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Improving estimates of GHG emission factors from livestock production systems on smallholder farms

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In sub-Saharan Africa, agriculture is estimated to account for over 60% of GHG emissions, primarily due to land use change and enteric methane production in ruminants; and over 80% of agriculture (both area and production) is smallholder systems. No empirical studies on enteric CH4 emissions and very few studies on GHG emissions from soils in these systems have been conducted. This study sampled 60 farms in western Kenya using static chambers (3 reps) and analyzed soils once for total C/N content, BD and texture and 4 times for soil IN concentration.

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Improving estimates of GHG emission factors from livestock production systems on smallholder farms

  1. 1. Improving es-mates of GHG emission factors from livestock produc-on systems on smallholder farms David Pelster, Klaus Bu1erbach-­‐Bahl, Mariana Rufino, John Goopy and Todd Rosenstock Introduc6on Lack of data on GHG emissions from African agriculture suggests inaccuracies in na6onal inventories • In September 2014 sub-­‐Saharan Africa, agriculture is es6mated to account for over 60% of GHG emissions, primarily due to land use change and enteric methane produc6on in ruminants; and over 80% of agriculture (both area and produc6on) is smallholder systems • No empirical studies on enteric CH4 emissions and very few studies on GHG emissions from soils in these systems • Current na6onal SSA inventories therefore are based on IPCC 6er 1 methodology; • Using emission factors from OECD states with large industrial farming systems that likely do not represent smallholder systems where manure applica6ons, not synthe6c fer6lizers are dominant source of nutrients, and where ruminant fodder is generally protein-­‐poor and food availability is oRen limited (The only available study [S. Africa] es6mated that the Tier 1 emission factors for ruminant CH4 produc6on is about 50% of actual emissions) Pictures Materials and methods Phase I: Targe-ng, priority seFng and infrastructure Landscape analysis and targe-ng Landscape implementa-on Set-­‐up of state-­‐of-­‐the-­‐art laboratory facili-es Training of laboratory and Phase II: Data acquisi-on Mul--­‐dimensional evalua-on of mi-ga-on op-ons field staff System-­‐level es-ma-on of Scalable and social acceptable mi-ga-on op-ons Produc-vity assessment 1. Soil GHG emissions from mixed smallholder farms did not vary by management (“intensive” vs extensive) however differences between land classes were noted 2. Soil cumula6ve emissions tended to be much lower than previous studies in OECD states 3. S6ll require measurements of enteric CH4 produc6on • Can emissions (g C m-­‐2 yr-­‐1) Cumula6ve C-­‐CO2 emissions (mg C m-­‐2 yr-­‐1) Cumula6ve C-­‐CH4 increase intensity of management (greater use of fer6lizers) without increasing soil GHG emissions • Suggests emissions (mg N m-­‐2 yr-­‐1) Cumula6ve N-­‐N2O that increased nutrient inputs that increase agriculture / livestock produc6on could be considered mi6ga6on as it also can decrease emissions per unit • Development of emission factors for using Tier II methodology to calculate na6onal GHG inventories • Determine feed strategies for increasing animal produc6on while reducing CH4 emission intensi6es David Pelster or Klaus Bu[erbach-­‐Bahl dpelster@cgiar.org ; k.bu[erbach-­‐bahl@cgiar.org ● P.O. Box 30709-­‐00100 Nairobi Kenya ● +254 20 422 3513 h[p://aghealth.wordpress.com ● www.ilri.org Acknowledgements: The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Interna6onal Livestock Research Ins6tute (ILRI) This document is licensed for use under a Crea6ve Commons A[ribu6on –Non commercial-­‐Share Alike 3.0 Unported License September 2014 Results Research into use mi-ga-on poten-al Phase III: Development of systems-­‐ level mi-ga-on op-ons Capacity building Phase IV: Implementa-on with development partners GHG measurements Profitability evalua-on Social acceptability assessment Joint scien-fic & stakeholder evalua-on UPCOMING • Stra6fied into 5 land classes (based on remote sensing); 3 field types (based on farm management); and 3 broad vegeta6on classes • Sampled 1x per week for one year at 60 farms in western Kenya using sta6c chambers (3 reps) • Analyzed soils once for total C/N content, BD and texture and 4 6mes for soil IN concentra6on • Classify livestock produc6on systems and determine herd numbers within each class • Plan to use enclosed respira6on chambers for measuring ruminant CH4 produc6on Soil GHG emissions over 6me: a) mg C-­‐CO2 m-­‐2 hr-­‐1; b) μg C-­‐CH4 m-­‐2 hr-­‐1; c) μg N-­‐N2O m-­‐2 hr-­‐1; d) Soil moisture content; e) soil IN (NH4 + NO3) concentra6ons Note: Do[ed ver6cal lines indicate plan6ng, while dashed lines indicate harves6ng Landclass: 1 = lowland subsistence farming; 2 = cash crops; 3 = highland subsistence farming; 4 = highland mixed farming; 5 = grasslands / pasture Cumula6ve soil GHG emissions over 6me for the 5 different land classes. Bars indicate 1 SEM

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