Introduction
Rapid population growth and economic development place a growing pressure on
increasing food production (Barrett, 2010). An increase in global crop
production of 100 % would be necessary to sustain the projected demand
for human food and livestock feed in 2050 (Tilman et al., 2011). Rice is the
staple food for nearly 50 % of the world's people, mainly in Asia
(Frolking et al., 2002). According to FAOSTAT (2010), approximately 600
million people in the Asia-Pacific region are suffering from hunger and
malnutrition. With the region's population projected to increase by another
billion by the mid-century, new approaches to increase food production are needed
(Chen et al., 2014). With a limited agricultural land area, the intensive
agricultural regions of China are facing serious environmental problems due
to large inputs of chemical fertilizer and low nitrogen use efficiency (NUE)
(Ju et al., 2009; Makino, 2011). Thus, integrated soil–crop system management
(ISSM), which redesigns the whole production system based on the local
environment and draws on appropriate fertilizer compounds and application
ratios, crop densities and advanced water management regimes, has been
advocated and developed to simultaneously increase crop productivity and NUE
with low carbon dioxide (CO2) equivalent emissions per unit product in
China (Chen et al., 2014). The key points of the ISSM are to integrate soil
and nutrient management with high-yielding cultivation systems, to integrate
the utilization of various nutrient sources and match nutrient supply to crop
requirements, and to take all soil quality improvement measures into
consideration (Zhang et al., 2011).
Carbon dioxide, methane (CH4) and nitrous oxide (N2O) are the most
important greenhouse gases (GHGs) that contribute to global warming (IPCC,
2013). The concept of global warming potential (GWP) has been applied to
agricultural lands by taking into account of the radiative properties of all
GHG emissions associated with agricultural production and soil organic carbon
(SOC) sequestration, expressed as CO2 eq. ha-1 yr-1
(Robertson and Grace, 2004; Mosier et al., 2006). Although agriculture
releases significant amounts of CH4 and N2O into the atmosphere,
the net emission of CO2 equivalents from farming activities can be
partly offset by changing agricultural management to increase the soil
organic matter content and/or decrease the emissions of CH4 and N2O
(Mosier et al., 2006; Smith et al., 2008). If global agricultural techniques
are improved, the mitigation potential of agriculture (excluding fossil fuel
offsets from biomass) is estimated to be approximately
5.5–6.0 Pg CO2 eq. yr-1 by 2030 (Smith et al., 2008). However,
the release of CO2 during the manufacturing and application of N
fertilizer to crops and from fuel used in machines for farm operations can
counteract these mitigation efforts (West and Marland, 2002). Therefore, when
determining the GWP of agroecosystems, there is a need to account for all
sources of GHG emissions, including the emissions associated with
agrochemical input (Ei) and farm operation (Eo) and sinks, e.g., soil
organic carbon (SOC) sequestration (Sainju et al., 2014).
Information on the effects of ISSM scenarios on GWP and greenhouse gas
intensity (GHGI) of agricultural systems is limited in China (Ma et al.,
2013; Liu et al., 2015). The annual rotation of summer rice-upland crop is a
dominant cropping system in China. Previous studies were mainly focused on
the initial influences of ISSM practices on CH4 and N2O emissions,
but did not account for the contributions of CO2 emissions from Ei and
Eo (Ma et al., 2013; Zhang et al., 2014). In this study, we evaluated GWP and
GHGI of rice-wheat crop rotation managed under several scenarios of ISSM by
taking CO2 equivalents emissions from all sources and sinks into account
for 3 years. We hypothesized that the ISSM strategies would reduce the
overall GWP and GHGI compared with local farmers' practice (FP). The
specific objectives of this study were to (i) evaluate the effects of
different ISSM scenarios on GWP and GHGI; (ii) determine the main sources of
GWP and GHGI in a rice-wheat cropping system; and (iii) elucidate the overall
performance for each ISSM scenario for different targets to increase grain
yields and NUE and reduce GWP and GHGI.
Materials and methods
Experimental site
A field experiment was conducted at the Changshu agro-ecological experimental
station (31∘32′93′′ N, 120∘41′88′′ E) in
Jiangsu province, China. This is a typical, intensively managed agricultural
area where the cropping regime is dominated by a flooded rice (Oryza sativa L.)-drained wheat (Triticum aestivum L.) rotation system. The
site is characterized by a subtropical humid monsoon climate, with a mean
annual air temperature of 15.6, 15.2 and 15.8 ∘C and precipitation
of 878, 1163 and 984 mm for 3 years, respectively. The soil of the field
is classified as an Anthrosol with a sandy loam texture of 6 % sand
(1–0.05 mm), 80 % silt (0.05–0.001 mm) and 14 % clay (< 0.001 mm),
which developed from lacustrine sediment. The major properties of
the soil at 0–20 cm can be described as follows: bulk density, 1.11 g cm-3;
pH, 7.35; organic matter content, 35.0 g kg-1; and total N,
2.1 g kg-1. The daily mean air temperatures and precipitation during the
study period, from 15 June 2011 to 15 June 2014, are given in the Fig. 1.
Daily mean air temperature and precipitation during the
rice-wheat rotation in 2011–2014 in Changshu, China.
Experimental design and management
A completely randomized block design was established in 2009 with four
replicates of six treatments, including no nitrogen (NN) and FP as controls,
and four ISSM scenarios at different chemical N fertilizer application rates
relative to the local FP rate (300 kg N ha-1) , namely ISSM-N1 (25 %
reduction), ISSM-N2 (10 % reduction), ISSM-N3 (FP rate) and ISSM-N4
(25 % increase). The designed ISSM scenarios (only for rice but not for
wheat) included a redesigned split N fertilizer application, a balanced
fertilizer application that included sodium silicate, zinc sulfate, rapeseed
cake (C / N = 8) providing an additional 112.5 kg N ha-1, and additional
phosphorus and potassium, and different transplanting densities, used as the
main techniques for improving rice yield and agronomic NUE. The agronomic NUE
was calculated as the difference in grain yield between the plots that
received N application and the NN plot, divided by the total N rate which
included chemical N fertilizer and N supplied from rapeseed cake in the
ISSM-N3 and ISSM-N4 scenarios. The details of the fertilizer applications,
irrigation and field management practices of the six different treatments
are presented in Table 1. Further information was described previously (Zhang
et al., 2014). Each plot was 6 m × 7 m in size with an independent
drainage/irrigation system.
The establishment of different treatments for the annual rice-wheat
rotations during the 2011–2014 cycle.
Scenario
NNa
FP
ISSM-N1
ISSM-N2
ISSM-N3
ISSM-N4
Rice-growing season
Chemical fertilizer application rate
0 : 90 : 120 : 0 : 0
300 : 90 : 120 : 0 : 0
225 : 90 : 120 : 0 : 0
270 : 90 : 120 : 0 : 0
300 : 108 : 144 : 225 : 15
375 : 126 : 180 : 225 : 15
(N : P2O5 : K2O : Na2SiO3 : ZnSO4, kg ha-1)
Split N application ratio
6 : 2 : 0 : 2
5 : 1 : 2 : 2
5 : 1 : 2 : 2
5 : 1 : 2 : 2
5 : 1 : 2 : 2
Rapeseed cake manure (t ha-1)
0
0
0
0
2.25c
2.25
Water regime
F-D-F-Mb
F-D-F-M
F-D-F-M
F-D-F-M
F-D-F-M
F-D-F-M
Planting density (cm)
20 × 20
20 × 20
20 × 15
20 × 15
20 × 15
20 × 20
Wheat-growing season
Chemical fertilizer application rate
0 : 90 : 180
180 : 90 : 180
135 : 90 : 180
162 : 90 : 180
180 : 108 : 216
225 : 126 : 270
(N : P2O5 : K2O, kg ha-1)
Split N application ratio
6 : 1 : 3
6 : 1 : 3
6 : 1 : 3
6 : 1 : 3
6 : 1 : 3
Seed sowing density (kg ha-1)
180
180
180
180
180
180
a NN, no N application; FP, farmers' practice; The four
integrated soil–crop system management (ISSM) practices at different nitrogen
application rates relative to the FP rate of 300 kg N ha-1 for the rice
crops and 180 kg N ha-1 for the wheat crops, namely, ISSM-N1 (25 %
reduction), ISSM-N2 (10 % reduction), ISSM-N3 (FP rate) and ISSM-N4
(25 % increase). Urea, calcium biphosphate and potassium chloride were
used as N, P and K fertilizers respectively. b F-D-F-M, flooding-midseason
drainage-re-flooding-moist irrigation. c 112.5 kg N ha-1 in the form
of rapeseed cake was applied as a basal fertilizer and included in the total N
rate for calculating agronomic NUE.
One midseason drainage (about 1 week) and final drainage before harvest
were used during the rice-growing season, whereas the plots only received
precipitation during the wheat-growing season. The N fertilizer was split
into a 6 : 2 : 0 : 2 or 5 : 1 : 2 : 2 ratio of basal fertilizer and topdressing for the
rice crop and a 6 : 1 : 3 ratio for the wheat crop. Phosphorus (P),
silicon (Si) and zinc (Zn) were all applied
as basal fertilizers for both crops, and
rapeseed cake manure was applied for the rice crop. Potassium (K) was added
as a split (1 : 1) application to the rice crop and all were added as basal fertilizer
for the wheat crop. The basal fertilization occurred at the time of rice
transplanting and wheat seeding. The topdressing was applied at the
tillering, elongation and panicle stages of the rice crop and at the
seedling establishment and elongation stages of the wheat crop. Above-ground
biomass including crop grains and straws were removed out of the fields for
all the treatments.
Gas sampling and measurements
We measured the CH4 and N2O emissions from each plot of the field
experiment over five annual cycles from the 2009 rice-growing season to the
2014 wheat-growing season. The initial 2-year measurements during the
2009–2011 rice-wheat rotational systems were described in our previous study
(Ma et al., 2013). Emissions were measured manually using the static opaque
chamber method. Each replicate plot was equipped with a chamber with a size
of 50 cm × 50 cm × 50 cm or 50 cm × 50 cm × 110 cm,
depending on the crop growth and plant height. The
chamber was placed on a fixed PVC frame in each plot and wrapped with a
layer of sponge and aluminum foil to minimize air temperature changes inside
the chamber during the period of sampling. Gas samples were collected from
09:00 to 11:00 using an airtight syringe with a 20 mL volume at intervals
of 10 min (0, 10, 20 and 30 min after chamber closure). The fluxes were
measured once a week and more frequently after fertilizer application or a
change in soil moisture.
The gas samples were analyzed for CH4 and N2O concentrations using
a gas chromatograph (Agilent 7890A, Shanghai, China) equipped with two
detectors. Methane was detected using a hydrogen flame ionization detector
(FID), and N2O was detected using an electron capture detector (ECD).
Argon-methane (5 %) and N2 were used as the carrier gas at a flow
rate of 40 mL min-1 for N2O and CH4 analysis, respectively.
The temperatures for the column and an ECD were maintained at 40
and 300 ∘C, respectively. The oven and FID were
operated at 50 and 300 ∘C, respectively. The
CH4 and N2O fluxes were calculated using a linear increase in the
two gas concentrations over time as described by Jia et al. (2012).
Topsoil organic carbon sequestration measurements
To measure the organic carbon content of the topsoil as described by Zhang
et al. (2014), soil samples were collected after the wheat harvest in 2009
and 2014 from all experimental plots at a plowing depth of 0–20 cm. The soil
organic carbon sequestration rates (SOCSRs) were calculated as follows (Liu
et al., 2015):
SOCSR(t C ha-1yr-1)=(SOCt-SOC0)/T×γ×(1-δ2mm/100)×20×10-1.
In Eq. (1), SOCt (g C kg-1) and SOC0 (g C kg-1) are the
SOC contents measured in the soils sampled after the wheat was harvested in
2014 and 2009, respectively. T refers to the experimental period (yr).
γ and δ2mm are the average bulk density and the gravel
content (> 2 mm) of the topsoil (0–20 cm), respectively.
GWP and GHGI measurements
To better understand the overall GHG impact of the rice-wheat crop rotation
managed under different ISSM scenarios, the GWP and GHGI were calculated
using the following equations (Myhre et al., 2013):
GWP(kg CO2eq.ha-1yr-1)=28×CH4+265×N2O+Ei+Eo-44/12×SOCSRGHGI(kg CO2eq. kg-1grain yield yr-1)=GWP grain
yield-1.
In Eq. (2),
Ei (kg CO2 eq. ha-1 yr-1), Eo (kg CO2 eq. ha-1 yr-1)
and SOCSR (kg C ha-1 yr-1) represent CO2
equivalent emissions from the agrochemical inputs, farm operations and soil
organic carbon sequestration rate, respectively. The global warming
potential of 1 kg CH4 and 1 kg N2O are 28 and 265 kg CO2
equivalents respectively (without inclusion of climate-carbon feedbacks),
based on a 100-year timescale (Myhre et al., 2013). 12 and 44 refers to
molecular weights of C and CO2, respectively. The grain yield is
expressed as the air-dried grain yield.
Therefore, the GWP of the cropland ecosystem equals the total CO2
equivalent emissions minus the SOC change per unit land area. In addition to
CH4 and N2O emissions, we considered CO2 equivalent emissions
associated with the use of agrochemical input (Ei), such as the manufacture
and transportation of the N, P and K fertilizers (Snyder et al., 2009), and
farm operation (Eo), such as the water used for irrigation (Zhang et al.,
2013) and diesel fuel (Huang et al., 2013a). The CO2 equivalent
emissions of N fertilizer were calculated as the mean value of the C
emissions of 1.3 kg C equivalent kg-1 N (Lal, 2004). Similarly, the
CO2 equivalent for irrigation was calculated from the total amount of
water used during the rice-growing season; the coefficient for the C cost
was 5.16 (kg C eq. cm-1 ha-1) originated from the value of 257.8 kg C eq. ha-1
for a 50 cm of irrigation provided by Lal (2004). The
CO2 equivalents of other Ei (P and K fertilization, manure, herbicide,
pesticide and fungicide applications) and Eo (tillage, planting, harvest and threshing) were recorded and also estimated by coefficients provided by
Lal (2004) since no specific coefficients were available for local
conditions. We collected the data specific to China's fertilizer manufacture
and consumption, and obtained the C emission coefficients to be 0.07 and 0.1 kg C eq. kg-1
of active ingredients for Si and Zn fertilizer,
respectively. The C emission factor for these farm operations depends on
diesel used as fuel or electricity. Chemical fertilizer was hand-broadcasted
for each fertilization event. Detailed information of each Ei and Eo
component for rice and wheat crop seasons are presented in Table 2.
Agricultural management practices for chemical inputs and farm operations in
the rice and wheat cropping seasons.
Rice season
Chemical input (kg ha-1)a
Farm operationb
N
P
K
Si
Zn
Herbicide
Insecticide
Fungicide
Irrigation
Tillage
Crop
Farm
Crop
Threshingc
planting
manure
harvest
Treatment
(cm)
(kg diesel ha-1)
(event)
(kg ha-1)
(kg diesel ha-1)
(kW h ha-1)
NN
0
90
120
0
0
1
13
2
78
20
1
0
6
74
FP
300
90
120
0
0
1
13
2.4
78
20
1
0
6
80
ISSM-N1
225
90
120
0
0
1
13
2.4
57
20
1
0
6
74
ISSM-N2
270
90
120
0
0
1
13
2.4
57
20
1
0
6
100
ISSM-N3
300
108
144
225
15
1
17
3.5
57
20
1
2250
6
100
ISSM-N4
375
126
180
225
15
1
26
5
57
20
1
2250
6
175
Wheat season
NN
0
90
180
0
0
1
5
2
0
17
1
0
5
61
FP
180
90
180
0
0
1
7
2
0
17
1
0
5
67
ISSM-N1
135
90
180
0
0
1
7
2
0
17
1
0
5
65
ISSM-N2
162
90
180
0
0
1
7
2
0
17
1
0
5
77
ISSM-N3
180
108
216
0
0
1
10
2.5
0
17
1
0
5
77
ISSM-N4
225
126
270
0
0
1
15
4
0
17
1
0
5
100
a There was no machinery used for fertilizer application.
b Tillage and crop harvest, crop planting and threshing were calculated
by diesel fuel (kg ha-1), event and electricity (kW h ha-1),
respectively.
c Electricity energy is calculated according to the power and working
hours. The power of the thresher is 15 kW in this experiment.
Statistical analysis
Repeated-measures multivariate analysis of variance (MANOVA) and linear
relationships were determined using JMP 7.0, ver. 7.0 (SAS Institute, USA,
2007). The F test was applied to determine whether there were significant
differences among practices, years and their interaction at P < 0.05.
One-way analysis of variance was conducted to compare the cumulative fluxes
of CH4 and N2O, and grain yield among the different treatments.
Tukey's Honestly Significant Difference (HSD) test was used to determine whether significant differences
occurred between the treatments at a level of P<0.05. Normal distribution
and variance uniformity were checked and all data were consistent with the
variance uniformity (P > 0.05) within each group. The results are
presented as the means and standard deviation (mean ± SD, n= 4).
Results
Crop production and agronomic NUE
During the three cropping rotations from 2011 to 2014, the rice and wheat
yields varied significantly among the treatments (Table 3). The grain yields
ranged from 5.83 to 12.11 t ha-1 for rice and 1.75 to 6.14 t ha-1
for wheat. On average over the three cycles, the annual rice yield of the FP
was significantly lower than that of the ISSM scenarios of ISSM-N1, ISSM-N2,
ISSM-N3 and ISSM-N4. Compared with the FP, rice grain yields increased by
10 and 16 % for the ISSM-N1 and ISSM-N2 scenarios, respectively,
i.e., with the lower N input, by 28 % for the ISSM-N3 scenario with the
same N input and by 41 % for the ISSM-N4 scenario with the highest N
input. However, we did not observe any significant increases in the
wheat-grain yields compared with the FP except for the ISSM-N4 scenario.
Statistical analysis indicated that rice and wheat yields from the 3
years were not significantly influenced by the interaction of cultivation
patterns and cropping year (Table 4).
Seasonal CH4 and N2O emissions, and yields during rice and wheat
cropping seasons in the three cycles of 2011–2014.
Rice season
Wheat season
Treatment
CH4
N2O
Yield
CH4
N2O
Yield
(kg C ha-1)
(kg N ha-1)
(t ha-1)
(kg C ha-1)
(kg N ha-1)
(t ha-1)
2011
NN
153 ± 10.8c
0.03 ± 0.05c
5.85 ± 0.08f
-0.48 ± 0.63a
0.45 ± 0.09d
1.74 ± 0.18d
FP
266 ± 25.3b
0.11 ± 0.08c
8.38 ± 0.35e
-0.48 ± 1.86a
1.43 ± 0.19b
5.67 ± 0.20b
ISSM-N1
212 ± 30.3bc
0.08 ± 0.03c
9.27 ± 0.26d
0.78 ± 0.97a
0.65 ± 0.11cd
5.05 ± 0.16c
ISSM-N2
220 ± 32.5bc
0.17 ± 0.11bc
9.79 ± 0.44c
2.25 ± 2.07a
0.80 ± 0.06c
5.71 ± 0.18b
ISSM-N3
518 ± 58.9a
0.38 ± 0.15ab
10.81 ± 0.26b
0.04 ± 3.23a
1.40 ± 0.10b
5.31 ± 0.26bc
ISSM-N4
561 ± 50.9a
0.37 ± 0.07a
11.76 ± 0.24a
-0.09 ± 1.40a
1.93 ± 0.09a
6.15 ± 0.15a
2012
NN
149 ± 25.8d
0.13 ± 0.10c
5.80 ± 0.22f
-4.32 ± 7.29a
0.65 ± 0.09d
1.73 ± 0.11c
FP
239 ± 34.5c
0.33 ± 0.11bc
8.72 ± 0.62e
4.85 ± 10.30a
2.13 ± 0.43ab
5.64 ± 0.34ab
ISSM-N1
226 ± 30.4cd
0.27 ± 0.07bc
9.43 ± 0.34d
1.46 ± 6.38a
1.39 ± 0.14c
4.94 ± 0.38b
ISSM-N2
228 ± 32.6cd
0.38 ± 0.29bc
9.99 ± 0.50c
-1.02 ± 0.84a
1.77 ± 0.38bc
5.78 ± 0.59ab
ISSM-N3
431 ± 26.8b
0.52 ± 0.16ab
10.92 ± 0.61b
2.45 ± 8.35a
2.19 ± 0.24ab
5.39 ± 0.39ab
ISSM-N4
536 ± 58.7a
0.78 ± 0.13a
12.24 ± 0.60a
5.91 ± 6.18a
2.61 ± 0.42a
6.10 ± 0.49a
2013
NN
101 ± 39.2b
0.16 ± 0.09b
5.84 ± 0.15f
-1.45 ± 1.34a
0.35 ± 0.06c
1.80 ± 0.03c
FP
141 ± 25.2b
0.43 ± 0.39ab
8.67 ± 0.26e
-3.70 ± 1.76a
0.80 ± 0.20ab
5.70 ± 0.30ab
ISSM-N1
135 ± 15.7b
0.19 ± 0.16ab
9.66 ± 0.29d
-1.00 ± 1.61a
0.49 ± 0.16bc
5.15 ± 0.20b
ISSM-N2
129 ± 32.2b
0.26 ± 0.13ab
10.15 ± 0.07c
-0.79 ± 1.60a
0.69 ± 0.24abc
5.80 ± 0.18ab
ISSM-N3
256 ± 45.6a
0.59 ± 0.42ab
11.14 ± 0.10b
-0.62 ± 1.14a
0.71 ± 0.10ab
5.51 ± 0.33ab
ISSM-N4
304 ± 22.3a
0.74 ± 0.40a
12.34 ± 0.16a
0.55 ± 1.68a
1.02 ± 0.11a
6.19 ± 0.63a
Average 2011–2013a
NNb
135 ± 19.6d
0.11 ± 0.05c
5.83 ± 0.04f
-2.08 ± 1.89a
0.48 ± 0.07d
1.75 ± 0.04d
FPb
215 ± 19.9c
0.29 ± 0.13bc
8.59 ± 0.25e
0.22 ± 3.96a
1.45 ± 0.24b
5.67 ± 0.16b
ISSM-N1b
191 ± 19.2c
0.18 ± 0.06c
9.45 ± 0.18d
0.42 ± 2.77a
0.84 ± 0.08c
5.04 ± 0.08c
ISSM-N2b
192 ± 11.6c
0.27 ± 0.12bc
9.98 ± 0.25c
0.15 ± 0.58a
1.08 ± 0.12c
5.76 ± 0.22ab
ISSM-N3b
402 ± 23.8b
0.50 ± 0.16ab
10.95 ± 0.13b
0.63 ± 3.51a
1.43 ± 0.05b
5.40 ± 0.16bc
ISSM-N4b
467 ± 39.2a
0.68 ± 0.15a
12.11 ± 0.28a
2.12 ± 2.57a
1.85 ± 0.16a
6.14 ± 0.35a
a Mean ± SD, different lower case letters within the same column
for each item indicate significant differences at P < 0.05 according
to Tukey's multiple range test.
b See Table 1 for treatment codes.
Repeated-measures analysis of variance (MANOVA) for the effects of
cultivation patterns (P) and cropping year (Y) on mean CH4 and N2O
emissions, and mean rice and wheat grain yields in the 2011–2014 cycle.
Crop season
Source
df
CH4
N2O
Yield
(kg C ha-1)
(kg N ha-1)
(t ha-1)
Rice
Between subjects
P
5
35.3***
3.71***
123***
Within subjects
Y
2
20.7***
0.88**
1.15**
P × Y
10
6.73***
0.15
0.37
Wheat
Between subjects
P
5
0.26
14.8***
76.3***
Within subjects
Y
2
0.55*
15.1***
0.08
P × Y
10
0.83
4.39***
0.05
Rice-wheat
Between subjects
P
5
37.2***
24.2***
153***
Within subjects
Y
2
20.5***
5.83***
0.70*
P × Y
10
6.50***
1.11
0.17
df – degrees of freedom, *P < 0.05, **P < 0.01, and ***P < 0.001
represent significant at the 0.05, 0.01 and 0.001
probability level, respectively.
The agronomic NUE for the rice and wheat of the fertilized plots ranged from
9.2 to 16.1 and 19.5 to 24.7 kg grain kg N-1, respectively (Fig. 2). The
higher NUE in the wheat season was mainly due to the relatively lower N
fertilizer (40 %) rates used for wheat compared with that for rice. As
expected, the rice agronomic NUE significantly increased by 75, 67, 35 and
40 % for the ISSM-N1, ISSM-N2, ISSM-N3 and ISSM-N4 scenarios,
respectively, compared with the FP (Fig. 2). For the wheat crop, the
agronomic NUE increased by 12 and 14 % in the ISSM-N1 and ISSM-N2
scenarios, respectively, and slightly decreased in the ISSM-N3 and ISSM-N4
scenarios compared with the FP, mainly because the current ISSM strategy was
only designed for rice and not wheat production.
Rice and wheat agronomic nitrogen use efficiency (NUE) in
2011–2014 in Changshu, China. Different letters indicate a significant
difference between treatments (p < 0.05). See Table 1 for treatment
codes.
CH4 and N2O emissions
All plots showed similar CH4 emission patterns, being a source in the
rice season and negligible in the wheat season (Fig. 3). During the three
annual rice-wheat rotations from 2011 to 2014, the CH4 fluxes ranged
from -3.89 to 99.67 mg C m-2 h-1. The seasonal CH4 emissions
varied significantly among the treatments during the rice-growing season
(Table 4, Fig. 3). No significant difference was found between the FP,
ISSM-N1 and ISSM-N2 plots. Temporal variation was significant during the
three cycles (Table 4, P < 0.001). Averaged across years, the
CH4 emission was greater in the ISSM-N3 and ISSM-N4 plots than in the
NN, FP, ISSM-N1 and ISSM-N2 plots (Table 3, P < 0.05). However,
compared with the NN plots, the FP, ISSM-N1 and ISSM-N2 plots with inorganic
fertilizer application resulted in increased CH4 emission rates of 59.9,
41.9 and 43.0 %, respectively, averaged over the rice-growing seasons.
The CH4 emission rates were further enhanced by 198.5 % in the
ISSM-N3 plots and by 246.7 % in the ISSM-N4 plots.
Seasonal variation of methane (CH4) fluxes from the
rice-wheat rotation cropping systems from 2011 to 2014. The black and gray
part in the figure separates different grain growth periods. See Table 1 for
treatment codes. The solid arrows indicate fertilization.
The annual N2O fluxes varied from -33.1 to 647.5 µg N2O-N m-2 h-1,
with most N2O emissions occurring during the
wheat-growing season after fertilization events, and several small emission
peaks during the rice-growing season (Fig. 4). With respect to the N
application effect, the annual cumulative N2O emissions for all four
ISSM scenarios were significantly higher than that in NN (P < 0.05).
Relative to the FP plot, the ISSM-N1 and ISSM-N2 scenarios decreased
the annual N2O emissions by an average of 41 and 22 %,
respectively (Table 3). The ISSM-N4 scenario significantly increased the
cumulative N2O emissions by 46 % (P < 0.05) because this
system received highest inorganic N fertilizer (25 % higher than that in
FP) and additional N via manure application compared to the FP,
although there was no significant difference between the ISSM-N3 and FP
plots.
Annual GWP and GHGI
Based on the perspective of the carbon footprint, we included the GHG
emissions associated with all of the inputs (Ei and Eo), and SOC
sequestration was expressed as kg CO2 eq. ha-1 yr-1. The
CO2 equivalent emissions associated with Ei and Eo are presented in
Table 5. The CO2 equivalents rates from N fertilizer dominated not only
the chemical input section (67–75 % of Ei) but also the total CO2
equivalents from agricultural management (45–50 % of the sum of the Ei
and Eo). Irrigation was the second largest source of CO2 equivalents
associated with agricultural management after N fertilizer (19–31 % of
the sum of the Ei and Eo). The GWP ranged from 8425 to 22 711 kg CO2 eq. ha-1 yr-1
for the NN and the ISSM-N4 plots, respectively (Table 6).
Although fertilized treatments increased the annual CH4 and N2O
emissions in comparison with the NN plot, it also increased the SOC
sequestration in these cropping systems. Of the main field GHGs that were
directly emitted, CH4 accounted for 59–78 % of the GWP in all
plots. An increase in the annual SOC content led to a significant decrease in
the GWP (contributed 5–9 % decrease of the GWP except in the NN plot).
Concerning the CO2 equivalents from agricultural management practices, emissions
associated with Ei (2493–4300 CO2 eq. ha-1 yr-1) were higher
than those associated with Eo (1296–1708 CO2 eq. ha-1 yr-1)
in the fertilized plots. There was no significant difference in the annual
GWP observed between the FP, ISSM-N1 and ISSM-N2 plots (Table 6). Across the
3 years, ISSM-N1 and ISSM-N2 slightly reduced the GWP by 12 and 10 %,
respectively; however, ISSM-N3 and ISSM-N4 significantly increased the GWP by
an average of 55 and 84 %, respectively, in comparison with the FP.
Agricultural management practices for chemical input and farm operation
and contributions to carbon dioxide equivalents (kg CO2 eq. ha-1 yr-1)
in the annual rice-wheat rotations from 2011 to 2014
(chemical input and farm operation used in each year were similar except
for irrigation water).
Treatment
Chemical input (kg ha-1)a
Farm operationc
N
P
K
Si
Zn
Herbicide
Insecticide
Fungicide
Irrigation (cm)b
Tillage
Crop
Farm
Crop
Threshing
and raking
planting
manure
harvest
2011
2012
2013
(kg diesel ha-1)
(event)
(kg ha-1)
(kg diesel ha-1)
(kW h ha-1)
NNd
0
180
300
0
0
2
18
4
75
80
80
37
2
0
11
135
FP
480
180
300
0
0
2
20
4.4
75
80
80
37
2
0
11
147
ISSM-N1
360
180
300
0
0
2
20
4.4
50
65
55
37
2
0
11
139
ISSM-N2
432
180
300
0
0
2
20
4.4
50
65
55
37
2
0
11
177
ISSM-N3
480
216
360
225
15
2
27
6
50
65
55
37
2
2250
11
177
ISSM-N4
600
252
450
225
15
2
41
9
50
65
55
37
2
2250
11
275
Chemical input (Ei) (kg CO2 eq. ha-1)
Farm operation (Eo) (kg CO2 eq. ha-1)
NN
0
132
165
0
0
46
338
53
1419
1514
1514
127
23
0
37
36
FP
2288
132
165
0
0
46
375
59
1419
1514
1514
127
23
0
37
39
ISSM-N1
1716
132
165
0
0
46
375
59
946
1230
1041
127
23
0
37
37
ISSM-N2
2059
132
165
0
0
46
375
59
946
1230
1041
127
23
0
37
47
ISSM-N3
2288
158
198
58
6
46
506
79
946
1230
1041
127
23
62
37
47
ISSM-N4
2860
185
248
58
6
46
768
129
946
1230
1041
127
23
62
37
73
a The carbon emission coefficients were 1.3,0.2,0.15, 6.3, 5.1 and 3.9 C
cost (kg C eq. kg-1 active ingredient) per applied nitrogen fertilizer,
phosphorus, potassium, herbicide, insecticide and fungicide, respectively,
as referred to in Lal (2004). We collected data specific to China's
fertilizer manufacture and consumption, and then estimated carbon emissions
coefficients were 0.07 and 0.1 C cost (kg C eq. kg-1 active ingredient)
per applied Si and Zn fertilizer, respectively.
b The carbon emission coefficient for irrigation was 5.16 C cost (kg C eq. cm-1 ha-1)
as referred to in Lal (2004). c The carbon emission coefficients were
0.94 C cost (kg C eq. kg-1 diesel) for tillage, raking and harvesting, 3.2 C cost (kg C eq. event-1)
for crop planting, 0.0075 C cost (kg C eq. kg-1) for farm
manure application and 0.0725 C cost (kg C eq. (kW h)-1) for threshing,
as referred to in Lal (2004).
d See Table 1 for treatment codes.
The GHGI was used to express the relationship between GWP and grain yield.
The GHGIs (kg CO2 eq. t-1 grain) in this study ranged from 712 to
1245 kg CO2 eq. t-1 grain (Table 6). The significant difference in
the GHGI of grain was found between the FP and the ISSM strategies. Compared
with the FP, ISSM-N1 and ISSM-N2 significantly reduced the GHGI by 14 and
18 %, respectively, mainly due to the increased grain yield and SOC
sequestration as well as reduced GHG emissions for the ISSM strategies of
reasonable N fertilizer management and suitable planting density. Although N
fertilizer or organic/inorganic combination fertilizer application reduced
the SOC losses caused by crop cultivation and increased the grain yields, the
GHGIs were generally higher for the ISSM-N3 and ISSM-N4 scenarios than the
ISSM-N1 and ISSM-N2 scenarios due to further increases in CH4 and
N2O emissions.
Seasonal variation of nitrous oxide (N2O) fluxes from
rice-wheat rotation cropping systems in three annual cycles over the period
2011–2014. The black and gray part in the figure separates different growth
periods. See Table 1 for treatment codes. The solid arrows indicate
fertilization.
Discussion
Grain yield and agronomic NUE as affected by ISSM strategies
Grain yields are directly related to fertilizer management. The MANOVA
results indicated that the rice and wheat grain yields were significantly
affected by the cultivation strategies (Table 4, P < 0.001), which
is in agreement with previous results (Chen et al., 2011; Zhang et al.,
2011). Compared with the FP, rice yields increased significantly by all four
ISSM scenarios (Table 3). However, the wheat grain yield decreased
significantly when the N fertilizer rate was reduced by 25 % (N1
scenario). It has been reported in previous studies that ISSM strategies can
effectively improve the rice grain yield (Ma et al., 2013; Liu et al., 2015).
First, the adjusted transplanting density for the ISSM-N1, ISSM-N2 and
ISSM-N3 scenarios would produce a positive effect on rice yield by
influencing rice colony structure, which agreed with Wu et al. (2005).
Second, split application of N fertilizer to match crop demand in the
ISSM-N1, ISSM-N2, ISSM-N3 and ISSM-N4 scenarios would significantly increase
agronomic NUE and rice yield which had been reported previously by Liu et
al. (2009). In the present study, ISSM-N1 and ISSM-N2 significantly increased
annual rice production by 10 and 16 %, respectively, in comparison with
the FP (Table 3). This finding is consistent with the results of
Peng et al. (2006), who reported that a 30 % reduction in the total N rate during the
early vegetative stage did not reduce the yield but slightly increased it
when combined with the modified farmers' fertilizer practice. Third,
integrated management of three macronutrients, N, P and K, as well as the two
micronutrients, Si and Zn, were considered as essential for sustainable high
crop yields. Additional Si and Zn fertilizer for the ISSM-N3 and ISSM-N4
scenarios would support better seedling establishment and reduce both biotic
and abiotic stress, thus producing higher yields (Wang et al., 2005; Slaton et
al., 2005; Kabata-Pendias and Mukherjee, 2007; Hossain et al., 2008). As
expected, when the total N rate was at the FP rate and/or increased by
25 %, in combination with other ISSM strategies (e.g., rapeseed cake
manure, additional P and K, applying Si and Zn fertilizer), the rice yield in
these ISSM-N3 and ISSM-N4 plots increased substantially by 28 and 41 %,
respectively. Based on a long-term fertilizer experiment, Shang et al. (2011)
reported that organic fertilizer incorporation significantly increased the
early rice grain yield. This may have resulted from the organic fertilizer
applied in combination with adequate nutrients contributing to alleviating
potential yield-limiting factors of rice.
It has been suggested that N losses vary depending on the timing, rate and
method of N application, as well as the source of N fertilizer (Zhu, 1997).
In addition to high rates of N and improper timing of N application, rapid N
losses (via ammonia volatilization, denitrification, surface runoff and
leaching) are important factors that cause low agronomic NUE of irrigated
rice in China (Peng et al., 2006). Compared with the FP plot, the rice
agronomic NUE was significantly increased by 75, 67, 35 and 40 % under
the ISSM-N1, ISSM-N2, ISSM-N3 and ISSM-N4 scenarios, respectively (Fig. 2).
The higher rice agronomic NUE in our study over the experimental period could
be due to reduced N losses by leaching and volatilization as well as the
improvement of N bioavailability in the rice crop season (Zhao et al., 2015).
Organic/inorganic combination fertilizer application also increases uptake by
crops compared with the traditional farmers' practice (Peng et al., 2006).
These findings suggest that the ISSM strategy is an effective method for
improving grain yield and agronomic NUE for future sustainable rice
agriculture in China.
Mean global warming potential (GWP) and greenhouse gas intensity (GHGI) over
the three rice season, wheat season and annual cycles of the 2011 rice
season–2014 wheat season.
Treatment
CH4
N2O
Ei
Eo
SOCSR
GWPa
Grain yield
GHGIb
kg CO2 eq. ha-1 yr-1
t ha-1 yr-1
kg CO2 eq. t-1 grain
Rice season
NN
5026 ± 733d
44 ± 20c
424
1601
-396 ± 164c
7492 ± 706d
5.83 ± 0.04f
1285 ± 123b
FP
8035 ± 742c
121 ± 53bc
1859
1603
585 ± 198ab
11032 ± 555c
8.59 ± 0.25e
1285 ± 68b
ISSM-N1
7132 ± 716c
75 ± 24c
1502
1191
246 ± 218b
9654 ± 800c
9.45 ± 0.18d
1021 ± 81c
ISSM-N2
7186 ± 434c
112 ± 49bc
1716
1198
355 ± 97ab
9858 ± 484c
9.98 ± 0.25c
989 ± 67c
ISSM-N3
15005 ± 888b
208 ± 66ab
2037
1260
691 ± 252a
17818 ± 786b
10.95 ± 0.13b
1626 ± 54a
ISSM-N4
17427 ± 1463a
284 ± 60a
2626
1280
773 ± 174a
20844 ± 1452a
12.11 ± 0.28a
1720 ± 108a
Wheat season
NN
-78 ± 71a
201 ± 28d
310
104
-396 ± 164c
934 ± 214b
1.75 ± 0.04d
533 ± 125a
FP
8 ± 148a
605 ± 99b
1206
105
585 ± 198ab
1339 ± 129b
5.67 ± 0.16b
236 ± 21b
ISSM-N1
16 ± 103a
351 ± 32c
991
105
246 ± 218b
1217 ± 342b
5.04 ± 0.08c
241 ± 68b
ISSM-N2
6 ± 22a
451 ± 49c
1120
108
355 ± 97ab
1329 ± 109b
5.76 ± 0.22ab
231 ± 26b
ISSM-N3
23 ± 131a
598 ± 20b
1302
108
691 ± 252a
1340 ± 290b
5.40 ± 0.16bc
247 ± 48b
ISSM-N4
79 ± 96a
772 ± 66a
1674
114
773 ± 174a
1867 ± 175a
6.14 ± 0.35a
305 ± 33b
Rice-wheat rotation
NNd
4948 ± 704dc
246 ± 26d
734
1705
-792 ± 327c
8425 ± 711d
7.58 ± 0.04d
1111 ± 94b
FP
8043 ± 858c
725 ± 49b
3065
1708
1170 ± 396ab
12371 ± 583c
14.26 ± 0.36c
868 ± 29c
ISSM-N1
7141 ± 709c
426 ± 55c
2493
1296
491 ± 435b
10871 ± 990c
14.50 ± 0.14c
750 ± 68d
ISSM-N2
7192 ± 424c
563 ± 86c
2836
1306
709 ± 193ab
11187 ± 552c
15.74 ± 0.44b
712 ± 52d
ISSM-N3
15028 ± 833b
806 ± 77b
3339
1368
1383 ± 503a
19158 ± 761b
16.36 ± 0.18b
1171 ± 37ab
ISSM-N4
17506 ± 1396a
1056 ± 58a
4300
1394
1545 ± 348a
22711 ± 1438a
18.26 ± 0.46a
1245 ± 93a
a GWP (kg CO2 eq. ha-1 yr-1)= 28 × CH4 +265 × N2O +
Ei +Eo - 44/12 × SOCSR, Ei
(agrochemical input), Eo (farm operation), SOCSR (SOC sequestration rate)
is divided by 2 to roughly estimate the GWP from rice and wheat season,
respectively. All other items were actually measured for each season.
b GHGI (kg CO2 eq. t-1 grain) = GWP/grain yields
c Different lower case letters within the same column for each item
indicate significant differences at P < 0.05 based on Tukey's multiple
range tests.
d See Table 1 for treatment codes.
CH4 and N2O emissions as affected by ISSM strategies
During the 3 years, the annual cumulative CH4 emissions, on average,
varied from 133 to 469 kg C ha-1 yr-1 (Table 3), and these values
fell within the range of 4.1 to 1015.6 kg CH4 ha-1 observed
previously in a rice field (Huang et al., 2004). Methane emissions were
highest during rice season, but only during the flooding period, mainly
because CH4 was produced in the anaerobic zones of submerged soils by
methanogens and is oxidized into CO2 by methanotrophs in the aerobic
zones of wetland soils and in upland soils (Le Mer and Roger, 2001). The
MANOVA results indicated that obvious effects of cultivation patterns and
years on CH4 emissions were found during the rice-wheat rotations (Table 4,
P < 0.001). The CH4 emissions were not significantly
affected by the cycles but affected by crop season (Table 6, Fig. 3). In this
study, no significant difference in CH4 emission was observed between
the FP, ISSM-N1 and ISSM-N2 plots. However, compared with the FP plot, the
ISSM-N3 and ISSM-N4 scenarios emitted 87 and 118 % more CH4,
respectively (Table 6), which is probably due to the incorporation of the
organic rapeseed cake manure. Previous reports support the observations that
CH4 emissions were significantly increased with the application of
organic amendments (Ma et al., 2009; Thangarajan et al., 2013; Zou et al.,
2005). Apparently, additional application of Si and Zn fertilizers had no
significant effect on CH4 and N2O fluxes, which was consistent with
the result of Xie et al. (2015). Moreover, rice growth was found to be
significantly increased under the ISSM-N3 and ISSM-N4 scenarios. In this
case, the organic matter inputs such as root litter and rhizodeposits in the
ISSM-N3 and ISSM-N4 scenarios were probably also higher than in the other
plots; and thus soil C input, which served as an additional source of
substrates for the methanogens in the rice paddies, likely contributed to the
increase in CH4 emissions (Ma et al., 2009). Finally, because the rice
plants acted as the main pathway for CH4 transports from the soil to the
atmosphere, the higher biomass may have facilitated more CH4 emissions
(Yan et al., 2005).
Denitrification and nitrification are the main processes that produce
N2O in the soil (Paul et al., 1993). The N2O emission patterns
varied during the rice- and wheat-growing seasons which were partially
associated with the anaerobic conditions prevailing in a rice paddy. Changes
in the soil water content strongly influenced the soil N2O emissions and
resulted in negligible N2O emissions when the rice field was flooded
(Fig. 4), which is consistent with previous reports (Akiyama et al., 2005;
Murdiyarso et al., 2010). When the soil water content was below saturation,
N2O emissions increase with soil moisture; however, N2O emissions
gradually decreased with the soil saturation condition (Rudaz et al., 1999).
A relatively high N2O peak was observed in the first 2 weeks of the
wheat-growing season (Fig. 4), possibly because soil changes from flooded to
drained conditions may have enhanced N2O release (Deng et al., 2012).
Alternation of drainage and flooding may induce large amounts of N2O
emissions, particularly in fertilized systems; this has commonly been shown
in earlier studies (Wang et al., 2012; Xiong et al., 2007; Zou et al., 2005).
The seasonal and annual rates of N2O emissions were significantly
affected by the cultivation practices and years (Table 4). Compared with the
FP plot, the ISSM-N2 scenario significantly decreased the seasonal N2O
emissions in this study, which may have resulted from a reduction in the N
fertilizer rate (Tables 1, 3). The total N2O emissions decreased by
7–38 and 26–42 % in the rice and wheat seasons, respectively,
when the conventional N management (300 kg N ha-1 for rice and
180 kg N ha-1 per crop for wheat) changed to optimum N management
(225–270 kg N ha-1 for rice and 135–162 kg N ha-1 per crop for wheat). It is
likely that more N2O was emitted (Mosier et al., 2006) as a result of
the additional N made available to the soil microbes through N fertilizer
application, which also probably contributed to increased CH4 emissions
(Banger et al., 2013). Strategies that can reduce N fertilization rates
without influencing crop yields can inevitably lower GHG emissions (Mosier et
al., 2006). Nitrogen leaching and volatilization are the important components
of reactive N releases but are not included in the current GHG budget.
GWP and GHGI as affected by ISSM strategies
The GWP in our study (10871–22711 kg CO2 eq. ha-1) with the ISSM
strategies was higher than that in a double-cropping cereal rotation
(1346–4684 kg CO2 eq. ha-1) and a rice-wheat annual rotation
(290–4580 kg CO2 eq. ha-1) reported by Huang et al. (2013b) and
Yang et al. (2015), respectively. Dominant CH4 emissions as well as
additional CO2 emissions due to the use of machinery/equipment for
irrigation and farm operations under the ISSM strategies may increase the
GWP more than in other cropping systems (emit more CO2 equivalent
emissions of 2439–5694 kg CO2 eq. ha-1 for agricultural
management practices in the present study). However, the current GWP was
comparable to that of a double-rice cropping system
(13407–26066 kg CO2 eq. ha-1) (Shang et al., 2011). The GHGIs, which ranged from
0.71 to 1.25 kg CO2 eq. kg-1 grain in this study, were slightly
higher than previous estimates of 0.24–0.74 kg CO2 eq. kg-1 grain
from rice paddies with midseason drainage and organic manure incorporation
(Qin et al., 2010; Li et al., 2006), but were lower than the DNDC model
estimates for continuous waterlogged paddies (3.22 kg CO2 eq. kg-1
grain) (Li et al., 2006). Differences in GWP or GHGI were found in the
cultivation patterns over the three rice-wheat rotations (Table 6). The
ISSM-N1 and ISSM-N2 scenarios with optimized ISSM strategies led to a lower
GWP than the FP by a certain extent, but there were not significant
differences among the FP, ISSM-N1 and ISSM-N2 plots (Table 6). Compared with
the FP, the ISSM-N1 and ISSM-N2 scenarios significantly reduced the GHGI,
which was mainly due to higher yields. In spite of the similar GWP compared
with the FP plot, the lowest GHGI (0.71 kg CO2 eq. kg-1 grain) was
obtained under the ISSM-N2 scenario. This finding is consistent with the
suggestion made by Burney et al. (2010), i.e., that the net effect of higher
yields offsets emissions. It is well known that CH4 emissions dominate
the GWP in rice paddies (Ma et al., 2013; Shang et al., 2011). In comparison
to the GWP (12371 kg CO2 eq. ha-1 yr-1) and GHGI
(0.87 kg CO2 eq. kg-1 grain) of the FP, the ISSM-N3 and ISSM-N4 scenarios
increased both the GWP and GHGI, mainly because these scenarios notably
increased the CH4 emissions compared with the FP, which resulted in
relatively higher GWP (Table 6).
Agricultural management practices that change one type of GWP source/sink
may also impact other sources/sinks and therefore change the GWP and GHGI
(Mosier et al., 2006; Shang et al., 2011). Although the N fertilizer plots,
especially those with the incorporation of organic fertilizer, increased the
annual CH4 and N2O emissions, they increased the SOC sequestration
in this cropping system, which is in agreement with previous reports (Huang and
Sun, 2006). This may be due to the incorporation of rapeseed cake and
enhanced below-ground crop residue associated with higher crop productivity
(Ma et al., 2013). In the present study, the ISSM-N2 scenario with ISSM
strategies decreased the CH4 and N2O emissions as well as the
energy consumption related to irrigation and the manufacture and transport
of N fertilizer (depending on coal combustion), ultimately leading to a
decrease in the GWP relative to the FP plot. Moreover, despite the lower N
fertilizer input, the grain yield did not decline and the GHGI of the
ISSM-N2 scenario was thus lower than of the FP plot, indicating less
consumption of CO2 equivalents per unit of grain produced. We
demonstrate that high yield and agronomic NUE, together with low GWP, are
not conflicting goals by optimizing ISSM strategies.
Main components of GWP and GHGI and implementation significance for the
ISSM strategies
Determining the main components of the GWP and GHGI in specific cropping
systems is very important for mitigating GHG emissions in the future,
because the benefits of C sequestration would be negated by CH4 and
N2O emissions and the CO2 equivalents released with the use of
high N fertilizer application rates (Schlesinger, 2010). In the current
study, the five main components of the CO2 equivalents for the GWP were
ranked in decreasing order of importance as follows: CH4 emissions > agrochemical
inputs of N fertilizer > farm
operations related to irrigation > SOC sequestration > N2O
emissions (Table 6). Of the two crops, CH4 and irrigation were
important for rice, but less important for wheat, in which N2O losses
were expected to have a higher weight (Table 2). Methane emissions, the most
important component of GWP in this typical rice-wheat rotation system, could
be further mitigated by some other strategies, such as reasonable irrigation
(Zou et al., 2005; Wang et al., 2012).
Although N fertilizer application increased SOC sequestration when it was
applied with rapeseed cake manure, this benefit was consistently
overshadowed, on a CO2 equivalent basis, by the increases in CH4
and N2O emissions (Table 6). Similar results have been reported, i.e.,
GHG emissions substantially offset SOC increases (Six et al., 2004). It is
possible that the realization of reducing the GWP and GHGI in China should
focus on increasing the SOC and simultaneously decreasing the CO2
equivalents from CH4 emissions and N fertilizer inputs. Several studies
reported possible methods for these types of mitigation strategies, such as
optimizing the chemical fertilizer application amount and rate (Ju et al.,
2011), the amount of water used for irrigation (Gao et al., 2015) and the
timing and rate of N using the in-season N management approach, as well as
improving the N fertilizer manufacturing technologies (Zhang et al., 2013)
and using nitrification inhibitors or polymer-coated controlled-release
fertilizers (Hu et al., 2013).
China is a rapidly developing country that faces the dual challenge of
substantially increasing grain yields and at the same time reducing the
substantial environmental impact of intensive agriculture (Chen et al.,
2011). We used the ISSM strategies to develop a rice production system that
achieved mean yields of 10.63 t ha-1 (an increment of almost 24 %)
and an agronomic NUE of 13.20 kg grain kg N-1 (an increment of 43 %)
in long-term field experiments compared with current farmers' practices. The
ISSM redesigned the whole production system only for the rice crop based on
the local environment, drawing on appropriate fertilizer varieties and
application ratios, crop densities and an advanced water regime management.
If the ISSM strategies were also developed for the rotated wheat crop, the
overall performance of the whole rice-wheat system would be much improved,
with further increases in yield and reductions in the GWP and GHGI. We
conclude that the ISSM strategies are promising, particularly the ISSM-N2
scenario, which is the most favorable to realize higher yields with lower
environmental impact. The proposed ISSM strategies can provide substantial
benefits to intensive agricultural systems, and can be applied feasibly using
current technologies.