Estimation of N2O and CH4 emissions in field study and DNDC model under optimal nitrogen level in rice-wheat rotation system
The Science of The Total Environment,
Год журнала:
2025,
Номер
974, С. 179168 - 179168
Опубликована: Март 25, 2025
Язык: Английский
Appropriately delayed flooding before rice transplanting increases net ecosystem economic benefit in the winter green manure-rice rotation system
Resources Environment and Sustainability,
Год журнала:
2024,
Номер
18, С. 100173 - 100173
Опубликована: Окт. 10, 2024
Язык: Английский
Variation in the Content and Fluorescence Composition of Dissolved Organic Matter in Chinese Different-Term Rice–Crayfish Integrated Systems
Sustainability,
Год журнала:
2024,
Номер
16(12), С. 5139 - 5139
Опубликована: Июнь 17, 2024
This
study
examines
the
fluorescence
characteristics
of
dissolved
organic
matter
(DOM)
in
soils
from
different
periods
rice–crayfish
integrated
systems
(RCISs)
China.
Utilizing
three-dimensional
excitation–emission
matrix
(3D-EEM)
spectroscopy,
investigated
hydrophobicity,
molecular
weight
distributions,
and
properties
DOM
2-,
5-,
7-year
RCIS
operations,
with
rice
monoculture
(RM)
serving
as
a
control.
The
findings
indicate
that
initial
2
years
an
RCIS,
factors
such
straw
deposition,
root
exudates,
crayfish
excretions
increase
carbon
(DOC)
release
alter
composition,
increasing
humic
acid
content
soil.
As
system
matures
at
5
years,
improvements
soil
structure
microbial
activity
lead
to
breakdown
high-molecular-weight
substances
rise
small-molecular-weight
amino
acids.
By
mark,
aquatic
ecosystem
stabilizes,
there
is
humification
index
DOM.
These
variations
are
essential
for
understanding
effects
farming
on
quality
sustainability.
Язык: Английский
Increased anaerobic conditions promote the denitrifying nitrogen removal potential and limit anammox substrate acquisition within paddy irrigation and drainage units
Feile Du,
Yinghua Yin,
Limei Zhai
и другие.
The Science of The Total Environment,
Год журнала:
2024,
Номер
951, С. 175616 - 175616
Опубликована: Авг. 19, 2024
Язык: Английский
Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots
Toxics,
Год журнала:
2024,
Номер
12(10), С. 737 - 737
Опубликована: Окт. 12, 2024
To
predict
the
behavior
of
aromatic
contaminants
(ACs)
in
complex
soil-plant
systems,
this
study
developed
machine
learning
(ML)
models
to
estimate
root
concentration
factor
(RCF)
both
traditional
(e.g.,
polycyclic
hydrocarbons,
polychlorinated
biphenyls)
and
emerging
ACs
phthalate
acid
esters,
aryl
organophosphate
esters).
Four
ML
algorithms
were
employed,
trained
on
a
unified
RCF
dataset
comprising
878
data
points,
covering
6
features
cultivation
systems
98
molecular
descriptors
55
chemicals,
including
29
ACs.
The
gradient-boosted
regression
tree
(GBRT)
model
demonstrated
strong
predictive
performance,
with
coefficient
determination
(R
Язык: Английский