soil organic carbon map of mexico

In Mexico, modern soil survey started 48 years ago.

62,000 sites with data carbon.

Sampling period: 1969-2014Distance (average): 5.06 kmNational cover: 93.6%

Sampling period: 2014-2016Distance (average): 14.7 km National cover: 58,8%Directed sampling. INEGI.

Sistematic sampling. CONAFOR

Objective

Concentrations, Stocks, Emissions, Dynamic of change and Uncertainties

Histosoles de Mixquic: 600 Mg C ha-1 (30cm profundidad)

National Net Loss 2016= 1.87 Tg.y-1

0.03% national stockUi = 59%

Degr

adac

ión

Defo

rest

ació

n

803 A ha-1 62.5 tonC ha-1

458 A ha-1 40.1 tonC ha-1

278 A ha-1 20.2 tonC ha-1

82 A ha-1 12.1 tonC ha-1

45 A ha-1 8.5 tonC ha-1

8 A ha-1 0.2 tonC ha-1

Symbology for a closed primary forest transition at the mesa central of Mexico: BQc (Bosque de encino cerrado), VSAc/BQ (Vegetación secundaria arbórea de bosque de encino), VSac/BQ (Vegetación secundaria arbustiva cerrada de bosque de encino), VSaa/BQ (Vegetación secundaria arbustiva abierta de bosque de encino), VSha/ BQ (Vegetación secundaria herbácea de bosque de encino). VSaa/MC (Vegetación secundaria arbustiva de matorral crasicaule). VSaa/ BJ (Vegetación secundaria arbustiva abierta de bosque de táscate). VSaa/BPQ (Vegetación secundaria arbustiva abierta de bosque de pinoencino). VSaa/SBC (Vegetación secundaria arbustiva abierta de selva baja caducifolia). PNa-b (Pastizal natural abierto o muy abierto). PNd (Pastizal natural extremadamente abierto). TA (Agricultura de temporal). R (Agricultura de riego). PIa-b (Pastizal inducido abierto o muy abierto). PId (Pastizal inducido extremadamente abierto). Erosión (Áreas desprovistas de vegetación o con evidencias de erosión superficial fuerte o extrema).

A ha-1 Trees per hectare.

tonC ha-1 Tons of soil organic carbon per hectare.

Note: The thickness of the lines represents the frequency of change.

Degr

adac

ión

Defo

rest

ació

n

Reduction of uncertainties

+ Improving the sampling design.

+ Reducing the heterogeneity between the field and laboratory protocols.

+ Correcting errors during the process of spatial propagation.

100 m

2 m

FIRST SAMPLINGSistematic. Central Cylinder 4” x 30cm. Auxiliar augers 1” x 0-30, 30-60cm.The objective is to know the behavior of the organic carbon across very small study areas. Profile Cylinder Auger

4 m

11 m

400 m2

Landscape B

Landscape C

Landscape D

Landscape E Landscape Fcross section

Landscape A(representative)

100 m

2 m

SECOND SAMPLINGSoil profile 1.5 m width. uses soil profiles. Samples of genetic horizon. Representative. The object of this sampling is to calibrate the systematic errors of both the national grid and the two depth ranges of sampling. Profiles Cylinder Auger

Landscape B

Landscape C

Landscape D

Landscape E Landscape Fcross section

Landscape A(representative)

400m2

100 m

2 m

THIRD SAMPLINGA net of locations is used for a rapid data acquisition. The object is to densify the most important variables related to organic carbon in the first thirty centimeters of depth. Cilindro

Landscape A(representative)

Landscape B

Landscape C

Landscape D

Landscape E Landscape Fcross section

100 m

2 m

INTEGRAL SAMPLINGDensification and representativeness. Uncertainty below 40%.

Profiles Cylinder Auger

Landscape A(representative)

Landscape B

Landscape C

Landscape D

Landscape E Landscape Fcross section

400m2

Mineral layer

Fibric Layer

Sapric Layer

Stocks of SOCSapric-Hemic 0.61 PgHemic-Fibric 0.35 Pg

Soil Organic Carbon in the layer Sapric-Hemic

We harmonized all the methods needed to quantify organic carbon.

1) Sample preparation

2) Bul density: coarse, fine and organic fraction.

3) Total Carbon, Shimadzu, TOC 5000A/5050.

4) Organic matter. Dry combustion (LOI).

5) Carbon Spectroscopy by NIR and chemometric.

6) Carbon and Nitrogen by Total Analyzer (LECO).

7) Carbon and Nitrogen by Elemental Analyzer Flash 2000-N-C Soils Analyzer.

8) Total Nitrogen by digested Amonium.

QUALITY CONTROL IN ORGANIC CARBON DATADistance, Azimut, Tree frecuency, Normal diameter, Crown.

QUALITY CONTROL IN ORGANIC CARBON DATADistance, Azimut, Tree frecuency, Normal diameter, Crown.

Map of the processes of organic carbon

Physiography/Geology

Microclimate

Genesis-Morphology

Landuse changes

Soil Organic Carbon

1:100,000

1:20,000

1:50,000

1:250,000

Microrelief 1:1,000

Initial agreed value of uncertainty

Soil Organic Carbon Mapadvantages

1) They show a large quantity of data in an adequate spatial distribution.

2) We have a quality control in all processes - field, laboratory and propagation of data.

3) Our values of concentrations and stocks of organic carbon are reasonable precision.

4) Since 2016, we have a permanent control of uncertainties.

5) The map represents in a highly detailed manner the losses and gains of organic.

2007

Basic codes of change

1 Loss due to deforestation

2 Loss due to degradation

3 Gain due to recuperation

4 Other change

There is now better detail in the delimitation of erosion and deforestation. In the medium term Mexico will remarkably develop its carbon gains-loss estimates from this type of mapping.

Minatitlán, Veracruz

2014

Minatitlán, Veracruz

Basic codes of change

1 Loss due to deforestation

2 Loss due to degradation

3 Gain due to recuperation

4 Other change

There is now better detail in the delimitation of erosion and deforestation. In the medium term Mexico will remarkably develop its carbon gains-loss estimates from this type of mapping.

Loss Carbon due to deforestation in Cenotillo, Yucatán

Loss Carbon due to deforestation in Cenotillo, Yucatán

NASA rightNASA wrong

Aparent change Real change

Deforested area NASA, 2014

Recovered area NASA, 2014

Change PNUD,

2014

2007

2014

Source: UDEL

Source: UDEL

Know

ledg

e

Tecnology

InteroperabilityConceptuals, Cartographics and Reference Bases

Teledeteccion, Learning Machine, Bigdata

Soil indicators

GEICARBON

DEGRADATION

BIODIVERSITY

EDUCATION

FERTILITY

DROUGHT

National System of Information and Monitoring Soils

(SNIMS)

Erosion

Desert

Contam

Concent

Stocks

Emission

Biomasa

Salud

CartDigital

Publicac

Cursos

Mitigac

Evaluac

Erosion rate (ton.ha-1.a-1)

Bacterian Biomass Coef (g.mm-2)

Emmision rate CO2 (ton.ha-1.a-1

)

COSM factor(ton.ha-1

)

Edafic Drought Vulnerability Index(0.0-1.0)

Desertificatión index (0.0-1.0)

COSM factor(%)

C:N Quality factor

Toxicity index (0.0-1.0)

Drought Frecuence Index (DFI)0.0-1.0

Stability ped(Rd/Rnd)

Productivity index(0.0-1.0)

Productividad

Sostenibilidad

QuimiometríaNational model

GEICARBON

DEGRADATION

BIODIVERSITY

EDUCATION

FERTILITY

DROUGHT

National System of Information and Monitoring Soils

(SNIMS)

Doubts and suggestionsomar.cruz@inegi.org.mx

México. November, 2016.

Báez Aurelio INIFAP. GuanajuatoBarbosa Paulo JRC-EUROCLIMA. ItaliaCarrao Hugo JRC-EUROCLIMA. ItaliaCarrillo Oswaldo FAO. MéxicoCruz Carlos Omar INEGI. AguascalientesCueto José INIFAP. DurangoCuevas Rosa REDLABs. Ciudad de MéxicoEtchevers Jorge COLPOS. MéxicoGarcía Samuel CONAFOR. JaliscoGonzález Irma INIFAP. NayaritGonzález René FAO. MéxicoGuerrero Armando COLPOS. Tabasco

Hidalgo Claudia COLPOS. MéxicoJarquín Aarón ECOSUR. TabascoLeyva Juan CONAFOR. JaliscoMario Guevara UDEL. USA.Martínez Magarita INEGI. AguascalientesMichelle Jose Ma FAO. MéxicoMorfín Jorge PNUD-REDD. México.Padilla Juliana COLPOS. MéxicoSaynes Vinisa COLPOS. México.Sosa Isabel REDLABs. México.Vargas Rodrigo UDEL. USA.Vargas Ronald FAO-GSP. Italia.

Interdisciplinary Work Team

Thanks to the participation of more than 30 experts in digitalization and image interpretation, as well as the conceptual support of Dr. Juan Gallardo Lancho, Dr. Juan José Ibañez and Dr. Peter Schad and the motivational impulse of Eng. Jesús Carrasco Gómez, Eng. Enrique Serrano Gálvez, Biol. José Luis Ornelas de Anda, Eng. Francisco Jiménez Nava, Dr. Rainer Baritz and Dr. Luca Montanarella.