Mapping global soil Carbon stocks and sequestration potential John Latham Renato Cumani UN/FAO Environmental Assessment and Monitoring Unit FAO, Rome, April 16, 2009 1 Food and Agriculture Organization of United Nations
Mapping global soil Carbon stocks and sequestration potential SOFA Report 2007 Payment for Environment Services Modeling Approach for first assessment CarboAfrica Project 2 Food and Agriculture Organization of United Nations
Ecosystem Services SOFA 2007 1. Identify locations of technical potential to supply Ecosystem Service (hotspots, soil carbon gap, sheet erosion + hydrosheds) 2. Identify locations where agricultural producers have good potential to provide environmental services because: They are not very productive for agriculture and: -- providing ES could improve ag. productivity (soil carbon) -- providing ES could may be more productive than ag. (biodiversity) 3. Show the extent to which poor people are located in areas where PES may have good potential and discuss possible benefits/problems with PES program implementation in these areas. 3
Three situations where agricultural producers could supply ES: 1. Changing production practices, but remaining in agriculture Identify locations in agriculture that have high potential off-site impacts (sheet erosion + hydrosheds) Identify locations in agriculture likely to benefit from ES supply (depleted fertility + soil carbon gap) Identify locations where agriculture could contribute to biodiversity (hotspots + areas in agriculture) 2. Converting land in agriculture to non-agricultural uses Identify locations in low productivity agriculture that have high potential ES supply (hotspots + areas in agriculture) 3. Avoiding land conversion to agriculture How do we map potential ES supply from agricultural producers? Identify locations at risk of conversion and likely ag. Productivity (neotropics areas at risk+ suitability for ag.) 4
Spatial Analysis Identification of products to support SOFA Main geospatial products to support topics discussed in the report: Potential for below ground carbon sequestration Areas in Agricultural production with high soil carbon gap Areas at risk of deforestation and low agricultural suitability Low agricultural suitability and biodiversity hotspots Areas in Asia in agricultural production with high rates of water erosion Highly eroded areas in agriculture production with high soil carbon gap Poverty, biodiversity hotspots and low agricultural suitability Poverty, eroded lands in agriculture and high potential for soil carbon 5
Spatial extent: Global and Regional Spatial dataset requirements Product characteristics Spatial data formats: Grids, ArcInfo format, ( Shapefiles, Coverages ), Tables Spatial data resolution: 5 arc-minutes Spatial reference: Geographic projection, WGS 84 Map Layouts: Layers: continental boundaries, major rivers thematic layers Frames: 1-4 frames Cartographic elements: legend (color scheme) map title Map Size: A4 format, (19 cm) portrait or landscape Output formats: EPS for printing, JPEG for screen viewing and Google Earth for online viewing 6
Source A Source B Source.. Z Spatial operations Spatial operations Methodological development Spatial operations INTERMEDIATE A INTERMEDIATE B INTERMEDIATE..Z Spatial operator RECLASSIFIED INTERMEDIATE A Spatial operator RECLASSIFIED INTERMEDIATE B Spatial overlay OUTPUT A Spatial operator RECLASSIFIED INTERMEDIATE..Z QC Report Vector / Raster / Various reference Systems / Various scale and resolutions Data Conversion Spatial Reference / Re-projection Spatial Adjustment / Merging/ Resampling / Extraction / Selection Intermediate results Conditional evaluation, Classification Boolean conditions, Masked, Reclassified Selected and reclassified intermediate results are inputs to Overlays Overlays, intersects, multivariate analysis, suitability analysis Output(s), edited, cleaned, classified, filtered, dissolved, aggregated, 7 controlled, validated
Preparation of Spatial dataset Collect data sources: Collect data from FAO, NRC, AG, GeoNetwork, UNSTAT, FGGD, ISRIC, GLC2000, SRTM, GTOPO30 (to name few of them) internal and external databases Create Database: Spatial data is organized to a central location at NRCE server Spatial reference: Project data to Geographic projection, WGS 84 Data conversion: Convert data from grids and vector formats with various resolution to global and or regional grids Database Harmonization: Integrated spatial data from various sources into database ( in terms of extent, pixel size, snapping, cell window environments, completeness and homogeneity) 8
Input variables Soil Conditions Climate Conditions Moisture Conditions Land Cover Conditions M O D E L L I N G Estimating Soil Carbon Gap methodology 1 Actual Soil Carbon Potential Soil Carbon Sequestration C A L C U L A T I O N Soil Carbon Sequestration Gap (Low Actual and Medium to High Potential) 1 Model based on the methodology for Global Conditions for Soil Carbon Sequestration FAO 9
Preparation of Inputs SOIL CARBON GAP Input: Suitability of Soil Condition, thematic climate, Soil Moisture, Land Cover, Actual Soil Carbon Output: Soil Carbon Gap, GEO WGS84, 5 arc-minutes, global, raster (based on methodology for estimating Actual and Potential Soil Carbon Sequestration) Criteria: Low actual soil carbon and Medium to High Potential Soil Carbon Sequestration AGRICULTURAL PRODUCTION Input: GLC2000, GEO WGS84, 30 arc-second Output: Agricultural Production, GEO WGS, 5 arc-minutes, global, raster Criteria: select class 16, 17,18 from GLC2000 10
Preparation of Inputs: Avoided deforestation and low agricultural suitability AVOIDED DEFORESTATION Input: Avoided deforestation (vector) Output: Combined avoided deforestation, GEO WGS84, 5 arc-minutes, Central and Latin America, raster Criteria: Projected areas of deforestation 2000 2010 (cropland and pasture expansion) 11
Preparation of Inputs: Low agricultural suitability and biodiversity hotspots LOW AGRICULTURE SUITABILITY Input: combined suitability of Land and rainfed crop and pastures for intermediate inputs, Output: Low Agricultural Suitability, GEO WGS84, 5 arc-minutes, global, raster Criteria: Very low and low suitability areas (PSI=0 and CSI <20, PSI=1-10, CSI<20) BIODIVERSITY HOTSPOTS Input: Biodiversity Hotspots (vector) Output: Biodiversity Hotspots, 5 arc-minutes, global, raster Criteria: inland Biodiversity hotspots areas Database Citation Conservation International 2005. Biodiversity Hotspots: Species Database Downloaded from www.biodiversityhotspots.org on 18 March 2005 12
SOFA 2007: Carbon Gaps 13 Food and Agriculture Organization of United Nations
Potential to sequester additional carbon in Soils 14 Food and Agriculture Organization of United Nations
Potential to sequester additional carbon in Soils on croplands 15 Food and Agriculture Organization of United Nations
Biodiversity hotspots on croplands poorly suited to rainfed agriculture 16 Food and Agriculture Organization of United Nations
Biodiversity hotspots in areas with low agricultural suitability and high poverty rates 17 Food and Agriculture Organization of United Nations
Areas at risk of deforestation and Low agricultural suitability 18 Food and Agriculture Organization of United Nations
CarboAfrica (http://www.carboafrica.net) is a multi-partner project aiming at setting up GHG fluxes monitoring network of Africa, in order to quantify, understand and predict GHG emissions in Sub-Saharan Africa and its associated spatial and temporal variability. FAO and GTOS are both partners in the Carboafrica project. A full dataset is expected to be available by March 2010. FAO datasets Carboafrica will produce: CarboAfrica ecosystem-model based maps of ecosystem carbon and water balance components over Sub- Saharan African with associated uncertainties; map carbon sequestration and nutrient cycling in standing biomass according to forest management scenarios; analysis of land cover changes which can be useful for the assessment of deforested and/or degraded areas, and the related assessment 19 of lost biomass and emitted carbon.
FAO datasets CarboAfrica and Soil Analysis CarboAfrica is carrying out intensive seasonal measurements campaigns of soil CO 2, N 2 O and CH 4 fluxes. Some examples are: - study of the heterogeneity of soil properties and fluxes - soil rewetting experiment after drought - belowground biomass dynamics of savanna 20
FAO datasets CarboAfrica and Modeling Activities CarboAfrica is developing a complex modeling framework to generalize and upscale (to regional and continental scale) ecosystem level carbon observations by integrating local and spatial data into data- and process-oriented models together with land surface schemes of different complexity. Models of different complexity (LPJ-DGVM, JULES, ORCHIDEE, and LPJ-GUESS) are used. Uncertainty analysis: - the uncertainty of existing ongoing CO2 flux observations will be estimated - areas and conditions with large uncertainties will be identified Models and soil - model of soil C and N is being developed, considering soil moisture 21 and soil heterotrophic respiration.
Application of the IPCC guidelines for GHG reporting Mapping emissions from cropland and grasslands using FAO data IPCC guidelines can be used in conjunction with FAO datasets to derive maps of croplands and pasture emissions at a generalized level Datasets and Maps are in development for selected countries to calculate emissions from croplands, referring to the chapter 5 of IPCC guidelines: above ground woody biomass, below ground biomass, soil carbon The use of modeling incorporating remote sensing data is suggested for detecting the extent of pastures due to complexity and lack of data for Nat.derived agricultural statistics. By using FAO datasets IPCC guidelines can be used to derive maps of croplands emissions at a detailed (i.e. sub-national level) or a more generalized level (per country calculation at a regional or global scale) both for cropland and pastures. 22
Application of the IPCC guidelines for GHG reporting Available or under development FAO datasets Cropland Production, Extent, Yield, Land use Thermal regime global Suitability and occurrence of pasture global Net productivity for pasture and projected expansion of pasture Global Land Use Systems database 2008 Global Agro-Environmental Zoning (GAEZ) Length of growing period Harmonized world soil database Global Land Cover 2000/GLOBCOVER Global map of irrigated areas Land cover AFRICOVER Crop calendars GIEWS Corifa - Country rice facts FAO Rice information vol.3, 2002 Cropland sub national statistics Plant management statistics (IPNIS) Fertilizers statistics Digital soil map of the world SOTER Soil and terrain of Southern Africa 23
Using the national scale data available in FAO the following indicators at tier 1 can be calculated for cropland: above-ground biomass soil carbon methane from rice Application of the IPCC guidelines for GHG reporting In a following phase, results of this modeling can be used as a basis to calculate GHG emissions from pastures that can be used in global or regional scale calculations for the following list of indicators: above-ground biomass soil carbon non-co2 emission for burning from pastures National emission scenarios 24
Using the detailed sub-national scale data available in FAO and collaborating with selected countries, the following IPCC indicators for cropland remaining cropland at tier 1 or 2 can be calculated: above-ground biomass below-ground biomass soil carbon methane from rice Application of the IPCC guidelines for GHG reporting Sub-National emission scenarios 25
FAO datasets Global estimates of gaseous emissions of NH3, NO and N2O from agricultural land The publication Global estimates of gaseous emissions of NH3, NO and N2O from agricultural land (2001) provides a comprehensive review of the literature about emissions of NH3, N2O and NO, and examines the regulating factors, measurement techniques and models. It draws these data together and generates global emission estimates that can serve as a basis for further addressing the issues of efficiency and environmental impact. 26
FAO Potential of soil for climate change mitigation About 89% of the mitigation potential of agriculture could be achieved through soil C sequestration in developing countries. Soil C stocks can be increased and maintained through improved land use and management, aimed at either increase C fixed into the soil and reduce C losses. However, up to now soil C sink has not received much consideration in current GHG reduction policies. 27
FAO Measurement capabilities Soil C-contents can be measured with a high degree of accuracy with an instrument error even lower than 2% (depending on the different methods). There are equipment and protocols that have been applied for decades. The are models to predict soil C stocks that have been applied for more than 20 years. There are hundreds of long-term field experiments globally on soil C dynamics. 28
FAO Measurement challenges Soil C content is often high variable within an individual field. Annul changes are usually small respect to the C stock (even less than 1%), hence there is a low signal to noise ratio difficult to be detected over short time periods. There are few existing inventory measurement system for soil C. 29
FAO Integrated approach Therefore, there are many capabilities for reliable measurements of soil C, but the main concern is due to the choice of rigorous protocols and an efficient and replicable sampling design. The best option is an integrated use of field measurements and model based approaches, including also the development of new remote sensing capabilities. Aggregated field measurements provide the means to estimate uncertainty and correct for potential bias in the model-based estimates, and the models provide the capability to interpolate the results for varying climate and soil conditions and this capture the spatial heterogeneity. 30
FAO Requirements Set up and support re-measurable sampling locations, that can be precisely relocated, to reduce the influence of spatial variability. Establish a common data archive containing the widest global information and available for use Validate remote sensing tools with ground based methods for monitoring and verification of management practice implementation Establish a set of rigorous field and lab protocols applicable world wide. 31
The application of the 3 CA principles helps to increase soil C stock 1- Minimum soil disturbance (0-tillage + direct seeding) 2- Permanent soil cover 3- Crop Rotations or associations FAO Conservation agriculture (CA) and climate change mitigation 32
Zero Tillage Zero Tillage Food and Agriculture Organization of United Nations Conventional Agriculture CO 2 Conservation Agriculture low soil organic matter Soil Organic Matter = Drought Resistance Action of Soil Biota Structure/Porosity Biological Tillage Mechanical Tillage How does CA work? High Soil Organic Matter 33
Agriculture and Climate Change Agriculture mitigating climate change Globally 5 bill ha (5. 10 9 ) under crop and pasture (= 40% of total land) 20% of that in Africa Significant impact on climate change Potential C-capturing 0.25-2.5 bill t/year Additionally emission reductions 50-60% 34
Agriculture and Climate Change Agriculture mitigating climate change options for emission reduction: no-tillage farming: 60% reduction in fuel 20% reduction in fertilizer/pesticides 50% reduction in machinery C-sequestration 0.05-0.2 t. ha -1. y -1 no burning, no CO 2 release 35
Agriculture and Climate Change Agriculture mitigating climate change options for emission reduction: methane: aerobic rice cultivation change in livestock diet/stocks nitrous oxides: avoid soil compaction/water logging change in N-fertilizer management change in irrigation practice 36
Agriculture and Climate Change Conservation Agriculture Carbon Offset consultation (West Lafayette/Oct. 08): CA systems sequester carbon but: Protocols must contain the three CA principles Minimum amount of residues to return depend on climatic conditions Adequate nutrient levels necessary to facilitate soil-life and SOM build up 37
Thank you 38 Food and Agriculture Organization of United Nations