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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. 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