely plays an important role in the phosphate loss through leaching, since mineral fertilizers are
quickly absorbed by the soil and manure is hardly transported through the soil pores because of its structure.
The phosphate loss to groundwater occurs through a leaching process and is described in a similar way in
(Prasuhn 2006) and in (Nemecek, A. Heil et al. 2004), but (Nemecek, A. Heil et al. 2004) do not take into account some site-specific correction factors applied in (Prasuhn 2006). The phosphate loss through leaching
is related to:
-
An average P quantity lost through leaching for a given land use type
-
A factor related to the P fertilization through slurry application
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-
A drainage coefficient
The site-specific correction factors in (Prasuhn 2006) are set to their default value (=1).
Mineral fertilizers and manure are not considered for the evaluation of phosphate loss through leaching,
since they play a minor role compared to slurry, in the same manner as for drainage (Prasuhn 2006).
7.1.4 Design of the global phosphorus loss model
The global phosphorus loss model and the data flows are visualized in Figure 7.2 and Figure 7.3. The light
blue elements refer to the inputs that have to be typed in by the user (or default value) and the yellow elements refer to the intermediate outputs, which are needed for the calculation of different phosphorus losses.
The outputs, phosphorus loss to surface water, phosphate loss to surface water and phosphate loss to
groundwater, are outlined in green.
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Figure 7.2: Structure of the erosion model and data flows.
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Figure 7.3. Structure of the phosphorus model and data flows
The inputs that have to be typed in by the user are described below (54).
7.2
Computation
As mentioned in 7.1.1, the assessment of phosphorus loss in particulate form requires the quantification of
soil erosion. We thus describe first the computation of soil erosion and then the computations of phosphorus
loss in different forms.
7.2.1 Erosion
The USLE (Universal Soil Loss Equation) is expressed as (Wischmeier and Smith 1978):
A = R * k * LS * c1 * c 2 * P
A = Potential long term annual soil loss [t ha-1 yr-1]
R = Erosivity factor [MJ mm ha-1 h-1 yr-1]
k = Erodibility factor [t h MJ-1 mm-1]
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LS = Slope factor [-]
c1 = Crop factor [-]
c 2 = Tillage factor [-]
P = Practice factor [-]
The erodibility factor, the crop factor, the tillage factor and the practice factor values are available in tables
(see 7.3). The erosivity factor and the slope factor have to be computed in order to determine the potential
long term annual soil loss.
Erosivity factor
The erosivity factor R represents the erosive force of rainfall (Nearing 2001). The numerical value of the R
factor should quantify the raindrop impact effect and provide information about the related rate and amount
of runoff (Wischmeier and Smith 1978).
The original relationship to derive the erosivity factor R (Wischmeier and Smith 1978) is robust and used until
now in the Revised Universal Soil Loss Equation (RUSLE) which is the current technology applied in the
USA for conservation planning and compliance (Nearing 2001). This relationship uses the total rainstorm
energy and the maximum 30 minutes rainfall intensity. These quantities are difficult to obtain globally and
cannot be used here.
People working on global climate and using global circulation models also meet some difficulties establishing
such a relationship based on detailed data (Nearing 2001). A statistical relationship between the erosivity
factor and only the average annual precipitation has been found for the USA (Renard and Freimund 1994)
with correlation factors of 0.73 and 0.81%. This relationship will be used in the present work, since no detailed data can be used and no global map of the R factor exists to our knowledge.
This is an approximation and we use the equation beyond the geographical area for which it was developed.
After having consulted some erosion experts, it seems that there is currently no other simple solution that
aims at evaluating erosion globally. The current practice consists in establishing a regression equation for
each location or region where erosion has to be computed. Such an approach is unfeasible at the global
scale.
The erosivity factor is computed according to (Renard and Freimund 1994):
⎧
0.0483 * P1.61
R=⎨
2
⎩587.8 − 1.219 * P + 0.004105 * P
if
if
P ≤ 850 mm
P > 850 mm
R = Erosivity factor [MJ mm ha-1 h-1 yr-1]
P = Annual precipitation [mm yr-1]
In (Renard and Freimund 1994) annual precipitation amounts are used in order to compute the erosivity factor. We consider the irrigation provided to the crop in addition to the annual precipitation since the water provided through irrigation can contribute to erosion too. In the SQCB tool, we thus consider this value for the
parameter P in the previous equation:
P = precipitat ion + irrigation * 0.1
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The precipitation, expressed in [mm yr-1], is given by the table relating the rainfall to the ecozone (see 7.3.1),
or by the user, if his production zone is located in a mountainous ecozone. Irrigation, in [m3ha-1], is supplied
by the user (see 7.4.1).
The used equation works well for locations with a summer-type precipitation distribution (minimum of one
summer month with more than 15% of the annual average precipitation) or with a uniform annual precipitation distribution (no month with more than 15% of average annual precipitation). It performs badly for locations with a winter-type precipitation distribution (minimum of one winter month with more than 15% of the
annual average precipitation) (Renard and Freimund 1994). A correction factor of 0.1 is applied for the winter-type locations, e.g. many stations in West Europe (Schwertmann 1987), since the R factor estimated with
the equation given above would be too high. The correction factor is based on the knowledge for Switzerland
and on a comparison with literature values for Switzerland (Prasuhn 2007), (Nemecek, A. Heil et al. 2004).
The R factor is thus finally given by:
⎧0.1 * R if
R=⎨
⎩ R
w int er − type
otherwise
The type of precipitation distribution is given by the user (see 7.4.4).
Slope factor
The slope factor LS is computed with the original equation described in (Wischmeier and Smith 1978).The
only adjustment consists in transforming the input data from the SI (International System of Units) units to the
American metric system. Indeed this formula requires length in feet whereas the user types it in meters in.
⎧⎛ L * 3.28083 ⎞ 0.2
S 2
))
⎟ * (65.41 * (sin(
⎪⎜
72.6
100
⎠
⎪⎝
⎪⎛ L * 3.28083 ⎞ 0.3
S 2
))
⎟ * (65.41 * (sin(
⎪⎜
⎪⎝
72.6
100
⎠
LS = ⎨
0.4
⎪⎛⎜ L * 3.28083 ⎞⎟ * (65.41 * (sin( S )) 2
⎪⎝
72.6
100
⎠
⎪
0.5
⎪⎛⎜ L * 3.28083 ⎞⎟ * (65.41 * (sin( S )) 2
⎪⎩⎝
72.6
100
⎠
S
)) + 0.065)
100
S
)) + 0.065)
+ 4.56 * (sin(
100
S
+ 4.56 * (sin(
)) + 0.065)
100
S
+ 4.56 * (sin(
)) + 0.065)
100
+ 4.56 * (sin(
if
S < 1%
if
1 % ≤ S < 3 .5 %
if
3 .5 ≤ S ≤ 5 %
if
S > 5%
LS = Slope factor [-]
L = Slope length [m]
S = Slope [%]
7.2.2 Phosphorus loss in particulate form to surface water: soil erosion
The phosphorus loss in particulate form to surface water is computed according to (Prasuhn 2006), with the
site-specific correction factors set to the default value (=1):
Pe = A * r * e * conc *
d 1
1
* *
12 y 1000
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Pe = Phosphorus loss to surface water [kgP kgproduct-1]
A = Potential long term annual soil loss [tsoil ha-1 yr-1]
r = 0.2 = fraction of phosphorus loss which effectively reaches the surface water system [-]
e = 2 = enrichment factor [-]
conc = 1 = Phosphorus concentration in soil [kgP tsoil-1]
d = Duration of cultivation [months]
y = Yield [tonsproduct ha-1]
The potential long term annual soil loss has been computed previously (7.2.1).
The value for the coefficient expressing the fact that only a certain proportion of the eroded phosphorus effectively reaches the water system is taken from (Prasuhn 2006).
The value for the enrichment factor is taken from (Roy 2003). (Prasuhn 2006) considers a value of 1.86.
The phosphorus concentration in the soil is assumed as being equal to 1 kgP tsoil-1. In (Prasuhn 2006), a
slightly lower value is assumed for Switzerland (0.95 kgP tsoil-1).
The coefficient related to the duration of cultivation is applied because the phosphorus loss through erosion
is computed with an annual soil loss. If we do not apply this period correction, we would not only consider the
phosphorus loss during the crop period but also those occurring during the whole year, what is not desirable.
The coefficient related to the yield is used in order to relate the phosphorus loss to one kilogram of product. If
we do not apply this coefficient, we would obtain the phosphorus loss per hectare instead of per kilogram of
product.
The values of the two other variables, duration of cultivation and yield, are supplied by the user (7.4).
The phosphorus concentration in soils varies greatly, depending on the bedrock, on volcanic activity and on
other parameters. No map or database does exist concerning the phosphorus concentration in soil for the
whole world. We consider a unique mean value of 1 kgP tsoil-1 in this project, which is a valid assumption for a
quick assessment. For a consistent and accurate assessment, we should however improve this aspect: the
phosphorus concentration can reach 8 kgP tsoil-1 in volcanic areas.
7.2.3 Phosphorus loss in dissolved form (phosphate) to surface water: run-off and drainage
Run-off
If the slope is less than 3%, no phosphate loss through run-off occurs (Prasuhn 2006).
If the slope is greater or equal to 3%, phosphate loss through run-off are computed like in (Prasuhn 2006)
with the site-specific correction factors set to their default value (=1):
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Pr = k r * (1 +
0 .2
0 .7
0.4
1
1
95
*
f +
s * c Ps 2 O 5 +
m * c Pm2 O 5 ) *
80
80
80
y 1000 31
Pr = Phosphate loss through run-off [kgPO4 kgproduct-1]
k r = Average run-off [kgP ha-1 yr-1]
f = Mineral fertilizer [kgP ha-1]
s = Liquid organic fertilizer (slurry) [m3slurry ha-1]
c Ps 2 O 5 = Concentration of P2O5 in the slurry [kgP m-3slurry]
m = Solid organic fertilizer (manure) [kgmanure ha-1]
c Pm2 O 5 = Concentration of P2O5 in the manure [kgP m-3slurry]
The value of the average run-off is given in a table (7.3.7) and the value of the other parameters, mineral fertilizer, liquid and solid organic fertilizer is supplied by the user (7.4).
The coefficient of 95/31 is used in order to express the results in kilogram of phosphate instead of phosphorus.
The concentrations of P2O5 in slurry and manure are extracted from (Walther, Ryser et al. 2001).
The coefficient related to the yield is used in order to express the phosphate loss per one kilogram of product. If we do not apply this coefficient, we obtain the phosphate loss per hectare instead of per kilogram of
product.
Drainage
The phosphorus loss through drainage is expressed as in (Prasuhn 2006) (with the site-specific correction
factors set to their default value):
Pd = k l * (1 +
1
1
95
0 .2
*
s * c Ps 2 O 5 ) * d * *
80
y 1000 31
Pd = Phosphate loss through drainage [kgPO4 kgproduct-1]
k l = Average leaching [kgP ha-1 yr-1]
d = Drainage
The value of the average leaching is given in a table (7.3.6) and the amount of liquid organic fertilizer is supplied by the user (7.4.11).
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The concentration of P2O5 in slurry is extracted from (Walther, Ryser et al. 2001).
The drainage coefficient is equal to 6 if conventional drainage is performed and equal to 0 without conventional drainage.
product.
Phosphate loss to surface water
We have to sum the losses through run-off and through drainage computed previously in order to get the
phosphate loss to surface water:
Psw = Pd + Pr
Psw = Phosphate loss to surface water [kgPO4 kgproduct-1]
Pd = Phosphate loss through drainage [kgPO4 kgproduct-1]
Pr = Phosphate loss through run-off [kgPO4 kgproduct-1]
7.2.4 Phosphorus loss in dissolved form (phosphates) to groundwater: leaching
Phosphate loss to groundwater is calculated according to (Prasuhn 2006) (with the site-specific corrections
set to their default value):
Pl = k l * (1 +
1
1
95
0 .2
*
s * c Ps 2O 5 ) * d * *
80
y 1000 31
Pl = Phosphate loss through leaching [kgPO4 kgproduct-1]
k l = Average leaching [kgP ha-1 yr-1]
d = Drainage
The value of the average leaching is given in a table (7.3.6) and the value liquid organic fertilizer is supplied
by the user (7.4.11).
The concentration of P2O5 in slurry is extracted from (Walther, Ryser et al. 2001).
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The drainage coefficient is equal to 0 if the user answer is “yes” to the question concerning the conventional
drainage and equal to 1 if the answer to this question is “no”.
product.
7.3
Tables
The tables contain the values of all the parameters needed to perform the assessment of phosphorus losses
which are not supplied by the user. The input data typed in by the user are used directly to compute phosphorus loss but also indirectly to derive other parameters needed for this computation. This allows a quick
assessment without demanding detailed data from the user.
7.3.1 Annual rainfall – ecozone
The annual rainfall is needed to compute the phosphorus loss in particulate form due to soil erosion. In this
project, the use of the ecozone as a climate (temperature, precipitation) unit avoids the use of detailed climate data, such as temperature or rainfall gridded datasets and avoids the direct question to the user. In this
respect, it has to be stressed that the ecozone concept is a coarse representation of the rainfall regimes (and
temperature regimes) occurring at the Earth surface. Furthermore there is not a unique description of the
major climate zones: the FAO ecozone classification and the Köppen climate classification, for example, are
very close one to each other but are not similar (FAO 2001). Attributing a mean annual rainfall value to each
ecozone is a rough estimation and, for a detailed and accurate assessment, the use of climate gridded datasets would be, to our point of view, the only solution.
The values assumed here are averages of values found in the FAO ecozone classification (FAO 2001) and
on the SD FAO map6.
The ecozones corresponding to mountain systems cannot be simplified in such a table, since the variability
of rainfall is too high in these zones. Therefore the user has to provide a value for the annual rainfall in these
cases. For the other ecozones, the considered values are:
6
http://www.fao.org/sd/Eidirect/climate/Eisp0002.htm
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Table 7-1: Mean annual precipitation for each ecozone
Ecozone
Mean annual precipitation [mm yr-1]
Tar
2500
Tawa
1500
Tawb
1000
Tbsh
500
Tbwh
50
Scf
1200
Scs
700
Sbsh
400
Sbwh
200
TeDo
1500
TeDc
600
TeBsk
300
TeBWk
150
Ba
500
BB
400
7.3.2 Erodibility k – USDA soil order
The erodibility of a soil corresponds to the difference in the eroded soil quantity between two soils under the
same conditions (same rainfall, same slope, etc.) due to soil properties. It represents the soil susceptibility to
erosion (Wischmeier and Smith 1978). The units are [t h MJ-1 mm-1].
It is a quantitative value experimentally determined, which should be measured directly or determined by using a nomograph.The nomograph is a graphical representation of an equation using particle size parameter,
percent of organic matter, soil structure code and profile permeability class (Wischmeier and Smith 1978),
which can not be provided by the user. Therefore a simplified method is used to derive the erodibility k.
We use the USDA soil orders (USDA 1999) in this project in order to derive the erodibility. This solution is not
optimal since the properties determining the erodibility k can vary considerably between two soils in a same
USDA soil order. Several solutions do exist but are rather complex:
1. Measure the soil properties in the location of the crop cultivation and compute the erodibility. This solution is practically not used since it requires going in this location, getting more than one soil sample
and carrying out laboratory analyses.
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2. Use some detailed datasets about the soil types and the derived properties (like the Digital Soil Map
of the World CD-ROM7 for instance). This solution would lead to more accurate results but would require some additional efforts for the tool programming. This solution should be preferred if performing a deeper assessment as the quick assessment done in SQCB.
3. Use a map with more detailed soil units (suborder for instance). Such a map does not exist for the
whole world to our knowledge. Such an approach is time consuming for both the developers, who
have to find a map for each country, and for the user, who has to find the map corresponding to his
production zone in an important collection of maps.
The USDA soil orders (USDA 1999) cannot be used directly for the erodibility determination. We first have to
assign a textural class to each soil order and then to identify the erodibility factor k in a table given by the
Ontario Ministry of Agriculture, Food and Rural Affairs8, which relates an erodibilty factor k to each textural
class.
The first step is carried out by using the characterization data given in (USDA 1999). They give values of
clay content, silt content and sand content in different soil layers. We use the data given in the upper horizon, since this horizon will be eroded. We then apply the texture triangle, in order to get a textural class, using the previously used clay content, silt content and sand content. We could do it for all USDA soil orders
except the histosols, for which no data were available.
We finally used the table given by the Ontario Ministry of Agriculture, Food and Rural Affairs and some literature values (Rodriguez, C.D. Arbelo et al. 2004), (Armas 2004), (Vopravil, M. Janecek et al. 2007), (Xuezheng and Dongsheng 1999), (Mati and Veihe 2001) for the erodibility factor k. When several values were
found, we simply averaged them. A conversion factor of 0.1317 is applied in order to convert the factor k
from U.S. customary units to SI units. For histosols, we consider a value of 0.04 which corresponds to a relatively low susceptibility to erosion. This value is not very important, since histosols are usually not used for
agriculture (peats and mucks) and only cover 1% of the global ice free land surface9. If an assessment for a
histosol is done however, the results could deviate substantially from results that would be obtained with the
appropriate erodibility factor k.
7
http://www.fao.org/ag/agl/agll/dsmw.HTM
8
http://www.omafra.gov.on.ca/english/engineer/facts/00-001.htm#tab2
9
http://soils.ag.uidaho.edu/soilorders/i/Histosols.jpg
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Table 7-2: Erodibility for each USDA soil order
USDA soil order
K factor [t h MJ -1 mm -1]
Alfisol
0.04
Andisol
0.066
Aridisol
0.040
Entisol
0.026
Gelisol
0.040
Histosol
0.040
Inceptisol
0.040
Mollisol
0.059
Oxisol
0.040
Spodosol
0.026
Ultisol
0.026
Vertisol
0.046
7.3.3 Crop factor c1 - crop
The original USLE method combines the crop and the tillage factors in a single factor (Wischmeier and Smith
1978). Some values are provided in (Wischmeier and Smith 1978) for specific crops in specific locations. We
cannot use them in this project since they do not correspond to our crops and our locations. A literature
study (Mazlan, Y. Zulkifli et al.), (David 1988), (Roose 1976), (Hardy 2004) has been carried out and average
value for the c1 factor could be attributed to each crop:
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Table 7-3: Crop factor for each crop
crop
c1 factor
Potato
0.4
Sugar beet
0.5
Sugar cane
0.3
Sweet sorghum
0.4
Rapeseed
0.5
Soya
0.4
Palm
0.2
7.3.4 Tillage factor c2 – tillage method
This factor was originally combined with the crop factor in a single value (Wischmeier and Smith 1978). The
tillage methods are often poorly defined and the values not explained. The determination of this factor has a
strong subjective component. The Ontario Ministry of Agriculture, Food and Rural Affairs10 provides a simple,
comprehensive and concise dataset. We have used this dataset for the current project. A tillage factor c2 is
given for each tillage method available in the input dataset:
Table 7-4: Tillage factor for each tillage method
Tillage method
Fall plow
C2 factor
1
Spring plow
0.9
Mulch tillage
0.6
Ridge tillage
0.35
Zone tillage
0.25
No tillage
0.25
7.3.5 Practice factor P – anti-erosion practice
The original values (Wischmeier and Smith 1978) for the practice factor P are not usable for our purpose,
due to their complexity. They are dependent on several other parameters, like maximum slope length and
strip width among others. We use the dataset of the Ontario Ministry of Agriculture, Food and Rural Affairs11.
10
http://www.omafra.gov.on.ca/english/engineer/facts/00-001.htm#tab4b
11
http://www.omafra.gov.on.ca/english/engineer/facts/00-001.htm#tab5
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Table 7-5: Practice factor for each anti-erosion practice
Anti-erosion practice
P factor
Up & down slope (no practice)
1
Cross slope
0.75
Contour farming
0.5
Strip cropping, cross slope
0.37
Strip cropping, contour
0.25
7.3.6 Average P leaching – crop
A mean value for the leaching rate expressed in [kgP ha-1 yr-1] is given for each land use category in (Nemecek, A. Heil et al. 2004):
-
0.07 kgP ha-1 yr-1 for arable land
0.06 kgP ha-1 yr-1 for permanent pastures and meadow
We have to assign a land use category to each crop in order to use these average P leaching values.
We define all cultures as arable land cultures. The usual cultures, potato, sugar beet, rapeseed and soya are
also considered as arable land in (Nemecek, A. Heil et al. 2004). For the exotic cultures, the FAO database
ecocrop (FAO) indicates that palm and sugar cane are arable irrigated cropping systems. Based on the information given in the FAO database crop water management (FAO), sweet sorghum is also cultivated as an
arable land culture as well.
A value of 0.07 kgP ha-1 yr-1 is thus assumed for all the considered crops.
7.3.7 Average run-off – crop
A mean value for the runoff rate expressed in kgP ha-1 yr-1 is given for each land use category in (Nemecek,
A. Heil et al. 2004):
-
0.175 kgP ha-1 yr-1 for open arable land
-
0.25 kgP ha-1 yr-1 for intensive permanent pastures and meadow
-
0.15 kgP ha-1 yr-1 for extensive permanent pastures and meadow
Each crop is assigned to a land use category, in the same way as for leaching. All crops are considered as
open arable land as explained above.
A value of 0.175 kgP ha-1 yr-1 is thus assumed for all the considered crops.
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7.4
Inputs
The user has to provide some information about the planed biofuel production in order to compute the related phosphorus emissions. A default value is available for some of the required inputs, but others are compulsory. The default values are presented in the next section.
The user has to be cautious regarding the units when typing in the inputs. The units are International System
of Units (SI) units or derived units. All the input concerning resources are related to a surface of one hectare
and to the period of cultivation. The set of inputs required for the computation of phosphorus losses is the following.
7.4.1 Irrigation
The irrigation has to be supplied by the user in [m3ha-1]. It corresponds to the water quantity supplied through
irrigation to one hectare of the considered crop.
For annual crops (potato, sugar beet, sweet sorghum, rapeseed and soybean), it is the water amount supplied between sowing and harvest. For perennial crops, oil palm and sugar cane, it corresponds to the water
amount supplied between two harvests. In case of perennial crops, we should consider the water and the
fertilizers supplied during the first unproductive phase too. These amounts should be shared over all harvests. Consequently, the user has to know how much water and fertilizers he applied during this unproductive phase and he has to know how many harvests the trees will furnish before being cut. We do not consider
the water and fertilizer amounts used in the first unproductive lifetime of the crop in SQCB. This is a simplification which will lead to a possible underestimation of the phosphorus emissions for perennial crops.
7.4.2 Ecozone
The user finds this information by locating his production zone in the corresponding ecozone map. The ecological zones, or ecozones, are defined as zones or areas with relatively homogeneous natural vegetation
formations, and coinciding roughly with the Köppen-Trewartha climatic types (FAO 2001).
The possible answers are:
-
Tar: Tropical rainforest
-
Tawa: Tropical moist deciduous forest
-
Tawb: Tropical dry forest
-
Tbsh: Tropical shrublands
-
Tbwh: Tropical desert
-
TM: Tropical mountain systems
-
Scf: Subtropical humid forest
-
Scs: Subtropical dry forest
-
SbSh: Subtropical steppe
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-
SBWh: Subtropical desert
-
SM: Subtropical mountain systems
-
TeDo: Temperate oceanic forest
-
TeDc: Temperate continental forest
-
TeBSk: Temperate steppe
-
TeBWk: Temperate desert
-
TeM: Temperate mountain systems
-
Ba: Boreal coniferous forest
-
Bb: Boreal tundra woodland
-
BM: Boreal mountain systems
7.4.3 Annual rainfall
The ecozone concept is a relatively good indicator of the mean annual precipitations in flat regions.
In mountainous regions, the precipitation amount can be very different from one location to another according to the altitude, the mountainside orientation as well as other local effects. The ecozone concept is thus
too rough in such regions and cannot provide information about the annual rainfall. The user has to provide
the annual rainfall value for his production zone when it is located in a mountainous region (TM, SM, TeM or
BM). The units are [mm yr -1].
It is usually easy to find such information by consulting the regional or national meteorological office.
7.4.4 Winter-type precipitation distribution
-
Yes
-
No
The user has to answer “Yes” if there is at least one winter month with more than 15% of the annual average
precipitation in his location. Otherwise the answer is “No”.
7.4.5 USDA soil order
The user finds the correct soil order related to his production zone by identifying his production zone in the
soil order map. A soil order is the highest level of soil classification in the USDA classification system12. At
this classification level, soils vary greatly within a given unit. Consequently, the utilization of these rough soil
12
http://www.uwsp.edu/geo/faculty/ritter/glossary/S_U/soil_order.html
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categories to derive other information (clay content for instance) can lead to inaccurate or wrong results. For
a detailed assessment, a lower level of classification should be selected or field analyses should be carried
out.
-
Alfisol
-
Andisol
-
Aridisol
-
Entisol
-
Gelisol
-
Histosol
-
Inceptisol
-
Mollisol
-
Oxisol
-
Spodosol
-
Ultisol
-
Vertisol
7.4.6 Slope
The slope is the mean slope of the field expressed in [%]. This value should represent the mean slope of the
whole field.
Example 1.
If the considered crop is cultivated on a field consisting of flat terrain (0.2%) and of a hilly zone (average of
10%), we have to average these two values proportionally to their importance. If the flat terrain extends on a
length of 200m and the hilly zone on a length of 500m, we have to average the values as followed:
slope =
0.2 ∗ 200 + 10 ∗ 500
= 7.2%
200 + 500
This value of 7.2% has to be typed in by the user.
Example 2.
If the field is inclined in several directions, the user has to consider the slope in the direction where the average slope is the steepest. If the previous field has the 7.2% slope in the North-South direction and a 15%
slope in the East-West direction, then he has to consider the East-West slope of 15%.
The 15% value has to be typed in by the user.
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7.4.7 Slope length
The slope length refers to the slope typed in by the user previously. It is given in [m]. The length is the total
length of the whole field, in the direction of the steepest slope.
Example 1.
In the example 1 of the previous section, the slope length is the total of the flat zone length and of the hilly
zone length. It corresponds to the sum of 200m and 500m, that means 700m.
The user has to type in 700m.
Example 2.
In the example 2 of the previous section, the slope length in the North-South direction is 700m and the slope
7.2%. But the slope in the East-West direction is steeper and the user has to consider the field length in the
East-West direction. If the East-West side of the field has a length of 300m, this length is considered.
The user has to type in 300m.
7.4.8 Crop
The SQCB tool can be used for computing the phosphorus emissions related to the cultivation of different
crops for biofuel. The considered crops in this project are:
-
Potato
-
Sugar beet
-
Sugar cane
-
Sweet sorghum
-
Rapeseed
-
Soybean
-
Oil palm
The present tool cannot be used for others crops cultivated for biofuel production, such as corn, rye, etc.
7.4.9 Tillage method
Tillage is the agricultural preparation of the soil by plowing, turning or ripping it. Tillage is performed by farmers in order to incorporate fertilizers and crop residues, to prepare the seedbed, to control weeds and to
conserve soil and water. Several tillage methods exist and the terminology is not always univocal. We consider some of them in the present project. If another type of tillage is performed for the considered crop, the
user should select the most similar one in the set of possible answers. They are:
-
Fall plow
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It corresponds to a traditional plowing after the harvest. This plowing aims to turn over the upper layer of the
soil, burying the crops residues of the harvest and bringing fresh nutrients to the surface (Ghazavi 2004). It
aerates the soil and prepares it for the cold months to come. It can be performed with some sophisticated
machines or with a rudimentary plow pulled by horses or oxen.
-
Spring plow
It corresponds to a traditional plowing, like the fall plow, but carried out before a new sowing, after the cold
months. One of its functions is to break up the soil for planting the seeds13. It can be performed with some
sophisticated machines or with a rudimentary plow pulled by horses or oxen too.
-
Mulch tillage
Mulch tillage, also known as conservation tillage, reduced tillage or minimum tillage, is a tillage method leaving at least 30% of the soil surface covered by residues14. Mulch tillage can be achieved with a chisel plow,
with discs or with rotary till implements for instance.
-
Ridge tillage
Ridge tillage is a tillage method maintaining permanent ridges on which row crops are grown15. After harvest,
the crop residues are left. The new seeds are planted in the ridge, after pushing residues aside16 .
-
Zone tillage
Zone tillage or strip tillage is a reduced tillage method that limits soil disturbance to the area of the planting
row, and leaves the areas between the crop rows undisturbed17. It refers to a system where strips 5 to 20 cm
in width are prepared to receive the seed whilst the soil along the intervening bands is not disturbed and remains covered with residues.
-
No till
No-till systems, or zero tillage systems, do not use tillage for establishing a seedbed. Crops are simply
planted into the previous year's crop residue18. The seeds are drilled into the soil without prior land preparation19.
7.4.10 Anti-erosion practice
Anti-erosion practices, or erosion control, refer to practices aiming to prevent or control erosion. Several
practices do exist and only some of them are considered in this project20. If the anti-erosion method applied
13
http://lcweb2.loc.gov/ammem/award97/ndfahtml/ngp_farm_plow.html
14
http://www.omafra.gov.on.ca/english/environment/field/tillage2.htm
15
http://attra.ncat.org/attra-pub/consertill.html
16
http://www.oisat.org/control_methods/cultural__practices/soil_tillage.html
17
http://ohioline.osu.edu/aex-fact/0507.html
18
http://www.no--till.com/
19
http://www.fao.org/Ag/magazine/0101sp1.htm
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in the field is not in the selection list, the user has to choose the most similar one. We consider these practices in SQCB:
-
Up & down slope
Up and down slope cropping systems are traditional cropping systems. They correspond to “no anti-erosion
practice” systems. It means that crop is cultivated in the direction of the slope of the hill21. It is the opposite of
cross slope cultivation. In such systems, the crops do not represent an obstacle for the run-off water flooding
downhill. The high velocity of run-off involves a high erosion potential22.
-
Cross slope
Contrary to up and down slope farming, cross slope farming consists in cultivating (planting, tilling and other
farming operation) perpendicular to the direction of the hill slope23. The crops break the downhill run-off and
redirect the flow in a perpendicular direction, reducing the velocity of the flow and the related erosion.
-
Contour farming
Contour farming entails performing all planting and tillage operations on or near the same elevation24. The
crop rows follow the contours. Water is held in between the contours, improving moisture content of the soil
and reducing erosion due to run-off.
-
Cross slope farming with strip cropping is a cross slope cropping system where two or more crops are cultivated in strips alternatively. We usually cultivate a strip of soil-protective crop (erosion resistant crop) in alternatively with a strip of less soil-protective crop (non- or less erosion resistant crop)25, both perpendiculars
to the slope.
-
Strip cropping, contour
Contour farming with strip cropping is the same as cross slope farming with strip cropping but the crop strips
are cultivated following the contours and not perpendicular to the slope.
7.4.11 Liquid organic fertilizer
The liquid organic fertilizer (slurry) amount has to be supplied by the user in [m3slurry ha-1]. It is the amount of
slurry applied per hectare to the considered crop during the growth period. The possible types of slurry and
their phosphorus content are described in Table 7-6. A dilution factor of 40:60 (slurry:water) is assumed if the
user does not enter his dilution factor.
20
http://www.uiweb.uidaho.edu/wq/wqbr/wqbr27.html
21
http://www.agr.gc.ca/nlwis-snite/index_e.cfm?s1=pub&s2=hw_se&page=135
22
http://www.gov.pe.ca/af/agweb/index.php3?number=71758
23
http://www.agr.gc.ca/nlwis-snite/index_e.cfm?s1=pub&s2=hw_se&page=135#AnchorCrossslope37516
24
http://topsoil.nserl.purdue.edu/nserlweb/weppmain/overview/contourb.html
25
http://www.tarahaat.com/water_StripCrop.aspx
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Table 7-6: Types of slurry and phosphorus content
Type of slurry
Phosphorus content [kg P2O5 m-3]
1.8
Pigs
3.5
The growth period is intended to be the period between sowing and harvest for annual crops (potato, sugar
beet, sweet sorghum, rapeseed and soybean). The growth period for perennial crops (sugar cane and oil
palm) is considered as the time period between two consecutive harvests. As mentioned for the irrigation, we
should also take into account the slurry amount applied during the unproductive phase at the beginning of
growth for the perennial crops and share this amount between all the harvests of the crop life. We neglect
this amount in SQCB for simplicity reasons and this will lead to an underestimation of phosphorus losses in
the case of perennial crops.
7.4.12 Solid organic fertilizer
The solid organic fertilizer (manure) amount has to be supplied by the user in [kgmanure ha-1]. It is the amount
of manure applied per hectare to the considered crop during the growth period. The possible types of manure and their phosphorus content are described in Table 7-7.
Table 7-7: Types of manure and phosphorus content
Type of manure
Phosphorus content [kg t -1]
Pigs
2.2
7
Hens (from deep pits)
25.6
Hens (from belts)
11.5
Broilers
19
The same remark as done in the previous section (liquid organic fertilizer) can be done here about the
growth period and the underestimation of phosphorus losses in the case of perennial crops.
7.4.13 P mineral fertilizer
The P mineral fertilizer amount has to be supplied by the user in [kgP ha-1]. It is the quantity of P mineral fertilizer applied per hectare to the considered crop during the growth period. The user has the choice between
typing in only the global amount of P mineral fertilizers and typing in more detailed information about P mineral fertilizers used. The possible P mineral fertilizers which can be selected by the user are described previously (Table 3.3 and Table 3.4).
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The same remark as done in the previous sections (liquid organic fertilizer and solid organic fertilizer) can be
done here about the growth period and the underestimation of phosphorus losses in the case of perennial
crops.
7.4.14 Duration of cultivation
The duration of cultivation has to be supplied by the user in [months]. This period corresponds to the time
period between sowing and harvest for annual crops (potato, sugar beet, sweet sorghum, rapeseed and
soybean) and between two consecutive harvests for perennial crops (sugar cane and oil palm).
The entry of fractions is possible for this parameter. That means that if a crop is cultivated during 75 days for
instance, the user has to type in 2.5 months.
7.4.15 Yield
The yield of product has to be supplied by the user in [tons ha-1]. This quantity represents the amount of
main product harvested per hectare. It is not the amount of whole crop (main product + co-products + residues) harvested per hectare.
For potato, we consider the harvested amount of tubers per hectare for instance and, for soybean, the harvested amount of beans per hectare.
7.4.16 Conventional drainage
Conventional drainage is intended as drainage performed with a dense drainage pipe system, comprising
pipes (generally plastic pipes, sometimes ceramic pipes), pipe couplings, catch basins and/or manholes,
drainage material and so on26. If the user performs such a type of drainage in the considered crop cultivation,
he has to answer “yes” to this question. If he performs another type of drainage (with only few pipes or a surface drainage system for example), or if he does not perform drainage at all, the answer is “no”.
7.5
Default values
Some default values are defined for some parameters in the SQCB tool in order to allow a rough assessment of environmental impacts also when the user cannot provide all the needed information. In such a case
however, the results can be very unrepresentative of the specific situation of the considered biofuel production and should be considered with a lot of caution. Their reliability is limited and a more detailed assessment
should be performed in order to have a good appreciation of the real situation. They can however give a first
idea about the environmental compatibility of a given biofuel production system.
The more accurate information is supplied by the user, the more the tool is used at its maximal capabilities.
26
http://www.ecy.wa.gov/programs/sea/pubs/95-107/components01.html
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7.5.1 Irrigation
If the user does not provide any information about irrigation, we assume that he does not supply water
through irrigation to the considered crop.
Default value: 0 [m3ha-1].
7.5.2 Ecozone
There is no default value for the ecozone. The user has to locate his production zone on the corresponding
map provided with the tool and to select the correct ecozone corresponding to his production zone. This is
compulsory information and no assessment can be carried out without it.
Default value: -
The user has to provide a value for this parameter if his production zone is located in a mountainous zone.
We cannot provide a default value in such a case, since, as mentioned before, the rainfall is very variable in
mountainous zones.
Default value: -
The user has to provide information about the time distribution of the annual precipitation in order to adapt
the erosivity calculation. If he does not, we assume that the precipitation distribution throughout the year
does not correspond to a winter-type distribution.
Default value: no
There is no default value for the USDA soil order. The user has to locate his production zone on the map
provided with the tool and to select the correct USDA soil order corresponding to his production zone. This
information is compulsory and no assessment can be carried out without this data.
Default value: -
7.5.6 Slope
We consider the value in (Nemecek, A. Heil et al. 2004) as default value for this parameter. This value is a
mean value for Switzerland and will not be realistic in most case. The user should type in a value in order to
get better results.
Default value: 3%
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7.5.7 Slope length
We consider the value in (Nemecek, A. Heil et al. 2004) as default value for this parameter. This value is a
mean value for Switzerland and will not be realistic in most case. The user should type in a value in order to
get better results.
Default value: 75 m
7.5.8 Crop
No assessment can be conducted without this information. The environmental impacts are crop-dependent
and it would not make any sense to calculate any impact without knowing the considered crop. The user has
to provide this information; it is a compulsory input.
Default value: -
A fall plow is assumed per default for the tillage method since it is the conventional tillage method and it corresponds to the experiment conditions in (Wischmeier and Smith 1978) without correction. If the user performs a less invasive tillage method, he has to select it in the selection list, in order to reduce his phosphorus
emissions related to soil erosion.
Default value: fall plow
Up and down slope cultivation is the traditional cultivation system, corresponding to no anti-erosion practice.
We assume that the user does not perform conservation farming if he does not provide any information
about his anti-erosion practice. Such a case corresponds to the experiment conditions in (Wischmeier and
Smith 1978) without correction.
Default value: Up & down slope (no practice)
The default values for the liquid organic fertilizer originate from the ecoinvent database. For each country or
region, a reference dataset in the database ecoinvent has been chosen.
For any country in Asia for instance, the dataset corresponding to the Chinese production or to the Malaysian production is selected in the ecoinvent database. If this dataset is not available, the dataset corresponding to the Brazilian production is selected. That means that if a user produces sugar cane in Pakistan and
doesn’t provide data about the liquid organic fertilizer used, a dataset for sugar cane in China or Malaysia
will be searched in the ecoinvent database and if this dataset doesn’t exist, the dataset for Brasilia will be selected in the ecoinvent database.
The fertilizer amount applied is however highly correlated with the yield and is also related to the pedoclimatic conditions, the level of plant protection applied and to the agricultural traditions or legislation. The
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default value only makes sense if these parameters are similar in the considered country and in the reference country.
Considering the previous example, the Brasilian dataset of ecoinvent for liquid organic fertilizer applied on
sugar cane is significant for the liquid organic fertilizer applied on sugar cane in Pakistan only if the relevant
parameters described above are similar.
The default values for the solid organic fertilizer originate from the ecoinvent database. For each country or
region, a reference dataset in the database ecoinvent has been chosen.
Like for liquid organic fertilizer, the solid organic fertilizer applied on a given crop is highly correlated with the
yield and is also dependent on the pedo-climatic conditions, the level of plant protection applied and to the
agricultural tradition or legislation. For example, the dataset for solid organic fertilizer applied on a given crop
in Brazil in the ecoinvent database is only valid for another country in South America if the production of the
considered crop in this country has the same yield and similar pedo-climatic conditions and agricultural practices than in Brazil.
7.5.13 P Mineral fertilizer
The default value for the P mineral fertilizer originates from the ecoinvent database. For each country or region, a reference dataset in the database ecoinvent has been chosen.
The amount of P mineral fertilizer used is highly correlated with the yield and related to the pedo-climatic
conditions, the level of plant protection applied and the agricultural practices and legislations. Therefore the
reference dataset for example for Brazil is only adapted if the yield of the considered crop and the pedoclimatic conditions and agricultural practice are similar in the considered African country and in Brazil.
The default values are extracted from (Nemecek, A. Heil et al. 2004) for the “conventional” crops: 5 months
for potato, 6.3 months for sugar beet, 10.6 months for rapeseed and 4.5 months for soybean. For the exotic
crops, we use the FAO database ecocrop (FAO). For the sweet sorghum, it indicates that grains reach maturity between 90 and 120 days. We take the average of 3.5 months. It considers sugar cane and oil palm as
perennial crop. We should take the time period between two consecutive harvests. Since this period is variable, we take an average of 12 months for these two crops. The user should type in the exact period in order
to get better results.
Default values:
-
5 months for potato
-
6.3 months for sugar beet
-
12 months for sugar cane
-
3.5 months for sweet sorghum
-
10.6 months for rapeseed
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-
4.5 months for soybean
-
12 months for oil palm
7.5.15 Yield
This information is compulsory. The yield varies considerably from a country to another one and from a specific situation to another one. It is not possible to assess environmental impacts without knowing the yield,
since phosphorus emissions are strongly depending on the yield.
Default value: -
Conventional drainage with drains (outflow pipes) is frequently performed in some industrialized countries
(Switzerland) but is relatively seldom performed in the largest part of the world (developing and emerging
countries). In these countries, a less complex drainage system is generally preferred with open canals instead of pipes.
Default value: no
7.6
Online help for the inputs
blue question mark. By clicking on it, the user can access to the help text. The help text is as short as possible. For most of the help texts, some words are underlined in blue and can be selected with the mouse. They
7.6.1 General
Please mind the units indicated for each parameter. We use the International System of Units (SI) in this
tool. If you use a different units system in your workaday, please convert your data in the International System of Units before typing them in.
7.6.2 Irrigation
Please indicate the water amount that you supply through irrigation to the considered crop for a surface of
1 ha during the cultivation period. The units are m3/ha. If you cultivate the considered crop on a larger or
smaller surface, you have to convert your value. Be careful to only consider the water that you supply for this
crop!
Cultivation period
The cultivation period is the period between sowing and harvest for annual crop and between two consecutive harvests for the perennial crops.
Conversion
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Example: if you cultivate potatoes on a 3 ha surface, with a cultivation period of 4 months (duration between
sowing and dig up), and you apply 60 m3 of water during this period, the irrigation value, that you have to
type in, is 20 m3/ha (=60/3).
7.6.3 Ecozone
Please find the map corresponding to the continent of your production zone in the provided set of maps and
then locate your production zone on this map. Look at the key on the map in order to determine in which
ecozone your production zone is located.
Ecozone
If your production zone is located at the limit between 2 ecozones, select the one that seem you to be the
most representative for your production zone.
If the selected ecozone is a mountainous ecozone, i.e. tropical mountain systems, subtropical mountain systems, temperate mountain systems or boreal mountain systems, please look at the next question “Annual
rainfall”.
You have to provide the annual rainfall in mm/yr if you are in a mountainous zone.
Annual rainfall
Please consult the meteorological office of your region or of your country in order to find this information.
Most of them have website and it should be quite easy to find this information. If it is not the case, you can
try to use generic websites such as http://www.worldclimate.com/ in order to find it.
Pleas answer “yes” if there is at least one winter month with more than 15% of the annual average precipitation in your production zone.
Please locate your production zone on the provided map and look at the key in order to find the USDA soil
order corresponding to your production zone.
Map
Note that you can download this map and have it in a better resolution.
7.6.7 Slope
Please type in your slope in %.
Slope
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It is important that you type in a value corresponding to the mean value of the slope over your whole field.
Slope
You can for example use a topographical map or a digital elevation model (DEM) in order to find your slope
value. It is better that you type in an approximated value than no value. If your field has a rough incline of
10% in the South-North direction and a rough inclination of 2% in the East-West direction, please type in
10%.
Whole field
If the half of your field is flat followed by a side, you should not consider the slope of the side only but average this value with a 0° value, representing the flat part of your field.
7.6.8 Slope length
Please type in the length of the slope in meters.
Length
It generally corresponds to the length of your field in the slope direction. In the previous example, you should
type in the length of the South-North side of your field (corresponding to the 10% slope in the previous example).
7.6.9 Crop
Please select the crop that you want to use for biofuel production.
Please select a tillage method.
Tillage
Tillage is the agricultural preparation of the soil by ploughing, ripping, or turning it. Please select the tillage
method that you perform on your field. If you perform more than one of the proposed methods, please select
the lowest one in the selection list. If your tillage method is not in the selection list, try to find the most similar
in the list.
Tillage method
The available tillage methods are:
-
Fall plow
-
Spring plow
-
Mulch tillage
-
Ridge tillage
-
Zone tillage
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-
No tillage
Fall plow
Traditional plowing after the harvest. Aim: turn over the upper layer of the soil, bury the crops residues of the
harvest and bring fresh nutrients to the surface. It can be performed with some sophisticated machines or
with a rudimentary plow pulled by horses or oxen.
Spring plow
Traditional plowing carried out before a new sowing, after the cold months. Aim: break up the soil for planting
the seeds among others. It can be performed with some sophisticated machines or with a rudimentary plow
pulled by horses or oxen too.
Mulch tillage
Mulch tillage is also known as conservation tillage, reduced tillage or minimum tillage. It is leaves at least
30% of the soil surface covered by residues. Mulch tillage can be achieved with a chisel plow, with discs or
with rotary till implements for instance.
Ridge tillage
Ridge tillage is a tillage method maintaining permanent ridges on which row crops are grown. After harvest,
the crop residues are left. The new seeds are planted in the ridge, after pushing residues aside.
Zone tillage
Zone tillage is also called strip tillage. It is a reduced tillage method that limits soil disturbance to the area
(strips of 5 to 20 cm) of the planting row, and leaves the areas between the crop rows undisturbed.
No tillage
No-till systems, or zero tillage systems, do not use tillage for establishing a seedbed. Crops are simply
planted into the previous year's crop residue. The seeds are drilled into the soil without prior land preparation.
Please select an anti-erosion practice. If you perform an anti-erosion practice that is not proposed in the selection list, try to find the most similar in this list.
Anti-erosion practice
The available practices are:
-
Up and down slope
-
Cross slope
-
Contour farming
-
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-
Strip cropping, contour slope
Up and down slope
Crop is cultivated in the direction of the slope of the hill. It is the traditional cropping system. It corresponds to
a “no anti-erosion practice” system.
Cross slope
Cultivating (planting, tilling and other farming operation) is done perpendicular to the direction of the hill slope
Contour farming
Crop rows follow the contours. Contour farming entails performing all planting and tillage operations on or
near the same elevation
Cross slope cropping system where two or more crops are cultivated in strips alternatively. The crop stripes
are perpendicular to the slope.
Strip cropping, contour slope
Contour farming where two or more crops are cultivated in strips alternatively. The crop stripes follow the
contours.
Please type in the applied amount of slurry in m3 per hectare. This is the amount that you applied on the
considered crop during the cultivation period.
Cultivation period
The cultivation period corresponds to the period between sowing and harvest for annual crops and to the period between two consecutive harvests for perennial crops.
Please type in the applied amount of manure in kg per hectare. This is the amount that you applied on the
considered crop during the cultivation period.
Cultivation period
7.6.14 P Mineral fertilizer
Please type in the applied amount of phosphorus mineral fertilizer in kg P2O5 per hectare. This is the
amount that you applied on the considered crop during the cultivation period.
Cultivation period
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Please type in the duration of cultivation of the considered crops in months. You can type in some fractions.
Fraction
If the duration of cultivation is for example 105 days, please type in 3.5 months. This duration equals the period of time between sowing and harvest for annual crops and to the period between two consecutive harvests for perennial crops.
7.6.16 Yield
Please type in the yield of the main product of the considered crop on in tons/ha.
Please answer “yes” if you perform drainage with a dense drainage pipe system.
Drainage pipe system
It comprises pipes (generally plastic pipes, sometimes ceramic pipes), pipe couplings, catch basins and/or
manholes, drainage material and so on.
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8
Modeling of nitrate emissions
Authors: Anne Roches, Gerard Gaillard, Ruth Freiermuth
Nitrate emissions from agricultural nitrogen inputs can cause eutrophication (Mc Isaac 2001) and are an environmental concern. In this project, the nitrate emissions are referred to the nitrate losses through leaching.
Several methods can be used in order to quantify nitrate leaching (Ren 2003), (Li 2006), (Barraclough 2006),
(Moreels 2003).
Accurate modeling of nitrate leaching requires simulations of both soil hydrological and biogeochemical
processes (Li 2006). It is unfortunately not possible to do it for a quick assessment of environmental impacts,
since such simulation models are complex. More simple models based on regression or mechanistic equations do exist (Barraclough 2006), (De Willigen 2000), (Roy 2003). But they should be applied only in the
same ranges of the data used for their setup. They can be used for a quick assessment but should not be
applied for an accurate description of nitrate leaching needed for political decision making, legal processes
or academic research.
The regression model in (De Willigen 2000) has been used for a nitrogen balance assessment in the whole
Sub-Saharan Africa (Roy 2003). This large application and the explanation in (Roy 2003), about the considerable literature review used in order to setup the model, have convinced us that the model in (De Willigen
2000) is appropriate for a quick assessment on large scale.
8.1
Origin of model and model structure
We use the regression model described in (De Willigen 2000). This model relates the nitrate leaching to
these parameters:
-
Amount of fertilizer nitrogen
-
Amount of nitrogen taken up by the crop
-
Amount of nitrogen in soil organic matter
-
Precipitations
-
Percentage clay
-
Layer thickness
The regression model is based on data within those ranges:
-
Precipitation: 40-2000 mm
-
Clay content: 3-54%
-
Layer thickness: 0.25-2m
A regression equation such as in (De Willigen 2000) should be applied only for interpolation, i.e. within the
ranges of the data used for the regression. We are in the range of the given data for the layer thickness (see
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8.3.4). For precipitation, only one value is above the limit of the recommended values (see 7.3.1) and, for
clay content, two minor soil types have lower clay contents (see 8.3.3).
We are not in the optimal conditions for applying this regression equation. It should however be sufficient for
a quick assessment and, to our knowledge, no other simple method has been applied on a global scale.
8.1.1 Design of the nitrate loss model
The nitrate loss model and the data flows can be visualized in the following chart. The light blue elements refer to the inputs that have to be typed in by the user (or default value) and the output, nitrate loss, is outlined
in green.
Figure 8.1. Structure of the nitrate model and data flows
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8.2
Computation
We can notice some small differences between the equation given in (De Willigen 2000) and in (Roy 2003):
-
Root depth is used instead of layer thickness in (Roy 2003)
-
Organic carbon content is used instead of the amount of nitrogen in soil organic matter in (Roy
2003)
-
Nitrogen fertilizer is restricted to mineral nitrogen fertilizer in (Roy 2003)
We compute the nitrate leaching according to (De Willigen 2000) and (Roy 2003) with small adaptations:
[
]
P
⎡
⎤1 1
N = ⎢ 21.37 +
0.0037 * S + 0.0000601 * C org − 0.00362 * U ⎥
c*L
⎣
⎦ y 1000
N = Nitrate loss through leaching [kgN kgproduct-1]
P = Annual precipitation and irrigation [mm yr-1]
c = Clay content [%]
L = Root depth [m]
S = Nitrogen supply [kgN ha-1]
C org = Organic carbon content [%]
U = Nitrogen uptake [kgN ha-1]
Negative values are avoided by testing them:
⎧N
N =⎨
⎩0
if
if
N ≥0
N <0
In the equation described in (Roy 2003), the annual precipitation amounts are considered without mentioning
irrigation. We consider that the water amount supplied through irrigation also contributes to the nitrate leaching and is thus added to the precipitation amounts (see equation described in 7.2.1).
In the same way as in (Roy 2003), we use the root depth (not the layer thickness) and the organic carbon
content (not the amount of nitrogen in soil organic matter).
We do not restrict the nitrogen fertilizer to the mineral fertilizer as in (Roy 2003), but we consider the nitrogen
supplied through organic and mineral fertilizer.
The coefficient related to the yield is used in order to relate the nitrate loss to one kilogram of product. If we
do not apply this coefficient, we obtain the nitrate loss per hectare instead of per kilogram of product.
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Some parameter values are found in the tables or supplied by the user: precipitation (7.3.1), irrigation (7.4.1),
clay content (8.3.3) and root depth (8.3.4).
Some others require simple computations: nitrogen supply, organic carbon content and nitrogen uptake.
8.2.1 Nitrogen supply
We consider the nitrogen supplied with the mineral fertilizers and with the liquid and solid organic fertilizers:
S = f + s * c Ns + m * c Nm
S = Nitrogen supply [kgN ha-1]
f = N Mineral fertilizer [kgN ha-1]
c Ns = Concentration of N in the slurry [kgN m-3slurry]
m = Solid organic fertilizer (manure) [kgmanure ha-1]
c Nm = Concentration of N in the manure [kgN kgmanure-1]
The value of the applied amount of mineral fertilizer, liquid and solid organic fertilizer is supplied by the user
(8.4).
The concentrations of N in slurry and manure are extracted from (Walther, Ryser et al. 2001).
8.2.2 Organic carbon content
The mean values for the organic carbon content are given per 3000 m3 of soil in Table 19.3. We have to
convert it to percent (mass fraction). We need the bulk density in order to carry out this conversion. As a
rough approximation, a single value is taken for all soils. For a more precise assessment, we should consider
a bulk density for each soil unit.
The conversion is computed in this way:
EMPA
C org = C org
*
1
1
*
*100
3000 1.3
C org = organic carbon content [%]
EMPA
C org
= organic carbon content given in Table 19.3 [tons Corg 3000m-3]
A bulk density of 1.3 tons of soil per cubic meter is assumed (average), based on the values found in (USDA
1999) and the values given by the American bulk density calculator27.
27
http://www.pedosphere.com/resources/bulkdensity/worktable_us.cfm
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8.2.3 Nitrogen uptake
The nitrogen uptake is computed as followed:
U = Unit _ uptake * y
U = nitrogen uptake [kgN ha-1]
Unit _ uptake = unit uptake [kgN tonsproduct -1]
8.3
Tables
The tables contain the values of all the parameters needed in order to perform the assessment of nitrate loss
and which are not supplied by the user. The input data typed in by the user are indeed used directly to compute nitrate loss but also indirectly to derive other parameters needed for this computation. The dataset of
inputs is reduced as much as possible in order to ensure the use of the tool by a non-expert person.
8.3.1 Annual rainfall – ecozone
This table has already been described in 7.3.1.
8.3.2 Organic carbon content – ecozone
A rough approximation of the organic carbon content in the soil is made in Table 19.3 using the IPCC values
(IPCC 2006) for each climate region. It would be worthwhile using a more accurate organic carbon content
by using more detailed data.
The values in (IPCC 2006) are given in tons of organic carbon per hectare in the first 30 cm of soil. This is
equivalent to tons of organic carbon per 3000m3.
The table is presented in Table 19.3.
8.3.3 Clay content – USDA soil order
The clay content was already used in order to determine the texture class and then the erodibility factor k of
each USDA soil order (see 7.3.2). The values given in (USDA 1999) are:
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Table 8-1: Clay content for each USDA soil order
USDA soil order
Alfisol
clay content %
28
Andisol
10.4
Aridisol
17.2
Entisol
3.5
Gelisol
23.7
Histosol
2
Inceptisol
4.9
Mollisol
21.1
Oxisol
53.9
Spodosol
1.8
Ultisol
12.3
Vertisol
49.0
For the histosol, no data is available in (USDA 1999). These soils are comprised primarily of organic material. The mineral material content should be minor and therefore a low clay content of 2% is assumed. This
very rough approximation could lead to important errors in the nitrate leaching computation for these soils.
The risk is however quite low since these soils cover less than 1% of the global ice free land surface and
since they are usually not used for agriculture28.
8.3.4 Root depth – crop
The FAO database crop water management (FAO) gives values for the rooting system depth for potato,
sugar beet, sugar cane, sweet sorghum and soybean. There is unfortunately no data about rapeseed or
palm. The FAO database ecocrop (FAO) gives one meter as standard depth for the oil palm rooting system.
The Idaho University carried out a study about nitrogen removal with rapeseed and found that nitrogen was
efficiently removed until a depth of three feet29, which roughly corresponds to 0.9 meters.
28
http://en.wikipedia.org/wiki/Histosols
29
http://www.uiweb.uidaho.edu/wq/wqfert/cis785.html
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Table 8-2: Root depth for each crop
Crop
Root depth [m]
Potato
0.45
Sugar beet
1
Sugar cane
1.5
Sweet sorghum
1
Rapeseed
0.9
Soya
0.65
Palm
1
The root depth can greatly vary according to the soil type, the maturation of the plant, the water availability,
the concurrence with other plants, etc. These values are mean values and should not be considered as an
absolute reference. The dataset is homogeneous with the exception of palm and rapeseed.
8.3.5 Unit uptake – crop
The regression equation used for calculating nitrate emissions needs the quantity of nitrogen that is taken up
by the whole plant. The yield however refers to the main product in this project. We thus have to express the
nitrogen uptake of the whole plant but expressed per ton of main product.
The FAO database ecocrop (FAO) gives values for oil palm and sweet sorghum:
-
The oil palm (whole plant) takes up 6 kg nitrogen per ton of fruits
-
The sweet sorghum (whole plant) takes up 50 kg nitrogen per ton of grains
We have to calculate the nitrogen uptake for the other crops, since no data has been found in (FAO). The
results are:
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Table 8-3: Unit uptake for each crop
Crop
Unit_uptake[kg N/tons]
Potato
3.75
Sugar beet
4.5
Sugar cane
2.3
Sweet sorghum
50
Rapeseed
44.7
Soya
77.1
Palm
6
The calculations are described below.
Potato
(Walther, Ryser et al. 2001) gives those values:
-
Tubers: Nitrogen uptake=135 kg N/ha; Harvest=45 tons/ha
-
Crop residues: Nitrogen uptake=25 kg N/ha; Harvest=18 tons/ha
The nitrogen uptake of the whole crop expressed per ton of tuber (main product) is obtained by calculating:
⎡ kgN ⎤
135 ⎡ kgN ha ⎤ 25 ⎡ kgN ha ⎤ 18 ⎡ t residues ha ⎤
⎢
⎥+
⎢
⎥* ⎢
⎥ = 3. 6 ⎢
⎥
45 ⎣ ha ttubers ⎦ 18 ⎣ ha t residues ⎦ 45 ⎣ ha ttubers ⎦
⎣ ttubers ⎦
The Bayerische Landesanstalt für Landwirtschaft30 gives those values:
-
Tubers: 3.5 kg N/t
-
Crop residues: 2 kg N/t
-
Proportion: 0.2 t residues/ 1 t tubers
Computing the nitrogen uptake of the whole crop expressed per ton of tuber (crop product) with these data
leads to:
⎡ kgN ⎤ ⎡ kgN ⎤
⎡ t residues ⎤
⎡ kgN ⎤
3 .5 ⎢
⎥ + 2⎢
⎥ * 0 .2 ⎢
⎥ = 3 .9 ⎢
⎥
⎣ ttubers ⎦ ⎣ t residues ⎦
⎣ ttubers ⎦
⎣ ttubers ⎦
We consider the average of these 2 values: 3.75 kg N/t tubers.
Sugar beet
(Walther, Ryser et al. 2001) gives these values:
30
http://www.lfl.bayern.de/iab/duengung/mineralisch/10536/
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-
Beet: Nitrogen uptake=137 kg N/ha; Harvest=65 t/ha
-
Crop residues: Nitrogen uptake=150 kg N/ha; Harvest=50 t/ha
The nitrogen uptake of the whole crop expressed per ton of beet (crop product) is obtained by calculating:
⎡ kgN ⎤
137 ⎡ kgN ha ⎤ 150 ⎡ kgN ha ⎤ 50 ⎡ t residues ha ⎤
⎢
⎥+
⎢
⎥* ⎢
⎥ = 4. 4 ⎢
⎥
65 ⎣ ha tbeets ⎦ 50 ⎣ ha t residues ⎦ 65 ⎣ ha tbeets ⎦
⎣ tbeets ⎦
The Bayerische Landesanstalt für Landwirtschaft gives those values:
-
Beets: 1.8 kg N/t
-
-
Proportion: 0.7 t residues/ 1 t beets
Computing the nitrogen uptake of the whole crop expressed per ton of beets (crop product) with these data
leads to:
⎡ t residues ⎤
⎡ kgN ⎤
1 .8 ⎢
⎥ + 4⎢
⎥ * 0 .7 ⎢
⎥ = 4 .6 ⎢
⎥
⎣ tbeets ⎦ ⎣ t residues ⎦
⎣ tbeets ⎦
⎣ tbeets ⎦
We take the average: 4.5 kg N/t beets.
Sugar cane
Neither (Walther, Ryser et al. 2001) nor the Bayerische Landesanstalt für Landwirtschaft give some values
for sugar cane, since it is not produced under European climate conditions.
Several values can be found in the literature for the nitrogen concentration in the different parts of the plant
and for the ratio between the stalks and the residues (INRA, (Woytiuk 2006), (Hassuani 2005), (Kee Kwong
1987), (Rehm 1984) ). They sometimes differ greatly. We combine them in all the possible ways in order to
see how the results vary. The results for different combinations of the values found in the literature vary to up
to 200%! Here, we chose to use the average value of all the possible combinations: 2.3 kg nitrogen per ton
of stalk.
The differences between the values indicate that the unit nitrogen uptake can vary greatly and a more detailed assessment of this parameter should be done in order to improve the results.
Rapeseed
(Walther, Ryser et al. 2001) gives those data:
-
Seed: Nitrogen uptake=105 kg N/ha; Harvest=3.5 t/ha
-
Crop residues: Nitrogen uptake=49 kg N/ha; Harvest=6.5 t/ha
The nitrogen uptake of the whole crop expressed per ton of seed (crop product) is obtained by calculating:
⎡ kgN ⎤
105 ⎡ kgN ha ⎤ 49 ⎡ kgN ha ⎤ 6.5 ⎡ t residues ha ⎤
⎢
⎥+
⎢
⎥*
⎢
⎥ = 44 ⎢
⎥
3.5 ⎣ ha t seeds ⎦ 6.5 ⎣ ha t residues ⎦ 3.5 ⎣ ha t seeds ⎦
⎣ t seeds ⎦
The Bayerische Landesanstalt für Landwirtschaft gives those values:
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-
Seeds: 33.5 kg N/t
-
-
Proportion: 1.7 t residues/ 1 t seeds
Computing the nitrogen uptake of the whole crop expressed per ton of seeds (crop product) with these data
leads to:
⎡ t residues ⎤
⎡ kgN ⎤
33.5⎢
⎥ + 7⎢
⎥ * 1 .7 ⎢
⎥ = 45.4 ⎢
⎥
⎣ t seeds ⎦ ⎣ t residues ⎦
⎣ t seeds ⎦
⎣ t seeds ⎦
The results differ only slightly and the average value is used for this study.
Soybean
(Walther, Ryser et al. 2001) provides the following data:
-
Beans: Nitrogen uptake=150 kg N/ha; Harvest=2.5 t/ha
Crop residues: Nitrogen uptake=88 kg N/ha; Harvest=2.5 t/ha
The nitrogen uptake of the whole crop expressed per ton of beans (crop product) is obtained by calculating:
⎡ kgN ⎤
150 ⎡ kgN ha ⎤ 88 ⎡ kgN ha ⎤ 2.5 ⎡ t residues ha ⎤
⎢
⎥+
⎢
⎥*
⎢
⎥ = 95.2 ⎢
⎥
2.5 ⎣ ha tbeans ⎦ 2.5 ⎣ ha t residues ⎦ 2.5 ⎣ ha tbeans ⎦
⎣ tbeans ⎦
The Bayerische Landesanstalt für Landwirtschaft provides the following data:
-
Beans: 44 kg N/t
-
-
Proportion: 1 t residues/ 1 t beans
Computing the nitrogen uptake of the whole crop expressed per ton of beans (crop product) with these data
leads to:
⎡ kgN ⎤
⎡ kgN ⎤ ⎡ t residues ⎤
⎡ kgN ⎤
44 ⎢
⎥ + 15 ⎢
⎥ *1⎢
⎥ = 59 ⎢
⎥
⎣ tbeans ⎦
⎣ t residues ⎦ ⎣ tbeans ⎦
⎣ tbeans ⎦
These two results differ substantially. We take the average in this project but, for a more detailed and accurate assessment, some further investigations should be done in order to check if this value is appropriate as
a mean value for the whole world.
8.4
Inputs
The user has to provide some information about the planned biofuel production in order to quantify the related nitrate emissions. A default value is available for some of the required inputs, but others are compulsory. The default values are presented in the next section.
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The user has to be cautious regarding the units when typing in the inputs. The units are International System
of Units (SI) units or derived units. All the input concerning resources are related to a surface of one hectare
and to the period of cultivation.
Some required inputs are already used for the computation of phosphorus emissions and have been described previously:
-
Irrigation (see 7.4.1)
-
Ecozone (see 7.4.2)
-
Annual rainfall (see 7.4.3)
-
USDA soil order (see 7.4.5)
-
Crop (see 7.4.8)
-
Liquid organic fertilizer (see 7.4.11)
-
Solid organic fertilizer (see 7.4.12)
-
Duration of cultivation (see 7.4.14)
-
Yield (see 7.4.15)
An additional input is required: the N mineral fertilizer. For liquid and solid organic fertilizers, the possible
types and their nitrogen contents are described in Table 5.2.
8.4.1 N mineral fertilizer
The N mineral fertilizer amount has to be supplied by the user in [kgN ha-1]. It is the quantity of N mineral fertilizer applied per hectare to the considered crop during the growth period. The user has the choice between
typing in only the global amount of N mineral fertilizers or typing in more detailed information about the N
mineral fertilizers used. The possible N mineral fertilizers which can be selected by the user are described
previously.(Table 3.3 and Table 3.4)
The growth period is intended to be the period between sowing and harvest for annual crops (potato, sugar
beet, sweet sorghum, rapeseed and soybean). The growth period for perennial crops (sugar cane and oil
palm) is considered as the time period between two consecutive harvests. We should also take into account
the N mineral fertilizer amount applied during the unproductive phase at the beginning of growth for the perennial crops and share this amount between all the harvests of the crop life. We neglect this amount in
SQCB for simplicity reasons and this will lead to an underestimation of nitrate losses in the case of perennial
crops.
8.5
Default values
Some default values are available for some parameters in the SQCB tool in order to allow a rough assessment of environmental impacts when the user cannot provide all the needed information. In such a case
however, the results can be very unrepresentative of the specific situation of the considered biofuel production and should be considered with a lot of caution. Their reliability is limited and a more detailed assessment
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should be performed in order to have a good appreciation of the real situation. They can however give a first
idea about the environmental compatibility of a given biofuel production system.
The default values have been already given for several inputs required for the computation of nitrate emissions:
-
-
Ecozone (see 7.5.2)
-
-
-
Crop (see 7.5.8)
-
-
-
-
Yield (see 7.6.16)
The default value for the N mineral fertilizer has to be described.
The default values for the N mineral fertilizer originate from the ecoinvent database. For each country or region, a reference dataset in the database ecoinvent has been chosen. For any country in Africa for instance,
the dataset corresponding to the Brazilian production is selected.
Since the amount of N mineral fertilizer applied is highly correlated with the yield and related to the pedoclimatic conditions, the level of plant protection and the agricultural practices or legislation, the reference dataset for Brazil is only adapted if the yield of the considered crop and the pedo-climatic conditions and agricultural practice are similar in the considered African country and in Brazil.
8.6
Online help for the inputs
blue question mark. By clicking on it, the user accesses to the help text. The help text is as short as possible.
For most of the help texts, some words are underlined in blue and can be selected with the mouse. They
The online help has already been described for most of the inputs:
-
-
Ecozone (see 7.6.3)
-
-
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-
Crop (see 7.6.9)
-
-
-
-
Yield (see 7.6.16)
The online help for the N mineral fertilizer has to be described.
Please type in the applied amount of nitrogen mineral fertilizer in kg N per hectare. This is the amount that
you applied on the considered crop during the cultivation period.
Cultivation period
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9
Modelling of further emissions in agriculture
9.1
Pesticides emissions
For the pesticides emissions, a simplified modeling following the one used in the ecoinvent report ‘Bioenergy” (Jungbluth, Chudacoff et al. 2007) is used. It is assumed that all inputs of pesticides are emitted in the
nature. Therefore the emissions of the specific pesticides are equal to the inputs of pesticides.
9.2
Heavy metal emissions
For the calculation of heavy metal emissions in soil, the same modelling as the one used in the ecoinvent report ‘Bioenergy” (Jungbluth, Chudacoff et al. 2007) is used. The difference between input of heavy metals
with fertilizer and seeds and the output with the plant is assumed to be equal to the emissions in soil. The
heavy metal content of plants and products is taken from Nemecek and Kägi (2007) (see Table 2.1).
Table 9.1: Source for the heavy metal contents of inputs and outputs in agriculture
Inputs
Table in Nemecek and Kägi
(2007)
Mineral fertilizers
Table A.2 (Annex)
Organic fertilizers
Table A.3 (Annex)
Biomass
Table A.1 (Annex)
9.3
Heavy metals in ground- and river-water
As these emissions need a very complex modeling and as they don’t have a great importance in the results,
the values for these emissions are carried over from the reference data set of ecoinvent.
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10
Modelling of fuel production
10.1
Computation
The detailed computation of fuel production could be expressed as follows (Figure 2).
Table:
Energy consumption X1
SQCB_Energy_input
…..
Xn
Table:
Computation
Allocation
Factors
Table:
Process data step 1
Table:
Process data step 1
Calculation
Allocation
main product
Chemicals
Crop input
Amount main product
Price main product
Amount by-product
Price by-product
Figure 10.1: Calculation chart for fuel production.
In the following the detailed calculation steps are explained. Within the first step the user determines whether
he will use ‘default values or ‘enter data for processing”.
When the user wants to use default values the following progress is recommended:
1. Check if more than one input meet the tagging ‘subcategory = plant production’
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2. If YES: Summarize all inputs to the first input and change the name to ‘MIfp2” and delete the other
inputs tagged as ‘subcategory = plant production.”
3. If NO: Change the name of the input meeting the tag to ‘MIfp2”. The reason for this is elaborated in
chapter seven.
4.
All other data in the process table are not adapted.
10.1.2 Enter data for processing
When the user wants to enter data for processing the following main progress steps are recommended:
1. Data entry by user (for detailed description see 1.1).
2. Devide all flows by the Allocation factor relevant for the process (Recalculate the allocation applied
in order to apply new allocation factors).
a. Select the relevant allocation factor from table ‘SQCB_Allocation” by comparing the ID of the
main product with the ID of the allocation factor.
b. Devide all flows by the allocation factor applied for the main product (e.g. 1/0.8 =1.25)
a. NO: Change the name of the input meeting the tag to ‘MIfp2”.
b. YES: Delete all flows (except the first) and change the name of the first flow to, for example,
‘MIfp2”.
4. Take-over the data entered by the user
a. CROP INPUT: Add the amount of crop input entered by the user to the ‘MIfp2” flow.
b. FOSSIL ELECTRICITY: Replace electricity mix in the process data with the name and
amount of electricity entered by the user.
c.
FOSSIL HEAT: Check if an amount greater than zero has been entered.
i. NO = delete all flows tagged as ‘heating system” in the subcategory and go to next
step.
ii. YES= delete all flows tagged as ‘heating system” in the subcategory and add heat
type and amount entered by the user to the process data set.
d. ENERGY FROM BIOMASS: Check if an amount greater than zero has been entered.
i. NO = delete all flows tagged as ‘cogeneration” in the subcategory field and go to
next step.
ii. YES= delete all flows tagged as ‘cogeneration” in the subcategory and add the flow
‘wood chips, burned in cogen 6400kWth” and the related amount (entered by the
user) to the process data set.
5. Calculate new allocation factors using the amount and the price of the main and by-product.
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a. If only one by-product is produced with one unit of the main product, the formula to calculate the allocation factor of the main product is determined as fallows:
Afmp=Fmp/(Fmp+Fbp) [z.B. 5/(5+1)=0.8]
with Afmp
and
Fmp=Amp*Pmp
where Fmp
and
= Allocation factor main product
= Turnover main product
Amp
= Amount of the main product
Pmp
= Price of the main product
Fbp=Amp*Pmp
where Fbp
= Turnover by-product
Abp
= Amount of the by-product produced with one kg of the main product
Pbp
= Price of the by-product
b. However, if more than one by-product is produced with a unit of the main product the following formula should be applied.
Afmp=Fmp/(Fmp+∑Fbpn) [z.B. 3/(5+∑1,2)=0.5]
with Afmp
and
Fmp=Amp*Pmp
where Fmp
and
Fbp1
= Turnover of first by-product
Abp
= Amount of the 1 by-product produced with one kg of the main p.
Pbp
= Price of the 1 by-product
Fbp2=Abp*Pbp
where
and
Fbp1=Abp*Pbp
where
and
Fbp2
= Turnover 2nd by-product
Abp
= Amount 2nd by-product produced with one kg of the main p.
Pbp
= Price 2nd by-product
Fbpn=Abp*Pbp
where
Fbp2
= N-faktor by-product
Abp
= Amount of the n by-product produced with one kg of the main p.
Pbp
= Price of the n by-product
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Amp
Pmp
6. Multiply all flows in the process table (except the output of the main product (1)) with the calculated
allocation factor of the main product.
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11
Modelling of fuel refining
The second process step is similar in most requirements. However, some minor adoptions are necessary
with respect to the web-form and the computation.
11.1
Computation
The detailed computation of fuel production could be expressed as follows (Figure 2).
Table:
Energy consumption X1
SQCB_Energy_input
…..
Table:
Xn
Computation
Allocation
Factors
Table:
Process data step 2
Table:
Process data step 2
Calculation
Allocation
main product
Chemicals
Input process 1
Amount main product
Price main product
Amount by-product
Price by-product
Figure 11.1: Calculation chart for fuel refining.
In the following the detailed calculation steps are explained. Within the first step the user determines whether
he will use ‘default values or ‘enter data for processing”.
When the user wants to use default values the following progress is recommended:
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6. If YES: Summarize all inputs to the first input and change the name to, for example, ‘MIfp3” and delete the other inputs tagged as ‘subcategory = plant production.”
7. If NO: Change the name of the input meeting the tag to ‘MIfp3”.
8.
All other data in the process table are not adapted.
11.1.2 Enter data for processing
When the user wants to enter data for processing the following main progress steps are recommended:
1. Data entry by user (for detailed description see 1.1).
2. Devide all flows by the allocation factor relevant for the process (Recalculate the allocation applied in
order to apply new allocation factors).
a. Select the relevant allocation factor from table ‘SQCB_Allocation” by comparing the ID of the
main product with the ID of the allocation factor.
b. Devide all flow values by the allocation factor applied for the main product (e.g. 1/0.8 =1.25)
a. NO: Change the name of the input to MIfp3. The reason for this is elaborated in chapter
seven.
b. YES: Delete all flows (except the first) and change the name of the first flow to MIfp3.
4. Take-over the data entered by the user
a. INPUT PROCESS 1: Add the amount of input entered by the user to the Input_per_kg_RME
flow.
b. FOSSIL ELECTRICITY: Replace electricity mix in the process data with the name and
amount of electricity entered by the user.
c.
FOSSIL HEAT: Check if an amount greater than zero has been entered.
i. NO = delete all flows tagged as ‘heating system” in the subcategory and go
to next step.
ii. YES= delete all flows tagged as ‘heating system” in the subcategory and
add heat type and amount entered by the user to the process data set.
d. ENERGY FROM BIOMASS: Check if an amount greater than zero has been entered.
i. NO = delete all flows tagged as ‘cogeneration” in the subcategory field and
go to next step.
ii. YES= delete all flows tagged as ‘cogeneration” in the subcategory and add
the flow ‘wood chips, burned in cogen 6400kWth” and the related amount
(entered by the user) to the process data set.
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5. Calculate new allocation factors using the amount and the price of the main and by-product.
a. If only one by-product is produced with one unit of the main product, the formula to calculate the allocation factor of the main product is determined as fallows:
Afmp=Fmp/(Fmp+Fbp) [z.B. 5/(5+1)=0.8]
with Afmp
and
Fmp=Amp*Pmp
where Fmp
and
Amp
Pmp
Fbp=Abp*Pbp
where Fbp
= Turnover by-product
Abp
= Amount of the by-product produced with one kg of the main product
Pbp
= Price of the by-product
b. However, if more than one by-product is produced with a unit of the main product the following formula should be applied.
Afmp=Fmp/(Fmp+∑Fbpn) [z.B. 3/(3+∑1,2)=0.5]
with Afmp
and
Fmp=Amp*Pmp
where Fmp
and
Amp
Pmp
Fbp1
= Turnover of the first by-product
Abp
Pbp
Fbp2=Abp*Pbp
where
and
Fbp1=Abp*Pbp
where
and
Fbp2
= Turnover 2nd by-product
Abp
Pbp
Fbpn=Abp*Pbp
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where
Fbp2
= N-faktor by-product
Abp
= Amount of the n by-product produced with one kg of the main p.
Pbp
= Price of the n by-product
6. Multiply all flows in the process table (except the output of the main product (1)) with the calculated
allocation factor of the main product.
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12
Modelling of transport to storage
Normally, since the transport is less important when focusing on the impacts of the whole value chain, the
transport to the storage in Switzerland is computed using generic transport processes. Each transport
process is related to a continent region. Since each country is related to such a region the relevant transport
process could be selected by the country entered by the user. However, the transport processes for ethanol
and methyl ester differ with respect to their emissions. For this reason the transport processes are distinguished as regards the type of fuel transported, i.e. they are hold two times, once for methyl ester and once
for ethanol. Thus, each transport process must be related to a specific kind of fuel. In detail the following
computation is recommended.
1. Select the transport process related to the specific biofuel by using the direct relation between fuel
type and transport processes.
2. Determine the continent region related to the country entered by the user and use the
‘SQCB_link_transport_to_region” table (Table 12.1) to select the relevant transport process, i.e. all
flows related to this transport process.
3. Change the main input flow to ‘MIfp6”. Do not change the value. The reason for this is elaborated in
chapter nine.
Table 12.1: Relation between world region and transport processes.
worldregion_un_code
14
17
15
18
11
29
13
5
21
143
30
34
35
145
151
154
39
155
53
54
1
worldregion_name_en
Eastern Africa
Middle Africa
Northern Africa
Southern Africa
Western Africa
Caribbean
Central America
Southern America
Northern America
Central Asia
Eastern Asia
Southern Asia
South-Eastern Asia
Western Asia
Eastern Europe
Northern Europe
Southern Europe
Western Euope
Australia and New Zealand
Other Ozeania
World
Transport_process_location_id
AF
AF
AF
AF
AF
US
BR
BR
US
CN
CN
AS
MY
AS
RER
RER
RER
RER
CN
CN
CN
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13
Modelling of car use
13.1
Operation of car
The following computation is recommended.
1. Select the operation process related to the specific biofuel.
2. Change the flow tagged with ‘biomass” in the category and as ‘fuels” in the subcategory into
‘MIfp5”. Do not change the value. The reason for this is elaborated in Chapter 12.
13.2
Transportation of 1 pkm
The following computation is recommended.
4. Select the transport process related to the specific biofuel.
5. Change the flow tagged with ‘transport systems” in the category field in ‘MIfp6”. Do not change the
value. The reason for this is elaborated in chapter seven.
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14
Normalization to functional unit
Figure 6 shows the life cycle stages of RME.
Cultivati-
1.9 kg
on
Fuel pro-
1.0087 kg
duction
1 kg rape
Fuel refi-
1.0005 kg
ning
1 kg oil
1 kg RME
Transport 0.0555 kg
to stora1 kg RME
ge
Operation
0.629 km
Use
1 pkm
1 km
Figure 6: Life cycle stages of RME without normalization to the functional unit of one pkm.
Calculation direction
6.
5.
[Flows] * [kg rape/fu]
[Flows] * [kg oil/fu]
1. Cultivation
0.0669 kg
2. Fuel
production
4.
3.
[kg RME/fu] * [Flows]
[kg RME/fu] * [Flows]= etc.
3. Fuel
0.0352 kg
refining
0.0349kg
4. Transport to
storage
2.
1.
[km/fu] * [Flows]= kg RME/fu
5. Opera-
0.0349 kg
tion
6. Use
0.629 km
Material flow direction
Figure 14.1: Life Cycle stages of RME with the calculation to normalize all inventory flows to the functional unit of one pkm.
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1 pkm
The flows of each life cycle stage refer to the outcome of 1 unit of the main product. In this context, the figure
presents the flows after all process related computations have been applied. In other words, the flows do not
refer to the function unit (fu) of ‘driving one person km (pkm) with biodiesel from rape”. Thus, the flows related to each life cycle stage must be normalized (Figure 7).
Basically each of the input and output flow of a specific process refers to the output of one unit of the main
product (e.g. all flows from fuel production refer to the output of 1 kg rape oil). In order to normalize all flows
to the functional unit of (‘driving one person km (pkm) with biodiesel from rape”) the following computation
must applied.
14.1
Computation of Use
The (input and output) flows and the related value of the ‘USE” process are already normalized to the functional unit, i.e. they refer to ‘driving one pkm”. Consequently, no computation must be applied.
14.2
Computation of Operation
All flows related to the process ‘Operation” refer to the output flow of one km. In order to normalize the flows
to the functional unit all values in the process must be multiplied with the amount of km required to drive on
pkm, i.e. 0.629 km. In detail, the following process is recommended:
1. Select all inventoried flows of process 5.
2. Select the main input flow of process 5 + 1 (MIfp 6) and multiply all flows selected with MIfp6. This
can be expressed with the following formula:
Np=Fxi*MIfp
with
Npf
=
Normalized process flows
Fxi
=
Each flow related to process n
MIfp =
Main input flow of process 5 + 1 (i.e. the process follow on the
process actually calculated).
For example, by multiplying the main input flow (MIfp5 = 0.0555) with the required input of the ‘Use”
process (MIfp6 = 0.629), the new input (MIfp5 = 0.0349) refers to the functional unit. In other words,
the amount of RME required for the operation meet now the demand for driving one pkm.
3. Write the calculation results to the process.
14.3
Computation of Transport to storage
In order to normalize the ‘Transport to storage” process the above calculated amount of the ‘main input flow”
is required. Using the MIfp of process 4 + 1 the Npf of process 4 can be calculated using the above determined progress.
C:\Dokumente und Einstellungen\TZiep\Eigene
Dateien\Projekte\sqcb\23_Webdescription\SQCB_Background_report_20090327_091027_oMais.doc
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14.4
Computation of fuel refining
By multiplying the MIfp of process 3 + 1 with all flows of process 3 the Npf of process 3 can be calculated.
Use the same progress as determined above.
14.5
Computation of fuel production
14.6
Computation of cultivation
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15
Computing the environmental impacts
After the normalization to the functional unit all process, i.e. all flows referring to these processes, must be
multiplied with the impacts values (UBP06 and GWP100) stored in SQCB_IMPACT_TABLE. In this context,
the following progress is recommended.
1. Select process n.
2. Multiply the value of each flow (except the MIfp and all flows tagged as ‘Main product” in the
idsqcb_crop_flowcategory, idsqcb_process_flowcategory, …..etc.) with the corresponding impact
value for GWP100 and UBP06 in table SQCB_IMPACT_Table where Flow_id of process n =
Flow_id SQCB_IMPACT_TABLE.
3. Write results, i.e. all flow attributes to results table (or René table).
4. Iterate until all process are computed
The main input and output of a process are not computed, i.e. not multiplied with the respective impact factor, and not written to the results table. The reason is that the SQCB_IMPACT_TABLE include the impact of
all process flows available in Ecoinvent 2.0. The stored GWP100 value to, for example, the process ‘rape oil,
at oil mill” includes all impacts caused by the life cycle of rape oil, i.e. the impacts of rape cultivation are already included in this value. Thus, when both the main input and output flows would be included, the results
would by far too high.
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16
Fossil reference data
In order to assess the environmental impacts a specific biofuels, reference data should include fossil and renewable systems. This calls for the availability of the environmental performance of (i) different fossil system
and (ii) different biofuel systems. In order to compare the calculated impacts of a specific biofuel with a reference system both the fossil and renewable system must refer to the same function, i.e. one pkm. Moreover,
this requires the clear classification of the reference systems to a specific biofuel system. Furthermore it is to
determine how detailed the impacts of the reference system should be expressed.
In regards of the fossil reference system, only total impacts per pkm are relevant. Both the value for
GWP100 and UBP06 per pkm can be obtained from the SQCB_IMPACT_TABLE. The fossil system is only
represented by:
6588 transport, passenger car, petrol, EURO3
Other fossil references are not used.
CH
pkm
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17
Description of technical aspects
Authors: Tobias Ziep, Prof. Volker Wohlgemuth, René Weichbrodt
17.1 Framework
The calculation model is developed as a module for the open source web content management system
Drupal. The used programming language is PHP. Drupal already provides a user management, a menu system and a template system to handle the outputs. These components do not need to be developed. Furthermore Drupal provides powerful Application Programming Interfaces (hereafter APIs) that enable the developer to use core functions like form building and database access and manipulation in a simple and secure way. The form validation and control, is realized with Javascript.
Drupal and the SQCB module use a MySQL database to store all data. MySQL is an open source relational
database management system.
17.2 Components
The SQCB module can be divided into 6 components.
Administration component
The administration component handles the import, entry and connection of master data into the database
tables. This master data can be subdivided into structure data, flow data from ecoinvent processes, geographical data and calculation parameters (see figure 8).
Figure 17.1: simplified schema of master data
Structure data, geographical data and calculation parameters are entered and connected via forms in the
administration backend. This area of the SQCB is only accessible by users with the role ‘administrator”.
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The ecoinvent flow data are imported via an xml interface. The administrator uploads xml files in ecoSpold
format that contains one or more processes. In the next step the contents of this file are partially displayed to
the administrator. Additional information can be applied to the process or the individual flows. Then the contents are imported into the corresponding database table.
The administrator can add, edit or delete master data. If the administrator deletes data or a dataset, all dependent data is also deleted to ensure the consistency of data.
Questionnaire component
The questionnaire component is a part of the user frontend. It displays a dynamic multipage form that is logically divided in the four main pages: basic data, cultivation, processing and social criteria. If the user chooses to enter data for more than one provider the data for cultivation (page 2) has to be entered for each provider. Due to the large amount of information that is needed to calculate the effects of the cultivation this
page is subdivided into 4 steps that are again represented by form pages. Hence the total number of form
pages that have to be filled by the user depends on the number of providers (see figure 9).
Figure 17.2: Structure of the questionnaire
The form pages are built with the Drupal Forms API. The user entries are saved on page change (‘Next” or
‘Back” button) and are stored as a structured array in the users persistent PHP session. The data saved in
the session is called user data. If the fields were already filled with data, they are prefilled with the values
contained in the user data on page load.
The form pages are controlled with Javascript to increase the usability. Form fields are hidden or disabled
depending on the users selection. Some form fields have clickable descriptions with a small help logo. When
clicked a box on the right side is enabled that contains a help text.
Another part of the questionnaire is the navigation bar on the right side. It displays all pages in a hierarchical
order, highlights the actual page and shows the quality of the entered data. It uses the user data as data basis. The page captions are clickable and can be used for quick form navigation.
All strings on the form pages are translatable. The strings can be extracted to a ‘GNUgettext’ compatible
format (.po file). This file can be translated with a special translation editor or a text editor.
Validation component
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The validation component checks the user data for completeness, plausibility and the properness of datatypes. If errors are found, a short description and a link to the corresponding form page are displayed.
Calculation component
Before the calculation is started the temporary results tables are cleaned from preceding calculation data.
The best available ecoinvent processes are chosen depending on the country and the crop from form page 1
– basic data and by implication the crop type and fuel type (see figure 10). Then the flows for the steps in
figure 10 are calculated. The calculation for the cultivation process is executed for each provider.
All of the calculation points except ‘cultivation – land use change” are connected with an imported ecoinvent
process. If the user chooses to use default values, the flows from these processes are taken as calculation
results. If the user enters detailed values the calculation is done based on the models and rules described in
the preceding chapters. These detailed calculations process the user data and the master data.
Calculation
order
Calulation point
1
2
3
4
5
6
7
Default values
possible?
User entry
possible?
Process selection
depending on?
provider x
cultivation
land use change
no
yes / mandatory
-
provider x
cultivation
mineral fertilizer
yes
yes
country
provider x
cultivation
organic fertilizer
yes
yes
country
provider x
cultivation
pesticides
yes
yes
country
processing
process 1
yes
yes
country
crop type
processing
process 2
yes
yes
country
crop type
yes / mandatory
no
country
fuel type
transport
8
operation
yes / mandatory
no
fuel type
9
usage 1pkm
yes / mandatory
no
none / generic
Figure 17.3: Calculation points
The resulting flows are tagged with the session ID, the provider number and the calculation point. They are
written into the results table. The session ID connected to every result dataset ensures that the SQCB is multi-user capable.
Thereafter the results from step 1 are normalized to the functional unit of 1pkm (see chapter 11) and are written into the normalized results table.
Evaluation component
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The unnormalized and normalized flow results are evaluated with the impact factors from the impacts table.
The results are written into the corresponding table. The evaluation methods included are UBP06, GWP and
ecoindicator99.
The evaluation results are aggregated per page, provider and step and written to the presentation results table.
Presentation component
The presentation component shows the results in different charts. The user can choose the evaluation method and the detail. The charts are generated live using the PHP PEAR image_Graph package. As a basis
for chart generation the normalized and unnormalized evaluated results and aggregated results tables are
used. Thus a comparison between providers (functional unit kg) as well as the total result compared to the
fossil reference (functional unit pkm) can be provided.
17.3 Interaction of Components
In the main the workflow of the SQCB is a linear process. The only exception is the administration component. It is only used to set up the master data. All other components take input of its predecessor and therefore depend on it. All components handle data from the database or the user session.
Figure 17.4: Interaction of the components
The starting point is the questionnaire component. The information provided by the user is written into the
session. The validation component checks this data. Only if no errors occur the calculation component is
started. It reads data from the database and the session calculates and writes the results back to the database. The evaluation component reads these results, applies the evaluation and writes the results back to
the database. The presentation component displays the final results.
17.4 Requirements
The Sustainability Quick Check for Biofuels can be installed on any web server that meets the following requirements:
•
PHP 5.2.x
o
GD 2.x with png support
103/129
o
simpleXML 1.x
o
PHP PEAR with image_graph package 0.7.2
•
MySQL 5.x support
•
Drupal 6.x installed
To access and use the Sustainability Quick Check for Biofuels the client has to meet the following requirements:
•
internet connection
•
a webbrowser with javascript and cookies enabled
The SQCB was tested with the following browsers:
•
Internet Explorer 6 and 7
•
Mozilla Firefox 3
•
Opera 9.63
•
Google Chrome 1
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18
Managing the database
Authors: Tobias Ziep, Volker Wohlgemuth, René Weichbrodt
This chapter describes the administration pages in an brief overview. Additionally the relationship between
data objects is explained.
18.1
Geographical Data
Figure x: relationship between geographical data items
The geographical data is used to describe calculation parameters and ecoinvent processes. A country is assigned to a world region. A world region has one or more ecozones. The land use transformation data is valid for a combination of world region, ecozone and land use type.
World Regions
Input fields:
Field name
World Region Name (en)
World Region Name (es)
World Region UN Code
Field type
text
text
number
Description
English name of the world region
Spanish name of the world region
World region code after UN specification
Field type
text
text
number
drop down
Description
English name of the country
Country short name from ecoinvent
Country code after UN specification
World region a country is assigned to
Field type
text
text
text
Description
English name of the ecozone
Spanish name of the ecozone
Ecozone short description
Countries
Input fields:
Field name
Country name (en)
Country short
Country UN Code
World Region
Ecozones
Input fields:
Field name
Ecozone Name (en)
Ecozone Name (es)
Ecozone Short
Land Use Types
Input fields:
105/129
Field name
Land Use Type Name (en)
Land Use Type Name (es)
Field type
text
text
Description
English name of the land use type
Spanish name of the land use type
Worldregion Ecozone
The administrator can assign an ecozone to a world region. The connections are displayed in a filterable list.
Field name
World region
Field type
drop down
Ecozone
drop down
Description
World region added in the world region
administration page
Ecozone added in the ecozone administration page
Land Use Change From
On this page the administrator can enter the parameters that are used for land use calculation. These values
represent the C stock for a land use ‘from” transformation. In the first step this page shows a form where the
administrator can choose a land use type and a world region. If this form is submitted a list with all connected
ecozones is shown. The administrator can enter land use from data for each ecozone.
First step:
Field name
Land use type
Field type
drop down
World region
drop down
Description
Land use type added in the land use type
administration page
administration page
Second step:
Field name
C Stock ‘from”
Field type
number
Description
Land use C stock ‘from” parameter valid
for land use type and world region chosen
in first step
Land Use Change To
On this page the administrator can enter the parameters that are used for land use calculation. These values
represent the C stock for a land use ‘to” transformation. In the first step this page shows a form where the
administrator can choose a land use type and a world region. If this form is submitted a list with all connected
ecozones is shown. The administrator can enter land use from data for each ecozone.
First step:
Field name
Land use type
Field type
drop down
World region
drop down
Description
Land use type added in the land use type
administration page
administration page
Second step:
106/129
Field name
C Stock ‘to”
18.2
Field type
number
Description
Land use C stock ‘to” parameter valid for
land use type and world region chosen in
first step
Structure data
Figure 18.1: relationship between structure data items
A crop type determines a fuel type and describes processing combinations valid for this crop type. A
processing combination is connected to two process types. A process type is connected to one or more
processes that are represented by imported ecoinvent processes.
Fueltype
Input fields:
Field name
Fuel type Name (en)
Fuel type Name (es)
Field type
text
text
Description
English name of the fuel type
Spanish name of the fuel type
Field type
text
text
drop down
Description
English name of the crop type
Spanish name of the crop type
Fuel type the crop type is connected to
Crop type
Input fields:
Field name
Crop type Name (en)
Crop type Name (es)
Fuel type
Process Types
Input fields:
107/129
Field name
Process type Name (en)
Process type Name (es)
Field type
text
text
Description
English name of the process type
Spanish name of the process type
Processing Combination
A processing combination is a combination of two processing processes that is connected to a crop type. If
there are two or more combinations for the processing of a crop type, the user can choose the one he is using.
The administrator can connect three different steps to a crop type. In this version of the SQCB only two
process steps are used. The third step is integrated for possible extensions.
Input fields:
Field name
Crop type
Process 1
Process 2
Process 3
Field type
drop down
drop down
drop down
drop down
Description
Crop type the combination is valid for
Process type step 1
Process type step 2
Process type step 3
Mineral Fertilizer
The administrator can enter mineral fertilizers in the SQCB. They are displayed in the questionnaire if the
user chooses to enter detailed values.
Input fields:
Field name
Name (ecoinvent)
Name (es)
Unit
Ecoinvent number
Visible
Base Element
Field type
text
text
Text
number
checkbox
drop down
Description
English name of the fertilizer
Spanish name of the fertilizer
Base unit of the fertilizer
Ecoinvent ID of the fertilizer
Flag to set the visibility in the questionnaire
Base element of the fertilizer (N, P or K)
Organic Fertilizer
The administrator can enter organic fertilizers in the SQCB. They are displayed in the questionnaire if the
user chooses to enter detailed values.
Input fields:
108/129
Field name
Name (en)
Name (es)
Unit
Ecoinvent number
Visible
category
Field type
text
text
Text
number
checkbox
drop down
Description
English name of the organic fertilizer
Spanish name of the organic fertilizer
Base unit of the organic fertilizers
Ecoinvent ID of the fertilizer
Flag to set the visibility in the questionnaire
Category of the organic fertilizer (liquid or
solid)
Pesticide
The administrator can enter pesticides in the SQCB. They are displayed in the questionnaire if the user
chooses to enter detailed values.
Input fields:
Field name
Name (ecoinvent)
Name (es)
Unit
Ecoinvent Number
18.3
Field type
text
text
text
number
Description
English name of the pesticide
Spanish name of the pesticide
Base unit of the pesticide
Ecoinvent ID of the pesticides
Ecoinvent Imports
Figure 18.2: relationship between ecoinvent flow data and structure data
The flow data that is used for the calculation is imported from different ecoinvent process xml files. The main
processes are crop cultivation (crop), processing (process), transport, operation and usage. A crop cultivation process is connected to a country and a crop type. The single flows in this process are categorized by
flow categories that have to be applied during the import process. A processing process is connected to a
country and a process type. Analogous to the cultivation process the single flows also have to be categorized. The transport process is connected to a country and a fuel type. The operation process only depends
on a fuel type. The usage process is generic and has no other connections.
For all import pages a file upload form is displayed that allows the user to upload a xml file.
109/129
Crop Flow Categories
A crop flow category describes a flow from a cultivation process. The flow categories are used during the
calculation to distinguish flows.
Input fields:
Field name
Crop Flow Category name
Field type
text
Description
English name of the crop flow category
Crops (Cultivation Processes)
The uploaded file is parsed and some contents are displayed to the user.
Field name
Crop display name (en)
Crop display name (es)
Baseline yield in kg/ha
Crop type
Field type
text
text
number
drop down
Description
English name of the crop cultivation process
Spanish name of the crop cultivation process
baseline yield of the crop cultivation process
Crop type the cultivation process is connected to
The country is auto-assigned depending on the location value from the imported file.
A table with all contained flows and a drop down box for the crop flow category selection is displayed below
the form.
Process Flow Categories
A process flow category describes a flow from a processing process. The flow categories are used during the
calculation to distinguish flows.
Input fields:
Field name
Process Flow Category name
Field type
text
Description
English name of the process flow category
Processes (Processing Processes)
Input fields:
Field name
Process display name (en)
Process display name (en)
Process type
Allocation Factor
Default
Field type
text
text
drop down
number
checkbox
Description
English name of the process
Spanish name of the process
Process type this process is connected to
Allocation Factor used for calculation
Default flag for process, if no process for a
country is available use the default flow for
calculation
110/129
A table with all contained flows and a drop down box for the process flow category selection is displayed below the form.
Transport (Transport Processes)
The uploaded file is parsed and some contents are displayed to the user. The transport process import file
contains more than one transport process. Thus a list of the names is displayed to the user.
Input fields:
Field name
Fuel type
Field type
drop down
Description
fuel type the process is connected to
Operation (Operation Processes)
Input fields:
Field name
Fuel type
Field type
drop down
Description
fuel type the process is connected to
Usage (Usage Processes 1pkm)
The uploaded file is parsed and some contents are displayed to the user. The process is generic. No input
by the administrator is required.
General Information
All administration pages have a list of already added/imported items. The list displays the most important information and provides edit- and delete-functionality.
111/129
19
Annex
Table 19.1: ‘Transformation from’ table as implemented within the SQCB. The ‘ from_c_stock –value’ is expressed in t carbon.
idsqcb_landuse_type
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sqcb_worlregion_un_name idsqcb_ecozone from_c_stock
Eastern Africa
1
216
Eastern Africa
2
180
Eastern Africa
3
98
Eastern Africa
4
73
Eastern Africa
5
39
Eastern Africa
6
116
Middle Africa
1
216
Middle Africa
2
180
Middle Africa
3
98
Middle Africa
4
73
Middle Africa
6
116
Northern Africa
5
39
Northern Africa
6
116
Northern Africa
8
114
Northern Africa
9
80
Northern Africa
11
85
Southern Africa
1
216
Southern Africa
2
180
Southern Africa
3
98
Southern Africa
4
73
Southern Africa
5
39
Southern Africa
6
116
Southern Africa
7
77
Southern Africa
8
114
Southern Africa
11
85
Western Africa
1
216
Western Africa
2
180
Western Africa
3
98
Western Africa
4
73
Western Africa
5
39
Western Africa
6
116
Caribbean
1
211
Caribbean
2
161
Caribbean
3
142
Caribbean
6
131
Central America
1
211
Central America
2
161
Central America
3
142
Central America
6
131
Central America
6
85
Central America
10
45
Central America
11
131
Southern America
1
211
Southern America
2
161
Southern America
3
142
112/129
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Southern America
Southern America
Southern America
Southern America
Southern America
Southern America
Southern America
Southern America
Southern America
Southern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Northern America
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Eastern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
Southern Asia
South-Eastern Asia
South-Eastern Asia
4
5
6
7
8
9
11
12
14
16
2
7
8
9
10
11
12
13
14
15
16
17
18
19
20
11
13
14
15
16
19
20
2
6
7
11
13
14
15
16
17
1
2
3
4
5
6
9
10
11
16
1
2
78
39
131
185
148
85
131
208
65
150
131
185
148
85
45
131
208
170
65
65
150
102
62
75
0
126
165
65
65
150
75
0
116
236
165
126
165
65
65
150
102
236
195
117
73
39
160
80
45
161
150
236
195
113/129
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
South-Eastern Asia
South-Eastern Asia
South-Eastern Asia
South-Eastern Asia
South-Eastern Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Western Asia
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Northern Europe
Northern Europe
Northern Europe
Northern Europe
Southern Europe
Southern Europe
Southern Europe
Southern Europe
Southern Europe
Southern Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Other Oceania
Other Oceania
Other Oceania
Other Oceania
World
3
4
6
7
11
5
6
7
8
9
10
11
13
14
15
16
13
14
15
16
17
18
19
20
12
13
17
19
8
11
12
13
14
16
12
13
16
17
19
1
3
4
7
8
9
10
12
16
1
2
3
6
1
117
73
160
219
161
39
236
165
109
75
45
126
165
65
65
150
165
65
65
150
102
62
75
0
94
165
102
75
45
60
94
165
65
150
94
165
150
102
75
64
39
39
77
45
45
45
94
86
64
53
39
60
67
114/129
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
55
38
38
38
59
61
28
28
28
45
101
87
41
41
64
26
26
26
0
26
21
19
19
19
33
35
19
19
19
25
60
61
29
29
38
14
17
14
0
86
86
86
86
86
86
88
88
88
88
88
87
87
87
115/129
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
15
16
17
18
19
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2
3
4
5
6
7
87
87
146
146
146
0
26
21
19
19
19
33
35
19
19
19
25
60
61
29
29
38
14
17
14
0
48
52
35
35
35
71
69
29
29
29
49
83
89
86
39
38
60
65
63
0
84
74
50
50
50
78
96
116/129
7
7
7
7
7
7
7
7
7
7
7
7
7
World
World
World
World
World
World
World
World
World
World
World
World
World
8
9
10
11
12
13
14
15
16
17
18
19
20
54
54
38
63
96
98
54
54
74
42
37
42
6
117/129
Table 19.2: ‘Transfromation_to” table as implemented within the SQCB. The ‘ from_c_stock –value’ is expressed in t carbon.
idsqcb_landuse_type
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
idsqcb_worldregion_un_name
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
World
idsqcb_ecozone
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
to_c_stock
26
21
19
19
19
33
35
19
19
19
25
60
61
29
29
38
14
17
14
0
84
74
50
50
50
78
96
54
54
38
63
96
98
54
54
74
42
37
42
6
118/129
Table 19.3: Average organic carbon content (SOC) in t carbon.
Climate Zone
Eco_Code
Tropical wet
Tropical moist
Tropical dry
Tropical dry
Tropical dry
Tropical montane
Warm temperate moist
Warm temperate dry
Warm temperate dry
Warm pemperate dry
Warm temperate moist or dry
Cool temperate moist
Cool temperate moist
Cool temperate dry
Cool temperate dry
Cool temperate moist or dry
Boreal moist
Boreal dry
Boreal moist and dry
Tar
Tawa
TAWb
TBSh
TBWh
TM
SCf
SCs
SBSh
SBWh
SM
TeDo
TeDc
TeBSk
TeBWk
TeM
Ba
Bb
BM
SOC_AVG_t_C
59
48
34
34
34
55
55
25
25
25
40
81
81
38
38
59
22
22
22
119/129
20
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