This
paper
explores
the
growing
interest
in
soil
health,
emphasizing
its
importance
optimizing
crop
production,
ecosystem
function,
and
biodiversity.
Defined
by
USDA-NRCS
as
soil’s
capacity
to
function
a
vital
ecosystem,
health
involves
filtering
contaminants,
cycling
nutrients,
supporting
infrastructure,
regulating
water
movement.
Traditional
approaches
quantifying
focus
on
chemical,
physical,
or
biological
properties,
often
calling
for
more
integrated
measurement
method.
While
practices
enhancing
such
no-tillage,
cover
crops,
biodiversity,
have
long
been
promoted,
their
broader
impacts
hydrologic
cycle
are
less
documented.
aims
fill
this
gap
reviewing
literature
practices’
effects
providing
evidence
guidelines
policy-
decision-makers.
It
highlights
benefits
of
improved
including
increased
infiltration,
higher
yields,
reduced
greenhouse
gas
emissions.
Applied Sciences,
Год журнала:
2023,
Номер
13(10), С. 6290 - 6290
Опубликована: Май 21, 2023
Gross
primary
productivity
(GPP)
is
an
important
indicator
in
research
on
carbon
cycling
terrestrial
ecosystems.
High-accuracy
GPP
prediction
crucial
for
ecosystem
health
and
climate
change
assessments.
We
developed
a
site-level
method
based
the
GeoMAN
model,
which
was
able
to
extract
spatiotemporal
features
fuse
external
environmental
factors
predict
Tibetan
Plateau.
evaluated
four
models’
behavior—Random
Forest
(RF),
Support
Vector
Machine
(SVM),
Deep
Belief
Network
(DBN),
GeoMAN—in
predicting
at
nine
flux
observation
sites
The
model
achieved
best
results
(R2
=
0.870,
RMSE
0.788
g
Cm−2
d−1,
MAE
0.440
d−1).
Distance
vegetation
type
of
influenced
prediction,
with
latter
being
more
significant.
different
grassland
types
exhibited
sensitivity
(Ta,
PAR,
EVI,
NDVI,
LSWI)
prediction.
Among
them,
site
located
alpine
swamp
meadow
insensitive
changes
factors;
accuracy
steppe
decreased
significantly
Kobresia
also
varied
factor
changes,
but
lesser
extent
than
former.
This
study
provides
good
reference
that
deep
learning
achieve
simulation
when
considers
spatial,
temporal,
factors,
judgement
made
by
conforms
basic
knowledge
relevant
field.
Abstract
Understanding
the
interactions
between
structures
and
functions
underlying
regime
shifts
in
dryland
social-ecological
systems
(SESs)
how
they
respond
to
climate
change
is
critical
for
predicting
managing
future
of
these
ecosystems.
Due
high
spatiotemporal
variability
sensitivity
drylands
ecosystem
natural
anthropogenic
disturbances,
it
challenging
predict
state
SESs.
This
theme
delves
into
mechanisms
geographical
heterogeneity
resilience
maintenance
stability
SESs
that
involve
threshold
behaviors.
We
emphasized
importance
considering
both
biotic
abiotic
factors
identify
drive
evolution
drylands.
The
research
frontier
involves
understanding
ecohydrological
socioeconomic
processes
a
geographically
diverse
scale-dependent
context,
developing
comprehensive
indicators,
models,
multivariable
approaches,
development
effective
management
strategies
can
maintain
sustainability
face
ongoing
global
environmental
changes.
Agronomy,
Год журнала:
2024,
Номер
14(4), С. 830 - 830
Опубликована: Апрель 17, 2024
Meadows
are
the
most
important
source
of
feed
for
extensive
livestock
farming
in
mountainous
conditions,
as
well
providing
many
environmental
services.
The
actual
socioeconomic
situation
and
climate
change
risk
its
conservation.
That
is
why
finding
optimal
management
important.
To
do
so,
predictive
models
a
useful
tool
to
determine
impact
different
practices
estimate
consequences
future
scenarios.
Empirical
good
analytical
tool,
but
their
applications
limited.
Dynamic
can
better
newer
scenarios,
even
if
there
dynamic
models,
adaptation
into
grassland
production
estimation
scarce.
This
article
reviews
suitable
grass
mountain
meadows
when
data
on
agricultural
(mowing,
grazing,
fertilization)
forage
value
available,
considering
conservation
plant
biodiversity.
Abstract
The
aboveground
biomass
(AGB)
of
grassland,
a
crucial
indicator
productivity,
is
anticipated
to
widespread
changes
in
key
ecosystem
attributes,
functions
and
dynamics.
Variations
grassland
AGB
have
been
extensively
documented
across
various
spatial
temporal
scales.
However,
precise
method
disentangle
long-term
effects
from
short-term
on
assess
the
attribution
explanatory
factors
for
change
remains
elusive.
This
study
aimed
quantify
impact
climatic
factors,
soil
properties,
grazing
intensity
changes,
utilizing
data
spanning
1980s
2000s
Northern
China.
Co-regression
model
was
explored
separate
AGB,
while
Generalized
Linear
Model
(GLM)
utilized
analyze
contributions
variables
AGB.
approach
effectively
avoids
issues
related
regression
mean
mathematical
coupling.
results
revealed
that
influence
variables,
texture
could
be
decomposed
into
long-term,
random
effects.
Long-term
explained
73.6%
variation,
whereas
effect
only
accounted
5.9%
change.
Additionally,
divided
direct
indirect
effects,
with
explaining
1.3%
4.6%
relative
importance
assessed,
identifying
parameters
precipitation
as
main
driving
area.
introduces
robust
methodology
enhance
performance
distinguishing
contributing
sustainable
development
ecology
similar
regions.
This
paper
explores
the
growing
interest
in
soil
health,
emphasizing
its
importance
optimizing
crop
production,
ecosystem
function,
and
biodiversity.
Defined
by
USDA-NRCS
as
soil’s
capacity
to
function
a
vital
ecosystem,
health
involves
filtering
contaminants,
cycling
nutrients,
supporting
infrastructure,
regulating
water
movement.
Traditional
approaches
quantifying
focus
on
chemical,
physical,
or
biological
properties,
often
calling
for
more
integrated
measurement
method.
While
practices
enhancing
such
no-tillage,
cover
crops,
biodiversity,
have
long
been
promoted,
their
broader
impacts
hydrologic
cycle
are
less
documented.
aims
fill
this
gap
reviewing
literature
practices’
effects
providing
evidence
guidelines
policy-
decision-makers.
It
highlights
benefits
of
improved
including
increased
infiltration,
higher
yields,
reduced
greenhouse
gas
emissions.
