SOILS INVENTORIES

We can consider two basic approaches:

A. Samples points extracted from a population of points.B. Mapping.

For the first an example (at european level) is the “IntensiveMonitoring of Forest Ecosystems in Europe” (formerly ICPForests).For the second an example is the “Soils Geographical Databaseof Europe” briefly presented at the beginning of this session.

The two approaches do not conflict, but aims at different targets.

I will describe and briefly analyze some of the problems encounteredduring the activities of an experts group charged of the so-calledHARMONIZATION for SGDB Europe scale 1 M all around ALPS;

in other words the homogenisation of national databases, producedseparately in different times by France, Switzerland, Austria, Slovenia

and Italy.

The first and the main task was centered on the transboundaryharmonization of the geographic database (i.e. Maps) along the

national borders (the so-called 50 km buffer zone), but this exerciserequested also a coordination of SMU (Soil Mapping Units), both as

polygons on the map as from the point of view of contents.Mapping Units was characterized by combinations of different STU

(Soil Typological Units), defined according an array of variables(attributes specifically devised by the european dataset) and by aSoil Name (FAO-Unesco 1990 revised Legend and FAO-Unesco

1974 Legend).

A graphical example of the maps used as starting point.This was the situation between Slovenia and Italy:

parts of Alps, down to south around Trieste (Carso and Istria).

A better example can be this:The situation at the border between Switzerland and Italy.

All the data bases was stored using Arc/Info GIS, but here I show you a photograph of the map base,because at that time (and now too) I preferred an handicraft approach to mapping (I consider myself a

craftsman in soil mapping); other coworkers will transfer marks and maps on an electronic support,but operation start in the brain, and basic concepts and choices pertain to human brain, not to machines.

I selected a sample area (window) for presenting and discussing theproblems connected to the exercise of harmonization.

The reasons of this selection are not very important, but will beclarified during the presentation.

In the next slide you will see a magnification of the area.

The analysis about major problems that can arise during an exerciseof harmonization will be conducted using LOGICAL RULES

IF THE DATA, OPTION OR CHOICE ARE CONGRUENT TOTHE RULE, THE PROCESS CAN GO ON

(i.e. THE RESULTS WILL BE A SHARED INFORMATION)

IF THE CHOICE IS A VIOLATION OF THE RULE,THE PROCESS MUST BE STOPPED, OTHERWISE THE RESULTS

WILL BE A MISLEADING INFORMATION = MISINFORMATION

SMU STU PC SOIL SOIL90 Clear NAME (FAO '90) TS1 TS2 S1 S2 A1 A2 MA1 MA2 ZMIN ZMAX U1 U2 DT TD1 TD2 RO IL WRW1 W2 W3 CFL Original NAME

10 10 100 Uk LPu Umbric Leptosol 2 3 2 4 3 4 212 500 1000 1 3 2 2 4 2 1 2 2 0 0 M Sol humocarbonaté

11 11 100 Bh CMdu Dystric [Humic] Cambisol 1 2 1 4 2 3 112 110 300 900 7 6 5 1 2 1 1 1 1 2 8 M Sol brun acide trés humifer

12 12 100 Dd PDdu Dystric [Humic] Podzoluvisol 1 2 3 4 3 1 713 500 1000 3 4 5 1 2 1 1 1 2 0 0 M Sol ocre-podzolique trés humifer

13 13 100 Qc CMda Dystric [Albic ?] Cambisol 1 2 1 0 2 1 110 200 300 1 0 5 1 2 1 1 1 1 2 0 M Sol brun acide sableux

14 14 100 H PHh Haplic Phaeozem 1 2 2 4 0 0 130 210 500 1000 7 1 5 1 2 1 1 1 1 2 8 H Pheaozem

15 15 100 Jc FLc Calcaric Fluvisol 1 3 1 0 2 1 110 400 650 12 6 5 1 3 1 1 1 1 2 8 H Fluvisol brut

16 16 100 P PZh Halpic Podzol 1 2 2 4 3 4 700 1300 2000 1 5 5 1 2 1 1 3 2 0 0 M Podzol

17 17 100 Dd PDd Dystric Podzoluvisol 1 2 2 4 2 3 700 900 1300 1800 1 5 5 1 2 1 1 3 2 0 0 M Sol ocre-podzolique

18 18 100 Bm LPm Mollic Leptosol 2 1 2 1 3 4 900 200 1500 2000 1 5 5 2 1 1 1 3 2 0 0 L Sol brun riche en mull

19 19 100 Ud LPd Umbric Leptosol 1 2 2 1 2 3 700 900 1500 2900 19 0 0 0 0 1 0 3 0 0 0 L Sol humo-silicaté

20 20 100 G GL Gleysol 2 4 1 2 21 31 110 400 500 1 0 0 2 4 1 1 3 1 1 1 H Gley

22 22 100 Gh GLdn(?) Dystric [Sodic ?] Gleysol 4 5 2 1 0 0 313 232 800 2000 1 0 0 4 5 0 2 3 1 1 2 H Gley pélitique

23 23 100 I LPi Gelic Leptosol 1 0 4 0 4 9 700 200 2900 3000 0 0 0 0 0 0 0 0 0 0 0 H Régosol litique

SMU STUPCSMUSOIL SOIL90 Clear NAME (FAO '90) TS1 TS2 S1 S2 A1 A2 MAT1MAT2 ZMIN ZMAX U1 U2 DT TD1 TD2 RO IL WRW1 W2 W3 CFL

266 878 40 Id LPd Dystric Leptosol 2 0 3 0 4 1 730 800 800 2600 1 5 5 0 0 3 1 1 2 0 0 V

879 35 U LPu Umbric Leptosol 2 0 3 0 4 1 730 800 800 2600 1 5 5 0 0 3 1 1 2 0 0 V

880 15 L LV Luvisol 2 0 3 0 4 1 730 800 800 2600 1 5 5 0 0 1 1 1 2 0 0 V

874 5 Bd CMd Dystric Cambisol 2 0 3 0 4 1 730 800 800 2600 1 5 5 0 0 1 1 1 2 0 0 V

881 5 Bh CMu Humic Cambisol 2 0 3 0 4 1 730 800 800 2600 1 5 5 0 0 1 1 2 2 0 0 V

267 882 30 Id LPd Dystric Leptosol 2 0 2 0 3 1 800 730 0 2600 1 0 5 0 0 3 3 2 2 0 0 V

883 20 Ie LPe Eutric Leptosol 2 0 2 0 3 1 800 730 0 2600 1 0 5 0 0 3 3 2 2 0 0 V

884 20 Ic LPe Eutric Leptosol 2 0 2 0 3 1 800 730 0 2600 1 0 5 0 0 3 3 2 2 0 0 V

885 20 r r rocks 2 0 2 0 3 1 800 730 0 2600 1 0 0 0 0 0 0 0 0 0 0 V

886 5 U LPu Umbric Leptosol 2 0 2 0 3 1 800 730 0 2600 1 0 5 0 0 3 3 2 2 0 0 V

887 5 E LPk Rendzic Leptosol 2 0 2 0 3 1 800 730 0 2600 1 0 5 0 0 3 3 2 2 0 0 V

268 888 50 E LPk Rendzic Leptosol 2 0 4 0 4 1 200 250 500 2500 1 0 5 0 0 3 1 1 2 0 0 V

889 30 Ic LPe Eutric Leptosol 3 0 4 0 4 1 200 250 500 2500 1 0 5 0 0 3 1 1 2 0 0 V

890 10 Be CMe Eutric Cambisol 2 0 4 0 4 1 200 250 500 2500 1 0 5 0 0 1 1 1 2 0 0 V

891 10 Bec CMc Calcaric Cambisol 2 0 4 0 4 1 200 250 500 2500 1 0 5 0 0 1 1 1 2 0 0 V

272 905 45 Be CMe Calcaric Cambisol 2 0 3 0 4 1 200 250 50 1600 5 3 5 0 0 1 1 2 2 0 0 V

906 30 Lo LVh Haplic Luvisol 3 0 3 0 4 1 200 250 50 1600 5 3 2 2 3 1 1 2 1 1 0 V

907 25 E LPk Rendzic Leptosol 2 0 3 0 4 1 200 250 50 1600 5 3 5 0 0 3 3 1 2 0 0 V

282 948 50 Bd CMd Dystric Cambisol 1 0 3 0 3 1 730 450 700 1500 5 1 5 0 0 1 1 1 2 0 0 V

949 35 Pl PZb Cambic Podzol 1 0 3 0 3 1 730 450 700 1500 5 1 5 0 0 4 3 1 2 0 0 V

950 8 U LPu Umbric Leptosol 1 0 3 0 3 1 730 450 700 1500 5 1 5 0 0 1 2 1 2 0 0 V

951 7 Lo LVh Haplic Luvisol 1 0 3 0 3 1 730 450 700 1500 5 1 5 0 0 1 1 2 2 0 0 V

283 952 50 Bd CMd Dystric Cambisol 2 0 3 0 4 1 450 800 300 1500 5 1 5 0 0 1 1 1 2 0 0 V

953 30 Be CMe Calcaric Cambisol 2 0 3 0 4 1 450 800 300 1500 5 1 5 0 0 1 1 1 2 0 0 V

955 10 Lo LVh Haplic Luvisol 2 0 3 0 4 1 450 800 300 1500 5 1 5 0 0 1 1 2 2 0 0 V

947 5 Id LPd Dystric Leptosol 2 0 3 0 4 1 450 800 300 1500 5 1 5 0 0 1 1 1 2 0 0 V

954 5 Ie LPe Eutric Leptosol 2 0 3 0 4 1 450 800 300 1500 5 1 5 0 0 1 1 1 2 0 0 V

294 995 40 Pl PZb Cambic Podzol 1 0 4 0 4 1 730 800 900 2200 5 0 5 0 0 4 3 1 2 0 0 V

996 30 Po PZh Halpic Podzol 1 0 4 0 4 1 730 800 900 2200 5 0 2 1 2 1 1 2 2 0 0 V

998 20 Bd CMd Dystric Cambisol 1 0 4 0 4 1 730 800 900 2200 5 0 5 0 0 1 1 2 2 0 0 V

999 10 Id LPd Dystric Leptosol 1 0 4 0 4 1 730 800 900 2200 5 0 5 0 0 3 3 1 2 0 0 V

SWITZERLAND Code number 41

ITALY

Code number 39

FIRST LOGICAL RULEThe landscapes present in the area are not very dissimilar.The valley systems north of the Lakes Region are arranged accordinga similar pattern; the Delta Elevation do not change greatly in IT andSZ; nor the climatic factors; bedrock are mainly metamorphic; therecent geological history (i.e. glaciation and deglaciation events) canbe presumed quite homogeneously distributed.

QUESTION: This similarity (geometric arrangement or SMU pattern) isrecognizable on the map ?

NO

DECISION: If you want a SHARED INFORMATION rearrange thepattern, i.e. redrawn the geometric data set.This decision can be applied only to the part of the window interestedby the aforeside description.

Let me open a brief digression.The origin of the basic geometric data sets presented for Italy andSwitzerland must be searched in remote times: For Switzerland the digitized base was derived from the Atlas de la Suisse(scale 1:500.000, 2nd edition, 1984; elaborated in the Inst. de CartografieEcole Polytech. Fédéral de Zurich, by Prof. Frei & Peyer and coworkers).For Italy the digitized base was derived from the Soil Map of Italy (originalscale 1:1M, 1966; prepared by Prof. Mancini and coworkers, with changesand subsequent modifications in 1984 and following years).

The basic concepts used by the different authors were not very dissimilar.

Mapping units, types of soils and their distribution were decided by thesupposed influence of pedogenetic factors (mainly climate, vegetation,bedrock and some very general ideas about landscape). Both maps werenot derived from a generalized survey of true soils, but by ideas elaboratedfrom few profiles, the so-called “zonal soils” (*), allotted on the national land. (*) According to D. Dent and A. Young (1980) a ZONAL SOIL is commonly regarded as one in which the properties areprimarily determined by climate. Experience suggests a revised definition: a zonal soil is the soil type that was thought

to be present before anyone went to have a look.

Photogrphic reproduction of Sols de la Suisse. Vue d’ensemble

Partial Photogrphic reproduction of Soil Map of Italy.

SECOND LOGICAL RULESoil Mapping Units in soilscapes that present similarities must be congruent, i.e. described with similar assemblage of soil types.

QUESTION: There are some SMU in the central part of the window thatare described in similar way, i.e. present soil types with names, an array of variables and a percentage of coverage into the SMU that are congruent ?

NO

DECISION: Rearrange the component descriptions of SMU’s pertainingto soilscapes that can be devised as similar, i.e. choose those parts oflandscapes (geometric data set) that can be declared (or supposed)similar and build up a new shared data set of SMU’s.This decision (and the subsequent exercise) assume the observance ofa third logical rule. next

THIRD LOGICAL RULESoil Typological Units pertaining to similar landscapes must be described in a congruent way, i.e. each STU that can be present in SZ and IT andis used to describe soil bodies very similar must have an identical (or verysimilar) array of variables. This identity guarantees soil scientists andusers of the database that everyone is talking about same objects.An administrative boundary cannot separate overlapping objects and relative concept (SHARING INFORMATIONS ON SOIL TYPES).

QUESTION: There are some STU in the central part of the window thatare described in similar way, i.e. present soil types with names, an array of variables and a percentage of coverage into the SMU that are

congruent ? NO

DECISION: Build up new shared tables of SOIL TYPES. STU tables are athe conceptual bases of any soil data base.

Photographic reproduction of West-Northern Alps. Base Soil Map.Draft edition, scale 1:500.000, prepared for discussion of border harmonization.

Photographic reproduction of East-Northern Alps. Base Soil Map.Draft edition, scale 1:500.000, prepared for discussion of border harmonization.

Example extracted from the semplified legend proposed for the harmonization exercise.Boxes report a synthetic definition of the major landscape characterization.

SUGGESTIONS

The basic starting point for a process of harmonization would be:A. Use the “true soil” information, i.e. the point samples (profiles)B. Homogenize the data of profiles (extracted and selected from regional and national databases).C. Grouping the harmonized single profiles into sub-populations.D. Into each sub-populations try to create classes of conceptual entities (Soil Types, Series, Soil Bodies, etc.).E. Define major landscapes, and draw a geometric database.F. Create groups (assemblages) of soil types that are logically present in each landscape (soilscapes).G. Match any one of this soilscape with the surveyed areas that can be reasonably collected and allotted to the geographical database.