Title Studies on the method of efficient utilization of soil surveydata( Dissertation_全文 )

Author(s) Kosaki, Takashi

Citation Kyoto University (京都大学)

Issue Date 1982-01-23

URL https://doi.org/10.14989/doctor.k2673

Right

Type Thesis or Dissertation

Textversion author

Kyoto University

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STUDaES ON THE METHOD OF EFFeCiwwff

UTeLIZATION OF SOeL SURVEY DATA

TAKASHI KOSAKI

1981

i-l

STUDIES ONTHEMETHOD OFEFFICIENT

UTILIZATIONOF SOIL SURVEYDATA

bv

TAKASHI KOSAKI

/

Subrnitted to in partial the

A THESIS

the Graduate School fulfillment of thedegree of Doctor of

of Kvoto Universitv .L ".requirernents forAgriculture

November 1981

ACKNOWLEDGEmeNT

I am especially grateful to Dr. Kazutake Kyuma, Professor

of Soil Science, Kyoto University, who, for nearly five years,

kindly guided and supervised my work throughout the course of

the study and sustained in me the keenness and enthusiasm ne-

cessary for an investigation of this nature.

I am very deeply indebted to Mr. Takuichi Hosoi, Headman

of Kaya Township, who allowed me to use the official facility

that helped much in carrying out the soil survey in 1977-1979.

--He also showed deep concern m my personal welfare durmg my

stay in Kaya Township. ' My thanks are due to Dr. Keizaburo Kawaguchi, Emeritus

Professor of Kyoto University, who offered me the opportunity

-- -- -to study in the University of Wisconsin for one year. Durmgmy stay in the United States, I had a chance to observe the

computerized soil testing program which interested me very much

and motivated my research on soil information system.

Special thanks go to Dr. Hisao Furukawa, Associate Pro-

fessor of Soil Science, the Center for Southeast Asian Studies,a'nd Mr. Kiyoshi Torii of Department of Agricultural Engineering,

Kyoto University, for the guidance in the field survey and in

computer programming, respectively.

I wish to express my sincere gratitude to the staff members

of the Laboratory of Soil Science, Kyoto University; Dr. Yoshiro

Matsuo, Associate Professor, Dr. Toshiyuki Wakatsuki, presently

Associate Professor of Soil Science, Shimane University, Dr.

Hideaki Furukawa, Mr. Nagao Okagawa for their invaluable dis-

cussions in every phase of the study, and Miss Uta Nakaoku for

miscellaneous matter. Appreciation is also extended to Dr. Koyo

Yonebayashi, Associate Professor of Soil Science, Kyoto Pre-

fectural University, and Dr. Hayao Fukui, Associate Professor

' -- 1-

of Agronomy, the Center for Southeast Asian Studies, for offering

many helpful suggestions.

I acknowledge with many thanks the cooperation and assist­

ance of all colleagues and students of the Laboratory of Soil

Science, Kyoto University. Messrs. E. Watanabe, S. Araki, M.

Fujiwara and Y. Kachi shared tough works in carrying out soil

survey in Kaya Township. Messrs. F. Yoshida and S. Morita worked

with me for one year and contributed very much to the studies

shown in Chapters 7 and 8, respectively.

Finally, hearty thanks are given to my dear class-mates;

Drs. S. Nakagawa and F. Sato, Messrs. Y. Asano and Y. Mano for

encouraging me throughout the study, and Mrs. R. Sato for typing

the preliminary drafts, for correcting the final manuscript and

for always maintaining a helpful attitude.

T.K.

- II -

CONTENTS

List of Tables

List of Figures

pART I Computer-based Soil Data Management System (CosMAs)

cHAPTER 1 Research Needs for Soil Information System

CHAPTER 2 Collection, Storage and Retrieval of Soil Survey Data

2.1 Introduction

2.2 Standardization of Data Collection

2.2.1 Currently used description card 2.2.2 Proposed description card for COSMAS

2.3 Data Mana'gement

2.3.1 Input 2.3.2 Storage and retrieval 2.3.3 Output 2.3.3.1 Statistics 2.3.3.2 "DECODE" program

2.4 Discussion

CHAPTER 3 Graphic Representation of Soil Survey Data

3.1 Introduction

3.2 Data Input 3.2.1 Location of a sampling site 3.2.2 Polygon data

3.3 Data Management 3.4 Output Programs

3.4.1 Program "PLOT" 3.4.2 Program "lrRANSECT" 3.4.3 Program "CONTOUR" 3.4.4 Program "AUTOMAP" 3.4.5 Program "}CAP"

3.5 Discussion

--t- IU -•

. Vl

--tVlll

3

5

5

6

67

8

1213141415

15

19

19

21

2122

24

24

2425272933

34

CHAPTER 4 Summary and Conclusion

4.1 What has COS]Y[AS done?

4.2 Where is COSMAS going?

PART II Practical Use of COSMAS - Kaya Case Study

CHAPTER 5 Description of Survey Area ' 5.1 Location

5.2 Climate

5.3 J.anduse

5.4 Physiography

5.5 Geology 5.6 Vegetation

CHAPTER 6 Soil Survey

6.1 Profile Observation and Soil Sampling

6.2 Laboratory Analyses of Soil Samples

6.3 Forest Soils 6.4 Lowland Soils

CHAPTER 7 Numerical Approach to Soil Material -- Classification -

7.1 Introduction

7•2 study Area and Employed Data

7.3 Soil Material Class Establishment

7.3.1 Hayashi's theory of quantification 7.3.2 Application and results

7.4 Sample Allocation 7.4.1 Discriminant analysis 7.4.2 Application and results

7.5 Discussion and Conclusion

CHAPTER 8 Automated Map Compilation

. 8.1 Introduction

No. 3

-IV-

36

36

37

41

41

42

43

43

45

45

49

49

49

53

53

56

56

59

61

6164

70

7073

77

82

82

CHAPTER

8.2 Compilation of Soil Map 8.2.1 Soil survey and employed data 8.2.2 Method of data handling 8.2.2.1 Numerlcal classification of soil type 8.2.2.2 Program "AUTO]wap" 8.2.3 Results 8.2.3.1 Taxonomic class establishment 8.2.3.2 Sample allocation 8.2.3.3 Area delineation 8.2.4 Discussion and conclusion

8.3 Compilation of an Interpretation Map - Suitability Map for Upland Crop Cultivation -

8.3.1 Selection of soil attributes influencing soil suitability for upland crop cultivation 8.3.2 Generation of assessment maps 8.3.2.1 Soil water regime 8.3.2.2 Soil tilth 8.3.2.3 Chemical fertility 8.3.3 Results 8.3.4 Discussi'on and conclusion

9 Summary and

9.1 Numerical

9.2 Automated

Further Research

-- tClassification

Map Compilation

Needs

83

8384848485859196

-96

98

991OO1OO102103103106

107

107

108

References

Appendix

Appendix

I Profile Descriptions and the Representative Soils

U Profile Descriptions and the Representative Soils

Analytical Datain the Mountain

Analytical Datain the Lowland

for

for

109

117

137

-v-

List of Tables

Tab1e

Tab1e

Table

Tab1e

Tab1e

Table

Tab1e

Table

Table

Tab1e

Table

Table

Table

Table

Table

Tab1e

Tab1e

Table

Tab1e

Tab1e

Table

3-1

3-2

5-1

5-2

6-1

6-2

7-1

7-2

7-3

7-4

7-5

7--6

7-7

7-•8

7-9

7-1O

7-11

8-1

8•-2

8-3

8-4

-i .Segment defmition.....................................Soil attribute list...................................

Climatic data of the study area.......................

Landuse of Kaya Town.ship....•.......•.•••.••..•••.••..

Brief description of method of laboratory analysis....

Legend of soil map......••..•.•..•..••..••••....•••..•

Conventional soil material classification at sampled .sltes................................................. ---Attribute-class lis•t for soil material classification.

Eigenvalues, correlatlon coefficients and cumulativeratios of total variance derived from "Hayashi 3"analysis for soil material classification.............

Numeric values assigned to soil attribute-classesfor soil material classification..............••.•.•..

Numeric values assigned to profiles for soil materialelassification..................................•...•.

Statistics for each soil material group .......••..•Prior probabilities for respective soil materialgroups......•••.•.•••.••..•.•..•••...••..••...•••.••..Eigenvalues and relative percentages for deriveddiscriminant functions for soil materialclassification...........................•••.•••..•...Numerical assignment of soil material group forsample profiles.••.....•...••..••••..•.•..••••..••..••Cross--table between field judgement and numericalassignment on soil material classification ofsample profiles.............................•....••...Clay mineral composition of sampled soilsrepresentative of soil material groups................Attribute--class list for soil type classification.....

Numeric values assigned to soil attribute-classesfor soil type classification..........................

Numeric values assigned to profiles for soil typeclassification........................................Eigenvalues, correlation coefficients and cumulativeratios of total variance derived from "Hayashi 3"analysis for soil type classification.................

23

23

42

43

50

55

60

65

65

66

68

70

73

74

75

77

79

85

86

87

89

- Vl --

Tab1e

Table

Table

Table

Tab1e.

Tab1e

Table

Tab1e

Tab1e

8-5

8-6

8-7

8-8

8-9

8-1O

8-11

8-12

8-13

Statistics for each soil type .............••....•.. 91Eigenvalues and relative percentages for deriveddiscriminant functions for soil type classification... 92

prior probabilities for respective soil types......... g2

Numerical assignment of soil type for sample profiles. 93crossLtable between field judgement and numericalassignment on soil type classification................ g6

classes set for soil water regime assessment.......... Iol

classes set for soil tilth assessment...'.............. Io2

classes set for chemical ferti!ity assessment......... Io3

classes set for suitability assessment for uplandcrop cultivation....................••.....•.......•.• 105

-t-- Vll --

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

Fig.

2-1

2--2

2-3

3-1

3-2

3-3

3-4

3-5

3-6

3--7

5--1

5-2

5--3

5-4

5-5

6--1

6-2

7-1

7-2

7-3

7•-4

7-5

7-6

8-1

List of Figures

Standardized description card........................ 9

Fundamental structure and data flow in COSMAS........ 12

Output by "DECODE" program.........................•. 16

Schematic model of polygon data...................... 22

Site attribute plotting map.......................... 26

Transect-attribute diagram........................... 27

rsoline map based on Ex. CalEx. Mg................... 28

Principle of "AUTO)4AP" program....................... 30

Delineation in a unit triangle......................• 32

Example of area extraction........................•.• 33

Kaya Township, Kyoto Prefecture, and surrounding area 41

Pattern of soil water regime.....................••.• 42

Physiographic map of Kaya Township................... 44

Geologic map of Kaya Township..............•••.•e•••• 46

Vegetation map of Kaya Township..................•.•. 47

Location map of sarnpling sites in the study area..... 51

Soil map of lowland of Kaya Township...............•. 54

Location of sampled profiles on the west slope of 60Mt. Oye............••••••••••••••••••••••••••••••••••Schematic representation of response pattern andHayashi's theory of quantification No. 3............. 62

Scattergram of sampled sites plotted with the firsttwo nurneric scores assigned by "Hayashi 3" for soilmaterial class establishment......................... 69

Scattergram of sampled sites plotted nith thefirst two discriminant scores for classifying .soil material.................••..••.....••..••...... 76

Comparison between nume'rical and conventionalclassification of soil material...................... 78

X-ray diffractogram for clay fractions of sampled soils representative o.f soil materials............... 80

Scattergram of sampled sites PIotted with the first two numeric scores assigned by "Hayashi 3" for soil type establishment........................•..••.•.••• 90

---- Vlll -

.Fig.

Fig.

Fig

Fig

Fig

Fig

.

.

.

.

8-2

8--3

8-4

8-5

8--6

8--7

Scattergram of sampled sites plotted with the firsttwo discrlminant scores for classifying soil type..•• 95

Comparison between numerically and conventionallycompiled soil maps................................... 97

Soil water regime map................................101

Soil tilth class map.................................102

Chemical fertility map...............................104

Suitability map for upland crop cultivation..........105

.- IX -

PART I

L

Computer-based Soil Data

( COSMAS

Managernent

)

System

cHApTER 1 Researcb Needs for Soil Information System

' During the last decade some soil scientists attempted to

develop soil data bankS by using a computer (John g!tE. g.IL., 1972a,

b; Rudeforth, 1975; Lee Åí.!tL a--1•, a976; Haantjens s};t g.IL., 1975a,b,

c). The motivations. were: first, the amount of soil data obtained

from projects for soil surveys is rapidly increasing and its

efficient management is desired; secondly, those data are,expected

to'serve many people in various fields of study such as agricul-

ture, land use planning and so forth; thirdly, the computer has

become a very easy and popular tool to handle.

In 1975 the first international meeting for "Soil Informa-

tion Systems" was held in Wageningen, Holland. Two additional

meetings were held in Australia and Bulgaria, and the papers pre-

sented at these meetings were published in "Soil Information Sys-

tems" (Bie, 1975), "Uses of Soil Information Systems" (Moore and

Bie, 1976) and "Developments in Soil Information Systems" (Sadovski

and Bie, 1977). Soil Information System (SIS) naturally contrib-

utes to pedological and edaphological studies, but the most import-

ant role of SIS, as Dumanski pointed out (Dumanski, 1978), is

data management service. At present, a few SISs have been estab-

lished in Canada, the Netherlands, the U.S.A., Australia, France

and West Germany (Dumanski .g!tL a.IL.., 1975; McCormack e.--t a-1.,1978).

Bulgaria, Rumania, U.S.S.R. and other east-European countries also

have their own SISs, whose main aims are crop monitoring and opti-

mizing production factors (Garbouchev eJt; a,IL., 1978). When compared

to the previous soil data bank's data management, analysis, re-

presentation and system manipulation, it is obvious that both types

of SIS have been greatly improved.

Normally, all the survey data are rounded into a soil map by

an experienced surveyor and then all the interpretation maps are

derived from the soil map. However, data should be rounded only

-3-

1n the step of interpretation, but not in the step of compilation

of a soil map. In the author's view, a soil map is no more than

one of the output forms of the stored data, not the sole base for

interpretations. Therefore, the construction of a data bank

containing raw soil data obtained in both field and laboratory

with an effective management system is highly desired. The author

has started a study of the methodology and practical use of a soil

data management system. It should be noted here that the purpose

of the construction of the system is to provide a convenient tool

for general data management for a wide range of users, not to

provide a special data bank for a certain research project.

Aiming at an efficient utilization of soil survey data, the

author experimentally built up a Soil Information System, named

as Computer-based Soil Data Management System (COSMAS), so as to

meet the following points;

1. The same descriptive terms should be employed when describ-

1ng similar features in taking the field record of a soil

survey. The irreducible minimum of the description should

be taken and stored regardless of the surveyor's intention

or purpose of the survey.

2. Computer manipulation for soil data handling sould be easy

for anyone who wants the data.

3. A cartographic unit should be included for the graphic re­

presentation of the raw and processed data in a base map.

The author will detail the structure and functions of COSMAS

1n the following two chapters and describe in Part II how COSMAS

helps us to perform data retrieval and analysis for practical use

of soil data for agriculture.

- 4 -

cHApTER 2 Collection, Storage and Retrieval of Soil Survey Data

.2.1 Introduction

rn order to satisfy the requirements of a soil information

system as mentioned in Chapter 1, first of all, a procedure of,

data collection m',"st be established wlth defined terminology.

Many methods of data collection are already known, such as field

notebook, marksense card, punch card, voice recorder and so on.

Among these various methods a standardized description eard is

best fittbd to field survey, provided it is well organized,

because it is simple to fill out, easy to read and no special

equipment is needed (Kloosterman, 1975).

Secondly, the system rnust be based on a well established

data rnanagement system so as to satisfy basic requirements of data

retrieval. In addition, the system is desired to involve the

functions of data analysis, that is, statistical analysis, when

aiming at an efficient use of the bulk of soil survey data.

Simple operation of the system is also highly required, since

the system may be used by those who are not specialized in computer

manipulation. Therefore, Statistical Package for the Social

Seiences(SPSS), which is one of the currently used data management

system, is suitable for COSMAS. SPSS carries out general data

management and statistical analyses, but does not satisfy a special

requirement of soil information retrieval. Then, some separate

programs are to be prepared in compensation. For example, it

takes considerable time and labor to retrieve site and horizon

descriptions and analytical data in text and tabulated forms.

Hence, it is necessary to provide an output program for COSMAS

for quick and easy retrieval of raw soil data in a conventional

form.

-5-

2.2 Standardization of Data Collection

2.2.1 Currently used description card

Some people have already designed description cards fitted

.to their own data banks. Roughly speaking, two types of descrip-tion card were designed, one by Haantjens s:!tL al. (1975a,b,c) and

Hazelden et al. (1976), and the other by Lee s2JtL a.-1. (1976)•

In the description card designed by Hazelden all data are

to be written in numerical codes assigned for their system. In

this method, a surveyor has to memorize the numerical code cor-

responding to a field appearance prior to a survey, or check it in

the translation table in the field. It is a very troublesome and

error- prone procedure. Furthermore, those recorded data are noteasily understandable in the laboratory without translation. Since

two types of description card, detailed form and short form, are

proposed, two different types of data management are needed, and

it is complicated for the users to retrieve those data. To avoid

complexity in the data processing, it is not advisable to have .dual structures for data storage. Some important attributes are

missing in Hazelden's system: land form and ground water level,

which are among the most improtant keys to understanding or inter-

.preting soils in lowland areas. Haantjens also employed a numeri'

cal code method. His method has the same shortcomings as those

mentioned above. Moreover, his description card is more bulky

and tedious to handle, and is not well organized, because differ-

ent types of information (for instance, observations obtained in

the field and estimates based on laboratory analyses) are assigned

to be recorded together on the same pages. On the other hand, a multiple choice method is employed by

.Lee. All possible terms are assigned on the card, and a numerrcal or letter code corresponding to a particular descriptive term

is ticked or written in the box. His method is fitted to field

-6-

use and is very easily understood in the laboratory without any

translation. In his system, however, none of the site descrip-

tions is included in the computer--processed system. Some of the

site attributes are, of course, very difficult to standardize as

specified terms, but it is desirable to select the nearest term

corresponding to the actual feature from a set of preselected

terms. otherwise an actual feature may be expressed in many dif-

ferent ways, and the attribute cannot be used for screening of the

required features. Topographical data, land form, and land use

are often used for screening an area out of a soil map; therefore,

those should be included in the system. Furthermore, most of the

attributes, including multistate-ranked attributes, are given as

numerical or letter codes in the card. Ithen the terms of multi--

state-unranked attributes are well defined, as in the case of

"soil texture", they need not be shown on the card. A letter

code such as "SL" for sandy loam can be put on the card to de-

scribe a state of soil texture.

2.2.2. Proposed description card for COSMAS

The author designed an improved description card employing

a multiple choice system, and letter codes as well as numerical

codes. The description card is designed to meet the following .POmts; ' 1. Possible descriptive terms are given for such attributes

except for some whose terms are very familiar to us or

which have a large number of state descriptions, such as

soil texture. Furukawa (1979) defined all possible terms,

from which a surveyor can choose the one most nearly cor-

responding to the actual feature. He also defined their

letter codes, which are to be written on the card. Sur-

veyors have no difficulty in learning the code for each

-7-

attribute prior to field work or checking it on a trans-

lation table in the field. The data obtained in this man-

ner are easily understandable not only in the field but also

in the laboratory.

2. Numerical codes are given for the multistate--ranked attrl-

butes (e.g. stone abundance). These are ticked or written

in assigned boxes.

3. Scaled attributes are measured with appropriate apparatuses

and their readings are written in the boxes in assigned units

(e.g. ground water level (cm)).

4. Letter codes are standardized to express the multistate--

unranked attributes. These are very familiar to us, and

easier to use and clearer to understand than numerical

codes.

5. For missing records, "999" and blank are assigned to the

numerical code and letter code, respectively. Likewise,

for irrelevant records such as "mottle size" and "mottle

color" for soil without mottles, "O" (zero) and "i" are

. glven.

The given terms are assigned to fit mainly the conditions

of Japan and Southeast Asian countries. It may take 15 to 20

minutes to fill out the description card for one profile. The

front and back sides of the card are used for site description

and horizon description,respectively, as shown in Figure 2-1.

2.3 Data Management

SPSS (Statistical Package for the Social Sciences) was em-

ployed for data management. SPSS is one of the program packages

that are adapted to the FACOM M-200 computer in the Kyoto Univer-

sity Data Processing Center, and includes many subprograms not

-8-

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only for data storage and retrieval management but also for stat-

istical analyses such as regression analysis, factor analysis and

so on (Nie et al., 1975; Miyake and Yamamoto, 1977).

' There have been some studies of soil data banks as mentioned

. before. They included data management programs such as mput, screening and retrieval procedures, but not programs for stat-

zstical analyses• Statistical analayses have to be used when

handling a large number of soil data. The data, if they are

retrieved only in the form of a numerical or letter code and

figures of measurements, are almost useless to the users.

The larger the number of data, the more difficult they are to com-

prehend. Therefore, the system should be provided with some

means to handle a huge data matrix; that is, statistical programs.

This is one of the reasons why SPSS was introduced to COSMAS.

Secondly, SPSS has remarkably developed functions of file manage-

ment which is very important ln natural sciences as well as in

social sciences. And finally, SPSS is very easy to use even for

those who are unfamiliar with a coTnputer. A few control cards

and task cards can command any job steps such as data storage,

selection, conversion, and so on.

Some of the well--established soil information systems are

also based on some kinds of program packages for data management

and processing. For instance, "G-EXEC," "GRASP"r "SYMAP" and

"SPSS" were introduced by the Dutch (Bie, 1977) and a German group

(Lamp, pers. comm.). CanSIS was based on "RAPID" for its data

,management (Kloosterman, pers. comm.). The fundamental strue-

ture and data flow of COSb4AS is shown in Figure 2-2 and detailed . m the following. The management of the polygon data stored in the cartographic file will be described in the following chapter.

-- 11 -

Cartographie

File

Data Storage andRetrieval Prograin mp

Fig. 2-2

USERCOMMANDS

Analytical Dat a Fiie

Processed Data

Fundarnental structure and

---, Horizon Site lDesenptiOn Descnption I

File File l l ' I Subprograms E l 1 l Subprograms t J

Output (Ex.

data

,PrograinsDECODE)

flow in COSM. AS

2.3.1 Input

All description data including letter code and numerical code

are put into files through 80-column punch cards or a time sharing

system without any translation between the description card and

' .data mput. Analytical data are also put into a file as scaled attributes.

At present, analytieal data include pH (H20 and IN KCI), exchange'abie cations (Na+ , K+ , ca2+ and Mg2+), cation exchange capacity,

organic carbon content, total nitrogen content, total phosphorus,

phosphorus absorption coefficient, available phosphorus, available

-- 12 --

silica, free oxideS (Fe203, ]hn02, A1203), sand content, silt con-

tent, clay content, CIN ratio, percent base saturation, bulk den-

sity, particle density, porosity, water content and water satu-ration percentage. The file is still large enough in its capacity

to accornmodate additional attributes.

For each input, COS)CAS requires two 80-colurm punch cards

of site description, two of horizon description and two of ana-

lytical data for each horizon. The COSMAS file system normally

has the capacity for six horizons of description and analytical

data. Therefore, dummy data should be put into the files when

the total number of horizons is less than six. Free format or

fixed format can be qhosen by the users for input. The data

rnust be thoroughly checked for errors prior to storage in the

files.

2.3.2 Storage and retrieval

Three SPSS files are ready to store the data mentioned above.

They are the Site Description File which includes the data recorded

on the front side of the description card, the Horizon Descrip-

tion File which consists of the data written on the back side of

the description card, and the Analytical Data File composed of

physical and chemical data obtained in the laboratory analyses.

Although the data are stored in the three files separately, all

of them can be cross-referenced with one another. Therefore,

no problems will arise when the data, stored in three different

files, simultaneously undergo processing.

All the data are stored in exactly the same form as those

written on the field description card. They can be transformed

into the forms favorable for expected manipulation by SPSS. For

example, subprogram "RECODE" can transform a letter code into a

numerical code or vice versa, a numerical value into a numerical

, -- 13 -

code by de'signating threshold values, and so on. Subprogram

"COMPUTE" can make up a new series of data from existing data.

For example, porosity, water saturation, moisture content and

particle density of soils can be calculated from the data of

three phase distribution and bulk density and added to the file

in existence. Subprogram "SELECT IF" is used for data screening.

Any logical requirement can be given to the system according to

the users' interests. For instance, the combination of subpro-

grams "SELECT IF" and "LIST CASES" is capable of data extraction

from 500 cases (profiles) ba$ed on such a requirement as "Give

all the profile descriptions of the profile whose soil type is

Brown Lowland Soil," wlien those two subprograms are specified as

fol1ows; * rk* SELECT IF (TUI EQ "BLS") ( LIST CASES CASES=500/VARIABLES=ALL * "TUI" is the abbreviated form assigned to a soil attribute •

"Taxonomic unit 1 (soil type)•"

** "BLS" is the abbreviated form assigned to "Brown Lowland -Soil . rT

Subprogram "MERGE FILES" takes up all or some of the attributes

in the individual files to build up a new file so that it may

undergo further processing. Soil data can be handled easily and

efficiently with the help of various combinations of the functions

of the SPSS data management system.

2.3.3 Output

2.3.3.1 Statistics

SPSS has many statistical subprograms whose output formats

are inherently fixed. Some commonly used subprograms are briefly

described in the following.

Subprogram "CONDESCRIPTIVE" produces statistics such as a

-imean, standard deviatibn, standard error, variance, kurtosis,

-14-

,skewness, range, maximum and minimum. Subprogram "FREQUENclEs"

generates histograrns from data values with the statistics men-

tioned above. Subprogram "SCATTERGRAM" and "TRIANGRAM" produce

scattergrams from the data of any two and three attributes,

respectively, together with some statistics such as correlation

coefficient. Subprogram "REGRESSION" carries out multiple reg-

ression analysis and subprogram "ANOVA" executes the analysis of

variance. Factor analysis and cluster analysis can be done with

subprograms "FACTOR" and "CLUSTER", respectively. '

2.3.3.2• "DECODE" program

This program was prepared to translate stored data in coded

forms into a conventional text. The output is readily published

as a part of soil survey report. The program was written in

FORTRAN and is now avai!able as a library program open to anyone

who wants to use it. Users are able to get full descriptions of

site and horizon characteristics, as well as analytical data for

the profiles that interest them through this program. It sets

us free from the time-consuming and error-prone job of preparing

of soil profile description and the tabulation of analytical data.

Figure 2-3 shows the output frorn the "DECODE" program. The de-

tails of this program and the user manual are given in the ref-

erence material (Laboratory of Soil Science, Kyoto University,1981).

2.4 Discussion

The main purpose of this pilot study of a .System is to work out and test the methodology

.is dreated and handled.. Therefore, the format

output may be modified, if necessary. However,

Structure of COS)IAS will be maintained even if

soil information

by which the system

of the input and

the fundamental

COSMAS undergoes

-- 15 -

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- 17 --

minor modifications in the future. The followings were accom-

plished in this study:

1. standardization of the profile description card and termi-

nology, and

2. construction of a data management system.

The standardized description card enables us to understand

and describe soil characteristics in a soil survey using the ter-

minology defined by Furukawa. The data can be clearly understood

and easily utilized by a large number of potential users who are

not specialists in the soil survey field. However, the termi--

nology put forward in this paper for COS]Y[AS has not been accepted

unanimously, and it will have to be modified when a standard

terminology is defined for a nation-wide system of soil informa-

.tlon.

Recently developed soil information systems are, to some

extent, based on currently used data base management systems.

SPSS was introduced to COSMAS for data management and analyses,

first because of the provision of statistical subprograms, and

secondly beeause of its ease of use for those unfamiliar with a

cornputer. SPSS has been developed for general use for datamanagement and statiStical analyses; therefore it does not always

satisfy a large variety of the output requirements of a soil in-

formation system. In order to meet special output requirements,

the author provided additional programs, one of which is "DECODE•" "This program cooperates with SPSS and generates profile descrip--

tion and tabulation of the analytical data, thus saving a great

deal of time and labor.

-18-

CHAPTER 3 Graphie Representation of Soil Survey Data

3.1 Introduction

Soil information is almost always retrieved in map form for

practical use. Generally speaking, an interpretation map must

be prepared for an individual problem in agriculture, land use

planning, etc., to present the results of the analysis of stored

data. Also in the field of researoh, soil information, especially

when it consists of a large number of data, is easier to under-

stand, when it is represented as a inap or graph. However, it is

well known that the production of a map requires considerable

time and labor, and the cornpilation of a special purpose map even

more.

A modern soil information system must, therefore, be pro-

vided with an automatic cartographic unit to satisfy the needs

mentioned above. COS)CAS's automatic cartographic unit has the

following three objectives (Zuijlen, 1975):

1. to speed up production of maps,

2. to reduce cost, and

3. to allow production of special purpose maps.

Two types of data may be obtgined in a so" survey: site

data and polygon data. The former include geographical andgeological descriptions and soil morphological features of each

horizon at the observation sites. The latter include the data

which are obtained as an area delineated on thematic maps such

as an air-photo interpretation map, cadastral map and others

which are prepared in a conventional way. The author has already

described the procedure for management and retrieval of site data.m Chapter 2. In this chapter, a method of graphical represen-tation of stored data, including both site and polygon data, will

be described.

-19-

Many papers have been published on the graphical represen­

tation of soil survey data. Procedures for digitization, storage

and retrieval of polygon data and their attributes were proposed

by Johnson (1977) and Sykes and Petersen (1980). Other soil in­

formation systems employ an elementary cell as a data unit which

is characterized by many soil attributes (Webster and Burrough,

1972a,b; Nichols, 1975; Cormack, 1976; Beeren, 1977; Legros and

Hensel, 1977; Ragg, 1977; Tilman and Mokma, 1980). In these

methods, the data must be considered homogeneous in a given poly­

gon or unit cell. The distribution of data is, however, not as

simple as mentioned above, hence, the site data and the polygon

data should be stored in separate files and retrieved as required.

Furthermore, the unit cell storage method requires huge amounts

of storage space, therefore it is better suited for a regional

information system, but not for a large, nation-wide information

system.

Cameron and Toogood (1970) employed site data to extrapolate

a two-dimensional representation to show the distribution pattern

of the required attributes. The application of this method is

considered to be restricted to certain soil attributes, which

are scaled or multistate-ranked ones. Bie ~ al. (197~) reported

the procedure of compilation of an interpretation map based on a

combination of site data and polygon data introducing the dis­

crete approach method by de Gruijter and Bie (1975). It ~s use­

ful in the map compilation stage to divide the area into elemen­

tary cells for the logical overlay of some given attributes as

Bie et al. showed.

Site and polygon data should be stored and managed in sep­

arate files to facilitate combination of those data as needed.

COSMAS gives priority to the polygon data for the compilation of

special purpose maps, provided the polygon is considered homo­

geneous on the attribute concerned. When the polygon does not

- 20 -

prove so, site data are used for the preparation of the map.

This is the author's philosophy for the preparation of a special

purpose map Four of the programs, which were completed for automatic

cartography in order to facil.itate graphical representation of

tvifo types of data, will be described in the following part. The

data used here were obtained in a detailed soil survey of Kaya

Township, Kyoto Prefecture. Samples were taken at an average

density of one per every four hectares.

3.2 Data Input

3.2.1 Location of a sampling site

Each sampling site is marked on a base map-'when it is ob-

served in the field. The location is then stored as grid ref-

erence in the COSMAS Site File mentioned in Chapter 2. A grid

reference is described relative to a given geodetic point, measured

in kilometers northward and eastward from that point. In the

Kinki District of Japan, where Kyoto is located, a geodetic point

(E136 N36) is used in describing the locations. A negative sign

is assigned to southward and westward shifts. For example:

E136 N36, -153.254, O09.080

It is a time-comsuming and error-prone job to get a grid

reference for a sampling site on the base map by means of the

scale. "TABLET", which is a digitizer connected to the FACOM

-i t-M-200 computer of Kyoto Unwersity Data Processing Center, isemployed to record the grid references of the sampling sites.

The base map, on which each sampling site is marked, is placed

on TABLET, and an attached cursor or stylus is moved to touch

the mark on the map, thereby the location of the site being

digitized in an X,Y-coordinate system with a given origin.

- 21 ---

3.2.2 Polygon data

Polygon data consist of boundaries and attributes of all

delineated areas within a map. A boundary is recorded as segments,

each of which is defined by identification numbers of such points

as nodes and inflection points, and identification numbers of the

areas that are delineated by the segment. As shown in Figure 3-1,

area 1 is delineated by a boundary which is composed of three

segments; Sl, S2 and S3. Segment Sl is defined by an initiatingnode (Nl), a terminating node (N2), an initiating inflection point

(Il), a terminating inflectiori point (I3) and identification

numbers of the areas (1 and 3). Likewise, the other segments

(S2, S3, S4,...) are defined by those data as shown in Table 3•-1.

I12 Nl @ Il

I3 IIOS4

Ill

Legend

-N el s

Fig. 3-1 Schematic model of polygon data

Node

Inflection pomtSegment

The.In a form

manner as

area are

location of nodes and inflection points

of a grid reference by means of TABLET

the ones of sampling site. Attributes

recorded for each of the mapping units.

-22-

.is recordedin the same

of a delineated

Table 3--2 shows

Tab1e 3-1 Segment definition

Segment No.

Node No.init. term.

-ltLuglLg!-!!i,gg-Rgla!l..!!g.:.nflection tN

init. term. Area Identification

1

2

3

4

.

.

.

.

1

2

1

2

.

.

.

.

2

3

3

4

.

.

.

.

1

4

12

7

.

.

.

.

3

6

12

11

.

.

.

.

1

1

1

2

.

.

.

.

3

2

o

3

.

.

.

.

Tab1e 3--2 Soil attribute list

MappingUnit No.

Soil Type

SoilVariety

Physiographic Unit Texture Stoniness Slope

1

1

2

3

4

10

.

.

.

GLL

GRL

GRL

GLL

GRL

GRL

. . .

ALL

UGALL

ALL

ALL

ALL

. . .

FP

FP

FP

FP

FAN

vp

. . .

FI,f

M

Mc

M

.

.

.

PM

. . •

Abbreviations

Soil Type :Soil Variety:PhysiographicTexture :Stonmess :Slope :

GLL(gley lowland soil), GRL(grayALL(all horizon), UG(undergroundunit: FP(flood plain), VP(valleyF(fine), M(medium), C(coarse)PM(pebble many)FLAT(flat), G.SL(gently sloping),

lowland soil)gley), RG(reverseplain), FAN(fan)

ROL(rolling)

gley)

FLAT

FLATFLAI'

FLA:[r

G.SL

ROL

. . .

-23-

a part of an attribute list of a soil map compiled by a conven-

tional way. Mapping unit (1), which has two sets of attributes,

shows a soil complex. All the data such as segment definitions,

locations of the points relevant to the boundary and attributes

of a delineated area are stored in the Cartographic File of COSMAS.

3.3 Data Management

Grid references of the sampling sites are stored as one

of the attributes of the COSMAS Site File, so that they are'under

the management of SPSS like any other attributes. The funda-

mental structure and data flow of COSMAS was described in Chapter2.

Polygon data are stored in the Cartographic File, which is

independent of the other three SPSS files of COSMAS; the Site

Description File, Horizon Description File and Analytical Data

File. The polygon data can be managed through the output program

"MAp" which will be discussed later. '

3.4 Output Programs

3.4.1 Program "PLOT"

' it i The program "PLOT" transcribes required data, stored m the

COS]vrAS files, onto their respective locations on a map. What we

call a site attribute plotting rnap shows distribution of the data

of one or more attributes. It was formerly a tedious process to

transcribe the data one by one from the field notes onto a map.

However, with this program, any data can be easily and accurately

retrieved and represented. Therefore, every interpretation map

can be compiled from the raw data stored in the file, rather than

from soil map.

-24-

For example, when preparing a soil map, it is, first of all,

necessary to show the distribution of such data as soil type,

variety, stoniness and texture class in a form of a site attri-

bute plotting map as shown in Figure 3-2. Then, the boundaries ' dtare located taking into account the result of air-photo mter-- 'pretation as well as those site data mentioned above. The soil

map and other interpretation maps prepared in that way are stored

as polygon data in the Cartographic File of COSMAS.

3.4.2 Prograrn "TRANSEC[r"

Transect-attribute diagrams are prepared using this program.

The change of topography and a given attribute along a transect

set by the user's reqdirement are presented on a graphic display

device or X-Y plotter. Two attributes can be selected to show

the relationship between them. Transect data is a simple and

informative tool in understanding the distribution of soils and

can be extended into two dimensions using knowledge of topogra-

phy of the surroundings, provided the transect is set to include

enough variability. However, it used to require great effort to

find an informative transect by trial and error. This program

features interactive query and response, so that it takes only

few seconds to get a diagram for any set of data stored in the

COSIylAS files. With the help of this program, we now have no

trouble in finding the most informative data set for the transect.

Figure 3--3 shows a diagram of the abundance of mottles in

the subsoils on the transect from site No. 258 to site No. 40

with soil type data as a reference. Two peaks, which correspond

to "many mottles," can be seen at two sites; No. 26g and No. 272.

It is easily found out that the feature, "many mottles" in the

subsoil, appears in association with Wet Brown Forest Soil (BF)'m the study area.

-25-

-•-omv:q-3oAblo-rj

--HomÅéCas

-3oghas

go

tllfl!- - 1///// !1//// L l/////,

1

v-c-- aj

o-m3 ortV A Å~

coaj c--3Q30E090A.QU tÅri ta-a aj •-

-AOoomÅrÅrÅr

År)ÅrÅrÅrÅr

ÅrÅrÅr

vCcr

-3oAL'

3o-p

1

has

-o

elp

11r

1![

111

11F

Hr11t

-•-om

r"

rll

r]F

IIr

111

nl1]1

---omvc

pt"

-3oA=3ogan

---

--Hoco

Åro-oovJoLO

pt

vcoptoA

tr

-i---+---

2

oe tt o •/•

t' uoo ,.t

ooovo/t )/., ceo,•

I rlr

t

`

# 7!

s !!! !/ 7f

/!!1!1111!!!!1!!!1!!!1!!11!!1!1!11!!!!1/lf!

IC

-- 26 -

AEMvor

ut

o

oo

ntuHupt:

.Hu"o-no-:A.Hp"-es

cu

-.Hco

ecI

an

.

co.-in

(M)loe .

soo.

800.

?oo.

600.

sao.

"ao.

eoo.

200.

Iee.

o.

DIAGRRM OFtigTTLE RBUNDFINCE

2Se.259.260.59251. 262. 265264.65.iG3269268. 267, 2S6.210, 27t.27227a67.

2n-e.

ili:

?.eDo(few)

t.soo

t.6ao

t.goo

t.2SOt.eoo (none)

ID YO.

e.o o.s i.e i•5 2•O 2-iKM)

Soil Type * : Dry Brown Forest Soil (BB) ' ftk : Moderately Moist Brown Forest Soil(drier subtype) (BD(d)) kkde : Wet Brown Forest Soil (BF)

Fig. 3-3 Transect--attribute diagram

3.4.3 Program "CON[rOUR"

' This program carries out the preparation of an isoline map

from the data of sampling sites. The users are able to display

the isoline map on the graphic display device, X-Y plotter and

printer plotter. The first one is used for determining the most

appropriate map and the latter two for publication of the final

version. An isoline map is prepared• for the scaled and multistate-

ranked attributes. This program was modified from "SDCONT," one

-- 27 -

2

o,

ut

o.

an

.9-?of:

oo eo o eeYeeooe eoooeO/ 9/" .tt

tJ

1'

o

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ersg

ve

e

e

e

joo

o

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e

e

e

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cc

v e a'e oe •oCoOD9eÅqOo

eeeo oecOooee ooooooee ooee o o

e"x

e

o

e

o

oe

o

,;tw.px.:/e/i'

A oo e,o!o OooOerfXe e e oeete eeeeeoeeYo :U} '1 e'SPXo '- oe . 'e :g i(/i`I L)

eeoo O o eae e e o e e ot e Prl aO ptÅíeeO o- oe Or oo oee '" oeo oeo oeo U]X e a- e eee oeo o s'OÅq o eoe oooo oeee oeeoe dXioeoo Ooeeeo e'e e C) o IIo st'cX o e. e e tt" o o o urfixe ,e o Li IX$( o- e e on tl It ll e e li tl o o o "11 o o o" el eoe oee oeoe o•eo eoee oeoo ooeGe eooe ooOo o e o oeoe eeoo e e a

o , NII LI 11 tl tl it ll tl ti lt ll li H II ll O e Oil H FI H 11 lt lt " +I n" ll te st ll O o e II H tl ll Ii ".el " tt II IL tt "- o o oe Il II U/ II : Jib: ll " "1OeOe e.

" lt tl tl."" H. Ii ll li ll lt o e eOOe Il li ll I}4#" lrt ll u Il l eO eO oOeoo Ie H tt ll u-"" Il " e O e e o Il lt ll - ll lt Il te tl e e ooeo ll - ll lt H lt l- Itl e e o eeooO g. H :I ll :Ag II ti tl Ooee Oo Oe e": eee it Llilx F/ h/ k/ e ?. o:e :e :e

e'oOooo Il L/ ::e g"xeO oe oO o ooe hSj ll lo o e e e • ee.tl sl e O oo oe ile ooee r. N7. eo eo oo ooeeo ee eo oo ÅëÅ~ oNXa e e oeo

--'~ .,,.,/iE.:iili'k•kl"i.ii

/ig'S:::: 5.;iii{:e ii

ooo ooeoee oeo ee eo eo e" I-N eee s e o II ll ll n 11- 11 " e e .r,ix e:o -"ll": g)llHni l: ,e: e:o o e cD lluev ll lt"tl" eo erse e oO " le CIP* :l Il ll : "O eOe .Fcsc:e:oy" "il fi il r/ il lli1 e:o o e rs )I " ll tt H 11 ll ll e oo ll" "le tl e ee o e b liU M" tl"ll ooO oO o e N l: ll """-: ll : ll eo OOO Htl tl ll oO o e o Il li"q-t :"ti S- tl ll O OOo ll lt li - 10 oVe e O#1) Ill)elt tlM e eOertO e l: ;/ ll ll tt ::"Rt5 ll ll oO oO o e IT le H 11 ft le-: :-lt l il e e e e oste ."xO ll :Y):t:e ll ll Il H:: eo Oee o " ll ll Il H lt II .n bl tt e eoO o 5:ll II X,H:IÅq" ll :':IP*:: e""'eO o n II lt ll "" lt el ll tl u ee tt tr o oO o " tl ll l"" ll lt Il 11 " ti eleO-rl)Åq O .ll )t " lt ll ll H 11 tt ll O Oxoe :: lll Il I"/ Il lleeoo oO et ll tt " o o lt tl " ll e e ' Il n II ll O N "H li ll"ll t"l" INi,. :., L' ll :: r: ::to

It u lt Il" tn ' " tt tl lt• r M }t tt

o.

ec

o ute-r- O

ut.

o

-EMv.

o.

r

n.

o

o.

o

-28-

oox .Å~

mÅ~tu

v.

xm"ovomtu

Dntufi

opt

.H-oco

Hst

l

an

.

oo.Hin

of the library subroutines of Kyoto University Data Processing

center, originally written by Fujii (1977). "SDCONT" carries

out the computation of a curved surface fitting to the actual

values of sampling sites and traces isolines on the surface.

The values of isolines are specified by a user.

Figure 3-4 shows the isoline map based on the ratio of the

content of exchangeable calcium (Ex. Ca) to that of exchangeable

magnesium (Ex. Mg) for the subsoil of the sampling site. The

data employed here are stored in the COSIylAS Analytical Data File.

This figure shows a valley inclined to the right. Geologically,

a granite hill and serpentine mountain are located in the upper

and lower parts of this figure, respectively. The ratio of Ex.

CalEx. Mg is generally lower in the serpentine area than in the

granite area. It can be seen in this figure that the low-ratio

part, which is represented by darker color, starts from the

bottom left side and spreads to the right. It indlcates that

soil material derived from serpentine, which has low ratio of

Ex. CalEx. Mg, came from the mountain, flowed and spread down the

valley to the lower part. The distribution pattern of the two

different soil materials derived from serpentine and granite can

be clearly seen.

3.4.4. Program "AUTOMAP"

This program converts point data to polygon data. Unlike

the program "CONTOUR" the program "AUTOMAP" is applicable to the

compilation of maps showing a two dimensional distribution of

multistate-unranked data.

Figure 3-5 shows the principle of this program. A unit

quadrilateral is delineated by connecting four observation sites.

For example, when two GLL (Gley Lowland Soil), one GRL (Gray Low-

land Soll) and one BLS (Brown Lowland Soil) profiles make up a

-- 29 '-

quadrilateral, and the probabilies* (figures ~n parentheses),

that they belong to their respective classes are designated, as

shown in Figure3-~there are two alternatives ~n delineating the

areas within the quadrilateral: (A) and (B).

(0.9) (1. 0)GLL GRL

[1. Iso-type effect

? 2. Distance effect

3. probability effectBLS GLL

(07

~GLL GRL GLL GRL

BLS(A)

GLL BLS(B)

GLL

Fig. 3-5 Principle of "AUTOMAP" program

First, the quadrilateral is divided into two unit triangles

by selecting one of the two possible diagonals. The following

factors are taken into consideration in statistically selecting

the more reasonable diagonal;

1. Isotype effect

A diagonal that connects two sites showing the same charac­

teristics or belonging to the same class is preferred.

* Probability (a) is given 1ll the discriminant analysis ( 0.0 ~

a ~1.0 ) that is used for assigning the class to which a sampie

belongs. In ordinary soil surveys, probability of class assign­

ment by a surveyor is regarded as unity.

- 30 -

2. Distance effect ' A diagonal that has a shorter distance is preferred.

3. Probability effect

A diagonal that has higher probability (the sum of the

probabilities of the two sites) is preferred.

In practice, the diagonal which shows a higher similarity calcu-

lated by the following equation isi chosesn

S= aD + bP +iwhere,

S : similarity of the two sites that form a diagonal

D : distance ratio (=1.0 or DllD2, when DlÅqD2) Dl, D2 ; length of the diagonals

P : sum of probabilities of two sites

a : coefficient for distance effect

b : coefficient for probability effect

i : coefficient for isotype effect

Coefficients a, b and i must be given by the user to produce a

reasonable map for his requirement. They. are, however, assigned

unities when left unspecified by the user. Coefficient i should

be added only when both of the sites which define the diagonal

have the same characteristics.

Selection of the more reasonable diagonal is followed by

boundary allocation within the unit triangle,. A boundary in the

unit triangle can be defined by a pair of dividing points that

are located on the side whose vertices have different character-

istics froTn each other. The following equation gives the co-ordinates of the dividing point.

D(x,y) = (pR12 * L2(x,y) + pR22 )k Ll(x,y)) ! (pR12 + pR22)

where ' D(x,y) : coordinates of a dividing point

Ll(X,Y): coordinates of the vertices L2(x,y)

PRI . probability of rnembership in assigned groups for PR2 ' the two vertices

-- 31 -

As shown in Figure 3--6, Dl, D2 and D4 are found as dividing

pomts. Pomt D3 is not adwidmg pomt, because two vertices(Vl and V3) have same characteristics. However, the eoordinates

and probability of D3 must be provisionally determined by the

above mentioned equation for defining the dividing point D4. The

coordinates of D4 are computed from those and probabilities of V2

and D3. Thus, the segments (Dl-D4) and (D4-D2) are registered as

boundaries to delineate subareas within the triangle. Likewise,

.the segments of the boundary must be determined for the other unit

triangle. Consequently three subareas are delineated in the quad-

rilateral and Figure 3-5 (B) is finally obtained.

GLL (Vl}

D2

BLS DI {V2}

Dl--D4 : Dividing point

Fig. 3-6 Delineation in a

GI,L(V3)

unit triangle

This procedure must be followed for all quadrilaterals con- 'cerned, so that the whole area is divided into subareas that are

homogeneous with resPect to a given soil attribute. The user must

predefine the quadrilaterals by the identifi.cation numbers of the

four sites concerned.

-32-

3.4.5 Program ")QXP"

This program reproduces a thematic map in full or in part

from the polygon data stored in the Cartographic File. Screening

of the delineated area can be done with one or more differentiae.

Two algorithms of screening are available for multiple differen-

tiae. One is ''AND" screening, that is, the data is extracted

when it satisfies both condtions "A" and "B." Another is "OR"

screening, that is, the data is extracted when it satisfies

either condition "A" or "B." The screening and retrieval of de-

lineated areas on the basis of the attribute stored in the Car-

tographic File are carried out through this program. An inter-

pretation map can be easily prepared from the soil map and other

ww extracted

1

area ( GLL or GRL(UG)}

i

l

1

1

1

I

Iy: :,l ,Ei ii l:l

Fig. 3-7 Example of area extraction

-33-

thematic maps, provided the interpretation can be derived from

the data of the attribute in the Cartographic File. This is the

conventional way of compiling an interpretation map on the basis

of the soil map. It is well known that this method is simple and

useful in many cases where the delineated area is considered

homogeneous relative to the attribute. Hence, this program has

great importance for rapid data screening and retrieval, and for

simple map preparation and publication at the user's request.

Figure 3-7 shows the areas and their identification numbers

extracted from the soil map on the requirements of "GLL(Gley low-

land soil type)" or "GRL(Gray lowland soil type) and UG(under-

ground gley variety)." Some drainage management may be advised

for cultivation of upland crops in the screened area.

3.5 Discussion +

The five programs described here enabled two-dimensional

retrieval of site data and management and retrieval of polygon

data, for which purpose the Cartographic File was provided to

COSMAS. It was proved that a two-dimensional display, such as

a map or diagram, is very useful in understanding at a glance

the distribution pattern and continuous change of data on a gi-ven

attribute. The polygon data, which is very important information

obtained from air-photo interpretation and other sources but which

requires much time and labor in its management, became easy to

manipulate. The conventional preparation procedure of an inter-

pretation map from the soil map was automated by program "MAP."

The output from these programs are obtainable on a graphic

display, an X-Y plotter and printer plotter. Hence, it is advis-

able that graphic display should be used for determination of the

appropriate format for the output by trial and error, while an

X-Y plotter and a printer plotter should be used for the final -

-34-

output in a completed format for publicatiop.

Some of the lnterpretation maps are compiled from the polygon

data, while others are done from site data or a combination of the

two. Lthen the interpretation can be done with the data stored

in the Cartographic F"e, that is, the required data for the

interpretation are considered homogeneous in a given polygon,

the interpretation maps are compiled on the basis of polygon de•-

lineations through the "MAP" program. When the data in the Car--

tographic File are, however, not useful in the interpretation,

but the site data are, the interpretation map must be compiled

from the site data. Program "CONTOUR" was prepared for such

situations. An isoline map, however, has limitations in its

applicability: it is good for the scaled and multistate-ranked

attributes, but not for multistate--unranked attributes. For such

a situation, a program "AUTOMAP" can be useci for the two-dimen-

sional retrieval of the data of multistate--unranked attribute.

In delineating an area in a map, program "AUTO]Y[AP" employs

the probabilities, with which observation sites belong to re-

spective classes assigned by a surveyor or by numerical classi--

fication. However, the probability at each site is not the

sole base of boundaty location. Physiographic change, vegetation

discontinuity, etc. must be taken into consideration, but they

are too difficult to be shown as an equation, by which a boundary.is located, unlike the probability. At present, the author wishes

to propose "AUTOMAP" program as the first approximation for a

procedure of automatic map compilation based on site information.

Those programs provide us with a consistent procedure of map

compilation on the basis of raw site data, thereby attaining high

reproducibility in map preparation.

-- 35 -

CHAPTER 4 Summary and Conclusion

4.1 Waht has COSMAS done?

A computer-based data management system was set up for col-

lection, storage and retrieval of soil survey data. The method

and terminology of soil descriptions were standardized for site

and horizon data collection in the field by introducing numerical

and letter codes. The description data as well as analytical data

are stored and managed in the files of the program package SPSS

(Statistical Package for the Social Sciences), which can be used

even by those who have no special training or knowledge of com-

puter techniques. SPSS carries out not only data management but

also statistical analyses. Data can be screened for users' re-

quirements and the profile description in a conventional text

form is generated through the "DECODE" program completed by the

author. A large number of raw data which are obtained in soil

survey has become accessible to users, employing standard ter-

minology and description.

Cartographic File was provided for the management of polygon

data which were derived from various maps prepared in a conven-

tional way. Five programs were provided to COSMAS for the graphic

representation of soil survey data. The graphic representation

of data as a map or graph is necessary for understanding and

interpreting soil survey data and for publishing the results.

Site and polygon data, raw or processed, are automatically

turned into a map or a diagram on a graphic display device, X-Y

plotter and printer p!otter through these programs.

Program ''PLOT" transcribes required data onto their obser--

vation sites to produce a soil-attribute plotting map. Program

"CONTOUR" prepares an isoline map for a given attribute•

-36-

program ''TRANSECT" gives a transect-attribute diagram to show the

relationship between a given attribute and the topography along

a transect set by a user'. Program "AUTO]vrAP" generates a map from

site information as regards to a given multistate-unranked soil

characteristics• Program ''MAP" controls the management and output

of polygon data which are stored in the Cartographic File. Whole

or screened areas of a thematic map ean be reproduced through

.this program.

Those programs feature interactive query and response on a

graphic display device, so that it becomes quick, easy and in--

expensive to find. the most appropriate map or diagram by trial

and error. The final output can be reproduced for publication

by means of an X-Y plotter and printer plotter, thereby saving

much time and labor.

soil

raw

It is concluded that COSMAS satisE',ies basic requirements of

information system with respect to efficient management ofsoil data, easy handling of the system and automated cartography.

4.2 wtiere is COSMAS going?

COSMAS was provided with basic functions and data for soil

inforrnation system, but is still under development. The author

recognizes the following lines along which COSMAS must be de-

veloped in the future;

1. improvement of functions (especially for automated car-

tography), and

2. cooperation with related information.

In the near future, it may be possible that some functions of

COSMAS are modified and improved due to development of pe-ripheral devices and that new functions are provided for COSMAS.

Such improvement of functions should be directed towards simple

and easy utilization of existing programs and subroutines in the

-37-

library programs, as well as towards development of new ones.

As far as the functional improvement is concerned, it is of greatimportance that those programs should be compatible with S' PSS

which carries out basic managernent of soil survey data in COS)CAS.

Otherwise, those programs may disturb the efficient operation of

COSDCAS.

COSINIAS is now capable of efficient management and rapid re-

trieval of soil survey data with a variety of output forms. How-ever, those data alone ' can hardly be sufficient for the practical

requirements of the agricultural management, planning, environ-

mental assessment and so forth. Thus, COSMAS must be able to link

its data to those which are made available in related field such

as crop management and meteorology. It is another important

problem to build up a procedure for linking those data together.

-38-

PART rl

Practical Use

- Kaya Case

of COSMAS

Study -

CHAPTER 5 Description of Survey Area

'5.1 Location

Kaya Township is located in the northwestern

.prefecture at a distance of 126km from Kyoto City.

about 6000 ha and its population is nearly 10,OOO

Figure 5-1 shows Kaya Township and its surrounding

part of Kyoto

. It occupies

in 1976.

area.

SEA OF JAPA rl

'"JakasaBayN

L...,

K,AYATown '

y.yoTo Pref.

N35KYOT•OCity

E135

O30(km)

Fig. 5-1 Kaya Township, Kyoto Prefecture, and surrounding area

-41-

5.2 Climate

Annual average temperature of the area is 140C and the high-

est and the lowest monthly temperatures are OOC in January and

300C in August, respectively. Precipitation is about 2,OOO mm

per annum in average with two maxima in winter (due to snow)

and in September (due to typhoon).

Table 5-1 shows some climatic data that have been obtained

for fifty years at Miyazu Meteorological Station which is adjacent

to Kaya Township. Figure 5-2 illustrates the pattern of soil

water regime calculated after Kyuma (1973a)using the above data.

As can be seen, soil moisture status shows surplus all year round,

thus, soil is subject to strong leaching condition.

Table 5-1 Climatic data of the study area

Month Jan. b'eb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec . Total

Temperature' 3.3 3.4 6.1 11.6 16.0 20.5 25.0 26.0 21.9 15.9 10.8 6.e -(eC)

.Potential

evapo- O.50 O.53 1.56 4.28 7.70 11.13 15.22 15.22 10.46 6.11 3.o-i 1. 25 7?.Otranspilation

(cm)Precipitation 20.90 17.13 14.23 12.32 10.71 16.86 16.05 17.02 23.18 17.05 13.0620 .58 199.09

(cm)

3OOTTiiTi

1OOmrn

--- -

Ponde

water

Oalin

--"-P--A.F:r::i:i•År-.II.R.un-off.-X-----t----l-------

-

Soil.moISt

Jan Feb Mar Apr May Jun JulAug Sep oct Nov Dec JanFIonth

Percolation ur-1OOmmd-----

-Fig. 5-2 Pattern of soil water regime

-42-

5.3 Landuse

' ' Most of the arable land is used for the cultivation of low-

land rice in summer and allowed to lie fallow in winter. Upland

crops are mainly cultivated in a garden andlor field around

houses. Twenty percent of the forest land is managed for pro-duction of commercial timbers such as Sugi (Cryptomeria japonica)

and Hinoki (Chamaecyparis obtusa), however the management is not

intensive. Landuse of Kaya Township is summarized in Table 5-2.

Table 5-2 Landuse of Kaya Township

(ha)

Lowlandpaddy

Uplandcrops

Naturalforest

Managedforest

Bambooforest

Residence Others Total

591 87 3515 922 101 69 654 5939

5.4 Physiography

The study area consists of a lowland formed by the Noda

River and surrounding mountains in the east, south and west.

The southern part of the lowland that has a relief class of '"rolling" to "hilly" is characterized as a fan, while the north-

ern part is classified as a flood plain or a valley p!ain.

River terraces can be seen on the sides of the lowland. The

relief of mountainous area ranges from "hilly" to "steep." The

mountains of southeastern part, whose bedrock is serpentine,

show smooth and simple slopes and have a few big peaks. On the

other hand, the rest of the area, whose bedrock is mainly granite,

has relatively complex slopes and consists of many small peaks.

-43-

z

C/ X,.xx) iJ' r,•Ac il/Jzi/f:,."y'w

k7pt,tt•,/•"

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-44-

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The lowest and the highest points ln the area are 8 m above

sea level in the flood plain of the Noda River and 832 m above

sea level on the top of Mt. Oye which is on the southeastern

border of the township.

Figure 5-3 shows a physiographic map compiled by an air-

photo interpretation. This map is stored as polygon data in

cartographic File of COSMAS with its attributes which charac-

terize delineated areas.

5.5 Geology

Geology of the area is quite simple except the southeastern

part. The bedrocks of the mountains in the western and north-

eastern parts are late mesozoic coarse-grained hornblende biotite

granite and biotite granite. In the southeastern part of the

area, the picture is more complex and bedrocks such as paleozoic

shales and.Iate mesozoic fine--grained granite and serpentine

are seen. The lowland consists of recent fluvial deposits that

make up valley floors and the lowest terrace. Pleistocene ter-race deposits can be seen only in Akeshi and Inanaki area. Figure

5-4 shows geologic map published by Geological Survey of Japan

(1965).

5.6 Vegetation

There is a clear difference in vegetation between the ser-

pentine area in the southeast and the rest of the areas dominated

by granite. The former is composed of low shrubs of deciduous

broadleaved and evergreen tree species and scattered poorly grown

red pine trees. Therefore, an openness of the plant cover is

a remarkable feature. The vegetation on granite areas has almost

-45-

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.,.it'e's,khpu`.a"--==fiL--L--ijo=ss....g-"'iii!=nN. 'O's"h`"--zg.....

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-46-

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-47-

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the same species composition as the one mentioned above, but all

trees are grown much taller and the area is c6vered with dense

canopy. Tree planting is more intensive and better managed in granite

areas than in serpentine area. The summit part of the mountains

in the southeast which is higher than 700 m above sea level is

covered with barnboo shrubs and Susuki (Miscanthus sinensis).

In both the areas, Sugi (Cryptomeria japonica) and Hinoki (Cha-

maecyparis obtusa) are planted in afforested areas. Natural

forest is a mixture of deciduous broadleaved trees such as Quer-

cus crispula, Quercus serrata, Quercus stenophylla and Quercus

crenata, and evergreen trees such as Camellia japonica, Eurya

japonica, Ilex crenata and Pieris japonica and Pinus desiflora.

There ean be sporadically seen bamboo forest (Phyllostachys

reticulata) on the foot of the mountains. Figure 5-5 shows the

vegetation map which was prepared by air-photo interpretation

and field observation. All the data of this map are stored in

Cartographic File of COSIY[AS.

-48-

CHAPTER 6 Soil Survey

6.1 Profile Observation and Soil Sampling

One hundred and ninety-six profiles and seventy-eight pro--

files were observed at densities of one per 4 ha in the lowland

and one per 9 ha in the mountain area, respectively. Figure

6-1 shows the locations of observation pits where soil samples

were taken.

Grid survey (approximately 200 m grid in the lowland and

300 m in the mountain) was carried out so that the sites of soil

observation and sampling may not be biased. Site and horizon

descriptions were collected on the standardized description card

shown in Chapter 2 and transferred into Site Description File and

Horizon Description File of COSMAS, respectively, for storage.

6.2 Laboratory Analyses of Soil Samples

Soil samples were analyzed for pH (water and IN KCI), ex--changeabie cations (K+ ,Na+, ca2+ , Mg2+), cation exchange capacity,

total nitrogen, total phosphorus, phosphorus absorption coef-

ficient, available silica, available phosphorus, free sesqui-

Oxides (Fe203, A1203, lm02), three phase distribution and particle

size distribution. Ibeom those data the followings were derived;

percent base saturation, CIN ratio, bulk density, particle density,

water content, pore content and water saturation percentage.

Table 6-1 gives a brief description of the methods of laboratory

analyses and references. All the data obtained are stored in

Analystical Data File of COSMAS.

-49-

Table 6-1 Brief description of method of laboratory analysis

pH (water, IN KC1)

Exchangeable cation

Cation exchangecapacity

Total carbon

Total nitrogen

Total phosphorus

Phosphorus absorp­tion coefficient

Available silica

Available phOSpho.uS

Free oxides

Three phasedistribution

Particle sizedistribution

measured electrometrically in a 1:5 soil/solution mixture of distilled water orIN KCl solution

IN ammonium acetate extraction, measlJredwith atomic absorption for ca2+ and Mj+and with flame emission for K+ and Nadeterminations

buffered neutral IN calcium chloridesolution medium (pH 8.2) measured withatomic absorption modified from Matsuo(1968)

Turin's combussion, titrated with Mohr'ssalt solution (Department of AgriculturalChemistry, Kyoto University, 1973)

digested in Bremner's method (1960),measured with ammonium electrode (Yone­bayashi and Hattori, 1980)

digested in Bremner's method, measuredcalorimetrically (Department of Agricul­tural Chemistry, Kyoto University, 1973)

neutral 25% diammonium hydrogen phos­phate mediumm,measured calorimetrically

acetic acid - sodium acetate buffer ex­traction (pH4.0), measured colorimetri­cally (Department of Agricultural Chem­istry, Kyoto University, 1973)

0.2N HCl extraction,measured calorimet­rically

0.2N oxalic acid extraction under anexposure of sunlight for nearly 5 hours(Matsuo, 1968), measured with atomicabsorption for Fe and Mn, and with in­ductive coupling plasma emission forAl (Fuwa and Haraguchi, 1980) determi­nations

taken as a soil core sample of 100 cc,actual volume (total volume of solidand liquid phases of the core) weremeasured, and percentages of three phaseswere calculated

dispersed in NaOH medium after organicmatter removal, followed by pipette method

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6.3 Forest Soils

The criteria used here for the classification of forest soils

are those proposed by the Forest Soil Division of Government

Forest Experiment Station (1976). Following soil types and sub--

types were encountered in the forest part of the study area; Dry brown forest soil - granular and nutty structur6 type (BB),

Moderately moist brown forest soil (BD), Moderately moist brown

forest soil - drier subtype (BD(d)) and Wet brown forest soil (BF).

As mentioned in 5.5, there can be seen several types of bed-

rock in the area. They are serpentine on the upper part, fine

grained granite on the middle part and coarse-to-medium grained

.granite on the lower part of the slope in the study area. Inaddition, one more soil material that is a mixture of serpentine

and fine grained granite can be seen in a relatively gently sloping

part of the mid-slope position that was previously disturbed by

landslides. Hence, there are altogether four different kinds of

soil material in the area. The site and horizon descriptions

with analytical data for the profiles representative of soil in

terms of soil type and soil material are given in Appendixl.

6.4 Lowland Soils

' The criteria used for the classification of lowland soil are

those proposed by Furukawa (1976). In the study area, the follow-ing soils are distributed: Gley lowland soil mainly occurs in the

back swamp oE flood plain and in the bottom of narrow valley

plains and shows gley color throughout the profile because of a

strong reductive condition. Gray lowland soil can be seen in the

area of flood plain, gently sloping terrace and gently sloping

to sloping valley plain and has horizons with dominantly gray

color (2.5Y to 7.5Y). Brown lowland soil occupies the area which

-53-

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-54-

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Table 6-2 Legend of soil map

.Mappzng Soil Soil Form Phase

UnitNo. Type .Varlety 'Physiogr. Soil 'Stonlness Relief.Unit Texture

1'GLL ALL. FP F FLAT

1 GRL VG FP M FLAT2 GRL ALL FP M FLAT3 GLL ALL FP M FLAT4 GRL ALL FAN c G.SLs GRL ALL TR F G.SL6 GLL ALL TR M SL6 GRL RG TR M SL

7 GLL ALL FP c FLAT7 GRL UG FP C FLAT8 GRL lALL vp M SL

9 GRL ALL vp c G.SL10 GRL ALL vp M PM ROL-l1 BLS ALL FAN M SL

11 GRL ALL FAN M PM SL

l2 PSG TR F ROL

13 BLS ALL FAN M ROL

14 PSG TR F SL

15 GRL ALL vp F CP SL

16 BLS ALL FAN M CP SL

17 GRL UG vp M G.SL

AbbreviatLons Soil Type : GLL(gley lowland soil), GRL(gray lowland soil), BLS(browii lowland soil), PSG(pseudogley soil) Soil VarLety ; ALL(all horizon), UG(underground gley), RG(reverse gley),

Form ; FP(flood plain), FAN(fan), TR(terrace), VP(valley plain), F(fine texture), M(medium texture), C(coarse texture) Phase ; PM(pebble rnany), CP(cobble profuse), FLAT(flat), ROL(rolling), G.SL(gently sloping), SL(sloping)

are classified as sloping fan or natural levee of flood plain,

shows brown to yellowish-brown color in soil matrix and rarely

undergoes reductive condition. Pseudogley soil is mostly ob-

served on a terrace and shows almost always heavy texture which

causes poor internal drainage. In such a condition, the soil

shows brown to reddish-brown colored mottles in the gray colored

matrix because of prolonged water saturation in a certain period

' . Figure 6-2 shows the soil map of the study area. Legend is

given in Table 6--2. Descriptions with analytical data for the

ti -- soil profiles representative of the mappmg umts are given in ' Appendix-II.

-- 55 -

CHAPTER 7 Numerical Approach to Soil Material Classification

7.1 Introduction

Soil material has been, heretofore, identified by an experi-

enced surveyor in the field, or by physical and chemical analyses

carried out in the laboratory. The surveyor's field judgement is

often subjective and uncertain, while detailed laboratory analyses

take considerable time and labor, particularly when handling a

large number of samples. Field descriptions should be used as

efficiently as possible in determining soil material because they

are easy to obtain. A numerical classitication method may well

permit highly reproducible determination of soil material. It

works to stated rules. Its advantage is not that the method isobjeetive - someone must initially fix all the procedures and

concepts - but that once this is done, the rules can be explicitly

stated, and they are uniformly and consistently applied throughout

the analysis, as Norris (1970) stated.

This study aims at generation of a procedure for classifying

soil material by introducing numerical manipulation of soil data.

'Since the objective of this study is to classify soil material

not only consistently and objectively but also in a manner sim"ar

to the conventional way, the rndst important thing is to find out

a rule by which surveyors classify soil material, and formulate

it into an appropriate procedure based on statistical methods.

Once the procedure is fixed, it determines soil material more

objectively and consistently than an experienced surveyor does.

The conventional procedure of a soil surveyor consists of

the following steps;

1. he selects soil attributes which distinguish a certain soil

material from the others ' 2. he sets up soil material classes in the study area, taking

-56-

into account the respoiise patterns of the samples to selected

soil attributes, and ' 3. he sets the'samples into one of the soil material classes he

has chosen. .[lhis chapter describes a numerical procedure to replace these

three steps.

For the first step, a surveyor selects, based on his experi--

"ence, some key attributes out of those obtained in both the field

and laboratory. Since the objective of this study is to carry out

a re!iable elassification close to the conventional one, the soil

attributes to be used herein should be the same ones which a sur-

veyor employs in his conventional classification.

Next, for setting up the classes various statistical analyses

were adopted, as reviewed by Norris (1970) and Arkley (1976).

.Principal component analysis (Cuanalo and Webster, 1970; Norris,1971; Kyuma and Kawaguchi, 1973, 1976), factor analysis (Arkley,

1971; Kyuma,1973b;Suh eJ! g.IL-., 1977a,b), and similarity ana!ysis

(Hole and Hironaka, 1960; Bidwell and Hole, 1964; RuSsell and

Moore, 1967; Campbell et al., 1970; Cuanalo and Webster, 1970;

Moore et al., 1972; Kyuma and Kawaguchi, 1976) were applied to

classification of soil groups at different categorical levels and

to classification of soil material, and to an evaluation of soil

tilth end fertility. These statistical methods are intrinsically

applicable only to "scaled attributes," thus, some of the soil

attributes employed must be scaled as numerical codes, whereas

they are not originally scaled. On the other hand, most of the

descriptive data obtained in a soil survey are multistate- ranked

or unranked, rather than scaled. Reyner (1966) and Muir ettL a.IL.

(1970) assessed similarities among all the horizons of sample

profiles by handling separately three kinds of soil attributes:

dichotornies, alternatives, and scales. An overall similarity

between soil profiles w'as then obtained on the basis of the averagesimilarity of matched pairs of horizons. Their procedure is rnuch

-57-

better forsimultaneous handling of multistate- ranked and unranked

Hayashi's theory of quantification No. 3 (Hayashi 3) was

originally proposed by Hayashi (1956) and applied to the classi-

fication of wild rice varieties by Takakura (1962) and to the

comparative study of character between the Japanese and the Jam

panese-Americans by Hayashi (1975). In both examples, all indi-viduals were assigned numerical' values, which imply similarities

among them based on, what is called, the "response pattern" of

qualitative andlor quantitative data for selected variables.

This is basically the same procedure that is followed by an experi-

-- -enced surveyor for understanding the relationship between a pair

of soil profiles on a given set of soil characters prior to clas-

sifying those individuals. The surveyor recognizes the similarity

between two individuals on the basis of their response patterns

to each of the soil attributes concerned with the classification.

Then he sets up several groups of individuals eqch of which shows

a different type of response pattern from the others as soil ma-

terial class. Therefore, among these statistical procedures,

Hayashi 3 is best suited and closest to the conventional procedure.

After the soil material classes have been set up, samples are

to be allocated'in one of them. It is possible to encounter a

sarnpl'e which cannot be allocated- into one of the material class.es

due to its'transitional characteristics between two .Qr more of .

them. Furthermore, the surveyor's judgement in the field is often

variable and hardly comparable to that of another surveyor. -In

such,si-tuations discriminant analysis is helpful, as described

by Webster and Burrough (1974) and Norris and Loveday (197D.

Discriminant analysis is capable of consistent allocation of a

sample into one of the predetermined groups. It is applicable to

"scaled attributes." However, no problem arises in handling the

multistate- ranked and unranked attributes employed for the clas-sification of soil material, provided the raw data- of those

-58-

'

attributes are first converted to numerical values.

The author proposes a combination of the two statistical

analyses, Hayashi 3 and discriminant analysis, for the numerical

procedure which replaces the conventional classification of soil

materia1.

7.2 Study Area and Employed Data '

The study area was on the west slop6 of the mountains in the

eastern part of Kaya Township. As shown in Figure 7-1 the area 'is divided into four regions on the basis of bedrock type: ser'

pentine area, mixed serpentine-granite area, fine grained granite

area and coarse-to-medium grained granite area. The fifty-six

soil prGfiles examined in this study were selected so that they

would cover the varieties of soil material in the study area.

As shown in Figure 7-1, most of the profiles were distributed qt

intervals of 300 m along four transect lines from the ridge down

to the valley, and the rest were between the regular samplingsites to detect rbore precise differences between soil materials

on one of the transects. As shown in previous studies (Webster

and Wong, 1969; Webster, 1973; Webster and Cuanalo, 1975) a tran-

sect gives, with limited man-power, great deal of information

about soils, provided it is laid to include all varieties of soil

attributes concerned. It suggests a two-dimensional distributionpattern of a given attribute, given a proper topographic inter-

pretation of the surroundings. By the conventional survey, each

sampled profile was classified into one of the soil material

classes suggested from the above mentioned bedrock types. Table7-1 summarizes the results of soil material classificatlon for

all sampled profiles. ' The soil attributes employed herein for the numerical clds- 'sification were selected from those which are stored in the COSMAS

files, namely Site Description File, Horizon Description File and

r 59 -

,t X • •L ' - .;t - ,' ,t 1 .•' .ii nl ' f'

: f! r•'f ,, L,1l

i L' ,J-, k-f, ,,1

ltl '•' ,1' t - ' '(t - ,"L/t -, - 'r •• 1.j:•r :ilJt-,i 'L '• '

L' t-.4 •t t. ' ,? ' L:1r• J .• n• f. ,t-

t .

'f'"R l., .

'

.LL ,. t '•-'

ti .1 ts- .i L, .,,,

-L- J' N ---i

-Jttt : /.tt

:b,t'"r(o 1'r 1.• •,tt)'.r '•,'fi I.i

it xl ,-

ltii'lll."l-sle'i,'..:".{-i"/,,n}ntS}.3"'. ,. !N 1.1).s i` G .. ;-. .:" 'i

lt.m' "' i -i-.

.-;1, 'F

-•l"1-I •-

,JJf' i'

)-- lt

"'p tJ ' " "

;R .

--

ttt

- ,e

G

'i"

tt /t

i.

.

-

t -t/

t-

.xJ"'i

.

.

b

'1

pl'le '•

/-t

- " t

1.

.e .,.e

-- .- t

..-

'

.r-

ll

r

,

'

•s":

.; d, ,7t..s7

, •/1 'x; t

• v.' t., :fill' S

lliRl,(,

I 1 , t/- ' km ! L. ',.s.'•;•Yl 1'

11 - -,-+

Mt.

,n ,, fL-.

gi

,7

ll-ili'lrsi,

liil]a

b;ivzlEcL.

: •t'gi]`'1'"rr,----'

t. ,,,,1.,i?lrl7, lf-r •.li:i•,:•i.,1.,

:s'

/--4 . t ..-e ) tt iv', E' .V !},tr";"LiE'l'" .fiil•1,t/L.

v

--

'fEiii.

.s ,-

't

t vY

x5it((.

i ,

.i l

.

. k

k eN

.!

- t-, -,- ' -'.l ti - - 1 -t-te p ,. . I. eh •:T .'s.:lt';n..,,"Yl" fih 1'h':L•Lv ,k

.,] };-b

tt: v-

-/. -.

, st

r

e

s- ,t-- .

• tr

.Fig

s

., ' tv ns '

-- s.

." ,-v l

if`'

il

1.0 ,,' ''1

t 1't }

Location Oye

rt/) }- .1t

1,,

tr .t

Sampled profile

o.s === ' .,-;ff,sgt

. 7-1

Table 7-1

E ,:J

- -

"t -.

tt

of

Conventionalsites

l )-- .t- --

,?,1, ,

sampled

-"- ,+-

r

". e ev

G .-- -

,ci

1- i•. t.r '

.

rT -e i -{ : ,b-: '1' -' I• 'dt i. NL. , XL- x'G} ,i

,..•"i'i'•{"'i`'i'i,.l-' l,'l•":,l.Åq,/,iiii'iIt"i.iii

- :-i.r t -. IS .'"- --ti,- -

.]. i

` son ptterlal ' ' O : eoaree-to-medSmN. , srttned sr'entte 7." ' Gr : 7tnc sreSned srsnite

S : Serpenttne .'' GS : Orantte + Serpa ttne '-s

..a-. .ptV ,,1 ' M - .. td; 1- r -- ";h,. '-' ' X•` ' ., .., N s t, '.-,- -lt- -t i Lk .t ny . L/

soil

;

i?t)

-t- -

-,-

.GS, . ' . -et

-t profiles

t'

.

-{tt-- i

` 'N"'

L

E,

d

1

ttt .-t

on

il.N

ei

1

.

.

,

,

.+..

s-

the

-il ts t- t- ` . . .- ,,- . . .b ei{',•.y"

. t.-L,V.' S,}Åq •

te`e .re

-"

west

classification

;

-.

-/

.

csle

I'i-i)iy`'llS`'IJIt'51f"

' - 4'. i- -Se

.-1 -. tEf.nt11 i ff• E.El."r/

' :.e.' - • -..L'

i'"` s"".4,.

-. -s. t -tc - ,; i.t .,i "' , 'r '= i.

i Ri' ttS"

. ' r• i3k" ,.L.-.../Sv'ii."

• L• t/F;,' -f s•'

';. '.' fmet

l•1•-l f' -J ( N.

'2. ;p"Åq'x

lrt .Vll)

veN""

"p"s

ma teria1

slope o:-

at sampled

,I

'

i

El

l

Type of soilmateria1

Total No.of profiles

Profile numbers

Serpentlnlc

Mixture ofserpentine and .granlteFine-grainedgran1t1c

Coarse-to-mediurn grainedgranLuc

13

7

6

23

27,261

30,

34,

22,52,272

28,29,262,

33,3S

49,SO

23,2453,S5,274

,S6,26S

,6S,

,51,

,2S,,63,

57.

263

267

26,66,

58,S9,258,259,260,

,264,269

,273

31,36,37,38,39.40.67,265,268,270,271,

l

-60-

l1

Etl

,Analytical Data File. They are the same soil attributes used by

a survryor for judging the type of soil material of each profile

in the study area. Program "TRANSECT," which was added to COSMAS

and described in Chapter 3, was very helpful in understanding the

relationship between soil attributes taken as site information,

e.g. soil drainage and bedrock type, and the one between site

information and its topographical location. The following soil

attributes were chosen: soil drainage (DRAIN), texture of subsoil

(LTEX), plasticity (LPLAS) and stickiness (LSTIC) of subsoil, size

(STSZ) and type (LSTTY) of stones, compactness of surface horizon

(UCOMPM) and subsoil (LCOMPM) measured with penetrometer, finger

penetrability (COMPF), color (chroma) of subsoil (LCHR), grade of

soil structure development (STRG), mode of parent material con-

tinuation (PMCONT), exchangeable magnesium content of subsoil (LMG),

water content of subsoil (LH20C), water saturation percentage (LH20

SP), air--phase percentage of subsoil (LAIR) and particle density

of subsoil (LPD).

7.3 Soil Material Class Establishment

7.3.1 Hayashi's theory of quantification No. 3 (Hayashi 3)

This is a method of classification of individuals based on

the similarity of response pattern with respect to attributes

having several attribute-classes. What we call "response pattern"

is the pattern of soil attribute--classes to which an individual

(i.e. soil sample) belongs, as shown in Figure 7--2 (A).

The check mark (V) indicates that the sarnple falls into this

particular attribute-class of selected soil attribute in question.

If the data matrix (A) is rearranged into (B) so that check marks

fall along a diagonal in the matrix, those individuals and attri-

bute-classes which are in adjacent positions in the matrix are

- 61 -

(A)

Attribute IIIClass 2 3 •1 2Individual

1

vv

v

vv.v

vv

vvvv

(B)

Attribute- I II I n I

Class' l I ' I '

Individual• 1 2 3 1 2

1 v v

2 v v

3 v v

4 v v

5 v v

6 v v

.Fig. 7-2 Schematic representation of response pattern and Hayashi's theory of quantification No. 3

-ftg o2 - a2 (U= Ob + aw 'where, ub2 iS the bdtween-variance for x.

' A'numerical value x is assigned to every attribute-class,when satisfying the abov6 mentioned condition. A numerical valtie

to be assigned to an individual, y, is obtained by summing up

nunierical values (x's) assigned to attribute-cla'sses to which the

individual is chdcked.

-62-

considered to be similar to each other. The mathematical objective of Hayashi 3 is to assign numerical

values X. and y. to the j-th attribute-class and the i-th individual jlrespectively, so as to maximize the correlation coefficient between

x and y. This procedure is equivalent to the rearrangement of

matrix as mentioned above, and consequently, both individuals and

-- tattribute-classes which have a similar response pattern are gwen

similar numerical values. ' When maximizing correlation coefficient p between x and y,the ratio-of within-variance ( ow2) to the total variance ( u2)

for one of the variables, say, x, is to be minimized. It is equal

to maximi4ing.cgrrelation ratio, n , which is defined as follows;

n 1. U'w2 IOb2 2 2 2)

'

Suppose that the responsb of the i-th individual to 'attribute-class is defined as follows;

6i(j) =[ 8j \:.e". I.tb:g2:gfi.:o the j-th ciass

Then between-- and total-variances are given as follows;

Ub2 =$i ]ii.(i.xj 6i(j))2- (" i.xj f.j)2

o2 =ii. xJ2' f.j- (ii xj f.j)2

where, f.j --- \ 6i(j)

1 fi. = Z 6i(j) j T =\ f.j-; fi. !2 c]To obtain x, which maximizes n2=ri:g:zE'b , one must solve the

eigenvector problem (H -n2F ) x = O, where the elements

F are as follows:

Hjk = \ 6i(j) 6i(k) - f•j.f.k

1 fi. T f.' f.k

Fjk =

The largest eigenvalue

the first dimensional

Likewise, thevector 2x define

thogonal to the

The numerical

s-th dimensionalSx's as follows;

the

'

j-th

general

of H and

J-r (j\k)f.j d f' g f•k (j.k)

.. 1. in2 and its associated eigenvector x defme

' numerical values to the attribute-•classes.next largest eigenvalue 2n2 and its associated eigen-

the second dimensional ones, whose axis is or-

former one, and so forth. value to be assigned to an individual on the

axis is computed from the linear combination of

-63-

IIH Syi = fi. gt Sxj 6i(j)

A detailed mathematical explanation of Hayashi 3 is given by

Hayashi (1956, 1975), Yasuda and Unno (1977), and Takakura (1962).

All computations are done with one of the subprograms of SPSS,

"HAYASI 3" (Miyake and Yamamoto, 1979), which is accessible in

COSMAS.

7.3.2 Application and results

Hayashi 3 starts with setting up attribute-c'lasses for each

of the selected soil attributes just as a surveyor does when clas-

sifying soil rnaterials, and Table 7-2 shows these attribute-

classes. Threshold values which define those attribute-classes

were assigned so as to distmguish a certain group of soil material

from the others. Hayashi 3 was applied to 56 soil profiles by em-

ploying their responses to 43 attribute-classes of 17 soil attri-

butes as seen in Table 7-2.

Table 7--3 shows the five largest eigenvalues (n2), with the

cumulative ratio of total variance for each. As shown in the

table, the first five eigenvalues explain 62.6 % of the total

variance. The solution of Hayashi 3 analysis, i.e. the numerical

values to be assigned to each attribute-class, was obtained as an

eigenvector corresponding to each of the eigenvalues. Table 7-4

gives five numerical values for each attribute-class derived from

the first five eigenvalues. ' Assigned numerical values roughly imply the similarities

between attribute-classes on the basis of individual's response

pattern in the data matrix. To the attribute-classes which were

given the five lowest values in the first solution of the analysis

(i.e. high water saturation percentage (LH20SP-III), fragments of

serpentine stones (LSTTY-III), high exchangeable magnesium content

-64-

Tab1e 7-2

'X---.*Attribute

Attribute-class list for soil material classification

DRAIN

LTEX

LPLAS

LSTIC

STSZ

LSTTY

LCOMPM

UCOMPM

COMPF

LCHR

STRG

PMCONT

LMG

LH20C

LH20SP

LAIR

LPD

* The

v.poor--m.well

LS SL L SILSCL

non-weakly

non-weakly

gravel

granlte

Åq18 O

Åq18 O

35 5

3

o

Åqo Åq20 O

Åq60 O

Åq25 O

Åq8

abbreviations in

well-excess

CL

moderately

mod.-strongly

s.pebbte

granite(fine)

18 0 25 0 18 0 25 0

4

68 2

oo 20 0 40 0 60 0 80 0 25 0 30 0 8Åq

describ ing

SICL SC LIC SICHC

strongly

pebble cobbleboulder

-serpentlne

25 OÅq

25 O(

attributes

3

40

80

30

must

mrnirlin

(code)

(Munse11)

(code)

(code)

me % % % glml

be referenced in 7.2.-

Tab1e 7-3 Eigenvalues, correlation coefficients and cumulativeratios of total variance derived from "Hayashi 3"analysis for soil rnaterial classification

Eigenvalue Correlationcoefficient

Cum.total

ratio of varlance

1

2

3

4

5

O.484

O.186

O.119

O.092

O.088

O.696

O.431

O.345

O.304

O.297

o

o

o

o

o

.

.

'

.

.

313

432

509

S69

626

-65-

Tab1e 7-4 'Numericfor soil

values assigned to soil material classification

attribute-classes

Attribute Class1 2

Solution 3 4 5

DRAIN I

II-1.128 1.109

-O.387 O.317

-O.206 O.312

-O.3S1 O.302

-O.615 O.511

LTEX I

IIIII

1.093-O.294-- 1.91S

-O.673 4.606-O.958

O.352-• 1.335-O.122

-O.356 2.419-O.478

-O.077-- 1.687 O.875

LPLAS I

IIIII

1.134-O.223-1.884

-O.899 3.110-O.763

O.206-O.O32-O.443

-- O.306 O.885-O.121

-O.046-1.792 1.3"

LSTIC I

II 1.023-1.307

-O.389 O.(59

O.OIO-O.073

-O.382 O.482

-O.343 O.396

STSZ I

IIIII

O.627-O.692-1.062

-O.428 O.727 O.369

O.568 1.292-2.979

-O.138 2.739-2.374

O.633-2.693 O.861

LSTTY I

II

IU

1.009 O.076-2.039

-O.609 3.373-1.147

O.420-1.827 O.334

-O.5(3 O.772 1.001

-O.556 O.580 O.440

LCOMPM I

IIIII

O.670-.285 O.806

O.784-O.231-2.840

O.149 1.121-4.055

O.541-l.405 1.669

1.298-O.404-4.567

UCOMPM I

II O.611-1.320

O.130-O.335

-- O.473 O.942

O.334-O.733

-O.O13-- O.O34

COM?F I

II O.S05-O.859

O.610-O.415

-O.286 1.008

l.472-2.217

O.267O.393

LCHR I

II-O.417 1.071

O.494-1.327

O.403-1.126

O.420-t.086

1.099-2.855

STRG I

II-- O.814 1.297

O.768-1.265

O.182-O.358

-O.2l9 e.34o

-- O.205 O.276

PMCONT I

II O.286-1.067

O.274-1.109

-O.323 1.085

-O.690 2.574

O.324-1.306

LMG I

IIIII

O.478-O.172-lr966

O.412 O.158-2.081

-O.493 3.259-O.874

-O.217 O.852 1.702

O.262-O.951-O.441

LH20C I

IIIII

1.536 O.463-- 1.375

-1.111 O.887-O.981-

O.660 O.571-1.258

2.082-1.294 1.157

"t .476

-O.798-O.682

LH20SP I

IIIII

1.067-1.09t-2.045

-O.045 1.732-1.512

O.501-2.2l3 3.008

O.294-1.844 IJ17

O.156 O.391-O.592

LAIR I

IIIII

-1.276 O.805 1.t15

O.314 O.896-O.978

O.063 1.364-O.971

-O.047-1.962 1.22• 7

o.ooo-1.977• 1.13:

LPD I

II O.284-O.564

O.494-- 1.038

1.178-2.434

O.209-O.(27

-O.251 O..t 45

-66-

(LMG-UI), heavy soil texture (LTEX-III), and strong plasticity

(LPLAS-III)) correspond individuals whose soil material is of

serpentine origin. On the other hand, the attribute-classes

showing the five highest values (i.e. Iow water content (LH20C-

I), weak:y developed stfucture (STRG--U), weak plasticity (LPLAS-I), high air-phase percentageÅqLAIR-III) and sandy soil texture

(LTEX-I)) are the characteristics of coarse--to-medium grained

granitic material. Likewise, the five attribute-classes (i.e.mediurn soil texture (LTEX-II), fragments of fine-grained granitestones (LSTTY-II), medium plasticity (LPLAS-II), medium water

saturation percentage (LH20SP-II), medium air phase percentage

LLAIR-II)), to which higher numerical values were assigned in the

second solutioR of the analysis, indicate the characteristics offine-grained granitic material. Thus, similar numerical values

were given to the attribute-classes showing representative char-

d-acteristics for each soil material distinguished by a surveyor 'in the Åíield.

Next, the scores to be assigned to an individual were computed

from a linear combination of these values with respect to each

solution, taking the response of the individual to each attribute-

class into consideration. Thus, those were also given in five

dimensions as shown in Table 7-5. These numerical scores imply

similarity between individuals. ' In order to set up the soil material class each profile was

plotted in two dimensional space, taking the first two scores

assigned to the individual as coordinates, as shown in Figure 7-3.

The first two eigenvalues explain 43.2 7. of the total variance as

shown in Table 7-3 and further eigenvalues contribute much less

than the first two eigenvalues. Thus, it is best to use only the

first two scores in representing the samples in a scattergrarn.

Each of the samples was plotted with a label of the soil material

group asSigned in the field by the surveyor. An ellipse shows a

statistical boundary within which 90 7. of the populatlon are

-67-

Tab1e 7-5 Numeric values ---classificatton

assigned to profiles for soil materia1

Profile No. Soil materialt(field judgement) 1 .2

NuTnerlc

3

values

4 5

22 23 24 2S 26 27 28 29 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 52 53 5S 56 57 S8 S9 63 65 66 67258259260261262263264265266267268269270271272273274

L1.1.

L1.4.4.4.f''

1.5.I..

tL,.

LLLLL1.1.

L1.

L1.1.s.E,.

I:.

1.

L1.4.4.4.4.1.5.1.1.4.4.4.4.4.ff/ -

5.4.1.

:.1-

1.T,.

1.

L1.

L:.' -

L

1). 7- 66 I;

t) . 5 E, 4 ;E]

E:I.E,41E O. .J: 64E O. 7- l t]4-1 . il, •:-4?6

-1 . 1 E, 1 ::t

-1.161E•- I] . :•:4E1::

I] . 1:; :I: tlt IIt

- i) . IIitil 7- E;

-iil.:.E:76-tll.1E,44 tlt . 44.:-!4

- t:f . 1:i 4 ,I; ::-:

tlt . 4 :-: F-'• l)-,

l] .; ;l:45 l.] . 4 t• FJ c'

[1. 7• [ii4S

[Il . E, l:1 LIi l.S

III . 7• l-i•)E,

[i . 4 •i' •r" :-'

+ .. 7t-1 .-- I] - t, 1' ,:d .:4

' .-l,',tr1+t U. -:• :, -eo l:L. 4 ':[ 'I: lll

t] . 1 :- i:-[ l[l

-[l . O•). 1) E,

-t.l- 1'::1:tt-

L" t, ti t• U llT . E, E, 4 •r.

. --'"e/ l.1.i.:,d- :,

- u . 'd 1:: t• t,

-l.1;E,'I]-1.E11:1E:-s].I7- :il1

- ''c -, l.l . Ll t+ -iL

- I] . 16 ?.7 •-

tll . E":E• 5

[r . E, ::': 'Jg 5

-l'i ' 1::t' t' d-b

- lli . ,r- III 1[1/:t

- 1 - l.] t/ ,y. :::

- i:t . E,E44

-1 . E•s;i •r-

-1 . [+ •I. 51

- l:1 . r:,7 t:14

- t-l=/":=-IJ.o•-ic,•.+ Lll . 7• 5•r-! 4]

iL E, 1 E, E,

-". ,:: +il E•7

. .-- -,c- lj . :, +• ,:, .-i

i!1.E,14S'1[1. E, 7• •r- iil

-+L S774 l.l . [1 :i l- 1.1

L]. 7- :-14tt.

- -:l . 0 5, /:- :"

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de Soil

1: 2: 4: s:

material 'Coarse'-to-rnedium grained granitic materialFine grained granitic materia)-Serpentinic rnaterialMixed material (granite and serpentine)

-68-

l

SCORE2

rk Serpentinic .materlal

A Fine-grained graniticmaterial

mgranLtrc

Coarse--to--medium-i

'material

AÅ~ Mixed .material

serpentine and'wrth,

granlte

A A'

x Az Åë•

Ox /

L

xm m

' m m

m mm L mm SCORE1

m m e

t t mÅëm a

t, + m

t

l:o t tt+MEi!]

s' mom

m

m

m

.Fig. 7--3 Scattergram of sampled sitestwo numeric scores assignedmaterial class establishment

plotted with the firstby "Hayashi 3" for soil

expected to fa11 (Okuno gt A,IL., 1972)• This was drawn using the

mean, standard deviation and varianee of the scores which were .assigned to the profiles classified into one group by a conven-

tional method as shown in Table 7--6.

The 90 % probability ellipses occupy different positions from

each other except for small areas of overlaps. Thus, profiles

with the same label cluster together and are fairly clearly dif--

ferentiated from each other in the two dimensional space created

by the scores, which are the first two solutions in Hayashi 3.

- 69 --

Tab1e 7-6 Statistics for each soil materia1 group

Soilqroup

materialt Statistics 1 2

Solution 3 4 5

1

2

4

5

meanvanances.d.

meanvanances.d.

meanvarlances.d.

meanvarlances.d.

O.506 O.083 O.288 O.077 O.088 O.296-1.029 O.028 O.168-O.442 O.113 O.336

-O.129 O.083 O.289 O.719 O.141 O.376-O.278 O.Ol4 O.117 O.491 O.111 O.333

O.061 O.104 O.323-O.059 O.025 O.157 O.057 O.148 O.38.5

-O.308 O.120 O.347

-O.O38,O.085

O.291 O.079 O.O17 O.131 O.070 O.089 O.298 O.035 O.179 O.423

-o.

. o.

-o. o. e.

o. o. o.

o. o. o.

Ol613036006405423205802114S

042074273

t soil

1 2 4 5

:

:

:

:

material groupCoarse-to-medium grained granitic rnaterialFÅ}ne grained granitic rnaterialSerpentinic materialMixed material (granite and serpentine)

Three- and higher'dimensional ordination of samples is expected to

better differentiate these probability ellipses. This means that

the numerical rnethods adopted herein reproduce more objectively

the soil material class establishment done in the field by a sur-

veyor. Consequently, in the study area four soil materials i.e.

serpentinic material, mixed material of granite and serpentine,

fine-grained granitic material and coarse-to-medium grained

granitic material, were not only conventionally but numerically

established by means of Hayashi 3.

7.4 Sample Allocation

7.4.1 Discrirninant aRalysis

Diserimxnant analysis begins with

distinguish between two or more groups

"groups'' are defined by the particular

-70-

the desire to statistically

of individuals. These

research situation. To

distinguish between the groups, the researcher selects a colleetion

of discriminating variables that measure characteristics by which

the groups may be best discriminated. The mathematical objective

of discriminant analy.sis is to weight and linearly combine those

discriminating variables in a certain fashion so that the groups

may be numerically discriminated.

A discriminant function, a linear combination of the dis--

criminating variables, takes the following form,

s Yi = VslXli + Vs2X2i + '''''''''' + VspXpi

where, Syi is the score of the s-th discriminant function of the

i-th individual, the v 's are weighting coefficients, or discrimi- snant coefficients, and x . is the value of the p-th discriminating Plvariable used in the analysis. The rnaximum number of functions

which can be derived is either one less than the number of groups

(g-1) or equal to the number of discriminating variables (p), if

there are more groups than variables. Each function is ortho-

gonal to the others.

Discriminant coefficients are computed so as to maximize the

ratio of the between-group variance to the within-group variance.

In general terms, it requires solving the general eigenvector

problem (B - XW )v =O, where B and W are, respectively, thebetween- and within-groups variance-covariance matrices. Thelargest eigenvalue iA and its associated eigenvectorlv definethe first discriminant function (ly). Likewise, further discrimi-

nant functions, S y (s=2,3,.....,g-1) are derived by using the next

largest eigenvalues, SX (s=2,3,.....,g-1) when "g-1" is less

than "p." Consequently, each individual is defined wi'th those

discriminant scores at most in (g-1) dimensional space.

Once the discriminant functions have been derived, they can

be used not only in allocating an unknown individual into one of

the predetermined groups, but also in testing the adequaey of a

classified individual. Under the assumption of a multivariate

- 71 -

normal distribution for each of the groups, the discriminant scores

can be converted into probabilitles of group membership, which are

to be used for allocating the individuals into appropriate groups.

The probability density (P..) of the i-th individual with

respect to the j-th group can be computed from the generalized

distance between the individual (i) and the centroid (m.) of the j -jnyth group. The density function for the normal distribution is as

follows:

p... I Ji .Jsil exp (-gx2ji)

where, U is the standard deviation computed from discriminantscores yi's, and x2 ji is the generalized distance between the

individual and the centroid, defined as follows: x2ji=..E(2LL:t[!gL{.Y .i 2M)2

Likewise, the probability densities with respect to other estab-

lished groups are also calculated. The relative probability of

membership in each group is obtained from those probability den--

sities. In addition, .e. priori knowledge of group probabilities

are also taken into account, when available.

Therefore, the relative probability of k--th group membership

for the s-th individual, P(Gk/ys), is given as follows;

p(Gkly,) = qkPksl \qjPjs J

where, qj is an -g- priori probability of j-th group. When no a

priori know-ledge of group probability is available, 11g is equally

`assigned for each group as the g priori probability. Finally, anindividual is allocated into the group, for which the individual

has the highest probabi!ity of membership.

The above mentioned procedures for deriving discriminant

functions and allocating an individual are detailed in several

multivariate statistics texts, such as Morlya and Iguchi (1976),

-72-

and Cooley and Lohnes (1971). All the computations in

analysis were done with one of the subprograms of S?SS,

NANT" (Nie et al., 1975).

discriminant

"DISCRIMI-

7.4.2 Application and results

In order to allocate individual profiles into one of the pre-

defined soil material classes, discriminant analysis was perforrned

using the results of the quantification of each individual earried

out in 7.3. The samples which were included within the 90 9. prob-

ability ellipses computed for the respective soil material classes

in 7.3 using the scores of the samples, were used to derive the

di•scriminant functions. The five dimensional numerical scores 'which were obtained for each individual by the linear combination

of five dimensional numerical values assigned to each soil attri-

bute-class taking the response pattern of the individual into

consideration, were employed as discriminating variables in the

analysis.

Consequently, fifty out of fifty-six profiles were used artd

tested for the adequacy of their surveyor's class allocation and

the rest were set into appropriate classes using the derived dis-

criminant functions. As shown in Table 7-7, prior probabilitywas given by the relative size of ea' ch soil material class: that

Table 7-7 Prior probabilities for respective soil material groups

Coarse--to-mediurngrained graniticrnaterial

Fine grainedgranitic material

Mixed rnaterial(granite + serpentine)

Eerpentinicrpaterial

No. of sarnples

Priorprobability

30

O.536

6

O.107

7

O.125

13

O.232

-73-

is, the ratio of the number of individuals which fell within the

90 % probability ellipse for the class to the total number of in-

dividuals used in the analysis.

Three discriminant functions were derived from three eigen-

values, as shown in Table 7-8. A sample profile was assigned

Table 7-8 Eigenvalues and relative percentages for derived discriminant functions for soil material classification

Discriminantfunction Eigenvalue

Relativepercentage

1

2

3

11.638

2.294

O.058

83.2

16.4

O.4

three discriminant scores by these derived discriminant functions.

Table 7-9 shows discriminant scores, probabilities of membership

in each soil material group and the group assignment for each of

the samples. As shown in Table 7--8 the first two discriminant

functions accounted for 99.6 % of the variation between groups.

Thus, the results of the analysis can be represented in the two- 'dimensional space created with these two discriminant functions,

leaving out the third diseriminant function. As shown in Figure

7-4, the samples were plotted on the scattergram by the discrimi-

nant scores computed from the first two discriminant functions as

the coordinates. In the scattergram, the statistically computed

territorial boundaries are also shown. The sample which fell within

these boundaries were judged members of the respective soil ma-

terial classes. The samples were plotted on the scattergram with

the label of soil material class assigned by the surveyor in the

field.

-74-

Table 7-9 Nurnerical assignmentsample profiles

of. soil materia1 group for

?rofile Ne. Soil rnaterial group t

Field Numeriealjudgement assignment

?robabi1ity

t 2

of membership

4 5

ft

Dlserlmmant

1 2

sceres

3

22 23 24 25 26 27 28 29 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 52 S3 55 56 57 58 59 63 65 66 672582S9260261262263264265266267268269270271272273274

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•-- a•.

-f'' 1:;.

:.d-

[I.- :tl -

::e 1:: .

-1]t.

1. E.

/--•.: E, 4 7

[t E, 4 l..

E, •r- E: 5

411L:.:-"q

;-: 4 E• E,

e641e Ea ::-•: E•

-YT }11f4:.d

1:.ELI2i; Ei4

S11E-E:! E•4

E,4l:!t1 E• i--- 7

T ::: 5, 1 '

:i: E. 4 f,

•:- 5, E, E• :

:!: E, 4 5,

':: ;' c' 1-l

:I- 7= 4E,

l-: :i: 5, 4

1 E, •F.•4

'-. '- t, E

r• -;gta1 i"' -'

44el"- C, :,7..b--LlE, E! 1 :-::

'-- ;I5:.,

:E• 1 E: 7=

:" f :i: ':+:

b l L' tr-,

•:l •r- :i: 1

.: fi:I 7• E,

LT;1 ::: E-

E• r• f-5

ls'-:ltt:

lll ;-,- E, r-

liitU-L-:6i:.i•-.

5, 1:: 7 4

E, 4 l:, 1

5 1 ::-: :"

'J'-rv-tLLVLE,E.E4E,5l:i1

[lE,7E•4E. 1 t7:

e'f' ::: Ll

l• E, :J, 4

E, 74 •:.

4 E, -I-, !l:

E4:i:7l:t4E.P

E, I E,4

4E,4tll

- 1:l

- l::

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l:t

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II-:

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-II

-il

l.t

LIS

t:!

[t

l:I

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.

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.

.

.

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.

.

.

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.

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.

-

.

-. 1•-t"-1. t" 4. -:.: t

-1.--

l:: .

-- 1.-1.

v'l•-t' (1. 1:.-1.

..- III.

- III .

-o.-ille

-1.-(L.- III .

L h:i '

-+[l-

l:I.

+Cl. III .

-1[l.

I] .

-1. +Ii .

- l:+ .

MTel- ;,i ..TET .'-L.: 1 O

4E: 9. 7

5 t) Ed 7•

l[l l:1 7- ]-

'Jg EL 5 7•

OE :- :-:

i- 7• 44

7-4E7•-1•T ; E: 1

7• 14E4 r• o6E E, :-: L"

:, -:, T i:L

WLt ,.,i)4E,i,

4t, LSV.Il Es 7' SIi

l1!,g'::: e, ,. IJ

4":l'::

EEf,4'i 7•' 1•'Er

:i:Et4E:

1 •). E• •)

44 E, :J

,l• :.: :i: :.,

t., -,ct:'t'-+'l4 E, ':a 1

Te-,-7t .a -:]i

T"t-J.'-ii t:1 :-

4 111 1 t-

4111O .:T E, f,

ei 4 :':::

IB4415.::4:g 1 4•).

17- ':g1

"e"--,l• .d 1' C

T:; 1 4 /It

E, •-. 4a•

54 ;,': 1

t) 7- 4'4

•:.. r• cll::

14E.4:.E4r-1

: 7• 'rgi)

E, ii: 7- f-

E, 7- 7- :-:

II147• ::

7• 1:-":IE,

t' ?' i 1::

r]45i'J. E• 5 E,

,;•:•e=t-.. -t. .1i

;::546

-III.114i]I- t:s . 17 •:-4

1. :!: ;1 1:: 6

-O. E, F. ill ?,

-o. a•; f, •r.

-1 . 41:: J-: 1

-1. III11EJ-1 . 4 ill O:-•

-:-• . [:•: 1 1 .E,

:J . t,t::: u-i

O. 4 4 1 :.;1 . 1:-:E• E, "

-,li. E, 7• 55,

- fll . Ill:IE9

- l) . 7 f- E- -?,

-t].77.6CJ l]. .:: ). I:l E:

-l] . 1 11t 9. 1.t

l:t . .:: •", l:l E:

l:t . 5, IZ,E /]-,

1) . .=., 1-, 1 4

- t) . ::: ,.:: 64

I.1 . .:t44ti 1 . ]i:i: 77•

•- 1. :-• E•4tll

-+II.47. :i,1

-i:i . 7 [i III [l

-1).4ClEl:l-B. E, E•47.'

-1 . dl"]49. I:1 . E' 1 E' O

- l.l . •tt. :::4

-ill.5:it64- ilt . :-L4 t)4

-1'. E,4ga•- [l . 1::4 lliEi

1 . i,4;S6 ;.: . .:: ,d, 4 F

[1 . 7- C-! E. ::

O . E, /:: [l .'-.

t]. 7• E,37 i:b v '-• 64 L)

1.111•r- 1..E• [1 E,4 1 . E, [i E. 7•

! . 7• •:" .6 3•

c'. e' V• Ll tt

1]. •) 1:+ 7- 4

l.I . ,:1. t, ,:: t.l

IIi . [.: 1• ,:" ;i,

1) . !i:71 E•

I] . 4e. 1)-, E,

- I]l . .`.'t [-: t, '.t

-E• .16JP. 6

-t:1.E:1SE- 1 . I:l6;:6

de Soil

1:2:4:5:

material groupCoarse-to-medium grained granitic rnaterial.Fine grained granitie materialSerpentinic rnaterial ,Mixed material (granite and serpentine)

-75-

1

i SCORE 2

N 1 Soil materialNNNxNNritorialboundary(s)

(GS)

NAN-

NNAxAXN

,1-

A"lAl1lt11111i

(Gf

-11ln!ee

Coarse-to--medigrainedgranit

FinegrainedgSerpentine(S)

Granite+Serp

SCORE!x 1

Ae •eeeeee

eA E eeee

t eAA

'

AA ! e ( G)A

1tt

ll

eFig. 7-4 Scattergram of sampled sitesfirst two discriminant scoressoil rnaterial

(G)

granite

Serpentine

plotted with the for c!assifying

(Gf)

(GS)

Three groups, labeled serpentinic, fine grained granitic,

and coarse-to-medium grained granitic materials, are compactly

clustered and separate from each other. Samples with mixed ma--

terial of serpentine and granite are somewhat dispersed but occupy

an area between the two end-member groups. It is reasonable that

samples of mixed material show such a distribution pattern in the

scattergram, considering their characteristics and geographical

location in the study area.

'

-76-

7.5 Discussion and Conclusion

The results of numerical classification of the soil samples

into appropriate soil'material classes are compared with the sur-

veyor's field observation in Table 7-10. Fifty-two out of fifty-

six profiles (92.9 %) were numerically allocated into the same

classes as those assigned by the surveyor. All samples belongingto the ' two end-members of soil material, coarse-to--medium grained

granitic material and serpentinic material, were correctly clas-

sified. However, some profiles belonging to the other two classes

Tab1e 7-1O Cross-table betweenassignment on soilsample profiles

field judgement and numericalmaterial classification of

Field

judgement

No. of

individuals G

Numerical assignment

Gf S GS

G

Gf

s

GS

30

6

!3

7

30

2

o

o

o

4

o

1

o

o

13

1

o

o

o

5

Percent

G; Gf :

s: GS :

of "grouped" individua!s correctly classified : 92.9 g

Coarse-to-medium grained granitic materialFine grained granitic materialSerpentinic rnaterialMixed material (granite + serpentine)

were mlsclassified. Some soil characteristics are common to both

fine grained granitic and coarse-to-medium grained granitic ma-

terials. Hence, it is quite difficult to distinguish one from the

other by those soll attributes. On the other hand, the avarage

characteristics are very difficult to define for the mixture of

-77-

r

serpentinic and fine grained granitic materials because of the

varied degree of mixing of these two. Hence, it is highly possible

that sorne samples are allocated into one of the end-member groups

of soil. material, to which the sample is statistically closer than

to the average characteristics of the mixture, even if it is ac-

tually the mixture of the two different soil materials. Figure 7-5 shows on a map the resvlts of sample allocation by

discriminant analysis, together with the delineation of soil rna--

terials established by field observation and air-photo interpre-

tation by the surveyor. The geographical distribution of samples

belonging to a certain soil material group concurs with the de--

lineations made by the surveyor. The profiles which were mis-

classified by the numerical method are located in the transitional

i-"r;)N--

x .})7i: .,

f:ft"e ,

6e Y,.i ,,,.

";i'.'L' • I, ' ' ,IH ,./ G aj.,.:t, ..

r.tt./ ..S Et}i;.,,,gi"`'/:'" G

L boSl uterSa ,- 1' :

i Coarse-to-nedtum ..-'.' e srained srantte CG)

' ' - rtne sT'atned srsntte CGr) •:i•' r,1 A serpentine (s) ' .

'' t- A Orsnite+Serpentine Cos) . t.- :

. ,. O O.S 1.0 km , ,",,st s l-' q• 1= ==1 ,, ' •a ii.?S;, -i '. Is;)rs sv :•,,':'' ,"' ,''• .

Fig. 7-5 Comparison between classification of

G e"

e /t.

6f

e.

GS •1

.--

e

'

:

L

4.-'-t

ti

tt,-t'

-/t t

)s-.

'" 1 s'.t

t ;;t-tt-

. . .-.etiis•

.

:- iNiL:n. k l./

t' -t-'• - M7:( - ,.v -i rt. ' t'v ---';/.'.-- ` .

.1. IiN :•'

. •. I -L . .

N

'rr .i.

N) .t:

.", .' -s tt t m-t - --t t. Qri -bo ,N .ny .,

i' ?1•ttS-.,,.

:7, 'X• i•.. . ,

' -i. t ' s. --': :- k"

tu t -.t "L , . ,T••

N •, ' ' :'' ''..l

-i+ er .• . IL airt- 'il,

"s f'

-.-

,',i ]J, .,x,-

numerical andsoil rnaterial

-78-

s- -kl•A

.i S . A' 1Åq .

','et "e ..,k'( .i .r

'

conventional

s

A

.

- 1•A

År

5

, •( N-t s -/t# lt --- -el.-egJ!t7rl

' N" kv s ge(sie'

l

'l'

..

F

/

l;

lI

area between two different soil material zones. Indeed they were

very difficult for the surveyor to classify in the field as well.

Clay mineral compositions of the four soil materials were

examined by X-ray diffraction analysis. Potassium- and magnesium-

saturated parallel orientation clay specimens of representative ,)samples, which were allocated into four different soil materials

by the numerical procedure, were prepared and examined semi-

quantitatively under different treatments, such as air drying (KAD)

and heating at 3000C (K300) and 5500C (K550) for the potassium-

saturated specimen and air drying (MgAD) and glycerol solvation

(MgGly) for the magnesium--saturated.one. Figure 7-6 shows X-ray

diffractograms for those samples. Clay mineral cofnpositions oÅí

the four soil materials are summarized in Table 7-11.

Serpentinic material contains chlorite mineral resulting from

weathering of serpentine. On the other hand, granitic materials

both fine grained and coarse-to-medium grained, have a generally

similar composition, and show a prominent peak of kaolin minerals

Tab1e 7-11 Clay mineral composition of sampled soilsrepresentative of soil material groups

Clay mineralsSamp1e Mt vt Ch It K

260-B

65-B

34-B

270-B

2

2

++(Al)++

++(Al)+

+(Al) -

+ ++

+++

+++

+++

Mt ;vt ;

Ch ;It ; K;

Montmori11oniteVermiculite(Al) : including AlChloriteIlliteKaolin minerals

interlayered vermiculite

-79-

"•t,v/'v•

4"Al

itrV"k

lx

Serpentine-.oraniteTnixed ma[erial Prefile No. ; 65 Horilen ; B. L

evLvY"

X"INvvirvrM

k,vl,•`.vKwr"tsi,i!,

MgAD

MgGly

KADK300

Ypt.,i

pt

Serpentinicmaturiai Profile N'o. , 260 Horizon ; B

pW.Y

MgAD

x IV .,MgGIY 1xwhtW.,)/,rfK",g,

K550

t5 10 5 (2e) 15 10 5(2e)

i,,/i,/iii"

Lrwy'i•

Medium-grainedgranitic material Profile No. 270

MgAD

hMgGly

VVil"N'sts'tNiLv""v'L'u.i,,!/v"'KAD

X".N...-s. .vK]OO '`"Vx-vny./f K5SO

Fine-grainedgranitic material Profile No. ' Horizon

af"'kas"lh),x::.x,xletrw'.,llllll",i,

X'A,-.",.,d,Lt"S""/V u.-,,,,t K300

;.'i

zl,iile`.',,v,.,,,,l//fr'7"tN`tsS"",lllx.',`r:.,.,,,,,

15 10 5 (2e)T'H"---"'•---r--

IS 10 s (2•e) (Cu K(,)

'Fig. 7-6 X-raysoils

diffractogram .representatlve

for of

c1aysoil

.fractlonsmateria1s

of sampled

-80-

but no chlorite peak. Mixed material of serpentine and granite

shows an intermediate pattern between the above two and shows a

srnall peak of chlorite. Although hardly any difference can be

seen between the fine .grained and the coarse-to-medium grained

granites in their clay mineral composition, a remarkable difference

is seen in their soil texture: clay loam for fine grained granite

and sandy loam for coarse-to-medium grained granite. Hence, the

results of sample allocation by the numerical method were supported

by the fact that the clay mineral composition and the particle size

distribution pattern of a sample from a certain soil material dif-

fered clearly from the other samples.

The author concludes that the numerical procedures which.in-

clude soil material class establishment and sample allocation,

successfully simulate the conventional procedures used by an ex-

perienced surveyor. Webster (1974) selected representative samples

according to the surveyor's judgement for the derivation of dis-

criminant functions for each of the soil series. In this studythe representative samples were statistically chosen by Hayashi 3

analysis and the 90 % probability ellipses. Thus, the soil ma-

terial classification was carried out without any biases of the

surveyor, except the procedures of soil attribute selection and

attribute-class setting. This study has proved that this procedure

can reproduce the classification performed by a surveyor, provided 'soil attribute selection and attribute-class setting are thought-

fully done. This study makes it possible to identify soil material

objectively and reproducibly when a sample is taken in the studied

area or its surroundings and data such as those employed in this

study are available.

- 81 --

CHAPTER 8 Automated Map Compilation

8.1 Introduction

Thematic maps and interpretation maps are compiled and

published as part of the outcome of a soil survey. Such maps

include many delineated areas, each of which is considered homo-

geneous with regard to certain characteristics of the soil. To

define these delineated areas, there are three possibilities;

the retrieval of polygon data based on associated soil character-

istics, delineation of areas within a map based on the similarity

of site data, and the combination thereof.

Defining delineated areas has so far been done by an ex-

perienced surveyor and, thus, has been time-consuming and some-

what subjective. If a computer automatically processes the raw

data and gives results similar to the conventionally processed

ones, then every user, even a non-specialist of soil survey,

can obtain such maps in accordance with his interests.

As stated in Chapter 3, there are many papers which describe

input-output procedures for thematic maps which have already been

conventionally prepared. However, very few papers deal with methods

of map preparation using raw data, including site and polygon data.

Cameron and Toogood (1970) and Davies and Roberts (1976) prepared

contour maps on nutrient status and trace element status, re-

spectively, by using site data. Campbell (1978) and Webster and

Burrough (1972) employed discriminant analysis and similarity

analysis, respectively, to locate the boundaries between soil

series. Bie et al. (1978) produced a map showing payment classes

for water charges from site and polygon data by the use of a

discrete approach (de Gruijter and Bie, 1975).

COSMAS is equipped with some programs aiming at fast, objecm

tive and reproducible representation of soil data in map form.

-- 82 -

In this study, an

data manipulation

map and a suitabi

for an example of

applicability of these

is tested for automatic

lity map for upland crop

interpretation maps.

programs and comp i1at i.on

cultivation

statistical

of a soil

on lowland

8.2 Compilation of Soil Map

A soil map shows a distribution of different kinds of soils,

each of which has its unique characteristics as compared to the

others. A surveyor compiles a soil map by the following steps:

1. taxonomic class establishment ,

2. sample allocation into one of the classes, and

3. defining mapping units and area delineation for each of

these units by the information obtained in step 2 foT

every sampled site.

The first two steps can be replaced b: numerical classification

procedures. For the third step, COSMAS's "AUTOMAP" program is

employed for delineating homogeneous subareas within a map.

8.2.1 So" survey and employed data

The study area was in Kaya Township, Kyoto Prefecture. !nthe lowland of Kaya Township, 156 profiles were investigated with

the survey procedure standardized in chapters 2 and 3. 0bser-

vation pits were distributed at a density of, one per 4 ha. For

those profiles, site descriptions, horizon descriptions and

analytical data are available through COS)GAS.

Four soil types were distinguished by a surveyor in the

!owland of the study area: Gley Lowland Soil (GLL), Gray Lowland

Soil (GRL), Brown Lowland Soil (BLS) and Pseudogley Soil (PSG).

The soil attributes to be employed here must be selected so thatthey distinguish these four soil types most effectively. Such

-83-

soil attributes coincide with those used in the conventional

classification of soil type by a surveyor and are: land form, slope,

water table, surface drainage, internal drainage, soil drainage (combination of surface drainage and internal drainage), accumu-

lation, consistency (dry), stickiness, plasticity, color (hue,

value, chroma), air-phase percentage, ferrous ion and soil texture.

8.2.2 Method oÅí data handling

8.2.2.1 Numerical classification of soil type

First of all, we must numerically eatablish taxonomic classes.

The author applied Hayashi's theory of quantification No. 3

(Hayashi 3) to the classification of soil material in Chapter 7

and concluded that it ean reproduce the surveyor's procedure of

class establishrnent. After taxonomic classes are established,

the samples are a!loeated into them. Discriminant analysisis the most popular and appropriate method for this task as stated

in Chapter 7.

Thus, the author employed Hayashi 3 analysis and discrimi-

nant analysis in combination for numerically classifying samples

m terms of taxonomic class (soil type). All computation wascarried out by the subprograms of SPSS, "HAYASI 3" and "DISCRIMI-

NANT.,i

8.2.2.2 Program "AUTOMAP"

The outcomes of numerical classification of sample profiles

in terms of soil type are point data. To form a soil map showing

the distribution of some different kinds of soll, these data must

be compiled into homogeneous polygons, i.e. subareas. In such asituation, COSMAS's "AUTOMAP" program was used for automatieally

--convertmg pomt data to polygon data.

-84-

8.2.3. Results

8.2.3.1 Taxonomic class establishment

Hayashi 3 starts 'with setting up attribute•-classes for each

of the selected soil attributes. Threshold values which define ' - e-those attribute--classes were assigned so as to differentiate the

soil types. Hayashi 3 was performed on 156 soil profiles using

39 attribute--classes of 16 soil attributes as shown in Table 8--1.

Table 8-2 and 8-3 show the results of quantification of

attribute-classes and individual soil profiles, respectively.

As shown in Table 8--4, eigenvalues from which the first two numeri-

cal values were derived explain 33.9 % of the total variance. The

contribution of lower-order eigenvalues are relatively small com-

pared to the first two. Thus, in order to establish the taxonomic

classes, every profi!e was plotted in two dimensional space, using

Table 8-1 Attribute-class list for soil type classification

Class I II.!IIunit

Attribute

Landfiorra 4-14 15-1617 -20(code)Slope O-5 5Åq -degreeWatertable O--100 -- 1OOÅr

.Surf.drainage pond.-slow med.-v.raptd

Int.drainage none--slow 'med.-v.rapid

Soildrainage v.poor-inperf. mod.-excess

Accumulation none weakly-strongly

Consistency(dry) loose-v.friab. friab.--hard

Stickiness non-modierate. strongly

Plasticity non-moderate. strongly

Color(hue) SYR-10YR 2.5Y-SY7.5Y-10BGMunsell(value) 1-3(chroma) 1--2-

Airphase7. O.O-5.0 5.0-20.020. O--100.0%Ferrousion none s!ow-moderate. imnediately'Soiltexture S,LS,SL,L,SiL CL,SCL,SCSiCL,LiC,SiC,HC-

-85-

Tab1e 8-2 Numericfor soil

values type

assigned to soilclassification

attribute-classes

Attribute Class1 2

Solution

3 4 5

Land form I

IIIII

2. o.-1.

533314138•

-O.398-O.29Q O.265

1.332-• 2.71.1

O.629

o. o.-o.

54e103276

-O.578-O.823 O.564

Slope I

u-1 1

.228

.142 O.680-O.651

1.120-• 1.039

o-o

.196

.206 1.217-1..148

Watertable

I

II o.-1.

514036

-O.575 1.120

-O.482 O.978

-o 1

.784

.5t9-O.079 O.149

Surfacedrainage

I

II-o 1

.447

.843 O.480-1.068

-O.190-O.155

o-o

.425

.865 O.638-1.936

Internaldramage

Soildrainage

Accumulation

I

II

I

II

I

II

o.-o.

o-o

o

-o

663995

.347

.314

.123

.146

l.224-- 1.824

1.849-1.624

O.665-O.739

O.O09O.O05

O.183-O.146

O.091-O.083

O.213-O.342

O.189-O.186

-1 1

.540

.657

-O.399 O.584

-O.908 O.787

-1.331 1.4-17

.Conslstency(dry)

Stickiness

I

II

I

II

-- 1.

o.07t856

-O.821 2.727

-1.006 O.802

-O.194 O.622

O.708-O.560

-O.305 1.054

-O.292 O.216

-- oo

.16.,

.500

-O.546 O.438

-O.396 1.317

Plasticity I

II-1 2

.080

.O08-O.438 O.803

-O.O39 O.095

-oo

.156

.260-O.734 1.370

Color(hue)

I

IIIII

o-o-o

.771

.779

.511

-1.612 O.247 2.186

-O.226-O.387 O.695

-o. 3.-1.

963446537

O.685-2.175 O.822

Color(value)

I

TIIII

-o-o

o

.267

.587

.814

-O.977 O.660-O.515

-3.867-O.323 i.736

o-1 1

.827

.202

.202

1.8i2 O.528-1.2s.t

Color(chroma)

I

IIIII

-oo

1

.615

.580

.641

O.679-1.789-1.34• 7

-O.857 O.909 2.310

-o.

L-o.

079350422

O.085 (.840-2.490

'

Air phase 7. I

IIIrl

O.429-O.808-1.335

O.(83-O. 7• 57

-3.409

-O.S61 O.9(4 3. .1 05

oo

-5

.031

.26L,

.163

O.186-O.098-2.590

Ferrous ion I

IIII T.

o-o-o

..129

.499

.605

-1.385 O.895 2.649

O.397-1.090 O.869

-o 3-3

.1 7,;

.1695T5

O.653-1.382 O.379

Texture I

IIIII

-1-o 2

.46S

.504

.410

-O.384-O.17i O.675

2.782-2.2,17 O.640

o-o

o

.495

.569

.337

-O.576-O.S68 1.5S(

-86-

Tab1e 8-3 Numeric values -- 'classification

.asslg ned to profiles for soil type

Profile Ne.Soil typei '

(field judgement)1 2

'Nurneric

3

values

4 s

486061626468

70. 71 72 73 74 75 76 77 78113114

115lf6lt7118119120121122123t24t25126127128129t3013113213313413S13613713813914014114214314414514614714814915115215315415SIS6157158159160161

3.LL3.!-3.1.3.3.3.3.3.?t-

3.r-J.

2.:tt-

L3.s?-.

3.4.3.4.))t

4.1:-: .

/:-: .

L).

•:{.

::-

lr. .

Ir. .

r-.

.:h.

.3.:.- t

':t '

3.:t'

4.::-

a.:.: d

2-•.

/].- -

::t

1.3..

1.4.El-

3.4.:.-t

1.:-:-

E•.

E•.

z..

::-

a.r..

'[l. 47?u L'

--).6S3E- O. .5. 08 9.

- O. 4 7 :- L)

-1].54E4-U.37:.O-i:I.65S4-O. :. 445- [i . :{ ?, S6

-o. .:-: e6s

-• l). ff, T• [[r.

-O. E,7Obh-V. ..th6U U.Ib.Sb-- +].E746-". 7-• 5tS9- [1 . S':: iP 5,

U. ::7[il,- III . 1:;SS4

l). I)5l:tS 1] . ':: 1:: :.d E,

1. E•54•- l].63E:6 o. tP. :i: 6, 9.

-".119L. 1 . :-t-.::6i5

- t].E l) 9. 4

- [1 . t) •:--4:!:

III . 1:T [I58

Cl . C: YS3 Cl . 7 1 :-: ':-

U.EL.t!8- l.t . IJ 8 t.1 ty.

- i:1 . 1:: 1 7- :•

- [l . ::: :" 16

1].7; 7• g t:l. E,1 7• O

t:l . /::4E, dli

-- 1].3E/::5

-l].4486 1 . Et IIII::i-:

l:l . [l •r-- 7. 1

l:l . i)4 [1 '::

- L-l . t, U:St

-- U.4L.t,b-[I. I:l 4). 7• :J

- a . .:! '.:r4.::

-[i.S;::E]6- IIE . 4 E• E, Et

-Ll . IJ :: ::lb

1. ll"ILE:1- [l . 4 :ÅÄ': :.L 1]:

-". I] EttE 1. [I E,4E,- +L t]si) fll

-i.1.t,if::l

-- III. a• 141

t).1!!:1(1 O. .:: 1 •-. d

[t.•-EiE1 l).E•1f,1 IJ . .:: e, LF c:

-- O. 1::147

-(1. L)194 ,).492E, O. 71 t)O- O. 4S 9. 7

t).O174-k). 0963 O. 61 ll-O. E,55S-1. 1:t Cl6:.

-l:I.7636--

O. f, f, Ct LT

- t) . 4F. a7

- I] . E, t) iP. O

- e:t . E: l:+63

-l:t . 1 9. E:6

-O.1l)64-i]. 167• :- [i . 7• 6E, 6

-U.bUV. b-O.1466- tll . 1]{E t]6

1). tll613;•

-l:1. 17E6- l:1 . Ei 1 t:l O

-- b].51 7- 1

-t:t. II:!9E:

- e. 3 6 1 ;1

-t).57- 'tgS

-i.t . bV. t, V,

-- l:l . 4E5 [t

- t:l . :-: 1 ::4

- l:l . 1:t t;. 3 9.

-Ct . E•4lltS

-O. 57. 0::-t) . L7 l]l45

-Cl.IEt47- }:1 . E31 1:1

-t].E-:l41-O. E4-r- [l

-- 1:l . 4E E•9

-- [t . (lEE, 7•

-CI. f, •]. F. [t

1) . [l ,). 4lr-,

- l:1 . 11,46 ?.

-O. :::S4 7•- l] . E, ::: 9, 6

-- [i.61413 1] . E• f,4 /I:

- l:t . :! •]. 5 t)

[t . 4 •::1]-:f

- i:1 . 1 •)1:-: l:T

-Cl.1E:tl;6- l) . E, 1 iP Ei

-IJ . 1.le t.1 1.]

[l . ':;E,4 1:1

tlt. 1]14a- l:i . •I: 1 E, 7••

1:i . 1 :II.I-; .::

IIL . a• iP. 45

l]. •:-::-1 7. 111

-t.:.t,t,dI.l cl . :-l::-:4r-

+:1 . -:l !I:4 E'

-O.3tL,93 O. 3•9S5 l) . tlP6f,4

-,).E9.71 O . O4 1:-"]1

-o. 1:{ 7e7 fj . 499 lll

[1.-E•I]17

O.4693 t).6e34 tll. t- 7• 5,1

t:t . E, E,49.

I). t]S4* 1] . 1 E: 'd c'

l:I . 55a• l]

1] . E•SE3- [+ . 1 •I- E• E,

O. 1 [I E,9

-' lll.47'77'

-O. E: 7- Ei5

-- 1).61 [-:1

):l . :-i ,J E, 1

-IL 1 :S• 9. 1

V . :. d I.1b

-- l.l . U:::bb

i.1.t:;1::b- Cl . f,E4 7•

- III . :: :l4,r-

- ,:l . J-• ELE, Ei

Cl.E•67.7 s] . [i ii 54

U . .t 'd :s ts

- l:t . E, 1:: 'I :. -

-[l . 41 :. IJ

- 1] . 1 E, III 1::

-[t. 1:-:111- 1[l . 1 L:: [l5

- tj . bl t Ll `f. c'

- t) . /:l t747

- 1:t . I] E, ::-: -:-

1).3t:b1Ei- 1) . :i: t[l4E]

111 . 1 :i, 7• 6

l:i . 1 ,), E' :)-+

l:S . Ed44.:1

-- [1 . E, (r41

-[1.0446 1] . :1 S[I4i•

- Ell . i tr, all E,

-- 11I . E' 4':: 9

T:f . 1] E.46•

1:L . E/ E, 1 E:

-O. E• E,4II-l

s:t . i:i4(,E

- [t . b fi E, ?•'•

[t.4414-I:t.45tll4- tll . E, :-: 1 E,

- )] . T• t] :.1:[

llt.1E,11-i].O4E,i]-l-I.d1.:;::1;!

- l] . /-,: :" 2. 0

L

D. (t5fi6- t) . 444 lr-,

-- [,.47- S8 O. ..:, i:iS2-

- [1 . .). :l f, 1:t

t:l.1E61- i) . .:: 9. t. L'

-t).Ie. [1 Ll

-O.e147• O. [ll):-:B

-- i:l . 1 :-! 3:6

- 1:I . 1:-: S77

1] . III4E, 1:i

-[l.E•r-t1) 1:l . E, 4:l 1]:

t:i . J: 1)-, E: .:

I:l . 1 6:1 1

- 1.l . t l.I ".' .)'

-O. Ill E,6E

-- i:s . 1 :I:e 7••

- ili . tll 1:: l-i4

- Il . LI :S6 t-t

- r.). 1] :" 9. 3

- t:t . /:: [:: s, a•

-s:t.4E•E,E t:1. 1E7. 1

-- III. .:: •-4•).

- l:i . i• •) 1 :i:

-II:.1Cl13 t:1 . 1 3• l] :::

O.I•::47-- I:S . 1 :-• I] ::

- tll . 1:: E, 1 1:t

- 1]. i-: E,3 IET

-t).14frl-". t:l 7-• 1 ')-

-l).1•r-16-- o. 1 :: •r4s

l:l . 1 IIIE•4- IId . 1 ::! E, ::-, '

- [I . t[] -l 1 S

- l]. 1 l] 1:: 1],

U. c'34 i.1 III - l9vK 7'

Cl . 7• :-• 9. 6

- l) . 1!: 5, :i: 5

-tll. E, ).44

- [l. -I: E,45

-tJt.1;7- 9. E;

-[1 . :-] 7•• 1 1)

-b:1.1544 ill. 1 E, i!l l)

-il l]:-: -:•- .- .--.-+Il.11li: III . 1 9. .:: ::

IL 1 E,:I:7- I] . 1 :.44

O.7:.!:1 U. t• tJdts- tll . 1 IILf, •],

-Ci.1ESfll Ill . E• El E, .:-,

Ll . ;: /"4t•

-t:1.4151 O. IIn]14-l). t) E. 1: E,

O. tl;f-61

l).1•)19-l:l.EE79,- I:l. t) E: :1]

-- [t . E• l:1a3

1] . a1 a• 3L

-l).O177 1:i.I•747

-- 1). 1)5Ol1 t:e - it 1 1:1 1:t:

- l:I . f] E•9?

l:t . Ii•4Y1

1] . 11 7• 1

III.14t)1 tli . 4 lln:1 1

l:l . I• i]aC-

- i:l . III5tr,4

-t]. I]eEt4-I.t . c:.1bb

t) . E5E F.

-III. 1 •::S"

-,[+.1666-+:l.1S15,- Cl . 14 :. 1

l].:1:t4:i:- F:l . :. 4). E,4

lll . E, f, Ei E•

l) . 544 ,S

-l].4:i:41-i:1. [1 7- E, .5

t:t . [i56b'

[t . :"54/:l

III.EL). 1e

-- ,) . :. 1 9. S

1] . 3. 1 :-: Et

1:; . t]E, 1)6

IIt . E• l. 1 g.

-- [l. i•46 E,

IJ . I.ibEV-[t.T117••-tli. fi)15l]

l]. Ill59.6- tll . tll Å}:-,e1::

- l) . Ei 1 E: •rg

l] . LII/:!E•7

1) . I: 1 5, 7•

- 1:) . [l .:: 7- 9.

IIf . 4-:,• [t 7

' -t Tte -1-II -- i-, .- •-,L" •" ']o E' E' E' 6

-- tli.EE,E1- l) . 5: [l4 :-:

-b] . ill') :1f

i:r . .? ') [I E•

O. 1ll-: :•l9

i] . 1[l 1 .Z: 7•

-tlt . +Il i•,• 7= E•

- tL E III E, 7

- t:t . 1 OI- 7•

-U. I] 'V. t,t

-87-

Table 8-3 (cont'd)

Profile No.Soil type S

(field judgement) 1 2

Nurnerie values

3 4

162 163164

167 168 169 170 171172173174175176177t78179t80181182183t84185186187188189t90191192193t94195196197198t992002012022032042052062072082092102M21221321421S216217'218219220221222223224225226

L L a,.

1. 3. 1. 2. e. L ?t-

:t--

1.1:.; i

1.2-.

LE•.

1.1.1.:t--

E,.

:.--

L).

.::-

:.l t

4.4./].- -

3..

I{'

4.4.:.-

'7"'

Ekr-'.

/:-: .

4.3.E,.

E•.

4.L:.

:l.

a.fJ.

4.4.4.e.?.cn

.

,:1.

'e.

1.1.1.1.a.3,.

t"1.

-[t.454S - [l . E: tll4l•

-l).a371 - O . [t t-: E: 1

- L) . 4E• E, E•

-- O. 1 9, 3E,

-[t.4t]17 - [t . :: 1 t.e

[l.E,4Ei3 - I) . EtS7• E

t.t. t.IUb4 - t] . 46 [-l E•

[l. r' 5L Ea fs

- l] . ,-:, 67e

-[I . E, 9. :.5

-- slt . :-r E, 9 ':-

- l.t . t,Vt' ,E

-tZl.444Ei t[1 . tld E:7 E,

- t:1 . :: t7:4E,

I] . tll E. 1 a'

-l:I.419. 9. t] . 1:: 15 7•

-[l.4Eil!t1 lll . E, 7 E• Z-

(l.E,1F4 1 .. I)Ei46 1. 1 [i 9. 1 I:l . 1 [: E, 7

III . E: E, :-: Et

-- tll . 1 t:- E, 5

1. [i41S - -t- "1 •n l-l . V, tr, .) c

-- I:1 . s[C• E:7

1). t]119, - tll . [{ E,71

-1:l. 347• S -- t:l. 19Et1

[1. 5,316 -t:1.14e4 " --ttdetl Lttft-1:- [t. 1)45,Er

1.1SE,4 1] . 5 :"[t7 -,:l. .E, 7. 1 9,

-t).44IIhS -1:l.1t.9.9 1. [S6[l1 [t. i). :: FJ7

O. .:::::48 - Cl . 1 :: Ll2

-i].1Ete5 -CI.::1E7 -O. EiE78 - i] . 7• 1:-: 9. 5

- 1:i . 41 a. 9,

-t].4l:S[l O.477O CI . E, 34e - 1] . ::9E- O

-O. 4 1] Cl /:s

- +:1 . 1 E: E,3

[l . :lE: (l [l

1:l . Si? E: [1

I[1 . 1 t[1 i). E,

[i.1E•5,e O.67• [t7 - tll . .:-:! E, O

i:1 . E, 1 9. 7

-,:,.17E,: l] . 44E: 9.

tJl . 5:i: :-, E

- s:i . E, E: UEt

- l) . .t-=, 64 :t

f!l . f, 7- 64

1) . 1]7 .--) 7

[t . 4ir- 1 1

tll . :-: e•74'

1-1 . t, t:b U

[l . E4 [: [1 1::

llt.79. 5. 3 r:t. :i: /:: :: 1

l-d. t: 1-l V, F.

Cl.4763-O. 5:1 1) 1

t]. 1:: E• 7• 4

h:r . t] e, E, ;7•

- O. 1 7• :. 9

-[l.E,3a4- i) . :-:E1 E,

-IIt. 1 9. 19

- [l . E t[l? :-,

-I:1.a:.scl- 1) . E, Jr-46

- l:I . .::SE,g

TII . 17 Ei 9.

-[1.SE:8O-l[l.41:.4

t) . .:-: E: 1 ;

e. 1 9, ,O-, :1

-l:,.563e 1:1 . 4EIJ O

-O. ?.7• (s4

I].4188 III . I-: 1-: !.1?

-[i.114! ,].1SS2- I:1 . 44 :1 1

1:1 . s:t,C.:E,3

- l:i . E,444

[t . I[i7 D t:l

-l:1 . .'6E•4

t:1. 1467 tll . [16[ie

t].Ej113 1:li . 1 4E:S

-- [s . 4E, 1:-:S

-+). 1 ;• E, t:l

(1 . r,:56E:

1:1 . E, E: 58

[t . Er6O7 t). E, a, E; 1

}l. :"Il68- C,. 7- 1:.5

(l . Et Cv,61

o. tteL: 1)

O.1493 IJ.4E44 l:s. i Cs7e

-- Ct . E• 351

- tll . 1 E. Cl 5,

-i:1.114S -)[l.14gs -t:t . 1 E• 7• CI

-tll.1E:E:6-III.E,171-El.5f-42

Cl . E•7 07 ill.[t1e1 [1 . E• EL 1a

-i.l.V4b6- 1) . t[iE:9*

[t. [t1 l:"]

1:I.EE:e7 [1 . sl4 E, 5 9.

-O. tll l. 9, S

- Cl . tll445

t) . 4E: E•2 1[t . [l E-: •). 9

- [l . a•464

i).11E4 i.1. I-l t, l.".I

E] . [-: i•,- E, S:1

[t . :! E:47"

IIt.4Et::S 1:t . 1:1 1 1:-: ::

1:l . III lll 7 4

ill . [-: h: Cl2

Cl. :{ 9. I:16

-[4 . f:tE,4O

[1 . I: E,47

-t:1.E7E6- II] . E' 7' L' -r"'

-- [l . 1 f,t7:5

-- [t . I" E-7$

ill . 47-• 5E:

[1 . 1 E: a.6

1) . [: ClF4

CT. t. 9. I)6

- t) . :. E, 9. 3

Cl . J-: E.46

1) . E, Ll 16

- 1] . Eg: 1E

III . E• 9. 9. 3

Cl . 1 St l]4

O. :-: E, 1 E,

-i). 7• 166- tll . E• E: 7 1

1] . 1 !E:67•

t:r.E,5::S f) . ;-; :! tl 1

lli.461Cl lll . 1 E, t]e

-IJI.I[i161- l.l . Ll .Sbe

-o.ae r• g. 1] . 4E•E l]- Ili . 1) E: d). 6

ill . at6g6

:It. Et9::6 - 1] . 1 '). 'ra E,

- l:l . I[1 Et E, 6•

- IIt . I:i •) [l 7•

-ill.179.d [t . 1) 1:t [: 6

-t]. slEO41 r). .3'1 E: 7

i[1 . 1 S6 ')"

-- t] . tll? :H 1

-i::.1E•E,9- slt. 4 'i 'P. 7

-- l:l. 1? [-: 9.

I:i . 5 Ei E, Lcr,

t:e. :s E' 1S

- i.t. 1 :::de

;].11•5e- I) . 1: Z.4 l:l

- IIi . 4 E, 1 .9

- t:I . .'i t;. os

t).3':a. 7r 1

Cl . E7 F] b-}

-l:I. t)E5,a- Cl . tlt :-:66•

lj . 1 V. dc: -.'

-- l[t . 1446

-- 1). 1 llt i:s .'-'t

O. I S I. 9-O. III 2.46

t] . 1 :-:S1

-t).4116-l:l.1467

tll . 1 [-: 13 O

- t[l. O76ill

-Ct.174? t:l.Et-::9

O.IE,ES-t:1.1Et-9. . 1:1 - Er 9• 65

-[I.?649 [l . 4 lnll 1

L).4SE•.5. [l. 1 t)S's'

{s . 4E: 5,3 l:t . J-: E' -r' 1

1:i. 7• 167

l).17g5-O. i[l 1 t:t 9.

O. E•F6e- III . [t E;E3

C,.5S15 O.O149 l] . E, t1 46

Ct . 44 9. 6

t). 1E1 t) [:l . [t ILI33

O. [t93S-l). Cl9ed9- t).1 7• 7- 9

L: . 4 1.t LY. d

t].14e8 t:e.1361- l) . Oh: U:-

- 88 --

5

i:t. 1 E:l48 o . :-: [t 1:")

[i.1776 1) . Cl49 [l

i).3157 - ,). 1S E, 7•

l:l . 3.914 rJ . E• 56 e.

-O. (I Et90

[i.E•481 I:l . 1488

-O. I):-:44- l:1 . t:l [l57

-- IIt . :-: h: 93

-Cl.3:i4B [l. IJi193 o. I:lsr• s-

i:t. 0413 1t. Ed g. 17

Cl. I)418 tll . ES38 [I .S1 /],4 i:i. E, 9. 55

t:t e ::7 S3

l:i. 5,E9. t.

[l . 1 1 E•S

-O.15,84-El. [l9. Ll.E:

-- O. ':-:567

O.17E3-e.1843-III.al:lee-- t]. O7•• 15

-O. C167• 8

-l:1.3418-t].4649-[1.49,6S CI . 6G :{4

O. I•S4 1-Ll.5t:l!5-Ll. OE 9. 4 Ll. :-:37• a

-- O. I:1F31

-o.ee4s e:1.0O54-O. 4S59- o. :s s Lr, s

-- o. os.ss Ct . 6E, 19

-I).8143-U.4-586- ci . f, eg s.

-O.1730-O. 141 0.

-O. (l407-,).1163-t:l . III687

-lll.1391 l) . I.) -, '•• 4O

-- O. 1 1,:- 9.

0.4814-tli . I.558 t:t . 4591

Table 8-3 (cont'd)

Profile No.Soil typet(field judgement) 1 2

.Numeric

3values

4 5

22722822923023123223323423523623723823924024124224324424S2462472492SO2S12522532542552562S7

:.k Y

1.:tt-

E•.

:'i-

L).

1.4.3.1.

L1.

L1.E•.

3..

1.I=:.--

Le:L1.I..

L:.: -

4.Ilt: '

::t

:.: -

- t:1 . 5E: 9, 4

-1:l.4E,1S O. 7S1 Cl 1:i. ; I:t P:lS

-1:I.E•E,53- l:t . 'E, 1!, 1 E,

-O.444E, 1] . SE, f,4 1:l . f,45,4

- 1.1 . 4 7• [-]

[l . tll E• 4 L-.

1:1 . III47 3,

llt . aL 1 E, :.

-111.1916- i:i . :! :J ELE

- t:t . 5, E, E•6

1] . IIi 1 l]:--

-Ct. 7• 1 [1 1]

- 1:t . [: f:l :" 7-

[1 . 1:-:E9. 1

r[i. :-: :'61

u. u:i; 7. E,

- I:s . 4 tll E: E'

1:l . ES E, 74

- l:t . 4E,4S

-- I) . 19E,7

1 . E, O/::/::

O.IE,Sl- l.l . Lt t' c' t:

- l) . 1 5g r-:t

- t:l . 1 1:i 1:-: ?

[t.;311 ,:t . E, 7• '• E,5

[1 . E• 9. 7• 5.

IIi . 1 l` E' [v

- Ll . 1 l:l Ed f"

tlt . F •) Et 3-,

-t:l.Ei4ill1-IIt. ..:548

s].441]3 l] . E, Ll -," E'

IIt. 7. E,eE [1. .t.I llt1

O. .i: E:6 L.

o. f,4e4-t:r.4Sf,9•

[1.7•5. 5. 7

-O. 1 1:t7• t:I

l:i . 1:: -F. e I.,

I:E.F6'19 tll . E, tr, 1 I[l

I:I.E:1:1::1

F:l . 5, :i g:. 7-

1:i . 4 t:t5 7-

l:i.4E,4Et- Ll . Ll e- 1-1 V.

- .- .. --- I.1 . I.fet, ,t

- 1:l . :--." Et4E,

-Cl . 19t;. 1

-I). E, 7• E,a

- [l . I[l4•I-:6

1:I . fj643

t-t . t.let. t.;

- 1:1 . 1]441

-t:1.4f,19 Ci.4442 1.4 . e :L} a• 7

[1 . III9E•3- i.l . u4 }.7 E,

1:t . 2"a16 T.l. u67Y- +) . /:-: E,7O

CI.34Et1- IJ: . IIt Et 1 4

-- 1.t.1::S3 i:1 . E•46 [l

1:t . 1 E, f.i t:1

t]..I:7::g-tll. I)7E7- 1:i . Ill 7- s:l9

l:t . :-: l[l :-, 3

t:I . Ci E, E, f-'

-Cl.[i41E:-l:t.1E:44 O.IE,41-U. 1.I111 Cl . 1:-ul" 1 5

- t[1 . ilt E:2 Ed

- t] . 4 [t 1:s3

-l) . Er 7- ES

[a . f, 'r4 ::s9

- tl: . .:. 7. 59

[l.1:::::6 t[E.4:J54 l:t . E•6:::2

1].1•;413-- t]. 1:t .: 4 Ci

III . :-' :iers

l] . O-]. 7- 5

- 1:l . 1 .:tE. ;.

-[l.EEi58-U. ;:C6U-t:i.E664- l) . :-: E,4E,

e. /.-.-: 1::E9

- I:i . I:l f- :i:7

- 1] . 4.:-: 7E

l] . 4[- 1[I Ed

O. 1:l 9. 1S- t:l . EJ E: .:-: f,

t] . E,4E,4

-1[l.4E,1[-- [i . 5i E,6E•

-}).1E,C.4-i:l.4E,14 I:t . i:I E, E: E,

-1:l. 1:l 9. 15

- [i . E• -, '-• E,7

- tll . Ii: i:1 t:1 O

r l]. a' 1EiE

-O. ILtE f"3 t) . t;IE•4 1

-[l. Cl4t-{ b'

L) . a•439 i).1161 O. a'684 [1. 1:I41 3.

-1:l.E11114 ct. E, r• !e

1:1 . 1 :. 18

- l:t . 4673

-[i. [iE,1e i:t.41 3• Cl C, . 1 7• 33

-O. 1 tr; Ed FJ

l:1 . :-•974

Cl . E', '• 4O

-O. I• E: 1e

Ct . 1434 C,. 47 9, 1

-Ct. 06S5 1] . e9. 17

-[t. i:I:I39

-[1.45l[1e-[s.13e,9 O. 1:1::5e

-;].l•466 [1 . :ll)Ea

-S).11(l1-O. .: E• J-:4

t soil

1: 2: 3: 4:

typeGley lowland soilGray lowland soilBrown lowland soilPseudogley soil

Table 8-4 'Eigenvalues, correlation eoefficientsratios of total variance derived fromanalysis for soil type classification

and cumulative"Hayashi 3"

Eigenvalue Corre!ationcoefficient

Cum.total

ratio of ` varlance

!

2

3

4

5

O.278

O.2l,9

O.i14

O.092

O.083

O.527

O.468

O.338

O.304

O.288

o

o

o

o

o

.189

.339

.417

.480

.536

-89-

the first two numerical scores assigned to indlviduals as coordi-

nates as shown in Figure 8-1. Each of the samples was plotted on

the scattergram with the soil type label assigned in the field by

the surveyor. An ellipse shows the statistical boundary within

which 80 % of the population are expected to fall. This was drawn

using the mean, standard deviation and variance of the numerical

scores assigned to the profiles belonging to one group as shown 'in Table 8-5.

m

m

A

m

M

mmmm mm tlP m

A A mA SL

AA

thAth

SOLUTION 2

m

OD

A ÅëthA

th

m

m

A

Ath Ath XA,A th

xxx x

A x xx x A x zz x x

x

x

Scattergram .two numenctype

x

'

xx

x

Ag

++

x/ x x

x

O xx:x llk

x

smm

mm m

mA+

A

mm

th

A

Legend

GLL M GRL A BLS X psG lnr

es

+

A

:4

A

x

A

t

t t SOLUTION 1

Fig. 8-1

•--

x

of sampled sites scores assignedestab1ishment

x

a

x

t

Åë

'-- t

tt

At

plotted with the firstby "Hayashi 3" for soil

-90-

Table 8-5 Statistics for each soil type

soil type Statistics1 2

Solution 3 4 5

GLL

GRL

BLS

PSG

mean -vanances.d.

meanvarlances.d.

meanvanances.d.

meanvanances.d.

-O.205 O.147 O.384

-- O.152 O.183 O.428

-O.O39 O.178 O.421

1.007 O.052 O.229

O.596 O.052 O.227

O.148 O.074 O.272

-O.445 O.047 O.217

-O.066 O.039 O.197

O.110 O.043 O.208

-O.043 O.111 O.332

-O.091 O.134 O.366

O.237 O.025 O.158

-O.225 O.061 O.247

O.292 O.055 O.234

-O.091 O.042 O.205

O.031 O.051 O.225

O.044 O.048 O.220

-O.038 O.097 O.312

O.048 O.082 O.287

-O.097 O.097 O.311

[ptie 80 % ellipses occupy separate areas in the scattergram

and most of the individuals are localized within one of these

ellipses. Consequently, four soil ty.pes were established as

taxonomic classes by means of Hayashi 3 analysis. They correspond

to the four soil types which were differentiated by the surveyor

in the field.

8.2.-3.2 Sample allocation

Discriminant analysis was performed to allocate the indi-

vidual into the appropriate soil type. The five numerical values

assigned to the individuals by Hayashi 3 were employed as dis-

criminating variables, and the 127 typical individuals which fell

within one of the 80 % probability ellipses were used for deriving

the discriminant functions.

Three discriminant functions were derived from three eigen-

values shown in Table 8-6. Each sample was given three discriml-

nant scores and allocated into a group in which the sample has

the highest probability of membership. Table 8-7 shows the prior

probabilities which were used to compute the probability of mem-

bership in each soil type for the individuals. The results of soil

- 91 -

Table 8-6 Eigenvaluesdiscriminant

and relative percentages functions for soil type

for derivedclassification

-- .Discrimmantfunctlon

EigenvalueRelativepercentage

1

2

3

8.977'

2.343

O.942

73.2

19.1

7.7

Tab1e 8-7 .Prior probabilities for .respectlve soil types

GLL GRL BLS PSG

No. of sample

Priorprobability

30

O.236

35

O.276

47

O.370

15

O.1l8

type assignment for the individuals were summarized in Table 8-8.

Figure 8-2 shows a scattergram of individuals which were plotted

with the first two discriminant scores, and a statistically com-

puted territorial boundary for each soil type. As shown in Table

8--6, since the first two discriminant functions explain 92.3 % of

the variance among soil types, the third discriminant function is

--negligible in representing the results of the analysis. In Figure

8-2 each individual is represented with the label which was as- 'signed in the field by the sufveyor. A comparison between field

judgement and numerical assignment in the soil type classification

is summarized in Table 8-9.

For 140 out of 156 profi!es (89.7 %), the same soil type

was assigned as in the field survey. The author concludes that

the numerical procedure gives an objective and satisfactory results

in classifying soil types of the individual soils.

- 92 --

Tab1e 8-8

profile No.

48 60 61 62 64 68 70 7t 72 73 74 75 76 77 78 113 t14l1511611711811912012112212312412S126127128129130f31132133134135136137138139140141142143144145146147148149t511521531S415515615715815916016t

162163

.!64167168169170171

Numerica1 assignment of soil type for samp1e profiles

SoilField.Judgement

type*

NumericalaSslgnment

Probabi

1

lity

2

ef membershipk

34Discriminant scores

33

1 1 lr.

13

133

's•

33

33L)

L:

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1

313

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-93-

Tab1e

Profile No.

172173174t75176177178t79.t80181182183184185186187188189190191192t93194195196197198199200201202203204205206207208

'209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241

8-8 (cont'd)

SoilField]udgement

typeh

Nuuierica1ass1g.nment

'

1 3 E 1 l-,

1 e 1 E• 1 1 1 s E :-

E• :-1.

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4 4 /;t

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1

1

1

1:.i

1•:

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1

21

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1

1

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1:.-

Probability

12of rnembershibk

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t].1E,Et:1 . 1.l lj 1-l

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t:1. I] III t)

1 . tl"[ll:i

III. t.l

II:.O

e. :- :5' E:

ti.'s:Ie,1 . [1 t] tlt

1 . i:1 l[l 1[i

1 . 0 i[1 lll

[j . 1 :l:5

U. ::: b, t"

o . [l t:1 l:I

1.E. t.i 1.1 Lt

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LI . L4 1.t l.t

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IIS. [l L) tll

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l-{ . ,-. 1.".:

LI . ,l. ,:4 l-l

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l]l. III

[l. t:l

tll. f]

t[1 . i:l

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l] . tls

l.l . t.l l-1 t.I

l.:. 1-S l.l l.l

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l.t.V'"ds.s. ,:I. .-. ,-

Ll . I-M.l U

1 . v t.l t-i

[T.Oi].4):!!4O. •). 9. 5

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tll . 1 E• :•

1-f. S.tIJ,t

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li -'-i'g---..I.l . i-t l.1 V

l:r . •). 9E,

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i) . +[] IInll

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l].9e7u.v'svIll. •F, E:S

t-1 - IJ t.1 1.i

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t[l. (l 14

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1.1 - Ll 1.1 l.i

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l.i - i.I LI +.i

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l.I. d-I 1.:::

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l.I . d.H.l L4

1 . [l t]1 t:i

l.l . LN) t.1

l:l . tll O t]

lt ittMl.--..ILr. ::"ll1

Lt. 'Y. c' l.t

t.i . E.l :.H-i

1 - II] 1:I1:t

111. I:: F:1 tll

1.{. Ll I.d•d

l.l. I.I LIe

lt iHlll.---.1.l. i.E LI tJ

Li . t-H-EU

lll . :5Ji:

tll . [Ila/:1

1[I . [1

1/l. 1)

Ll . V IJ l.I

Lt. IJ

t-l . 4) Ll t.1

1 . i:1 ilHIE

t.i . 1.1 I.H.t

U. Ul.t . 1-l Ll i-t

V. IJt.I - Ll l.I l.i

l) . I] lll t[l

O. III [i 1

l). t:l1E•

ili. s:t

t.l . I.1 tJE

l:i . •l 7- 5

tJl . I]

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l:I; l]S

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1:I. t)

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I.l . Li l.1 l-l

U. UU i 1.1 tJ Itl

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l[l' t:t

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l:l . U ilS 11L

[l. I]

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1 e tll 1] i]1

1 . 1:t 1) [t

[1. t] .:: E,

i]. ')- 7- [-e

U. L: 1-rs.l

1 . t:1 1] i[l

1 . tll 1:1 t)

t-f. I.l I.1 l.l

1]. t[i11

l}. Ill

1:l. I]

I]. I:t

l.1 . I.1 fJ I.:

M IHILI---.-t]. tll1S

1-t . tJ t.H.I

1 . IIt t:t Cl

[i. t) t:f1

1-b . ITI 1.I LI

LI. I.S

t-l . i.l t.1 t.1

1. IIn) L[1

t[l . •). 91

l.1 . e,ed

O. tlt

t] . ill

t]t. Ill

t-: i +'l l-l s't

f.t. }.l

1.t. Li

I.I.UU . I.l i] l.t

IJi . I] ]/I O

IIi. 1:T

U. UUIjt't' 1]

l:i . t]1

l] . L:l

i:t.elll . 1) Ci4

l[s. 111 1 '.-.l

o. t:l

i:t . IIH) (l

Cl. i:f

ti '- '-F:-I- -t-

1] . III E• Ei

t]. ci

I:;' {[l

1:l . ;:I

I]. [I

III. Ill

Sll . i[E

-94-

Discriminartt scores

3

-E. 1 .:]•]E.

:t . E, 4E •)

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1.11t.4- :-! . 7- E, 1 :l

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-E•.E4[:I:-S. 7• 5, E:4

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-U. ddt, IJ •-, c--,t" L.":, -- 5. .I. I] •]. 1

/-., . :.l 1.l 4 ::

i:{ . 16E• E,

5. 74•) I-i ':-: . E• 1 4 l]

::.4a•4II: 4. I:i4l••) IIt . E, 1 E• E,

/II . F. 7. !i: a,

L.44E:1- l• . E: l[s 1 /::

-1.64i':-; li, . 5 1:s iE::.:

-1 . E; 41 l)

4. :::E, ::t4

-1. 1 [- E:7-1.•)E:i:E: E. :!I E: f- 1

i:1 . 1 1 a• E'

1. E,1•I. E•

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- [d . 4lt 1 •).

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1 . ,r,aE• E,

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-l•.4:i7E•-e.t•'"t.:: t t 4t ttt -- -, -.,'-,v +t -. .t- /.- ). t, [,L•1-1. E, •). •) Z-i

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- [l . c'• E: E, S

-- 1:I. 7• 7 7• 1::

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II-:. 167• i-

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-- 1 . 94 e,6 1 . 7- 7• .:i• 7-

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E.64E:9 1 . Iil 1::45,

::.l•514 t]. •). 7- E, E,

Ill.41 7, :?•

tll . 99 :i: .1:

(1 . 41 :t: /:-:

.:: . I-l t, 4 ,L-:

tlt . E, E, 1 :;

E. E, 7• 04 1. 5, 9. t:: 1

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1.4t[lE•:",

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1. 54 4 7•- t) . ili•) C. F

-- IIi . E, .r :. t,

-I].51E•;"-1. II]5; tl,

-1 ; [t /--.-; lll 9.

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1. :i;172

Table 8-8 (cont'd)

Profile No. SoilFieldjudgement

typet

Numericalasslgnment

ProbabLlity of membershipk

1 2 3 4 1

Dlscrlmlnant

2

scores

3

24224324424S2462472492502Sl2522532S425S256257

rs

1tr.

E•

1

z.

!

1

l•

1/:-:

41]

:t

::

. • :.:

1e'

L:

1

z.

1

1

e1

t'

41:-:

3•

III. [l l). OO I]1 . 1.H.l 1.1 t.1. I.N.I t.t

t-S . LN.l 1-1 1 . i.1 1.l 1.1

t:I. I-J 11u:: t] . 1 9?

1. 1:t 1:H) tll. [I t) lll

t:1. 1[t 1[11 V. V. V. V.

1. I:flllIJI F-i. LlUU

1. IIt L:"li t]. 1:1 il":t

". E• E, i]l tls. r- 4d]

1. ,] ,li ili [i. ,:i t:i t:i

llt. I:t tll tll i:1. E: II-:1

I:l. I:i [l. i:t

1). t]t CI. IIH:J l)

t]. I:1 tll. t:1 []1

t]. O 1.I. iJ l-t l.S

1 . i] sli [t

t] . i]1

t.i . I.H.l [I

1-I . 1.l :-t l.1

l]. I]

l.h . LI LI l-t

tlt. 1:t

l:l. I)

l.4 . 1.{ 1.I t.I

III . 1[l

t:l . 1 E, 9.

t.l - Li 1.I i.i

t.l. V. V. V.

IJ. 'V. 'V. V.

1. i) [uli

tll . tlt S] O

CI. CI

[1. t[t

lll. 1]

. I]. I:F

III. 1[1

O. [l Cl. I:I

1.r . Li t.l i.l

11I. i:l

U . LHtl l.]

1. III t:H]

l.T . I.i U iJ

1.t . i.U.H.l

1) . t:1 t) IIt

r.. I) E• 7- 1

-4. E, 43f,-". 7•• 7- :E E:

-- a• . 7 E,:o

-. :; . •-. Ea•4

-a' . I-'5o [I

-4. S• 1 E,1-4 . E 1:-:7 E,

-1 . E: a• •). 7"

-:-:. e, L;•E3

l:l. 1 iJ-1 4

:.,. :-: :t 1 1

1.I5E,E, 1. E, :" 1 di

1:l . E,4•i tll

1 . E, Cl :iiE,

- 1 . 'I:E, 1:: 7•

1.46t]i1 1:t.7b:1E:1

-1.E,1tli5 l:1 . a• S• 9, 4

-1 . 4E• E, i•'•

l] . 1 9. 47-•

-- i . :. •r- E,6

l:1 . tlt45 t:l

1] . 5, 7 E, E;

-4 . f, 1]-:5:-•

- t[I . i• 1) 55,

ltl. t:[ id. 1.I l.1

1 . E Ct E, 7•

-1 . t:C-: 1S

-- 1.Ei11i 1 . 9, S 1::6

l) . 44 l. E:

- 1 . t] :i: 8 tr

:. . 4E• E4

-1 . E: O1 F•,•

-:..:.f-51 [1. h) t",1 t:1

-- 1. E:g1S l) . Ll •). 85

tL 1 9. 9Ei- 1 . :: f, 1:t /::

- 1. 07 5 E,

-•- i] . 6• 7• rc-,

t soil

1: 2: 3: 4:

typeGley lowland soil (GLL)Gray lowland soil (GRL)Brown lowland soil (BLS)Pseudogley soil (PSG)

SCORE 2

Legend

(GRL) GLLNNxNltsA GRLA

BLseNAAAZtS eleeeeeleeeeeeee

el-eeee

PSG[]

11"ldAAAA SCORE1

:1N-AA"-NAoA 1e,evt

1eee'e(BLS)

1--NA-IN NAA!ÅqE]

Nl(GLL)År'A

/k--i-e--'k-- K---eo

DD[]MDDD

Territorialboundary

/ (PSG)/ o

I o

.Fig. 8-2 Scattergram of sampledtwo discrirninant scores

'sltes for

plotted with theclassifying soil

firsttype

- 95 --

Table 8-9 Cross-table between field judgernent and numerical assignment on soil type classiflcation

Field

judgement

No. of

individuals

Numerical assignment

GLL GRL BLS PSG

GLL

GRL

BLS

PSG

36

44

59

17

32

o

o

o

-1.

40

2

1

o

3

52

o

o

1

s

16

Percent of ''grouped'' individuals correct!y classified : 89.77.

8.2.3.3 Area delineation

Numerically assigned soil type and the probability of mem-

bership for each sampling site were used to delineate areas which

are homogeneous with respect to soil type. The mapping unit wasidentical with taxonomic unit ln this study. The final map wasautornatically prepared by a printer plotter (VERSATEC 1200) which

is one of the peripheral devices of the FACOM M.200 computer of

Data Processing Center of Kyoto Universlty. A soil map qgmpiled

by the numerical procedure is shown in Figure 8-3 (B) in compari-

son with the one compiled by the conventional procedure (A).The conventional soil map was herein recompiled on the basis of

the difference in soil type.

8,2.4 Discussion and conclusion

The distribution pattern of each soil type is generally

quite similar between (A) and (B). The numerically compiled

soil map consists of more irregular, complicated and mosaic-

shaped delineations than the other. This is because "AUTOMAP"

has not yet been equipped with a function for recognizing

"inclusions" unlike conventional map compilation. The surveyor

-96-

#+

;+

+#t##:

"J ut OJ cr - utoomaww ---- ---- --- T -- - -

++++++++++++++++

co

atufi

H.Hoco

B•:

g8

za

--tu:o

.HpaÅëÅr

qoovatu

AÅq

-t++ +

+++++++++

+++el++++++

+;i;9ZIZII

)15 ")t

x-eltu

o.HpÅëfi

:aa.oco

3"opg

.Yl

Iil

98

n1

co

.oo

.Hin

-97-

sometimes neglects an area around the site of soil type X and

shows that area as being oecupied by soil type Y, when soil type

X occupies too small an area to delineate within the area of soil

type Y at the given scale. Secondly, the program is not capable

of setting up ''soil complexes."

On the other hand, "AUTOMAP'' makes the raw data available

to a user; therefore, he can adjust them, if necessary, in ac-

cordance with his interest. Even a user who is not specialized

in soil surveys can prepare a map showing the distribution pattern

of any required data, raw or processed. ' One thing must be mentioned here about the location of the

boundaries. As shown in Figure 8-3, the exact locations of the

boundaries are not identical between (A) and (B), because the

method of compilation is entlrely different. As the results of

this study, if the nurnerically and conventionally eompiled soil

maps have similar distribution pattern of soil, the boundaries

drawn in the conventional map are normally more reliable. However,

the numerical method might make new boundaries in an area wheye

no delineation occurs on the conventional map, and those boundaries

might be of importance in cornpiling a map. The user himself is

responsible for deciding which of the boundaries, conventional or

numerical, should be adopted.

8.3 Compilation of an Interpretation Map - Suitability Map for Upland Crop Cultivation -

In order to serve to practical purposes, soil survey dataare usually required to be compiled into interpretation mapg.

The compilation of a suitability map for upland crop cultivationon lowland paddy areas is described here as an example of inter-

pretation map compilation. An interpretation map is not always

compiled based on a single factor, which is derived from a soil

-98-

attribute or a group of soil attributes. Hence, it comes out

through overlaying of several assessment maps, which are prepared

for each of the factors concerned. Compilation of the interpre--

tation map is carried out through the following three steps;

1. selection of soil attributes relevant to the problem,

2. generation of an assessment map for each of the selected

attributes or groups of attributes, and

3. class setting and overlaying of the assessment maps.

It is one of the biggest problems in agriculture of Japan to

convert paddy rice cropping to upland crop cultivation in the

lowland area because of an imbalance between a great deal of sur-

plus of rice production and deficit of upland crops production

such as wheat, soybean , vegetables, forage crops and so on.

Ministry of Agriculture, Forestry and Fisheries of Japan made a

proposal of conversion from paddy rice to upland crop cultivation

on 390,OOO ha out of total rice cultivated area of 2,881,OOO ha

and 439,OOO ha (113%) was actually converted in 1979 (Agricultural

Production Bureau, 1979; Statistics and Information Department,

Ministry of Agriculture, Forestry and Fisheries, 1979). However,

this policy is not always accepted unanimously by the farrners due

to complicated socio-economic situations. Although many problems

remain unsoJlved yet, the assessment of land suitability for con-version of the landuse should be the most basic and important task

for a soil scientist, if an efficient utilization of the limited

arable land of Japan should be aimed at.

8.3.1 Selection of soil attributes influencing soil suitability for upland crop cultivation '

Three factors must be considered for an assessment of the

suitability for upland crop cultivation. ' -- 1. Soil water regime ' 2. Soil tilth

- •99 --

3. Chemical fertility

Three factors are ranked in the above order in their importance,

provided the suitability is determined in terms of the cost for

' adapting an area to upland crop cultivation.

The first is soil water regime, which affects oxidation -

reduction condition of root zone of the cultivated crops. This

factor is characterized by such soil attributes as ground water

level, water content, ferrous ion content, soil color, mottles,

and soil structure. This is the costliest factor to modify, be-

cause certain means of drainage must be installed to rnake the

land suitable for upland crops. . Next is the tilth factor which is characterized by stonmess, structure and consistency, latter two being a function of soil

texture.

Finally chemical fertility should be considered. It is

, rather easy and inexpensive to modify this factor in Japan as

compared to the preceding two other factors. The assessment of

the study area on the third factor is based on such soil attri-

butes as cation exchange capacity, calcium saturation per6entage, pH, exchangeable cations (K+, ca2+, Mg2+), available phosphorus

and silica.

8.3.2 Generation of assessment maps

8.3.2.1 Soil water regirne

The soil water regime is one of the most important differ- entiatlng characteri'stics of the four soil types; GLL, GRL, BLS

and PSG. Therefore, polygon data, stored in Cartographic File

of COSMAS, should better be used for the preparation of assess-

ment map• on this factor rather than.site data, because it was

shown in 8.2 that the conventionally cornpiled soil map was similar

to the one prepared by numerical method based on site observations•

- 100 -

/

As shown in Table 8-10, three classes were set up by the use

of two soil attributes which are stored in Cartographie File; soil

type and variety. Figure 8-4 shows the rnap of suitability class

in terms of soil water regime. This map was prepared by the use

of the "MAP" program of COSMAS.

Table 8--10 Classes set for soil water regime assessment

SuitabilityClass

.SoilType SoilVariety

I(good) BLS ALL

GRL ALLII(poor) PSGGRL UGIII(v.poor)GLL ALL

E cLAss ii1mp1 rti CLAss III

ndrdi

ilii:f:qi if'l E iL r' liilY]:l,rl•i'iii

;sti'

Fig. 8-4 Soil water regime map

- 101 -

8.3.2.2. Soil tilth

The assessment map on soil tilth can also be prepared from

the polygon data through "MAP" program. As shown in Table 8-11,

three classes were set up based on two soil attributes stored in

COSMAS's Cartographic File; stoniness and texture class. Figure

8-5 shows the assessment map on soil tilth.

Table 8-11 Classes set for soil tilth assessment

SuitabilityClass

Stoniness Texture Class

I(good) none M 'c

II(poor-1) CP,PM M ,c

III(poor-2) none F

'//. )){i

'i//"

.ECEND

rri ,...LAS,S IL--J5.c/td,:F,ig'"i•.Aqqii

S:-:.a.-n"."

EL,rll '"L""• m

"N

11"l ''1 '

s"

Åq

7 "NV4.l..;Al L )

o,71 h ffs"t-/L71t) j

gtJ t' t .r[,y'

.,1.f.:-w

..t.' izt,is:.-

e /'• 'i !M

fr .A

.t.-.y71A'"

2 km

'8-5 Soil tilth class

- 102 -

.Fig. maP

8.3.2.3. Chemical fertility

The data of soil attributes relevant to this factor are

grouped into several classes according to the criteria used for

the nation-wide soil survey of arable land by the Agricultural

Production Bureau, Ministry of Agriculture, Forestry and Fisheries.

Table 8-12 summarizes all classes for each of the soil attributes

concerned. A tentative fertility class at an individual site is 'given as the class of a soil attribute which gets the lowest

Table 8--12 Classes set for chemical fertility assessment

Suitability CEC PH Casat. Exchangeable(mg) Available(mg)

Class (me) (7e) cao MgO KO2 P20s sio2

I 20.0Åq 5.S Åq- 50.0Åq 200Åq 25Åq 15Åq 10Åq 15Åq

II 20.0-6.0 5.5-5 .o 50.0-30.0 200-1OO

25-10 15-8 1O--2 15-5

III Åq6.0 Åq5.o Åq30.0 Åq1OO Åq10!

Åq8 Åq2 Åq5

ExampleIII I II III I II II II I

assessment class among all the attributes used, as shown

example of Table 8-12. Tentative fertility classes for the individual sites

reclassified; class I (high fertility) for the tentative

I and II, and class II (low fertility) for the tentative

IU. Then, the assessment map was compiled by the use of

MAP" program of COSMAS, and is shown in Figure 8--6.

.

m an

were

classes

class

"AUTO-

8.3.3 Results

A suitability map for upland crop cultivation was produced

by overlaying three assessment maps mentioned above. As the

chemical fertility is easier to modify as compared to the other

- 103 -

y.SK'

N

tsN. ".x .-..• xssN...c.tt,l:•;S•lj

'Y 1'" "rÅr-,

LEGEND 2f,.Åq.I. !x':, ]rL--"[,ci.A,s,s,, -IN cii" --c

O 1 2km Fig. 8-6 Chemical fertility map

'two factors, it was thought appropriate to take it up as a secondary

factor. Therefore, suitability class was prlmarily determined

by the other two factors; soil water regime and tilth. Three

classes were set up, as shown in Table 8-13. Figure 8-7 shows

the final output of a suitability map for upland crop cultivation

on the lowland paddy area.

In this study area, class I is the most suitable land, where

no problem may arise in converting the landuse from paddy to up-

land crop cultivation. Class II is poorly suitable land,. which

consists of two subareas: poorly drained area and area hqrd to

plow. Hence, preparation of raised beds and stone removal, re-

spectively, are advisable. Class III is very poorly suitable

land because of too wet water regime, which would cause wet in-.jury to .crop roots. Installation of a drainage system is indis--

pensable. ,

- 104 -

Table 8-13 Classes set for suitabilitycrop cultivation

assessment for upland

SuitabilityClass

SoilWaterCondition Tilth

I good good

II Poor poor-1 ,2

'

III v.poor -h-

l

II

III

low

ri' ifiihlll' " Ill

idi'11•I'diifi,lileipag

ii lllii,

esl,I•

2 km = === ==

Fig. 8-7 Suitability map for gpland crop cultivation

' A secondary category as determined by chemical fertility

status (high and low) is provided to each of the classes, as shown

in Figure 8-7. Dotted areas, which have low fertility status,

may be advised to adopt more intensive management of soil fer-

tility.

- 105 -

8.3.4 Discussion and conclusion

An interpretation map has, hitherto, been compiled from a

soil map, which is, in itself, a product of manipulation of raw

data obtained in a soil survey. Thus, a lot of loss or distortion

m origmal mformation is mevitable m the process of prepara-tion of interpretation maps. It is preferable' to make use of the

raw or original data for preparing interpretation maps.

This study has proved that soil map and lnterpretation maps

can be prepared as one of the output Åíorms from raw data, site

or polygon, stored in the files by the use of the functions of

automated cartography of COSMAS. "AUTOMAP" program is now avail-

able for the procedure of site-polygon data conversion, whlch was

used to be done by an experienced surveyor, giving high repro-

ducibility and objectivity.

Consequently, even a user, who is not specialized in the soil

survey, is able to prepare a map according to his interest. It

was shown that any assessment map on a single factor could be

automatically prepared by cosMAS's output programs. However,over' laying of several assessment maps cannot yet be automatically

processed. Bie et al. (1978) employed a diserete approach (de

Gruijter and Bie, 1975) as an example of the methods in solving

this problem. This is one of the problems which should be solved

by COSMAS in the near future.

- 106 -

CHAPTER 9 Summary and Further Research Needs

The aim of this case study was to test the applicability of

COSMAS's functions for an efficient use of soil survey data on

the fundamental and practical problems. Soil classification and

map compilation have been carried out by an experienced surveyor

accompanying certain subjectivity and variability. In order to

avoid such drawbacks, search for appropriate methods of numerical

classification of soils and automated map compilation were at--

tempted in this study with some success.

9.1 Numerical Classification

Soil material classification and soil type classification

were accomplished for soil samples obtained in the west slope of

Mt. Oye and lowland area, respectively, in Kaya Township. Two

numerical procedures, Hayashi's theory of quantification No. 3

and discriminant analysis, were employed in the following two

steps,respectively, to simulate the procedure taken by an experi-

enced surveyor in the field; category-class establishment based

on the similarity between samples with regard to their response

pattern to the soil attributes and sample allocation into estab-

lished classes. Soil attributes used here were selected in ref-

erence to surveyor's field procedure. About 90 % of the samples

examined were allocated by means of the numerical methods to the

same class as done by the surveyor. Therefore, the procedure

proposed in this study, which is the combination of soil attribute

selection, Hayashi's theory of quantification No. 3 and discrimi-

nant analysis, is believed useful for classifying soil material

and soil type.

Since this is a mere case study carried out in a small area,

we cannot say that the procedures adopted herein.are applicable

- 107 -

everywhere to produce reasonable and reproducible classification.

But we believe that the methodology or the principle underlying

the used procedures are widely applicable, regardless of the area

or the category at which classification is attempted.

9.2 Automated Map Compllation

Soil map and suitability map for upland crop cultivation on

lowland were prepared by the use of programs implemented for COSMAS. Programs, "AUTO]Y[AP" and ''}GAP," were very useful for area delineation

from site data and for area retrieval from polygon data, respectively.

ThereÅíore, a m"ap can, now, be compiled with respect to any single

or multiple soil attributes which are stored as site or polygon

data in the corresponding files of COS)4AS.

A soil map prepared by "AUTOMAP" was satisfactory as the

first approximation in automated map cornpilation, but it was still

too crude to reproduce the actual changes in physiography. Need-

less to say, the more samples are available the more detailed out-

put can be. However, the amount of labor also increases veryrapidly with the number of samples. There is one way to obtain a

huge amount of data with high density, speed and relatively low

cost: it is remote sensing. The author expects that remotely

sensed data processed by "AUTOMAP" would give much more detailed

and informative output to users. As can be seen in the latest

publication (Burroff and Morrison, 1980), handling of remotely

sensed data is becoming more exact and precise, but only a few

relationships between these'data and soil characteristics are

known. Future study should be directed towards elucidation of

those unknown relationships, since both the hard and soÅítware

already exist for collection, storage and retrieval of these data.

- 108 -

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-- 109 -

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•- ile -

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- 112 -

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- 113 -

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- 114 -

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pp52-60

-- 115 -

Appendix I

Profile Descriptions and Analytieal Data

for the Representative Soils in the Mountain

i

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