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Agus Challenges for agric ghg quant Nov 10 2014

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Presentation at workshop: Reducing the costs of GHG estimates in agriculture to inform low emissions development
November 10-12, 2014
Sponsored by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the United Nations (FAO)

Published in: Science
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Agus Challenges for agric ghg quant Nov 10 2014

  1. 1. Challenges for agricultural GHG quan4fica4on Fahmuddin Agus Indonesian Soil Research Ins5tute Jl. Tentara Pelajar No 12, Cimanggu, Bogor 16114, Indonesia F_agus@litbang.pertanian.go.id Interna'onal Workshop Reducing the costs of GHG es4mates In agriculture to inform low emissions development www.litbang.deptan.go.id Opening Panel, Rome, Italy, 10-­‐12 Nov. 2014
  2. 2. 3 Ques4ons 1. What approaches are currently used to es5mate GHG emissions from agriculture? 2. Have you developed any innova5ons to reduce the cost of greenhouse gas es5mates? 3. What are the major challenges and priori5es for improving es5mates of agricultural greenhouse gases?
  3. 3. Na4onal Communica4ons, BUR REDD+ NAMAs Land-­‐based (Forestry + Agric.) Transporta5on Energy Industry Wastes LAMAs (Provincial) 1. CO2 from LUC 2. CO2 from drained peat oxidation 3. CO2 and CH4 emissions from forest and peat fire 4. CH4 from rice field 5. N2O from N fertilizer and animal manure 6. CH4 from enteric fermentation
  4. 4. Rela4ve importance of land-­‐based GHG emissions ? Land use change and peat decomposi5on contributed about 87% of the total land based emissions and thus the 26% na5onal emission reduc5on target can only be achieved if emissions from these sources can be reduced significantly
  5. 5. 1-­‐2. Current approach and Innova4on to reduce the cost Aspect Current approach Cost reduc4on Innovtn. 1. Land use change and peat emissions Ac5vity data o 23 x 23 LU/Lcover change matrix from landsat TM 1:250,000 scale; 3-­‐5 yearly o Overlay of the 23 land cover classes with peat soil map Emission factors: o IPCC (2006), IPCC (2013) for peat oxida5on and na5onally generated data Adapt the available data to the 23 Land cover types 2. CH4 emissions from rice field Ac5vity data o Areal of lowland rice, harvest index o Harvest area o Area by rice variety o Area by irriga5on system (con5nuous flooding vs intermifent) Priori5ze on area by irriga5on system Emission factor o IPCC (2006), local research data
  6. 6. Land cover classes and emission factors No . Penggunaan lahan Time averaged C stock (t/ha) Emisi (t CO2 ha-­‐1 th-­‐1) Remarks 1 Primary dryland forest 195 0 Mineral soil, assumed zero 2 Secondary dryland forest 169 0. Mineral soil, assumed zero 3 Primary mangrove 170 0 Mineral soil, assumed zero 4 Secondary mangrove forest 120 0 Mineral soil, assumed zero 5 Primary swamp forest 196 0 IPCC (2006) 6 Secondary swap forest 155 19 IPCC (2013) 7 Timber planta5on 64 73 IPCC (2013) 8 Estate planta5on 63/40 (OP) 40 IPCC (2013) 9 Annual upland agriculture 10 51 IPCC (2013) 10 Mixed upland Agriculture 30 51 IPCC (2013) 11 Shrub 30 19 IPCC (2013) 12 Swamp Shrub of is manual 30 19 IPCC (2013) 13 Savanna/grassland digi5zing 4 35 IPCC (2013) 14 Paddy Field 2 34 IPCC (2013) 15 Swamp6) 0 0 Flooded, assumed zero (IPCC 2013) 16 Ponds6) 0 0 Flooded, assumed zero (IPCC 2013) 17 Transmigra5on 10 51 Assumed similar with annual crop 18 Seflement 4 35 Assumed similar with savanna, 19 Airport 0 0 Assumed zero 20 Mining 0 51 Assumed same as bareland 21 Bareland 2.5 51 IPCC (2013) 22 Water body 0 0 Waterlogged, assumesd zero 23 Others (cloud cover) ? ? Refer to the previous or subsequent LU 23 The 23 x matrix LUC generated landsat from TM (mostly screen) on
  7. 7. 1-­‐2. Current approach and Innova4on to reduce the cost (con4nued) Current approach Cost reduc4on Invtn. 3. N2O Emission from fer4lizers Ac5vity data o Amount of N fer5lizers Already very simple method Emission factor o IPCC (2006) and na5onally generated data for AG C 4. Emissions from animal husbandry Ac5vity data CH4 from enteric fermenta5on o Livestock popula5on o No separa5on between conven5onal and befer quality feed Emission factor o IPCC (2006), research on-­‐going for country specific
  8. 8. 3. Challenges and priori4es for improvement • MRV and assessment of GHG emission is rela4vely new for most stakeholders, especially at sub-­‐na4onal level • Despite the Presiden4al Regula4on No. 71/2011 on MRV, stakeholders see li]le (short term) incen4ves for MRV, no market whatsoever for carbon emission reduc4on Need to look at the synergy between adapta4on and mi4ga4on
  9. 9. Management Adapta4on Mi4ga4on Intermifent irriga5on for rice Larger plan5ng area with the same volume of water Reduced CH4 emission Balanced and efficient fer5liza5on Higher yield and befer plant vigor Lower emissions from fer5lizers Mul5strata farming on drought prone areas The tree crop component is more tolerant to and can s5ll produce during long dry seson Enhancement of C by the tree component Improvement of livestock feed Increase weight gain Decreased CH4 emission from enteric fermenta5on • Treat adapta4on as the entry point for GHG quan4fica4on • Quan4fy mi4ga4on as the extra benefits
  10. 10. 3. Challenges and priori4es for improvement on Ac4vity data and Emission Factors Source Challenges and priori4es 1.a. Land use change and peat emissions o Development of sub-­‐na4onal emission factor o Reducing uncertainty of ac4vity data of peat and forest fire emissions 2. CH4 emissions from rice field 3. N2O Emission from fer4lizers o Improve ac4vity data by using the rate of N fer4lizer applica4on by cropping system 4. Emissions from animal husbandry o Development of emission factors by feed composi4on o Assessment of average animal body weigh by age class by region
  11. 11. H C H C H C Thank you

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