agronomy

Article
Effect of Alternate Wetting and Drying on the Emission of
Greenhouse Gases from Rice Fields on the Northern Coast
of Peru
Ida Echegaray-Cabrera 1 , Lena Cruz-Villacorta 2 , Lia Ramos-Fernández 3, * , Mirko Bonilla-Cordova 1 ,
Elizabeth Heros-Aguilar 4 and Lisveth Flores del Pino 5

1 Science Faculty, Universidad Nacional Agraria La Molina, Lima 15024, Peru;

20170031@lamolina.edu.pe (I.E.-C.); 20170330@lamolina.edu.pe (M.B.-C.)
2 Department of Territorial Planning and Doctoral Program in Engineering and Environmental Sciences,
Universidad Nacional Agraria La Molina, Lima 15024, Peru; lenacruz@lamolina.edu.pe
3 Department of Water Resources, Universidad Nacional Agraria La Molina, Lima 15024, Peru
4 Agronomy Faculty, Universidad Nacional Agraria La Molina, Lima 15024, Peru; lizheros@lamolina.edu.pe
5 Center for Research in Environmental Chemistry, Toxicology and Biotechnology, Universidad Nacional
Agraria La Molina, Lima 15024, Peru; lisveth@lamolina.edu.pe
* Correspondence: liarf@lamolina.edu.pe

Abstract: The cultivation of rice is one of the main sources of greenhouse gas (GHG) emissions due
to continuously flooded irrigation (CF), which demands large volumes of water. As an alternative
solution, alternate wetting and drying (AWD) irrigation has been developed as a water-saving
strategy. This study was conducted at the Experimental Agricultural Station (EEA) in Vista, Florida,
in the Lambayeque region located on the northern coast of Peru. Thus, it was analyzed the effect of
AWD irrigation at different depths (5, 10, and less than 20 cm below the surface) compared to CF
control on methane (CH4 ) and nitrous oxide (N2 O) emissions and rice grain yield. AWD treatments
Citation: Echegaray-Cabrera, I.; reduced CH4 emissions by 84% to 99% but increased N2 O emissions by 66% to 273%. In terms of
Cruz-Villacorta, L.; Ramos-Fernández,
Global Warming Potential (GWP), the AWD10 treatment demonstrated a 77% reduction and a Water
L.; Bonilla-Cordova, M.;
Use Efficiency (WUE) of 0.96, affecting only a 2% decrease in rice grain yield, which ranged between
Heros-Aguilar, E.; Flores del Pino, L.
11.85 and 14.01 t ha−1 . Likewise, this study provides sufficient evidence for the adoption of AWD
Effect of Alternate Wetting and
Drying on the Emission of
irrigation as a strategy for the efficient use of water resources and the mitigation of GHG emissions in
Greenhouse Gases from Rice Fields on rice cultivation in the study area, compared to continuous flooded irrigation.
the Northern Coast of Peru. Agronomy
2024, 14, 248. Keywords: global warming potential; water management; grain yield
https://doi.org/10.3390/
agronomy14020248

Academic Editor: Jose Manuel

1. Introduction
Gonçalves
Rice is a fundamental food source for over 60% of the global population [1]. Currently,
Received: 15 December 2023 it is cultivated on approximately 153 Mha, which is equivalent to 11% of the world’s arable
Revised: 17 January 2024
land [2]. With the increasing global population, the demand for rice is estimated to rise by
Accepted: 22 January 2024
56% by the year 2050 compared to the production level of 25.1 million tons recorded in
Published: 24 January 2024
2001 [3]. Therefore, there is a need to increase rice production to meet this demand.
Rice cultivation is one of the major sources of greenhouse gas (GHG) emissions, such
as methane (CH4 ) and nitrous oxide (N2 O) [2]. These GHGs exert a significant influence
Copyright: © 2024 by the authors.
on global warming, as they have warming potentials 28 and 273 times higher than that of
Licensee MDPI, Basel, Switzerland. carbon dioxide (CO2 ), respectively [4].
This article is an open access article The irrigation system used in rice fields, the choice of varieties, and fertilizer manage-
distributed under the terms and ment have a significant impact on these emissions from rice fields [5]. Traditional irrigation
conditions of the Creative Commons methods, such as continuous flooding (CF), not only lead to the loss of water and nitrogen
Attribution (CC BY) license (https:// resources but also turn rice fields into a significant source of CH4 [6]. On the other hand,
creativecommons.org/licenses/by/ good agricultural management practices and genetically improved rice varieties can reduce
4.0/). GHG emissions in fields by 20% to 50% [7].

Agronomy 2024, 14, 248. https://doi.org/10.3390/agronomy14020248 https://www.mdpi.com/journal/agronomy

Agronomy 2024, 14, 248 2 of 18

Climate change will affect water availability in agriculture due to extreme events
such as floods and damage to irrigation infrastructure [8]. This threatens food security in
rice-producing countries, and adaptation strategies are required to maintain sustainable
production [9].
In recent years, non-continuous flooding irrigation methods have been developed as
a solution, reducing water use by up to 38% without affecting yield [10]. The Alternate
Wetting and Drying (AWD) irrigation regimen stands out as one of the most studied and
globally employed methods [5]. It involves cycles of wetting and drying the soil, leading
to changes in soil moisture and redox conditions [5,6,11]. It has been proven that this
irrigation regimen can reduce GHG emissions by up to 40%, as it decreases CH4 emissions
by enhancing aerobic processes in the soil during the drying period [10,11].
However, this reduction is offset by an increase in N2 O emissions in terms of GWP [11,12].
For example, Islam et al. [5] reported a 46% increase in cumulative seasonal N2 O emis-
sions. Additionally, some studies claim that alternating between aerobic and anaerobic
periods increases N2 O emissions through nitrification and denitrification processes, respec-
tively [12–15].
Differences in soil textures, climate, and field management practices create controversy
regarding this new irrigation regimen and its effects on GHG emissions [14,16]. For instance,
Ariani et al. [14] compared GHG emissions under AWD between coarse (loamy sand) and
fine (silty clay) soil textures. Meanwhile, Sha et al. [16] conducted their studies under
the AWD regimen in a temperate monsoonal continental climate with an average annual
temperature of 8 ◦ C and average precipitation of 716 mm over a 3-year period. Another
influencing factor is the drying depth threshold, as rice crop roots need to extract water
for optimal yield and to avoid plant stress [12]. Therefore, it is important to generate
information on greenhouse gas emissions in rice cultivation in different agroecological
zones and irrigation management practices.
Field-level measurements will also help develop baseline data for studies in other rice
fields with similar agroecological characteristics, soil types, and management practices.
This will enable farmers, researchers, and policymakers to develop mitigation strategies
and planning for climate-smart agriculture [17].
Therefore, this study focused on investigating CH4 and N2 O emissions at three differ-
ent AWD irrigation levels (5, 10, and greater than 20 cm depth) and their influence on grain
yield compared to the conventional CF regimen. The specific objectives of the study were
to (i) quantify CH4 and N2 O emission flows, (ii) calculate the Global Warming Potential
(GWP) and its relationship with the crop yield (YGWP), and (iii) determine the emission
factors (EF) for CH4 and N2 O.

2. Materials and Methods

2.1. Location and Experimental Design
The experiment was conducted from January to June 2023 at the Agricultural Experi-
mental Station (EEA) Vista, Florida, of the National Institute of Agricultural Innovation
(INIA) (06◦ 43′ 34′′ S, 79◦ 46′ 44′′ W, and an altitude of 35 m above sea level), at the tropical
coastal agroecological zone located at km 8 on the Chiclayo to Ferreñafe road in the district
of Picsi, province of Chiclayo, Lambayeque region. According to Köppen and Geiger, the
climate is classified as arid and warm (BWh). It is important to highlight that despite
the majority of the population in the study area being engaged in rice production and
commercialization, the Lambayeque region is characterized by frequent droughts [18].
The experiment covered a total area of 1100 m2 , divided into four plots measuring
24 m in length and 11 m in width, with each plot further divided into three subplots
(Figure 1). Different irrigation regimens were established in each plot, with the control
plot using continuously flooded irrigation (CF) with a constant water depth of 5 cm until
two weeks before harvest. The other plots used AWD5 , AWD10 , and AWD20 irrigation,
corresponding to depths (H) of −5, −10, and ≤−20 cm, respectively. The reference point
was the ground level relative to the soil surface based on the decline in water level. PVC
Agronomy 2024, 14, x FOR PEER REVIEW 3 of 18

Agronomy 2024, 14, 248 two weeks before harvest. The other plots used AWD5, AWD10, and AWD20 irrigation,3 of cor-
18
responding to depths (H) of −5, −10, and ≤−20 cm, respectively. The reference point was
the ground level relative to the soil surface based on the decline in water level. PVC per-
forated piezometers
perforated piezometers were installed
were installed cmcmbelow
5050 belowthe
thesoil
soilsurface
surfaceto
to control
control the flooding
the flooding
depth of the plots [6,19]. Irrigation water was sourced from the Tinajones reservoir
depth of the plots [6,19]. Irrigation water was sourced from the Tinajones reservoir and and
distributed through the main channel to the channels feeding the plots. An observational
distributed through the main channel to the channels feeding the plots. An observational
design with
design with repeated
repeated measures
measures waswas considered.
considered.

Figure 1.
1. Location
Locationofofthe study
the area
study on on
area thethe
northern coast
northern of Peru
coast (a), Lambayeque
of Peru regionregion
(a), Lambayeque (b), Agri-
(b),
cultural Experimental Station (EEA) INIA—Vista Florida, Lambayeque
Agricultural Experimental Station (EEA) INIA—Vista Florida, Lambayeque (c).(c).

2.2. Meteorological Characterization

Meteorological data from the study area encompassed a series of key variables, variables, in-
in-
cluding
cluding air
air temperature
temperature (Ta),
(Ta), relative
relative humidity
humidity (HR),
(HR), solar
solar radiation
radiation (Rs),
(Rs), and
and wind
wind speed
speed
(Vv). Thesedata
(Vv). These datawere
wererecorded
recorded every
every minute
minute using
using a portable
a portable automatic
automatic station
station (ATMOS(AT-
MOS 41, METER, Pullman, WA, USA) during the greenhouse gas (GHG)
41, METER, Pullman, WA, USA) during the greenhouse gas (GHG) monitoring hour. monitoring hour.
Daily
Daily precipitation
precipitation data
data (Pa)
(Pa) were
were recorded
recorded at
at the
the Vista
Vista Florida
Florida automatic
automatic weather
weather station
station
(SENAMHI) throughout the crop development (Figure
(SENAMHI) throughout the crop development (Figure 2). 2).
Agronomy 2024, 14, x FOR PEER REVIEW 4 of 18
Agronomy 2024, 14, 248 4 of 18

Figure 2.2.Variation
Figure VariationininTaTa(a),
(a),HRHR(b), RsRs
(b), (c),(c),
andand
Vv Vv
(d) every five five
(d) every minutes during
minutes the monitoring
during hour
the monitoring
with
hourthe
with portable automatic
the portable station.
automatic DailyDaily
station. variation in Ta in
variation and
Tacumulative precipitation
and cumulative during
precipitation crop
during
development (e) with
crop development (e) the
withVista Florida
the Vista SENAMHI
Florida automatic
SENAMHI station.
automatic station.

2.3. Soil and Irrigation Water Characterization

2.3. Soil and Irrigation Water Characterization
The soil has a loamy sand texture (26% sand, 39% silt, 35% clay), electrical conductivity
The soil has a loamy sand texture (26% sand, 39% silt, 35% clay), electrical conduc-
(EC) of 0.42 dS m−1 ), pH of 7.64, cation exchange capacity (CEC) of 220 meq kg−1 , organic
tivity (EC) of 0.42 dS m ), pH of 7.64, cation exchange capacity (CEC) of 220 meq kg ,
matter (OM) of 1.22%, total nitrogen (N) of 0.11% (N-NO of 14.01 ppm, N-NH3 of 29.40 ppm),
organic matter (OM) of 1.22%, total nitrogen (N) of 0.11% (N-NO of 14.01 ppm, N-NH 3 of
organic carbon of 0.71%, available sulfur of 3.76 ppm, bulk density (da) of 1.41 g cm−3 real
29.40 ppm), organic carbon of 0.71%, available sulfur of 3.76 ppm, bulk density (da) of
density (dr) of 2.67 g cm−3 , porosity of 47.2%, field capacity (FC) of 29.76 cm3 cm−3 , wilting
1.41 g cm real density (dr) of 2.67 g cm , porosity of 47.2%, field capacity (FC) of 29.76
point (WP) of 16.27 cm3 cm−3 , CaCO3 of 4.02%, P of 12 ppm, K of 376 ppm, exchangeable
cm cm , wilting point (WP) of 16.27 +cm cm +, CaCO3 of 4.02%, P of 12 ppm, K of 376
cations (Ca2+ de 180.5, Mg2+ de 28.1, Na de 2.5,2+K de 9.7) meq kg−1 , soluble B of 0.42 ppm,
ppm, exchangeable cations (Ca de 180.5, Mg de 28.1, Na de 2.5, K+ de 9.7) meq kg ,
2+ +
soluble gypsum of 0.01%, exchangeable sodium percentage (ESP) of 1.14, sodium adsorption
soluble B of 0.42 ppm, soluble gypsum of 0.01%, exchangeable sodium percentage (ESP)
ratio (SAR) of 0.08 meq L−1 , Pb of 14.82 ppm and Cr of 13.50 ppm.
of 1.14, sodium adsorption ratio (SAR) of 0.08 meq L , Pb of 14.82 ppm and Cr of 13.50
The irrigation water has a pH of 7.34, EC of 0.31 dS m−1 , cations (Ca2+ of 1.91; Mg2+ of
ppm.
0.43; Na+ of 0.59; K+ of 0.10) meq L−1 , anions (Cl−1 of 1.00; HCO3 2− of 1.89; SO4 2− of 0.29)
−1 irrigation water has a pH of 7.34, EC of 0.31 dS m , cations (Ca2+ of 1.91; Mg2+ of
The
meq L . The irrigation water classification is C2-S1, indicating low sodium and salinity
0.43; Na+with
content, of 0.59;
a SARK+ of
of 0.55. meqanalyses
0.10) All L , anions
were (Clperformed
−1 of 1.00; HCO32− of 1.89; SO42− of 0.29)
at the Soil, Plant, Water, and
meq L . The
Fertilizer irrigation
Analysis water of
Laboratory classification
the FacultyisofC2-S1, indicating low sodium and salinity
Agronomy—UNALM.
content,
The with
waterausage
SAR of 0.55.
was All analyses
measured using were performedmethod.
the volumetric at the Soil, Plant, Water,
To enhance and
precision,
Fertilizer Analysis Laboratory of the Faculty of Agronomy—UNALM.
a water balance that included an account for cumulative precipitation, percolation, and
The water usage was
crop evapotranspiration wasmeasured
used usingusing
thethe volumetric
AquaCrop method.
model To enhance
[20]. The precision,
Penman-Monteith
a water balance that included an account for cumulative precipitation, percolation, and
Agronomy 2024, 14, x FOR PEER REVIEW 5 of 18

Agronomy 2024, 14, 248 5 of 18

crop evapotranspiration was used using the AquaCrop model [20]. The Penman-Monteith
equation
equation was
was employed,
employed, considering
considering all
all parameters
parameters related
related to
to energy
energy exchange
exchange and
and latent
latent
heat flux [21]. As shown in Table 1:
heat flux [21]. As shown in Table 1:

Table 1.
Table 1. Water
Water balance
balance in mm according
in mm accordingtotothe
thedifferent
differentirrigation
irrigationregimens.
regimens.

Components CF AWD5 AWD10 AWD20

Components CF AWD5 AWD10 AWD20
Precipitation 164.6 164.6 164.6 164.6
Precipitation 164.6 164.6 164.6 164.6
Irrigation 1997 1428 1434 1447
Irrigation 1997 1428 1434 1447
Percolation
Percolation 1723.3 1723.3 1180.9 1180.9 1206.9
1206.9 1330.7
1330.7
Evapotranspiration
Evapotranspiration 738 738 722.6 722.6 717.3 717.3 729.9
729.9
WaterUse
Water UseEfficiency
Efficiency (WUE)
(WUE) is determined
is determined by theby theofratio
ratio of the
the grain grain
yield yield
to the to the volume.
irrigation irrigation volume.

2.4. Crop Management

planting of
The planting of the
the INIA
INIA 515—Capoteña
515—Capoteñavariety varietywaswasconducted
conductedininseedbeds
seedbedsonon 2
2 January
January 2023.
2023. Thirty
Thirty daysdays after
after planting
planting (DAP),
(DAP), twotwo seedlings
seedlings were were transplanted
transplanted per
per hill
hill with
with a spacing
a spacing of 0.25 × 0.25× cm.
of 0.25 0.25The
cm.cropping
The cropping
system system
at the at the experimental
experimental site wassite was
domi-
dominated by a seasonal rotation of rice and wheat; however, in the last
nated by a seasonal rotation of rice and wheat; however, in the last season before the ex- season before
the experiment,
periment, a riceharvest
a rice seed seed harvest was conducted.
was conducted. After
After this this harvest,
harvest, the residues
the residues were
were burned
burned and incorporated into the land preparation before transplanting.
and incorporated into the land preparation before transplanting. The fertilization dose The fertilization
dose250-120-50
was was 250-120-50in theinform
the form of urea,
of urea, diammonium
diammonium phosphate,
phosphate, and and potassium
potassium sulfate,
sulfate, re-
respectively
spectively (Figure
(Figure 3).3).
One One hundred
hundred percent
percent of of P and
P and K and
K and 36% 36%
of of
NN were
were applied
applied at
at transplanting, while the remaining nitrogen fertilizer was distributed
transplanting, while the remaining nitrogen fertilizer was distributed equally in the bud- equally in the
budding,
ding, tillering,
tillering, and and
cottoncotton setting
setting stages
stages [22].[22].

Figure 3. Greenhouse gas (GHG) monitoring and nitrogen fertilization (a), Crop
Crop phenology
phenology (b).
(b).

2.5. Sampling
2.5. Sampling and
and Analysis
Analysis of
of GHG
GHG
Gas samples
Gas samples were
were collected
collected using
using the
the closed
closed static
static chamber
chamber method
method toto monitor
monitor CH CH44
and N O emissions. The opaque polyethylene chamber consisted of a 30 cm
and N2O emissions. The opaque polyethylene chamber consisted of a 30 cm tall base with
2 tall base with
aa diameter
diameter of of43
43cm,
cm,equipped
equipped with 5 cm
with diameter
5 cm holes
diameter spaced
holes at 22at
spaced cm22intervals allowing
cm intervals al-
water entry and exit. The base was hermetically sealed to the drum, which
lowing water entry and exit. The base was hermetically sealed to the drum, which was was 84 cm tall
84
andtall
cm 50 cm
andin50diameter, throughthrough
cm in diameter, a hydraulic seal (Figure
a hydraulic 4). A gas
seal (Figure 4).sampling connection
A gas sampling con-
was
nectionincorporated into oneinto
was incorporated side ofside
one the of
drum, consisting
the drum, of a silicone
consisting hose hose
of a silicone attached to a
attached
three-way valve connected to a 60 mL syringe for gas sample extraction. Additionally, a
to a three-way valve connected to a 60 mL syringe for gas sample extraction. Addition-
thermometer was installed at the top to measure the chamber’s internal temperature [23].
ally, a thermometer was installed at the top to measure the chamber’s internal tempera-
To ensure homogeneous gas distribution during sample collection, a fan powered by a
ture [23]. To ensure homogeneous gas distribution during sample collection, a fan pow-
portable battery was installed inside [7,24–26].
ered by a portable battery was installed inside [7,24–26].
Agronomy 2024, 14, x FOR PEER REVIEW 6 of 18
Agronomy 2024, 14, 248 6 of 18

Diagramofofthe
Figure4.4.Diagram
Figure theopaque
opaqueclosed
closedstatic
staticchamber.
chamber.

Monitoring took place on the same day as nitrogen fertilization application, three days
Monitoring took place on the same day as nitrogen fertilization application, three
after it, and then every 15 days to assess changes in phenology and irrigation management.
days after it, and then every 15 days to assess changes in phenology and irrigation man-
The sampling was carried out in the morning between 8:00 and 11:00 a.m., under clear
agement. The sampling was carried out in the morning between 8:00 and 11:00 a.m., under
skies. For each sampling date, samples were taken at 0, 20, 40, and 60 min [6]. Samples
clear skies. For each sampling date, samples were taken at 0, 20, 40, and 60 min [6]. Sam-
collected throughout the crop growth period were immediately transferred to empty 15 mL
ples collected throughout the crop growth period were immediately transferred to empty
glass vials sealed with a rubber protector (EXETAINER, Labco Limited, Lampeter, UK) for
15 mL glass vials sealed with a rubber protector (EXETAINER, Labco Limited, Lampeter,
later laboratory measurement.
UK) for later laboratory measurement.
GHG concentrations were measured using a gas chromatograph (GC-2014, Shimadzu,
GHG
Kyoto, Japan) concentrations were
equipped with measured
flame using
ionization a gas(FID)
detectors chromatograph
for CH4 and (GC-2014, Shi-
electron capture
madzu, Kyoto, Japan) equipped with flame ionization detectors (FID) for CH 4 and elec-
detectors (ECD) for N2 O at the Greenhouse Gas Laboratory (CIAT, Cali, Colombia) [23].
tron capture detectors (ECD) for N2O at the Greenhouse Gas Laboratory (CIAT, Cali, Co-
lombia) [23]. of Transparent and Opaque Chambers
2.6. Correlation
Blocking light under opaque chambers during greenhouse gas (GHG) sampling can
2.6. Correlation
impact of Transparent
GHG production, and Opaque
transport, Chambersprocesses [27]. Additionally, the absence
or emission
Blocking
of sunlight light under
reduces plantopaque chambers
photosynthetic duringby
capacity greenhouse gas (GHG)
causing complete sampling
or partial can
closure
impact GHGthus
of stomata, production,
limiting transport,
external CO or2 emission
absorptionprocesses
by leaves [27]. Additionally,
[28]. As this effect the absence
has the po-
oftential
sunlight reduces
to reduce GHGplant photosynthetic
emission capacity
rates through by causing
plants, complete
comparative or partial between
measurements closure
oftransparent
stomata, thusandlimiting
opaque external
chambers CO 2 absorption
were conducted.by leaves
The aim [28].
wasAstothis effect has
establish the po-
a correction
tential to reduce
relationship GHG the
through emission rates through
correlation between plants, comparative
GHG emissions measurements
measured between
in both chambers.
transparent and opaque
The transparent chambers
chamber were conducted.
consisted The aim
of a square metal basewas to 0.26
(area m2 , height
establish a correction
0.15 m)
permanentlythrough
relationship installedtheincorrelation
the soil at abetween
depth ofGHG 10 cm in each subplot
emissions measured(Figure 5. The
in both 1 m tall
chambers.
chamber body was placed
The transparent chamber onconsisted
the flangeofata the top metal
square of the base
base (area
with 0.26 m2 , height
a hydraulic seal0.15
[14].
m) permanently installed in the soil at a depth of 10 cm in each subplot (Figure 5. The
The chamber was covered by a lid containing the connection for gas sample collection and
1
m tall chamber body was placed on the flange at the top of the base with a hydraulic seal
the thermometer, like the opaque chamber. Inside, two fans connected to a portable battery
wereThe
[14]. installed
chamber forwas
air mixing
covered[1,29].
by a lid containing the connection for gas sample collection
and the thermometer, like the opaque chamber. Inside, two fans connected to a portable
battery were installed for air mixing [1,29].
Agronomy 2024, 14, x FOR PEER REVIEW 7 of 18
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14, 248
x FOR PEER REVIEW 7 of 18
7 of 18

Figure 5. Diagram of the transparent closed static chamber.

Figure 5. Diagram of the transparent closed
closed static
static chamber.
chamber.
A set of paired measurements with transparent and opaque chambers was carried
A
A set
out in three 2.5 m measurements
setofofpaired
paired 4.0 m plots with
× measurements transparent
installed
with and and
opaque
in the Irrigation
transparent chambers
Experimental
opaque was
chambers carried
Area out lo-
(AER),
was in
carried
three 2.5 m × 4.0 m plots installed in the Irrigation Experimental Area (AER),
out in three 2.5 m × 4.0 m plots installed in the Irrigation Experimental Area (AER), lo-
cated within the campus of the National Agrarian University La Molina located within
(12°00′05″ S,
◦ 00′ 05′′ S, 76◦ 57′ 06.5′′ W,
the
catedcampus
76°57′06.5″ of
withinW,thethe
altitude
campus 233ofmthe
National Agrarian
above University
sea level)
National La Molina
between
Agrarian (12
November
University 2022 and
La Molina May 2023
(12°00′05″ S,
altitude
(Figure 233 m above
6). W,
76°57′06.5″ altitudesea233
level)
m between
above seaNovember 2022 and
level) between May 20232022
November (Figure
and6).May 2023
(Figure 6).

Figure 6.
Figure 6. Closed
Closed static
static chambers
chambers in
in the
thefield:
field: transparent
transparent (a)
(a) and
and opaque
opaque(b).
(b).
Figure 6. Closed static chambers in the field: transparent (a) and opaque (b).
The
The data
data gathered
gathered from
from both
both chambers
chambers was
was performed
performed forfor the
the statistical
statistical analysis
analysis of
of
normality
normality and distribution.
andgathered
The data fromItboth
distribution. waschambers
concluded
wasthat the
the distribution
for the was
distribution
performed was non-parametric.
non-parametric.
statistical analysis of
Consequently,
normality andBox-Cox
Consequently, Box-Cox transformations
transformations
distribution. were
wereused
It was concluded that to
used toassess
theassess the
thenecessary
distribution necessary adjustments.
adjustments.
was non-parametric.
Pearson’s
Pearson’s R,
R, for
for the
the relationship
relationship between
betweenchambers,
chambers, for
forCH
CH and
and N
N
Consequently, Box-Cox transformations were used to assess the necessary adjustments.
4 4 22OO flux,
flux, was
was 0.823
0.823
and 0.693,
and 0.693, respectively.
respectively.
Pearson’s Resulting
R, for theResulting in Equations
in Equations
relationship (1) and (2):
(1) and (2):
between chambers, for CH4 and N2O flux, was 0.823
and 0.693, respectively. Resulting in Equations (1) and (2):
Ln(CH4,o ) = 1.3796 Ln(CH4,t ) + 1.1293 (1)
Agronomy 2024, 14, 248 8 of 18

where CH4,o is the CH4 ; emission flux from the opaque chamber, and CH4,t is the CH4
emission flux from the transparent chamber, both expressed in mg m−2 h−1 .

Ln( N2 O,o ) = 0.7723 Ln( N2 O,t ) − 0.9227 (2)

where N2 O,o is the N2 O emission flux from the opaque chamber, and N2 O,t is the N2 O
emission flux from the transparent chamber, both expressed in mg m−2 h−1 .

2.7. Calculation of GHG Emissions

Emission fluxes were determined from the slope through linear regression, the concen-
tration of CH4 or N2 O against chamber closure time [13,30]. Then, the slope was converted
to mass per unit area per unit time (mg m−2 h−1 ) through Equation (3) [3,29]:

slope ( ppm min − 1) × P × Vc × MW × 60

Emission rate o f CH4 and N2 O = (3)
R × Tk × Ac

where P is pressure under normal conditions, Vc is the gas chamber volume in m3 , MW is

the molecular weight of the respective gas, 60, min h−1 , R is the ideal gas constant 0.082057
in atm m−3 kmol−1 K−1 , Tk is the temperature inside the chamber expressed in Kelvin, and
Ac is the chamber area in m2 .
Considering the results obtained in the correlation between the transparent and opaque
chambers, the correction equation was applied to the calculated emission rate, using
Equation (1) for CH4 flux and Equation (2) for N2 O flux.
The GWP of CH4 and N2 O was calculated with Equation (4):
 
GWP kg CO2 equivalent ha−1 = ( TCH4 × 28 + TN2 O × 273) (4)

where TCH4 is the total accumulated CH4 emissions (kg ha−1 ), TN2 O is the total accu-
mulated N2 O emissions (kg ha−1 ), and 28 and 273 are the GWP values for CH4 and N2 O,
respectively, relative to CO2 over a 100-year horizon [4].
The yield-scaled global warming potential was calculated with the following Equa-
tion (5) [11]:
PCG
YGWP = (5)
Yield
where YGWP is the total GHG emissions per unit of grain yield (kg CO2 eq kg−1 grain yield).
The scale factor for AWD was estimated by dividing cumulative AWD emissions by
cumulative CF emissions [5].
The emission factor (EF) was estimated by dividing cumulative AWD emissions by
the GHG measurement period.

2.8. Data Analysis

The normality of distribution and homogeneity of variances for each treatment were
assessed using the Shapiro-Wilk test and the Bartlett test, respectively. The results indicated
a non-normal distribution; therefore, an observational design with repeated measures using
the non-parametric Kruskal-Wallis test was employed. Cumulative emissions, grain yield,
GWP, YGWP, and EF of each treatment were compared with the control group (CF) using
the Dunn test [31]. For the statistical calculations, R Studio software (v2023.06.1) was used.
A significance level of α = 5% was considered.

3. Results
3.1. Methane CH4 Emission Dynamics
The temporal variation of CH4 throughout the experiment period, from the ger-
mination stage to post-harvest, is depicted (Figure 7). The magnitudes and trends of
CH4 emission flux varied with AWD treatments, crop growth phase, and meteorological
 3. Results
3.1. Methane CH4 Emission Dynamics
Agronomy 2024, 14, 248 The temporal variation of CH4 throughout the experiment period, from the germina- 9 of 18
tion stage to post-harvest, is depicted (Figure 7). The magnitudes and trends of CH4 emis-
sion flux varied with AWD treatments, crop growth phase, and meteorological conditions.
An increase An
conditions. in emissions
increase inwas observedwas
emissions during the tillering
observed duringstage
the (61 and 65
tillering DAS)
stage (61under
and
both
65 DAS)AWD andboth
under CF AWD
irrigation
and CF regimens,
irrigationwith emissions
regimens, with ranging
emissionsfrom 0.167
ranging to 0.167
from 2.778
mg2.778
m h −1
−2 htrend
to mg.m This persisted
. This for the CFfor
trend persisted condition, while therewhile
the CF condition, was athere
rapidwas
decline for
a rapid
the AWD
decline fortreatment (Table 2). (Table
the AWD treatment A second2). Aincrease in CH4 emissions
second increase were observed
in CH4 emissions during
were observed
the flowering
during stage (103
the flowering stageand 107
(103 DAS)
and 107 under both AWD
DAS) under and CF
both AWD irrigation
and regimens,
CF irrigation with
regimens,
−2 h−1 .
emissions ranging from 0.028 to 8.124 mg m h .
with emissions ranging from 0.028 to 8.124 mg m

Figure 7. Temporal
Figure TemporalVariation
VariationofofCH
CH Flux
44 Flux(mg m m−h2 h)−during
(mg 1 thethe
) during crop development
crop under
development CF irri-
under CF
gation regimen (a), AWD (b), AWD (c), and AWD (d).
irrigation regimen (a), AWD5 (b), AWD10 (c), and AWD20 (d).
5 10 20

The results
results indicate
indicate that
that CH
CH44 emissions under CF, ranging from 0.025 0.025 to
to 17.924
17.924
mg m−2 hh−1,,were were significantly
significantly higher
higher than those under other AWD treatments.
treatments. The maxi-
The max-
− 2 −1
mum
imumCH CH44emission
emissionvaluesvalueswere
were2.778,
2.778,0.493,
0.493,and
and0.177
0.177mgmgmm hh for forAWD
AWD55,, AWD
AWD10 10,
and
and AWD20 20,, respectively.
respectively.
From 7 MarchMarch(62 (62DAS),
DAS),anan unorganized
unorganized tropical
tropical cyclone
cyclone namednamed “Cyclone
“Cyclone Yaku” Yaku”
was
was present
present nearnear the north
the north and central
and central coastcoast
untiluntil 18 March
18 March (73 DAS)
(73 DAS) [32]. presence
[32]. This This presence
facil-
facilitated
itated the theentry entry
andand accumulation
accumulation of of moistureononthe
moisture theoccidental
occidentalwatershed.
watershed.As As aa result,
result,
intense rainfall and unprecedented daily precipitation records occurred along
intense rainfall and unprecedented daily precipitation records occurred along the north- the northern
coast, significantly
ern coast, impacting
significantly the hydrological
impacting regimen
the hydrological during
regimen the experimental
during period.
the experimental pe-
riod.
Agronomy 2024, 14, x FOR PEER REVIEW 10 of 18
Agronomy 2024, 14, 248 10 of 18

Table 2. Flux of CH4 and N2O Emissions under Continuous Flooding (CF) and AWD Treatments.
Table 2. Flux of CH4 and N2 O Emissions under Continuous Flooding (CF) and AWD Treatments.
C-CH4 (𝐦𝐠 𝐦 𝟐 𝐡 𝟏 ) N-N2O (𝐦𝐠 𝐦 𝟐 𝐡 𝟏 )
Fecha DAP −2 h−1 ) 2 −1 )
CF AWD5 C-CH AWD
4 (mg
10 m AWD20 CF AWD N-N
5 2 O (mg
AWD m−10 h AWD20
Fecha DAP
11 February 2023 38 0.025 0.008
CF AWD0.013
5 AWD 0.014
10 AWD 0.007
20 CF 0.010 AWD 5 0.027
AWD 10 0.021
AWD20
15 February 2023 42 0.289 0.033 0.023 0.016 0.008 0.043 0.081 0.379
11 February 2023 38 0.025 0.008 0.013 0.014 0.007 0.010 0.027 0.021
6 March 2023
15 February 2023 61 0.857
42 1.102
0.289 0.384
0.033 0.0230.177 0.0160.016 0.0080.013 0.043 0.0340.081 0.009
0.379
10 March 2023 2023 65
6 March 1.110
61 2.778
0.857 0.177
1.102 0.3840.167 0.1770.019 0.0160.017 0.013 0.0300.034 0.028
0.009
20 February 20232023 75
10 March 5.816
65 0.603
1.110 0.129
2.778 0.1770.023 0.1670.011 0.0190.178 0.017 0.6230.030 0.148
0.028
24 March 2023 202379
20 February 17.924
75 0.242
5.816 0.022
0.603 0.1290.048 0.0230.011 0.0110.131 0.178 0.3200.623 0.059
0.148
24 March
2 April 2023 2023 88 79
0.769 17.924
0.223 0.242
0.002 0.0220.028 0.0480.029 0.0110.045 0.131 0.0400.320 0.059
0.019
2 April
6 April 2023 2023 92 88
3.263 0.769
0.831 0.223
0.083 0.0020.011 0.0280.016 0.0290.010 0.045 0.0330.040 0.019
0.041
6 April
17 April 2023 2023 103 92
8.214 3.263
1.353 0.831
0.307 0.0830.047 0.0110.019 0.0160.020 0.010 0.0190.033 0.041
0.014
21 April 2023 2023 107
17 April 103
6.188 8.214
1.122 1.353
0.493 0.3070.028 0.0470.008 0.0190.028 0.020 0.019
0.028 0.014
0.211
21 April 2023 107 6.188 1.122 0.493 0.028 0.008 0.028 0.028 0.211
7 May 2023 123 4.996 0.310 0.102 0.053 0.018 0.003 0.020 0.010
7 May 2023 123 4.996 0.310 0.102 0.053 0.018 0.003 0.020 0.010
11 May11 May 2023 127
2023 5.902
127 0.263
5.902 0.050
0.263 0.0500.006 0.0060.017 0.0170.013 0.013 0.0240.024 0.007
0.007
31 May31 2023
May 2023 147 0.020
147 0.003
0.020 0.015
0.003 0.0150.092 0.0920.076 0.0760.018 0.018 0.026
0.026 0.027
0.027
2 June2 2023
June 2023 149 0.002
149 0.042
0.002 0.016
0.042 0.0160.015 0.0150.046 0.0460.046 0.046 0.0100.010 0.024
0.024
7 June7 2023
June 2023 154 0.025
154 0.005
0.025 0.004
0.005 0.0040.039 0.0390.027 0.0270.030 0.030 0.0050.005 --
9 June9 2023
June 2023 156 156
0.006 0.006
0.020 0.020
0.031 0.0310.014 0.0140.050 0.0500.018 0.018 0.0250.025 0.016
0.016

3.2. Dynamics
3.2. Dynamics ofof N
N22O
O Emissions
Emissions
The temporal
The temporal variation
variation of
of NN22O
O throughout
throughout thethe experiment
experiment period,
period, from
from germination
germination
to post-harvest, is observed (Figure 8). The increase in emissions during
to post-harvest, is observed (Figure 8). The increase in emissions during the maximum the maximum
tillering stage (75 and 79 DAS) under both AWD and CF irrigation regimens corresponds
extreme drought,
to a period of extreme drought, with emissions
emissions ranging from 0.011
0.011 to
to 0.623
0.623 mgmg m
m−2 h h−1..
Some highhighvalues
valueswere
wereobserved
observed after
after urea
urea fertilization,
fertilization, withwith emissions
emissions ranging
ranging from
from 0.008
0.0080.379
and and mg m−mg
0.379 2 hm
−1 h
on 42 on
DAP 42 and
DAPbetween
and between
0.008 0.008 mg m−
and 0.211
and 0.211 mg2 hm
−1 h
on 107onDAP,
107
DAP,
for thefor the AWD
AWD irrigation
irrigation regimen.
regimen.

Figure 8. Temporal
Temporal Variation
VariationofofNN2O
2 OFlux
Flux(mg(mgmm−h2 h− ) 1during thethe
) during crop development
crop under
development CFCF
under ir-
rigation regimen
irrigation regimen(a),
(a),AWD
AWD5 (b),
5
AWD
(b), AWD 10 (c),
10
and
(c), AWD
and 20 (d).(d).
AWD 20
Agronomy 2024, 14, x FOR PEER REVIEW 11 of 18
Agronomy 2024, 14, 248 11 of 18

The
The highest
highest peaks
peaks of
of N
N22OOemission
emissionoccur
occurunder
under the theAWD
AWD regimen,
regimen, 0.178,
0.178, 0.623,
0.623, and
and
0.379
0.379 mg m hh for
mg m − 2 − 1
for AWD
AWD55, ,AWD
AWD1010, ,and
andAWD , respectively,
AWD2020 , respectively,compared
comparedto tothe
theemission
emission
under
under CF,
CF, which was 0.029 mgmg mm−2 hh−1. .

3.3. Cumulative
3.3. CumulativeEmissions
Emissionsof
ofCH
CH44and
andNN2O 2O
The effects of irrigation regimens significantly
The effects of irrigation regimens significantly influenced
influenced < 0.05)
(𝑝 (p < 0.05)
the the cumula-
cumulative
tive greenhouse
greenhouse gas emissions
gas emissions (Table (Table
3). For 3).
CH4For CH4 , significant
, significant differences differences among treat-
among treatments are
ments are Values
observed. observed.
rangeValues 1.59 kg
fromrange from ha 1.59 ha−AWD
forkgthe
1
for20the AWD20 regimen
irrigation irrigationtoregimen
108.55
−1
to ha
kg 108.55under
kg hathe under the CFIn
CF regimen. regimen.
Figure 9a, In the
Figure 9a, the significance
significance and Spearman’s
and Spearman’s R values areR
values are observed. Cumulative CH emissions were significantly
observed. Cumulative CH4 emissions were significantly higher under the CF irrigation
4 higher under the CF
irrigation regimen. The AWD irrigation regimen reduced CH emissions,
regimen. The AWD irrigation regimen reduced CH4 emissions,4 decreasing by 84%, 96%, decreasing by
84%, 96%, and 99% in AWD
and 99% in AWD5, AWD10, and , AWD
5 AWD , and AWD
1020, respectively.
20 , respectively.

Effectofof
Table3.3.Effect
Table Irrigation
Irrigation Regimens
Regimens andand Their
Their Levels
Levels on Rice
on Rice Yield,Yield, CH4Nand
CH4 and N2 O Emissions,
2O Emissions, Emis-
Emission
sion Factor,
Factor, GWP,GWP, and YGWP.
and YGWP.

Grain
Grain Yield
Yield EUA
EUA Emission(kg
Emission (kghaha−−11)) EF (kg
EF (kg ha
ha−−11dd−1−)1 )
Water Regimens GWP a a YGWP bb
Water Regimens (t ha )
−1 (kg m ) −3 CH4 N 2O CH4 N 2O GWP YGWP
(t ha−a1 ) (kg m−3 ) CH4 a N2 O a CH4a N2aO
CF 14.01 0.70 108.55 0.63 0.92 0.01 3211.54 a 0.23 a
CF 5 14.01 a 0.70 108.55 a 0.63 a 0.92 a 0.01 a 3211.54 a 0.23ba
AWD 11.85 b 0.83 17.72 a 1.05 a 0.15 a 0.01 a 782.11 b 0.07
AWD b 0.83 17.72 a 1.05 a 0.15 a 0.01 a b b
AWD105 11.85
13.72 c 0.96 4.02 b 2.36 b 0.03 b 0.02 b 782.11
755.58 b 0.07
0.06 c
AWD2010 13.72 cc 0.96 b b b b b 0.06cc
AWD 13.32 0.92 4.02
1.59c c 2.36
2.24c c 0.03
0.01 cc 0.02b
0.02 755.58b
656.46 0.05
AWD20 13.32 c 0.92 1.59 2.24 0.01 b b 0.05 c
a WUE (regarding the water use efficiency; kg m 0.02 656.46
) is calculated by dividing grain yield by irriga-
a WUE (regarding
tion volume. b GWP (global
the water usewarming
efficiency; kg m−3 ) iskg
potential; CO2 equivalent
calculated by dividing hagrain
) ofyield
CH4by and N2O was
irrigation cal-
volume.
b GWP (global warming potential; kg CO equivalent ha−1 ) of CH and N O was calculated
culated using GWP values of 28 and2 273 for CH4 and N2O, 4 respectively.
2
c YGWPusing (global
GWP warming
values of
28 and 273atforyield
CH4 scale,
and N2kg
O, respectively. c YGWP (global warming potential at yield scale, kg CO equivalent
potential CO2 equivalent kg grain yield) was calculated by dividing 2the global
−1 −1
kg grainpotential
warming yield) wasby calculated
yield (kgbyha dividing
). the global warming potential by yield (kg ha ).

Figure
Figure 9. Non-parametric
Non-parametric Spearman
Spearmancorrelation
correlationand
and Dunn
Dunn statistical
statistical testtest between
between irrigation
irrigation regi-
regimens
mens
basedbased
on CHon4 CH₄ emission
emission (a) (a)
and Nand
2 O N₂O emission
emission (b). (b).
The The
(*) (*) indicates
indicates significant
significant differences
differences be-
between
tween treatments (𝑝 <
treatments (p < 0.05). 0.05).

Regarding
Regarding cumulative
cumulative N N22OOemissions,
emissions,significant
significantdifferences
differencesamong
among treatments
treatments are are
observed.
observed. Values
Valuesrange
rangefrom 0.63 kg
from0.63 kghaha−1 for
for the
the CF
CF irrigation
irrigation regimen
regimen toto 2.36
2.36 kg ha−1
kg ha
under
underthetheAWD
AWD1010regimen.
regimen. In In
Figure 9b,9b,
Figure thethe
significance
significance andand
Spearman’s
Spearman’s R values are ob-
R values are
observed.
served. Cumulative
Cumulative N2N O emissions
O2emissions were
were significantlyhigher
significantly higherunder
underthetheAWD
AWDirrigation
irrigation
regimen. The
regimen. TheAWD
AWDirrigation
irrigation regimen
regimen increased
increased N N22OOemissions
emissionsatatall
alllevels,
levels,increasing
increasingby by
66%, 273%,
66%, 273%, and
and 255%
255% in in AWD
AWD55, ,AWD
AWD1010, and
, andAWD
AWD , respectively.
, respectively.
2020
Thehighest
The highestcumulative
cumulativemethane
methane (CH4emissions
(CH₄) ) emissions from
from crops
crops are are observed
observed during
during the
the vegetative stage across all treatments. In contrast, when it comes
vegetative stage across all treatments. In contrast, when it comes to the total N₂O emis-to the total N 2O
emissions based on crop growth stages, the maturity phase produces the
sions based on crop growth stages, the maturity phase produces the most emissions in the most emissions in
the CF system. Whereas in the AWD system, the growth phase, coinciding
CF system. Whereas in the AWD system, the growth phase, coinciding with nitrogen fer- with nitrogen
fertilization
tilization results
results in the
in the maximum
maximum emissions
emissions (Table
(Table 4). 4).
Agronomy 2024, 14, 248 12 of 18

Table 4. Cumulative emissions according to the phenological stage of rice cultivation.

Phenological Emission CH4 (kg ha−1 ) Emission N2 O (kg ha−1 )

Stage CF AWD5 AWD10 AWD20 CF AWD5 AWD10 AWD20
Vegetation 64.017 25.703 2.214 1.021 0.168 0.740 1.970 1.489
Reproductive 24.926 3.930 0.697 0.107 0.068 0.066 0.104 0.101
Ripening 51.989 4.816 1.611 0.467 0.303 0.165 0.256 0.622
Post-Harvest 0.030 0.046 0.030 0.076 0.092 0.072 0.025 0.030

3.4. Rice Yield, Water Use Efficiency, GWP, YGWP, and Emission Factors
Irrigation regimens significantly influenced (p < 0.05) yield, GWP, and YGWP
(Table 3). In all three AWD treatments, GWP was reduced compared to the CF regimen.
GWP values were 782.11, 755.58, and 656.46 kg CO2 equivalent ha−1 for the AWD5 , AWD10 ,
and AWD20 regimen, respectively, while for the CF regimen, values reached 3 211.54 kg
CO2 equivalent ha−1 . The reduction in GWP compared to the CF regimen was 76%, 77%,
and 80% for the AWD5 , AWD10 , and AWD20 regimens, respectively.
With respect to WUE, AWD irrigation demonstrates higher efficiency, with 0.83, 0.96,
and 0.92 kg m−3 for AWD5 , AWD10 , and AWD20, respectively; thus, there is a 28% reduction
in water use in AWD irrigation compared to CF.
YGWP values were 0.065, 0.054, and 0.048 kg CO2 equivalent kg−1 for the AWD5 ,
AWD10 , and AWD20 regimens, respectively, while for the CF regimen, a value of 0.229 kg
CO2 equivalent kg−1 was obtained. The AWD regimen reduced YGWP values compared
to the CF regimen by 71%, 76%, and 79% for the AWD5 , AWD10 , and AWD20 , regimens,
respectively. No significant differences were shown between treatments (p < 0.05), regard-
ing YGWP.
The CH4 EF ranged from 0.01 kg ha−1 d−1 for the AWD20 regimen, while for the CF reg-
imen, a value of 0.92 kg ha−1 d−1 was obtained. The N2 O EF ranged from 0.005 kg ha−1 d−1 .

4. Discussion
4.1. CH4 Emission Dynamics
CH4 emissions from rice fields were influenced by the AWD irrigation regimen [11].
In Figure 7, it can be observed that the highest emission rates were under the CF regi-
men. Although these emission rates are comparable to those recorded in other locations
(Table 5) [3,5,14,16], there are studies reporting higher [2,12,19] or lower values [6,10].
According to the crop phenology, CH4 emissions increase as the plants grow un-
til reaching the flowering stage. This increase is due to the optimal development of
aerenchyma tissue, especially in the early stages of plant development, leading to increased
exudate release and fermentation of easily degradable soil organic matter [10]. Thus, peak
emission levels were recorded during the vegetative stage (17.924 mg m−2 h−1 in CF and
2.778 mg m−2 h−1 in AWD) and the reproductive stage (8.214 mg m−2 h−1 in CF and 1.353
mg m−2 h−1 in AWD). This increase can also be explained by microbial degradation, root
exudate release, and microbial biomass growth during the maximum tillering phase [14].
These results are consistent with previous research [10,12,16].
The decrease in emissions under the CF regimen started during the maturation stage
(0.002–0.025 mg m−2 h−1 ), which coincides with the period when irrigation is suspended,
where there is greater availability of oxygen in the rhizosphere. Furthermore, soil aeration
promotes the oxidation of CH4 by methanotrophic bacteria in underground soil layers and
consequently reduces CH4 emissions [3].
Despite this, these CH4 fluxes were higher throughout almost the entire crop devel-
opment compared to the fluxes under the AWD treatment. Despite the climatic and soil
conditions of previous studies, they reported differences when compared with the CF
treatment (Table 5). This can be attributed to the fact that in rice cultivation systems, the
transfer and release of CH4 to the atmosphere mainly occur through three mechanisms,
Agronomy 2024, 14, 248 13 of 18

with the most important being the diffusion of gas dissolved in interfaces between water
and air, as well as between soil and water [33]. This diffusion process can be promoted by
the porosity of the soil; in this study the soil texture was sandy loam. This restricts the time
available for methanotrophs to degrade CH4 [14].
The aerated conditions of the AWD treatment could be affected by the strong and ab-
normal rainfall caused by the natural phenomenon Cyclone “Yaku”. These coincided with
the beginning of the tillering period (Table 4), which resulted in an increase in emissions
because the soil remained saturated, generating longer anoxic conditions [34].

Table 5. Maximum flux of CH4 and N2 O emissions under continuously flooded irrigation and AWD
treatments according to various authors.

CH4 Emission N2 O Emission

Climate by N1 (mg m−2 h−1 ) (mg m−2 h−1 )
Site Season Soil Year Ref.
Köppen (kg ha−1 )
CF AWD CF AWD
Alabama, Humid
Dry Loamy 2013 105 - - 0.01 0.06 [13]
United States subtropical
Daca, Clay 2018 35 34 0.13 0.12
Savanna Dry 78
Bangladesh loam 2019 19 14 0.05 0.05
[5]
Mymensingh, 2018 7 3 0.03 0.06
Monsoonal Dry Loamy 90
Bangladesh 2019 7 5 0.04 0.03
2018 19 17 0.08 0.09
Daca, Dry Clay
Savanna 2019 78 20 13 0.09 0.08 [3]
Bangladesh loam
2020 17 13 0.09 0.08
Dry 2017 180 29 29 0.26 0.3
Wet 150 25 21 0.2 0.3
Guangzhou, Dry winter Clay 2018
Dry 160 26 25 0.21 0.31 [12]
China subtropical loam
Wet 150 31 31 0.15 0.24
2019
Dry 180 39 33 0.4 0.31

Hubei, 2021 7 4 0.01 0.2

Cool summer Wet Loamy 180 [6]
China 2022 7 5 0.01 0.23

Hung Yeng, Dry winter Dry 30 24 - -

Clay 2017 - [19]
Vietnam subtropical Wet 84 96 - -
Jakenan,
Loamy 10 7 0.1 0.1
Indonesia
Monsoonal Dry 2020 120 [14]
Wedarijaksa, Loamy
1 0.8 0.12 0.14
Indonesia clay
2017 17 3 0.9 1.2
Warm
Liaoning, China continental Dry Loamy 2018 180 4 1.5 0.3 0.4 [16]
summer 2019 3 2 0.05 0.09
Mymensingh,
Monsoonal Dry Loamy 2019 180 7 4 - - [10]
Bangladesh
Wet 2020 28 9 0.5 0.9
Tamil Nadu, India Savanna 180 [2]
Dry 2021 20 8 0.8 0.8
1 fertilizante nitrogenado.
Agronomy 2024, 14, 248 14 of 18

4.2. Dynamics of N2 O Emission

N2 O emissions in rice fields are directly influenced by the AWD irrigation regimen
and the quantity of fertilizers used. Figure 8 shows that the highest N2 O emission rates
occur when using the AWD irrigation regimen (0.003–0.623 mg m−2 h−1 ), compared to
emissions under the CF regimen (0.007–0.076 mg m−2 h−1 ).
It is important to highlight that recent research has observed higher emission peaks
under the AWD regimen [2,16], although most of them are lower than the maximum
value recorded in this study (0.623 mg m−2 h−1 ), as detailed in Table 2 [3,5,13]. This could
be due to the use of a high amount of nitrogen fertilizers (250 kg N ha−1 ), which is the
conventional dose in this study area, as reported by local farmers [35]. It is important to
mention that this nitrogen amount exceeds that used in previous research, as shown in
Table 5. Therefore, it led to higher inorganic nitrogen production and excessive growth of
the nitrifying microorganism population. Moreover, the lower moisture conditions during
intermittent drainage periods favor N2 O overproduction [12]. The alternation between
oxygenated and anoxic conditions during the AWD regimen can enhance nitrification
and denitrification processes, depending on oxygen availability [3]. It is suggested that
the alternation of soil wetting and drying stimulated N2 O production due to the use of
endogenous nitrogen released from soil organic matter, originating from both fertilizer
application and nutrients released by plant roots [16].
However, N2 O emissions were also affected by the long periods without irrigation due
to a break in a water distribution channel between days 70 and 80 DAP. This interruption
had a major impact on emissions under AWD (0.178–0.623 mg m−2 h−1 ). Despite this
change in conditions, emissions under CF did not increase significantly, as the soil remained
waterlogged for an extended period, resulting in complete denitrification [15,36].

4.3. Effect of Water Regimens on Cumulative GHG Emissions

Water management affected CH4 emissions from rice cultivation. In this study, AWD
irrigation significantly reduced (p < 0.05) CH4 emissions compared to the conventional
farmers practice (Table 3). These results are consistent with previous findings (Table 5) [5,12],
with reductions of 99% in the AWD20 treatment. Because, intermittent aeration makes an
oxygen-rich soil environment, resulting in CH4 oxidation by methanotrophs, causing a drop
in CH4 emissions (1.670 kg ha−1 para AWD20 ). It has been reported that up to 80% of CH4
produced during the rice cultivation season is oxidized by these methanotrophs [17,34].
In contrast, rice cultivation under the CF regimen creates an anaerobic soil environment,
i.e., a reducing environment. Leading to a low redox potential (−150 mV), this medium
favors the anaerobic decomposition of complex organic substances by methanogens [5,14].
Generating two important reactions: the reduction of CO2 with H2 derived from organic
compounds or methylated compounds and the decarboxylation of acetic acid, which is
known as methanogenesis, driving the production of CH4 [37].
Water management also had a significant impact on cumulative N2 O emissions
(Table 3). Under CF conditions, N2 O emissions were minimal (0.631 kg ha−1 ). In con-
trast, in fields with AWD irrigation, emissions were significantly higher, with the highest
in the AWD10 treatment (2.354 kg ha−1 ). The variation in water regimens, transitioning
from CF to AWD, influenced nitrification and denitrification rates, depending on oxygen
availability. During the flooding period, nitrification of ammonium ions (NH4 + ) is low,
inhibiting N2 O production [3]. However, during the drying cycle, the upper soil layer
initially becomes aerobic, but the lower layer remains anaerobic, even if the water level is
more than 15 cm below the soil surface [14]. This explains that despite the AWD20 treatment
having the longest aeration time (2.243 kg ha−1 ) it did not surpass the cumulative emissions
of the AWD10 treatment (2.354 kg ha−1 ).
Agronomy 2024, 14, 248 15 of 18

4.4. Effect of AWD Irrigation on Emission Factors, Grain Yield, Water Use Efficiency, GWP
and YGWP
Water management influenced the CH4 emission factor. Under the AWD regimen,
values ranged between 0.01 and 0.92 kg ha−1 d−1 , compared to the CF regimen, where
0.92 kg ha−1 d−1 was obtained. It is relevant to mention that the value obtained in the CF
regimen falls within the range of values presented by the IPCC for South America (0.86–
1.88 kg CH4 ha−1 d−1 ) [38]. FE measured by the IPCC is based on specific assumptions,
such as the absence of organic amendments in the fields and aeration conditions for 180 days
before planting, conditions that were applied in our experiment.
Regarding grain yield, a decrease is observed with respect to the AWD treatments. It
is possible that this decrease is due to the rapid drainage of water in the sandy loam soil,
which caused the plant to suffer from drought stress, in addition to the high temperatures
in the area [15]. However, for AWD10 only a 2% decrease was recorded. This suggests that
increasing soil air exchange with AWD can provide sufficient oxygen to the root system to
facilitate the mineralization of soil organic matter. This increases soil fertility and improves
rice production, which does not happen in the AWD5 treatment [39].
In contrast, the maximum grain yield value is observed in the CF regimen (14.01 t ha−1 ),
which coincides with the maximum CH4 emissions (140,963 kg ha−1 ). This is related to
optimal vegetative and root development, which generates an increase in available carbon
and root exudation. The latter is a substrate used by methanogenic bacteria that cause high
yields and emissions of CH4 [12,33].
It is observed that AWD irrigation could increase WUE mainly due to the reduction in
the amount of irrigation [40]. Of the three AWD levels, AWD10 had the highest efficiency
due to its high performance and low irrigation level due to intermittent drainage periods,
which is an alternative for times of low water supply for irrigation.
On the other hand, the AWD scale factor for CH4 varied between 0.01 and 0.16,
significantly lower values than those presented by the IPCC (0.41–0.72 kg CH4 ha−1 d−1 ),
corresponding to the water regimen with multiple drainage periods [38]. The IPCC also
specifies that crop fields must have a period without flooding. However, in this study,
this period was interrupted due to the presence of Cyclone “Yaku”, explaining the notable
difference in emission factor values in the AWD regimen.
Different water regimens revealed a trade-off relationship between CH4 and N2 O
emissions. Despite the 100% increase in cumulative N2 O emissions with AWD irrigation
compared to CF irrigation, this only offset less than 1% of the total GWP. Overall, the AWD
irrigation regimen reduced GWP by 77% compared to the CF irrigation. These results
confirm that the total GWP in rice fields is mainly determined by CH4 emissions, even
though N2 O (265 kg CO2 ) has a higher radiative forcing in terms of CO2 [41]. Consequently,
CH4 represents the main contributor to GWP in rice cultivation, accounting for over 90% of
the total GWP. In this study, CH4 emissions represented 94.78% in AWD and 98.9% in CF.
These findings align with previous research [6,12,16,34]. In other studies, it is mentioned
that the primary contributor to GWP in CF irrigation corresponds to N2 O due to variations
in drainage to field capacity during fertilization, leading to increased N2 O emissions [15].
Therefore, the most effective measures to reduce GWP and greenhouse gas emissions in
rice cultivation should focus on reducing CH4 emissions.
The YGWP, or the relationship between total GHG emissions and grain yield, was used
to measure the efficiency and sustainability of a rice management system. Similarly to the
GWP, AWD irrigation demonstrated the potential to reduce YGWP by an average of 75%
compared to CF irrigation [5,16]. Although the AWD20 regimen presented the lowest YGWP
value (0.05), it is considered that the AWD10 regimen effectively mitigates GWP, as it only
reduces grain yield by 2%. Additionally, neither treatment shows significant differences
regarding YGWP. This means that AWD irrigation has an environmental improvement
effect, contributing to a reduction in water use with the additional potential of saving fossil
fuel-based energy and reducing CO2 emissions, which is why it can be considered as a
Agronomy 2024, 14, 248 16 of 18

strategy for mitigation for decision-makers and policymakers. In addition, it supports the
state’s commitment to the United Nations Framework Convention on Climate Change.

4.5. Challenges and Viability

In this study, the challenge was due to the transportation of the closed static chambers
to the field, which is why they were replaced with a lighter material using opaque chambers.
In addition, the presence of abnormal precipitation caused by Cyclone “Yaku” significantly
altered soil moisture conditions, generating variations in greenhouse gas (GHG) emissions
in the proposed treatments. Despite these challenges, AWD irrigation stood out as a low
GHG emission regimen, establishing itself as an effective mitigation option to reduce
emissions in rice fields.
This is an irrigation system practiced in the Lambayeque region during periods of
drought caused by a lack of rain [18]. However, this regimen can be applied in any season
to benefit the predominantly family-based agriculture practiced by the population. This
can contribute to the economic viability of the locality and ensure food security.

5. Conclusions
In the context of climate change, both the availability of water resources and food
security have significant risks. For this reason, the AWD method becomes relevant by
greatly reducing greenhouse gas emissions and the demand for irrigation water. In this
study, grain yield and greenhouse gas emissions were evaluated under the CF regimen and
irrigation levels AWD5 , AWD10 , and AWD20 . An average 93% reduction in CH4 emissions
was observed, as well as a 198% increase in N2 O emissions. Regarding grain yield, it
experienced a decrease of 15%, 2%, and 5% for the AWD5 , AWD10 , and AWD20 levels,
respectively. With respect to WUE, AWD irrigation shows greater efficiency, with 0.83, 0.96,
and 0.92 kg m−3 for AWD5 , AWD10 , and AWD20 , respectively, and a 28% water reduction
in AWD irrigation. Despite the increase in N2 O emissions, the GWP was mainly influenced
by the reduction of CH4. This resulted in a notable decrease in GWP under the AWD
irrigation regimen. This pattern was also reflected in the total GHG emissions in relation
to grain yield (YGWP), being 0.07, 0.06, and 0.05 kg CO2 eq kg−1 grain yield for irrigation
regimens AWD5 , AWD10 , and AWD20 , respectively. The findings highlight the importance
of a more detailed and specialized approach in AWD10 treatment, considering its minimal
impact on grain yield. The results of this study support the adoption of AWD irrigation
as a strategy to mitigate CO2 emissions while contributing to the reduction of water use.
This approach acquires relevance in the socioeconomic and climatic context of the northern
coast of Peru, since it safeguards the supply of rice in the population’s diet during times
of drought.

Author Contributions: Conceptualization, L.F.d.P. and L.R.-F.; methodology, E.H.-A., I.E.-C. and M.B.-
C.; validation, L.R.-F., L.F.d.P. and L.C.-V.; investigation, I.E.-C., M.B.-C. and L.R.-F.; resources, L.R.-F.,
L.C.-V. and L.F.d.P.; data curation, L.R.-F., I.E.-C. and M.B.-C.; writing-original draft preparation
I.E.-C., M.B.-C. and L.R.-F.; writing-review and editing, L.R.-F., L.C.-V. and L.F.d.P.; supervision,
L.R.-F., E.H.-A., L.C.-V. and L.F.d.P.; project management, L.R.-F. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by the National Scientific Research and Advanced Studies Pro-
gram (PROCIENCIA) of PROCIENCIA-Peru, under the project “Implementation of the technological
tool in the development of a precision system with remote sensors to optimize the use of water and
reduce the emission of greenhouse gases in rice fields for the benefit of farmers in the Lambayeque
region” (Project No. PRO501078113-2022-PROCIENCIA-Peru).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Agronomy 2024, 14, 248 17 of 18

Conflicts of Interest: The authors declare no conflicts of interest.

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