Spatial Statistics,
Год журнала:
2022,
Номер
50, С. 100639 - 100639
Опубликована: Фев. 15, 2022
For
the
better
part
of
20th
century
pedologists
mapped
soil
by
drawing
boundaries
between
different
classes
which
they
identified
from
survey
on
foot
or
vehicle,
supplemented
air-photo
interpretation,
and
backed
an
understanding
landscape
processes
is
formed.
Its
limitations
for
representing
gradual
spatial
variation
predicting
conditions
at
unvisited
sites
became
evident,
in
1980s
introduction
geostatistics
specifically
ordinary
kriging
revolutionized
thinking
to
a
large
extent
practice.
Ordinary
based
solely
sample
data
variable
interest—it
takes
no
account
related
covariates.
The
latter
were
incorporated
1990s
onward
as
fixed
effects
regression
predictors,
giving
rise
with
external
drift
kriging.
Simultaneous
estimation
coefficients
variogram
parameters
best
done
residual
maximum
likelihood
estimation.
In
recent
years
machine
learning
has
become
feasible
huge
sets
environmental
obtained
sensors
aboard
satellites
other
sources
produce
digital
maps.
techniques
are
classification
regression,
but
take
correlations.
Further,
effectively
'black
boxes';
lack
transparency,
their
output
needs
be
validated
if
trusted.
They
undoubtedly
have
merit;
here
stay.
too,
however,
shortcomings
when
applied
data,
statisticians
can
help
overcome.
Spatial
pedometricians
still
much
do
incorporate
uncertainty
into
predictions,
averages
totals
over
regions,
errors
measurement
positions
data.
must
also
communicate
these
uncertainties
end
users
maps,
whatever
means
made.
Science,
Год журнала:
2022,
Номер
378(6622), С. 915 - 920
Опубликована: Ноя. 24, 2022
Grazing
represents
the
most
extensive
use
of
land
worldwide.
Yet
its
impacts
on
ecosystem
services
remain
uncertain
because
pervasive
interactions
between
grazing
pressure,
climate,
soil
properties,
and
biodiversity
may
occur
but
have
never
been
addressed
simultaneously.
Using
a
standardized
survey
at
98
sites
across
six
continents,
we
show
that
soil,
are
critical
to
explain
delivery
fundamental
drylands
Increasing
pressure
reduced
service
in
warmer
species-poor
drylands,
whereas
positive
effects
were
observed
colder
species-rich
areas.
Considering
local
abiotic
biotic
factors
is
key
for
understanding
fate
dryland
ecosystems
under
climate
change
increasing
human
pressure.
European Journal of Soil Science,
Год журнала:
2020,
Номер
72(4), С. 1607 - 1623
Опубликована: Май 21, 2020
Abstract
Spatially
resolved
estimates
of
change
in
soil
organic
carbon
(SOC)
stocks
are
necessary
for
supporting
national
and
international
policies
aimed
at
achieving
land
degradation
neutrality
climate
mitigation.
In
this
work
we
report
on
the
development,
implementation
application
a
data‐driven,
statistical
method
mapping
SOC
space
time,
using
Argentina
as
pilot.
We
used
quantile
regression
forest
machine
learning
to
predict
annual
stock
0–30
cm
depth
250
m
resolution
between
1982
2017.
The
model
was
calibrated
over
5,000
values
from
36‐year
time
period
35
environmental
covariates.
preprocessed
normalized
difference
vegetation
index
(NDVI)
dynamic
covariates
temporal
low‐pass
filter
allow
given
year
depend
NDVI
current
well
preceding
years.
Predictions
had
modest
variation,
with
an
average
decrease
entire
country
2.55
2.48
kg
C
−2
(equivalent
decline
211
Gg
C,
3.0%
total
Argentina).
Pampa
region
larger
estimated
4.62
4.34
(5.9%)
during
same
period.
For
2001–2015
period,
predicted
variation
seven‐fold
than
that
obtained
Tier
1
approach
Intergovernmental
Panel
Climate
Change
United
Nations
Convention
Combat
Desertification.
Prediction
uncertainties
turned
out
be
substantial,
mainly
due
limited
number
poor
spatial
distribution
calibration
data,
explanatory
power
Cross‐validation
confirmed
prediction
accuracy
limited,
mean
error
0.03
root
squared
2.04
.
spite
large
uncertainties,
showed
methods
can
space–time
may
yield
valuable
information
managers
policymakers,
provided
observation
density
is
sufficiently
large.
Highlights
tested
use
stock.
2017
3%
topsoil
time.
greater
IPCC
approach.
Accurate
requires
dense
sampling
Agronomy,
Год журнала:
2023,
Номер
13(1), С. 220 - 220
Опубликована: Янв. 11, 2023
Climate
models
project
that
many
terrestrial
ecosystems
will
become
drier
over
the
course
of
this
century,
leading
to
a
drastic
increase
in
global
extent
arid
soils.
In
order
decrease
effects
climate
change
on
food
security,
it
is
crucial
understand
environment
and
constraints
associated
with
Although
aridity
aboveground
organisms
have
been
studied
extensively,
our
understanding
how
affects
soil
processes
nutrient
cycling
lacking.
One
primary
agricultural
constraints,
particularly
locations,
water
scarcity,
due
which
soils
are
characterized
by
sparse
vegetation
cover,
low
organic
carbon,
poor
structure,
reduced
biodiversity,
high
rate
erosion
via
wind.
Increased
limit
availability
essential
plant
nutrients
crop
growth,
subsequently
pose
serious
threats
key
ecological
services.
The
increasing
salinization
another
major
environmental
hazard
further
limits
potential
These
can
be
ameliorated
yields
increased
through
case-specific
optimization
irrigation
drainage
management,
enhancing
native
beneficial
microbes,
combinations
amendments,
conditioners,
residue
management.
This
review
explores
technologies
ameliorate
maintain
output
