Direct field emissions natural resources v 1.1. (pdf) - EcoInvent

Methods of assessment of direct 

field emissions for LCIs of 

agricultural production systems 

Data v3.0 (2012) 

Thomas Nemecek and Julian Schnetzer 

Agroscope Reckenholz-Tänikon Research Station ART 

Zurich, August 2011 

This documentation file is mainly based on chapter 4 of ecoinvent report No. 15a (Nemecek 

et al. 2007) where the methods of assessment of direct field emissions for life cycle 

inventories (LCI) of agricultural crop production (ecoinvent data version 2) are described. 

Further elementary flows related to natural resources are described here, as well. This text 

represents an updated documentation of the methods and data sources used within the 

frame of updating agricultural LCIs for ecoinvent data version 3.

Life cycle inventories of Swiss and European agricultural production systems - Table of Contents 

Table of Contents 

TABLE OF CONTENTS ...................................................................................................................... 2 

ABBREVIATIONS ............................................................................................................................... 3 

1 BASIC ASSUMPTIONS ............................................................................................................... 4 

2 DIRECT FIELD EMISSIONS ..................................................................................................... 5 

2.1 EMISSIONS OF AMMONIA TO THE AIR ...................................................................................... 5 

2.1.1 The AGRAMMON model ................................................................................................. 5 

2.2 NITRATE LEACHING TO GROUND WATER ............................................................................... 8 

2.2.1 The SALCA-NO3 model ................................................................................................... 9 

2.2.2 The SQCB-NO3 model .................................................................................................. 12 

2.3 EMISSIONS OF PHOSPHORUS TO THE WATER ......................................................................... 15 

2.3.1 Phosphate Leaching to Ground Water .......................................................................... 16 

2.3.2 Phosphate Run-Off to Surface Water ............................................................................ 17 

2.3.3 Phorsphorous Emissions Through Water Erosion to Surface Water ............................ 17 

2.4 EMISSIONS OF N 2 O TO THE AIR .............................................................................................. 17 

2.5 EMISSIONS OF NO X TO THE AIR .............................................................................................. 18 

2.6 NUTRIENT INPUTS IN AGRICULTURAL SOILS ......................................................................... 19 

2.7 RELEASE OF FOSSIL CO 2 AFTER UREA APPLICATIONS .......................................................... 19 

2.8 EMISSIONS OF HEAVY METALS TO AGRICULTURAL SOIL, SURFACE WATER AND GROUND 

WATER ............................................................................................................................................... 19 

2.9 EMISSIONS OF PESTICIDES TO AGRICULTURAL SOIL ............................................................. 21 

3 NATURAL RESSOURCES ........................................................................................................ 22 

3.1 CO 2 FROM THE ATMOSPHERE ................................................................................................ 22 

3.2 LAND USE .............................................................................................................................. 22 

APPENDIX A ...................................................................................................................................... 26 

LITERATURE .................................................................................................................................... 29

Direct field emissions and elementary flows in LCIs of agricultural production systems - Abbreviations 

Abbreviations 

CH 

CH 4 

DM 

FAL 

FAT 

FiBL 

FU 

IPCC 

kg 

LCI 

LCIA 

LU 

m 2 

m 3 

N 2 O 

n.a. 

NH 3 

NO x 

SALCA 

SIP 

Switzerland 

methane 

dry matter 

Swiss Federal Research Station for Agroecology and Agriculture, Zurich-Reckenholz 

(today part of ART) 

Swiss Federal Research Station for Agricultural Economics and Engineering, 

Tänikon (today part of ART) 

Research Institute of Organic Agriculture, Frick, Switzerland 

functional unit 

Intergovernmental Panel on Climate Change 

kilogram (measurement of weight) 

life cycle inventory 

life cycle impact assessment 

livestock unit 

square metre (measurement of area) 

cubic metre (measurement of volume) 

dinitrogen monoxide 

not available 

ammonia 

nitrous oxide 

Swiss Agricultural Life Cycle Assessment 

Swiss Integrated Production 

3

Direct field emissions and elementary flows in LCIs of agricultural production systems - Basic Assumptions 

1 Basic Assumptions 

The following general assumptions are valid for the Swiss plant production system datasets covered by 

this documentation file: 

 

 

 

 

 

 

 

 

The field was assumed to have a slight slope of 5% (Nemecek et al. 2005, Appendix 3.1.3; value 

valid for the lowlands). The field slope mainly affects soil erosion and P-emissions to the water. 

For the European datasets the values given by local experts were used. 

Humus content was assumed to be 2%, clay content 20% and potential rooting depth 80 cm 

(Nemecek et al. 2005, Appendix 3.1.3). These factors affect the quantity of nitrate leached. 

The field is situated in the lowlands. The majority of arable crops are cultivated in the lowlands, 

and most seed production takes place there as well. Nevertheless, a large proportion of grassland is 

located in the hills and mountains, and although studies (Nemecek & Huguenin 2002, Nemecek et 

al. 2005) have shown that the differences between the lowlands and mountainous regions in terms 

of environmental impacts were found to be relatively small, this fact must be borne in mind. 

The soil was assumed to be of average erodibility. 

The field plot was assumed to have no artificial drainage. The majority of the fields and meadows 

in Switzerland are not drained 1 . For the canton of Zurich, for instance, the percentage of drained 

agricultural area lies between 7 and 38%, depending on the region (Schmid & Prasuhn 2000). For 

the other regions in Europe the same assumption was made. 

Fertilisation follows current recommendations (Walther et al. 2001). In order to obtain direct 

payments 2 , the farmer must have a balanced nutrient balance. The fertilising recommendations 

(Walther et al. 2001) form the basis for calculating the nutrient balance. Consequently, it is likely 

that farmers generally follow these recommendations. Nevertheless, it is possible to deviate from 

these recommendations to a certain extent: there is a tolerance of up to 10% for a positive nutrient 

balance. Furthermore, a farmer may apply more fertilisers than recommended to one crop, and less 

to another. 

No special measures are taken to prevent soil erosion, except the application of green manure for 

spring-sown crops. This is in accordance with the data source chosen for the use of machinery 

(LBL et al. 2000). 

The average density of livestock units (LU) per hectare was set at 1.3 LU/ha (BLW 2003). This 

value was used to calculate the potential N-mineralisation of the soil, except for the extensive 

meadow, where no fertiliser is applied at all. No distinction has been made between integrated and 

organic farming, even if fertilising practise is different. Organic farms apply more manure to 

arable crops than do integrated farms. If the entire crop rotation is considered, however, this 

difference almost disappears (FAT 2000a), since the farmyard manure is applied to a larger extent 

to the meadows in the integrated farm. 

The Swiss plant production inventories in ecoinvent refer to this “standard situation”. In conditions 

differing from this situation, the emissions may differ substantially from the values in ecoinvent data. 

1 Personal communication from V. Prasuhn, ART, September 2002. 

2 Verordnung über die Direktzahlungen in der Landwirtschaft (Direktzahlungsverordnung, DZV), 7.12.1998. 

4

Direct field emissions and elementary flows in LCIs of agricultural production systems - Direct Field Emissions 

2 Direct Field Emissions 

2.1 Emissions of Ammonia to the Air 

Ammonium (NH 4 + ) contained in fertilisers can easily be converted into ammonia (NH 3 ) and released 

to the air. Agriculture is the biggest source of ammonia emissions in Switzerland. For 2000, Thöni et 

al. (2007) estimated the total emissions of NH 3 to be 53,000 tonnes, thereof 93% from agriculture. 

Animal husbandry (emissions in the stable, during manure storage and spreading) is the largest source. 

About 30% of the excretions of N are lost in the form of ammonia. By taking appropriate measures, 

these emissions could be reduced by about 20-40% (Menzi et al. 1997). 

Ammonia contributes to acidification and the eutrophication of sensitive ecosystems. Its impact is 

mainly local and regional. 

A comparison of different emission factors for ammonia can be found in Menzi et al. (1997). 

2.1.1 The AGRAMMON model 

Geographic scope of application: global 

The losses of NH 3 emissions were calculated based on the model Agrammon (www.agrammon.ch), a 

model especially designed for the assessment of NH 3 emissions from agriculture on either farm scale 

or on a regional scale. The relevant modules of the model applied here are, on the farm scale, 

„application‟, referring to emissions from the application of farm manure, and „plant production‟, 

referring to emissions from the application of mineral and recycling fertilisers. The model structure 

and technical parameters can be found in Agrammon Group (2009a, b). 

Parameters required by the model that were not available from the production inventories were 

complemented with standard values proposed in the user interface implemented online, e.g. the 

fractions of slurry application techniques in practice (cf. Tab. 2.1 & Tab. 2.2). These standard values 

shall reflect a representative distribution of alternatives in agricultural practices and do not necessarily 

correspond to the „basic system‟ in Agrammon and, thus, the resulting correction factors deviate from 

1 (cf. following paragraph). The standard values are based on a survey described in Kupper et al. 

(2010) and a corresponding summary of the model parameters deducted from this survey (SHL 2010). 

‘Application’ of farmyard manure 

The module „application‟ is subdivided into three categories which are application of liquid manure, of 

solid manure and of poultry manure. The formulae for all sub-modules follow the same principle: 

NH 3 –N = TAN * (er + c_app) * c x 

NH 3 –N = nitrogen emissions in form of NH 3 (kg N/ha) 

TAN = Total ammonium nitrogen; this is considered equal to the soluble nitrogen content 

(Agrammon Group 2009b) and is calculated as the product of amount of farmyard manure 

(kg/ha) and the corresponding soluble nitrogen content (kg N/kg manure) according to Flisch et 

al. (2009) (kg N/ha) (Tab. 2.3) 

er = emission rate; this is a constant emission rate for each type of farm manure (%/100 of 

TAN) (Tab. 2.4) 

c_app = correction factor that influences the emission rate; it refers to the amount of manure 

per application and its degree of dilution; applies only for liquid manure (dimensionless). 

c x = correction factor x; this refers to various parameters of the crop production system; for the 

basic system assumed in Agrammon c x = 1; c x < 1 has a reducing effect on NH 3 –emissions, c x > 

1 an increasing effect (dimensionsless, see Tab. 2.1 for the explanation of the variables). 

5


c x = c_tech * c_soft * c_season for liquid manure 

c x = c_incorp_time * c_season for solid and poultry manure 

The calculation of correction factors is described in Agrammon Group (2009a). The correction factors 

of liquid manure are characterised in Tab. 2.1; for solid and poultry manure the same correction 

factors apply (Tab. 2.2). In the case of solid and poultry manure, the date of base dressing was used to 

determine c_season. 

Tab. 2.1 Correction factors (c x) for application of liquid manure 

c x Description Calculation Applied 

c_app 

c_tech 

c_soft 

c_season 

represents the amount of manure per application (applied 

standard value: 30 m 3 /ha) and its degree of dilution (40% 

liquid manure : 60% water) 

represents the technical equipment applied for slurry 

spreading 

equipment: 

splash plate 90 

trailing hose 10 

% of application: 

represents the proportions of manure applied on hot days 

and in evening hours 

application in evening hours 20% 

application on hot days 

sometimes 

represents the proportions of manure applied in summer 

(June-August) and the rest of the year 

based on crop 

data 

based on 

standard 

values 

based on 

standard 

values 

based on crop 

data 

value 

-0.029 

0.97 

0.96 

variable 

Tab. 2.2 

Correction factors (c x) for the application of solid and poultry manure 

c x Description Calculation Applied 

value 

c_incorp_time 

c_season 

represents the time span between manure 

application and incorporation 

incorporation of manure: 

within 1 day 20 

within 3 days 20 

after more than 3 days 10 

no incorporation 50 

% of application: 

represents the proportions of manure applied in 

summer (June-August) and the rest of the year 

based on 

standard values 

based on crop 

data 

cattle/pig: 

0.88; 

poultry: 

0.82 

variable 

Tab. 2.3 

Nitrogen contents of different types of manure. N total = total nitrogen content; N soluble = soluble nitrogen 

content (Flisch et al. 2009), this is considered to be equal to TAN. The values for liquid manure apply to 

liquid manure without addition of water. 

Animal category Manure type Unit kg N soluble/unit 

Cattle liquid manure kg/m 3 2.3 

low-excrement liquid manure kg/m 3 3.2 

staple manure kg/t 0.8 

solid manure from loose housing kg/t 1.3 

6


Pigs liquid manure kg/m 3 4.2 

solid manure kg/t 2.3 

Poultry broiler manure kg/t 10.0 

laying hen manure kg/t 6.3 

laying hen litter kg/t 7.0 

dried poultry litter kg/t 9.0 

Tab. 2.4 

Nitrogen emission rates (er) of different animal categories and manure types 

Animal category manure type er (% TAN) 

Cattle Liquid 50 

Solid 80 

Pigs Liquid 35 

Solid 80 

Poultry Solid, from growers, layers and other poultry 30 

Solid, from broilers and turkeys 65 

‘Plant production’ 

The module „plant production‟ is subdivided into the sub-modules „agricultural area‟, „mineral 

fertiliser‟ and „recycling fertiliser‟. The sub-module „agricultural area‟ defines a standard emission of 

2 kg NH 3 -N/ha from the leaf surface of plants. As a similar level of emission could be expected from 

any other vegetated area and is not due to agricultural practice, this sub-module was left out of 

consideration. The sub-module for recycling fertilisers – like compost and liquid or solid digestate – 

is, so far, only applied to sugarcane production in Brazil, where vinasse, a by-product of sugar 

production and considered a „liquid digestate‟, is applied to fields for nitrogen fertilisation (Tab. 2.5). 

The NH 3 emissions from applied mineral fertilisers are calculated by constant emission factors for 

each group of fertiliser. Instead of the emission factors suggested in Agrammon group (2009a) (15% 

for urea and 2% for all other mineral fertiliser) a set of emission factors was applied that distinguishes 

a greater number of different fertiliser groups (Asman 1992; Tab. 2.6). 

Tab. 2.5 

Parameters used in calculation of NH3-N emissions due to vinasse application on sugarcane fields in Brazil. 

Parameter Value Source 

Total N content of vinasse [g/l] 0.27 Jungbluth et al. 2007 

Fraction of TAN of total N in vinasse [%/100] 0.5 Flisch et al. 2009 

Emission factor of TAN from liquid digestate [%/100] 0.6 Agrammon Gruop 2009b 

7


Tab. 2.6 NH 3-emissions from mineral fertilisers (% N emitted in form of NH 3). 

Type of fertiliser Emission factor for NH 3-N 

ammonium nitrate, calcium ammonium nitrate 2 % 

ammonium sulphate 8 % 

urea 15 % 

multinutrient fertilisers (NPK-, NP-, NK-fertilisers) 4 % 

urea ammonium nitrate 5.7 % *) 

ammonia, liquid 3 % 

*) The weighted average of ammonium nitrate (2/3 of N) and urea (1/3 of N) was taken, since no emission factor is 

given by Asman (1992). 

2.2 Nitrate Leaching to Ground Water 

Nitrate (NO 3 

- ) is either supplied to the soil by fertilisers or produced by micro-organisms in the soil 

via the mineralisation of organic matter. Nitrate in the soil can be absorbed as a nutrient by the plants. 

In periods of heavy rainfall, however, precipitation exceeds soil evaporation and transpiration of the 

plants, which leads initially to saturation of the soil with water, and afterwards to percolation to the 

ground water. As nitrate is easily dissolved in water, the risk of leaching is high. This situation is quite 

frequent in Switzerland. 

The risk of nitrate leaching is highest in autumn and winter, when precipitation often or always 

exceeds uptake by the plants. Moreover, nitrogen mineralisation is generally highest in late summer, 

when the nitrogen often cannot be taken up by the plants (Stauffer et al. 2001). 

Experiments have shown that it is not the choice of crops but rather the succession of crops in a crop 

rotation that is determining the amount of nitrate leached (Stauffer et al. 2001). Since the modules in 

the ecoinvent database are life cycle inventories of products taking into account one single crop only, 

the succession of crops can only partly be taken into account. This fact should be borne in mind when 

interpreting the nitrate leaching values. 

Nitrate losses are undesirable for several reasons: 

 

 

 

From the agricultural point of view, valuable nutrients are lost from the soil, increasing the need 

for fertilisers. 

Nitrate in ground water used as drinking water may have a toxic impact to humans. Although the 

acute toxicity of nitrate is low, nitrate is easily converted into nitrite, which has a higher acute 

toxicity and is supposed to be indirectly carcinogenic (Surbeck & Leu 1998). 

Once ground water becomes surface water, nitrate contributes to eutrophication and also induces 

emissions of nitrous oxide, a major greenhouse gas (Schmid et al. 2000). 

The tolerance level for nitrate in drinking water is 40 mg/l in Switzerland and 50 mg/l in the EU, while 

the Swiss quality goal is 25 mg/l maximum. Results from the Swiss monitoring network NAQUA 

(Greber et al. 2002) show that these levels are exceeded only in areas with arable crops, or in fruitand 

wine-growing areas. In areas with forests or permanent grassland, these levels have never been 

exceeded. This shows the importance of arable crops and soil cultivation in nitrate leaching. 

Nitrate emissions to ground water can be estimated by simulation models, although this method is very 

complex and time-consuming and does not always lead to very satisfactory results (Oberholzer et al. 

2001). A comparison of different methods for estimating nitrate leaching is given in Audsley et al. 

(1997). 

Depending on the country of crop production different models were used to calculate nitrate leaching. 

A model by Richner et al. (in prep.) specifically for the application to conditions in Switzerland 

(SALCA-NO3) was applied to Switzerland and other European countries, where similar conditions are 

found. For non-European countries the SQCB-NO3 model was used, a geographically unspecific and 

8


simpler model (de Willigen 2000, in: Faist Emmenegger et al. 2009). Both models are described 

below. 

2.2.1 The SALCA-NO3 model 

Geographic scope of application: Europe 

The model SALCA-NO3 calculates the expected nitrate leaching and comprises the following 

elements (Richner et al. in prep.): 

 

 

 

 

 

Nitrogen mineralisation from the soil organic matter per month 

Nitrogen uptake by vegetation (if any) per month 

Nitrogen input from the spreading of fertiliser 

Soil depth 

Factors not considered: 

o 

o 

Amount of seepage 

Denitrification 

The following description is an extract of the full description of the model SALCA-nitrate by Richner 

et al. (in prep.). The reader is referred to this report for further information. 

The model of Richner et al. (in prep.) calculates the expected nitrate leaching of arable crops, 

meadows and pasture land considering not only crop rotation, soil cultivation, nitrate fertilising but 

also nitrate mineralisation from the soil organic matter, nitrate uptake by the plants and various soil 

conditions. The model is valid for the Swiss lowland and adjoining regions. The calculation bases on 

the monthly difference between the amount of mineralized nitrate in the soil and the nitrate uptake of 

the plants. Furthermore, the nitrate leaching risk from fertiliser application during inappropriate time 

periods is taken into account. The expected nitrate leaching of pastures rises because of locally high 

nitrate concentrations. Therefore the total amount of nitrate on pastures is calculated from the number 

of animals, the grazing duration and the grazing period. 

The total expected nitrate leaching of an arable crop is assessed by the sum of the monthly values 

within the assessment period starting one month after the harvest of the former crop and ending in the 

month of harvesting of the given crop. 

The model distinguishes three different regions – valley, hill and mountain – each of which defines a 

specific climate regime. Virtually all Swiss plant production inventories in ecoinvent refer to 

production in the valley region, hence the model parameters shown below refer to that selfsame 

region. 

Tab. 2.7 shows the expected nitrogen mineralisation for default settings of clay and humus content in 

the valley region. The monthly mineralisation may be increased by certain intensive soil cultivation 

operations aerating the soil and thereby promoting microbial activity. 

Tab. 2.7 

Expected nitrogen mineralisation (N min m, kg N per ha and month, from Richer et al. in prep.) in soils with 

15% clay, 2% humus and N input from farm manure of 1 LU/ha in the valley region. Intensive soil cultivation 

means treatment by a rotary cultivator or a rotary harrow in the respective month. In months where there is 

no intensive soil cultivation, the values “Without intensive soil cultivation” are used. 

Jan. Feb. March Apr. May June July Aug. Sept. Oct. Nov. Dec. 

Without intensive soil cultivation 0 0 6 9 12 15 17 21 23 12 6 0 

With intensive soil cultivation 0 0 10 15 20 25 29 38 38 20 10 0 

9

Clay 

content(%) 


Nitrogen mineralisation was further corrected for clay and humus content of the soil (Tab. 2.8) as well 

as for green manuring and tillage of pastures (see Richner et al. in prep.). 

Tab. 2.8 

Correction factors of nitrate mineralization (%) against the clay and humus content of the soil. 

Humus content (%) 

40 - 30 % - 30 % - 25% - 15 % 

Nitrogen uptake by vegetation was estimated based on the model STICS (Brisson et al. 2003) with a 

high temporal resolution (100 time steps from sowing to physiological maturity of the crop in 

question). These nitrogen uptake functions were determined for the crops grass, protein peas, barley, 

potatoes, maize, rapeseed, soy beans, sunflower, wheat and sugar beets assuming for each crop a 

standard yield and a corresponding standard nitrogen uptake as given in Flisch et al. (2009). Nitrogen 

uptake of other crops was approximated by these functions or by combinations of them (for details 

refer to the appendix of Richner et al. in prep.). Variations in nitrogen uptake due to yields deviating 

from the standard yield were accounted for by scaling the nitrogen uptake relative to the difference 

between standard and real yields. 

Nitrogen input from farm manure is considered in terms of livestock units per hectare (LU/ha). Based 

on the average number of livestock units of farms in the Swiss lowlands (BLW 2003), this parameter 

(St) was set to 1.3 LU/ha for all calculations (average of the years 2000 to 2002), except for the 

extensive meadow, where St=0, since no fertiliser is applied (see chapter 1), and for other meadow 

types, where St varies in the range of 1.3-1.5. The basic values of nitrogen mineralisation which refer 

to 1 LU/ha linearly decrease or increase with St by 10% per 1 LU/ha. For the European datasets, a 

farm without livestock was assumed (St=0). 

The risk of nitrogen leaching due to fertiliser application is dependent on the crop and the month in 

which fertiliser was applied (Tab. 2.9.; Richner et al. in prep.). 

Tab. 2.9 Risk of nitrogen leaching (fraction of potentially leachable nitrogen of the N applied through fertilisers in %, 

from Richner et al. in prep.). 

Months Winter cereals Maize, 

soya 

beans 

sowing 

year 

harvestyear 

harvestyear 

Winter rape seed 

and green manure 

sowing 

year 

harvestyear 

Potato, 

sugar and 

fodder 

beets 

harvestyear 

Faba 

beans, 

protein 

peas 

(spring 

sown) 

harvestyear 

harvestyear 

Sunflowers 

Permanent 

meadow 

Int 

Permanent 

meadow 

Ext 

January 100 50 100 100 20 100 100 100 20 20 

February 100 30 100 100 10 100 100 100 10 20 

March 100 10 100 100 0 50 50 50 0 0 

April 100 0 80 100 0 30 30 30 0 0 

May 100 0 70 100 0 10 0 0 0 0 

June 100 0 0 100 0 0 0 0 0 0 

July 100 - 0 100 - 0 0 0 0 0 

August 100 - 0 80 - 0 - 0 0 0 

10


Months Winter cereals Maize, 

soya 

beans 

Winter rape seed 

and green manure 

Potato, 

sugar and 

fodder 

beets 

Faba 

beans, 

protein 

peas 

(spring 

sown) 

Sunflowers 

Permanent 

meadow 

Int 

September 90 - 0 0 - 0 - - 0 0 

October 90 - - 0 - - - - 0 0 

Permanent 

meadow 

Ext 

November 90 - - 20 - - - - 10 20 

December 90 - - 20 - - - - 20 20 

The correction of the expected nitrate leaching due to fertiliser application against the depth of the soil 

is listed in Tab. 2.10. 

Tab. 2.10 

The correction of the expected nitrate leaching due to fertiliser application against the depth of the soil 

(Richner et al. in prep.) 

Soil depth (cm) Correction (%) 

> 100 0 

91-100 +5 

81-90 +10 

71-80 +15 

61-70 +20 

51-60 +25 

41-50 +30 

≤ 40 +35 

There is no leaching water during the intensive vegetation period because the evapotranspiration is 

similar or higher than the precipitation. Therefore usually no nitrate leaching occurs during this period. 

For various crops fertilising is only possible shortly before the growing period due to agronomic or 

technical reasons. The model accumulates the monthly values of nitrate mineralisation, nitrate uptake 

by the plants and the nitrate from fertilising during this period (Tab. 2.11). 

Tab. 2.11 

Accumulation of the monthly values of nitrate mineralisation, nitrate uptake by the plants and the nitrate 

from fertilising for various crops (Richner et al. in prep.). 

Month 

Crop J F M A M J J A S O N D 

winter cereal 

spring cereal 

maize, soybean 

potato 

sugar beet, fodder beet 

sunflower 

fava bean, protein pea (spring sown) 

fava bean, protein pea (autumn sown) 

permanent meadow 

As nitrate leaching is strongly dependent on the availability of water percolating the top soil which, in 

turn, is dependent on precipitation, a correction factor is introduced, in addition to the model SALCA- 

NO3, for regions other than the Swiss lowlands to which the model is adapted. This „nitrate leaching 

11


transformation factor‟ represents the ratio of winter precipitations (Octobre to March) of the region in 

question and of Reckenholz (Switzerland, site of model calibration) as most leaching occurs in this 

period. The results of SALCA-NO3 are multiplied by the respective transformation factor. For Swiss 

production systems this factor is constantly set to 1. For all other regions, the considered winter 

precipitations and transformation factors are presented in Tab. 2.12. 

Tab. 2.12 

Winter precipitations and nitrate leaching transformation factors for the different regions 

Winter precipitations 

(October-March) in mm 

Nitrate leaching 

transformation factor 

Swiss lowlands (site Reckenholz) 433 1 

Barrois 381 0.88 

Castilla-y-Leon 266 0.61 

Saxony-Anhalt 183 0.42 

Europe *) 219 0.51 

*) In this case, rye is the only affected crop which is mainly produced in Eastern Europe. Winter precipitation was 

calculated as the average of all available weather stations in the Polish middle and low lands 

(www.klimadiagramme.de). 

2.2.2 The SQCB-NO3 model 

The SQCB-NO3 model is reported in Faist Emmenegger et al. (2009) and is an adaption of a formula 

developed by de Willigen (2000). The formula calculates the leaching of NO3-N and is a simple 

regression model of the form: 

P 

N 21.37 

0.0037* 

S 0.0000601* Norg 0.00362* 

U 

c* 

L 

where: 

N = leached NO3-N [kg N/(ha*year)] 

P = precipitation + irrigation [mm/year] 

c = clay content [%] 

L = rooting depth [m] 

S = nitrogen supply through fertilisers [kg N/ha] 

N org = nitrogen in organic matter [kg N/ha] 

U = nitrogen uptake by crop [kg N/ha] 

It must be mentioned that in Faist Emmenegger et al. (2009) the formula has been taken from Roy et 

al. (2003), where it is not reported correctly (p. 51, formula “OUT3”), stating C org instead of N org 

(details for calculation of N org from C org see below). 

The SQCB model provides relatively simple approaches to assess most of the required input 

parameters. P and C org are determined through the ecozone in which the crop is produced. The 

ecozones for the whole globe are defined and presented as maps in FAO (2001). Fix values for carbon 

content in the upper 30 cm of soil and for annual precipitation are assigned to each ecozone (see Tab. 

2.13). The carbon content in tonnes per 3000 m 3 (1 ha [area] * 30 cm [depth]) is converted into mass 

fraction by the formula: 

C org [%] = C org [t/3000 m 3 ] * (1 / 1.3 t m -3 ) * 100. 

In case of irrigation, the amount of irrigation water [mm] is added to the precipitation in order to 

obtain the parameter P. Precipitation values for montane ecozones were calculated as an average – 

 

12


weighted if sufficiently detailed data were available – of the annual precipitations of all representative 

weather stations available at www.klimadiagramme.de. 

Where several ecozones were covered by the crop producing region of the respective transforming 

activity, the model was applied to each ecozone and an average nitrate leaching rate for the whole 

producing region was calculated from the ecozone-wide results, weighted by the contribution of each 

ecozone to crop production – in terms of harvested acreage or production volume according to data 

availability. The same applied for several USDA soil orders within one producing region or ecozone 

(see below). 

The information for weighting the contribution of sub-units (by ecozone or USDA soil order) were 

gained from comparisons of the ecozone or soil map, respectively, with a map of the spatial 

distribution of crop production in the respective country or with equivalent spatial information. This 

information was taken from USDA (2009) for maize, wheat, potatoes, soybeans, rice, rape seed and 

cotton in the USA, from USDA (2004a) for soybeans and Goldemberg (2008) for sugarcane in Brazil, 

and from Hsu & Gale (2001) for cotton and Zhao (2008) for sweet sorghum in China. 

Tab. 2.13 

FAO ecozones and their assigned carbon content and annual precipitation. Due to high variability in 

precipitation, no values are given for montane ecozones. For these ecozones precepetation values have to 

be researched in each individual case. (From Faist Emmenegger et al. 2009) 

FAO ecozones 

Carbon content 

[t/ha in upper 30cm 

= t/3000 m 3 ] 

Annual precipitation 

[mm] 

Tropical wet 59 2500 

Tropical moist 48 1500 

Tropical dry 34 1000 



Tropical montane 55 - 

Warm temperate moist 55 1200 

Warm temperate dry 25 700 

Warm temperate dry 25 400 

Warm pemperate dry 25 200 

Warm temperate moist or dry 40 - 

Cool temperate moist 81 1500 

Cool temperate moist 81 600 

Cool temperate dry 38 300 

Cool temperate dry 38 150 

Cool temperate moist or dry 59 - 

Boreal moist 22 500 

Boreal dry 22 400 

Boreal moist and dry 22 - 

They clay content c is defined by the USDA soil order of a producing region or its sub-unit, 

respectively. A constant value for clay content is assigned to each USDA soil order based on USDA 

(1999) (see Tab. 2.14). The maps for defining sub-units of production regions or ecozones by soil 

orders were taken from USDA (1999), as well, and more detailed maps especially for the USA from 

the USDA website (http://soils.usda.gov/technical/classification/orders/). 

Tab. 2.14 USDA soil orders and their assigned clay contents. (From Faist Emmenegger et al. 2009) 

USDA soil order clay content [%] 

Alfisol 28.0 

Andisol 10.4 

13


USDA soil order clay content [%] 

Aridisol 17.2 

Entisol 3.5 

Gelisol 23.7 

Histosol 2.0 

Inceptisol 4.9 

Mollisol 21.1 

Oxisol 53.9 

Spodosol 1.8 

Ultisol 12.3 

Vertisol 49.0 

The rooting depth for several crops is given in the SQCB report by Faist Emmenegger (2009). The 

missing values were taken from other literature. Values and sources are presented in Tab. 2.15. Where 

information was available, values from the SQCB report were replaced by values from FAO (2011). 

Tab. 2.15 

Crops and their rooting depth as assumed for calculations. 

Crop Rooting depth [m] Source 

Potatoes 0.5 FAO 2011 

Sugar cane 1.6 FAO 2011 

Sweet sorghum 1.5 FAO 2011 

Rape seed 0.9 SQCB report 

Soybeans 0.95 FAO 2011 

Oil palm 1.0 SQCB report 

Wheat 1.2 FAO 2011 

Maize 1.35 FAO 2011 

Rice 0.6 Mishra et al. 1997 

Cotton 1.35 FAO 2011 

The nitrogen supply S was taken from the unit process itself and, if necessary, converted to nitrogen 

supply per hectare by multiplication with the the yield given in the respective unit process‟ general 

comment field. 

The nitrogen uptake U is given for several crops in Faist Emmenegger et al. (2009). For the remaining 

crops nitrogen uptake was taken from literature. Tab. 2.16 presents the nitrogen uptake per hectare; 

original values are given in kilogrammes per tonne of product in the SQCB report and are converted to 

tonnes per hectare here. For the other crops literature values were treated in the same way and values 

were adjusted by the ratio of yields in literature and in the unit process of the transforming activity, if 

necessary. In the case of soybeans, only 40% of the values given in the SQCB report are considered as 

nitrogen uptake in order to reflect the fact, that the remaining 60% are fixed from the air and are not 

directly relevant to the balance of nitrogen supplied through fertilisers and mineralised from the soil 

organic matter (Schmid et al. 2000). 

Tab. 2.16 

Crops and their nitrogen uptake as assumed for calculations. Note that one crop can have several different 

values according to different yields and/or different literature sources for the respective countries. 

Crop 

Producing Nitrogen uptake Source 

country [kg N/ha] 

Potatoes USA 154 SQCB report 

Sugar cane Brazil 152 SQCB report 

Sweet sorghum China 193 SQCB report 

Rape seed USA 53 SQCB report 

14


Crop 

Producing Nitrogen uptake Source 

country [kg N/ha] 

Soybeans USA 81 SQCB report 

Soybeans Brazil 78 SQCB report 

Oil palm Malaysia 150 SQCB report 

Wheat USA 51 Flisch et al. 2009 

Maize USA 196 Flisch et al. 2009 

Rice USA 119 Swain et al. 2006 

Cotton USA 89 Boquet & Breitenbeck 2000 

Cotton China 135 Dong et al. 2010 

To calculate the organic nitrogen N org in soil [kg N/ha] from the soil organic carbon content C org [%] 

the following quantities are needed: 

 

soil volume V [m 3 /ha] 

V is taken to be 5000 m 3 , which means that the upper 50 cm of soil are considered 

(according to pers. comm. J. Leifeld, ART, 2011), assuming the same carbon content for 

30-50 cm depth as calculated above for 0-30 cm depth. 

bulk density D b [kg/m 3 ] 

 

 

Bulk density is taken to be 1300 kg/m 3 , which is the standard value from the SQCB report. 

C/N ratio r C/N [dimensionless] 

The C/N ratio is taken to be 11. This is the mean value of the range (10-12) determined 

through literature research (Batjes 2008; Scheffer 2002; Eggleston et al. 2006) and 

consultation of experts (pers. comm. J. Leifedl, ART). 

ratio of N org to N tot (total soil nitrogen) r Norg [dimensionless] 

The C/N ratio expresses the ratio of C org and N tot . The ratio r Norg is needed calculate N org 

from N tot , which is calculated in a first step applying the C/N ratio. r Norg is assumed to be 

0.85 (Scheffer 2002). 

N org is calculated by the formula: 

N org is the mass of organic nitrogen contained in the upper 50 cm of soil. Naturally only a fraction of 

this mass is mineralised and, hence, available for uptake by plants and leaching to the ground water. 

This fraction is determined by the mineralisation rate, which is 1.6% here and implicitly included in 

the regression coefficient of the term N org . 

2.3 Emissions of Phosphorus to the Water 

Phosphorus (P) is an important plant nutrient and must be supplied to the plants in sufficient 

quantities. A part of the phosphorus is lost to water due to leaching, run-off and soil erosion through 

water. As phosphorus is a limiting nutritional element in natural water bodies, P emissions can cause 

eutrophication (Prasuhn & Grünig 2001). Soil erosion by wind is not considered here. 

We distinguish between three different kinds of phosphorus emissions to water: 

 

leaching of soluble phosphate (PO 4 ) to ground water (inventoried as “phosphate, to ground 

water”), 

15


 

 

run-off of soluble phosphate to surface water (inventoried as “phosphate, to surface water”), 

erosion of soil particles containing phosphorus (inventoried as “phosphorus, to surface water”). 

The emission models SALCA-P (Prasuhn 2006) developed by ART are applied in ecoinvent data. A 

comparison of different emission coefficients is given by Audsley et al. (1997), Schmid & Prasuhn 

(2000) and Prasuhn & Grünig (2001). 

The following factors are considered for the calculation of P emissions in ecoinvent data inventories: 

 

 

 

 

type of land use 

type of fertiliser 

quantity of P in fertilisers 

type and duration of soil cover for the calculation of the soil erosion (C-factor). 

For other factors, considered in the model SALCA-P, default values are used: 

 

 

 

 

distance to next river or lake 

topography 

chemical and physical soil properties 

drainage. As the field was assumed to have no drainage (see chapter 1), the emissions to surface 

water through drainage were not taken into account. 

The model takes soil erosion, surface run off and drainage losses to surface water and leaching to 

ground water into account. 

It should be borne in mind that the values are valid for the soil and site parameters chosen. Changes in 

soil conditions or in cropping practice could lead to emissions substantially different from the ones 

calculated in ecoinvent data. 

The key factors of the model are listed below. Please see Prasuhn (2006) for detailed calculations. 

2.3.1 Phosphate Leaching to Ground Water 

P leaching to the ground water was estimated as an average leaching, corrected by P-fertilisation: 

P gw = P gwl * F gw 

P gw = quantity of P leached to ground water (kg/(ha*a)) 

P gwl = average quantity of P leached to ground water for a land use category (kg/(ha*a)), which 

is 0.07 kg P/(ha*a) for arable land and 

0.06 kg P/(ha*a) for permanent pastures and meadows. 

F gw = correction factor for fertilisation by slurry (-) 

F gw = 1 + 0.2/80*P 2 O 5sl 

P 2 O 5sl = quantity of P 2 O 5 contained in the slurry or liquid sewage sludge (kg/ha). The values of 

P 2 O 5 -content were taken from Walther et al. (2001). 

16


2.3.2 Phosphate Run-Off to Surface Water 

Run-off to surface water was calculated in a similar way to leaching to ground water: 

P ro = P rol * F ro 

P ro = quantity of P lost through run-off to rivers (kg/(ha*a)) 

P rol = average quantity of P lost through run-off for a land use category (kg/(ha*a)), which is 

0.175 kg P/(ha*a) for open arable land, 

0.25 kg P/(ha*a) for intensive permanent pastures and meadows and 

0.15 kg P/(ha*a) for extensive permanent pastures and meadows 

F ro = correction factor for fertilisation with P (-), calculated as: 

F ro = 1 + 0.2/80 * P 2 O 5min + 0.7/80 * P 2 O 5sl + 0.4/80 * P 2 O 5man 

P 2 O 5min = quantity of P 2 O 5 contained in mineral fertilisers (kg/ha) 

P 2 O 5sl = quantity of P 2 O 5 contained in slurry or liquid sewage sludge (kg/ha) 

P 2 O 5man = quantity of P 2 O 5 contained in solid manure (kg/ha) 

The values of P 2 O 5 -content for slurry and manure were taken from Walther et al. (2001). 

2.3.3 Phorsphorous Emissions Through Water Erosion to Surface Water 

P emissions through erosion of particulate phosphorous to surface water were calculated as follows: 

P er = S er * P cs * F r * F erw 

P er = quantity of P emitted through erosion to rivers (kg P/(ha*a)) 

S er = quantity of soil eroded (kg/(ha*a)) 

P cs = P content in the top soil (kg P/kg soil). The average value of 0.00095 kg/kg was used. 

F r = enrichment factor for P (-). The average value of 1.86 was used (Wilke & Schaub 1996). 

This factor takes account of the fact that the eroded soil particles contain more P than the 

average soil. 

F erw = fraction of the eroded soil that reaches the river (-). The average value of 0.2 was used. 

The amount of eroded soil S er is calculated according to Oberholzer et al. (2006, Appendix A4.1). 

2.4 Emissions of N 2 O to the Air 

Nitrous oxide or dinitrogen monoxide (N 2 O) is produced as an intermediate product in the 

denitrification process (conversion of NO 3 

- into N 2 ) by soil micro-organisms. It can also be produced 

as a by-product in the nitrification process (conversion of NH 4 + into NO 3 

- , Schmid et al. 2000). The 

total emissions of N 2 O caused by the Swiss agricultural sector in 1996 were estimated at 8,600 tonnes. 

N losses in the form of N 2 O are closely linked to the nitrogen cycle in agriculture; intensive agriculture 

with a high input of nitrogen fertiliser contributes to the increase in N 2 O-emissions. N 2 O is a 

greenhouse gas with a high impact. 

Calculations of N 2 O emissions are based on the IPCC method for calculating N 2 O emissions 

(Eggleston et al. 2006). Direct emissions of N 2 O and indirect or induced emissions are included. In the 

case of indirect N 2 O emission, nitrogen is first emitted as NH 3 or NO 3 

- and subsequently converted to 

N 2 O. 

Direct N 2 O emissions [kg N 2 O] from mineral and organic fertilisers and from crop residues were 

calculated on the basis of the total nitrogen content (N tot [kg N]). The factor of 1.0% N lost as N 2 O was 

used. Induced N 2 O emissions [kg N 2 O] from ammonia (NH 3 ) were considered using a factor of 1.0% 

17


of N in emitted NH 3 , induced emissions from nitrate (NO 3 

- ) with a factor of 0.75% of N in leached 

NO 3 

- . According to the new IPCC-guidelines, no emissions are calculated from biological nitrogen 

fixation. 

The content of total nitrogen in farmyard manure was taken from Walther et al. (2001). 

The contents of total nitrogen in crop residues are taken from Walther et al. (2001) and the amounts of 

crop residues from Nemecek et al. (2007) as such, or scaled by the yields of the reference products 

(and by-products) based on the latter. For exceptions see Tab. 2.17. 

N 2 O = 44/28 * (0.01 (N tot + N cr ) + 0.01 * 14/17 * NH 3 + 0.0075 * 14/62 * NO 3 

- ) 

N 2 O = emission of N 2 O (kg N 2 O/ha) 

N tot 

N cr 

NH 3 

NO 3 

- 

= total nitrogen in mineral and organic fertilisers (kg N/ha) 

= nitrogen contained in the crop residues (kg N/ha) 

= losses of nitrogen in the form of ammonia (kg NH 3 /ha) 

= losses of nitrogen in the form of nitrate (kg NO 3 

- /ha). 

Tab. 2.17 Literature sources of amounts and nitrogen contents of crop residues (other than Walther et al. 2001) 

Crop Amount of crop residue Nitrogen content of crop residue 

Rice Nemecek et al. (2007) Swain et al. (2006) 

Cotton Boquet & Breitenbeck (2000) Boquet & Breitenbeck (2000) 

Sweet sorghum Khaledian et al. (2010) Keskin et al. (2009) 

Oil palm Khalid et al. (1999) 

(577 kg N/ha in standing biomass considered as N in crop residues, 

allocated over 25 years of plantation lifetime) 

Sugar cane 

No crop residues due to burning of fields before harvest. 

2.5 Emissions of NO x to the Air 

During denitrification processes in soils, nitrous oxide (NO x ) may also be produced. These emissions 

were estimated from the emissions of N 2 O 3 : 

NO x 

= 0.21 * N 2 O 

Since this process is not one of conversion from N 2 O to NO x , but a parallel process, no correction of 

the N 2 O emissions is required. 

This equation includes the direct NO x emissions from fertilisers and the soil only. Other sources such 

as tractor exhaust gases are included in the respective inventories. 

3 personal communication from Grub, 1996 

18


2.6 Nutrient Inputs in Agricultural Soils 

The input of nutrients (N, P, K, Ca, etc.) into the agricultural soil was not inventoried as emissions to 

the soil for the following reason: The inventories of agricultural products in ecoinvent data are based 

on the fertilising recommendations (Walther et al. 2001). These recommendations in turn are based on 

the assumption that the fertiliser should cover the needs of the plants. In a first step, the export of 

nutrients through the products (main- and co-products) was calculated. In a second step, the 

recommended fertiliser dose was calculated by accounting for various other aspects. The nutrients 

supplied to the soil will therefore either be exported in the products or lost to the air or water. The 

quantity of nutrients in the soil should not be changed on average in the long term. 

2.7 Release of Fossil CO 2 after Urea Applications 

During the urea production process, CO 2 is used, which is chemically bound in the urea molecule. 

After application and transformation processes in the soil, this CO 2 is released to the atmosphere. Per 

kg of applied urea-N, 1570 g of fossil CO 2 are released that are inventoried as “Carbon dioxide, fossil” 

to “air, low population density” in ecoinvent data. 

2.8 Emissions of Heavy Metals to Agricultural Soil, Surface Water 

and Ground Water 

According to an analysis of the heavy metals that are causing problems in Swiss agriculture (Kühnholz 

2001), the following seven were selected for the inventories in ecoinvent data: 

Cadmium (Cd), Chromium (Cr), Copper (Cu), Lead (Pb), Mercury (Hg), Nickel (Ni) and Zinc (Zn). 

Typical heavy-metal content of agricultural and non-agricultural soils is given by Desaules & 

Dahinden (2000). 

Kühnholz (2001) gives a comparison of different emission factors and methods for calculating heavy 

metal balances. 

The heavy metal emissions were calculated by SALCA-heavy metal (Freiermuth 2006). Inputs into 

farm land and outputs to surface water and groundwater are calculated on the basis of heavy metal 

input from seed, fertilisers, plant protection products and deposition. Residues left on the field are not 

considered, because they do not leave the system. For erosion of soil average heavy metal contents for 

arable land, pastures, meadows and intensive crops are used. The amount of eroded soil is calculated 

as for P-emissions with the method described in Oberholzer et al. (2006). An allocation factor is used 

to distinguish between diffuse and agriculture-related introduction (Freiermuth 2006). We give only a 

summary description of the method here. For a full description, the reader is referred to Freiermuth 

(2006). 

Three types of emissions are considered in ecoinvent data: 

 

 

 

Leaching of heavy metals to the ground water (always positive values) 

Emissions of heavy metals into surface waters through erosion of soil particles (always 

positive values) 

Emissions of heavy metals to agricultural soil (positive or negative values according to the 

results of the balance). 

The following sources were used to calculate heavy-metal contents: 

 

Mineral fertilisers: Desaules & Studer (1993, p. 153), see Tab. A. 2 in the Appendix, 

Farmyard manure: Menzi & Kessler (1998) and Desaules & Studer (1993, p. 152), see Tab. A. 3 

in the Appendix, 

19


pesticides: FAW & BLW (2000), 

 

biomass (seed and products from plant production): Houba & Uittenbogaard (1994, 1995, 1996 & 

1997), von Steiger & Baccini (1990) and Wolfensberger & Dinkel (1997); Bennett et al. (2000) & 

for Nickel Teherani (1987) for rice; generic mean of biomass for cotton due to lack of data with 

mass allocation to fibre and seed (Freiermuth 2006); see Tab. A. 1 in the Appendix. For grass 

seed, the values of wheat grains were used; for clover seed, the values of protein peas. 

Heavy metal emissions into ground and surface water (in case of drainage) are calculated with 

constant leaching rates as: 

M leach i = m leach i * A i 

M leach i 

agricultural related heavy metal i emission 

m leach i average amount of heavy metal emission (Tab. 2.18) 

A i 

allocation factor for the share of agricultural inputs in the total inputs for heavy metal i 

Tab. 2.18 Heavy metal leaching to groundwater according to Wolfensberger & Dinkel (1997). 

Cd Cu Zn Pb Ni Cr Hg 

mg/ha/year 50 3600 33000 600 n.a. 21200 1.3 

Heavy metal emissions through erosion are calculated as follows: 

M erosion i = c tot i * B * a *f erosion * A i 

M erosion agricultural related heavy metal emissions through erosion [kg ha -1 a -1 ] 

c tot i 

total heavy metal content in the soil (Keller & Desaules 2001, see Tab. 2.19 [kg/kg]) 

B amount of soil erosion according to Oberholzer et al. (2006) [kg ha -1 a -1 ] 

a accumulation factor 1.86 (according to Prasuhn 2006 for P) [-] 

f erosion erosion factor considering the distance to river or lakes with an average value of 0.2 

(considers only the fraction of the soil that reaches the water body, the rest is 

deposited in the field) [-] 

A i 


[-] 

Tab. 2.19 Heavy metal contents in mg per kg soil (from Keller & Desaules 2001). 

Land use 

Cd 

[mg/kg] 

Cu 

[mg/kg] 

Zn 

[mg/kg] 

Pb 

[mg/kg] 

Ni 

[mg/kg] 

Cr 

[mg/kg] 

Hg 

[mg/kg] 

Permanent grassland 0.309 18.3 64.6 24.6 22.3 24.0 0.088 

Arable land 0.24 20.1 49.6 19.5 23.0 24.1 0.073 

Intensive crops 0.307 39.2 70.1 24.9 24.8 27.0 0.077 

The balance of all inputs into the soil (fertilisers, pesticides, seed and deposition) and outputs from the 

soil (exported biomass, leaching and erosion), multiplied by the allocation factor is calculated as an 

emission to agricultural soil. 

M soil i = (Σ inputs i - Σ outputs i ) * A i 

Some of the values for emissions of heavy metals to the soil are negative. This means that more heavy 

metals are exported than imported. It must, however, be borne in mind that these heavy metals are 

transferred either to the water bodies or to the products harvested from the field (food, feed and straw). 

20


A certain fraction of the heavy metal input into the soil stems from atmospheric deposition. The 

deposition would occur even without any agricultural production and is therefore not charged to the 

latter. An allocation factor accounts for this. The farmer is therefore responsible for a part of the inputs 

only (the rest stems mainly from other economic sectors), therefore only a part of the emissions is 

calculated in the inventory. 

A i = M agro i / (M agro i + M deposition i ) 

A i 


M agro i total input of heavy metal from agricultural production in mg/(ha*year) (fertilisers + 

seeds + pesticides) 

M deposition i total input of heavy metal from atmospheric deposition in mg/(ha*year) (Tab. 2.20) 

In cases, where M agro i = 0, i.e. no agricultural inputs to the soil occur, A i also becomes 0. 

Tab. 2.20 Heavy metal deposition (see Freiermuth 2006). 


Deposition 

[mg/ha/year] 700 2400 90400 18700 5475 3650 50 

2.9 Emissions of Pesticides to Agricultural Soil 

All pesticides applied for crop production were assumed to end up as emissions to the soil. The 

amounts of pesticides used as inputs were thus simultaneously calculated as outputs (emissions to 

agricultural soil). For many active ingredients there are no own LCIs; in these cases, the amounts of 

active ingredients are aggregated and inventoried by their chemical class, for which an LCI exists, as 

assigned in Sutter (2010, Tab. 2.25). As emissions, though, the active ingredients appear under their 

specific name. Only for the inputs “pesticides, unspecified”, “fungicides, unspecified” and 

“insecticides, unspecified”, no corresponding emission flow could be assigned. Field emissions 

resulting from these admittedly small quantities of substances were thus not considered. 

21

Direct field emissions and elementary flows in LCIs of agricultural production systems - Natural Ressources 

3 Natural Ressources 

3.1 CO 2 from the Atmosphere 

The CO 2 bound from atmosphere is considered a natural resource and is represented in the datasets as 

the exchange with the environment “carbon dioxide, in air”. Only harvested biomass is accounted for 

in this quantity, i.e. products and by-products. Moreover, the CO 2 content of the planted seeds was 

subtracted from the CO 2 content of the products in order to avoid double counting. Crop residues 

remaining in the field are not considered, since these usually decompose within a few years. The 

change in soil organic-matter content was not considered either, i.e. the organic C-content of the soil 

was assumed to be constant. Exceptions are cases, where a land use change occurred and the C-content 

of the soil is decreasing. 

The binding of CO 2 from the atmosphere was estimated from the C-content in dry matter multiplied by 

the stoichiometric factor 44/12, based on the assumption that the carbon in the biomass is completely 

sourced from the air. The references of C-content of products and seeds are given in the respective 

datasets („exchange properties‟). Basically, if no direct reference of a C-content could be found, it was 

estimated from the composition of the respective biomass assuming the following C-contents of the 

components (in dry mass): 44% in carbohydrates (including fibres), 75% in fats and 0% in ash 4 ; 53% 

in proteins (Rouwenhorst et al. 1991). 

The basic form of the formula for the calculation of “carbon dioxide, in air” is: 

44/12 * (RP.C_content * RP.amount * RP.DM 

+ (BP_1.C_content * BP _1.amount * BP_1.DM + … + BP_X.C_content * BP_X.amount * BP_X.DM) 

- (SD_1.C_content * SD_1.amount * SD_1.DM +… + SD_X.C_content * SD_2.amount * SD_X.DM) ) 

where RP represents the reference product, BP_X the by-products, and SD_X the seed inputs; C_content is 

the non-fossil C-content [kg C/kg dry mass], amount is the absolute amount of the flow of product or 

seed [kg DM; kg FM; unit; ...], and DM is the dry matter in one unit of the respective flow [kg/unit]. 

Attention must be paid when applying these datasets in life cycle studies (e.g. if straw is combusted to 

produce heat or food products are consumed): the CO 2 released from the agricultural products and byproducts 

must be considered as an emission of biogenic CO 2 , CO, CH 4 or other C-compounds. 

3.2 Land Use 

Land occupation was calculated from the duration of land use (taking account of the time from soil 

cultivation until harvest), and from the yield per area unit, if the reference function was kg of product 

instead of ha. Different categories of land occupation were used depending on crop and country 

specific characteristics (see Tab. 3.1; for details on irrigation refer to documentation of inputs from 

technosphere of the respective crop dataset); extensive and organic production was classiefied as 

„extensive‟ land use, intensive integrated and conventional production as „intensive‟ land use. The 

duration of land use considered for annual crops varies (either from sowing to harvest or 12 months) 

and can be obtained through the ratio land occupation/land transformation. For permanent crops a 

duration of 12 months is used per crop year. 

Land transformation was calculated on the basis of the area required to produce 1 kg of product. In the 

case of 1 ha as a reference unit, areas of preceding and current land use sum up to 10‟000 m 2 , each. 

For permanent crops the amounts of transformed land are divided by the lifetime of the crop, e.g. 28 

years for oil palm plantations. The current land use form corresponds to the categories listed for land 

4 Personal communication from Jens Leifeld, ART, 23 April 2002. 

22


occupation in Tab. 3.1. The preceding land use form depends on crop and country specific 

characteristics: 

 

 

 

For Swiss arable crops and temporary grassland (CH) the preceding land use form was 

assumed to be 71% arable land (“transformation, from annual crop, non-irrigated”) and 29% 

meadow (sown on arable land; “transformation, from pasture, man made”) for all winter 

crops. These percentages correspond to the proportions of arable crops and leys out of the total 

arable surface in Switzerland (293,000 ha arable crops (71%), 118,000 ha leys (29%), 411,000 

ha total (100%) arable surface in 2000), taken from BLW (2001, p. A4). The type of use 

before establishment of the crop Green manure is not established after meadow or pasture (as 

this would cause the meadow to assume the function of a green manure), but is always 

established between two arable crops. Land transformation to green manure was therefore 

calculated 100% as “transformation, from annual crop, non-irrigated”. The spring-sown crops 

were assumed to follow a green manure. In these cases too, land transformation was calculated 

as 100% “transformation, from arable, non-irrigated”. Specifications of extensive and 

intensive land use follow the same pattern as for land occupation (see Tab. 3.1). 

For Swiss permanent grassland (CH) it was assumed that land use is contstant and 

grasslands are not being transformed from other land uses. In fact this corresponds to the 

current situation in Switzerland. Specifications of extensive and intensive land use follow the 

same pattern as for land occupation (see Tab. 3.1). 

For arable crops in Europe (DE, FR, ES) crop rotations were assumed that did not include 

grassland and where all crops were produced in conventional, non-irrigated systems. Hence 

the preceding land use was always classified as “transformation, from annual crop, nonirrigated, 

intensive”. 

For rye in Europe (RER) it was assumed that transformed land originally consisted of 71% 

annual crops and 29 % temporary grassland (grown on arable land). The percentages are based 

on German agricultural statistics (Statistitisches Bundesamt 2003). 

 

 

 

 

For energy crops in Germany (DE) (mischanthus, willow in short rotation coppice, as well 

as mischanthus rhizome for propagation) it was assumed that land use was originally arable 

production, but not further specified. For willow stem cuttings (DE) it was assumed that land 

use was originally an annual crop (non-irrigated, intensive). 

For arable crops in USA and China (US & CN) crop rotations were assumed that did not 

include grassland. Hence, the preceding land use was always classified as “transformation, 

from annual crop”. No specifications were made with respect to irrigation, as non-irrigated 

crops may precede irrigated crops and vice versa. 

For soybean in Brazil (BR) the original land use and respective fractions of transformed land 

were calculated as shown in Tab. 3.2. The fractions of land transformed from non-arable areas 

(land use change) are summarised in the exchange „transformation, from cropland fallow 

(non-use)‟ and listed in detail as inputs from technosphere („land, recently transformed from 

forest, year 1‟, etc.). For details of accounting for land use change and related emissions see 

“Guidlines_LUC_Calcs" - pdf-document on the dedicated talk page of ecoinvent 

(www.ecoinvent.org/documentation). 

For sugarcane in Brazil (BR) the original land use was assumed to be sclerophyllous 

shrubland for 0.97% of the sugarcane area, the remainder area was assigned to annual crop, 

non-irrigated. The 0.97% correspond to the annual expansion rate of the sugarcane area in 

Brazil (Mathias 2005a & 2005b). This expansion is assumed to occur on shrubland because an 

expansion into tropical rainforest areas is unlikely due to the wet climate and weak 

infrastructure, unsuitable soils and high production costs (J. Granelli (José Granelli & Filhos 

Ltds), personal communication, 2005; D. Aronson (Petrobras), personal communication, 

2005; R.B. Ortolan (agronomist at Copercana), personal communication, 2005). The fractions 

of land transformed from non-arable areas (land use change) are summarised in the exchange 

„transformation, from cropland fallow (non-use)‟ and listed in detail as inputs from 

technosphere („land, recently transformed from forest, year 1‟, etc.). For details of accounting 

23


 

for land use change and related emissions see “Guidlines_LUC_Calcs" - pdf-document on the 

dedicated talk page of ecoinvent (www.ecoinvent.org/documentation). 

For oil palm plantations in Malaysia (MY) it was assumed that 100% of the area were being 

transformed from tropical rainforest. This assumption is based on the rapid expansion of palm 

oil plantations in the recent years in Malaysia (150,000 ha in 10 years, USDA 2004c). The 

fractions of land transformed from non-arable areas (land use change) are summarised in the 

exchange „transformation, from cropland fallow (non-use)‟ and listed in detail as inputs from 

technosphere („land, recently transformed from forest, year 1‟, etc.). For details of accounting 

for land use change and related emissions see “Guidlines_LUC_Calcs" - pdf-document on the 

dedicated talk page of ecoinvent (www.ecoinvent.org/documentation). 

Tab. 3.1 

Categories of land occupation as applied by country and crop. SIP = Swiss Integrated Production. 

Country Crop Category Comment 

CH 

CH 

CH 

CH 

Europe 

(RER, DE, 

FR, ES) 

DE 

DE 

arable crops (SIP 

extensive & organic) 

arable crops (SIP 

intensive) 

grassland production 

(SIP/organic, 

extensive) 

grassland production 

(SIP/organic, 

intensive) 

arable crops 

(conventional), incl. 

willow stem cutting 

production 

perenniel energy crops 

(willow in short rotation 

coppice, miscanthus & 

miscanthus rhizome) 

willow stem cutting 

production 

occupation, annual crop, nonirrigated, 

extensive 


intensive 

occupation, pasture, man 

made, extensive 

occupation, pasture, man 

made, intensive 


intensive 

occupation, permanent crop, 

non-irrigated, intensive 


intensive 

US cotton, potato, wheat occupation, annual crop Partly irrigated. 

US rice occupation, annual crop, 

irrigated, intensive 

US rape seed, soybean occupation, annual crop, nonirrigated, 

intensive 

All Swiss crops are assumed not to be 

irrigated. 

All Swiss crops are assumed not to be 

irrigated. 

All Swiss grasslands are assumed not 

to be irrigated. Persistence of extensive 

permanent grassland: 50 years. 

All Swiss grasslands are assumed not 

to be irrigated. Persistence of intensive 

permanent grassland: 20 years; 

temporary grassland: 3 years. Note that 

temporary grassland is always 

considered intensive, also in organic 

farming. 

All European arable crops are assumed 

not to be irrigated. 

All German arable crops are assumed 

not to be irrigated. 

An annual production system is 

assumed; non irrigated. 

100% of rice in US is assumed to be 

irrigated. 

No irrigation. 

MY palm fruit bunches occupation, forest, intensive 100% of oil palm plantations in MY are 

assumed to be irrigated. 

BR soybean, sugarcane occupation, annual crop, nonirrigated, 

intensive 

CN 

cotton 

sweet sorghum 

occupation, annual crop, 

irrigated, intensive 

Non-irrigated production is assumed for 

sugarcane and soybean for the whole 

of BR. 

100% of cotton and sweet sorghum in 

CN is assumed to be irrigated. 

24


Tab. 3.2 

Calculation of transformed land use categories for soybean cultivation in Brazil, taken from Jungbluth et al. 

2007 (calculation based on USDA 2004b, Bickel & Dros 2003) 

Land use Brazil total South Brazil North Brazil 

total area in 2004 [Million ha] 23.5 11.0 12.5 

total area in 2003 [Million ha] 

(Total are in 2004 – rise per year) 

21.8 10.6 11.2 

Rise of the total area [Million ha] 

In the last 5 years: 

6.1. Million ha in North Brazil 

1.7 0.5 1.2 

2.3 Million ha in South Brazil 

% of total cultivated area in Brazil 47 53 

Transformation from arable area [Million ha] 21.5 10.6 10.9 

Transformation to pasture [Million ha] 

(rise per year * 49% / 2 years) 

0.299 0 0.299 

New area [% of the total cultivated area] 

(rise of the total area + transformation to pasture) 

8.4 4.2 12.2 

From rainforest [% of the new cultivated area] 0 49 

From Cerrado Ecosystems [% of the new cultivated area] 100 51 

Transformation from tropical rainforest [Million ha] 0.74 0 0.74 

Transformation from shrub land [Million ha] 1.23 0.46 0.77 

Transformation from arable area [Million ha] 21.52 10.58 10.94 

Transformation from tropical rainforest 

[% of the total cultivated area] 

3.2 0 5.98 

Transformation from shrub land 


5.2 4.2 6.2 

Transformation from arable area 


91.6 95.8 87.8 

25

Direct field emissions and elementary flows in LCIs of agricultural production systems - Appendix A 

Appendix A 

Tab. A. 1 Heavy-metal contents of plant material (mg/kg dry matter, from Freiermuth 2006). 


[mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] 

Generic mean 0.10 6.6 32.0 0.54 1.04 0.55 0.04 

Grass / Hay 0.13 8.6 40 1.2 1.68 1.09 0.15 

Maize grains 0.03 2.5 21.5 0.3 1.16 0.32 0 

Maize silage 0.1 5 34.5 1.61 0.48 0.7 0.01 

Wheat grains 0.1 3.3 21.1 0.2 0.2 0.2 0.01 

Wheat straw 0.2 2.5 9.6 0.6 0.6 0.7 

Barley grains 0.03 4.3 26.6 0.2 0.1 0.1 

Barley straw 0.1 4.8 11.1 0.6 0.8 1.2 

Rye straw 0.1 3.2 13 0.4 0.7 0.5 

Potatoes 0.04 6.45 15 0.55 0.33 0.57 0.09 

Rape seed 1.6 3.3 48 5.25 2.6 0.5 0.1 

Faba beans 0.04 6 30.1 0.87 1.3 0.69 0 

Soya beans 0.06 15.1 47.7 0.08 5.32 0.52 0 

Protein peas 0.09 10 73 0.16 0.83 0.32 0.01 

Sugar beets 0.4 12 36.4 1.16 1.08 1.775 0.095 

Rice grains 0.02 5.27 43.9 0.96 0.97 0.49 

26


Tab. A. 2 Heavy-metal contents of mineral fertilisers (mg/kg nutrient) according to Desaules & Studer (1993). No data available on Hg. Source: Freiermuth (2006). 

Cd Cu Zn Pb Ni Cr 

mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg 

Mineral fertilisers (%N/%P2O5/%K2O/%Mg) nutrient nutrient nutrient nutrient nutrient nutrient 

Urea (46/0/0) kg N 0.11 13.04 95.65 2.39 4.35 4.35 

Calcium ammonium nitrate (20/0/0) kg N 0.25 60.00 155.00 5.50 90.00 10.00 

Ammonium nitrate (27.5/0/0) kg N 0.18 25.45 181.82 6.91 47.27 14.55 

Ammonium sulphate (21/0/0) kg N 0.24 19.05 142.86 5.24 8.57 9.52 

Calcium ammonium nitrate (27/0/0) kg N 0.19 8.52 100.00 5.93 12.59 2.96 

Magnesium ammonium nitrate (23/0/0/5) kg N 0.43 56.52 4.35 4.35 21.74 6.09 

Generic mean N 0.21 22.25 121.43 5.37 17.17 7.81 

Triple superphosphate (0/46/0) kg P2O5 113.04 97.83 650.00 7.61 95.65 567.39 

Superphosphate (0/19/0) kg P2O5 52.63 121.05 852.63 578.95 105.26 342.11 

Thomas meal (0/16/0) kg P2O5 1.56 250.00 425.00 75.00 125.00 12212.50 

Hyperphosphate/raw phosphate (0/26/0) kg P2O5 50.00 115.38 915.38 23.85 76.92 611.54 

Generic mean P 51.32 118.22 751.32 49.42 100.46 589.46 

Potassium chloride (KCl) (0/0/60) kg K2O 0.10 8.33 76.67 9.17 3.50 3.33 

Potassium sulphate (0/0/50) kg K2O 0.10 4.00 64.00 6.60 1.60 4.00 

Raw potassium (0/0/26/5) kg K2O 0.19 173.08 153.85 11.54 11.54 173.08 

Lime kg CaO 0.12 4.00 8.00 3.60 12.20 314.00 

Generic mean K 0.11 6.17 70.33 7.88 7.52 88.54 

27


Tab. A. 3 Heavy-metal contents of farmyard manure and organic fertilisers (g/unit, compiled by Freiermuth 2006 from from Menzi & Kessler (1998) and Desaules & Studer (1993, p. 

152)). Dry matter (DM) contents from Walther et al. (2001, Tab. 44). 


Farmyard manure 

[mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] [mg/kg DM] DM-content 

Cattle liquid manure 0.178 37.1 162.2 3.77 4.3 3.9 0.4 9.0% 

Cattle slurry 0.16 19.1 123.3 2.92 3.1 2.1 0.6 7.5% 

Cattle staple manure 0.172 23.9 117.7 3.77 4.3 3.9 0.4 19.0% 

Cattle manure form loose housing 0.151 22.0 91.1 2.81 4.3 3.9 0.4 21.0% 

Pig liquid manure 0.21 115.3 746.5 1.76 8.6 6.7 0.8 5.0% 

Pig solid manure 0.21 115.3 746.5 1.76 8.6 6.7 0.8 27.0% 

Litter from broilers 0.292 43.8 349.2 2.92 40 10 0.2 65.0% 

Litter from belts from laying hens 0.2525 39.6 468.4 2.235 7.9 5.5 0.2 30.0% 

Litter from deep pits from laying hens 0.2525 39.6 468.4 2.235 7.9 5.5 0.2 45.0% 

28

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