gefsoc project

Eleanor MilneOn behalf of The GEFSOC Project Team

GEFSOC Project

Towards a generic system for estimating soil carbon stocks and changes at the

regional and national scale

Project Aim

To develop generic tools which quantify the potential impact of land use/managementand climate scenarios on carbon sequestration in soils at national/regional level

• The University of Reading U.K. (Coordinating Institution)• Universidade de Sao Paulo, Brazil

• Higher Council for Research and Technology / Badia Research and Development, Jordan

• Kenya Soil Survey, Kenya

• National Bureau of Soil Survey and Landuse Planning, India

• International Institute for Applied Systems Analysis, Austria

• ISRIC – World Soils Information, The Netherlands

• Colorado State University, U.S.A.

• Institut de recherche pour le developpement, France

• Rothamsted Experimental Station, U.K.

• The Hadley Centre, U.K.

• UNEP (GEF Implementing Agency)

Partners

Why assess SOC stocks and fluxes?

PROTECT

DEPLETE

INCREASESource WRI 2001

Kyoto allows ‘carbon emissions to be offset by demonstrable removal of C from the atmosphere’

•Article 3.3 afforestation, reforestation

•Article 3.4 improved management of agricultural soils

‘Verifiable changes in carbon stocks’

‘Transparent and verifiable manner’

‘Achievement of real, measurable benefits’

Why assess SOC stocks and changes?

Totals = 120 Experiments, 31 Models

Europe:86 Experiments20 Models

N. America:10 Experiments7 Models

S. America:3 Experiments

Australasia:8 Experiments3 Models

Asia:10 Experiments1 Model

Africa:3 Experiments

Problem of distribution

Problem of scale

Plot Scale

Regional Scale

National Scale

Most studies

Few studies

Very few studies

1. Regression based approaches (Gupta and Rao 1994, Smith et al 2000, 2001)

2. Regression approaches based on spatial soil databases (Kern and Johnson 1993, Kotto-Same et al 1997)

Project Approach

Approaches used estimate changes in SOM/SOC include

Assume constant rates of change

Project Approach

Spatial DatabasesSimulation

model

ActiveSOM

SlowSOM

PassiveSOM

Residues

PlantGrowth

CO2 CO2CO2

CO2

CO2

Spatial Results

3. Dynamic SOM models linked to spatial data bases

*This approach is dynamic*

Soil C Models

Roth C

Century

Two of the most widely used SOM models

Evaluated under many conditions (including forestry, grasslands and arable in the tropics)

Top performance in comparison of 9 models (Powlson et al 1996, Smith et al 1997)

Used in regional applications

Model GIS linkage

Roth C SOC model

Century ecosystem model – plant productivity sub-model

Case Studies

JordanBrazilian

Amazon

Kenya Indogangetic Plains, India

The Brazilian Amazon Case Study

Area: The Brazilian Amazon over 5 million km2

Terrain: The Amazon Basin

Climate: Up to 3000mm/yr rain

Soils: Oxisols, Ultisols and Alfisols

Major land use change: Forest to pasture, increasingly forest and pasture to crops

The Brazilian Amazon Case Study

Moraes et al (1995) 47 Pg top 100cm

21 Pg top 20cm

Cerri et al (2000) 41 Pg top 100cm

23.4 Pg top 30cm

Bernoux et al (2002)

22.7Pg in the top 30 cm

The Indo-gangetic Plains Case Study

Area: The Indian Indo-gangetic Plains, 650,000 km2

Terrain: Flat alluvial plains

Climate: Arid- humid. 300mm – 1600mm increasing west to east

Soils: Entisols, Inceptisols and Alfisols

National Bureau of Soil Survey and Land Use Planning

Indogangetic Plains, India

Landuse change: Changes in crops and cropping practices

The Indo-gangetic Plains Case Study

Jenny and Raychaudhuri (1960)

Gupta and Rao (1994) All India 24.3 Pg Sub soil depth 44-186cm

Velayuthum et al (2000) All India 20.99 Pg top 30cm, 63.19 Pg top 150cm

Bhattacharyya et al (2000) SOC stock in The Great Plains 3.28 Pg top 30cm, 10.53Pg top 150 cm

Bhattacharyya et al (2003) SOC stock in IGP 0.63 Pg in top 0-30cm 2Pg top 150cm

The Kenya Case Study

Kenya Soil Survey

Kenya

Area: The whole of Kenya, 582,646 km2

Terrain: Coastal and eastern plains, the central and western highlands, the Rift Valley Basin and the Lake Victoria Basin

Climate: 150 (arid) to humid 2500 mm/yr

Soils: Entisols, Alfisols, Oxisols, Ultisols and Alfisols

Jordan Case Study

HCST

Area: Whole of The Hashemite Kingdom of Jordan (89,206 km2)

Terrain: Elevation -392m –1754m

Climate: Subhumid (~600 mm) to hyperarid (<50 mm/yr). 80% desert (<200mm/yr).

Soils: Inceptisols, Vertisols, Aridisols, Entisols

Methodology

COMPAREESTIMATE CURRENT SOC STOCKS

QUANTIFY THE IMPACT OF LAND USE CHANGES ON C SEQUESTRATION IN SOIL

ASSIST POLICY FORMULATION

EXISTING TECHNIQUES

IDENTIFY LONG TERM EXPERIMENTAL

DATA SETS

SOIL C MODELS

DEVELOP COUPLED MODELLING/GIS TOOLS

GIS

SOILS

CLIMATE

LANDUSE

COLLATE & FORMAT NATIONAL SCALE

DATASETS

Model Evaluation




EXISTING TECHNIQUES


DATA SETS

SOIL C MODELS


GIS

SOILS

CLIMATE

LANDUSE


DATASETS

% SOC

Sample depth

BD

IGP Case Study

Fertiliser and Compost Trials from the ‘All India Coordinated Research Project’ Swarup (2000)

Location of LTEs in the Indo gangetic plains

Farmer with potatoes, West Bengal

Source: Singh et al (2004) report by NBSS&LUP prepared for the GEFSOC Project

IGP Case Study

Source: Singh et al (2004) report by NBSS&LUP prepared for the GEFSOC Project

• Majority of land use change - forest to pasture

• Datasets from long-term experiments: lacking

• Important: obtain time-series of soil C measurements

• Strategy: to collate information on as many land use chronosequences as possible, to cover the range of soils and climate

Brazilian Amazon Case Study

15 yrs8 yrs3 yrsPrimary forest

Age of pasture

A) Models were run for 7,000 years to reach equilibrium

B) Models were run to the period of 100 years of pasture


Century RothC

Faz. BosqueState: Pará

Lat. 2º 48' Long. 47º 20'

Soil type: Yellow Latosol(Oxisol)

Grass: B. briz, P. max, B. hum

Temp: 27.2 oC Precip: 1750 mm

Soil texture: Very clayey

Chronosequence:

Forest Pasture 4 yearsPasture 6 yearsPasture 10 yearsPasture 12 yearsPasture 15 yearsPasture 17 years


Faz. Bosque


0

20

40

60

80

100

0 5 10 15 20

Pasture age (yr)

Tota

l Soi

l C (M

g ha

-1)

0

2000

4000

6000

8000

10000

0 5 10 15 20

Pasture age (yr)

Tota

l Soi

l C (g

m-2

)

RothC

Century

TeixeiraState: Amazonas

Lat. 2o 30' Long. 60o 01'

Soil type: Yellow Latosol (Oxisol)

Grass: B. humidicula

Temp: 26.7 oC Precip: 2075 mm

Soil texture: Very clayey

Chronosequence:

Forest Pasture 1 yearPasture 2 yearsPasture 6 yearsPasture 7 yearsPasture 8 years


Teixeira


0

1000

2000

3000

4000

5000

6000

7000

8000

0 5 10 15

Pasture age (yr)

Tota

l Soi

l C (g

m-2

)

0

10

20

30

40

50

60

70

80

0 5 10 15

Pasture age (yr)

Tota

l Soi

l C (M

g ha

-1)

RothC

Century

EVALUATING THE MODELING PROCEDURES(using all sites within the eleven chronosequences)

Model r CD CRM RMSE M n% g m-2

Century 0.91 0.98 0.027 16.19 88.04 73

RothC 0.88 1.07 -0.008 17.28 -0.24 51

Perfect fit 1.00 1.00 0.00 0.00 0.00

r: sample correlation coefficient, CD: coefficient of determination, CRM: coefficient of residual mass; RMSE: root mean square error, M: mean difference between observations and simulation.

Statistical tests


Other Case Studies

Kenya LTEs

-KARI 25 yr crop study

-Manure and Fertiliser trials 14 yrs

Jordan

-LTE from Syria

-Time series data from NCARTT

National Data




EXISTING TECHNIQUES


DATA SETS

SOIL C MODELS


GIS

SOILS

CLIMATE

LANDUSE


DATASETS

Soils - SOTER

• SOTER: World Soils and Terrain Database

• Soil Map of the World is now largely outdated

Each SOTER unit is an area of land with a distinctive, and often repetitive, pattern of:

•landform•surface form•parent material•slope•soils

SOTER databases:–Brazil –Kenya–Jordan –India SOTER under prod.

Missing data?1.Collate additional measured data

2.Expert knowledge and common sense

3.Scheme of taxotransfer rules

Soils - SOTER

Batjes N.H. (2003) A taxotransfer rule based approach for filling gaps in measured soil data in primary SOTER databases GEFSOC Project Report/ Rep 2003/03 ISRIC – World Soil Information, Wageningen

Gaps in soil attributes needed for modelling e.g. BD

Case study countries

WISE

Soils - SOTER

National Data




EXISTING TECHNIQUES


DATA SETS

SOIL C MODELS


GIS

SOILS

CLIMATE

LANDUSE


DATASETS

•Pluviometric stations

CAMREX, ABRACOS

Model/GIS coupling




EXISTING TECHNIQUES


DATA SETS

SOIL C MODELS


GIS

SOILS

CLIMATE

LANDUSE


DATASETS

Scenario Development




EXISTING TECHNIQUES


DATA SETS

SOIL C MODELS


GIS

SOILS

CLIMATE

LANDUSE


DATASETS

DEVELOP LAND USE BASELINE AND CHANGE SCENARIOS

IIASAIIASA

Stage 1. – The production of spatially explicit land use/management information for a base year (2000)

Stage 2. – Projections of land use changes for the year 2030

Scenario Development

Accounting of soil carbon stocks

IIASAIIASA

Geographic Datasets for Down-scaling• Population

LandScan 2001, CIESIN• Terrain

GTOPO30, SOTER• Climate

climate grids (Tx, Tn, Pr, Sf, Wi, RH) • Land Cover

IFPRI/FAO land cover, continuous 4 typesGLC 2000 Kassel/FAO irrigation shareProtected areas

• Transportation infrastructure• AEZ Suitability (modeled)

crops, pastures, trees; NPP• Farming systems

Stage 1. Base year 2000

Site and regional scale results for up-scalingprovided by the case studies

Stage 1. Base year (2000)

Eg. Kenya - production data from the agricultural census documents (put into electronic format by KSS)

Eg. Brazil - digital vegetation maps from IGBE, management practices from statistical bulletins

Down-scaling of global projections/model output

Stage 2. Projections to 2030

•Population projections

•Urbanization level

•Land use: FAO AT2015/30Cultivated, irrigation, multi-cropping indexProduction, Harvested areas(all major crops and livestock)Fertilizer use Consumption, Trade

•Climate change

Incorporate expert knowledge of local

production systems andregional land use plans

The Final Output

•Comparable georeferenced data sets of soils climate and landuse

•Comparable estimates of current SOC stocks

•Land use change scenarios for 2000 – 2030

•Comparable estimates of SOC change under these scenarios

A transferable system for estimating SOC A transferable system for estimating SOC stocks and changes in a range of soil and stocks and changes in a range of soil and

climatic conditionclimatic condition

For each case study area:

GEFSOC Project

www.reading.ac.uk/GEFSOC

Funding

The Global Environment Facility

The United Nations Environment Programme

gefsoc project

Documents