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GHG emissions and mitigation potential in Indian agriculture

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University of Aberdeen and the International Maize and Wheat Improvement Center (CIMMYT) have been collaborating to use the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) Mitigation Options Tool (CCAFS-MOT) to estimate greenhouse gas emissions from Indian farming and identifies cost-effective mitigation options. Sylvia Vetter has presented a poster with preliminary results of this project at EGU – European Geosciences Union General Assembly in Vienna in April 2016.
Authors: Sylvia Vetter, Diana Feliciano, Jon Hillier, Clare Stirling, Tek Bahdur, Pete Smith.

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GHG emissions and mitigation potential in Indian agriculture

  1. 1. GHG Yield N P T Total emissions tonnes/ ha kg/ha mm grad C kg/ha kg/kg Legume 0.37 0.99 426.9 25.12 257.27 0.43 Wheat 4.44 161.99 543.11 24.97 1289.8 0.29 Millet 1.71 55.48 475.26 25.16 817.58 0.43 Sugarcane 56.1 184.8 1076.23 24.2 2039.3 0.04 Coconut, Cotton, Sesamum 1.79 88.26 432 25.12 669.25 0.41 Rice 3.72 161.13 612.33 24.68 3080.91 1.22 GHG emissions and mitigation potential in Indian agriculture Sylvia H. Vetter1,*, Diana Feliciano1, Jon Hillier1, Clare M. Stirling2, Tek Bahdur2, Pete Smith1 1 Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK; 2 International Maize and Wheat Improvement Center (CIMMYT) Introduction India is one of the world’s largest greenhouse gas (GHG) emitter, accounting for about 5% of global emissions with further increases expected in the future. The Government of India aims to reduce emission intensities by 20-25% by 2020 compared with the 2005 level. In a recent departure from past practice the reconvened Council on Climate Change stated that climate change in agriculture would include a component that would focus on reducing emissions in agriculture, particularly methane and nitrous oxide emissions. Method The CCAFS Mitigation Option Tool (CCAFS-MOT) estimates GHG emissions from various crops (e.g. barley, maize, sugarcane), crop groups (e.g. vegetables, legumes) and livestock production in different regions. By bringing together several different empirical models to estimate GHG emissions, CCAFS-MOT provides policy-makers across the globe with the reliable information needed to make informed decisions about emissions reductions within agriculture. An excel version of the CCAFS-MOT can be downloaded: https://ccafs.cgiar.org/mitigation-option-tool- agriculture#.VpTnWL826d4 For the presented analysis the Tool was programmed in MATLAB to allow spatial modelling and a comprehensive analysis. Input: soil (texture, organic C, pH, bulk density, N content) and climate (annual precipitation, temperature) information; management (crop, yield, fertiliser, tillage practice, residue management) and for rice additional water management information Output: GHG emissions are estimated in terms of carbon dioxide equivalent per hectare (kg CO2eq ha-1) and carbon dioxide equivalent per unit of product (kg CO2eq kg-1). Mitigation potential for multiple options in kg CO2eq ha-1 Data: Plot level data on crop yield and fertilizer application was obtained from Directorate of Economics and Statistics of the government of India: http://eands.dacnet.nic.in/Plot- Level-Summary-Data.htm. Data on temperature and rainfall were obtained from Global Climate data: http://worldclim.org/. Soil data corresponding to GPS location were obtained from The Global Soil dataset for Earth System Modelling: http://globalchange.bnu.edu.cn/research/soilw. Mitigation options: Option 1: no tillage Option 2: no tillage (rice) Option 3: Planting basins Option 4: Legume intercropping/rotation with legumes Option 5: Residue incorporation Option 6: improved water management (general) Option 8: Use of cover crops GHG emissions for crops in India • Lowest GHG emissions per hectare are produced by legumes, followed by cereals • Highest emissions per hectare are produced by paddy rice, sugarcane, potato and onions • Emissions per kg yield show a different order with lowest GHG emissions for coconut, sugarcane, onions, barley and potato. The lower emissions per kg yield result from the higher yield that is produced on one hectare for these crops • Paddy rice shows in both lists the highest GHG emissions. The GHG emissions of rice management differ immensely with the water management (single drainage, multiple drainage). Sorted by Emissions per ha Emissions per yield crop kg/ha crop kg/kg Masur,Lentil 260.25 Coconut 0.00029 Soyabean 321.83 Sugarcane 0.06096 Groundnut 344.04 Onion 0.09504 Urad,Blackgram 352.09 Barley 0.18034 Coconut 363.34 Potato 0.18535 Arhar,Redgram 422.91 Soyabean 0.24911 Gram 444.06 Groundnut 0.30568 Mustard,Rapeseed 481.42 Wheat 0.31254 Jowar 494.95 Maize 0.31485 Peas 504.33 Masur,Lentil 0.33134 Barley 561.18 Bajra 0.37672 Moong,Greengram 597.69 Peas 0.38471 Bajra 636.50 Mustard,Rapeseed 0.39680 Ragi 751.45 Ragi 0.47289 Safflower 751.65 Arhar,Redgram 0.51275 Maize 797.50 Gram 0.54047 Sunflower 826.24 Jute 0.59166 Wheat 947.71 Jowar 0.60071 Sesamum 1416.62 Urad,Blackgram 0.63021 Jute 1483.59 cotton 1.12294 Onion 1610.65 Sunflower 1.12646 cotton 1656.79 Moong,Greengram 1.33883 Potato 4210.67 Safflower 1.61506 Sugarcane 5083.94 Sesamum 2.12707 Paddy Rice 7593.74 Paddy Rice 4.70139 Haryana Example Sugarcane: Bihar GHG yield N P T Total emissions tonnes/ ha kg/ha mm grad C kg/ha kg/kg yield Legume 0.88 21.85 1021.41 26.02 319.79 0.38 Maize 3.29 106.94 1031.54 25.67 930.08 0.26 Wheat 2.47 111.6 1043.78 25.63 867.69 0.36 Potato 15.33 104.06 994.98 25.82 1607.98 0.1 Coconut, Cotton, Sesamum 1.32 54.22 1084.33 25.47 978.36 0.8 Rice 2.13 91.59 1050.29 25.57 2015.98 1.68 Example: Maize: • Precipitation is nearly twice as high in Bihar compared with Haryana • Legumes result in a lower yield (tonnes/ha) in Bihar even if the fertilizer amounts are much higher than in Haryana • For wheat and rice: yield and fertilizer use is much higher in Haryana than in Bihar, what results in higher GHG emissions per hectare but the emissions per kg yield are lower because of the higher yield in Haryana Mitigation Scenario Bihar: • Increase in all fertiliser to increase yield • Residue is less burned and more incorporated • No till increases soil organic carbon (SOC) Mitigation Scenario Haryana: • Synthetic fertiliser ↓; organic fertiliser ↑ • Residue is less burned and more incorporated • No till increases soil organic carbon (SOC) Mitigation Scenarios: Stakeholder meetings provided a wide range of possible and definite scenarios (management, policy, technology, costs, etc.) for the future to mitigate emissions in agriculture as well as how to increase productivity. In a first analysis the sum of the given mitigation options for Haryana and Bihar is calculated. *Contact: Sylvia Vetter School of Biological Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen AB24 3UU, Scotland, UK E-mail: sylvia.vetter@abdn.ac.uk State analysis GHG emissions calculated for crops of two different states in India and general mitigation potential for different single options Conclusion • GHG emissions are highest for rice management • Management differs widely over India what results in a wide range of GHG emissions and a need for different mitigation options for different regions/states • Mitigation potential is high in rice production due to a change of water management • Mitigation potential is high in fertiliser management, depending on region a decrease or increase of fertiliser can decreases GHG emissions per kg yield and influence production positively

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