Black Sea Journal of Agriculture,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 17, 2024
Soil
organic
carbon
(SOC)
is
an
important
indication
of
soil
health
and
helps
to
sustain
fertility.
As
a
result,
determining
its
composition
the
factors
that
influence
it
critical
for
long-term
nutrient
management,
especially
in
controlled
conditions
such
as
greenhouses.
This
study
utilizes
machine
learning
classify
SOC
content
greenhouses
built
on
pyroclastic
deposits
Isparta
region.
A
dataset
276
samples
eight
variables—clay
(%),
silt
sand
electrical
conductivity
(EC),
pH,
elevation,
slope,
aspect—were
used
model
values.
was
classified
into
five
classifications:
very
low
(2.3%).
In
this
study,
models—Logistic
Regression
(LR),
K-Nearest
Neighbors
(KNN),
Support
Vector
Machine
(SVM),
Decision
Tree
(DT),
Random
Forest
(RF)—were
evaluated
using
cross-validation
determine
their
classification
accuracy,
precision,
recall,
F-score,
ROC
area.
(RF)
(DT)
outperformed
other
models,
with
RF
achieving
highest
overall
accuracy
(76.4%),
precision
(77.3%),
AUC
(0.904),
followed
by
DT
at
75.4%
0.874.
shows
practicality
models
categorizing
content,
highlighting
importance
fertility
control
greenhouse
conditions.
To
improve
efficacy,
future
studies
should
include
more
auxiliary
variables,
physical
chemical
qualities
lithological
data,
well
wider
range
types.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(6), P. 2427 - 2427
Published: March 10, 2025
As
artificial
intelligence
(AI)
technology
continues
to
advance
and
iterate,
various
industries
have
undergone
intelligent
reformation.
China’s
animal
husbandry
industry,
given
its
importance
for
people’s
livelihoods,
is
no
exception
this
transformation.
Using
AI
in
field
becoming
increasingly
common
since
it
not
only
improves
production
efficiency
but
also
revolutionizes
traditional
business
models.
Animal
science
a
fundamental
discipline
that
drives
the
progress
of
by
studying
growth,
breeding,
nutritional
needs,
feeding
management
livestock
poultry.
This
explores
advanced
veterinary
theories
technologies
epidemic
prevention
control.
The
ultimate
objective
ensure
high-quality
sufficient
products
fulfill
demands
both
daily
life.
It
predicted
deep
integration
into
will
bring
unprecedented
opportunities
industry.
study
aims
explore
impact
on
students’
learning
experiences
future
educational
directions.
By
situating
research
within
context
current
developments
technology,
we
hope
provide
valuable
insights
educators
policymakers
employ
questionnaire
survey
perceptions
attitudes
students
majoring
from
agricultural
institutions
China
toward
integration.
results
practical
references
cultivation
development
talent
Carbon Balance and Management,
Journal Year:
2024,
Volume and Issue:
19(1)
Published: May 29, 2024
Abstract
Climate-smart
agriculture
can
be
used
to
build
soil
carbon
stocks,
decrease
agricultural
greenhouse
gas
(GHG)
emissions,
and
increase
agronomic
resilience
climate
pressures.
The
US
recently
declared
its
commitment
include
the
sector
as
part
of
an
overall
climate-mitigation
strategy,
with
this
comes
need
for
robust,
scientifically
valid
tools
GHG
flux
measurements
modeling.
If
is
contribute
significantly
mitigation,
practice
adoption
should
incentivized
on
much
land
area
possible
mitigation
benefits
accurately
quantified.
Process-based
models
are
parameterized
data
from
a
limited
number
long-term
experiments,
which
may
not
fully
reflect
outcomes
working
farms.
Space-for-time
substitution,
paired
studies,
monitoring
SOC
stocks
emissions
commercial
farms
using
variety
climate-smart
management
systems
validate
findings
experiments
provide
process-based
model
improvements.
Here,
we
describe
project
that
worked
collaboratively
producers
in
Midwest
directly
measure
organic
(SOC)
their
at
field
scale.
We
study,
several
unexpected
challenges
encountered,
facilitate
further
on-farm
collection
creation
secure
database
stock
measurements.
