Download Climate change and agriculture in Asia: A case study for methane

 1 Climate change and agriculture in Asia: A case study for
methane emission due to rice cultivation
Prabir K. Patra1, Akihiko Ito1,2 and Xiaoyuan Yan3
1. Research Institute for Global Change, JAMSTEC, Yokohama, 236-0001, Japan
2. National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
3. Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
Abstract.
Temperature and rainfall (climate) have mostly determined traditional agricultural
practices on the Earth. As the population increased so as the demand for food, during
the green revolution during the 1940s through the 1970s, series of research and
developments have occurred in order to increase agriculture production around the
world. Agricultural activities are associated with the emission of greenhouse gases,
notably methane (CH4) and nitrous oxide (N2O), which are important for
anthropogenically influenced climate change. Thus the feedbacks between the
agricultural and climate are important. Here we have used (1) three sets of emission
inventories to estimated emission of CH4 from rice paddies, and (2) a terrestrial
ecosystem model using two independent parameterizations, primarily to understand
the controlling climate and human factors on the CH4 emission during the period of
1920-2009. The results suggest contemporary CH4 emission from the rice cultivation
areas is 42.3±3.3 (model) and 14.8-41.7 (inventory) Tg-CH4 for the 2000s, although
there are clear disagreements in spatial distributions of different emission inventories.
The total CH4 emission is simulated to be 25.7±3.1 Tg-CH4 during the 1920s, and
increased at the fastest rate during the periods of green revolution. The role of trends
in climate played only secondary role for CH4 emission increase simulated by the
terrestrial ecosystem model.
1. Introduction
Agriculture is the main source income for the majority of population living in the
South and Southeast Asia. For instance, about 70%, 30% and 20% of the total land in
South Asia, Southeast Asia and East Asia, respectively, are designated as agricultural
land (FAOSTAT, 2010). Traditional agricultural practices over these regions are
naturally rainfed due to strong monsoons. However, in the past several decades,
modern technologies for water harvesting in dams or smaller storages have become
more common. For example, the rice cultivation in Japan and China are almost 100%
irrigated and the complete inundation of paddy fields is only exceptions. The
controlled irrigation is beneficial for both increasing rice production and reduction of
methane (CH4) emission (Nishimura et al., 2004; Minamikawa et al., 2006; Yan et
al., 2009). Such mutually beneficial methodologies are often required for convincing
the farmers to change their agricultural practices.
2 Nevertheless, the main source of water in the tropical countries is warm precipitation.
As per the Intergovernmental Panel on Climate Change (IPCC, 2007), the tropical
areas are more vulnerable to the erratic rainfall and prolonged periods of droughts. A
wide variety of crops, most prominently rice, are grown in Asia due to favourable
climate. The extreme climate change and environmental degradation can lead to crop
yield and ecosystem health through droughts, enhanced ozone (e.g., Parthasarathy et
al., 1988; Chameides et al., 1999). However, higher amount of CO2 in the ambient air
may also cause increase sequestration of carbon by the ecosystem, through the so
called CO2-fertilization effect (Inubushi et al., 2003).
The statistical database on rice paddies cultivation area, yield and production for each
country are available from the statistical database of the Food and Agriculture
Organization (FAO) of the United Nations (FAOSTAT, 2010; hereinafter FAO10;
www.fao.org/corp/statistics/en). FAO10 is based on country level data on agricultural
activity, such as the state-wise crop area and production, for each of categories.
According to FAO10, the rice cultivation area increased only marginally since the late
1900s to the 2000s (Table 1). Figure 1 shows the share of major rice producing
countries in the world, with China (29%) and India (20%) topping the list. While the
rice cultivation area of most other countries have continued to increase to meet
demand for most popular stable diet of the Asian population, the cultivation area for
China has decreased steadily since the 1970s. This is mainly because the Chinese
demand has been fulfilled by raising the yield most dramatically (by a factor of 2.2
between 1960s and 2000s) among the major rice producing nations, while that for
India has increased by a factor of 2. Among the top 4 rice producing nations, India
has the lowest yield, at about the half that in China.
Table 1: Trends in rice cultivation area (in millions of hectre, MHa) and yield (within
parenthesis in tonnes/Ha) for top 4 rice producing countries in 2000s and global totals
(source: FAO10; see also Fig. 1).
1960s
Global
114.13
(2.078)
China
29.83
(2.833)
India
36.05
(1.496)
Indonesia
7.38
(1.859)
Bangladesh 9.31
(1.684)
1970s
125.30
(2.483)
35.61
(3.585)
38.63
(1.735)
8.42
(2.653)
9.90
(1.779)
1980s
129.05
(3.147)
33.34
(5.058)
40.65
(2.202)
9.68
(3.859)
10.33
(2.197)
1990s
134.01
(3.691)
31.85
(6.004)
43.21
(2.777)
11.15
(4.336)
10.19
(2.747)
2000s
137.10
(4.084)
29.02
(6.306)
43.21
(3.091)
11.92
(4.613)
10.75
(3.755)
3 Production (million Tonnes) 700 600 500 400 300 200 100 0 Figure 1: Top 10 rice producing countries in 2009 as in FAO10 are shown in
comparison with world and Asian total productions (data source:
http://faostat.fao.org/site/339/default.aspx).
Many areas representing part of the Indo-Gangetic Plains are used for single rice crop
in subtropical conditions (Rainfed) and might pose threat of CH4 emission (e.g.,
Manjunath et al., 2006). A deep scientific understanding is needed for understanding
the past changes in CH4 emission from rice paddies in the past century in order to
predict possible future scenarios in to the coming decades and century. Here we use a
set of statistical dataset for rice cultivation over the 1960s-2000s, estimation CH4
emission from rice paddies using detailed emission factors, controls and flux
measurements, and a terrestrial ecosystem model for simulating CH4 emission from
the paddy fields and other inundated areas in Asia. The methodologies are described
in the next section. Results of the spatial and temporal variations in rice cultivation
and CH4 fluxes are discussed in Section 3, followed by the conclusions.
2. Data sources and model description
In this work two main sources of data are used (1) latitude-longitude distributions of
monthly-mean CH4 emissions from rice paddies based on gridded crop area,
inundation and emission factors (Yan et al., 2009; hereinafter Yan09), and (2) a
terrestrial ecosystem model simulated emission for the period of past 100 years (Ito
and Inatomi, 2011; hereinafter Ito11).
Yan09 methane emission from rice fields was estimated using the tier 1 method of the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2007), which
provides a default emission factor for a specific condition and scaling factors to
account for various conditions. The parameters required for this methodology include
the areas of irrigated, rainfed, and deepwater rice paddies; the proportions of irrigated
rice fields that are continuously flooded, intermittently flooded with single drainage,
4 and intermittently flooded with multiple drainage; the proportions of rainfed rice
fields that experience regular rainfall and are drought prone; the proportions of rice
that have a long non-flooded preseason, a short non-flooded preseason water status,
and a flooded preseason water status; the type and amount of organic fertilizers used;
and the length of the rice growing season. Data for the lowland rice area for the year
2000 were collected at the sub-national level for monsoon Asian countries from
various country statistics. The areas of rice fields that were irrigated and rainfed were
scaled according to Huke and Huke (1997) and the database of the International Rice
Research Institute (www.irri.org/science/ricestat/pdfs/Table%2030.pdf). Detailed
estimation for water regimes of rice paddy in both rice and non-rice seasons, organic
fertilizer application rate, and the length of the rice growing season can be found in
Yan et al. (2009).
Ito11 fluxes are obtained from the Vegetation Integrative SImulator for Trace gases
(VISIT) terrestrial ecosystem model simulated monthly CH4 emission from wetland
areas and consumption by upland soils (Ito and Inatomi, 2011). In the VISIT model
plant photosynthetic CO2 uptake, allocation, biomass growth, and mortality are
treated as ecophysiological processes (Ito and Oikawa, 2002). Recently, the model
has been improved to incorporate the nitrogen cycle for better representing the
variation of gas fluxes responding to weather and biological conditions (Ito, 2005).
VISIT model is composed of several functional compartments such as leaves, stems,
roots, dead biomass, and organic soil. To consider the uncertainty caused by
difference in parameterization schemes, VISIT incorporates multiple schemes for CH4
fluxes. CH4 emissions from the inundated land area are parameterized using a semimechanistic scheme (Walter and Heimann, 2000; hereinafter “WH”) and a simple
bulk scheme (Cao et al., 1998; hereinafter “Cao”). To include the spatial
heterogeneity of inundated land, CH4 fluxes are separately estimated for flooded and
non-flooded fractions of the ground surface depending on the water table depths. At
layers lower than the water table, CH4 production is estimated as a function of
temperature and plant carbon supply. To simulate inter-annual/decadal variability in
CH4 emission, VISIT model is run with meteorological forcing from the Climate
Research Unit (CRU) TS3.0 dataset for cloud cover, diurnal temperature range,
precipitation, daily mean temperature, monthly average daily maximum temperature,
and vapour pressure covering the period 1901-2006 at 0.5x0.5 degrees horizontal
resolution (Mitchell and Jones, 2005). The present simulation considers historical
land-use change on the basis of dataset by Hurtt et al. (2006), from which conversion
matrix (i.e., gross fractional change among primary forest, secondary forest, cropland,
and pasture) is available for each year. The fractional coverage by paddy field was
obtained from the dataset by Monfreda et al. (2008) for the year 2000; historical
change was assumed to be proportional to the temporal change in total cropland area
mentioned above.
3. Results and discussion
Figure 2 shows the latitude-longitude distribution of annual-mean CH4 emission from
the rice paddies as estimated by three emission inventory groups and the terrestrial
ecosystem model simulations using two different parameterization schemes. Overall,
there are similarities among all 5 cases, showing high emission intensity in the
Gangetic India Plain (GIP), Bangladesh, northern Southeast Asia (NSEA) and eastern
5 China. Yet there are striking differences in the detailed emission distributions, such as
the relative emission intensities between the east and west part of the Gangetic Plain
or the north and south part of China in the EDGAR3.2 (Emission Database for Global
Atmospheric Research; version 3.2) and REAS/Yan09 inventories. These contrasts
are even more prominent in the two VISIT simulation cases using Cao and WH
schemes. In Cao case, the low latitudes areas within each region exhibit stronger CH4
emission, e.g., the southern part of China, Bangladesh within the Indian subcontinent
and NSEA (10-15 N). In WH scheme, the emission intensity is more evenly spread
across all latitudes, depending mainly on the areas of rice cultivation. The total CH4
emissions from global rice fields also vary greatly from one of the earliest estimation
of 106 Tg-CH4/yr (Mathews and Fung, 1987) to the most recent emission inventory
of 26.7 (range: 14.8-41.7) Tg-CH4/yr (by Yan09). The EDGAR3.2 and Ito11
estimations (42.3±3.3 Tg-CH4/yr) are around the upper end of Yan09 estimation,
which is also about the total emission of rice paddies suggested by atmospheric
chemistry-transport modeling (Patra et al., 2009).
6 Figure 2: Spatial distributions of CH4 emissions from paddy fields as estimated using
satellite remote sensing inundation area (a; GISS; Matthews and Fung, 1987),
EDGAR3.2 (b; Olivier and Berdowski, 2001), database of field measurements (c;
REAS; Yan et al., 2009), and VISIT ecosystem model corresponding two emission
parameterizations (d: Cao et al., 1998 and e: Walter and Heimann, 2000).
As expected seasonality of CH4 emission from rice paddies depends strongly on the
rainfall over all parts of the Asia (not shown). The Yan09 emission show strongest
emission intensity over the southeastern India during the boreal autumn month
(September-November), in phase with maximum rainfall intensity as per the TRMM
(Tropical Rainfall Measuring Mission) satellite observations. Such agreements in the
months of maximum rainfall and CH4 emissions are also seen for southern and
northern parts of the Southeast Asia, and East Asia in Yan09 distributions. However,
7 the Ito11 emissions show early peak in emissions by about 2 months in the southern
India region. In the GIP region, Yan09 show delayed peak in emissions, while Ito11
emission peak is in good agreement with TRMM maximum rainfall.
To understand the human influence and climate variability on the CH4 emissions, we
show differences in VISIT/Ito11 emission differences between the averages for the
2000s and 1920s, and the variabilities (1σ) over the period of 1920-2009 (Figure 3).
Consistent increase in emissions are found most of the Asia over the past 90 years,
with highest increases (Fig. 3a,b; red shade) are clearly seen over the regions of dense
populations in Gangetic India plains, Bangladesh, northern Southeast Asia and China.
Only one place in China and India show decrease in CH4 emission (blue shade) due
to the shift in crop type.
Figure 3: Longterm trends (a,b) and variability (c,d) in annual mean CH4 fluxes as
simulated by the VISIT model (Ito11) using two parameterization schemes during the
period of 1920 and 2009.
The CH4 emission variabilites (Fig. 3c,d) also exhibit somewhat similar patterns as
the longterm trends in the tropical Asia region, because the variability is proportional
to the intensity of emission (ref. Fig. 2d,e). However, the emission variabilties are
extremely low over the temperate Asia regions. This contrast in tropical and
temperate Asia region arise from rice cultivation practices, e.g., irrigated vs. rainfed,
and the natural variability in rainfall. The South Asia experiences one of the most
intense monsoon systems in the world during the boreal summer, and with large
interannual variability (Patra et al., 2005 and references therein). Note here that the
magnitudes of change (Fig. 3a,b) in CH4 emissions are as great as 10 times compared
8 to the variabilities (Fig. 3c,d) over all of the Asian countries. This suggests that the
human influence, by intensifying agricultural activity, has dominated the changes in
CH4 emission over the past 100 years compared to the variation/change in climate.
Rice_Cao
Wetland_WH
Wetland_Cao
Figure 4 shows the time series of CH4 emissions from three part of broadly divded
Asia20
regions for the period 1920-2009. The increase in CH4 emission during 1920 to
2010 are attributed to (1) higher level of carbon assimilation by the rice paddies,
because the concentration of CO2 increased from ~300 to 390 ppm which led to
10 level of soil carbon input the methanogenic bacteria, and (2) increase in rice
higher
cultivation areas during the period of green revolution, the 1940s through the 1970s.
In the0VISIT simulation, temporal change in rice cultivation area was derived from
(1) current fraction of paddy cultivation area, and (2) historical cropland area. Here,
we assumed that paddy/cropland ratio has been constant through the period of
20
simulation.
Even though, the increase in CH4 emissions is attributable to the increase
in rice cultivation activities, large quantitative uncertainty remains. It should be
mentioned that the VISIT simulations do not include effects of management practices
10
(such as water regime and the use of organic fertilizer), and the change of rice yield
(biomass) (see Yan et al., 2009; Zhang et al., 2011 for further details).
0
20
10
Rice_WH
0
20
10
Southeast Asia
South Asia
East Asia
0
3
Soil Oxid.
Units: Tg-CH4 yr
2
1
0
1920
1940
1960
Year (A. D.)
1980
2000
Figure 4: Time series of annual mean CH4 emissions from paddy field areas as
simulated by the VISIT ecosystem model for the three Asian regions using two
different emission parameterization cases.
-1
9 4. Conclusions and outlook
We present the distributions of CH4 emission from rice paddies over the Asian
region, estimated using the field studies of emission rate and remote sensing data of
rice cultivation area (bottom-up) as well as a terrestrial ecosystem model simulated
emissions using two CH4 production schemes. The model results over the period 1920s and 2000s are used for understanding the role of climate and human influences on CH4 emission increase and variability. CH4 emission from rice
paddy is very variable, and affected by many factors, however, there is no long-term
monitoring over a specific site under the same management practice. Therefore there
is no observed trend.
There is a clear trade-off between CH4 and N2O emission form rice paddies,
especially as affected by water management (e.g., Gao et al., 2011). However, the
increase in N2O emission was mostly due to the rapid increase in nitrogen fertilizer
application. The direct effect of nitrogen fertilizer on methane emission rice paddy is
not certain yet, likely not much. An indirect effect of nitrogen fertilizer on CH4
emission is that fertilizer increase crop yield and biomass, and thus substrate for
methane production. Further studies are required to delineate the coupling between
gases emission from the agricultural ecosystems.
It should be noted that there remain several uncertainties in the model simulation. In
the present model, impacts of irrigation and human management on rice growth and
CH4 production were not fully captured (i.e., handled in a simplified manner). As
attempted by Yan et al. (2009), this is important for evaluating the mitigation
potential in terms of climate change. As noted earlier large fraction of rice cultivation
in the South Asia are not mechanized. With the rapid industrial development in this
region, we believe the agricultural sector will be revamped in the coming decades.
That means the agricultural sector will become a more important source of
greenhouse gases, through the burning of fossil fuels for running the machineries.
Acknowledgement. We thank Tapas Bhattacharya for useful feedback on the
organization of this chapter content. This work is partly supported by JSPS/MEXT
KAKENHI-A grant number 22241008 and APN project number ARCP201111NMY-Patra/Canadell.
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