ArticlePDF Available

Improving the accounting of field emissions in the carbon footprint of agricultural products: a comparison of default IPCC methods with readily available medium-effort modeling approaches


Abstract and Figures

Purpose The estimations of greenhouse gas (GHG) field emissions from fertilization and soil carbon changes are challenges associated with calculating the carbon footprint (CFP) of agricultural products. At the regional level, the IPCC Guidelines for National Greenhouse Gas Inventories (2006a) Tier 1 approach, based on default emission factors, insufficiently accounts for emission variability resulting from pedo-climatic conditions or management practices. However, Tier 2 and 3 approaches are usually considered too complex to be practicable. In this paper, we discuss different readily available medium-effort methods to improve the accuracy of GHG emission estimates. Methods We present four case studies—two wheat crops in Germany and two peach orchards in Italy—to test the performance of Tier 1, 2, and 3 methodologies and compare the estimated results with available field measurements. The methodologies selected at Tier 2 and Tier 3 level are characterized by simple implementation and data collection, for which only a medium level of effort for stakeholders is required. The Tier 2 method consists of calculating direct and indirect N2O, emissions from fertilization with a multivariate empirical model which accounts for pedo-climatic and crop management conditions. The Tier 3 method entails simulation of soil carbon stock change using the Rothamsted carbon model. Results and discussion Relevant differences were found among the tested methodologies: in all case studies, the Tier 1 approach exceeded the Tier 2 estimations for fertilizer-induced emissions (up to +50 %) and the measurements. Using this higher Tier approach reduced the estimated CFP calculation of annual crops by 4 and 21 % and that of the perennial crop by 7 %. Removals related to positive soil carbon change calculated using the Tier 1 approach also exceeded the Tier 3 calculations for the studied annual crops (up to +90 %) but considerably underrated the Tier 3 estimations and measurements for perennial crops (−75 %). In this case, the impact of the selected Tier method on the final CFP results was even more relevant: an increase of 194 and 88 % for the studied annual crops and a decrease of 67 % for the perennial crop case study. Conclusions The use of higher Tiers for the estimation of land-based emissions is strongly recommended to improve the accuracy of the CFP results. The suggested medium-effort methods tested in this study represent a good compromise between complexity reduction and accuracy improvement and can be considered reliable for the assessment of GHG mitigation potentials. Keywords Carbon footprint Crop management Fertilizer field emissions GHG accounting Regional variability Soil carbon change Tier methodologies
This content is subject to copyright. Terms and conditions apply.
Improving the accounting of field emissions in the carbon
footprint of agricultural products: a comparison of default IPCC
methods with readily available medium-effort modeling
Christiane Peter
&Angela Fiore
&Ulrike Hagemann
&Claas Nendel
Cristos Xiloyannis
Received: 29 May 2015 /Accepted: 4 February 2016 /Published online: 20 February 2016
#The Author(s) 2016. This article is published with open access at
Purpose The estimations of greenhouse gas (GHG) field
emissions from fertilization and soil carbon changes are chal-
lenges associated with calculating the carbon footprint (CFP)
of agricultural products. At the regional level, the IPCC
Guidelines for National Greenhouse Gas Inventories (2006a)
Tier 1 approach, based on default emission factors, insuffi-
ciently accounts for emission variability resulting from
pedo-climatic conditions or management practices.
However, Tier 2 and 3 approaches are usually considered
too complex to be practicable. In this paper, we discuss dif-
ferent readily available medium-effort methods to improve the
accuracy of GHG emission estimates.
Methods We present four case studiestwo wheat crops in
Germany and two peach orchards in Italyto test the perfor-
mance of Tier 1, 2, and 3 methodologies and compare the
estimated results with available field measurements. The
methodologies selected at Tier 2 and Tier 3 level are charac-
terized by simple implementation and data collection, for
which only a medium level of effort for stakeholders is re-
quired. The Tier 2 method consists of calculating direct and
indirect N
O, emissions from fertilization with a multivariate
empirical model which accounts for pedo-climatic and crop
management conditions. The Tier 3 method entails simulation
of soil carbon stock change using the Rothamsted carbon
Results and discussion Relevant differences were found
among the tested methodologies: in all case studies,
the Tier 1 approach exceeded the Tier 2 estimations
for fertilizer-induced emissions (up to +50 %) and the
measurements. Using this higher Tier approach reduced
the estimated CFP calculation of annual crops by 4 and
21 % and that of the perennial crop by 7 %. Removals
related to positive soil carbon change calculated using
the Tier 1 approach also exceeded the Tier 3 calcula-
tions for the studied annual crops (up to +90 %) but
considerably underrated the Tier 3 estimations and mea-
surements for perennial crops (75 %). In this case, the
impact of the selected Tier method on the final CFP
results was even more relevant: an increase of 194
and 88 % for the studied annual crops and a decrease
of 67 % for the perennial crop case study.
Conclusions The use of higher Tiers for the estimation of
land-based emissions is strongly recommended to improve
the accuracy of the CFP results. The suggested medium-
effort methods tested in this study represent a good compro-
mise between complexity reduction and accuracy improve-
ment and can be considered reliable for the assessment of
GHG mitigation potentials.
Keywords Carbon footprint .Crop management .Fertilizer
field emissions .GHG accounting .Regional variability .Soil
carbon change .Tier methodologies
Responsible editor: Ivan Muñoz
*Christiane Peter
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V.,
Institute of Landscape Systems Analysis, Eberswalder Straße 84,
15374 Müncheberg, Germany
Department of European and Mediterranean Cultures: Architecture,
Environment and Cultural Heritage, Università degli Studi della
Basilicata, Via San Rocco 3, 75100 Matera, Italy
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V.,
Institute of Landscape Biogeochemistry, Eberswalder Straße 84,
15374 Müncheberg, Germany
Int J Life Cycle Assess (2016) 21:791805
DOI 10.1007/s11367-016-1056-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 Introduction
Agriculture accounts for one third of global greenhouse gas
(GHG) emissions (Lal and Kimble 1997). If demand for food
and biomasses continues to increase, annual GHG emissions
from agriculture may increase proportionally, along with the
vulnerability of agro-ecosystems to climate change (Xiong
and Khalil 2009). However, agriculture also has a significant
potential to reduce GHG emissions, as soils are the second
largest carbon (C) sink after oceans (Lal and Kimble 1997).
In general, there are three options for climate change mitiga-
tion in agriculture. First, GHG emissions can be reduced by
improving the management of C and nitrogen (N) flows in
agro-ecosystems. Second, increasing the level of temporary
C storage through improved agricultural management prac-
tices can increase soil C sequestration. Third, the replacement
of fossil fuels with renewable fuels such as residues from
agricultural crop production also is possible.
During the last decade, the interest of companies and
policy-makers in carbon footprint (CFP) as a supporting tool
to assess the impact of food and biomass production on global
warming processes and as a tool to design impact reduction
plans has grown steadily.
Based on the completeness principle stated in ISO 14067
(2013), the most recent international reference standard about
CFP, all GHG emissions and removals that provide a signifi-
cant contribution to the CFP of the analyzed product system
should be included in the study.
Although field emissions from fertilization and crop resi-
due management (CO
and N
O) can contribute considerably
to the GHG balance of food and bioenergy products, they are
often disregarded in CFP studies (Bessou et al. 2013). The
same applies to CO
fluxes occurring due to changes in soil
C stocks subsequent to crop management change (Brentrup
et al. 2000; Petersen et al. 2013), which, following the ISO
14067 (2013), should be included in CFP if not already cal-
culated as part of land use change.
For the accounting of field emissions at country level, the
IPCC guidelines for National GHG Inventories (IPCC 2006a)
provide, in the fourth volume dedicated to Agriculture,
Forestry and Other Land Use sector (AFOLU), three calcula-
tion pathways (Tiers) characterized by different degrees of
complexity: Tier 1 includes low-accuracy methodologies,
which can be applied by using the default emission factors
provided by the IPCC; Tier 2 methodologies require the use
of national emission factors reflecting local pedo-climatic
characteristics; finally, Tier 3 methodologies are based on
model simulations or in situ measurements. Tiers 2 and 3 are
referred to as the higher Tiers in the following text.
At present, the most common practice is using Tier 1 meth-
odologies for field emission calculation in life cycle assess-
ment (LCA) and carbon footprinting of food and energy crops.
However, Tier 1 methodologies are intended for use at large
spatial scales, and they can generate substantial errors in pre-
dictions at finer spatial scales. In fact, at regional and sub-
regional levels, Tier 1 methods are not always sufficiently
accurate to account for the spatial variability of GHG emis-
sions due to different soil, climate, and management practices.
Conversely, higher Tiers (Tiers 2 and 3) are usually considered
too complex and time-consuming to be practicable in the de-
velopment of LCA studies. Therefore, the IPCC guidelines
recommend using a higher Tier for the key emission catego-
ries and provide decision trees to support identification of the
most suitable Tier.
There is an urgent need for the application and validation of
appropriate higher tier methodologies at farm, project, or plan-
tation scales, to address local issues with bioenergy and food
sustainability and identify local mitigation potentials (Smith
et al. 2007; Smith et al. 2012).
The aim of the present paper is to provide to CFP practi-
tioners higher Tier methods Breadily available^and Beasy to
implement^(with a medium effort) to assess field emissions
from fertilization and from soil carbon change consequent to
crop management change for the inclusion into CFP
assessment studies of agricultural products. We selected
higher Tier methods which match these requirements,
choosing the Tier 2 method based on the Bouwman et al.
(2002a,b) approach for estimating field emissions from fer-
tilization and the Tier 3 method for soil organic carbon (SOC)
change assessment based on simulations with the Rothamsted
C (RothC) model (Coleman et al. 1997); both methods are
described, respectively, in Sects. 2.2 and 2.3. These methods
have been applied to four case studies and compared with Tier
1 results and with measurements, in order to test their perfor-
mance. The measurements were not intended to be used as
Tier 3 approach, because they are not always readily available
for LCA practitioners, but could be used to confirm the valid-
ity of the model for the examined agro-ecosystem conditions.
A further goal of the paper is to assess and compare the
influence of the variability of regional inventory data on CFP
results, depending on the adoption of Tier 1, Tier 2, or Tier 3
assessment methods. The examined case studies, two winter
wheat crops in Germany and two peach orchards in Italy, are
described in Sect. 2.1. The case studies were deliberately se-
lected to represent different soil characteristics, climate condi-
tions, and crop types (annual and perennial) in order to test
how the different methodologies handle this variability.
2 Materials and methods
2.1 Field experiments
Four field trials were selected based on crop type (annual
crops and perennial crops), soil, and climate conditions.
Characteristics of all sites are presented in Table 2.Whilewe
792 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
assumed that no land use change occurred in our case studies,
there were confirmed changes of the crop management
The two winterwheat crops were cultivated at two different
sites in Germany (sites 1 and 2) and for 2 years (20112012
and 20122013). They were sown after plowing at the end of
September and harvested the following summer (end of June).
Both cropping systems were rain fed. Site-specific rates of N
fertilization were calculated based on field-sampled soil min-
eral N content in springtime and crop-specific target values
from the official recommendation system, which reflects ex-
pected crop N uptake during the season, and split into 50 %
mineral (calcium ammonium nitrate applied in cultivation pe-
riod 20112012: 101 kg N ha
at site 1 and 73 kg ha
at site
2 and in period 20122013: 105 kg N ha
at site 1 and
75 kg N ha
at site 2) and 50 % organic N fertilizer (digestate
applied in cultivation period 20112012: 135 kg N ha
at site
1 and 137 kg ha
at site 2 and in period 20122013:
98 kg N ha
at site 1 and 108 kg N ha
at site 2) (Table 2).
At sites 1 and 2, straw management was changed in the first
year of the assessment period (2011). Before the change, all
straw was taken off the field to be used for energy production
or animal feeding. After the change, it was left on the field and
incorporated into the soil. Data on C inputs from straw and
applied digestate were direct field data, and C inputs from
roots (C
) and root exudates (C
) were calculated based on
yield (Y) data, harvest index (HI), and shoot to root ratio
(S:R), using the following equations (Farina et al. 2013):
0:45 ð1Þ
0:45 ð2Þ
At site 1, the mean yearly yield of the two considered crop
cycles (20112012, 20122013) was 6.8 t (dry matter grain)
and 3.9 t (dry matter straw), while at site 2, it was 8.0 t (dry
matter grain) and 5.4 t (dry matter straw). Tier 1 and Tier 2
estimates were compared with measured GHG emissions,
originating from a joint research project investigating GHG
emissions from energy crops fertilized with fermentation res-
idues. In situ measurements of N
emissions were
conducted from sowing of winter wheat until the sowing of
the subsequent crop for the crop years 20112012 and 2012
Periodic N
O measurements were conducted one to two
times per month out at three permanently installed soil collars
(0.75×0.75 m) at each site, with higher resolution following
fertilization events. Emissions of N
O were measured by tak-
ing four consecutive 100-ml gas samples from static non-
flow-through non-steady-stateopaquechambers(closure
time 60 min, vol. 0.296 m
; Livingston and Hutchinson
1995) and subsequently analyzed using a gas chromatograph.
O fluxes were calculated based on the ideal gas law using
the R package Bflux 0.2-2^(Jurasinski et al. 2012), using
linear regression analysis with stepwise backward elimination
of outliers. The calculated flux rates were then averaged for
the respective measurement day and linearly interpolated to
determine total N
O exchange.
Ammonia volatilization was measured for 2 to 5 days im-
mediately following fertilization using the open dynamic
chamber Dräger-tube method of Pacholski et al. (2006).
Four stainless steel chambers were placed on pre-installed
stainless steel rings (104 cm
). Chamber air was pumped
through the system with a constant air flow (1 L min
), and
the actual NH
concentration in the chamber air was directly
determined in vol. ppm. Cumulative NH
losses were calcu-
lated by linear interpolation between measurements and in the
end were summarized.
The perennial crop field trials were conducted at two peach
orchards (sites 3a, 3b), located in Metapontino, the southern area
of the Basilicata region in Italy, which is devoted to fruit produc-
tion. They are characterized by the same rootstock and the same
training system of canopy, as well as similar pedo-climatic con-
ditions, orchard layout, and management regime. The orchard at
site 3a was planted in 2006 and the orchard at site 3b in 1996 and
removed after 15 years in 2011. In both orchards, a management
change occurred in the eighth year after plantation (2013 for site
3a, 2004 for site 3b) from conventional to sustainable manage-
ment. The conventional management regime consisted of soil
tillage (site 3b), chemical weed control (site 3a), and the burning
of pruning material. The sustainable management regime intro-
duced some innovative elements, including no tillage (site 3b),
mechanical weed control (grass cover mowed twice per year),
the chipping of pruning residues to be left in the field, and the
provision of 10 t of compost per hectare per year.
The life cycle of a fruit orchard can be divided into three
main stages: the young stage characterized by low yield and
grow of permanent structures, the mature stage characterized
by stable high yield, and the senescence stage characterized by
the decrease of yield. The farmers can decide in which stage
the orchard will be removed and replanted, and usually, it
happens at the end of the mature stage (Cerutti et al. 2010).
For peach trees, the young stage lasts 2 years and the mature
stage about 13 years. The amounts of C added to the soil
during the mature stage of the orchards (i.e., as crop residues
and organic fertilizers) were derived from direct field mea-
surements: figures regarding the dry matter of senescent
leaves, pruning residues, thinned fruit, and grass cover were
retrieved from field sampling performed at site 3b from 2004
to 2010 (from 8th to 14th year after establishment) and at site
3a in 20132014 (8th and 9th year after establishment), as-
suming a mean C content of 0.45 t C per ton of dry matter, and
a grass cover below-ground contribution of 20 % (Celano
et al. 2003). The C input during the young stage of the orchard
(senescent leaves and pruning material) was retrieved from an
Int J Life Cycle Assess (2016) 21:791805 793
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
experiment performed in a peach orchard located in the same
area, with same rootstock, same training method, comparable
management regime, and adapting data to the different tree
density per hectare (Sofo et al. 2005). The C input from root
turnover was calculated as 30 % of the treesabove-ground
biomass turnover (senescent leaves, pruning material, and
fruit yield) for the first 3 years and as 25 % for all other years
(Buwalda 1993).
Data collected from site 3b were used to compare SOC
change estimate after crop management change, performed
with Tier 1 and Tier 3 methods with measurements of soil C
content, performed at the beginning of the experimental peri-
od (2004) and after 7 years of management shifted to sustain-
able practices (2010). Each time, 30 soil samples at 30-cm
depth were taken from a 1-ha field at different distances from
the tree row line. SOC was determined by the potassium-
dichromate oxidation procedure (Heanes 1984). Total per-
hectare C stocks in the topsoil (030 cm) were calculated as
the weighted average of SOC measured in the 30 soil samples.
For site 3a, the complete CFP was calculated using primary
data from the field logbook about the amount of productive
inputs used to perform all agricultural operations during the
orchard establishment, as well as the young and mature phase
of the orchard life cycle; the SOC change of site 3a was also
estimated using Tier 1 and Tier 3 methods.
2.2 Scope of the CFP assessment
GHG emissions of sites 1, 2, and 3a were calculated and
included in the CFP accounting according to ISO standards
14040 (2006), ISO 14044 (2006), and ISO 14067 (2013). Our
selected functional unit was not the unit of product, but the
surface unit of the cropland (1 hectare), since the focus of the
study was not on the environmental efficiency of the produc-
tion, but on the methodology comparison (Cerutti et al. 2015).
In fact, the results of Tier 1 methods are usually expressed as
emissions per unit of hectare, as their first application is
intended to analyze emissions from national crop production.
Therefore, the results of higher Tiers and the field measure-
ment were calculated per hectare size, in order to become
comparable with Tier 1 values. However, it is important to
highlight that for global issues such as food security and glob-
al warming impact of food production, GHG emission assess-
ment and mitigation potential per unit product are often more
useful than the absolute emissions per unit area (Bennetzen
et al. 2012). Since CFP calculations per unit of product can
take into account the variability related to yield differences,
they are particularly suitable for comparative CFP studies of
the same product cultivated in different locations or with dif-
ferent farming practices. System boundaries were fixed from
cradle to farm gate, starting with production of all productive
inputs, e.g., seeds, fertilizers, pesticides, agricultural machin-
ery, and fuels (indirect emissions), and ending with the harvest
of the crop, encompassing all emissions along the production
chain. Crop cultivation and processing of agricultural products
can lead to multiple outputs, e.g., straw and grain from cereal
harvesting or biogas and digestate from anaerobic biomass
digestion. In CFP calculations, there are different methods to
allocate the process emissions to different products (Benoist
et al. 2012). Manure and digestate are productive input for the
crop life cycle and residues (by-products) for the livestock and
bioenergylife cycle. They are re-used in the same form at field
(non-treated). As Rehl et al. (2012) stated, there are many
ways to allocate organic fertilizer but the most logical one is
the economic value. However, usually, manure and digestate
are not sold by the farmers; therefore, it is difficult to
determine prices because it does not exist. We followed the
approach by Rehl et al. (2012) and used the open-loop alloca-
tion procedure (ISO 14067 2013, Sect. BAllocation
procedure for reuse and re cycling^)andtheeconomicindica-
tor with the market value and assumed that the by-products are
given away free of charge. Emissions from storage of organic
fertilizer and on field (application of fertilizer) were consid-
ered in the crop cultivation process where these emissions
occurring. The agri-footprint database (Blonk Agri-footprint
BV. 2014) also considers organic fertilizer (digestate and an-
imal manure) as residual products of biogas and animal pro-
duction system, so it does not include any emissions of the
biogas or animal production system, in order to avoid double
counting. Conversely, for compost, the production phase was
entirely included within the boundaries of the study, as it is not
reused in the same form (organic urban waste), but it is the
outcome of a recycling process, performed for agricultural
purpose only. For the peach case study 3a, the whole life cycle
of the orchard (site 3a) was included within the time bound-
aries, from orchard establishment until removal (15 years).
This approach is coherent with the most common practice of
LCA sectoral studies about fruit production from perennial
tree crops (Cerutti et al. 2010; Milà i Canals and Clemente
Polo 2003). The soil preparation prior to orchard establish-
ment, the establishment and removal phase, comprising the
production, and the disposal phases of materials constituting
the support structure (concrete and aluminum poles, steel
wire, and concrete blocks) were included in the boundaries,
extrapolating data about machinery operations for the removal
phase and end-of-life of treespermanent structure from
For the winter wheat case studies, direct data from two crop
cycles were considered (20112012 and 20122013).
Inventory data on the amount of productive inputs (fertil-
izers, pesticides, fuels, machinery) were mostly retrieved di-
rectly from our on-farm experiments. GHG emissions related
to the production of these productive inputs were based on the
Ecoinvent database v.2.2 and v.3.0 (Ecoinvent 2013). The
calculation of CO
O emissions from diesel com-
bustion was based on IPCC Tier 1 (2006b).
794 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The considered impact category was the global warming
potential (GWP)100 years with the characterization factors
from the CML 2001 method corresponding with IPCC 2007
(Ecoinvent 2013).
CFP calculations were divided into three main parts: (i)
field emissions from fertilization, (ii) soil C stock change sub-
sequent to crop management change, and (iii) all other emis-
sions from agricultural operations. The consequences of meth-
odological choices were analyzed by comparing the CFP re-
sults based on Tier 2 and Tier 3 approach with the reference
CFP results based on IPCC Tier 1 default accounting methods.
2.3 Methodologies for the assessment
of fertilization-induced field emissions
The simple IPCC Tier 1 method (IPCC 2006a) for calculating
direct emission of nitrous oxide (N
O) from managed soils
simply takes into account 1 % (uncertainty range 0.33%)
of the anthropogenic N inputs (mineral fertilizer, organic
amendments, and crop residues) at the field level. Indirect
O emissions take place through two pathways. The first
pathway is volatilization of N as NH
and NO
and their
deposition onto soil and water, accounted by IPCC Tier 1
method as 10 % (0.33.0 % uncertainty) of kilogram N ap-
plied from mineral fertilizer and 20 % (0.55.0 % uncertainty)
from organic amendments expressed as kilogram NH
-N +
-N. Only 1 % of these emissions from atmospheric depo-
sition of N volatilized from managed soils are accounted as
indirect N
O-N emissions. The second pathway of indirect
O emissions is leaching and runoff of N from fertilizer
application and crop residues, accounted by IPCC Tier 1
method only for regions where leaching and runoff occur as
30 % (1080 % uncertainty) expressed as kilogram N. Only
0.75 % of these emissions leaching and runoff are accounted
as indirect N
O-N emissions. This Tier 1 approach completely
disregards any impact of crop type, fertilizer type, manage-
ment system, and local climate conditions on the GHG
emissions except for the calculation approach for leaching
and runoffs which takes the regional risk for leaching into
account. However, considering all or some of these
agricultural characteristics in the calculation of N
O, NO,
and NH
emissions would more accurately reflect the
heterogeneity of the environmental and management
conditions occurring in agriculture. This would better allow
the identification of local GHG emission hotspots and to
evaluate options of reduction.
We chose a Tier 2 level modeling approach used by
Bouwman et al. (2002b) to determine direct and indirect
O emissions and the approach of Bouwman et al. (2002a)
for NH
volatilization. A Tier 3 level modeling approach for
estimating field emissions from fertilization was not tested,
since to our knowledge, it does not exist at present any ap-
proach that matches our requirements to be readily available
and easily implementable by the user. Both tested Tier 2 ap-
proaches have been validated on a large global dataset from
measured agricultural field emissions encompassing 846 N
measurements from 126 studies, 99 NO measurements from
29 studies, and 1667 NH
measurements from 148 studies
(Bouwman et al. 2002a; Bouwman et al. 2002b). These
methods should therefore demonstrate better performance at
the local scale and under different agricultural management
systems than the Tier 1 methods, thus reducing the uncertainty
of the estimates with respect to the global emission factors
used in Tier 1 assessments (IPCC 2006a). However,
implementing the Tier 2 approach after Bouwman et al.
(2002a,b) requires more detailed data, as shown in Table 2.
The multivariate empirical model of Bouwman et al. (2002b)
classifies the parameters influencing N
O and NO emissions
into specific categories for each factor. For N
O, the signifi-
cant parameters are fertilizer type and application rate, crop
type, soil texture, SOC, soil drainage, soil pH, and climate
type, but only data regarding fertilizer type and application
rate, SOC, and soil drainage are needed to calculate the NO
emissions. The climate condition has less influence on the NO
emissions, since these emissions appear to be more concen-
trated during the crop-growing season than N
O emissions.
During the growing season, climate condition varies less be-
tween climate types than during other seasons (Bouwman
et al. 2001). The model for ammonia NH
(Bouwman et al. 2002a) is similar to the Bouwman et al.
(2002b) approach, but the significant parameters are fertilizer
type, fertilizer application rate and method, crop type, soil
texture, soil cation exchange capacity (CEC), soil pH, and
climate type. The amount of indirect emissions can be con-
verted to N
O-N by multiplying the NO-N and NH
-N emis-
sions with the default value 0.01 (based on IPCC 2006a). For
reporting purposes, the total N
O-N emissions can be convert-
ed to N
O by multiplying the kilogram of N
O-N by 44/28
(ratio of molecular weight of N and N
O). For the NH
sions induced by organic fertilizers (i.e., slurry and manure,
digestate, poultry manure), we used the more detailed model
approach by KTBL (2009) for organic fertilization to calculate
-N emissions, with NH
volatilization depending on fer-
tilizer type, fertilizer application rate and method, daily tem-
perature, and a binary variable indicating whether the fertilizer
wasincorporatedwithin1h(Table1). CO
emissions from the
application of urea and liming were calculated based on Tier 1
IPCC (2006a)factors.
2.4 Methodologies for the assessment of emissions
from soil C stock change
The default international practice about GHG accounting in
the AFOLU sector (Tier 1) assumes that the soil carbon con-
tent is in equilibrium (steady state) when the last crop man-
agement change or land use change occurred more than
Int J Life Cycle Assess (2016) 21:791805 795
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
20 years before the assessed time frame. In this case, it is
assumed that C outputs as CO
emissions from organic matter
decomposition equal the C inputs from organic material added
to soil. As soon as the landuse or management regime (tillage,
soil cover, carbon input level, irrigation) changes, C input and
outputs become imbalanced and either C emissions or C se-
questration will occur. It may take several decades before the
system returns to steady state at a new equilibrium, and the
default time set by Tier 1 method is 20 years. To include the
soil C change into CFP assessment, different methods are
available. We tested and compared methodological ap-
proaches from Tier 1 and Tier 3, since Tier 2 national emission
factors for SOC change are not always Bready available^for
LCA practitioners and other existing models for both C and N
cycle in soil do not match our requirement for Bmedium-effort
modeling approaches.^
The simple Tier 1 method, explained in Chapter 5 of
Volume 4 (cropland remaining cropland) of the IPCC guide-
lines (IPCC 2006a), requires the application of Eqs. 3and 4.
The soil organic C reference (SOC
) under native vegetation
must be assigned based on six available soil types (high ac-
tivity clay, low activity clay, sandy, spodic, volcanic, wetland)
and nine climate regions (boreal, cold temperate dry, cold
temperate moist, warm temperate dry, warm temperate moist,
tropical dry, tropical moist, tropical wet, tropical montane).
Three relative stock change factors further describe the site:
(i) F
is related to land use (long-term cultivated, paddy rice,
perennial/tree crop, set aside), (ii)F
characterizes the tillage
regime (full, reduced, no tillage), and (iii) F
describes the
carbon input level (low, medium, high without manure, high
with manure). These factors come with individual error ranges
(between ±5 and ±50 %) and have to be defined for conditions
both before and after the change in management or land use
Using Eq. 3, the soil organic carbon content before
) and after (SOC
) the change can be calculated
as follows:
The difference between the final (new equilibrium,
) and the initial C stock (old equilibrium, SOC
indicates the soil C stock change in the topsoil (030 cm) over
a period of 20 years, expressed as tons of C per hectare. This
amount can be converted to atmospheric CO
stored in or
emitted from the soil by multiplying the tons of C by 44/12
(ratio of molecular weight of CO
and C):
ΔSOC t C year1
T¼default time period f or transition between equilibrium
SOC values;20 years
The selected Tier 3 methodology consists of a simulation of
the soil C turnover using the RothC model (26.3); Coleman
et al. 1997). To run the simulation, the model requires inputs
regarding the soil characteristics (i.e., clay content, considered
depth horizon, initial SOC), climate data (i.e., monthly aver-
age temperature, cumulative evapotranspiration, and rainfall),
and monthly soil C input (in tons of C per hectare), expressed
as net primary production (NPP). The output of the RothC
simulation of soil organic matter decomposition process is
the dynamic of C fluxes between soil carbon pools (resistant
and decomposable plant material, microbial biomass, and hu-
mified organic material) and the inferred CO
emissions in
atmosphere. The RothC simulations were run for different
time perspectives (T=20, 50, 100 years) and compared with
the default Tier 1 result (T= 20 years ). Equation 4was used as
well to include soil C change in the CFP case studies using a
Tier 3 method.
Before running the simulation, the initial value of the four
carbon pools (decomposable, resistant, biological, humus)
constituting the SOC in the RothC model was simulated using
the following procedure (initialization procedure):
The C stock of each C pool was set at 0 t C ha
. The
simulation was then run to equilibrium using the C input esti-
mated for the management regime before management
change. The equilibrium values of the SOC pools were used
Table 1 Ammonia-volatilization rate (based on KTBL 2009)
Fertilizer type NH
volatilization in % of the applied NH
-N Application method-related reduction of NH
emission in %
Drag hose
Drag shoe Manure
chisel plow
within 1 h
Cattle slurry 30 40 50 90 8 30 30 90 80
Digestate (thickened)
Pigslurry 1020257030 506090 80
Digestate (liquid)
Slurry 20
Mature manure 90
Poultry manure 90
796 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
as initial values for the simulation if the equilibrium obtained
for total organic C (TOC) stocks matched the measured initial
TOC; otherwise, the model was run in inverse mode to gen-
erate the required C input (CI
), using the equation suggested
by Mondini et al. (2012):
CIreq ¼CIiCmeas IOM
Csim IOM
 ð5Þ
where CI
is the monthly C input used in the first equilibrium
run, C
is the measured soil C stock to be matched, C
the simulated soil C stock after the first equilibrium run, and
IOM is inert organic matter, the small soil organic C compart-
ment resistant to decomposition (with an equivalent radiocar-
bon age of more than 50,000 years), which, in absence of
radiocarbon data, can be roughly estimated from total SOC
using the equation provided by Falloon et al. (1998):
IOM ¼0:049TOC1:139 ð6Þ
With the required C input (CI
), the model must be run again
to equilibrium to obtain the initial value of the SOC pools.
The RothC model can only simulate heterotrophic respiration
resulting from microbial decomposition of soil organic matter.
However, the autotrophic respiration of plants is implicitly in-
cluded as well, because the amounts of C inputs entered in the
model are expressed as net primary production (NPP) of bio-
mass, resulting from the balance between CO
absorbed through
photosynthesis and CO
released through dark respiration.
The crop management changes examined in our four
case studies concerned mainly the different amount of or-
ganic material returned or added to soil: In Table 3,the
carbon input stock change factors selected to evaluate
SOC change with Tier 1 method and the C input derived
from direct field data used to implement the RothC simu-
lation are summarized.
RothC can be considered as a reliable simulation tool of
carbon turnover in soil for arable land in cool or temperate
moist climates based on multiple validation campaigns
(Coleman et al. 1997; Falloon and Smith 2002;Ludwig
et al. 2007; Zimmermann et al. 2007), but there is a lack of
model application to perennial crops (Bessou et al. 2013).
Therefore, the results of RothC simulation were compared
with available measurements just at site 3b and not at sites 1
and 2, due also to the unavailability of medium-long-term
monitoring of SOC dynamic at these sites.
Tab le 2summarizes all data required for the implementa-
tion of the four tested methodologies.
3 Results and discussion
As shown in Fig. 1, in all case studies, new SOC equilibrium
after crop management change estimated using RothC model
(Tier 3) was reached in more than 20 years, the default time
period assumed in the Tier 1 approach. For site 1, the SOC
change at equilibrium is much lower if calculated using Tier 3
approach than using Tier 1. The Tier 3 value is outside the
range of uncertainty of the Tier 1 value (±51 %), resulting
from the propagation of errors declared for each stock change
factor in the IPCC (2006a) guidelines. For site 2, the equilib-
rium SOC change calculated using Tier 3 is also lower than
Tier 1 but falls within the forecasted Tier 1 error (±51 %).
The Tier 1 SOC change estimate is the same for the two
winter wheat case studies, as the same crop, soil (high activity
clay (HAC)), climate zone (cold temperate moist), and man-
agement practices were investigated, and thus, no difference
between the two sites is recognizable using the Tier 1 ap-
proach. In contrast, the curves resulting from the RothC sim-
ulation are very different, because site 1 is characterized by
lower soil clay content and a moister climate, which leads to a
slower rate of SOC rise due to the faster decomposition rate of
soil C pools. Moreover, the different amount of straw added to
soi1 as C input in the RothC simulations is higher at site 2 than
at site 1 in the considered time period 20112013, which leads
to different values of simulated SOC change. This difference
in C input is not appreciable using Tier 1 method because it is
only possible to choose between the qualitative C input levels
Blow,^Bmedium,^and Bhigh^(with or without manure), as
summarized in Table 3. Therefore, finer regional variation of
climate, yield, and soil texture cannot be represented using
Tier 1 methodology for SOC change estimates, in the case
of a crop management change.
For sites 3a and 3b, the Tier 3 SOC change at equilibrium is
much higher than the Tier 1 value and outside the forecasted
error range of Tier 1 (±172 %). In the Tier 3 simulation, the
succession of different peach orchard life cycles results in a
fluctuation of SOC within the orchard life cycle due to the
lower amount of crop residues during the establishment, the
young phase, the senescence phase, and the removal of the
orchard. The peak of SOC simulated at site 3a (Fig. 1)atthe
beginning of each orchard life cycle is due to the soil prepa-
ration with manure (60 t ha
). Moreover, for both orchards,
the simulation reveals an overall increasing trend of SOC be-
yond the single orchard life cycle, if pursued with sustainable
management practices.
As can be noticed in Fig. 1, the comparison of SOC change
measurements with estimates at site 3b reveals that Tier 1
method underestimates the measured SOC change after 7 years
of sustainable management practices of 9.73 t C ha
RothC simulation better represents the SOC increasing trend,
with an overestimation of 6.6 t C ha
. Tier 1 C input stock
change factors (Table 3) do not probably reflect the real
growth of soil C input after the incorporation of crop residues,
compost, and grass cover into the soil. RothC overestimation
could be due to the lack of consideration in the simulation of
soil condition variability across the orchard hectare: the
Int J Life Cycle Assess (2016) 21:791805 797
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Tab le 2 Characteristics of experimental sites and comparison of data requirements by tested Tier methods
Site 1 Site 2 Site 3a3b Data required to estimate
Field emissions from fertilization Soil carbon change
Country Germany (south) Germany (central) Italy (south) Tier 1 Tier 2 Tier 1 Tier 3
Geographical location 48° 59N 51° 00N 40° 14N; 16° 42E(site3a)
12° 39E11°39E 40° 23N; 16° 42E (site 3b)
Height above sea level (m) 431 247 16 (site 3a)
23 (site 3b)
Crop Crop type Wheat
Amount crop residues (t DM ha
) 3.9 5.4 6.8 Qualitatative
C content crop residues (% DM) 45 45 45
Fertilization Fertilizer type CAN
, digestate
, digestate
, compost
Method of fertilizer application Broadcast, incorporated Broadcast, incorporated Broadcast
Fertilizer amount (kg ha
2012: 375
, 2012: 270
, 2400
, 200
2013: 388
, 33,000
2013: 278
, 32,000
, 80,000
N content of fertilizer (%) 2012: 27
2013: 27
2012: 27
2013: 27
C content of organic fertilizer (% FM) 2.5
Soil Soil type (WRB classification) Stagnic Cambisol (HAC) Luvisol (HAC) Eutric Cambisol (HAC) ●●
Soil texture Loamy sand Silty clayey loam Sandy loam ●●
Soil bulk density (g/cm
) 1.7 1.5 1.5 (site 3a) 1.6 (site 3b) ●●
pH value 5.1 7.4 7.1 (site 3 a) 8.1 (site 3 b)
SOC before change 0.5 0.5 1.0 ●●
Soil drainage Good Good Good
Climate Climate region Temperate Temperate Temperate ●●
Precipitation (cumulative yearly in mm)
957 582 525 ●● Monthly value
Temperature (yearly in °C)
8.3 9.6 16.4 Specific days Monthly value
Evapotranspiration (cumulative
yearly in mm)
611 568 1262 ●● Monthly value
Management Tillage regime Converted to no tillage during
the experimental period
CAN calcium ammonium nitrate
digestate fermentation of biomass from energy crops and animal slurry
ON organic nitrogen fertilizer
AS ammonium sulfate
NPK nitrogen (N) + phosphorus (P) + potassium (K) compound
CN calcium nitrate,
Compost with 40 % mineralization efficiency
Cumulative amount of fertilizers distributed within the time boundaries of the study
Average values over the experimental periods
Winter wheat (Triticum aestivum L.)
Peach (Prunus persica)
798 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
variable soil moisture with the distance from the tree line due
to drip irrigation and the concentrated distribution of compost
and roots along the tree line (Montanaro et al. 2012).
In Table 4, the yearly rates of SOC changes are reported for
differenttime horizons (20, 50, 100 years). Generally, it can be
stated that with a longer time horizon, the yearly rate of SOC
Fig. 1 Simulations of SOC
change subsequent to the shift to
sustainable crop management,
performed using IPCC (2006a)
Tier 1 approach and using RothC
26.3 model initialized with
measured SOC before
management change (the gray
line represents monthly
simulation and the black one
yearly average)
Int J Life Cycle Assess (2016) 21:791805 799
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
change, expressed as CO
removed from atmosphere, de-
creases, since SOC change is always faster during the first
years after disturbance. This aspect has already been
highlighted in Petersen et al. (2013), where they suggested
using a 100-year time horizon when simulating SOC change
for CFP studies, based on a 100-year GWP calculation.
However, it is difficult to elaborate predictions in such a long
term, as many factors characterizing the agricultural sector
(e.g., land use, cropping systems, and management regimes)
are usually defined by highly volatile framework conditions
(e.g., consumer demand, economic trends, societal transfor-
mation, and public policy). For agricultural land use decision-
making, even 20-year continuous land use of the same kind is
not common and LUC should be considered at a more reason-
able time horizon. Furthermore, when changing the cultiva-
tion system each year, the effect of management change on the
SOC content is not stable and the uncertainty of the results is
very high. In order to more consistently compare Tier 1 and
Tier 3 methods, the yearly SOC change value derived from
20-year RothC simulation has been used and included into
CFP of the examined case studies (Fig. 2).
Figure 2shows the CFPs of sites 1, 2, and 3a and the
relative contribution of the three different GHG emission cat-
egories. The CO
removals from SOC change are reported
separately as prescribed by ISO 14067 (2013), because of
the temporary character of CO
storage in soil. For sites 1
and 2, the CFP represents the sum of all GHG emissions and
removals during a 1-year crop cycle, while for the site 3a CFP,
the entire 15-year peach orchard life cycle was taken into
account, with the last 8 years of management regime shifted
to sustainable practices. For CFP calculation of annual crops,
the whole crop rotation and the crop rotation-related effects
should be taken into consideration as stated by Brankatschk
and Finkbeiner (2015). Especially, crop residues can have a
great influence on the crop rotation effects, since crop residues
remaining on the field affect the subsequent crop through
influencing the physical, chemical, and biological soil proper-
ties and improving the soil fertility. The nutrients (N, P, K)
Table 3 Selected C input stock change factors selected to calculate SOC change with Tier 1 method and the C input derived from direct data used to
implement RothC simulation
Management practices Tier 1 C input stock change factor C input Tier 3 (t C ha
Site 1 Site 2 Site 3a Site 3b
Winter wheat
before change
Straw removed from
field + digestate addition
Representative for annual cropping with cereals
where all crop residues are returned to the
field. If residues are removed, then supplemental
organic matter (e.g., manure) is added. Also,
it requires mineral fertilization or N-fixing crop
in rotation.
2.22 2.24
Winter wheat
after change
Straw incorporated in
soil + manure addition
High with manure
(Error ±13 %)
Represents significantly higher C input over medium
C input cropping systems due to an additional
practice of regular addition of animal manure.
4.02 4.67
Peach orchard
before change
Pruning residues burned,
chemical weed control
(site 3a), and tillage
(site 3b)
(Error ±13 %)
Low residue return occurs when there is due to
removal of residues (via collection or burning),
frequent bare fallowing, production of crops
yielding low residues (e.g., vegetables, tobacco,
cotton), no mineral fertilization, or N-fixing crops.
1.8 1.8
Peach orchard
after change
Pruning residues
incorporated in soil,
mechanical weed control,
no tillage, compost addition
High without manure
(Error ±13 %)
Represents significantly greater crop residue inputs
over medium C input cropping systems due to
additional practices, such as production of
high-residue-yielding crops, use of green
manures, cover crops, improved vegetated
fallows, irrigation, frequent use of perennial
grasses in annual crop rotations, but without
manure applied
5.8 6.8
800 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
remaining in crop residues on field can be used by the subse-
quent crop and can result in a reduced fertilizer dose for the
subsequent crop. This problem can be accounted in the CFP of
the subsequent crop in two ways: allocating the respective
environmental burdens to the subsequent crop or a credit can
be given for the current crop if a reduced fertilizer dose is
recommended for the subsequent crop. So far, there is no
agreement about a uniform approach on how to include the
effects of the crop rotation in the CFP calculation of an annual
crop (Brankatschk and Finkbeiner 2015). We tried to include
the crop rotation effect for the winter wheat crops by using the
real crop cultivation data provided by the researchers from the
experimental sites. The amount of farming operating material
for each crop applied at field was calculated in advance in
consideration of the local pedo-climatic conditions, the char-
acteristics of the previous crop (overall fertilization strategy
Table 4 Results of SOC change,
expressed as CO
removed from
atmosphere, calculated with
different time horizons and
different Tier approaches for the
experimental sites
Experimental site Assessment
Time horizon (years) SOC change at
equilibrium (t C ha
SOC change
(t CO
1 Tier 3 RothC 1 1.13
20 0.33
50 0.26
100 5.18 0.19
Tier 1 20 28.8 (±14.7) 5.29
2 Tier 3 RothC 1 7.80
20 2.74
50 1.87
100 38.15 1.40
Tier 1 20 28.8 (±14.7) 5.29
3a Tier 3 RothC 20 2.99
50 2.63
100 55,2 2.03
Tier 1 20 7.4 (±12.7) 1.35
3b Tier 3 RothC 7 9.91
20 5.36
50 2.94
100 43.87 1.61
Tier 1 20 7.4 (±12.7) 1.35
Fig. 2 Comparison of CFP
calculated using the Tier 1 and
Tier 2/3 approach for the three
case studies (for sites 1 and 2,
mean values from two cultivation
years at each site, site 3 values are
totaled up over 15 years). SOC
change is calculated in a 20 years
time horizon
Int J Life Cycle Assess (2016) 21:791805 801
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
for the crop rotation), and the amount of nutrients available in
the soil (provided by the soil samples). Furthermore, the real
obtained yield and nutrient content at each site were used to
calculate the amount of SOC added to the field through crop
residues and digestate.
For site 1, the choice of either Tier 1 or Tier 3 for SOC
change estimation is a relevant decision, as SOC change cal-
culated with Tier 1 corresponds to 194 % of all other CFP
emissions (the agro-ecosystem is a C sink), while the Tier 3
estimate amounts to approximately 13 % of all other emis-
sions. For site 2, the Tier level selected for SOC change esti-
mation is less crucial, as in both cases, the agro-ecosystem is
considered to be a C sink, but the benefit calculated by Tier 3
is lower (145 against 221 % of all other CFP emissions offset
with Tier 1). In site 3a, the use of the Tier 3 method resulted in
a more realistic value of SOC change than Tier 1. During
8 years of sustainable management practices, 81 % of all other
CFP emissions can be offset through soil C storage, compared
to only 19 % estimated with Tier 1. Pursuing a sustainable
management regime, the Tier 3 simulation reveals that the
subsequent peach orchard life cycle would be a C sink, storing
152 % of the emissions from agricultural operations and field
emissions from fertilization in the soil.
For the annual crops, field emissions from fertilizer applica-
tion are an important factor, accounting for almost 50 % of the
emissions calculated using Tier 1 for sites 1 and 2. In contrast,
for the perennial peach crop, emissions from fertilizer applica-
tion contribute around 10 % (Fig. 2). In all case studies, the Tier
2 estimates of fertilizer-induced field emissions are lower than
the Tier 1 estimate (9 % for wheat at site 1, 46 % for wheat at
site 2, and 65 % for peach at site 3) and within the uncertainty
range of ∼−70 to +325 % reported for the default global emis-
sion factor from Tier 1. The consequence of using a higher Tier
(Bouwman et al. 2002b) to calculate field GHG emissions from
fertilizer application is a CFP reduction of 4 % for wheat pro-
duction at site 1, 21 % for wheat production at site 2, and 7 %
for peach production at site 3a. As the yield from annual crops
is strictly dependent on nutrient availability and weather
conditions during a relatively short cultivation period, the
fertilizer management system is often more intensive for the
short annual crop cycle than for perennial crops. Consequently,
the fertilizer management system has a larger influence on
overall field emissions for annual crops than for perennial
crops, which feature considerably lower fertilizer input
throughout their entire life cycle.
As reported in the IPCC (2006a) guidelines, the global de-
fault values (Tier 1) are, in some cases, adequate to determine
fertilizer-induced field emissionsas confirmed by our wheat
case study (site 1). However, in most cases, these factors should
be specified based on environmental conditions (climate and
soil characteristics) as well as on crop management conditions
(fertilizer type, fertilizer application method, and rate) as in our
second wheat (site 2) and our peach (site 3) case study.
To evaluate these findings, wecompared the Tier 1 and Tier
2 estimates for N
emissions from the two winter
wheat case studies with field-measured GHG emissions
(Fig. 3). For both sites and both gases, the Tier 1 and 2 calcu-
lations overestimated the measured fluxes. However, for NH
N emissions, Tier 2 estimates only deviate 1 and 10 % from
measured data of site 1 and site 2, respectively. In contrast,
Tier 1 calculated NH
-N emissions are 87 and 168 % higher
than measured emissions for site 1 and site 2, respectively.
Tier 2 estimates of fertilizer-induced N
O-N field emissions
are less accurate than estimates for NH
-N, but more accurate
than Tier 1 estimates. As Firestone and Davidson (1989)have
stated, the microbial processes of denitrification and nitrifica-
tion are the dominant sources of gaseous N emissions from
agricultural soil systems. Many factors associated with crop,
soil, water, climate, and fertilizer management can influence
soil turnover processes at all levels, e.g., organic matter de-
composition, denitrification, and nitrification. Consequently,
the heterogeneity of soil and weather conditions hamper a
sufficiently accurate representation of N
O, NO, and NH
emissions from a field using a model. As presumed, the Tier
2 estimates of fertilizer-induced N
O-N and NH
-N field
emissions were closer to the measurements than the Tier 1
estimations. Especially, the NH
-N field emission estimates
were very close to the measurements results. Most NH
emissions on field arise from organic fertilizer application,
but the modeling approach from Bouwman et al. (2002a)does
not distinguish between different organic fertilizer types, but
the used KTBL (2009) calculation method for organic fertil-
izer takes the fertilizer type, the temperature during applica-
tion of the organic fertilizer, and the application method into
account. Therefore, through combining the two modeling ap-
proaches (for mineral fertilizer Bouwman et al. (2002a)and
for organic fertilizer KTBL (2009)), the accuracy of the
modeling results could be increased.
Comparing modeling data with measurements can be prob-
lematical since measured data and modeled data have also a
risk of uncertainty. As Bouwman et al. (2002c) pointed out in
their study, the amount of N
O and NO emissions is influ-
enced by the measurement technique, the length of measure-
ment period, and the frequency of measurements per day. For
O emissions are longer measurement periods (>300 days)
and intensive measurements (1 per day) better to detect the
fertilization effect on the N
O emissions. However, for NO
emission, no significant differences in measurement frequen-
cy classes could be found. The frequency of measurement and
continuity is a sensitive factor to detect the fertilization effect
on the N
O emissions. In our case, N
O measurements were
conducted one to two times per month with higher resolution
following fertilization events; therefore, they are not continu-
ous and include a risk of uncertainty. To reduce this uncertain-
ty, the N
O fluxes arising in the periods between the measure-
ments were calculated using linear regression analysis as
802 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
explained in the Sect. 2.1. Ammonia volatilization was measured
for 2 to 5 days immediately following fertilization. On both sites
(1 and 2), the experimental length exceeded the 300 days and,
correspondingly, our measurement period implying a low risk of
uncertainty. The considered time frame for data collection can
influence the N
O emission result. The emissions that occur
during mineralization of organic matter after harvest can be
charged to the harvested crop or to the subsequent crop. Both
dataset of measurements, with a determined level of uncertainty
(Bouwman et al. 2002a,b). However, the Tier 1 modeling ap-
proach only considers the amount of N applied and is more
suitable for global- or national-scale calculation where the vari-
ability of environmental- and management-related factors is
not appreciable for the different regions (IPCC 2006a). Since
the Tier 2 modeling approach introduces more parameters to
account for the heterogeneity of local pedo-climatic and manage-
ment conditions, it is more suitable to represent field emissions at
farm or project scale.
Gabrielle et al. (2006) also compared different modeling
approaches for N
O emission calculation from winter wheat
on regional scale. We come to the same conclusion as
Gabrielle et al. (2006) that in the case of fertilizer-induced
field emissions, a higher Tier approach with a focus on the
aforementioned regulating factors, especially regional envi-
ronmental conditions, could therefore be used to adequately
detect mitigation potentials. As Fig. 3shows, our suggested
Tier 2 approach can be a good solution to estimate fertilizer-
induced field NH
-N emissions, as it takes these regional en-
vironmental and management conditions into account.
However, the results for fertilizer-induced field N
O-N emis-
sions modeling with the different Tier approaches were not
convincing; therefore, we recommend to continue testing at
more sites and with more crops to confirm our hypothesis.
The choice of the methodological approach (Tier level) can
considerably affect the CFP of agricultural products.
Therefore, sufficient transparency is required to inform rele-
vant parties about possible error and shortcomings introduced
by the selected method when applied to a case study. In this
paper, we identified appropriate, readily available, assessment
methods at the Tier 2 and Tier 3 level with medium efforts for
stakeholders and explored the consequences of these method-
ological choices on the CFPs of annual and perennial crops for
field GHG emissions from crop cultivation.
Only few site-specific data are needed to apply these higher
Tier approaches, which can be used to improve the accuracy
of the estimate of land-based GHG emissions from fertiliza-
tion and soil C change, thus supporting the assessment of the
agricultural mitigation potential and the development of GHG
reduction plans at farm level.
The results for fertilizer-induced field emission calculation
were consistent among the three case studies: using the higher
Tier (Tier 2) led to lower estimated field emissions from fer-
tilization at two sites and to almost equal emissions at one site
compared to results obtained with the Tier 1 approach and to a
more reliable estimate in agreement with field measurements.
Based on our results, we suggest the following recommenda-
tions: For annual crops, a higher Tier approach is particularly
important when estimating fertilizer-induced field emissions,
whereas for perennial crops, it has a minor impact on the CFP.
However, we cannot draw general conclusions on the efficacy
of default emission factors for annual and perennial crops
from this limited amount of data; therefore, further studies
are needed to confirm our findings.
Regarding soil C stock change, important differences were
found between results calculated with Tier 1 and Tier 3 meth-
odologies. Using the Tier 1 approach can lead to wrong esti-
mates due to equivocal interpretation of the carbon input stock
change factor (qualitative description of the amount of organic
material entered to soil) and to the lack ofspecification of local
pedo-climatic conditions. Tier 3 RothC simulations can con-
stitute a valid alternative, as local primary data about climate,
soil features, and carbon input can be entered in the model;
RothC simulation of SOC change after the modification of a
crop management routine showed more reliable results when
tested against available measurements than Tier 1 estimates. A
more frequent SOC monitoring campaign would be useful to
further test the models performance and calibrate it to or-
chardsfeatures. The present study has underlined the rele-
vance of SOC change from crop management change on
Fig. 3 Comparison of fertilizer-
induced N
O-N and NH
emissions on field calculated
using Tier 1 and 2 approach and
measured data, mean values from
two wheat cultivation periods at
each site
Int J Life Cycle Assess (2016) 21:791805 803
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
CFP of perennial crops, which cannot be always adequately
represented using a Tier 1 approach. Concerning annual crops,
crop rotations were not included in the RothC simulation, as
SOC change after land use change (crop change) was not
included in the scope of the assessment. However, the influ-
ence of SOC change on CFP of 1-year crop cycle could be
strongly related to the long-term SOC dynamic, subsequent to
crop choice and to the management regime, which determine
the amount of organic residues returned to soil. Thus, for
annual crops, a simulation approach is also advisable to eval-
uate SOC change as the default Tier 1 does not allow to rep-
resent the change of different crops in the rotation. Further
investigation efforts are needed in this direction. Similarly to
what was done in this paper for default carbon input level
stock change factors, it would be interesting to assess how
the default land use stock change factors (long term cultivat-
ed, paddy rice, perennial crops, set aside) of Tier 1 method
influence the performance of SOC change estimate with re-
spect to higher Tier approaches.
The outcomes of the present paper suggest that it is neces-
sary to foster more awareness and consensus within LCA
practitioners and policy-makers about the importance of in-
cluding regional field emissions into CFP of agricultural prod-
ucts, as it can considerably affect the results of the analysis.
Moreover, it is recommendable to use modeling approaches
for field emissionsestimate, taking into account local pedo-
climatic and crop management conditions, because this can
significantly improve the reliability of GHG accounting for
agriculture at farm level.
The higher-tiered methodologies for the calculation of
field emissions from fertilization and SOC change require
little additional effort compared with default Tier 1
methods, and thus, their practical application is advisable.
However, the development of user-friendly, crop-specific
tools underpinning these modeling approaches could more
efficiently increase the usefulness of CFP for agricultural
sustainability assessment at farm and regional landscape
Acknowledgments The authors want to express their gratitude to the
participants of the following research projects who provided the observed
data used for the CFP calculations: (i) BDevelopment and comparison of
optimized cropping systems for the agricultural production of energy
crops^(FKZ 22013008), funded by the German Federal Ministry of
Food and Agriculture through the Agency of Renewable Resources
(FNR); (ii) BInnovation for Quality and Sustainability of fruit and vege-
table production (IQuaSoPO),^funded by Measure 124 of the Rural
Development Program 20072013 of Basilicata Region (Italy); and (iii)
the joint research project BPotentials for the mitigation of climate-relevant
greenhouse gas emissions from energy crop cultivation for biogas
production^(FKZ 22021008), funded by the German Federal Ministry
of Food and Agriculture through the Agency of Renewable Resources
(FNR) e.V. We would especially like to thank Achim Seidel, Gawan
Heintze, and Madlen Pohl for their contribution of data on field NH
and N
O emissions and Anne-Katrin Prescher for reviewing the
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://, which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
Bennetzen EH, Smith P, Soussana J-F, Porter JR (2012) Identity-based
estimation of greenhouse gas emissions from crop production: case
study from Denmark. Eur J Agron 41:6672
Benoist A, Dron D, Zoughaib A (2012) Origins of the debate on the life-
cycle greenhouse gas emissions and energy consumption of first-
generation biofuelsa sensitivity analysis approach. Biomass
Bioenerg 40:133142
Bessou C, Basset-Mens C, Tran T, Benoist A (2013) LCA applied to
perennial cropping systems: a review focused on the farm stage.
Int J Life Cycle Assess 18(2):340361
Blonk Agri-footprint BV. (2014) Agri-footprintpart 2description of
dataversion 1.0. Blonk Consultants Gouda, the Netherlands.
Bouwman AF, Boumans LJM, Batjes NH (2001) Global estimates of
gaseous emission of NH
, NO and N
O from agricultural land.
International Fertilizer Industry Association and Food and
Agriculture Organization of the United Nations, Rome
Bouwman AF, Boumans LJM, Batjes NH (2002a) Estimation of global
volatilization loss from synthetic fertilizers and animal manure
applied to arable lands and grasslands. Global Biogeochem Cy. doi:
Bouwman AF, Boumans LJM, Batjes NH (2002b) Modeling global an-
nual N
O and NO emissions from fertilized fields. Global
Biogeochem Cy 16:1080
Bouwman AF, Boumans LJM, Batjes NH (2002c) Emissions of N
NO from fertilizes fields: summary of available measurements data.
Global Biogeochem Cy 16:1058
Brankatschk G, Finkbeiner M (2015) Modeling crop rotation in agricul-
tural LCAschallenges and potential solutions. Agr Syst 138:66
Brentrup F, Küsters J, Lammel J, Kuhlmann H (2000) Methods to esti-
mate on-field nitrogen emissionsfrom crop production as an input to
LCA studies in the agricultural sector. Int J Life Cycle Assess 5:
Buwalda JG (1993) The carbon costs of root systems of perennial fruit
crops. Environ Exp Bot 33:131140
Celano G, Palese AM, Xiloyannis C, (2003) Gestione del suolo. In:
Fiorino P (Ed.), Olea-Trattato di Olivicoltura. Il Sole 24 ORE
Edagricole S.r.L.. Calderini, Bologna, Italy, p 349363
Cerutti AK, Bagliani M, Beccaro GL, Bounous G (2010) Application of
ecological footprint analysis on nectarine production: methodologi-
cal issues and results from a case study in Italy. J Clean Prod 18:
Cerutti AK, Beccaro GL, Bosco S, De Luca AI, Falcone G, Fiore A,
Iofrida N, Lo Giudice A, Strano A (2015). Life cycle assessment
in fruit sector. In: Notarnicola B et al. (eds) Life cycle assessment in
the agri-food sector, Springer International Publishing Switzerland.
DOI 10.1007/978-3-319-11940-3_6.
Coleman K, Jenkinson DS, Crocker GJ, Grace PR, Klir J, Korschens M,
Poulton PR, Richter DD (1997) Simulating trends in soil organic
carbon in long-term experiments using RothC-26.3. Geoderma
Ecoinvent (Weidema BP, Bauer Ch, Hischier R, Mutel Ch, Nemecek T,
Reinhard J,Vadenbo CO, Wernet G) (2013) The ecoinvent database:
804 Int J Life Cycle Assess (2016) 21:791805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
overview and methodology, data quality guideline for the ecoinvent
database version 3,
Falloon P, Smith P (2002) Simulating SOC changes in long-term
experiments with RothC and CENTURY: model evaluation
for a regional scale application. Soil Use Manage 18(2):
Falloon P, Smith P, Coleman K, Marshall S (1998) Estimating the size of
the inert organic matter pool for use in the Rothamsted carbon mod-
el. Soil Biol Biochem 30:12071211
Farina R, Coleman K, Whitmore AP (2013) Modification of the RothC
model for simulations ofsoil organic C dynamics in dryland regions.
Geoderma 200:1830
Firestone MK, Davidson EA (1989) Microbiological basis for NO and
N2O production and consumption in soils. In: Andreae M, Schimel
ODS (eds) Exchange of trace gases between terrestrial ecosystems
and the atmosphere. Wiley, New York, pp 721
Gabrielle B, Laville P, Duval O, Nicoullaud B, Germon JC, Hénault C
(2006) Process-based modeling of nitrous oxide emissions from
wheat-cropped soils at the subregional scale. Global Biogeochem
Cy 20. doi:10.1029/2006GB002686.
Heanes DL (1984) Determination of total organic-C in soils by an im-
proved chromic acid digestion and spectrophotometric procedure.
Commun Soil Sci Plant Anal 15:11911213
IPCC (Intergovernmental Panel on Climate Change) (2006a) In:
Egglestone HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds)
Guidelines for National Greenhouse Gas Inventories. Volume 4:
agriculture, forestry and other land use. Prepared by the National
Greenhouse Gas Inventories Program. IGES, Japan.
IPCC (Intergovernmental Panel on Climate Change) (2006b) In:
Egglestone HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds)
Guidelines for National Greenhouse Gas Inventories. Volume 2:
energy. Prepared by the National Greenhouse Gas Inventories
Program. IGES, Japan.
IPCC (Intergovernmental Panel on Climate Change) (2007) In: Solomon
S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M,
Miller HL (eds.) Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate
Change, 2007. Cambridge University Press, Cambridge, and New
York .
ISO 14040 (International Organization for Standardization) (2006)
Environmental managementlife cycle assessment: principles and
framework. ISO 14040, Geneva.
ISO 14044 (International Organization for Standardization) (2006)
Environmental managementlife cycle assessment: requirement
and guidelines. ISO 14044, Geneva.
ISO 14067 (International Organization for Standardization) (2013)
Greenhouse gasescarbon footprint of products: requirements and
guidelines for quantification and communication. ISO 14067,
Jurasinski G, Koebsch F, Hagemann U (2012) Flux rate calculation from
dynamic closed chamber measurements, R package version 0.21,
available at: package=flux/.
KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft)
(2009) Faustzahlen Biogas. Vol 2. KTBL, Darmstadt.
Lal R, Kimble JM (1997) Conservation tillage for carbon sequestration.
Nutr Cycl Agroecosys 49:243253
Livingston GP, Hutchinson GL (1995) Enclosure-based measurement of
trace gas exchange: applications and sources of error. In: Matson PA,
Harris RC (eds) Methods in ecology. Biogenic Trace Gases,
Measuring emissions from soil and water. Blackwell Science
Malden, pp 1451
Ludwig B, Schulz E, Rethemeyer J, Merbach I, Flessa H (2007)
Predictive modelling of C dynamics in the long-term fertilization
experiment at Bad Lauchstadt with the Rothamsted Carbon
Model. Eur J Soil Sci 58(5):11551163
Milà i Canals L, Clemente Polo G (2003) Life cycle assessment of fruit
production. In: Mattsson B, Sonesson U (eds) Environmentally-
friendly food processing. Woodhead Publishing Limited and CRC
Press LLC, Cambridge and Boca Raton, pp 2953
Mondini C, Coleman K, Whitmore AP (2012) Spatially explicit model-
ling of changes in soil organic C in agricultural soils in Italy, 2001
2100: potential for compost amendment. Agr Ecosyst Environ 153:
Montanaro G, Dichio B, Bati CB, Xiloyannis C (2012) Soil management
affects carbon dynamics and yield in a Mediterranean peach orchard.
Agri Ecosyst Environ 161:4654
PacholskiA, Cai G, Nieder R, Richter J, Fan X, Zhu Z, Roelcke M (2006)
Calibration of a simple method for determining ammonia volatiliza-
tion in the fieldcomparative measurements in Henan Province,
China. Nutr Cycl Agroecosyst 74:259273
Petersen BM, Knudsen MT, Hermansen JE, Halberg N (2013) An ap-
proach to include soil carbon changes in life cycle assessments. J
Clean Prod 52:217224
Rehl T, Lansche J, Müller J (2012) Life cycle assessment of energy
generation from biogasattributional vs. consequential approach.
Renew Sust Energy Rev 16:37663775
Smith P, Martino D, Cai Z, Gwary D, Janzen H, Kumar P, McCarl B,
Ogle S, OMara F, Rice C, Scholes B, Sirotenko O (2007) Chapter 8.
Agriculture. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer
LA (eds) Climate change 2007: mitigation of climate change.
Contribution of Working Group III to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge and New York
Smith P, Davies CA, Ogle S, Zanchi G, Bellarby J, Bird N, Boddey RM,
McNamara NP, Powlson D, Cowie A, van Noordwijk M, Davis SC,
Richter DB, Kryzanowski L, van Wijk MT, Stuart J, Kirton A, Eggar
D, Newton-Cross G, Adhya TK, Braimoh AK (2012) Towards an
integrated global framework to assess the impacts of land use and
management change on soil carbon: current capability and future
vision. Glob Change Biol 18(7):20892101
Sofo A, Nuzzo V, Palese AM, Xiloyannis C, Celano G, Zukowskyj P,
Dichio B (2005) Net CO
storage in Mediterranean olive and peach
orchards. Sci Hortic-Amsterdam 107:1724
Xiong ZQ, Khalil MAK (2009) Greenhouse gases from crop fields.
Environ Sci Eng 113132
Zimmermann M, Leifeld J, Schmidt MWI, Smith P, Fuhrer J (2007)
Measured soil organic matter fractions can be related to pools in
the RothC model. Eur J Soil Science 58(3):658667
Int J Life Cycle Assess (2016) 21:791805 805
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... Other studies comparing the models selected in this study have been performed (Wu and McGechan 1998;Cannavo et al. 2008;Bockstaller et al. 2009;Nitschelm et al. 2018;Peter et al. 2016) for various reasons and using different approaches. Wu and McGechan (1998) compared Animo and Daisy (older versions) with two other mechanistic models (SOILN and SUNDIAL). ...
... Unlike the findings of Bockstaller et al. (2009), in the present study, SALCA is considered a user-friendly model compared with the mechanistic models, Daisy and Animo, but being related to the use by LCA practitioners. Peter et al. (2016) and Torrellas et al. (2018) and compared Tier 1, Tier 2 and Tier 3 approaches to the estimation of greenhouse gases (GHG) in wheat crops and peach orchards, and emissions from a cow manure biogas plant in Catalonia, respectively. Both works used IPCC (2006) as Tier 1 model, Tier 2 model in Peter et al. (2016) was Bouwman et al. (2002) and in Torrellas et al. (2018) was regionalized EF to Catalonia. ...
... Peter et al. (2016) and Torrellas et al. (2018) and compared Tier 1, Tier 2 and Tier 3 approaches to the estimation of greenhouse gases (GHG) in wheat crops and peach orchards, and emissions from a cow manure biogas plant in Catalonia, respectively. Both works used IPCC (2006) as Tier 1 model, Tier 2 model in Peter et al. (2016) was Bouwman et al. (2002) and in Torrellas et al. (2018) was regionalized EF to Catalonia. Regarding Tier 3 models, Peter et al. (2016) decided not to select any model justifying that, at the moment, there was no model readily available and easily implementable by the user, and Torrellas et al. (2018) used EF estimated from field measurement. ...
Full-text available
Purpose Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA. Methods Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts. Results and discussion According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N 2 O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner. Conclusions Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.
... Nitrous oxide (N 2 O) emissions from nitrogen-fertilization in agricultural soils can be estimated by using emission factors or more accurate empirical or processed-based models (Goglio et al. 2018). To date, it is still a common practice to use emission factors in LCA studies due to the complexity and resources required to build and run more advanced models (Peter et al. 2016). Nitrous oxide emission models can be divided into two macro-groups, according to the function they use to estimate the emissions from the N-input: linear or exponential. ...
... According to Bouwman et al. (2002a), annual N 2 O emissions are significantly affected by rate and type of N-fertilizer, crop type, soil texture, soil organic carbon, soil drainage, soil pH, climate type, and length of the experiment, while NO emissions mainly depend on rate and type of N-fertilizer, soil organic carbon, and soil drainage. Several studies (Philibert et al. 2012;Kim et al. 2013;Shcherbak et al. 2014;Peter et al. 2016;) confirm the overall quality of the estimation generated by this model. A similar model developed by the same authors (Bouwman et al. 2002c) was used to estimate the ammonia (NH 3 ) losses from mineral N-fertilization. ...
Winter camelina [Camelina sativa (L.) Crantz] and field pennycress [Thlaspi arvense L.] are oilseed feedstocks that can be employed as winter-hardy cover crops in the current cropping systems in the U.S. upper Midwest. In addition to provide multiple ecosystem services, they can be a further source of income for the farmer. However, using these cover crops is a new agricultural practice that has only been studied recently. The objective of this study was to assess and compare the environmental performance of a maize [Zea mays L.]-soybean [Glycine max (L.) Merr.] cropping system with different winter cover crops-camelina, pennycress, and rye (Secale cereale L.)-in the U.S. upper Midwest. Field experiments were carried out from 2016 to 2017 (2-year maize-soybean sequence) at three locations: Morris (Minnesota), Ames (Iowa), and Prosper (North Dakota). The environmental impact assessment was carried out using a "cradle-to-gate" life cycle assessment methodology. Four impact categories were assessed: global warming potential (GWP), eutrophication, soil erosion, and soil organic carbon (SOC) variation. Two functional units (FU) were selected: (1) 1 ha year − 1 , and (2) $1 net margin. When expressed with the FU ha yr − 1 , across the three locations cover crops had (a) lower eutrophication potential and water soil erosion, and (b) lower GWP if the cover crop was not fertilized with nitrogen. Camelina and pennycress were more effective than rye in reducing soil losses, while the three cover crops provided similar results for eutrophication potential. The results for the SOC variation were mixed, but the sequence with rye had the best performance at all locations. When expressed with the FU $ net margin, sequences including camelina and pennycress were overall the worst sequences in mitigating greenhouse gas emissions and nutrient and soil losses. This negative performance was mainly due to the seed yield reduction in the second year of the sequence for both the main cash crop (soybean) and the relayed-cover crop compared with the conventional sequence maize-soybean. Such result led to a lower net margin per hectare in the sequences including camelina and pennycress when compared with the control. The results of this study suggest that the introduction of camelina and pennycress as winter-hardy cover crops has a strong potential for reducing the environmental impacts of the maize-soybean rotation. However, a field management optimization of these cover crops in a relay-cropping system is needed to make them a sustainable agricultural practice.
... Therefore, emissions of NOx, NH3, NO3 − , and PO4 3− were included in our LCI due to its relevance for various impact categories, such as acidification and eutrophication [29]. Moreover, field emissions from fertilisation (N2O) affect GHG emissions [30] and, therefore, the climate change impact category. ...
Full-text available
This study aimed to estimate the environmental impact of barley production in the Basque Country, Northern Spain, using cradle-to-gate life cycle assessment (LCA) methodology, as well as to assess how methodological choices (i.e., the use of IPCC 2019 Guidelines versus allocation methods) can influence such estimation. The production of mineral fertiliser and the direct emissions of nitrous oxide (N2O) resulting from the application of nitrogen (N) fertiliser were identified as the two main contributors (40% and 30% of all greenhouse gas emissions, respectively) to the environmental impact of barley production. Pertaining to GHG emissions themselves, the use of calcium ammonium nitrate fertiliser was found to be the main contributor. Therefore, the optimization of N fertiliser application was established as a key process to reduce the environmental impact of barley production. The fertiliser-related release of N and phosphorous (P) to the environment was the main contributor to particulate matter formation, terrestrial acidification, and terrestrial and marine eutrophication. The incorporation of environmental data on NH3, NOx, NO3−, and PO43− to the LCA led to a more accurate estimation of barley production impact. A sensitivity analysis showed that the use of economic allocation, compared to mass allocation, increased the estimation of climate change-related impact by 80%. In turn, the application of the IPCC 2019 Refinement Guidelines increased this estimation by a factor of 1.12 and 0.86 in wet regions and decreased in dry regions, respectively. Our results emphasise the importance of the choice of methodology, adapted to the specific case under study, when estimating the environmental impact of food production systems.
... Some results from both projects have already been published, e.g. Fiedler et al. 2015, Fiedler et al. 2016, Heintze et al. 2017, Hoffmann et al. 2018, Lucas-Moffat et al. 2018, Peter et al. 2016, Pohl et al. 2015. Further information is provided in the final project reports (only in German). ...
Full-text available
Greenhouse gas (GHG) emissions as well as other gaseous emissions and agronomic variables were measured for three years (2011/2012 - 2014/2015) at eight experimental field sites in Germany. All management activities were consistently documented. The database (GHG-DB-Thuenen) stores these multi-variable data sets of gas fluxes (CO2, N2O, CH4 and NH3), crop parameters (ontogenesis, aboveground biomass, grain and straw yield, N and C content, etc.), soil characteristics (nitrogen content, NH4-N, NO3-N, bulk density etc.), continuously recorded meteorological variables (air and soil temperatures, radiation, precipitation, etc.), management activities (sowing, harvest, soil tillage, fertilization, etc.), and their metadata (methods, further information about variables, etc.). In addition, N2 data were measured and analyzed. Site-specific calculated C and N balances for the respective crops and crop sequences are also available.
... where the factor classes are the crop type (grass, −0.158), the fertilizer type and amount (ammonium nitrate ≤50 kg, 0.134; ≤100 kg, 1.936), the application mode (broadcast, −1.305), and the soil drainage (good, 0.946). The amount of indirect emissions can be converted to NO 2 emission by multiplying NO and NH 3 emissions by the default value 0.01 [28,29]. NO 3 -leaching was calculated according to the semi-empirical model of Di and Cameron [30] considering volatilization and denitrification: ...
Full-text available
Low iLUC risk feedstocks, such as lignocellulosic no-food crops, have been indicated as sustainable crops for the transition to a bio-based economy. Given the high output to input ratio and the environmental benefits that can be obtained from renewable heat production replacing fossil fuels, the present study addressed the biomass yield, CO2-sequestration, and life cycle assessment of giant reed (Arundo donax L.) and miscanthus (Miscanthus × giganteus Greef et Deuter) growing under different soil water availability and nitrogen fertilization for three consecutive growing seasons in a semiarid Mediterranean environment. Giant reed outperformed miscanthus, showed a higher CO2-sequestration and a lower overall environmental impact. In case of both crops, the irrigation effect was significant, while the one of nitrogen fertilization was not apparent. While giant reed responded positively to reduced irrigation, compared to its highest level, as the plantation became older, miscanthus needed high water volume to get most out its potential yield. Nonetheless, the growing season had also a significant effect on both crops, mainly when low yields were achieved following the establishment year. Unlike the environmental benefits in the impact categories “non-renewable energy use” and “global warming potential”, environmental burdens concerning ozone depletion, acidification, and eutrophication were observed, indicating that further improvements of the evaluation of impact assessment associated with bioenergy production might be necessary.
... A generic Tier 1 approach is recommended for countries that do not have local emission factors; Tier 2 requires country-specific emission factors; and Tier 3 is applied when there are detailed emission factors measured according to environmental and management conditions of a location (IPCC, 2006(IPCC, , 2019. Tier 1 approach is adequate for large-scale studies but erroneous for detailed GHG calculations (Peter et al., 2016). Assessments of livestock production systems on environment require detailed approaches (Reinecke and Casey, 2017). ...
Livestock is a major producer of agricultural greenhouse gas emissions in South Africa. Cattle methane (CH4) from enteric fermentation is the main source of the emissions. However, due to shortage of information to guide agricultural mitigation plans in the country, the main objective of this study is to investigate causal factors of the emissions from cattle in all nine national provinces. This study calculates provincial CH4 emission factors and factors (i.e. nitrogen excretion rate and average annual nitrogen excretion per animal) required for nitrous oxide (N2O) emissions from cattle manure management. The study further uses these factors and other values obtained from the literature to calculate cattle CH4 emissions from enteric fermentation and manure management. It also provides similar N2O emissions from manure management as well as urine and dung deposited on the pasture, range and paddock. The emissions are calculated for each cattle type: commercial dairy, commercial beef, subsistence and feedlot cattle. Cattle in South Africa produced a total of 35.37 million tonnes (Mt) of carbon dioxide equivalent (CO2e) emissions in 2019, inclusive of emissions from pasture, range and paddock. Methane from enteric fermentation accounts for 64.54% of the total emissions followed by emissions from pasture, range and paddock (27.66%). Manure management contributes 4.34% of N2O to the total emissions while this source also produces 3.45% of CH4 emissions. Commercial beef is responsible for 50.21% of the total emissions, followed by subsistence beef (36.72%), commercial dairy (10.52%) and feedlot cattle (2.52%). The Eastern Cape province is the highest producer of cattle emissions with 8.66 Mt CO2e, a quarter of the emissions. It is followed by KwaZulu-Natal (7.14 Mt CO2e, 20%) and the Free State (5.65 Mt CO2e, 16%). Gauteng province is responsible for the lowest producer of the emissions with 0.71 Mt CO2e (2%) of the total. South Africa’s emission factors are higher than values for Africa, indicating importance of developing national factors to avoid uncertainties in emissions. As a result of national landscape and environmental conditions, the eastern provinces of the country are major sources of cattle emissions in the country.
... At present, many existing studies have focused on the evaluation of agricultural carbon emissions. Among them, most studies used the simple summation method and emission coefficients recommended by IPCC Guidelines for National Greenhouse Gas Inventories in 2006 to measure carbon emissions in the agricultural sector (Richards et al. 2016, Tubiello et al. 2013, Peter et al. 2016. For instance, according to the IPCC guidelines, Han et al. (2018) measured the carbon emissions in the agricultural sector in China from 1997 to 2015, Zhang et al. (2019) estimated the agricultural carbon emissions in China's main grain-producing areas during the period from 1996 to 2015, and Tian et al. (2016) estimated carbon emissions from agricultural production in Hunan Province in China during the period from 1995 to 2010. ...
Full-text available
Carbon emissions in agricultural production activities have become an important source of accelerating climate warming. At present, low-carbon agriculture is not only an important means to mitigate climate warming, but also a necessary process of transformation from traditional agriculture to modern agriculture. Therefore, to achieve the sustainable development of agriculture in China’s Western Taiwan Straits Economic Zone (WTS Economic Zone), the governments should vigorously promote the upgrading and realize the development of low-carbon agriculture. By adopting the latest emission coefficients and the ordered weighted averaging (OWA) aggregation operator, this paper selected agricultural land use, rice paddies, crop production, livestock manure storage and livestock enteric fermentation as the five carbon emission sources, and measured agricultural carbon emissions in the WTS Economic Zone from 2010 to 2017. Thus, from the time perspective, the average agricultural carbon emissions in the WTS Economic Zone showed a fluctuating downward trend, from 762.64 × 103 tonnes in 2010 to 710.02 × 103 tonnes in 2017. From the spatial perspective, total agricultural carbon emissions among regions are quite different. To further clarify the factors affecting agricultural carbon emissions in the WTS Economic Zone, by applying the geographically and temporally weighted regression (GTWR) model, this paper selected the research and development intensity, the added value of agriculture, the proportion of agricultural labour force, the overall level of urbanization, per capita disposable income of rural residents and per capita arable land areas as the influencing factors, and then measured the direction and degree of the influences on agricultural carbon emissions in different temporal-spatial backgrounds. The results showed that the added value of agriculture, the proportion of agricultural labour force and per capita arable land areas had positive influences on agricultural carbon emissions, while the research and development intensity, the overall level of urbanization and per capita disposable income of rural residents had negative impacts. Although agricultural carbon emissions in the WTS Economic Zone have decreased in recent years, further measures can be taken to effectively reduce agricultural carbon emissions, and ultimately promote the development of low carbon agriculture according to the results of this study.
... The present study relied on RothC simulations, which use site-specific data on climate, soil features and carbon input. For these reasons, it is considered a more reliable approach than, for instance, the default emission factors proposed in the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006) commonly applied in LCA (Peter, Fiore, Hagemann, Nendel, & Xiloyannis, 2016). Calibration of the model using SOC data representative of multi-annual WPM cultivation could further improve its reliability. ...
Full-text available
Maize silage is the main biogas co‐substrate in Germany, but its use is often questioned due to negative environmental impacts. Perennial wild plant mixtures (WPM) are increasingly considered alternatives, as these extensive systems improve soil quality and enhance agrobiodiversity. Methane yields per hectare however do not match those of maize. This study examined whether the potential advantages of replacing maize with WPM for biogas production are counteracted by lower yields and associated effects. Life cycle assessment and life cycle cost assessment were used to compare the environmental and economic performance of electricity generation from WPM in two establishment procedures, ‘standard’ (WPM E1) and ‘under maize’ (WPM E2). These metrics were benchmarked against those of maize. The production of 1 kWh electricity was chosen as functional unit. The life cycle inventory of the agricultural phase was based on multi‐annual field trials in southwest Germany. Both WPM E1 and E2 had lower marine eutrophication and global warming potentials than maize. The GWP favourability was however sensitive to the assumptions made with regard to the amount and fate of carbon sequestered in the soil. WPM E1 performed less favourable than WPM E2. This was mainly due to lower yields, which could in turn result in potential indirect land use impacts. These impacts may outweigh the carbon sequestration benefits of WPM cultivation. Maize performed best in terms of economic costs, freshwater eutrophication, terrestrial acidification, fine particulate matter and ozone formation. We conclude that the widespread deployment of WPM systems on productive agricultural land should only take place if permanent soil carbon sequestration can be ensured. In either case, WPM cultivation could be a valid alternative for bioenergy buffers and marginal land where competitive yields of common crops cannot be guaranteed, but which could accommodate low‐input cultivation systems.
Much of the global egg industry is currently transitioning from conventional cage to alternative (i.e. enriched cage, single- and multi-tier free run, free range, and organic) housing systems. While this transition is primarily motivated by animal welfare concerns, it also has significant potential to alter the environmental footprint of egg production, which is the fastest growing livestock sector worldwide. Understanding the benefits, impacts and improvement opportunities characteristic of alternative systems is hence imperative to ensuring net-positive sustainability outcomes. This requires attention to current resource efficiency levels, key variables that influence efficiency, as well as the environmental impact mitigation potential of efficiency gains for specific interventions and housing systems. The current analysis reports a joint application of data envelopment analysis (DEA) and life cycle assessment (LCA) to industrial egg production systems based on a large data set collected from egg production facilities in Canada. It was found that egg farms are generally operating at high levels of efficiency relative to one another with respect to feed and pullet inputs per tonne of eggs produced both within and between housing system types. DEA results suggest that feed and pullet inputs could decrease across all housing systems between 3.55% and 13.22%, which translated to environmental impacts reductions of up to 17.27%. Least shrinkage and selection operator models were unable to identify key drivers of efficiency for any system except enriched colony housing where an increase in lay cycle length of 1 day was associated with minor increases in efficiency, and the use of brown birds was associated with a 0.95% decrease in efficiency. Further research is necessary to determine key drivers of efficiency that may represent priority strategies for farmers to increase efficiency and decrease environmental impacts. Scenario analyses were used to calculate the cumulative environmental impacts of egg production assuming different distributions of production across housing systems and that DEA-efficient conditions are realized for all farms in each scenario. In all scenarios, 0% of production was attributed to conventional cages, reflecting a complete transition away from conventional production systems over time. The most likely of these scenarios, which included large increases in proportions of enriched and multi-tier free run housing, and moderate increases in free-range and organic housing, exhibited between 90.3%-100.1% of current (i.e. non DEA-efficient) levels of environmental impacts.
Full-text available
The energy sector worldwide is a significant source of air pollutant emission. In Poland, the vast majority of heat and electricity is generated in coal-fired heat and power plants. There is a common belief that high greenhouse gas emissions from the energy sector in Poland are mainly due to the technological processes involving the conversion of energy by burning fossil fuels. However, coal mining also causes a high environmental burden. This paper aimed to determine the carbon footprint of a typical hard coal-fired heating plant in Poland, taking into account mining of hard coal, its transport to the heating plant and useful energy generation in the heating plant. The investigation carried out allowed comparing the process steps and determining which of them is the dominant source of the greenhouse gas emissions. The obtained results show that hard coal mining and hard coal transport account for almost 65% and 5% of total equivalent carbon dioxide emission, respectively. Energy transformations in the heating plant account for 30% of total equivalent carbon dioxide emission, where approx. 29% is due to hard coal burning and 1% due to electricity consumption. The relative shares of carbon dioxide, methane and nitrous oxide in total equivalent carbon dioxide emission account for approx. 91%, 4% and 5%, respectively.
Full-text available
Functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided.
Despite large efforts there are still methodological challenges to bring life cycle modeling closer to agricultural reality. Here, we focus on the inclusion of the effects occurring between the crops grown in the same agricultural field in temporal succession. These so called crop-rotation effects are caused by changes in physical, chemical and biological properties of the agricultural land over time (presence and availability of different micro and macronutrients, soil structure, soil texture, phytosanitary conditions, presence of weeds, etc.) due to the rotation of crops. Since a huge number of parameters contribute to crop-rotation effects, they cannot be easily measured. Therefore, LCA (Life Cycle Assessment) studies with system boundaries containing only one vegetation period have a limited ability to include these effects — unless explicit modeling measures have been taken to include individual crop-rotation effects. Existing approaches for the inclusion of crop-rotation effects are described, e.g. via transferring certain amounts of nutrients and their environmental burdens to subsequent crops. Still, many crop-rotation effects between crops are not covered in recent LCA methodology; corresponding gaps are identified and described. Examples include reduced input of agrochemicals via improved phytosanitary conditions, stabilization of yields via reduction of harvest failures, improved yields via improved soil texture, soil structure and improved conditions for soil organisms. Overall, most crop-rotation effects are not properly addressed in current LCA practice. Thus, LCA results and the quality of derived recommendations are negatively affected — for example incentives for the (unlimited) removal of crop residues are set based on LCA results without considering potential adverse effects on soil fertility. In other words, these gaps might lead to unintended free-rider problems. A new approach for the modeling of crop-rotation effects is suggested. It consists of six steps. First, align the system boundary during the inventory analysis to the level of the whole crop rotation system; second, determine all inputs of the whole crop rotation; third, do the same for the outputs; fourth, convert all outputs to a common agriculture-specific denominator, the so-called Cereal Unit; fifth, calculate an output-specific allocation share using the ratio of each individual output to the sum of all outputs of the crop rotation; and sixth, apply the allocation shares to the sum of each input-type — resulting in the output-specific allocated input. One major advantage of this approach is the integration of crop-rotation systems into LCA, including all relationships between the individual crops of the crop rotation. Using this approach, LCA practice becomes able to depict crop rotations more accurately and to avoid the current practice of ignoring the effects between individual crops. It might enable LCA to consider the fundamental agricultural principle of crop rotations and to include interactions between one crop and the subsequent crop. Since these crop-rotation effects influence soil fertility, yields and overall sustainability of agricultural systems, the reliability of the evaluation of environmental impacts might be affected. Thus, the ability to consider the entire spectrum of crop rotation effects should be integrated into agricultural LCAs.
Chapter Fruit products are generally considered to be some of the less environmentally damaging foods in occidental diets. In fact studies investigating the carbon footprint of different food choices have reported that fruit is the category with the least environmental impact. However, these studies use data from environmental assessments of generic fruit production, which take no account of specific issues within orchard systems and fruit supply chains. Indeed, modern food production is very diverse, with high levels of specialisation and complexity. These features inevitably affect methodologies in the application of LCA to food products and agro-systems. It is therefore important to study what has already been done regarding standardisation of application protocols in order to make appropriate comparisons between products. In the present chapter, a review of LCA application in fruit systems is presented: papers from international journals, national journals, and conference proceedings have been reviewed. In particular, it can be assumed that mainstream research on the LCA applied to fruit production systems began around 2005; most of the papers were published in 2010 and 2012 in conjunction with international conferences on LCA in the agri-food sector. The review covers all the main criteria for conducting an LCA in fruit production systems. Specific issues considered are: aims and scopes, system boundaries, product considered, functional unit, data origin, life cycle-based methodology adopted, and environmental impact assessment method used. Furthermore this chapter investigates two aspects that are rarely considered in LCA studies of fruit systems: the role of nurseries in determine environmental impacts and the carbon storage properties of orchards.