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Okumu GHG estimation for agric Kenya 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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Okumu GHG estimation for agric Kenya nov 10 2014

  1. 1. GREENHOUSE GAS ESTIMATION FOR AGRICULTURE IN KENYA By Michael Okumu Climate Change Unit Ministry of Agriculture, Livestock and Fisheries Presented during the FAO/CCAFS Workshop on Reducing costs of GHG EsEmates in Agriculture, November 2014, Rome Italy
  2. 2. INTRODUCTION • Kenya has a vision -­‐ The Kenya Vision 2030 -­‐ which is the country’s long -­‐ term development blueprint • aims at crea<ng a globally compe<<ve and prosperous country with a high quality of life for all its ci<zens by 2030. • To achieve the Vision 2030 which aspires to transform Kenya into a newly industrializing middle income country by 2030, Kenya has adopted a Low Carbon Climate Resilient Development Pathway. • Agriculture is the key economic driver to this Vision, it is threatened by Climate Change. • Being a signatory to UNFCCC, Kenya is commiPed to Green House Gas Emissions reduc<on (as evidenced by the adop<on of the Low Carbon Development pathway to its V2030)
  3. 3. INTRODUCTION • A na<onal inventory system incorporates all elements needed to es<mate, report and archive GHG emissions and sinks • Kenya did the First Na<onal Communica<on in 2002 • The Second Na<onal Communica<on is to be ready by December, 2014 • Main challenges – Technical exper<se – Data required • Currently – regular data is collected through the government repor<ng systems • Ad Hoc Surveys
  4. 4. Current GHG Emission Es<ma<on Approaches • Assembly of cropland data (area, Ha) – Area under perennials (tea, coffee, sugarcane, ) – Area under rice – irrigated; non irrigated – Area under annual crops – Area under hor<culture; oil crops • Assembly of crop residue data (area in Ha) – Area of harvested crop residue burnt • By administra<ve unit, by crop, on a <me scale (yrs) • Assembly of fer<liser data (tonnage) – Nitrogenous fer<lisers – Phospha<c – Potassium; others • Assembly of paddy rice data (by area, by year)
  5. 5. Current GHG Emission Es<ma<on Approaches • Wetlands Data – Area converted to wetlands; managed wetlands; croplands; sePlements; forestlands; grasslands; other lands – Area of land under managed wetlands/flooded areas (es<mated area under water for a specified period of <me)
  6. 6. Current GHG Emission Es<ma<on • Livestock Approaches popula<on (ha) – By type; by year; by admin unit • Area under grasslands (ha); Area under grasslands burnt(ha) – Managed grasslands (rangelands and ranches) • By year; by admin unit
  7. 7. Current GHG Emission Es<ma<on • Forestland Approaches data – Managed forestland outside/inside gazePed forest – Unmanaged forest – Area of land converted to forestland (cropland, sePlements, wetlands, grasslands, other lands) – Area of land under forestland burnt (ha) [managed forestland outside/inside gazePed forest; unmanaged forest land] – Amount of harvested wood product
  8. 8. Innova<ons • Data collec<on manuals (to enhance harmony in procedures and methods) • Use of modern technology – Mobile phones (rapid simple data transfer) – ACME Planimeter (es<ma<on of areas) • Training of data collectors (to improve skill, create uniformity of approach)
  9. 9. Major Challenges • Unsuitable formats of data • Incomplete data • Inadequate availability or accessibility of data • Data inconsistency • Unreliable and inadequately documented data sources • Non uniform data collec<on methods and formats • High cost of data collec<on