Supporting Information for “Modeling methane emissions from irrigated rice
cultivation in China from 1960 to 2050”
S1 Model description
S1.1 CH4MOD
The CH4MOD is a semi-empirical model that simulates the daily methane
emissions from rice paddies under various agricultural practices. This model consists
of two modules: the derivation of the methanogenic substrates and the processes of
methane production and emission. The former module simulates the production of the
methanogenic substrates that are primarily derived from rice root exudation and added
organic matter (i.e., crop residues and manure). The latter module simulates the
methane production from the available methanogenic substrates and the fraction of
emissions via rice plants and bubbles. The model used a logistic function with rice
grain yield as the input to simulate the growing rice biomass that is a key variable in
calculating the root exudates and the fraction of the methane emissions from plants
and bubbles (Huang et al., 1998). The daily changes in the soil redox potential (Eh)
were calculated with differential functions, according to various water manipulations
in the rice paddies (Huang et al., 2004; Xie et al., 2010). The influences of other
environmental factors, such as soil temperature and texture, on organic matter
decomposition and methane production were expressed as specific coefficient
functions (Huang et al., 1998). Effects of rising atmospheric CO2 on methane
emissions have been reported in several previous papers (Megonigal & and
Schlesinger, 1997; Inubushi et al., 2003; Xu et al., 2004; Zheng et al., 2006). With
results in these studies, Xie et al. (2010) developed functions to simulate the effects of
atmospheric CO2 concentration on methanogenic substrates and vascular CH4
transportation. The comprehensive details can be found in literature of Huang et al.
(1998, 2004) and Xie et al. (2010).
S1.2 Agro−C
The Agro−C model was developed to simulate carbon cycling in agro-ecosystems
on large scales. This model consists of two sub-models: the Crop−C sub-model and
the Soil−C sub-model. The Crop−C sub-model simulates the crop photosynthesis,
autotrophic respiration and net primary production (NPP) for rice and other crops that
rotate with rice (e.g., winter wheat and rape seed plant). The Soil−C sub-model
simulates the decomposition of input organic matter and soil organic carbon (SOC).
The nitrogen that is needed by the growing crops is supplied by the decomposition of
the organic matter in the soil and mineral fertilizers. Model validations with
independent datasets that cover areas with a range of climates, soils, crop rotations
and agricultural practices had been comprehensively conducted and the results
showed that the model was able to simulate crop NPP for rice, wheat and maize,
which are the staple grain crops in China. Details of the model architecture and can be
found in Huang et al. (2009).
Because the Agro−C model is able to simulate daily aboveground and underground
crop biomasses using meteorological and agricultural cultivation data, combining the
CH4MOD with the Agro−C model makes it possible to predict future methane
emissions from rice paddies. The comprehensive details can be found in literature of
Huang et al. (2009).
S1.3 FGOALs
Based on well-established physical principles, climate models have reproduced
observed features of recent and past climate changes, and there is now considerable
confidence that those Atmosphere-Ocean General Circulation Models can provide
credible quantitative estimates of future climate change, at least on continental and
global scales (IPCC, 2007). The FGOALS is a GCM (General Circulation Model) that
contributed to the 4th assessment report (AR4) of the IPCC (Yu et al., 2004; Yu et al.,
2002).1 In the present study, climate changes from 2000 to 2050 were projected using
the FGOALS, which was developed by the Institute of Atmospheric Physics at the
Chinese Academy of Sciences (IAP, CAS). Because the projections of climate change
depend heavily upon future human activity, climate models are run against scenarios.
There were 40 different scenarios that were grouped into 4 scenario families, each
making different assumptions for future greenhouse gas pollution, land-use and other
driving forces (IPCC, 2000). In the present study, the FGOALS model projected
climate changes for the A1B and B1 scenarios, which represented a balanced
emphasis on all of the energy sources and an ecologically integrated world.
Figure s2 shows the changes in the annual mean air temperature (Fig. s2a) and
precipitation (Fig. s2b) in China from 2010 to 2050, as projected by the FGOALS.
Under the A1B scenario, the atmospheric CO2 concentration will increase from 365
ppm (parts per million) in 2000 to 535 ppm in 2050, and the annual mean air
temperature in China will increase by 0.8C in 40 years. The B1 scenario set the
atmospheric CO2 increase at 87 ppm over the same period, less than the A1B scenario,
but their predicted temperature increases were comparable to one another. In the
eastern part of China, where the majority of the rice of the nation is cultivated, the
temperature changes under the A1B scenario (Fig. s2c) were generally lower than
under the B1 scenario (Fig. s2d). No significant trend in precipitation was observed in
the projection of the FGOALS for either the A1B or B1 scenarios. Therefore, the
projected inter-annual variations in the temperature and the level of precipitation were
±1C and ±100 mm, respectively (Fig. s2a, b).
S2 Data processing
S2.1 Climate data and crop phenologies
Daily mean ambient air temperatures are the only meteorological data that are
required to drive the CH4MOD model; however, the Agro−C uses the daily maximum
and minimum air temperatures, precipitation and solar radiation. Observations of
these parameters at 678 meteorological stations in China from 1960 to 2009 were
acquired from the National Meteorological Information Center (NMIC) at the Chinese
Meteorological Administration (CMA) (http://cdc.cma.gov.cn/). The spatial
interpolation algorithm of Thornton et al. (1997) was applied to each day to create
1
http://www.ipcc-data.org/ar4/model-LASG-FGOALS-G1_0−Change.html
continuous surfaces for the air temperatures and precipitation, representing the spatial
variation in the climate conditions in China from 1980 to 2009. Air temperatures
generally decrease with increasing altitude. The altitude correction was made by
establishing linear regression equations for the air temperature and the meteorological
station’s elevation before the interpolation process. The linear equations were then
applied during the interpolation process, referencing a 10 km × 10 km DEM (Digital
Elevation Model) dataset that was generated from 1:250,000 contour line maps from
the State Bureau of Surveying and Mapping of China. The daily solar radiation was
calculated using the methods of Thornton et al. (2000) and the daily air temperatures
and precipitation.
The planting/transplanting and harvesting dates of rice control the start and end of
the CH4MOD’s runs at each growing season, and the Agro−C model requires the
phenologies of the other crops that rotate with the rice. The data for the crop
phenologies originally consisted of iso-line maps that were edited by Zhang et al.
(1987) in the Atlas of Agricultural Climate in China. In addition, the
planting/transplanting, heading and harvesting dates for each season in the 10 km ×
10 km grid were spatially interpolated from the iso-lines using the TIN (Triangular
Irregular Network) technique (Aumann et al., 1991).
The outputs of the FGOALS for future climate change projections are daily
meteorological features, including the maximum and minimum temperatures,
precipitation and solar radiation needed by the CH4MOD and Agro−C models, with a
coarse spatial resolution of 2.8º × 2.8º for the geographical longitude and latitude. We
used statistical down scaling approaches (Wilby et al., 1998; Kidson & Thompson,
1998) to downscale the climate projections. Regression equations were established in
the first step of the down scaling to create relationships between each 10 km × 10 km
grid and its enclosing 2.8º × 2.8º grid with the data from the overlapping period of
2000 to 2009. The regression equations were then applied to the FGOALS outputs to
calculate the daily meteorological data for the 10 km × 10 km grid from 2010 to 2050.
S2.2 Organic matter amendment and nitrogen application
Decomposition of the amended organic matter in rice fields provides substrates
for methanogenesis to produce methane under anaerobic conditions. The addition of
organic matters (e.g., animal manure, green manure and crop straws) strongly
enhanced methane emissions from the rice fields (Denier and Neue, 1995; Wassmann
et al., 1996). It has been reported that declining green manure application may have
reduced the methane emissions from the rice paddies in China in the past (Denier,
1999). If the farm manure and crop residue application is considered, the overall
organic matter application did not show a declining trend, even though the amount of
mineral N fertilizer application increased rapidly after 1975 (NATESC, 1999; MAC,
2009). In 1949, the amount of organic N that was applied to the cropland was 1.45
million tons, with negligible amounts of mineral N fertilizer. As of 1990, however,
76% of the total fertilizer N was from mineral fertilizer, with the organic portion
increasing to 5.12 million tons (NATESC, 1999; MAC, 2009).
Scarce available data, however, make it difficult to elucidate the details of the
amount and type of organic matter that was incorporated into the rice fields at
different times of the year and in different regions of China, especially over past
decades. In the present study, we delivered census sheets to aged farmers and
agricultural technicians in every province except for Beijing, Tianjin, Shanghai and
Qinghai. More than 1,000 such census sheets were collected. Qualification of the
census data were made by statistical methods. The average and standard deviation
(stdev) were calculated, at the first step, for each of the five grand regions and each of
the five decades. The outliers 3 times the stdev away from the average were removed.
The average and standard deviation were then calculated again (Table 2). The
fractions of incorporated crop straw and the amounts of farmyard manure induced
from the census data were verified by comparing to the relevant data in publications
(e.g. NATESC, 1999; Yang et al., 2010). In addition to crop straw, the incorporated
crop residues also included dead crop roots and stubbles. According to Zhao and Li
(2001), stubble accounts for approximately 13% of the total straw’s dry weight, and
the average ratio of root-to-shoot at harvest is approximately 0.1 – 0.17 (Zhao & Li,
2001; Neue et al., 1990). The total amount of the straw was calculated using the
straw/grain ratio (ratiostraw/grain) and the grain yield. The ratiostraw/grain has changed with
rice cultivar evolution from 1.7 for traditional varieties before the 1960s to
approximately 1.3 for the modern semi-dwarf varieties of the 1980s and 0.92 at
present (Donald, 1962; Yoshida, 1981; Zhang & Zhu, 1997; Fu, 1997; Yang & Zhang,
2010). The values for each sector were then derived as follows: straw = ratiostraw/grain ×
grain, root = ratioroot/shoot × (straw + grain) and stubble = straw × 13%.
In an effort to reduce labor costs, a larger proportion of crop straws were being left
in the fields instead of being taken away for household fuel and animal feed
(NATESC, 1999). The increasing amount of crop NPP also contributed to the
enhanced amount of crop straw in the fields (Huang et al., 2007). The overall organic
matter that was incorporated into the crop fields thus increased from 1960 to 1990.
Due to the increasing crop biomass and the straw incorporation between the 1960s
and the 2000s, the amount of overall crop residuals incorporated in the rice paddies
was enhanced by 154% from 1.43 t ha−1 to 3.64 t ha−1. However, at the same time, the
application of farm manure decreased gradually. The increasing amended crop residue
overrode the weakening farm manure application, and the total incorporated organic
matter in the rice fields was significantly increased.
From 1970 to 1979, the crop residuals in the rice paddies in the main rice
cultivation regions (Regions I and II) totaled 1.82 to 1.90 tons of dry matter per unit
of harvested area. From 2000 to 2009, the amended crop residuals in the rice paddies
in Regions I and II were 4.26 and 3.55 t ha−1, respectively, which were 1.9 to 2.3
times those amounts in 1970 to 1979. Compared to Regions I and II, the crop residual
application in southwestern China (Region III) increased moderately, and the total
amount was approximately 2.39 t ha−1 during the period of 2000 to 2009, which was
an increase of 51%.
Extrapolating to the year 2050, a simple assumption was made about the fraction
of the crop straw and farm manure that will be applied to the land. We assumed that
the fraction of the added crop straw would increase by the rate that was calculated
using the census data for the past 20 years. A maximum value of 0.9 was set to limit
the fraction of the amended crop straw. Farm manure application levels would
continue to decrease, following the decreasing trend over the past 50 years. Green
manure only accounted for a minor proportion of the total organic matter and was
assigned a value of its average for 2000 − 2009.
Mineral fertilizer is now the major nitrogen supplier in China’s crop cultivation.
Although mineral fertilizer is not a direct input parameter of the CH4MOD model, it
is an important input variable of the Agro−C model to simulate crop growth.
Therefore, mineral fertilizer is needed to estimate future methane emissions with the
combined Agro−C and CH4MOD model. The present level of mineral fertilizer
application has reached its peak at 225−375 kg N ha−1 (Zeng & Li, 2004). The
environmental pressures that are related to excessive mineral N fertilizer application
in Chinese agriculture decrease the possibility of increasing its use in the future.
Therefore, we assumed that the mineral fertilizer application levels would remain the
same as they were between 2000 and 2005 in the future scenarios.
S2.3 Water management
Mid-season drainage is typical in the modern water management of Chinese rice
cultivation. The benefits of mid-season drainage in rice production include
suppressing extra tillers, improving root development to resist lodging and saving
water. The present water management strategies that are used in the rice paddies
include various compositions of continuous flooding, drainage and intermittent
irrigation at the different stages of rice growth (Gao & Li, 1992; Huang et al., 2004).
Continuous flooding for the course of the entire rice growing season is only applied in
certain down-sloping rice paddies in hilly regions, known as year-round waterlogged
(YRWL) rice paddies (Cai, 2000). Few reports were found on the locations of these
YRWL rice paddies (Cheng, 1989; Lin et al., 1986; Liu, 1986; SAA, 2006; Wang &
Sun, 1984; Xu, 1989; Zhu et al., 1996). With limited information in the literature, we
compiled the acreages of the YRWL rice paddies for each of the main rice cultivation
provinces. Referring to the work of Gao and Li (1992), we cataloged the patterns of
water management for the different rice cultivation strategies in China as follows: (1)
the early rice in a double rice rotation were exposed to flooding, drainage, re-flooding
and intermittent irrigation; (2) the late rice in a double rice rotation were exposed to
flooding, drainage and intermittent irrigation; (3) the single rice in northern China
were exposed to the same strategies as the early rice; (4) the single rice in southern
China were exposed to the same strategies as the late rice and (5) the YRWL rice
paddies used continuous flooding (Huang et al., 2006).
Compared to the continuously flooded rice paddies, mid-season drainage has
been shown to remarkably reduce methane emissions (Husin et al., 1995; Yagi et al.,
1996; Wassmann et al., 2000; Li et al., 2002). Since 1960, different water
manipulation techniques have been tested and promoted in the main rice cultivation
regions of China to improve rice production (Li, 2001; Zou et al., 2007; Mao, 1981;
Zheng, 1990; Xiong et al., 1992). Furthermore, the need for water conservation in
agricultural practices made rice field irrigation more complicated and efficient
(MWRUC, 1996). In the present study, we assumed that all of the rice paddies were
continuously flooded in 1960 and then gradually shifted to mid-season drainage
irrigation (Li et al., 2002). After 1970, all of the paddies, except for the YRWL rice
paddies, utilized the mid-season irrigation water regime (Xiong et al., 1992; MWRUC,
1996).
S2.4 Soil parameters
Soil characteristics influence methane emissions significantly and have been
parameterized in previous models for simulating rice paddy emissions. It is a common
practice to use soil maps and soil profile measurements to scale up models (Cao et al.,
1998; Matthews et al., 2000; Van Bodegom et al., 2002; Zhang et al., 2007). There
are more than 6,000 site-specific profile measurements for the soil parameters that are
available for model scale-up in China. These data were collected through the Second
National Soil Survey and other scientific research projects. Combining the profile data
with the 1:1,000,000 soil maps of China, the type-specific soil characteristics were
recalculated (Liu et al., 2006). The 10 km × 10 km soil feature raster data were then
created by rasterizing the 1:1,000,000 soil maps (Institute of Soil Science, Chinese
Academy of Sciences). The gridded databases of the soil parameters consisted of sand
percentages, soil organic carbon content, total nitrogen content and pH, which were
inputs for the CH4MOD and Agro−C models.
S3 Changes in rice cultivation of China
The methane emissions are strongly related to the above ground biomass or the
net primary production which is thus an important input parameter of the CH4MOD.
Because the data on rice production are readily available in regular statistics
yearbooks, it is typically the primary data that is used to estimate regional methane
emissions from rice paddies (Cao et al., 1996; Matthews et al., 2000), especially for
variations over long time periods (Denier, 1999, 2000; Li et al., 2004). To estimate
methane emissions from rice paddies in the past, the annual statistics of provincial
rice production and harvested areas between 1960 and 2009 were collected from
yearbooks and official publications (EBCAY, 1980 − 2010; CISNAR, 1990; MAC,
2009). The spatial distributions of the rice paddies in the late 1980s and in 2005 were
obtained from the Data Center for Resources and Environmental Sciences of the
Chinese Academy of Sciences (RESDC, CAS). We aggregated the spatial distribution
datasets into 10 km × 10 km grids using the original 1 km × 1 km grids. The statistical
annual harvested areas of single, early and late rice were then allocated into 10 km ×
10 km grids by referencing the aggregated rice paddy distribution grid data. An
annually and provincially varying rectification coefficient was calculated and was
used during the allocation procedure under the restriction that the total area of rice
harvested in the 10 km × 10 km grids for each province must be equal to the
corresponding statistical harvested area of the same province. A comprehensive
description of rasterizing the statistical data can be found in Huang et al. (2006).
Historical statistics have shown that, along with the development and adoption of
short-duration rice cultivars in the 1960s, urgent food supply requirements in China
have forced many rice paddy farmers to use double-rice crop rotation instead of the
traditional rice−upland crop rotation (Xiong et al., 1992). Typical traditional rice
varieties take 160 to 200 days to mature, whereas modern varieties take 90 to 130
days. This finding caused a great number of rice paddies to change from single-rice to
double-rice plantations in southern China in the mid-1970s. The harvested area of
double-rice rotation, therefore, increased by 67%, from 15 Mha in the 1960s to 25
Mha in the mid-1970s, whereas the harvested area of single-rice rotation decreased
accordingly (Table s1). By the mid-1970s, the total area that was harvested for rice in
China was more than 36 Mha when the area of double-rice rotation was at its
maximum (Fig. s3a). After 1980, the increase in rice productivity enabled many of the
double-rice rotation operations to revert back to single-rice-upland crop rotations
(Table s1). At the same time, rapid and consistent urbanization gradually occupied
more croplands, including rice paddies (Tong et al., 2003; Lin & Ho, 2003).
Rice is cultivated over a vast area of China, from 50ºN in northeastern China
southward to Hainan Island at 19ºN. Due to the different climate conditions, the crop
rotations in rice paddies are very complicated. In southern China, the crop rotations
generally consist of three components, two of which are rice and the third is an upland
crop (Region I). In the rice paddies of Regions II and III, the prevailing crop system
consists of a single rice rotation, with upland crops for the remainder of the year.
However, double-rice rotation has once been dominant during the 1970s (Table s1). In
eastern China (Region II), double-rice rotation accounted for 68.7% of the total
harvested area in 1975 but has reduced to 21.3% at present. In northeastern China
(Region IV), only one crop is planted per year because of the cool climate, but the
temperature in this region has increased rapidly due to climate warming. Between
1960 and 1980, the annual temperature was 3.47 to 3.60C at the Harbin
meteorological station, which is located north of Region IV. After 1980, this reading
increased to 4.94C in the 1990s. Accompanying with this warming, the area of the
rice paddies expanded from 0.52 Mha between 1960 and 1964 to 0.86 Mha between
1975 and 1979 and to 3.44 Mha between 2005 and 2009 (Table s1).
With the development of rice technologies, remarkable growth in rice productivity
has been achieved since the mid-1960s. In 1960, the rice grain yield in China was 2.0
to 3.1 t ha−1, which increased rapidly to 5.0 to 6.1 t ha−1 at end of the 1980s. After the
1980s, the yield of the double-rice rotations (i.e., the early rice and late rice rotations)
was nearly stable at approximately 5.5 t ha−1, whereas the yield of the single-rice
continued to increase to more than 7.0 t ha−1 (Fig. s3b). Depending on the variety of
the rice, field management and the environment, the harvest index (HI) of modern,
short-grain rice can be more than 0.5, compared to 0.3 for the traditional varieties
(Yoshida, 1981). Equal rice production with a higher HI has been reported to emit less
methane (Denier, 2000; Wang et al., 1997). Because a significant proportion of the
increase in rice productivity was due to improvements in the HI, it is not likely that
the rice biomass increased by the same magnitude as the rice production.
S4 Assumed scenarios of rice harvest area from 2010 to 2050
Changes in the harvested area in the future depend on contradictions between the
expansion of industrialization and the secure food supply that is needed by the
increasing population. To 2040s, the population of China will likely increase by 200
million to a maximum of 1.6 billion (UN Population Division, 1998). When food
consumption reaches 400−450 kg per capita at that time, the national total food need
will be 640−720 million tons, 140−220 million tons more than the present food
production in China (Feng, 2007). The challenges of the nation’s food security stem
from the shrinking cropland area, which began in 1979 due to rapid urbanization and
infrastructure construction. In 1999, the total cropland area was 130 Mha, and in 2005,
it was only 122 Mha (MAC, 2009), which is very close the limit that was set by
Chinese authorities. Industrialization results in the conversion of agricultural land into
settlement and factories, but food security in China sets a limit of at least 120 Mha of
cropland. It is likely that the rice paddy area will change little in the future because of
these two contradicting forces, except in northeastern China, where the
rice-harvesting area has increased significantly and will continue to increase along
with climatic warming.
In the present study, the impact of the changes in rice crop rotation on national
methane emission was found to be complicated. Limited by climatic conditions, the
changes in the crop rotations were ultimately affected by administrative policies (e.g.,
food security, urbanization and energy conservation), economic factors (e.g., grain
price, water resource costs, power, fertilizer, pesticides and labor efficiency) and
agricultural technologies (Tong et al., 2003; Yoshida, 1981). It is difficult to predict
the changes in the future cropland area and crop rotations because of the contradicting
requirements of the inevitable urbanization and the urgent food security of the nation.
To discuss the methane emission uncertainties that result from changes in the area that
is harvested for rice, two extreme land use scenarios involving rice cultivation were
evaluated: a) the area that was harvested for rice in each province increased gradually
to its historical maximum, except in the three provinces in northeastern China where
the rice paddies have been expanding and benefiting from climatic warming (the
increasing rate of the rice paddy area in northeastern China was assumed to be equal
to the average rate over the past 20 years, 9.2×104 ha per year); and b) the area that
was harvested for rice in each province decreased, except in the three provinces of
northeastern China where the area of the rice paddies remained the same as those at
present day. According to the study by Liang et al. (2005), China’s cropland area will
decrease to 99 Mha by the year 2050, 23.6% down from the area in the early 2000s.
We assumed that the area that was harvested for rice would decrease in proportion to
the changes in the total cropland area in the latter scenario.
The rice-harvest area depends on the rice paddy area and the rice rotations that
are conducted on this area. When the rice paddy area varied between 21 and 23 Mha
from 1960 to 2009, the rice-harvest area changed from 28 to 35 Mha and to 29 Mha,
due to alternations between single- and double-rice rotations (Table s1). In the two
extreme rice cultivation scenarios (Table s3), the rice-harvest area was between −6.9
and 9.9 Mha different from present day on a national scale. Due to the spatial
differences in the crop rotations and socio-economic conditions, the potential changes
in the harvested rice area for each region varied significantly. In Region I and II, the
rice-harvest area was 16.1 to 27.2 Mha in 2045 to 2049, which was −4.7 to 6.4 Mha
different from that in 2005 to 2009. The majority of the harvest area changes in the
two regions resulted from changes in crop rotation, rather than rice paddy conversions.
Due to the spatial differences in the crop rotations and socio-economic conditions, the
changes in the harvested rice area for each region varied significantly. In Region I and
II, the rice-harvest area was respectively 16.1 to 27.2 Mha in 2045 to 2049, which was
−4.7 to 6.4 Mha different from the value in 2005 to 2009. The majority of the harvest
area changes in the two regions were due to changes in crop rotation, rather than rice
paddy conversions.
In the increasing scenario (see the left panel of Table s3), the decreasing
single-rice area was doubly compensated by the growing double-rice area, when these
areas were converted into double-rice rotations. The decrease in the harvested area in
the two regions (see the decreasing scenario in the left panel of Table s3) was likely
due to the overall shrinking of the area of the rice paddies, when the single- and
double-rice areas decreased. In Region III, the increases in the rice paddy area may
have been trivial, but in the decreasing scenario, there could be losses of more than
27.8%. In contrast to Region III, the northeastern area of China (Region IV) exhibited
no reductions in rice paddy acreage in the future that were due to climate warming.
Instead, this area increased its amount of paddies during the period from 2045 to 2049
by 2.1 times its present amount to 7.3 Mha.
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Table s1 Average rice-harvested area in the present study’s regions of interest (×106 ha)
Period
I
1960 − 1964
1965 − 1969
1970 − 1974
1975 − 1979
1980 − 1984
1985 − 1989
1990 − 1994
1995 − 1999
2000 − 2004
2005 − 2009
S-R†
3.65
2.58
1.53
1.27
1.32
1.29
1.21
1.28
1.78
2.27
II
D-R‡
10.48
12.61
15.25
15.72
14.83
13.99
13.72
13.23
10.87
10.34
S-R
4.82
4.10
3.62
3.46
4.38
5.00
5.18
5.48
5.87
6.55
D-R
3.26
4.54
7.45
7.95
6.18
5.10
4.65
4.03
1.98
1.61
Region
III
S-R
D-R
4.00 0.55
4.72 0.50
4.41 0.91
4.19 1.03
4.72 0.28
4.63 0.20
4.40 0.19
4.18 0.15
4.48 0.10
4.39 0.07
† Single-rice rotation; ‡ Double-rice rotation
IV
S-R
0.52
0.63
0.75
0.86
0.90
1.40
1.60
2.17
2.65
3.44
Total
V
D-R
-
S-R
0.74
0.72
0.72
0.67
0.67
0.64
0.70
0.77
0.65
0.60
D-R
-
28.02
30.40
34.64
35.15
33.28
32.24
31.65
31.28
28.37
29.27