Assessing life cycle environmental impacts of inoculating soybeans in Argentina with Bradyrhizobium japonicum

Abstract

Purpose
To estimate life cycle impacts from introducing the yield-enhancing inoculant containing the nitrogen-fixing bacterium Bradyrhizobium japonicum and the signal molecule lipochitooligosaccharide (LCO) in Argentinian soybean production. The study focuses on soybeans grown in rotation with corn in the Buenos Aires province. We also provide the life cycle impact assessment for the inoculant production. The study represents a novel scope in terms of the studied crop, inoculant type, and location.

Methods
Consequential LCA is used to assess the cradle-to-gate soybean production systems with and without inoculant use. Stepwise is used for quantification of 16 impacts at mid-point level. Also, the LCA-based guidance of Kløverpris et al. (2020) is followed, and we divide the change in impacts caused by the inoculant’s use into four effects. The field effect accounts for changes in field emissions. The yield effect accounts for additional soybean production in the inoculant system that displaces soybean production elsewhere (system expansion). The upstream effect covers the inoculant production and the downstream effect covers post-harvest changes such as soybean transport and drying. Small plot field-trials data is applied in the biogeochemical model DayCent to estimate field emissions, among others.

Results and discussion
The use of this inoculant reduces environmental impacts from soybean production in all studied impact categories. The main contributing factor is the yield effect, i.e., reduced impacts via avoided soybean production elsewhere including reduced pressure on land and thereby avoided impacts in the form of indirect land-use-change (iLUC). The field effect is the second-largest contributor to the overall impact reduction. Upstream and downstream effects only had minor influence on results. The yield and field effects are closely tied to the yield change from the inoculant use, which was not fully captured in the DayCent modeling. Thereby, a potential underestimation of the environmental benefits of roughly 10% can be expected, corresponding to the difference of empiric yield data and the modeled yield data in DayCent.

Conclusion and recommendations
The use of this inoculant shows environmental benefits and no trade-offs for the 16 impacts assessed. Results depend primarily on avoided soybean production (the yield effect) which entails iLUC impacts in Brazil and USA, and to a lesser degree on field emissions modelled with DayCent. Better data and parametrization of DayCent, to better capture the change in yields and estimate field emissions, economic modelling for the system expansion assumptions, and accounting for uncertainty in iLUC modelling could improve the assessment.

Full text

https://doi.org/10.1007/s11367-021-01929-7
LCA FORAGRICULTURE
Assessing life cycle environmental impacts ofinoculating soybeans
inArgentina withBradyrhizobium japonicum
AngelicaMendozaBeltran1 · ClausNordstrømScheel2· NualaFitton3· JannickSchmidt4· JesperHedalKløverpris2
Received: 8 July 2020 / Accepted: 18 May 2021
© The Author(s) 2021
Abstract
Purpose To estimate life cycle impacts from introducing the yield-enhancing inoculant containing the nitrogen-fixing bacte-
rium Bradyrhizobium japonicum and the signal molecule lipochitooligosaccharide (LCO) in Argentinian soybean production.
The study focuses on soybeans grown in rotation with corn in the Buenos Aires province. We also provide the life cycle
impact assessment for the inoculant production. The study represents a novel scope in terms of the studied crop, inoculant
type, and location.
Methods Consequential LCA is used to assess the cradle-to-gate soybean production systems with and without inoculant use.
Stepwise is used for quantification of 16 impacts at mid-point level. Also, the LCA-based guidance of Kløverpris etal.(2020)
is followed, and we divide the change in impacts caused by the inoculant’s use into four effects. The field effect accounts for
changes in field emissions. The yield effect accounts for additional soybean production in the inoculant system that displaces
soybean production elsewhere (system expansion). The upstream effect covers the inoculant production and the downstream
effect covers post-harvest changes such as soybean transport and drying. Small plot field-trials data is applied in the biogeo-
chemical model DayCent to estimate field emissions, among others.
Results and discussion The use of this inoculant reduces environmental impacts from soybean production in all studied
impact categories. The main contributing factor is the yield effect, i.e., reduced impacts via avoided soybean production
elsewhere including reduced pressure on land and thereby avoided impacts in the form of indirect land-use-change (iLUC).
The field effect is the second-largest contributor to the overall impact reduction. Upstream and downstream effects only had
minor influence on results. The yield and field effects are closely tied to the yield change from the inoculant use, which was
not fully captured in the DayCent modeling. Thereby, a potential underestimation of the environmental benefits of roughly
10% can be expected, corresponding to the difference of empiric yield data and the modeled yield data in DayCent.
Conclusion and recommendations The use of this inoculant shows environmental benefits and no trade-offs for the 16 impacts
assessed. Results depend primarily on avoided soybean production (the yield effect) which entails iLUC impacts in Brazil
and USA, and to a lesser degree on field emissions modelled with DayCent. Better data and parametrization of DayCent, to
better capture the change in yields and estimate field emissions, economic modelling for the system expansion assumptions,
and accounting for uncertainty in iLUC modelling could improve the assessment.
Keywords Environmental impact· N-fixation· Inoculant· Soybean· Agricultural practices
Communicated by Greg Thoma.
* Jesper Hedal Kløverpris
jklp@novozymes.com
Angelica Mendoza Beltran
angelica.mendoza@uab.cat
1 Institute ofEnvironmental Science andTechnology (ICTA),
Universitat Autonoma de Barcelona (UAB), Barcelona,
Spain
2 Novozymes A/S, Biologiens vej 2, 2800Kgs.Lyngby,
Denmark
3 Institute ofBiological andEnvironmental Sciences,
University ofAberdeen, Cruickshank Building, 23
St. Machar Drive, AberdeenAB243UU, UK
4 Department ofPlanning, Aalborg University, Rendsburggade
14, room 1.431, 9000Aalborg, Denmark
/ Published online: 2 August 2021
The International Journal of Life Cycle Assessment (2021) 26:1570–1585
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1 Introduction
In the past, the main goals of agricultural optimization were
solely related to productivity improvement. For instance,
yield enhancements and economic optimization were pri-
oritized without further considering potential environmen-
tal trade-offs that some practices could give rise to (Foley
etal. 2011; van Noordwijk and Brussaard 2014). Today, the
drive for productivity is increasingly combined with a need
for agricultural sustainability (Leggett etal. 2017). This
implies that an important additional aim is to minimize
environmental impacts of food consumed. Some alterna-
tives to achieve this are for instance, reducing land use and
chemical use, or ensuring optimal nutrient and carbon bal-
ances in agricultural soils (Horrigan etal. 2002). Sustain-
able agricultural practices have already been widely docu-
mented (Tilman etal. 2001; Godfray etal. 2010; Rockström
etal. 2017) and inoculants—also known as biofertilizers—
is a technology combining agricultural optimization with
a sustainability focus (Santos etal. 2019). Agricultural
inoculants have been known for more than a century and
are broadly used while still holding further potential for
widespread use (Santos etal. 2019). Soybean inoculation
is largest in South American countries while in the USA
only about 15% of the area with soybean cultivation has
been inoculated (Santos etal. 2019).
Generally, inoculants contain microorganisms that tar-
get specific processes in growing plants or the surrounding
soil to enhance the health and growth of plants (Nadeem
etal. 2013) and therefore may change productivity. For
instance, the microbial phosphate inoculant P. bilaiae aids
the uptake of soil nutrients in corn plants causing higher
yields (Leggett etal. 2015) and overall environmental
benefits such as reduction of global warming and nutri-
ent enrichment impacts (Kløverpris etal. 2020). Likewise,
there is a specific type of agricultural inoculants within
the nitrogen (N) fixing category that focuses on symbi-
otic N-fixation. Availability of N is often the limiting soil
nutrient factor for plant growth (Andrews etal. 2003),
and thus, the symbiotic relation between the plant and the
microorganism that enhances N-fixation has potential to
increase crop yields. That is the case for the symbiosis
between soybean and the bacterial inoculant made with
Bradyrhizobium (Keyser and Li 1992). Several reviews
have shown the positive effects of a range of N-fixing bac-
teria, among others, on plant nutrient uptake and N avail-
ability (Andrews etal. 2003; Adesemoye and Kloepper
2009; Di Benedetto etal. 2017; Backer etal. 2018).
In addition to the productivity debate (Adesemoye and
Kloepper 2009; Backer etal. 2018), a broader environ-
mental assessment of the use of inoculants would be more
relevant for agricultural sustainability assessments. Recent
research has focused on identifying possible ways in which
inoculants may help in the reduction of environmental
impacts. For instance, Nadeem etal. (2013) reviewed
the potential of bacterial inoculants in future sustainable
agriculture and suggests that inoculants could reduce the
impact of biotic and abiotic stress factors such as pathogen
attach and extreme temperature, and thereby increase, for
instance, climate resilience of crops. Alori and Babalola
(2018) identified the additional potential of inoculants use
to reduce agro-chemicals such as pesticides and chemi-
cal fertilizers, similar to the study by Alves etal. (2003)
who found that N fertilizer could be completely avoided
for soybean produced in Brazil when properly inoculated
with Bradyrhizobium. The knowledge on the mechanisms
of action of the microbial inoculants plays a vital role in
their use for sustainable agriculture. For instance, under-
standing their relationship with nutrient flows, yields and
agricultural inputs, is vital information to understand their
environmental impacts. Yet, to our knowledge, only one
study quantified the environmental effects of introducing
inoculants in agricultural practices.
Kløverpris etal. (2020) use life cycle assessment (LCA),
as per ISO 14040 (ISO 2006), as a basis for articulating
specific methodological guidance (detailed in Sect.2.2)
with respect to assessing the environmental consequences
of alternative agricultural practices. With this methodology,
they assess the effects of introducing a microbial phosphate
inoculant as a yield-enhancer in the production of corn in
the USA. Results show that the environmental consequences
of introducing a microbial phosphate inoculant to corn are
significant environmental benefits with no trade-offs, in par-
ticular reduction of climate change, eutrophication and land
use change impacts. These benefits come from reduction in
direct emissions from the cropland and from reduced use of
land and other agricultural inputs elsewhere when more crop
can be grown on the same field. Besides this application of
the methodological guidance of Kløverpris etal. (2020), no
other crops, inoculant types and locations have been studied
with it or with LCA more broadly.
In the present study, we apply the LCA-based method-
ological framework described by Kløverpris etal. (2020)
to a new inoculant, crop rotation, and region. We study
soybean production in a corn-soybean rotation, with and
without the use of a novel N-fixing inoculant in the Buenos
Aires region of Argentina. The inoculant contains a natu-
rally occurring, root-nodulating, microsymbiotic N-fixing
bacterium called Bradyrhizobium japonicum and a signal
molecule called lipochitooligosaccharide (LCO). The inocu-
lant is manufactured by Novozymes and is nowadays mar-
keted under the name Nitragin Optimize® II (from here on
referred to as the inoculant) in Argentina. The signal mol-
ecule (LCO) increases the consistency and effectiveness of
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the rhizobium-soybean symbiosis. This molecule initiates
nodulation, stimulates the root system, and enhances nod-
ule development leading to enhanced N-fixation (Leggett
etal. 2017). The inoculant action on soybean cultivation
follows several steps. Newly emerged soybean plants rely
on their cotyledon leaves as a source of N. Once that supply
is exhausted, the N-deficient plant exudes flavonoid signal
molecules. The rhizobia bacteria in the soil sense the fla-
vonoids and release their own unique signal molecule, the
LCO molecule (nod factor). This signal molecule initiates
a process of root cell elongation and cell division creating
infection sites for the symbiotic rhizobia bacteria within the
soybean roots. This inoculant replaces the need for the plants
to wait until the nodulation cycle is complete by bypassing
the need to release the flavonoids from the plants and/or the
release of LCO. The LCO molecule is present on the seed
as well as the rhizobia inoculant, starting the growth process
and rhizobia nodulation at emergence. Nodulation timing,
root length, and volume can increase greatly. This changes
N and carbon (C) nutrient cycles, crop yields (Leggett etal.
2017), and general plant growth, thereby also affecting the
amount of plant residues.
This study will not only provide an assessment of
the environmental impacts of the use of LCO-fortified
Bradyrhizobium japonicum in soybean production in Buenos
Aires, Argentina but also the life cycle impact assessment
for the inoculant production which could be of use in other
LCA studies.
In the following sections, we first explain how we imple-
ment and capture the mode of action of the inoculant within
the goal, scope and life cycle inventory of the LCA. We then
present the results and discuss the limitations and uncertain-
ties around the LCA. Finally, we conclude and recommend
further improvements and research.
2 Methods
2.1 Goal andscope
The goal of this study is to quantify environmental conse-
quences of introducing an inoculant containing Bradyrhizo-
bium japonicum and LCO in conventional soybean produc-
tion from cradle-to-gate using a consequential LCA. The
functional unit is one metric ton (Mg) of dried soybeans
at farm, ready for the market (13% moisture at harvest and
11% moisture after drying). The soybean is cultivated in the
province of Buenos Aires, Argentina (referred to as AR)
in a corn-soybean rotation. The geographic scope is at the
province level, as the corresponding largest multi-annual
dataset for yields (n = 58) is available at this level (Leggett
etal. 2017). Field data collection took place between 2009
and 2013, and the temporal scope of the study is within the
short-term future. Figure1 shows the data flows from field
trial data to life cycle inventory data. The full inventory and
its calculation are described in coming sections.
The method used for life cycle impact assessment (LCIA)
is Stepwise 2006, version 1.7. The method is described and
documented in Annex II of Weidema etal. (2008) and in
Weidema (2009), and updates for nature occupation in
Schmidt and Saxcé (2016). The characterization module of
Stepwise is based on a combination of the Impact 2002+
method (Jolliet etal. 2003) and the EDIP 2003 method
(Hauschild and Potting 2005). For the detailed description
of the impact categories and methods in Stepwise, see the
electronic supplementary material (ESM), Sect.1.2. Results
are presented at mid-point level of impact, i.e., without nor-
malization and weighting.
2.2 Four environmental effects fromtheuse
ofBradyrhizobium japonicum
Two systems are compared as this study focuses on the
changes in environmental impacts from introducing the
inoculant. In the reference system (Ref), soybean is grown
conventionally in rotation with corn. Here, standard agricul-
tural inputs are applied to the field and then harvested and
dried to get an output of corn in year 1 and soybean in year 2.
The second system is the inoculant system which considers
soybean grown with Bradyrhizobium japonicum (referred to
as B.j-LCO) in year 2 following conventional corn produc-
tion in year 1. It is assumed that the corn output in uneven
years (year 1, 3, 5, …, 99) is unaffected by the introduction
of the inoculant for the biogeochemical modelling period of
100 years, as will be detailed further.
Following the approach by Kløverpris etal. (2020), the
two systems are divided into four categories, i.e., upstream,
field, yield, and downstream and the difference in impact
between the two systems within each category is referred
to as an “effect.” The upstream effect includes the produc-
tion of the inoculant in the inoculant system. The field effect
includes a change in direct emissions from the field in the
inoculant system in comparison with the reference system.
The yield effect considers the higher output of soybean in
the inoculant system per unit of area compared to the refer-
ence system. To ensure that the two systems provide the
same output, i.e., that the two systems are “equivalent” and
therefore comparable, the inoculant system is expanded to
include displacement of marginal soybean production else-
where. Finally, the downstream effect accounts for changes
in drying and transport from field to farm in the inoculant
system compared to the reference. See the ESM, Sect.1.1,
for details on the equations to calculate each effect.
Figure2 shows the reference and the inoculant systems.
Figure2 also shows the cultivated area in both systems (A),
the output of soybeans from area A in the reference system
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(Q), the increase in soybean output from area A when the
inoculant is used (∆Q) and the area of soybean produc-
tion displaced when the inoculant is used (B). These are
all important parameters to determine the individual effects
(ESM, Sect.1.1).
2.3 Inventory
The inventory data and assumptions for the effects’ model-
ling are described here. Details for the area A in the ref-
erence system and the inoculant system are available in
Table1.
2.3.1 Upstream effect: theinoculant production
The inoculant production takes place in a fermentation facil-
ity and has three main stages: the laboratory, the fermenta-
tion, and the post-fermentation stage. In general, all inven-
tory flows are estimated based on the inoculant’s production
of the total volume of liquid processed at the fermentation
facility in 2017. For the materials, energy, transport, emis-
sions, waste streams, and capital goods and services that are
included in this inventory, see the ESM, Sect.2.1.
Finally, the amount of inoculant used in the inoculant
system is 0.15 kg per ha, as estimated based on the rec-
ommended dose for all Nitragin Optimize® II products
for South America and the assumed standard seeding rate
(USDA 2019).
2.3.2 Field effect
Soybean cultivation and biogeochemical modelling with
DayCent The DayCent model was used to simulate the use
of the inoculant in the soybean production (Del Grosso etal.
2001, 2006; Parton etal. 2001). Initially, DayCent simu-
lates the corn-soybean rotation for the reference scenario.
For this, input parameters include temperature, precipitation,
soil texture, plant growth, and management events such as
fertilization and harvests. Data for these parameters were
obtained from a regional calibration of data for soybean
after corn production, combining typical productivity and
management practices sourced from agronomic experts from
Novozymes, with soil (most dominant soil type) and climate
(daily climate data averaged across the region) parameters
sourced from the Harmonized World Soil Database (HWSD)
and National Aeronautics and Space Administration (NASA)
databases, respectively.
Two additional assumptions were made to model the
inoculant mode of action system. First, the average per-
centage change in soybean yield achieved by the inocu-
lant system and simulated in DayCent is based on data
obtained from the field data for Buenos Aires (Sect.2.3.3).
Second, the effect of the inoculant in plant growth and N
uptake by the plant was simulated by gradually increas-
ing the parameter in DayCent that defines the maximum
amount of N fixed per gram of C fixed via net primary
production (NPP), if there is insufficient N within the
soil. This parameter is called the SNFXMX parameter
and it was gradually increased until the average percent-
age change in soybean yield with inoculant use from the
field was achieved in the simulations. Modification of this
parameter is used as a simple yet representative proxy to
characterize the inoculant’s mode of action, particularly
capturing the effect the inoculant has on plant growth and
on N uptake within the model. The baseline rate of N-fixa-
tion without the use of the inoculant had a value of 0.003 g
N/g C NPP i.e. this is the SNFXMX value for the reference
system. Simulations for both systems run for a 100-year
period. More details on DayCent modelling are provided
in the ESM, Sect.2.2.1. The key DayCent outputs were
the nutrient balances shown in the ESM, Sect. 2.2.2. These
are the base for the inventory of the cultivation process
emissions as explain below.
Cultivation emissions based on DayCent simulations DayCent-
modelled outputs include soil organic carbon (SOC) content,
CH4 emissions from aerobic and anaerobic reactions, N in
plant grain, as well as N-related emissions to air, i.e., N2O
(direct), NH3, NO, N2, and NO3
− emissions to water for the ref-
erence and inoculant systems. SOC changes between systems
were used to estimate CO2 emissions (see below). Change in
N2O emissions are observed because the inoculant can pro-
mote root growth and plant productivity. Over a long period of
time, the increase in plant productivity returns higher C inputs
into the soil, resulting in a higher soil C level in the inoculant
system compared to the reference system. This in turn results
in a slight increase in the corn yields (~0.4%) over the 100-year
period, too. Soil C and its retention within the soil is key to
soil health and its relationship with crop yields is well estab-
lished (Oldfield etal. 2019). Therefore, the modelled change
in corn yields may also be expected. However, the increase in
corn yield has conservatively been omitted in the LCA as it
is assumed that corn production inputs and outputs (including
emissions) remain the same in both systems. This is because,
while corn yields increase, the level of increase is so small
(~0.4%), it cannot be safely assumed that fertilizer applica-
tion or any other management activates around corn would
change due to the presence of the inoculant. Table1 shows the
emissions based on DayCent simulations as used in the inven-
tory for soybean, as well as the simulated emissions for corn,
for indication. The ESM, Sect.2.2.2, shows the uncertainty
parameters of the DayCent modelled emissions.
SOC‑related emissions The SOC emissions are calculated
with a time-independent approach elaborated by Schmidt
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Table 1 Inventory of soybean production in a corn-soybean rotation, with (B.j-LCO) and without (Ref) the use of inoculant. All flows normal-
ized to 1 ha
Flow Unit Ref B.j-LCO Data source (section)
Output Value σg
2 (pedigree
scores)***
Value σg
2 (pedigree scores)***
Soybean yield (DayCent) Mg 2.626 2.29 2.747* 2.13 Selected data from Leggett
etal. (2017), Sect.2.3.3
Inputs
Pesticide application ha 4.30 - 4.30 - Leggett etal. (2017),
Sect.2.3.2
Combine harvesting ha 0.30 - 0.30 - Ecoinvent v3.4, Sect.2.3.2
Tillage ha 0 - 0 - Leggett etal. (2017),
Sect.2.3.2
Sowing ha 1.00 - 1.00 - Ecoinvent v3.4, Sect.2.3.2
Fertilizing ha 1.00 - 1.00 - Leggett etal. (2017),
Sect.2.3.2
Phosphate fertilizer, as P2O5kg 11.45 - 11.45 - Selected data from Leggett
etal. (2017), Sect.2.3.2
Packaging, for fertilizers and
pesticides
kg 12.37 - 12.37 - Calculated proxy
Pyrethroid-compound kg 0.06 - 0.06 - Ecoinvent v3.4, Sect.2.3.2
Organophosphorus-compound,
unspecified
kg 0.53 - 0.53 - Ecoinvent v3.4, Sect.2.3.2
Phenoxy-compound kg 0.30 - 0.30 - Ecoinvent v3.4, Sect.2.3.2
Glyphosate kg 0.003 - 0.003 - Ecoinvent v3.4, Sect.2.3.2
Triazine-compound, unspecified kg 0.02 - 0.02 - Ecoinvent v3.4, Sect.2.3.2
Seeds kg 65.6 - 65.6 - (USDA 2019), Sect.2.3.2
Water l 0 - 0 - Leggett etal. (2017),
Sect.2.3.2
Transport field to farm tkm 41.55 3.18 (4,4,4,4,4,2.0) 43.46 3.18 (4,4,4,4,4,2.0) Calculated proxy, Sect.2.3.4
Drying of soybean l 68.22 2.46 (4,4,4,4,4,1.05) 71.36 2.46 (4,4,4,4,4,1.05) Calculated proxy, Sect.2.3.4
Inoculant kg 0 1.55 (2,2,1,2,1,1.05) 0.150 1.55 (2,2,1,2,1,1.05) Recommended dose,
Sect.2.3.1
Land occupation ha year−1 1 - 1 - Area A, Sect.2.3.2
Market for arable land {GLO} ha year 1 - 1 - iLUC, Sect.2.3.2
Avoided production
Soybean {GLO}| market for kg 0 - −121 28.9 Avoided production,
Sect.2.3.3
Emissions for soybean
NH3-N emissions to air kg N 3.66 3.47 3.67 3.45 DayCent, Sect.2.3.2
NO-N emissions to air kg N 0.76 4.83 0.77 4.79 DayCent, Sect.2.3.2
N2O-N emissions (direct) to air kg N 0.576 4.75 0.582 4.68 DayCent, Sect.2.3.2
N2-N emissions to air kg N 0.25 8.01 0.16 15.52 DayCent, Sect.2.3.2
CH4 emissions to air kg CH42.17 1.12 2.16 1.12 DayCent, Sect.2.3.2
N2O-N emissions (indirect)
to air
kg N 0.1067 - 0.1072 - IPCC, Sect.2.3.2
NO3
--N emissions to water kg N 8.33 10.28 8.37 10.13 DayCent, Sect.2.3.2
Phosphate emission to water kg P 0 - 0 - Dalgaard etal. (2006),
Sect.2.3.2
SOC CO2 emissions to air kg CO20 - −18.3 1.37*** DayCent, Sect.2.3.2
Emissions from corn**
NH3-N emissions to air kg N 5.42 - 5.56 - DayCent, Sect.2.3.2
NO-N emissions to air kg N 1.41 - 1.41 - DayCent, Sect.2.3.2
N2O-N emissions (direct) to air kg N 0.93 - 0.94 - DayCent, Sect.2.3.2
N2-N emissions to air kg N 0.17 - 0.28 - DayCent, Sect.2.3.2
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and Brandão (2013), building on the time-independent,
decay-function-based CO2 characterization factors described
by Petersen etal. (2013). Kløverpris etal. (2020) recom-
mends the use of the time-independent approach which
looks at the change in radiative forcing related to a single
event, in this case, one year of soybean production with
inoculant use. The inoculant application impacts the trend
of SOC over the subsequent time period. This trend can be
compared to the trend in the reference scenario (without
inoculant) and, in this way, changes can be estimated. The
estimated change in SOC emission are −18.3 in kg CO2 ha−1
year−1 for the 100-year modelled period (ESM, Sect.2.2.3,
and Appendix II). An additional annualized approach to
calculate SOC emissions (for 20 and 50 years) is described
in the ESM, Sect.2.2.3, and it is used in the sensitivity sce-
narios (see ESM, Sect.4).
Residues The LCA follows DayCent modelling of biomass
from residues (Abodeely etal. 2012). At harvest, DayCent
separates the entire above and belowground biomass into
grain and “other biomass.” The other biomass is divided into
a fraction returned to the field, i.e., left behind (residues)
and adding nutrients to either soy or corn (thus accounted
for in the nutrient balances), and the remaining is removed
from the field. The fraction removed has been set to 70%
of the total residues following previous DayCent runs from
Fitton etal. (2014, 2017). The difference of removed resi-
dues between inoculant and reference systems in Mg dry
matter ha-1 yr-1 for corn is 0.02 and 0.16 for soybean (ESM,
Sect.2.2.4). No inventory assumption was made on the
treatment of these removed residues because it is not clear
what happens to them after being removed and the range of
treatment options is large, as discussed in the ESM. This is
a cutoff of the system. More data collection from the field
practices could improve the assessment.
Other field emissions Other field emissions included in the
inventory were indirect N2O emissions to air and phosphate
emissions to water (Table1). Indirect N2O emissions were esti-
mated using the IPCC 2006 Guidelines for National Greenhouse
Gas Inventories Tier 1 (De Klein etal. 2006)and N-related
parameters calculated with DayCent, i.e., N-volatilization,
N-redeposition, N-leaching, ammonia emissions, NOx emis-
sions, and nitrate emission to water. Calculation details are
shown in the ESM, Sect.2.2.5.
Phosphate emissions are calculated based on Dalgaard
etal. (2006) as a fraction of 2.9% of the surplus of P, esti-
mated as the difference between phosphate fertilizer input
and phosphorus removal in the crop. Similar to DayCent
emissions, these emissions are only estimated for soybean
production as corn cultivation is assumed to remain the same
between systems. More details of this calculation are shown
in the ESM, Sect.2.2.5.
Other inputs to soybean cultivation Other inputs to soy-
bean cultivation considered are as follows: seeds, fertiliz-
ers and packaging, pesticides and packaging, energy use for
crop management (plant protection applications, harvest-
ing, tillage, ploughing, sowing, and fertilizing), and water
use (Table1). Assumptions and background processes used
for each input are described in the ESM, Sect.2.2.6. These
inputs remain the same on area A, for the reference and inoc-
ulant systems, and were added, for completeness. Land-use-
change (LUC) was also added for completeness and remains
equal between systems. It is accounted in the form of direct
* To avoid double counting, the yield entered in the SimaPro software for the inventories is 2.626 Mg/ha * year. The table shows the actual yields
to display the change between systems
** DayCent modelled emissions for corn are shown for indication here. It was assumed no inputs or outputs of corn production changed between
systems
*** Uncertainty information for selected flows of the reference and inoculant systems in AR. σg
2 is included in SimaPro together with lognormal
distribution for all flows to characterize their uncertainty, except for SOC for which the assumed distribution for CO2 emissions is normal in
order to account for the negative emissions
Table 1 (continued)
Flow Unit Ref B.j-LCO Data source (section)
Output Value σg
2 (pedigree
scores)***
Value σg
2 (pedigree scores)***
CH4 emissions to air kg CH42.08 - 2.08 - DayCent, Sect.2.3.2
N2O-N emissions (indirect)
to air
kg N 0.163 - 0.165 - IPCC, Sect.2.3.2
NO3
−-N emissions to water kg N 12.63 - 12.74 - DayCent, Sect.2.3.2
Phosphate emission to water kg P n.a - n.a - Dalgaard etal. (2006),
Sect.2.3.2
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and indirect land use change (dLUC and iLUC) and has the
same magnitudes for the reference and inoculant systems i.e.
one ha yr-1 of occupied arable land for dLUC and one ha
yr for iLUC, linked to the global marginal market for land
(ESM, Sect.2.2.7, and Schmidt etal. 2015) and (Table1).
For more details on LUC accounting and modelling, see the
ESM, Sect.2.2.6.
2.3.3 Yield effect
Yield modelling Field trial data (Leggett etal. 2017) was
collected for nine provinces in Argentina (ESM, Sect.2.3.1).
Yields had a relative increment of 6.4% due to the inoculant
use. These data were refined to a smaller dataset via a plot
analysis (ESM, Sect.2.3.1), resulting in a dataset that con-
tains data only from Buenos Aires, Argentina, with a relative
increment of yields of 5.1% (n = 58). The final relative yield
increment used in the LCA was determined with this dataset
and with DayCent simulations as explained in Sect.2.3.2.
The resulting modelled yield increase was 4.6% in Buenos
Aires. All yield-related values are shown in Table2. The
difference between change in modelled yields (4.6%) and
change in yields from the plot analysis for Buenos Aires
(5.1%) is about a 10%, yet it is the closest long-term match
achieved by means of the simulations. The impossibility to
replicate the plot analysis yields in DayCent is rooted in two
reasons. First, the annual modelled yield, and yield response
to the presence of the inoculant is not fixed and varies in
relation to the climate, as a consequence interannual vari-
ation in soybean yield is greater than the increase in yield
due to the inoculant use. Secondly, the change in yield, due
to the presence of the inoculant, was smaller in years with
projected lower yields.
The modelled relative change in yields leads to a total
change in production between the systems with and without
inoculant of 0.121 mg DM ha−1 year−1 (Table2). This is the
value used for the system expansion as the avoided soybean
production in the global marginal market of soybean due to
the inoculant use (Table1). We use the DayCent modelled
yields to keep consistency with the modelled emissions
within the LCI, despite them being lower than those from
the plot analysis. The use of lower modelled yields leads to
a conservative assessment of the benefits from the inoculant
use.
Global marginal market of soybean According to the meth-
odology to identify marginal suppliers in LCA described in
Weidema etal. (2009) and Weidema (2003), the marginal
producers (on a country level) are the first to react to changes
in the market, e.g. in demand. Similarly, they would be the
first producers to pause production increases if a new tech-
nology (e.g., a yield-enhancing inoculant) increased sup-
ply elsewhere. Hence, it is assumed that the producers, that
would first react to a change in demand, are located in the
countries with the recently largest increase in production
(i.e., an assumed reaction to increasing demands). Based
on a linear regression of FAOSTAT data from 2012 to 2016
(FAO 2018), the marginal suppliers of soybean in the global
market were assessed to be located in USA and Brazil, where
the largest increases were observed. Together, the two coun-
tries represent about 60% of the global production of soy-
beans. The distribution of 52% soybeans from USA and 48%
soybeans from Brazil in the marginal market corresponds to
a scaled share of global production but only accounting for
these countries (ESM, Sect.2.3.2).
The life cycle inventory for soybean production in the USA
and Brazil for this market is based on data from Schmidt and
De Rosa (2018). It includes inputs such as fertilizer, diesel,
land, irrigation, and outputs such as soybean production and
main field emissions (ESM, Sect.2.3.2). The input of land
is linked with the global marginal market of land used to
model iLUC (ESM, Sect.2.2.7). According to Schmidt etal.
(2015), there is a global market for land concerned with
production capacity of land, instead of land area. Countries
that supply land to this market are all countries that expand
their arable land and countries that intensify their existing
productive land. Thus, there is supply through expansion and
Table 2 Soybean yields with (B.j-LCO) and without (Ref) the use of the inoculant. DayCent modelled yields are used in the LCIs (values in
bold)
* Corresponds to 9 provinces in Argentina
** Corresponds to Buenos Aires, Argentina (number of plots = 58)
*** Arithmetic standard deviation
Yields [Mg dry matter ha−1 year−1]
Field trials (Leggett etal. 2017) Plot Analysis Modelled by DayCent
Ref B.j-LCO Change in yield
(ΔQ = B.j-LCO
– Ref)
Change in soybean
yield (%)
Change in soybean
yield (%)
Ref B.j-LCO Change in yield
(ΔQ = B.j-LCO
– Ref)
Change in
soybean yield
(%)
2.55* 2.72* 0.17 +6.4* + 5.1** 2.626 (0.66)*** 2.747 (0.69)*** 0.121 +4.6
1576 The International Journal of Life Cycle Assessment (2021) 26:1570–1585
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intensification. In fact, the global market of land operates
with different markets for land, i.e., (1) arable land, (2) inten-
sive forest land, (3) extensive forest land, and (4) grassland.
This delimits land types with different potential uses. Land
use change (LUC) can be direct (dLUC) if the reference use
of land is directly changed or indirect (iLUC) if it is induced
via the market i.e. the use of land by the crop under study is
what is considered as dLUC, while the supply of new land
caused by the need for compensating the production capac-
ity of the land required by the new demand is considered as
iLUC. In our case, the avoided production of soybean in the
global marginal market of soybean is linked to the global
marginal market of land as it is expected for the marginal
producers to avoid the need for new agricultural land.
2.3.4 Downstream effect—soybean transport anddrying
Once harvested, the transport of soybeans from field to farm
by tractor is added for both systems. Such transport has been
calculated as the yield in wet weight times the distance from
field to farm. Moisture at harvest is 13% and the distance
field-farm is 14 km (ecoinvent v3.4. data for soybean pro-
duction for Argentina). Similarly, the impact of transporting
soybean from field to farm in the global marginal market of
soybean, i.e., in Brazil and USA (needed for the substituted
soybean production in the inoculant system) was calculated.
The difference between the two systems covers the down-
stream effect of transport (ESM, Sect.2.4.1).
For drying of additional and substituted soybean, mois-
ture at storage was assumed to be 11% for all locations.
For more details on the drying calculations, see the ESM,
Sect.2.4.2.
2.3.5 Background processes
All processes used in the background of the reference and
inoculant systems are from the ecoinvent v3.4 database,
consequential model (Wernet etal. 2016). The two back-
ground processes that are not drawn from this database are
the global marginal market of soybean (Sect.2.3.3) and the
global marginal market of land used for iLUC modelling
(ESM, Sect.2.2.7, and Schmidt etal. 2015).
2.3.6 Uncertainty analysis andsensitivity scenarios
An uncertainty analysis was conducted to account for
parameter uncertainty among which for DayCent emis-
sions, yields, inoculant use and substituted soybean (see
ESM, Sect.5, for all details and Table1). Pairwise Monte
Carlo simulations are run, and based on these, we calcu-
late the discernibility analysis count per impact and the
statistical significance using a null hypothesis testing by
means of a paired t-test, for all impacts as well. This analy-
sis aims to show whether the mean of the relative impacts
of two systems are significantly different from each other.
For the sensitivity analysis, we performed the three one-
at-the time scenarios: (1) 20- and 50-year annualized SOC
emissions (as described by Kløverpris etal.2020) instead
of the time-independent approach, (2) higher impacts in
the production of the inoculant to address the missing
feedstocks in the inventory of this activity, and (3) exclud-
ing iLUC emissions that appeared as key contributors to
the impacts (ESM, Sect.4).
2.4 Impacts notincluded intheLCA
Two important environmental impacts are not explicitly
quantified in this study and deserve further research: soil
microflora and water impacts. Microbial inoculants can
impact the soil microflora temporarily and in the long
term, and these effects are still not well understood and
need further research (Trabelsi and Mhamdi 2013). The
effect of the inoculant, on synergistic effects, interactions
and co-inoculation of B.j-LCO and other microorganisms
on soybean productivity has been studied (Chibeba etal.
2015; Egamberdieva etal. 2016; Meena etal. 2018). Also,
the competitive behavior of the inoculant with indigenous
microorganisms has been studied, finding that it can be
used to enhance N-fixation and productivity in organic
soybean and may save chemical fertilizer use (Abou-
Shanab etal. 2017). To our knowledge, no studies cover
specifically the impacts of the use of B.j-LCO inoculant
on soil microflora and it would be an impact that needs
further developments in LCA.
For water impacts, the development of bigger crops with
higher yields will likely lead to higher evapotranspiration
from the specific field and thereby a higher “green” water
footprint (Mekonnen and Hoekstra 2011). In case the crops
are fully rain-fed, this is not a major issue, e.g., in Buenos
Aires soybean (Leggett etal. 2017). Also, higher yields due
to the inoculant use avoid more soybean production else-
where. Hence, the net water use is likely to be more or less
unaffected per Mg of soybean produced, although this will
depend on the efficiency of water use in the different loca-
tions. Besides irrigation water, some water is used in produc-
tion of the inoculant (upstream effect). This is likely off-set
by the water saved through the yield effect. We included the
use of water in the inoculant production and no irrigation
was assumed in the marginal producers of soybean. With
data for specific locations, this impact could be added to the
LCA impacts.
1577The International Journal of Life Cycle Assessment (2021) 26:1570–1585
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3 Results
3.1 Life cycle impact assessment results
Table3 shows the LCIA results per kg of inoculant as
implemented in this study.
Table4 shows the effects of introducing the inoculant
in soybean cultivation for the 16 environmental impact
categories considered. The total impacts for the reference
system are shown for comparison as well as the relative
changes in impacts caused by the inoculant. Impacts are
reduced for all impact categories in the inoculant system
with respect to the reference system. Among the four
effects, the yield effect is dominating the impact reduc-
tions. The field effect also contributes to impact reduc-
tions, particularly of global warming, whereas upstream
and downstream effects are relatively insubstantial.
Figure3 shows the contribution of different activities to
each impact category and each effect, for the two systems.
Negative contributions (i.e., reduction of impacts) are
shown for the avoided soybean production. These results
correspond with the yield effect and are therefore only vis-
ible for the inoculant system. Field emissions are the only
contributor to the field effect. The inoculant production
contributes to the upstream effect and finally, drying and
transport correspond with the downstream effect. More
detailed results for the contribution analysis, contribu-
tion of effects and total impacts are available in the ESM,
Sect.3.1.
3.2 Sensitivity anduncertainty analysis results
Here, we briefly describe key results for the sensitivity and
uncertainty analysis. Detailed results can be found in the
ESM, Sects. 4 and 5, respectively. For the first sensitivity
scenario, field CO2 emissions related to changes in SOC
are higher (less negative) than when estimated using the
annualized approach. Therefore, global warming impacts
are higher for the scenarios when the annualized estimates
are used. Thus, the field effect increases (is less negative)
and therefore the benefits of using the inoculant reduce. For
the second scenario, the upstream effect is less than 15%
of the total change in impacts, when the impacts per kg of
inoculant are 10 times the initially established. These results
suggest that including further feedstocks on the production
of the inoculant would have to increase more than 10 times
the impacts per kg of inoculant in order to meaningfully
change the upstream effect. Finally results for the third sen-
sitivity scenario show that all effects remain the same except
for the yield effect that substantially increases (is less nega-
tive) because iLUC emissions (CO2 emissions) are the larg-
est contributor to this effect and have been excluded in the
scenario. Global warming impacts reduce from around 7%
when excluding iLUC to 4% when including iLUC.
For the uncertainty analysis the discernibility analysis
shows that for all impacts (except for nature occupation),
results are not discernible, meaning that when accounting
for uncertainty it is difficult to assert whether the inoculant
system has better results than the reference. Yet, results for
the paired t-test show that for a p value of 5% and a cor-
rected p value of 2.9%, 16 out of 16 impacts are significantly
different. This means that the mean of the distribution of
the difference of impacts between reference and inoculant
systems, are significantly different from zero which is the
hypothesized mean, and therefore impacts are significantly
reduced in the inoculant system compared to the reference.
4 Discussion
4.1 Effects discussion
Results showed that the field and yield effects are the largest
contributors to the overall benefits of using the inoculant in
soybean grown with corn as previous crop. Upstream and
downstream effects contribute insubstantially to the change
in impacts from introducing the inoculant.
The field effect depends entirely on changes in soil emis-
sions from the area A as modelled in DayCent, because
emissions related to iLUC, field work, fertilizer, pesticides,
seeds, and other inputs do not change between the systems
for the area A. The field effect is negative (reduced impacts)
Table 3 LCIA of the production of one kg of inoculant for Stepwise
impacts at mid-point level
Impact category Unit Value per kg inoculant
Human toxicity, carcinogens kg C2H3Cl-eq 1.991E−02
Human toxicity, non-carc. kg C2H3Cl-eq 5.548E−03
Respiratory inorganics kg PM2.5-eq 2.974E−04
Ionizing radiation Bq C-14-eq 7.084E−01
Ozone layer depletion kg CFC-11-eq 2.997E−08
Ecotoxicity, aquatic kg TEG-eq w 1.014E+01
Ecotoxicity, terrestrial kg TEG-eq s 1.594E+00
Nature occupation PDF*m2a −8.375E−04
Global warming kg CO2-eq 3.523E−01
Acidification m2 UES 1.956E−02
Eutrophication, aquatic kg NO3
-
-eq 3.534E−04
Eutrophication, terrestrial m2 UES 2.267E−02
Respiratory organics pers*ppm*h 2.425E−04
Photochemical ozone,
vegetat.
m2*ppm*h 2.431E+00
Non-renewable energy MJ primary 1.143E+01
Mineral extraction MJ extra 8.587E−03
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for three impacts: global warming, respiratory organics, and
photochemical ozone formation impact on vegetation. Other
impacts have a positive field effect, i.e., the inoculant use
does not lead to reductions compared to the reference. A
closer look at global warming, for instance, shows that N2O
emissions from the area A have the largest increase with the
use of the inoculant and SOC-related CO2 emissions the
largest reduction, leading to a net reduction in greenhouse
gas (GHG) emissions from the area A (ESM, Table17). N2O
and SOC emissions/sinks are influenced by the increase in
biomass (plant and root growth) stimulated by the inoculant
and modeled in DayCent, not just for soybeans but also for
corn. The slight increase in corn yield over the 100-year
simulation period increases the return of carbon and nitrogen
to the soil via plant inputs, as a consequence this induces
additional carbon storage and nitrogen-related emissions.
Because these results for the field effect depend entirely on
the DayCent model outputs, uncertainties, and limitations
of this modelling approach are further discussed (Sect.4.2).
The yield effect is the largest contributor among the
effects. The yield effect is negative for all impact categories
by definition as it consists of avoiding the impacts of soybean
production in the global marginal market of soybean. The
larger the yield increase in the inoculant system compared
to the reference, the more impacts will be avoided and the
more negative the yield effect will be (direct proportion-
ality). These results depend primarily on the yield change
between systems, which is based on experimental data from
Leggett etal. (2017) and also on how this change was cap-
tured in DayCent (see Sect.4.2 for limitations). Also, the
benefit of avoiding soybean produced in this market is larger
when accounting for iLUC (0.86 kg CO2eq Mg−1 soybean)
compared to when not, i.e., excluding iLUC (0.24 kg CO2eq
Mg−1 soybean). N2O and CO2 emissions from iLUC are the
two main contributors to total GHG emissions of avoided
soybean production in this market (ESM, Table17). iLUC
modeling is thus important as are its limitations and uncer-
tainties, for instance data and identification of marginal land.
More strengths and weaknesses of the model are discussed,
also in comparison to other iLUC models, in De Rosa etal.
(2016). Among the most important strength is the suitabil-
ity of the model to be used within an LCA framework. For
instance, there is no conflict with the usual assumption of
full price elasticity in the markets (Weidema 2003), and the
Table 4 Effects of the use of inoculant on the impacts of soybean production per Mg soybean. Negative values refer to improvement compare to
the reference
Impact category Unit Upstream
effect (Eq.1)
Field effect
(Eq.2)
Yield effect
(Eq.3)
Down-stream
effect (Eq.4)
Total inoculant
effect (Eq.5)
Total ref. without
inoculant incl.
iLUC
Changewith B.j-
LCO versus ref
incl. iLUC (%)
Human toxicity,
carcinogens
kg C2H3Cl-eq 1.14E−03 0.00E+00 −2.21E−01 1.86E−03 −2.18E−01 5.53E+00 −4.0%
Human toxicity,
non-carc.
kg C2H3Cl-eq 3.19E−04 1.94E-04 −2.24E−01 6.39E−03 −2.17E−01 1.14E+01 −1.9%
Respiratory
inorganics
kg PM2.5-eq 1.71E−05 4.62E−04 −3.36E−02 2.07E−03 −3.10E−02 1.19E+00 −2.6%
Ionizing radiation Bq C-14-eq 4.07E−02 0.00E+00 −1.71E+01 1.40E−01 −1.70E+01 3.59E+02 −4.7%
Ozone layer
depletion
kg CFC-11-eq 1.72E−09 0.00E+00 −1.24E−06 5.91E−09 −1.23E−06 2.70E−05 −4.6%
Ecotoxicity,
aquatic
kg TEG-eq w 5.83E−01 1.49E−02 −6.14E+02 5.35E+00 −6.09E+02 8.79E+03 −6.9%
Ecotoxicity, ter-
restrial
kg TEG-eq s 9.17E−02 3.73E−02 −5.33E+01 8.35E−01 −5.24E+01 1.48E+03 −3.5%
Nature occupation PDF*m2a −4.82E−05 0.00E+00 −4.41E+01 −1.78E−02 −4.41E+01 1.34E+03 −3.3%
Global warming kg CO2-eq 2.03E−02 −6.06E+00 −3.97E+01 2.09E−01 −4.56E+01 1.12E+03 −4.1%
Acidification m2 UES 1.12E−03 1.14E−01 −5.70E+00 1.84E−02 −5.56E+00 1.84E+02 −3.0%
Eutrophication,
aquatic
kg NO3
−
-eq 2.03E−05 2.18E−02 −1.63E+00 4.69E−05 −1.61E+00 3.52E+01 −4.6%
Eutrophication,
terrestrial
m2 UES 1.30E−03 5.33E−01 −2.45E+01 2.09E−02 −2.39E+01 8.14E+02 −2.9%
Respiratory
organics
pers*ppm*h 1.39E−05 −1.45E−04 −5.98E−03 1.13E−04 −6.00E−03 2.88E−01 −2.1%
Photochemical
ozone, vegetat.
m2*ppm*h 1.40E−01 −1.18E+00 −7.44E+01 1.33E+00 −7.41E+01 3.49E+03 −2.1%
Non-renewable
energy
MJ primary 6.57E−01 0.00E+00 −1.51E+02 2.29E+00 −1.48E+02 3.56E+03 −4.1%
Mineral extrac-
tion
MJ extra 4.93E−04 0.00E+00 −7.77E−01 6.07E−03 −7.70E−01 2.05E+01 −3.8%
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accounting for land use emissions over time is being handled
by means of the global warming potential (GWP) concept in
line with the characterization of greenhouse gases usually
applied in LCA.
The displacement of marginal soybean production in Bra-
zil and the USA is important as much of the overall environ-
mental benefits of the inoculant are driven by this effect. It
is hence warranted to pay further attention to American soy-
bean markets. Studies show that the largest producers of soy-
bean namely Brazil, USA, and Argentina, affect one another
when changing their production as a consequence of price
and demand changes (Plato and Chambers 2004; Boerema
etal. 2016; Yao etal. 2018). For instance, an increased sup-
ply in South America reduces the soybean price in the USA
(Plato and Chambers 2004). This suggests that increased
supply in Argentina may change production in Brazil and
USA, but how production will change depends not only
on the price change but also on the demand, on production
costs, as well as on the stimulus for farmers to remain pro-
ducing soybean at a lower market price, among others. Cap-
turing such complexities and better estimating the short-term
effects that additionally produced soybean in Argentina will
have on the global market and marginal producers, would
require more sophisticated economic modelling, for instance
by a partial-equilibrium model. Such modelling can better
inform the assumptions made for the system expansion, i.e.,
determining the marginal producers and the effect that addi-
tionally produced soybean will have in the global marginal
market; thus, this is a matter for further research. Yet, we
believe the system expansion presented here is plausible and
representative of the dynamics of the global soybean market.
The upstream effect is positive for all impacts because
producing the inoculant is associated with an impact (in the
inoculant system) that is not part of the reference system.
Although the inoculant production inventory covers most
materials, transport, energy, and emissions reported by
Novozymes, three relevant materials are not included due
to lack of available relevant inventories for these materials.
A sensitivity scenario is explored to account for the possi-
ble effect of these inventories (ESM, Sect. 4). The scenario
confirms the relatively small contribution of this effect com-
pared to the other effects.
Finally, the downstream effect was either positive or
negative depending on the impact and the location. For
transport, we assumed the same distance from field to farm
for all locations thus the difference in impacts reflects only
the difference in yields. Similarly, for drying we assumed
the same moisture content of soybean in all countries;
thus, the difference in impacts only reflects the differ-
ence in yields. Estimates of the downstream effect would
improve with more location representative data on soybean
moisture and distances from field to farm.
4.2 DayCent uncertainties andlimitations
There are uncertainties and limitations around the replica-
tion of the inoculant action on soybean, as observed in the
field, within DayCent. The main assumption made is that the
inoculant only impacts soybean yield. This assumption could
lead to misrepresentations of some observed field behav-
ior and thus of the modelled emissions. For instance, the
field trial dataset reports yield and yield changes induced
Fig. 1 Data flows from field trials to life cycle inventories of reference and inoculant systems
1580 The International Journal of Life Cycle Assessment (2021) 26:1570–1585
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by the inoculant use. From the two, DayCent simulations
in this study reflect yield changes, misrepresenting, e.g.,
long-term soil or crop processes indirectly induced due to
the presence of the inoculant. Also, as detailed in Leggett
etal. (2017), the presence of the inoculant does not trigger
a unique response by the soybean crop. Yet, yield responses
to the inoculant within DayCent modelling in this study,
indicated an increase in yield always, possibly misrepre-
senting years where the yield decreased, as it reached its
genetic maximum (Leggett etal. 2017). In the absence of
ReferenceSystem(Ref)
InoculantSystem(B.j-LCO)
DisplacedMaterial/energy flow
Process
System expansion
Material/energyflow
Legend
Functional Unit
Displacedprocess
Energy (Fuel)
Capitalgoods
(machinery)
Corn Cultivation
(AR)
Area A(ha)
Freshcorn
Fertilizers
Energy (Fuel)
Capitalgoods
(machinery)Emissions
Crop residues
NetCO
2
-uptake
Land
Pesticides
Year 1
SoybeanCultivation
(AR)
Area A(ha)
Emissions
Crop residues
NetCO
2
-uptake
Year 2
Seeds
Fertilizers
Land
Pesticides
Seeds
Inputs
Inputs
Fresh Soybeans
Drying
Soybeans to market (Q)
Energy
Drying
Energy
Corn (Mg/ha *yr)
System expansion
Corn Cultivation
(AR)
Area A(ha)
Emissions
Crop residues
NetCO
2
-uptake
Year 1
SoybeanCultivation
(AR)
Area A(ha)
Emissions
Crop residues
NetCO
2
-uptake
Year 2
Inputs
Otherinputs
Coated
Seeds
Inoculant
production
Energy
Rawmaterials
Soybean
Cultivation
Area B
Inputs Fresh
soybeans
FreshSoybeans
Drying
Drying
Soybeans to market(Q+∆Q)
Energy
Soybeans to market (-∆Q)
Energy
Freshcorn
Drying
Energy
Corn (Mg/ha *yr)
Fig. 2 Reference (Ref) and the inoculant (B.j-LCO) systems for soybean production in a corn-soybean rotation
1581The International Journal of Life Cycle Assessment (2021) 26:1570–1585
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

more detailed experimental information that could be used
for model parameterizations and upgrade model predic-
tions, the approach adopted here is valid in providing an
understanding of the impact caused by the inoculant on
emissions as the assumptions underpinning model simu-
lations and implementation are in line with established
Fig. 3 Contribution analysis of the impacts of soybean production with (B.j-LCO) and without (Ref) the inoculant. A negative contribution
means a reduction of the impact. The field effect includes impacts from (in blue): field emissions, iLUC, field work, fertilizers, pesticides and
seeds; the downstream effect includes impacts from (in green) transport and drying; the yield effect includes impacts from the avoided soybean
(in yellow) and the upstream effect includes impacts from the inoculant (in black)
1582 The International Journal of Life Cycle Assessment (2021) 26:1570–1585
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

modelling methodologies outlined in other studies (Fitton
etal. 2017). Besides, we expect that using yield changes
as the key DayCent model driver could represent well the
interactions between the inoculant, the plant, the yields and
hence the modelled emissions because of model-intercom-
paring studies (Fitton etal. 2019), that show that outputs
from DayCent matched both the emissions field-data and
also data for more complex models that could explicitly
model the relationship examined.
Moreover, the method used here to implement yield
changes within DayCent is also uncertain. As the inoculant
aims to promote the uptake of N from the soil by the soybean
plant, replication of this effect was undertaken by modify-
ing the maximum amount of N fixed per gram of C fixed
via net primary production (NPP) if there is insufficient N
within the soil. Gradual increase of this parameter was used
to characterize the effect the inoculant has on plant N uptake
in DayCent. All other inputs such as soil, climate, manage-
ment including fertilization, chemical treatment and residue
production and treatment, and corn and soybean growth
parameters remained constant. Thus, only one parameter
change represents the inoculant mode of action. A limita-
tion of this approach is that it was not possible to achieve
the exact percentage change in yield observed on the field,
and values presented in this analysis are based on the closest
long-term match. Moreover, other parameters could change
in reality due to the effect of the inoculant, and thus, they
could be misrepresented by the approach leading to a misrep-
resentation of emissions too, as discussed for the field effect.
Additional field data on such parameters could help improve
this limitation.
4.3 LCA uncertainty andsensitivities
Results of the uncertainty analysis confirm the improve-
ments in soybean cultivation impacts due to the inoculant
use including parametric uncertainty. No trade-offs appear,
i.e., 16 out of 16 impact categories are significantly lower
in the inoculant system when accounting for uncertainty.
The results to the three scenarios can be found in ESM,
Sect.4. The first scenario shows that annualized approaches
to calculate SOC emissions lead to lower benefits in global
warming impacts than the time-independent approach.
Thus, the field effect increases and therefore the benefits
of using the inoculant reduce if the annualized approach
is used. The second scenario was already discussed in
Sect.3.1. The third scenario shows that all effects remain
the same except for the yield effect that substantially
increases as iLUC emissions are the main contributor to this
effect, i.e., CO2 emissions from iLUC. Particularly, Brazil
has a high conversion rate of forests into arable land mainly
for cattle ranching and more recently for soybean cultivation
(Boerema etal. 2016).
4.4 Rough perspectives forinoculant use inall
Argentinian soybean production
The average production of soybeans in Argentina from 2015
to 2018 was 53 million metric tons (FAO 2020). The inocu-
lant is currently used on 5.5% of Argentinian soybean fields
but is expected to have the potential to give a yield response
on 80% of these fields if applied at full scale (D’Alessio
2020). If the inoculant were applied at this scale, the global
warming benefit would correspond to a reduction in green-
house gas emissions of 1.9 million Mg CO2e [i.e., 80%,
53 million Mg 45.6 kg CO2e Mg−1]. While this is a rough
extrapolation of the results for the Buenos Aires province
to the rest of the country, it gives an indication of total ben-
efits the inoculant could potentially provide (order of mag-
nitude). An important assumption behind these estimates is
an increasing demand of soybean. Since mid-1990s, imports
of soybean by China, the largest world importer, have been
increasing. Nonetheless, in the last couple of years, imports
by China show a more stable behavior (FAO 2021) so this
may be an assumption to revise for the short-term future.
5 Conclusions
We compared the life cycle environmental impacts of soy-
bean cultivation with and without the use of an inoculant
containing Bradyrhizobium japonicum and the signal mol-
ecule lipochitooligosaccharide (LCO) in the Buenos Aires
province of Argentina. This cradle-to-gate assessment
included the cultivation of soybean, the inoculant produc-
tion, and the postharvest activities including transport from
field to farm and drying of soybean until ready for market.
The environmental benefits from introducing the inoc-
ulant were assessed by investigating the effect of this
inoculant in four main stages. The upstream effect covers
the inoculant production. The field effect consists of the
change in field emissions triggered by the increased pro-
duction per ha in the inoculant system. The yield effect
accounts for the fact that additional production in the inoc-
ulant system is expected to replace soybean production
in the global marginal market of soybean and its linked
impacts. The downstream effect compares the postharvest
impacts of the additional production in the inoculant sys-
tem and in soybean production elsewhere (avoided).
The inoculant use in soybean production in Buenos Aires,
Argentina, reduced all studied environmental impacts.
Hence, the use of this inoculant leads to overall environmen-
tal benefits and no trade-offs. This is a key finding contrib-
uting to knowledge about environmental impacts emerging
from inoculants’ use. The main contributor to this benefit is
the yield effect. Most of these avoided impacts come from
iLUC CO2 emissions, which result from the assumed system
1583The International Journal of Life Cycle Assessment (2021) 26:1570–1585
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

expansion. Avoiding soybean production in Brazil and USA
is crucial for the environmental benefits found in this study.
Hence, the marginal soybean producers and the effect of the
additional production of soybean induced by the inoculant
use should be carefully determined and is a matter for further
research. The field effect is the second largest contributor
to the overall reduction in GHG emissions, mostly due to
the effect of the increased SOC activated by the inoculant
use, as modelled by DayCent in this study. DayCent results
carry uncertainties and limitations of modelling the mode
of action of the inoculant given the parametrization of the
model. In the absence of field data to better model emis-
sions and dynamics of nutrients in DayCent, uncertainties
remain, and these results represent a modelling explorative
exercise valid to understand possible dynamics and environ-
mental impacts driven by the inoculant use. The downstream
and upstream effects contribute insubstantially to the total
change in impacts when using the inoculant. These conclu-
sions hold for this particular inoculant used in the province
of Buenos Aires, Argentina, in a corn-soybean rotation.
Some of the limitations of this study and topics for fur-
ther research that could further refine calculations and help
improve the robustness of conclusions include addressing
missing data on the supply chains of some feedstocks to
produce the inoculant, further study of the assumption of
avoided soybean production taking place at the global mar-
ginal market of soybean which could be tested with more
sophisticated economic models, additional field data col-
lection to better parameterize modelled field emissions in
DayCent, field data collection on the residues production
and treatment, and including uncertainty of iLUC emis-
sions. Finally, other impacts such as water, biodiversity
and microflora impacts should also be considered in order
to have more complete environmental assessments of inoc-
ulants use, with broader focus besides productivity gains.
Supplementary information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11367- 021- 01929-7.
Declarations
Conflict of interest Jesper H. Kløverpris is employed by Novozymes
who produce and market microbial inoculants as part of a larger port-
folio of biological solutions. Claus Nordstrøm Scheel was employed by
Novozymes when the study was conducted.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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