Changes in soil organic carbon (SOC) stocks are a major
source of uncertainty for the evolution of atmospheric CO2
concentration during the 21st century. They are usually simulated by
models dividing SOC into conceptual pools with contrasted turnover times.
The lack of reliable methods to initialize these models, by correctly
distributing soil carbon amongst their kinetic pools, strongly limits the
accuracy of their simulations. Here, we demonstrate that PARTYSOC, a
machine-learning model based on Rock-Eval® thermal analysis,
optimally partitions the active- and stable-SOC pools of AMG, a simple and well-validated SOC dynamics model, accounting for effects of soil management
history. Furthermore, we found that initializing the SOC pool sizes of AMG
using machine learning strongly improves its accuracy when reproducing the
observed SOC dynamics in nine independent French long-term agricultural
experiments. Our results indicate that multi-compartmental models of SOC
dynamics combined with a robust initialization can simulate observed SOC
stock changes with excellent precision. We recommend exploring their
potential before a new generation of models of greater complexity becomes
operational. The approach proposed here can be easily implemented on soil
monitoring networks, paving the way towards precise predictions of SOC stock
changes over the next decades.
Introduction
Soil organic carbon (SOC) plays an important role in sustaining soil
functions and associated soil ecosystem services worldwide (IPCC,
2019). It is the largest terrestrial organic carbon reservoir, with the
upper 2 m of soil storing 2400 Pg C, 3 times more carbon than the
atmosphere (Jobbagy and Jackson, 2000). A mere 4 per 1000 annual
decrease in SOC stocks (ca. 10 Pg C yr-1) may double the global annual
anthropogenic CO2 emissions, while an equivalent increase may
compensate them (Balesdent and Arrouays, 1999). This is
the concept behind the 4 per 1000 initiative (Rumpel
et al., 2018) that aims at increasing SOC stocks to fight global warming
while ensuring food security, two Sustainable Development Goals of the
United Nations (UN General Assembly, 2015). This initiative and
other political headway have put the question of managing SOC stocks and
assessing the global SOC sequestration potential at the top of political and
scientific agendas
(Vermeulen et al., 2019;
FAO, 2019; Amelung et al., 2020). Despite this particular attention, the
prediction of SOC stock changes remains very uncertain, which makes soils a
major source of uncertainty for the evolution of atmospheric CO2
concentration
(Todd-Brown
et al., 2014; He et al., 2016; Shi et al., 2018).
Models of SOC dynamics can predict future SOC stock evolution by simulating
carbon transfer into the soil mostly through plant organic matter inputs
and microbial SOC mineralization resulting in a CO2 flux from the soil
to the atmosphere. They can have structures of various complexities,
reflecting our mechanistic understanding of SOC dynamics
(Shi et al., 2018; Luo et al., 2016).
However, most models dedicated to prediction, including those used in Earth
system models, have a simple structure dividing SOC into conceptual pools
with contrasted turnover times
(Manzoni
and Porporato, 2009; He et al., 2016; Todd-Brown et al., 2014). These
multi-compartmental models of SOC dynamics are the best option we currently
have to foster science-based SOC preservation and sequestration actions,
given the strong uncertainty of more complex models
(Cécillon,
2021a; Dangal et al., 2021; Lee et al., 2020; Shi et al., 2018; Crowther et
al., 2019). Predictions of SOC stock evolution provided by such simple
models are very sensitive to the initial distribution of SOC amongst the
different kinetic pools
(Luo et
al., 2016; Smith and Falloon, 2000; Clivot et al., 2019). This makes the
question of partitioning of SOC kinetic pools a priority for improving the
accuracy of multi-compartmental SOC dynamics models
(Luo et al., 2016;
Taghizadeh-Toosi et al., 2020).
The most commonly used method to initialize the size of SOC kinetic pools is
to run spin-up simulations until a steady-state equilibrium for SOC is
reached, eventually matching the initial SOC stock measurement
(Wutzler and
Reichstein, 2007; Taghizadeh-Toosi et al., 2020). However, this method has
two well-known limitations. First, climatic SOC input and land-use or
land-cover data extending over long time periods required by this approach
are highly uncertain. Second, assuming steady-state equilibrium for SOC at
the onset of model simulations is often unrealistic. This is due to the
history of the simulated sites that often includes disturbances (e.g. fire), as
well as previous changes in climate, land use, and soil management that
prevent SOC pools with slow turnover times from being at equilibrium
(Wutzler
and Reichstein, 2007; Herbst et al., 2018; Oberholzer et al., 2014; Poeplau
et al., 2011; Clivot et al., 2019). Alternative initialization procedures
are needed to address these issues
(Wutzler and
Reichstein, 2007; Bruun and Jensen, 2002; Taghizadeh-Toosi et al., 2020).
In some models of SOC dynamics, like the AMG model
(Clivot et al., 2019), a default initial SOC
pool size distribution is prescribed according to basic information on
land-use history (i.e. long-term cropland vs. long-term grassland;
Clivot et al., 2019). This approach does not
take into account the effect of recent changes in land use or historical
soil management practices on SOC pool distribution. To better reflect the
effect of the frequent state of non-equilibrium of SOC on its partitioning
into conceptual kinetic pools, another approach has been proposed, relating
results from SOC fractionation methods with SOC kinetic pool sizes (e.g. Zimmermann
et al., 2007a, or Skjemstad et al., 2004, for the RothC – Rothamsted carbon – model; Dangal et al., 2021, for the DAYCENT – Daily Century – model). However, this approach also suffers from
important drawbacks. First, SOC fractionation procedures are tedious and
cannot be implemented on large-scale studies, though this problem may be
solved by using soil infrared spectroscopy or environmental variables and
machine learning
(Zimmermann
et al., 2007b; Viscarra Rossel et al., 2019; Sanderman et al., 2021; Cotrufo
et al., 2019; Lugato et al., 2021; Baldock et al., 2013; Barthès et al.,
2008; Lee et al., 2020; Dangal et al., 2021). Second, their reproducibility
is questionable
(Poeplau
et al., 2013, 2018), and third, their use of initializing model SOC pool
sizes has never been properly validated. A proper validation would require
showing that (1) the size of measured SOC fractions matches the one of model
kinetic pools and that (2) simulations of SOC dynamics are more accurate
using this initialization strategy, compared to default simulations (on
independent validation sites while other model parameters remain constant).
Reasonably good correspondence between measured or
soil-spectroscopy-estimated SOC fractions and modelled SOC conceptual pools
has been reported in a number of studies, though with some notable
discrepancies
(Leifeld
et al., 2009b; Herbst et al., 2018; Zimmermann et al., 2007a; Dangal et al.,
2021). Conversely, the studies that attempted to initialize model SOC pool
sizes using a SOC fractionation scheme generally reported no improvement in
the accuracy of simulations of SOC dynamics compared to a default or a
spin-up initialization approach
(Leifeld
et al., 2009a; Nemo et al., 2016; Cagnarini et al., 2019). Only two studies
showed that an initialization based on an SOC fractionation scheme yielded
more accurate simulations of observed SOC dynamics, but this was at the cost of
modifying the decomposition rate of SOC kinetic pools
(Skjemstad
et al., 2004; Luo et al., 2014).
An alternative approach using Rock-Eval® thermal analysis has
recently been proposed – under the name of the PARTYSOC model – to
estimate SOC kinetic pool sizes
(Cécillon
et al., 2018, 2021). PARTYSOC is a machine-learning model trained on
Rock-Eval® data of soil samples from long-term experiments
(LTEs) where the size of the centennially stable-SOC fraction can be
estimated (e.g. sites including a bare fallow treatment). PARTYSOC
incorporates recent key elements of the new understanding of SOC dynamics
(Dignac et al., 2017), showing that the centennially
stable-SOC fraction has specific chemical and energetic characteristics
that are measurable quickly (ca. 1 h per sample) and at a reasonable cost (less
than USD 60) using Rock-Eval®; it is thermally stable (i.e. high
activation energy), and it is depleted in hydrogen
(Barré
et al., 2016; Hemingway et al., 2019; Gregorich et al., 2015; Poeplau et
al., 2019; Chassé et al., 2021).
In this study, we tested if the PARTYSOC machine-learning model, built
on a totally independent dataset from north-western Europe, could be used
to initialize the distribution of SOC pools of the simple AMG model
(Clivot et al., 2019) and improve the
accuracy of its simulations. The default version of AMG is currently the
most accurate model for reproducing the observed SOC stock dynamics in
diverse French agricultural LTEs at the pluri-decadal scale
(Martin et al., 2019). The efficient
use of this model at sites covering an important pedological and climatic
variability (including oceanic, continental, and tropical climate) provides
further support to its robustness
(Levavasseur
et al., 2020; Farina et al., 2021; Saffih-Hdadi and Mary, 2008). In this
model, SOC is simply divided into two pools, the “stable SOC (CS)”
that is considered inert at the timescale of the simulation and the
“active SOC (CA)” that has a mean turnover time of a few decades. A
recent study (Clivot et al., 2019) determined
that the optimal initial proportion of stable SOC (CS/ C0) can
deviate from the model's default value (0.65 in croplands) so that a more
precise initialization of the CS/ C0 proportion would significantly
improve AMG simulations of SOC dynamics. Here, we hypothesized that the SOC
pool partitioning as determined by the PARTYSOC machine-learning model
(Cécillon
et al., 2021, 2018) would be close to the mathematically optimal one for the
AMG model, therefore, improving the accuracy of its SOC dynamics simulations
compared to default initialization. We tested our hypothesis on 32 treatments from 9 independent French agricultural LTEs (experiment
duration from 12 to 41 years with a median of 21 years) in which the AMG-optimal SOC pool partitioning could be determined by ex post optimization and for
which topsoil samples collected at the onset of the experiment were
available (Table 1). These LTEs were croplands established in different
pedoclimates that have experienced contrasted soil management practices and
land-use histories. All available initial topsoil samples were analysed with
Rock-Eval®, and the results were used in the European version
of the PARTYSOC model, PARTYSOCv2.0EU (Cécillon et
al., 2021), to compute the centennially stable-SOC proportion of each
topsoil sample.
Materials and methodsExperimental sites
This work was conducted on nine French agricultural LTEs (Supplement Fig. S1); s
7 LTEs including 29 treatments were selected from the
dataset presented in Clivot et al. (2019),
from sites with available initial topsoil samples. Two additional LTEs
(Colmar and Feucherolles) including a total of three treatments were
obtained from the dataset published in
Levavasseur et al. (2020), selecting
control treatments without organic amendments and with available initial
topsoil samples. Basic site and topsoil characteristics are reported in
Table 1 and Supplement Table S1. Information necessary to run AMG
simulations on a total of 32 treatments (initial soil physicochemical
properties, detailed information on management practices, and observed
climatic data during all experiments) were obtained from
Clivot et al. (2019) for the 29 treatments of
the 7 sites and from Levavasseur
et al. (2020) for the 3 treatments of the sites of Colmar and
Feucherolles.
Main information about the nine French agricultural long-term
experiments used in this study. All sites had been croplands for at least 20 years before the onset of all experiments. Additional site information
including climatic variability amongst sites and long-term history of land
cover is provided in Supplement Table S1. WRB: World Reference Base.
Our final soil sample set included 181 topsoil samples. At each site the
soil was sampled to include the whole ploughing depth (Table 1). At all
sites, except Boigneville where the soil was sampled in five sublayers, the
ploughing layer was sampled as one homogeneous layer. Of the final samples,
71 were from starting dates of the 9 LTEs; 24 were from LTEs intermediate
dates; and 86 were from LTEs final dates. All samples were air-dried or dried at
40 ∘C, sieved to <2 mm, and finely ground to <250µm using a ball mill (Retsch, Germany).
Rock-Eval® analysis of archive soil samples
All soil samples were analysed using a Rock-Eval 6® Turbo
apparatus (Vinci Technologies). The samples were first pyrolysed in an inert
N2 atmosphere, then oxidized under ambient air (O2). The heating
routine applied during pyrolysis was as described in Disnar et al. (2003),
starting with a 3 min isotherm at 200 ∘C followed by a
heating ramp of 30 ∘C min-1 up to 650 ∘C. For the
oxidation step, a 1 min isotherm was kept at 300 ∘C and was
directly followed by a heating ramp of 20 ∘C min-1 until 850 ∘C was reached, followed by a 5 min isotherm at 850 ∘C (Baudin et al., 2015; adapted from Behar et al., 2001).
Based on 5 generated Rock-Eval® thermograms, 18 parameters
were calculated for each sample and were then used as predictors by the
random forest model. These include total organic carbon (TOC; in g C per kg soil) – the amount of organic C released during the analysis as a
proportion of sample weight; pyrolysed organic carbon (PC; in g C per kg soil) – the sum of C released as HC, CO, and CO2 during the pyrolysis step; the ratio of PC to TOC (PC/TOC); the S2 peak area (g C per kg soil) – the hydrocarbon gas released within the range of the
pyrolysis temperature ramp; the ratio of S2 to PC (S2/PC); the PseudoS1 peak
area (g C per kg soil) – the sum of C released as HC, CO, and CO2
during the first 200 s of pyrolysis (after Khedim et al., 2021);
the hydrogen index (HI; in mg HC g TOC-1) – the amount of hydrocarbons
released as a ratio of TOC; and the ratio of HI to the oxygen index (HI/OIRE6)
– where OIRE6 is calculated as the amount of oxygen released as CO
and CO2 gases normalized to TOC. Finally, various temperature
parameters (T70HC_PYR, T90HC_PYR,
T30CO2_PYR, T50CO2_PYR,
T70CO2_PYR, T90CO2_PYR,
T70CO_OX, T50CO2_OX,
T70CO2_OX, and T90CO2_OX; in ∘C) are included in the predictor set. They describe evolution steps, namely
at which temperature a specific amount (e.g. 30 %, 50 %, 70 %, or 90 %) of a
given gas was released according to each thermogram (Cécillon et al.,
2018). It is important to note that no pre-treatment of
CaCO3-containing samples was necessary before Rock-Eval®
analysis. The slow pyrolysis and oxidation steps of the
Rock-Eval® method allow for distinguishing carbon of organic and
mineral form, since the latter is released above a given temperature. For
the calculation of all of the above parameters, only the part of each
thermogram corresponding to organic carbon was taken into account. For this
purpose, upper temperature integration limits for Rock-Eval®
temperature parameters were set to 560 ∘C for the CO and CO2
pyrolysis thermograms and to 611 ∘C for the CO2 oxidation
thermograms (Cécillon et al., 2018; Supplement Fig. S2). R
scripts used for computing Rock-Eval® parameters are available
on the Zenodo platform (Cécillon, 2021b).
The PARTYSOC machine-learning model
The most up-to-date European version of this model, calibrated on soils from
north-western Europe, used in this study, is described in detail in
Cécillon et al. (2021). This model uses the 18 above-mentioned Rock-Eval®
thermal analysis parameters as predictors and estimates the centennially
stable-SOC proportion in a topsoil sample. The model consists of a trained
non-parametric machine-learning algorithm, using the random forest approach
to estimate centennially stable-SOC proportions in unknown topsoils from
centred and scaled Rock-Eval® parameters. In this study the
obtained centennially stable-SOC proportion of each topsoil sample was
converted to centennially stable-SOC content by multiplying the predicted
proportion by the total SOC content. The PARTYSOCv2.0EU model,
available on Zenodo (Cécillon, 2021b), was used without any
adaptation.
The AMG model of soil organic carbon dynamics
The AMG model (Andriulo et al., 1999) was developed based on
the two-compartment SOC model proposed by Hénin and Dupuis
(1945). It is characterized by a simple
structure consisting of three carbon pools: fresh organic matter and two
SOC fractions, an active and a stable pool (Supplement Fig. S3).
The model allows for the transfer of carbon from the fresh organic matter pool
either to the atmosphere through microbial mineralization or into the active
pool. Organic carbon from the active pool is also subject to mineralization,
forming a second direct flux of CO2 from the soil into the atmosphere.
Soil organic matter (SOM) decomposition follows first-order kinetics with a rate defined by the
coefficient of mineralization k (yr-1), controlled by climatic
conditions and soil characteristics. The h coefficient controls the yield of
crop residue transformation into active carbon and depends on the type of
fresh organic matter. No carbon exchange with the stable-SOC pool is
possible, since it is considered inert and remains unchanged over the
simulation period (here from 12 to 41 years; see Table 1). Considering the
stable-SOC pool as mathematically inert at this timescale is in line with
consistent observations of a significant pluri-decadal persistent SOC
fraction in long-term bare fallows and C3–C4 vegetation change
chronosequences
(Barré
et al., 2010; Balesdent et al., 2018).
The AMG model can be mathematically described by two simple equations
(Clivot et al., 2019):
1QC=QCS+QCA,2dQCAdt=∑imihi-k⋅QCA,
where QC is the total SOC stock (Mg C ha-1), QCS is the stable-SOC
stock (Mg C ha-1) defined as a fraction of the initial SOC stock QC0
(see Sect. 2.6) constant for a specific treatment, QCA is the active-SOC
stock (Mg C ha-1), t is the time in years, mi is the annual C
input from organic residue i (Mg C ha-1 yr-1), h is a
coefficient representing the fraction of C inputs which is incorporated in
SOM after 1 year related to the type of organic residue, and k is the
mineralization rate constant associated with the active-C pool (yr-1).
The AMG parameters (h and k) have been determined by experimental results
(Clivot et al., 2019). This approach differs
from most multi-compartmental SOC dynamics models for which decay rates of
slower pools were calibrated indirectly, assuming an equilibrium state for
SOC (Wutzler and Reichstein, 2007). The
simple structure of the AMG model and the experimental determination of its
decomposition rates make it less susceptible to the problem of equifinality
compared to other multi-compartmental models of SOC dynamics
(Clivot et al., 2019;
Luo et al., 2016). Furthermore, AMG has been validated with δ13C tracer data of long-term alternative sequences of C4 and C3 crops
(Mary et al.,
2020).
The version of AMG used in this study was AMGv2, described in detail in
Clivot et al. (2019). Input data necessary
to run simulations of SOC stocks with AMG include crop type, annual crop
yields, and information regarding management of crop residues. These are used
to compute annual aboveground and belowground C inputs from plants, here
according to the method proposed by
Bolinder
et al. (2007) and adapted by Clivot et al. (2019). The coefficient of mineralization k (yr-1) is calculated
according to soil characteristics (clay and carbonate contents, pH, and C:N
ratio) and climatic conditions (mean annual temperature, precipitation, and
potential evapotranspiration; Clivot et al.,
2019).
Soil organic carbon pool partitioning in the AMG modelDefault CS/ C0 initialization
Two default values can be used for initialization of the SOC pool distribution
in AMG, depending on land-use history before the onset of simulations. The
initial proportion of CS/ C0 equals 0.65 for sites with a long-term
arable land-use history. Former long-term grassland sites are expected to
have lower CS/ C0, and the value of 0.40 was used in previous
studies
(Saffih-Hdadi
and Mary, 2008; Clivot et al., 2019). Since all sites used in this study had
been under arable land for at least 20 years before the onset of the
experiment, a default value of 0.65 was used.
PARTYSOC-based initialization of CS/ C0
The PARTYSOC-based initialization of CS/ C0 was derived from
data obtained with Rock-Eval® analysis of initial topsoil
samples from each LTE. Here, CS/ C0 was estimated using the
following simple four-step procedure: first, topsoil samples from the LTE's
onset were analysed with Rock-Eval®, and the 18 thermal
parameters described in Sect. 2.3 were calculated for each sample. Second,
the thermal parameters were used as input for the PARTYSOC
machine-learning model described in Sect. 2.4 which was run for this sample
set resulting in a sample-specific prediction of the centennially stable-SOC
proportion. Third, the obtained values were averaged per LTE. Fourth, the
site mean of the centennially stable-SOC proportion was used (as
CS/ C0) to initialize simulations of SOC stocks for the various
treatments of every site (the site standard deviation is reported in Fig. 1
and in Supplement Table S2). Supported by the evident common
land-use history shared by the multiple treatments of each site before the
onset of simulations and because the SOC stocks and centennially stable-SOC
contents were very homogeneous amongst each site, we also performed
simulations of 17 treatments for which soil samples from the onset of the
LTE were not available. In these cases, we considered that the
CS/ C0 of the treatment was equal to the mean value of the
respective site (Supplement Table S1 and S2).
Ex post optimization of CS/ C0
Following a least-squares optimization approach, the best fit of the AMG
model on observed SOC stocks time series was obtained, and the optimal
initial SOC pool partitioning (CS/ C0) was estimated accordingly
for each site (Clivot et al., 2019). In sites
with C3–C4 vegetation change chronosequences where δ13C
long-term monitoring data were available, the model was adapted to
simultaneously match the observed evolution of C, C3, and C4 stocks
(Clivot et al., 2019) for a given treatment.
Calculation of the centennially stable-SOC content
The content of the centennially stable-SOC pool of each LTE at initial,
intermediate, and final dates was estimated through multiplication of the
PARTYSOC estimates of the proportion of the centennially stable SOC at
a given date by the corresponding total SOC content previously determined
using elemental analysis (Clivot et al., 2019; Levavasseur et al., 2020).
For example, for the onset of an LTE where t=0, CS= CS/ C0⋅ C0, where CS is the stable-SOC content
(g C per kg soil), and C0 is the total SOC content (g C per kg soil) at time t=0.
Statistics
The fit between PARTYSOC predictions of the centennially stable-SOC
proportion and ex post AMG-optimized stable-SOC proportion was assessed by a linear
regression model. The same approach was applied for the evaluation of the
agreement between centennially stable-SOC content and ex post AMG-optimized stable-SOC content of initial samples. The evaluation of the performance of the AMG
model, for the different SOC pool-partitioning initialization methods, was
also based on simple linear regressions between simulated and observed SOC
stock values. Statistical terms used to express the strength and the
statistical significance of the relationships were the coefficient of
determination (R2) and the associated probability value (p value). Prediction
bias and model error were expressed as the mean difference (BIAS) and relative mean
square error (RMSE). The relative root mean square error (RRMSE) and the normalized
root mean square error (NRMSE) were used to compare the error of different datasets (with a different range of predictions)
(Smith et al., 1996; Wallach, 2006;
Otto et al., 2018).
3R2=∑i=1nOi-O‾⋅Si-S‾∑i=1nOi-O‾2⋅∑i=1nSi-S‾22,4BIAS=1n∑i=1nSi-Oi,5RMSE=1n∑i=1nSi-Oi2,6RRMSE=RMSEO‾,7NRMSE=RMSEOmax-Omin,
where O and S are the observed and simulated values; n is the number of
observations; O‾ and S‾ are the means of observations and
simulations, respectively; and Omax and Omin are the maximum and the
minimum value observed.
The observed and simulated total SOC stock change dQC was calculated as follows
for each treatment:
8dQCobs=QCobs,t2-QCobs,t1,9dQCsim=QCsim,t2-QCobs,t1,
where QCobs is the observed SOC stock at time t, QCsim is the SOC stock
at time t simulated with AMG, and t1 indicates the start and
t2 indicates the end of simulation period.
All data processing and statistical analyses were performed within the R
programming environment (version 3.4.2) (R Core Team,
2017). For plotting, packages ggpmisc (Aphalo, 2016), reshape2
(Wickham, 2007), and ggplot2 (Wickham, 2016) were
used.
ResultsAccurate soil organic carbon pool partitioning
Centennially stable-SOC proportion values were predicted by the
PARTYSOC machine-learning model
(Cécillon et al.,
2021) using Rock-Eval® data measured on initial topsoil
samples. The mean value for each independent site was plotted against the
stable SOC proportion as determined by ex post AMG optimization (Fig. 1). The
initial centennially stable-SOC proportion values predicted with
PARTYSOC ranged from 0.44 to 0.74, with a mean value of 0.59, whereas
ex post AMG-optimal estimations of stable-SOC proportion covered almost the same
range, from 0.45 to 0.74, with a mean value of 0.61. The two approaches were
strongly correlated (R2=0.63, significant at the p<0.05
level), with a linear regression slope close to 1 (a=0.9) and intercept
close to 0 (b=0.04), showing an unbiased relationship between
PARTYSOC estimates of the centennially stable-SOC proportion and the
ex post AMG-optimized stable-SOC proportion at the onset of the nine LTEs. Although
a slight discrepancy was observed for higher stable-SOC proportion values,
the results validate our hypothesis showing that the centennially stable-SOC
proportion determined by Rock-Eval® thermal analysis and the
PARTYSOC machine-learning model built on fully independent data
provides a good estimate of the optimal stable-SOC proportion of the AMG
model for unrelated French agricultural soils. When expressed as content (g C per kg soil), the fit between the PARTYSOC predictions of the
centennially stable SOC determined on initial topsoil samples and the
ex post optimized stable-SOC content values was excellent (R2=0.95; Supplement Fig. S4; optimal stable-SOC content ranged from
4.37 to 12.75 g C per kg soil across the nine sites). Furthermore, the
method appears to be reliable, since additional Rock-Eval®
measurements on topsoil samples from intermediate and final dates of the
LTEs showed that the PARTYSOC predictions of the centennially stable-SOC content remained remarkably constant during the experimental period at
most sites (Supplement Fig. S5).
Performance of the PARTYSOC model to predict the centennially
stable-SOC proportion compared to the ex post AMG-optimized stable-SOC proportion.
Points represent site-mean values based on initial topsoil samples from nine
independent French long-term experiments. Statistics refer to the linear
regression between x and y values (blue solid line). Horizontal error bars
show the uncertainty associated with the AMG-optimal stable-SOC proportion,
calculated as the standard deviation of treatment-wise AMG optimizations.
Vertical error bars represent the prediction error of the centennially
stable-SOC proportion values, calculated as the standard deviation of the
PARTYSOC model predictions on initial topsoil samples.
More accurate soil organic carbon simulations
In a second step, we investigated if a PARTYSOC-based initialization of
the SOC pool partitioning could improve the accuracy of SOC stock
simulations of the AMG model. To do so, we compared SOC stock simulations
obtained with three different initializations. We first ran AMG using the
default initialization method for the SOC pool partitioning (CS/ C0= 0.65, since all LTEs were under cropland for at least 2 decades before
their onset; Table 1). Then, we ran AMG simulations using the
PARTYSOC-based initialization method by defining CS/ C0 as the
site-mean centennially stable-SOC proportion determined by the PARTYSOC
model. Finally, we ran AMG using the ex post optimization method to initialize the
SOC pool partitioning for each site. For all three initialization
procedures, the simulated SOC stock change between the initial and last
sampling date for each treatment of each site was plotted against the
measured SOC stock change (Fig. 2a–c). Observed SOC stock change ranged
from +6 to -24 Mg C ha-1 for the 32 treatments. In spite of a
rather good mean agreement (RMSE = 5.95 Mg C ha-1), the AMG model
initialized with the default procedure provided predictions of SOC stock
change rather far from what was observed in two out of nine LTEs (Fig. 2a).
Using the PARTYSOC-based initialization method improved AMG simulations
compared to the default method, bringing them much closer to the observed
SOC stock changes (RMSE = 3.60 Mg C ha-1; Fig. 2a, b).
PARTYSOC-based initialization of AMG resulted in unbiased simulations
(BIAS = 0.06 Mg C ha-1) and a strong decrease in the mean error of
prediction. Unsurprisingly, AMG initialized using ex post optimized CS/ C0
proportions predicted SOC stock changes very close to the observed ones
(RMSE = 2.12 Mg C ha-1; Fig. 2c). AMG simulations from ex post optimized and
PARTYSOC-based initializations were remarkably comparable (Fig. 2b, c).
The SOC stock simulations produced with AMG for each independent treatment
are presented in Supplement Fig. S6.
Observed vs. simulated change in SOC stocks between the initial and
final date of 32 treatments from nine French long-term experiments. The
three panels show the performance of the AMG model for three different
initialization approaches. Initial SOC kinetic pool sizes were defined using (a) the default value for cropland (CS/ C0=0.65), (b) the
centennially stable-SOC proportion predicted by the PARTYSOC model, and
(c) the ex post AMG-optimized CS/ C0 proportion. Statistics refer to the
linear regression between x and y values (blue solid line). Points represent
the values for the 32 treatments for which AMG simulations were run.
It is noteworthy that the PARTYSOC-based initialization improved the
fit between observed and simulated SOC stock change, compared to AMG default
initialization, especially for treatments that experienced the greatest SOC
stock loss (Fig. 2a, b). In treatments that experienced no SOC stock change
or a slight increase in SOC stock, the PARTYSOC-based initialization
did not improve the simulations but resulted in highly reliable predictions,
similarly to AMG default or optimized initialization methods (Fig. 2a–c).
This is likely explained by the history of land cover and soil management
practices of the different sites. Sites presenting treatments with no change
or a slight increase in SOC stocks were predominantly sites with a long
cropland history (e.g. site of Boigneville; Supplement Table S1), for
which the default AMG CS/ C0 value of 0.65 is nearly optimal.
Conversely, the two sites, Kerbernez and Tartas, where the ex post optimized
CS/ C0 values were far below the default value (Fig. 1) have a more
complex history of land use and soil management practices. The site of
Kerbernez is former grassland (during the first half of the 20th century; Supplement Table S1) that was converted into cropland 2 decades before the implementation of its arable LTE, in 1958. The site
of Tartas was cultivated for a longer time before the LTE onset; however it
was turned to grassland for a period in the 19th century (Supplement Table S1) and received applications of poultry manure for several
years before the LTE began. In these two sites, characterized by an AMG-optimal CS/ C0 much lower than the default value, the PARTYSOC
machine-learning model predicted values very close to the optimal
CS/ C0 values (Fig. 1).
Discussion
Our study demonstrates that the PARTYSOC method based on
Rock-Eval® thermal analysis
(Cécillon
et al., 2018, 2021) can estimate the initial SOC pool partitioning of the
AMG model of SOC dynamics while improving its accuracy in a series of
diverse and independent French LTEs. Contrary to previous studies
(Skjemstad
et al., 2004; Luo et al., 2014), no modifications of the decomposition rate
of SOC kinetic pools were necessary to improve model predictions. The
PARTYSOC initialization method never severely affected the model
simulations, while it strongly improved them at sites where SOC stocks were
far from an equilibrium state due to historical changes in soil management
or land use. Areas with past changes in land use and soil management
represent a large yet poorly known part of arable land in France and Europe
(Fuchs
et al., 2015; Erb et al., 2017) where SOC stocks and slow-cycling SOC pools
are far from equilibrium
(Wutzler
and Reichstein, 2007; Herbst et al., 2018; Clivot et al., 2019;
Taghizadeh-Toosi et al., 2020). Therefore, by accounting for these legacy
effects of site history on SOC pool partitioning, the PARTYSOC-based
initialization of the AMG model should result in more accurate simulations
of SOC dynamics at a national or continental scale.
Our findings, combined with results reported in recent ensemble modelling
studies
(Martin
et al., 2019; Farina et al., 2021), suggest that despite its simple
structure and when properly initialized (e.g. using the PARTYSOC model) the
AMG model is unsurpassed for predicting observed SOC stock changes in French
agricultural LTEs and is amongst the best available modelling frameworks of
SOC dynamics in European arable land (Martin et al., 2019; Farina et al.,
2021). Our results demonstrate that there is still potential to increase the
accuracy of simple multi-compartmental models of SOC dynamics, bringing
their simulations very close to the observed values of SOC stock changes.
Developing other Rock-Eval®-based initialization methods
specifically designed to match the carbon pool design of other
multi-compartmental SOC dynamics models such as RothC
(Coleman et al., 1997) is a
promising research area. More generally, we recommend that the potential of
multi-compartmental SOC dynamics models be fully explored and exploited by
soil biogeochemists before a new generation of models of increased
complexity becomes operational. While new models including the diversity of
microbial communities and related processes are emerging (Lehmann et al.,
2020; Crowther et al., 2019), the uncertain structure and parametrization of
more complex models is hindering their application as robust predictive
tools (Shi et al., 2018). At the same time, simple conceptual models of SOC
dynamics like AMG combined with novel initialization methods and data-based
approaches such as PARTYSOC show promising improvements (Cécillon,
2021a; Dangal et al., 2021; Lee et al., 2020). The low prediction error of
the AMG model when its SOC pool distribution is initialized with the
PARTYSOC method even challenges the ability of more complex modelling
approaches to achieve better performance, given the uncertainty on observed
values of SOC stock changes
(Schrumpf et al., 2011).
The continental or worldwide implementation of the AMG model with the
PARTYSOC-based initialization of SOC pools distribution will require
additional work. First, the PARTYSOC machine-learning model
(Cécillon
et al., 2018, 2021) will have to be validated on a wider range of
pedoclimates. This method, initially built on LTEs coming from north-western
Europe (Cécillon et
al., 2018), has now been successfully extended to new soil types and a new
climate (tropical;
Cécillon et al.,
2021). The good agreement between AMG-optimal stable-SOC proportion values
and PARTYSOC predictions reported here suggests that most agricultural
LTEs with accurate AMG simulations could be used as reference sites for the
PARTYSOC model, lifting an important technical limitation to its
geographical expansion
(Cécillon et al.,
2021). Second, the improved accuracy of model simulations using a
PARTYSOC-based initialization will also have to be demonstrated for a
wider pedoclimatic range (i.e. worldwide LTEs; such as those referenced by the
International Soil Carbon Network; Nave et al., 2015). Third,
Rock-Eval® data from the new application areas will be
required. Rock-Eval® is a high-throughput technique that is
well adapted to the analysis of large soil sample sets provided by
large-scale soil monitoring programmes. We recommend implementing
Rock-Eval® measurements in national and continental soil
monitoring networks.
Conclusions
Combining Rock-Eval® thermal analysis with the PARTYSOC
machine-learning model should be considered an emerging key approach with
demonstrated ability to improve the accuracy of simulations of SOC dynamics,
complementary to other SOC cycling proxies
(Bailey et al.,
2018; Wiesmeier et al., 2019). The progressive large-scale delivery of these
complementary data related to SOC dynamics will strengthen model predictions
of SOC stock changes at the national to global scale, necessary for
implementing efficient climate change mitigation policies
(FAO, 2020).
Code availability
Code used to produce the results presented in this study, as well as the AMG
model, can be made available upon reasonable request. The code of the
Rock-Eval®-based PARTYSOCv2.0EU random forest
model is available on Zenodo at the permanent link of
10.5281/zenodo.4446138 (Cécillon, 2021b).
Data availability
Pedoclimatic and land management information used to run the AMG model are
published in Clivot et al. (2019) and can be obtained from Hugues Clivot and Fabien Ferchaud
upon request concerning seven sites and from Florent Levavasseur and Sabine Houot concerning the
remaining two sites (Levavasseur et al., 2020). The Rock-Eval®
data obtained and processed during this study are available upon request
from the corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-375-2022-supplement.
Author contributions
EK performed the research and produced the first version of the
manuscript. PB and LC designed the study with contributions from CC
and FB, whereas BM, FF, HC, FL, and SH organized the search for
archive soil samples, provided data needed to run AMG simulations as well as
the latest version of the AMG model, and helped with the interpretation of
the results. FL performed AMG simulations for the sites of Colmar and
Feucherolles. FB conducted all Rock-Eval® analyses. All
authors participated in multiple scientific discussions during the
preparation of this paper and finally reviewed and provided valuable
feedback during the writing process.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors would like to thank ADEME (Ministry for an Ecological Transition of the Ministry for Higher Education, Research and Innovation) and the ANR (French National Research Agency) for the financial support. The authors would
also like to thank Florence Savignac for her help in performing
Rock-Eval® analyses and all the people involved in
maintaining and monitoring the field trials used in this study over multiple
decades. We would also like to thank everyone involved in the conception and
creation of the AMG consortium, the AIAL database, and the SOERE PRO network
for kindly providing access to their data.
Financial support
This work was supported by ADEME (French Agency for Ecological Transition, under the Ministry for an Ecological Transition and the Ministry for Higher Education, Research and Innovation; PhD grant for Eva Kanari, PROTERR project) and the ANR (French National Research Agency; PhD grant for Eva Kanari, StoreSoilC project ANR-17-CE32-0005).
Review statement
This paper was edited by Alexey V. Eliseev and reviewed by Adrián Andriulo and one anonymous referee.
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