Agronomy,
Год журнала:
2024,
Номер
14(11), С. 2498 - 2498
Опубликована: Окт. 25, 2024
In
recent
years,
biochar
(BC)
and
biochar-based
soil
amendments
(CSAs)
have
been
widely
used
in
agriculture
the
environment.
present
study,
a
two-rice-season
field
study
was
conducted
to
explore
comprehensive
effects
of
applying
BC
(1%)
CSA
(0.5%
1%)
on
organic
carbon
accumulation,
acidification
amelioration
heavy
metal
availability
soil–rice
system.
The
results
show
that
pH
increased
by
0.5–1.7
units
0.3–1.0
units,
respectively,
early
rice
season
late
treated
compared
with
CK.
Soil
contents
were
18–30%
15–25%
amended
treatments.
addition,
available
phosphorus
largely
as
result
addition.
CaCl2
extractable
metals
(Cd,
Ni,
Cu
Zn)
simultaneously
decreased
or
amendments.
Cd
grain
significantly
reduced
25–48%
52–83%
treatments,
while
Zn
generally
not
affected.
uptake
Ni
also
CSA.
This
demonstrates
application
alone
combinates
inorganic
(limestone,
sepiolite
potassium
dihydrogen
phosphate)
can
improve
properties
nutrient
content
decrease
(especially
for
Ni)
accumulation
from
grain,
where
combination
is
more
effective.
Ecological Processes,
Год журнала:
2024,
Номер
13(1)
Опубликована: Май 1, 2024
Abstract
Background
Soil
organic
carbon
(SOC)
is
a
critical
component
of
the
global
cycle,
and
an
accurate
estimate
regional
SOC
stock
(SOCS)
would
significantly
improve
our
understanding
sequestration
cycles.
Zoige
Plateau,
locating
in
northeastern
Qinghai-Tibet
has
largest
alpine
marsh
wetland
worldwide
exhibits
high
sensitivity
to
climate
fluctuations.
Despite
increasing
use
optical
remote
sensing
predicting
SOCS,
obvious
limitations
Plateau
due
highly
cloudy
weather,
knowledge
on
spatial
patterns
SOCS
limited.
Therefore,
current
study,
distributions
within
100
cm
were
predicted
using
XGBoost
model—a
machine
learning
approach,
by
integrating
Sentinel-1,
Sentinel-2
field
observations
Plateau.
Results
The
results
showed
that
content
exhibited
vertical
distribution
cm,
with
highest
topsoil.
tenfold
cross-validation
approach
model
satisfactorily
efficiency
0.59
root
mean
standard
error
95.2
Mg
ha
−1
.
Predicted
distinct
heterogeneity
average
355.7
±
123.1
totaled
0.27
×
10
9
carbon.
Conclusions
High
topsoil
highlights
risks
significant
loss
from
human
activities
Combining
Sentinel-1
model,
which
demonstrates
importance
selecting
modeling
approaches
satellite
images
at
fine
resolution
m.
Furthermore,
study
emphasizes
potential
radar
(Sentinel-1)
developing
mapping,
newly
developed
fine-resolution
mapping
having
important
applications
land
management,
ecological
restoration,
protection
efforts
Remote Sensing,
Год журнала:
2024,
Номер
16(16), С. 3017 - 3017
Опубликована: Авг. 17, 2024
Soil
erodibility
(K)
refers
to
the
inherent
ability
of
soil
withstand
erosion.
Accurate
estimation
and
spatial
prediction
K
values
are
vital
for
assessing
erosion
managing
land
resources.
However,
as
most
K-value
models
empirical,
they
suffer
from
significant
extrapolation
uncertainty,
traditional
studies
on
focusing
individual
empirical
have
neglected
explore
pattern
differences
between
various
models.
This
work
proposed
a
universal
framework
selecting
an
optimal
soil-erodibility
map
using
enhanced
by
machine
learning.
Specifically,
three
models,
namely,
erosion-productivity
impact
calculator
model
(K_EPIC),
Shirazi
(K_Shirazi),
Torri
(K_Torri)
were
used
estimate
values.
Random
Forest
(RF)
Gradient-Boosting
Decision
Tree
(GBDT)
algorithms
employed
develop
which
led
creation
maps.
The
distribution
associated
environmental
covariates
also
investigated
across
varying
Results
showed
that
RF
achieved
highest
accuracy,
with
R2
K_EPIC,
K_Shirazi,
K_Torri
increasing
46%,
34%,
22%,
respectively,
compared
GBDT.
And
distinctions
among
variables
shape
patterns
been
identified.
K_EPIC
K_Shirazi
influenced
porosity
moisture.
is
more
sensitive
moisture
conditions
terrain
location.
More
importantly,
our
study
has
highlighted
disparities
in
Considering
data
distribution,
measured
values,
outperformed
others
estimating
plateau
lake
watershed.
aimed
create
maps
offered
scientific
accurate
method
assessment
Agriculture,
Год журнала:
2025,
Номер
15(3), С. 339 - 339
Опубликована: Фев. 4, 2025
The
accurate
prediction
of
soil
organic
matter
(SOM)
content
is
important
for
sustainable
agriculture
and
effective
management.
This
task
particularly
challenging
due
to
the
variability
in
factors
influencing
SOM
distribution
across
different
cultivated
land
types,
as
well
site-specific
responses
remote
sensing
data
environmental
covariates,
especially
black
region
northeastern
China,
where
exhibits
significant
spatial
variability.
study
evaluated
variations
on
importance
imagery
covariates
zones.
A
total
180
samples
(0–20
cm)
were
collected
from
Youyi
County,
Heilongjiang
Province,
multi-year
synthetic
bare
images
2014
2022
(focusing
April
May)
acquired
using
Google
Earth
Engine.
Combining
three
types
such
drainage,
climate
topography,
area
was
categorized
into
dry
field
paddy
field.
Then,
model
constructed
random
forest
regression
method
accuracy
strategies
by
10-fold
cross-validation.
findings
indicated
that,
(1)
overall
analysis,
combining
drainage
variables
May
could
attain
highest
accuracy,
ranked
follows:
(RS)
>
(CLI)
(DN)
Topography
(TP).
(2)
Zonal
analysis
conducted
with
a
high
degree
precision,
evidenced
an
R2
0.72
impressively
low
RMSE
0.73%.
time
window
monitoring
More
specifically,
optimal
frames
dryland
identified
May,
while
those
fields
concentrated
May.
(3)
In
addition,
diverse
observed
vary
types.
regions
characterized
intricate
fields,
contributions
assumed
heightened
importance.
Conversely,
featuring
flat
terrain,
roles
played
more
substantial
role
outcomes.
These
underscore
selecting
appropriate
inputs
improving
accuracy.
Applied Sciences,
Год журнала:
2025,
Номер
15(7), С. 3753 - 3753
Опубликована: Март 29, 2025
The
cultivated
land
in
the
black
soil
of
Northeast
China
(BSNC),
due
to
long-term
high-input
and
high-output
utilization,
is
facing
a
series
challenges
such
as
erosion,
compaction,
nutrient
loss.
However,
existing
quality
evaluation
(CLQE)
lacks
regional
specificity,
making
it
difficult
accurately
reflect
(CLQ)
characteristics
across
different
areas.
Therefore,
this
study
proposes
comprehensive
framework
that
integrates
both
functionality
degradation
risk,
establishing
an
assessment
system
consisting
18
indicators
comprehensively
evaluate
CLQ
BSNC
from
multiple
perspectives.
results
indicate
exhibits
declining
trend
north
south,
with
second-
third-grade
dominating,
accounting
for
75.68%
total
area.
overall
increases
west
east,
Liaohe
Plain
Region
(LHP)
performing
best.
Low-risk
primarily
concentrated
Songnen
(SNP)
Western
Sandy
(WS),
covering
38.55%
Additionally,
finds
trade-off
between
primary
productivity
function
resource
utilization
efficiency
regions,
while
synergistic
relationship
observed
maintenance
functions.
This
research
emphasizes
necessity
balancing
ecological
protection
achieve
sustainable
efficient
use
BSNC.