Forests,
Год журнала:
2024,
Номер
15(9), С. 1603 - 1603
Опубликована: Сен. 11, 2024
The
selection
of
suitable
tree
species
and
the
reasonable
allocation
planting
areas
are
important
measures
for
improving
soil
quality.
To
evaluate
quality
(SQ)
its
driving
factors
Pinus
tabuliformis
forests
in
loess
hilly
where
forestry
ecological
projects,
such
as
returning
farmland
to
forest
(grass),
have
been
implemented,
this
study
selected
P.
with
different
restoration
years
(1a,
6a,
11a,
18a,
22a)
Wuqi
County
used
grassland
before
afforestation
(PRG)
abandoned
(AG)
22
controls.
In
study,
physicochemical
indices,
fauna
herbaceous
plant
indices
obtained
via
principal
component
analysis
were
establish
a
evaluation
model
fuzzy
comprehensive
method
comprehensively
SQ.
Structural
equation
modeling
(SEM)
was
identify
key
affecting
SQ
forests.
goal
create
that
could
effectively
while
considering
all
relevant
factors.
findings
showed
that:
(1)
by
performing
on
27
indicator
factors,
first
six
components
had
eigenvalues
>
1,
cumulative
contribution
rate
90.028%,
encompassing
information
original
variables.
(2)
highest
index
(SQI)
0.592
(p
<
0.05)
restored
6a
forest,
whereas
lowest
SQI
0.323
1a
forest.
As
number
increased,
plantation
progressively
approached
long-term
grassland,
only
1.8%
difference
after
restoration.
woodland
83%
higher
than
1a,
following
restoration,
decreasing
trend
increasing
years.
Nevertheless,
increased
>52%
compared
early
stage
(1a)
31%
(PRG).
(3)
SEM
revealed
land
mainly
driven
physical
indicators,
indicators
negative
effect
evolution
different,
increase
years,
effects
an
overall
upward
trend.
European Journal of Soil Science,
Год журнала:
2024,
Номер
75(5)
Опубликована: Сен. 1, 2024
Abstract
Converting
monocultures
to
mixed
plantations
has
been
emphasized
improve
ecosystem
productivity
and
services.
However,
the
impact
of
tree
species
identity
on
phosphorus
(P)
bioavailability
in
acidic
soils
subtropical
China,
where
P
is
relatively
scarce,
not
fully
understood.
This
study
explored
changes
soil
biologically‐based
fractions
effect
mineral
microbial
properties
transformation
after
mixing
five
broadleaved
trees
(
Bretschneidera
sinensis,
Manglietia
conifera,
Cercidiphyllum
japonicum,
Michelia
maudiae
Camellia
oleifera
)
individually
with
coniferous
Pinus
massoniana
).
The
results
showed
that
most
significantly
increased
pH
citric
acid
decreased
exchangeable
Fe
3+
Al
activation
oxides
compared
monospecific
plantations,
which
reduced
precipitation
adsorption.
Mixed
planting
phosphatase
activity
altered
community
composition
P‐mobilizing
bacteria
carrying
phoD
pqqC
genes,
contributed
organic
mineralization
inorganic
(Pi)
desorption.
biomass
relative
rate
turnover.
Labile
(Enzyme‐P)
was
a
potentially
significant
source
soluble
Pi
(CaCl
2
‐P)
among
fractions,
plus
P.
Overall,
introducing
species,
especially
(e.g.
japonicum
,
high
litter
quality
belowground
secretions
acid,
phosphatase),
solubilization
recalcitrant
(HCl‐P),
desorption
chemisorbed
(Citrate‐P)
accumulation
Enzyme‐P,
thereby
increasing
available
pools.
Redundancy
analysis
demonstrated
were
mainly
driven
by
phosphatases,
cations,
floor
fresh
lignin/N
acid.
Altogether,
we
highlight
importance
choosing
mixtures
have
synergistic
or
complementary
effects
when
constructing
order
alleviate
limitations.
Forests,
Год журнала:
2024,
Номер
15(9), С. 1603 - 1603
Опубликована: Сен. 11, 2024
The
selection
of
suitable
tree
species
and
the
reasonable
allocation
planting
areas
are
important
measures
for
improving
soil
quality.
To
evaluate
quality
(SQ)
its
driving
factors
Pinus
tabuliformis
forests
in
loess
hilly
where
forestry
ecological
projects,
such
as
returning
farmland
to
forest
(grass),
have
been
implemented,
this
study
selected
P.
with
different
restoration
years
(1a,
6a,
11a,
18a,
22a)
Wuqi
County
used
grassland
before
afforestation
(PRG)
abandoned
(AG)
22
controls.
In
study,
physicochemical
indices,
fauna
herbaceous
plant
indices
obtained
via
principal
component
analysis
were
establish
a
evaluation
model
fuzzy
comprehensive
method
comprehensively
SQ.
Structural
equation
modeling
(SEM)
was
identify
key
affecting
SQ
forests.
goal
create
that
could
effectively
while
considering
all
relevant
factors.
findings
showed
that:
(1)
by
performing
on
27
indicator
factors,
first
six
components
had
eigenvalues
>
1,
cumulative
contribution
rate
90.028%,
encompassing
information
original
variables.
(2)
highest
index
(SQI)
0.592
(p
<
0.05)
restored
6a
forest,
whereas
lowest
SQI
0.323
1a
forest.
As
number
increased,
plantation
progressively
approached
long-term
grassland,
only
1.8%
difference
after
restoration.
woodland
83%
higher
than
1a,
following
restoration,
decreasing
trend
increasing
years.
Nevertheless,
increased
>52%
compared
early
stage
(1a)
31%
(PRG).
(3)
SEM
revealed
land
mainly
driven
physical
indicators,
indicators
negative
effect
evolution
different,
increase
years,
effects
an
overall
upward
trend.
