soil organic carbon map of mexico
TRANSCRIPT
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 [email protected]
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.