africa soil profiles database - johan leenaars

Africa Soil Profiles database

Johan Leenaars (March 22nd, 2012)

• Goal

• Results

• Workflow

• Accuracy

This presentation

Goal. Input to soil mapping

Goal

2 profiles per 1000 km2 (20 * 20 minutes)

(= 30 - 40,000)

Minimally : geo-referenced & GSM data

Goal

2 profiles per 1000 km2 (20 * 20 minutes)

(= 30 - 40,000)

Minimally : geo-referenced & GSM data

(to produce medium resolution soil property maps

as covariate for high resolution soil property mapping)

2010 baseline : 2770 profiles

Wise3

2 / 13000 km2

2010 incl. ISRIC sets : 4343 profiles

2 / 8000 km2

Excl. 2955 duplicates

2 / 8000 km2

2010 incl. ISRIC sets : 4343 profiles

2010 AfSP v1 : 7300 profiles

2 / 5000 km2


2 / 4000 km2


2 / 3000 km2


2 / 3000 km2

> 50,000 profile layers

Data density

AfSP

Data completeness

AfSP

Reconnaissance calculation:

Total OC stock = 100-200 Pg (10 ¹⁵)

Query of

preliminary versions

Query of


AfSP

Use of


AfSP

GSM showcase

Nigeria

(I. Odeh & H. Reuter)

Use of


AfSP

SOTER

Malawi

(K. Dijkshoorn)

Profiles representative for soil components, and WRB classified.

- Africa DSM modelling (M. Walsh & Jiehua)

- Malawi DSM showcase (T. Hengl)

- Data entry template GSP-FAO (R. Vargas)

- Data entry template University of Sydney working with CI, CM, GH, NG, SN (I. Odeh)

Use of


AfSP

AfSP Sharing of


World Soil Information Service

• Africa Soil Profiles database will be embedded into WoSIS database

• Data can be downloaded through WoSIS

• Additional data can be uploaded through WoSIS

AfSP Sharing

• Inventory

• Collect

• Analogue -> digital

• Process (collate, standardize, quality control)

• Serve

Compile

• Inventory

• Collect

• Analogue -> digital

• Process (collate, standardize, quality control)

• Serve

• Just do it !

Compile

• Lineage

- Datasets & reports

Compile Basic principles

• Lineage

- Datasets & reports (IP rights)

Compile Basic principles

• Lineage


• Soil observations & measurements

- Feature

- Attribute

- Method

- Value

Compile

• Lineage



- Feature (georeferenced profiles & layers)

- Attribute

- Method

- Value

Compile

• Lineage




- Attribute (x-y-z-t, map, class, site, layer-fld, layer-lab)

- Method

- Value

Compile

• Lineage





- Method

- Value

• Value standardization (Soter conventions)

- 1. Original data

- 2. Standardized data

- (3. Harmonized data / info)

Compile

• Lineage





- Method

- Value

• Value standardization (Soter conventions)

- 1. Original data - basic quality control

- 2. Standardized data - routine quality control

- (3. Harmonized data / info - full quality control)

Compile

Compile

1st record

Compile

Compile

http://library.wur.nl/WebQuery/isric/26971

Compile


OCR

Copy-paste

Compile

UTM36s

UTM34n Original profile site values

Compile

Standardized site values (according to eSOTER conventions)

WGS84 DD

Compile

http://library.wur.nl/WebQuery/isric/26971 Profile

point

location

Compile

Profile

point

location

map

Compile

Profile

point

location

description

Time inefficient and inaccurate (avg. accuracy = 0.03 DD)

Compile


OCR

Copy-paste

Profile

map unit

derived

location

Compile

Profile

map unit

derived

point

location

Compile

Profile

map unit

derived

location

domain

Not in AfSP

Compile

IRD

• Soil profile =

- Spatial point feature (AfSP)

- Spatial polygon feature (not in AfSP)

(no need to harmonize legend)

Compile

Quality review

Some sources of error

• Reporting of incorrect lab results

• Incorrect reporting of lab results

• Errors introduced during compiling process

Quality control

• Basic quality control, of source data upon entry

• Routine quality control, of collated data

• Full quality control, of in-pedon consistency

Quality control

• Basic quality control, of source data upon entry

- Original values and expressions

• Routine quality control, of collated data

- Standardized values assessed by various methods

• criteria applied in WoSIS enterprise environment

• Full quality control, of in-pedon consistency

- Harmonized values as if assessed by single method

• requires stratified and complete data

• criteria applied in WoSIS enterprise environment

Routine quality control

• Single attribute rules:

0 < OC < 1000 ‰

0 < clay < 100 %

0 < CEC < 150 cmol/kg

2 < pH < 11


pH H2O

pH KCl (& CaCl2)


0 < OC < 1000 ‰

0 < clay < 100 %


2 < pH < 11

• Multiple attribute rules:

4 < C/N < 45

Sand +silt +clay = 100%

(CEC OC + CEC clay) = CEC

pH H2O > pH KCl (& CaCl2), except geric


pH H2O

pH KCl (& CaCl2)


0 < OC < 1000 ‰

0 < clay < 100 %


2 < pH < 11

• Multiple attribute rules:

4 < C/N < 45

Sand +silt +clay = 100%

(CEC OC + CEC clay) = CEC

pH H2O > pH KCl (& CaCl2), except geric

What to do with inconsistencies ?


• Multiple attribute rules

CEC = f (org C, clay / content, nature)

clay

OC

CEC CEC

CEC

CEC

Prep to full quality control

Harmonize

Eff. Base Sat. ≈ f (pHH2O)

BSat

pH pH H2O pH H2O

BSat eBSat

eCEC

CEC pHH2O

pHCaCl2 pHKCl

pHH2O

-> Reduce variance ->

• Legacy soil data quality varies

Accuracy of final product


• An accurate final product (Africa soil property maps) requires evidence; both quality ánd quantity



• An accurate final product (Africa soil property maps) requires evidence; both quality ánd quantity


Soil data quality is use specific

Soil data quantity informs regression with covariates

Resolution !

Distribution !


100 * 100 km

Resolution !

Data quality is use specific

10 * 10 km

1 * 1 km


10 * 10 km

100 * 100 km

1 * 1 km

Resolution !



new soil data

Resolution !

0.1 * 0.1 km

10 * 10 km

100 * 100 km

1 * 1 km



new soil data

0.1 * 0.1 km

10 * 10 km

100 * 100 km

1 * 1 km



legacy soil data

10 * 10 km

100 * 100 km

1 * 1 km

Distribution !


Accurate evidence-based Africa soil map,

• at low-medium resolution

- Attainable with many legacy soil data, of varying quality

- Nót attainable with few new soil data, of constant quality


Accurate evidence-based Africa soil map,

• at low-medium resolution

- Attainable with many legacy soil data, of varying quality

- Nót attainable with few new soil data, of constant quality

= covariate for:

• at high resolution

- Attainable with many legacy data plus few new data

- Attainable with many new data (very EXPENSIVE)



Profiles

Evidence

cells

Legacy data

10 * 10 km

cells informed

Profiles

New data

(cells informed / M $)

60

Accuracy of final product Evidence

Observations

• Legacy soil data are compiled very cost efficiently compiled, but not cost free (man months)

• Limiting factor is capacity (man months)

• An efficient way to effectively acquire and compile digital soil profile data, in quantities required to map Sub Saharan Africa, is to allocate facilitative resources to increase focused capacity

- Capacity in house

- Capacity out house (actively involve dataholders)

Next steps

• AfSP 3 release (report)

• AfSP 4 in WoSIS (add profiles) - Datasets (priority)

- Entry

• In house : focused steady manual entry

• Out house : partnering with data holders / GSP manual entry

• Out house: web crawl / semi automated manual entry

• Out house : crowd sourced manual entry

• AfSP – AfSM (link profiles to map-units) • Derive soil data from maps (requires data standardisation)

• Derive covariate from maps (requires legend harmonisation)

Thank you

africa soil profiles database - johan leenaars

Education