Carbon
farming
trading
schemes
(CFTS)
are
emerging
as
a
nature-based
solution
to
contribute
the
mitigation
of
climate
change
by
capturing
carbon
from
atmosphere
and
storing
it
in
plant-soil
system
soil
organic
(SOC).
Increasing
SOC
for
CFTS
requires
improved
management
practices,
monitoring
through
sampling
measurements,
ex-ante
economic
evaluation.
The
implication
is
that
needs
be
quantified
reliably,
i.e.,
compatible
with
measurements
data
validation
certification
under
CFTS.
Multicompartmental
dynamic
models
(mSCDM)
have
been
widely
proposed
assessing
spatial
temporal
trajectories
stocks.
However,
overly
complex
structure
mSCDM
means
they
prone
overparameterization
overfitting,
poor
performance
unseen
data,
thus,
not
appropriate
present
paper
addresses
this
gap
describing
development
ProCarbon-Soil
(PROCS)
model,
designed
explicitly
CTFS
new
context
data-model
fusion.PROCS
holds
same
fundamental
biophysical
principles
most
applied
SCDM
has
advantage
improving
adherence
empirical
shortening
soil-plant
system's
state
variables
total
stocks
decomposability
allow
reducing
number
parameters
needed.
Plant and Soil,
Journal Year:
2023,
Volume and Issue:
495(1-2), P. 113 - 136
Published: Nov. 11, 2023
Abstract
Background
and
aims
Soil
organic
carbon
(SOC)
dynamics
are
vital
in
the
context
of
climate
change
sustainable
soil
management.
The
ẟ
13
C
signatures
SOC
powerful
indicators
tracers
fluxes
through
soils
transformation
processes
within
soils.
Depth
gradients
can
be
considered
as
their
archive.
However,
many
different
drivers
impact
simultaneously,
thus
hampering
interpretation.
Methods
Here
we
summarize
current
knowledge
about
drivers,
sources
determining
δ
matter
along
profiles.
Results
largest
profiles
(>
10‰)
have
been
observed
at
sites
where
vegetation
has
shifted
between
C3
C4
plants,
changing
isotopic
inputs.
In
without
such
changes,
typically
increase
by
1–3‰
from
topsoil
to
subsoil.
Three
main
reasons
for
this
(i)
decreasing
atmospheric
CO
2
(Suess
effect)
led
a
depletion
plant
biomass
2.0‰
since
1850,
(ii)
increasing
concentrations
also
depleted
1.8‰,
(iii)
fractionation
occurs
during
continuous
microbial
recycling
necromass
accumulation.
Moreover,
greater
mobility
C-enriched
hydrophilic
dissolved
other
input
may
Conclusions
External
climatic
affect
signature
inputs,
stronger
influence
on
compared
internal
processes.
Climate smart agriculture.,
Journal Year:
2024,
Volume and Issue:
1(1), P. 100001 - 100001
Published: April 24, 2024
Modeling
soil
organic
carbon
(SOC)
is
helpful
for
understanding
its
distribution
and
turnover
processes,
which
can
guide
the
implementation
of
effective
measures
(C)
sequestration
enhance
land
productivity.
Process-based
simulation
with
high
interpretability
extrapolation,
machine
learning
modeling
flexibility
are
two
common
methods
investigating
SOC
turnover.
To
take
advantage
both
methods,
we
developed
a
hybrid
model
by
coupling
two-carbon
pool
microbial
modeling.
Here,
assessed
model's
predictive,
mapping,
capabilities
process
on
Ningbo
region.
The
results
indicate
that
density-dependence
(β
=
2)
biomass
performed
better
in
parameters
microbial-based
C
cycle,
such
as
use
efficiency
(CUE),
mortality
rate,
assimilation
rate.
By
integrating
this
optimal
random
forest
(RF)
model,
improved
prediction
accuracy
SOC,
an
increased
R2
from
0.74
to
0.84,
residual
deviation
1.97
2.50,
reduced
root-mean-square
error
4.65
3.67
g
kg−1
compared
conventional
RF
model.
As
result,
predicted
exhibited
spatial
variation
provided
abundant
details.
Microbial
CUE
potential
input,
represented
net
primary
productivity,
emerged
factors
driving
Projections
under
CMIP6
SSP2-4.5
scenario
revealed
regional
loss
areas
was
mainly
caused
decreased
induced
climate
change.
Our
findings
highlight
combining
microbial-explicit
improve
understand
feedback
changing
climate.
Journal of Geophysical Research Biogeosciences,
Journal Year:
2024,
Volume and Issue:
129(6)
Published: June 1, 2024
Abstract
In
the
past
few
decades,
there
has
been
an
evolution
in
our
understanding
of
soil
organic
matter
(SOM)
dynamics
from
one
inherent
biochemical
recalcitrance
to
deriving
plant‐microbe‐mineral
interactions.
This
shift
driven,
part,
by
influential
conceptual
frameworks
which
put
forth
hypotheses
about
SOM
dynamics.
Here,
we
summarize
several
focal
and
derive
them
six
controls
related
formation,
(de)stabilization,
loss.
These
include:
(a)
physical
inaccessibility;
(b)
organo‐mineral
‐metal
stabilization;
(c)
biodegradability
plant
inputs;
(d)
abiotic
environmental
factors;
(e)
reactivity
diversity;
(f)
microbial
physiology
morphology.
We
then
review
empirical
evidence
for
these
controls,
their
model
representation,
outstanding
knowledge
gaps.
find
relatively
strong
support
representation
factors
but
disparities
between
data
models
diversity,
stabilization,
inputs,
particularly
with
respect
destabilization
latter
two
controls.
More
research
on
inaccessibility
morphology
is
needed
deepen
critical
improve
representation.
The
are
highly
interactive
also
present
some
inconsistencies
may
be
reconciled
considering
methodological
limitations
or
temporal
spatial
variation.
Future
must
simultaneously
refine
at
various
scales
within
a
hierarchical
structure,
while
incorporating
emerging
insights.
will
advance
ability
accurately
predict
