Conceptual Soil-Regolith Toposequence Models to Support Soil Survey and Land Evaluation

Assisting nonsoil specialists to identify soil types for landmanagement: an approach using a soil identification keyand toposequence models

G. J. GREALISH1,2 & R. W. FITZPATRICK

1,2

1CSIRO Land and Water, Private Bag No 2, Glen Osmond, South Australia 5064, Australia, and 2Acid Sulfate Soils Centre,

School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia

Abstract

Conventional soil survey information is often unclear except to specialists. An approach using soil

toposequences and a soil identification key was used to aid the translation of soil survey information

into a form suitable for a nonspecialist audience with a case study from Brunei. Soil Taxonomy was

used to characterize the major soil types; however, to assist end users, a complementary special-

purpose soil classification system was developed in the form of a soil identification key using plain

language terms in English that were also translated into Malay. Easily recognized soil features such as

depth, colour and texture were used to categorize soils to match Soil Taxonomy classes. To

complement the soil identification key, conceptual soil toposequence models presented the soil

distribution patterns in a visual format that local land users understood. Legacy soil survey

information along with a widespread distribution of 172 soil sites from 35 traverses in 16 study areas

provided a dataset to develop and test soil toposequence models and the soil identification key which

both proved reliable and robust. The approach demonstrated in Brunei could be applied to other

countries and landscapes.

Keywords: Soil Taxonomy, special-purpose soil classification, soil-landscape extrapolation, soil

survey, land use

Introduction

Conventional soil survey information can be of limited use

to farmers and nonsoil specialists because of the scientific

expertise required to understand and apply the soil

information (Dudal, 1987; Yaalon, 1996; Sanchez et al.,

2009; Fitzpatrick, 2004, 2013). Experienced pedologists are

in short supply and are rarely available to meet

land-users’ demands. In response, we devised an approach

that aids the translation of such soil survey information into

a form suitable for a nonspecialist audience, and results are

presented in an example from Negara Brunei Darussalam.

To achieve a significant degree of food security in

Brunei, there is a commitment to increase the level of self-

sufficiency in rice, fruit, vegetables and animal production.

This could be achieved through yield increases per hectare,

having more crops per year and by developing new areas

for agricultural production. To meet the country’s food

security requirements, information on major soil types and

their suitability for agriculture is needed to assist decision

makers with the reallocation of agricultural land (in some

cases forestry) to the most appropriate uses and to

recommend sustainable soil and nutrient management

practices.

Soil survey data for the entire country (Hunting Technical

Services, 1969) and for selected areas (Blackburn & Baker,

1958; ULG Consultants, 1982, 1983; Grealish & Fitzpatrick,

2013) describe soils and their distribution within Brunei.

Marumaya (1994) evaluates some of these surveys and

identifies their major weaknesses as the restricted coverage

and that no recognized international classification systems

had been used. Our investigations concluded that the

information on these soil survey reports while thorough and

appropriate for the time when the surveys were conducted

was now of limited use because:

Correspondence: G. J. Grealish.

E-mail: [email protected]

Received October 2012; accepted after revision December 2013

© 2014 British Society of Soil Science 251

Soil Use and Management, June 2014, 30, 251–262 doi: 10.1111/sum.12108

SoilUseandManagement

1. The scale of mapping was too broad for on-farm planning.

2. Most decision makers, including farmers and agricultural

advisors, struggled to understand and apply the

information shown on a soil map, soil map legend and

soil survey report.

3. The soil classifications used were not correlated to an

international system that would assist with transfer of

knowledge from similar soils, making it difficult to

implement and test new crop and soil management

practices used in other countries.

4. The soil data were not in a form that could be easily

interpreted for current land management challenges.

To implement land-use change, farmers and agricultural

advisors need to:

1. Identify soils and where they occur in the landscape to

produce farm plans at a more detailed scale than the

published maps or in unmapped areas.

2. Have soil types that are easy to understand.

3. Use soil types that correlate with an internationally

recognized soil classification.

4. Use soil types that can be directly linked with limitations

to land use and land-use suitability.

Identifying soils

Classifying soils provides a means for ordering soils into

groups with similar properties that facilitates transfer of

knowledge about the soil and land management performance

(e.g. Wilding & Drees, 1983; Dudal, 1987; Yaalon, 1996;

Fitzpatrick, 2013). Soil Taxonomy (Soil Survey Staff, 1999,

2003) and the World Reference Base (2006) are general

purpose soil classification systems used to communicate soil

information internationally. Soil Taxonomy was chosen for

this work because it is used elsewhere in the region where there

are similar climates and land uses (e.g. Philippines, Thailand).

In addition, Soil Taxonomy is the basis for the Fertility

Capability Classification (FCC; Sanchez et al., 2003) that can

be used to assess the limitations of land for agricultural uses as

part of land suitability evaluation. However, for local users,

Soil Taxonomy has limitations that include the reliance on

laboratory analyses and the specialized terminology and

language used to classify and name soils (Drohan et al., 2010;

Fitzpatrick, 2013). To improve the impact of soil survey data,

the knowledge and ability of local people need to be taken into

account (Sillitoe, 1998). Presenting this information in the

form of a simplified soil classification linked to Soil Taxonomy

allows local, nontechnical users to identify soils using their

own language and would improve the uptake and use of soil

data (Fitzpatrick, 2013).

Soil location in the landscape

Conventional soil maps are produced based on the surveyors’

understanding of soil classes, and their distribution in the

landscape. Milne (1935) describes a soil catena as a sequence

of soils occurring on the same parent material and related to

each other by topography. Topographic variation influences

soil processes such as soil erosion and soil solute movement

that impacts on the other downhill members of the soil

sequence, thereby developing the linkage between soil types

(Milne, 1935; Huggett, 1975; Conacher & Darylmple, 1977).

Soil associations describe a geographic association of soil

types rather than a process-based relationship (Conacher &

Darylmple, 1977). A soil toposequence describes a soil

association that can be defined in terms of topography, but

does not necessarily imply the more strictly defined process-

based linkage of a soil catena.

Soil toposequence models provide a conceptual under-

standing of soil and landscape relationships on a hillslope

(Huggett, 1975) and are developed intuitively by soil

surveyors’ observations to assist with soil mapping and

delineation of map units. Farmers’ understanding of soil

variation is also strongly influenced by terrain, so reasonable

agreement is likely (Barrera-Bassols et al., 2009). While soil

survey maps and map legends provide information on how

soils vary across an area, soil toposequence models can be

used to bridge the gap and graphically convey information

about soil variation in a form that nonsoil experts understand

(e.g. Grealish et al., 2013).

The scale of soil maps is often too coarse for use in farm

planning. A simplified soil identification system combined

with a toposequence model can help farmers, and their

advisors delineate the soils on a farm at an appropriate

scale. They also allow nonexperts to identify and delineate

soils outside mapped areas that have similar landscapes.

Aim

The aim was to present an approach that would assist

people such as farmers and agricultural advisors who do not

necessarily have a background in soil classification and

mapping to independently identify soil types to support their

land management decisions. The approach combines soil

toposequence models with a user-friendly, special-purpose

technical classification system, demonstrated by a case study

from Brunei.

Method

The approach requires an experienced soil surveyor to acquire

and interpret conventional soil data and then distil and

represent the information in a conceptual toposequence model

and a nontechnical, special-purpose classification system using

a soil identification key. The soil surveyor constructs

conceptual toposequence models using information from the

legacy survey reports and from limited field investigations.

The next step is to develop a simple soil identification key that

© 2014 British Society of Soil Science, Soil Use and Management, 30, 251–262

252 G. J. Grealish & R. W. Fitzpatrick

honours the same classification sequence used to identify soils

in Soil Taxonomy.

Case study area

Negara Brunei Darussalam is a small country of about

5,300 km2 consisting of two slivers of land on the northwest

coast of Borneo, bordering Malaysia and the South China

Sea. The study consists mainly of the hill country areas of the

Tutong and Temburong Districts (Figure 1). The hill

country’s lithology consists of interbedded and tilted shales

and siltstones (Sanddal, 1996). Flat narrow valleys are

surrounded by hills that often have very steep slope gradients

(>30%). Land use on flat areas is a mix of animal grazing and

vegetable crops, and on the slopes are fruit orchards. In small

areas where slope gradients have been reduced by terracing,

vegetables are grown, while steeper areas have regrowth or

native forest. Climate is equatorial tropical, characterized by

high temperatures throughout the year with an average annual

temperature of about 28 °C, annual rainfall exceeding

2300 mm, high rainfall intensity and humidity ranging from

70% to 98%. Seasons are poorly defined.

Field investigations and soil characterization

Survey over the entire area was not possible because of

resource constraints, access and very difficult terrain.

Therefore, sixteen representative areas (Figure 1) were

selected for study with 35 traverses consisting of 172 site and

profile investigations. At 24 of the sites, some soil layers

were sampled for laboratory analysis. The information

presented is based on data and publications from a larger

project – Soil Fertility Evaluation/Advisory Service in

Negara Brunei Darussalam (Grealish et al., 2007). Potential

site locations within the representative survey areas were

determined using satellite images (LandSat 7 ETM+ 2001),

hard copy topographic maps (Survey Department Brunei

Darussalam), a previous 1:100 000 scale soil survey (Hunting

Technical Services, 1969), a general geology map at

1:200 000 scale (Sanddal, 1996) and from discussion with

local farmers, agricultural advisors and research staff from

the Department of Agriculture.

Observation and sampling sites were located along field

traverses to produce a sequence of sites for investigation at

different slope positions from crests to lower slopes or valley

flat areas. Difficult terrain and thick vegetation prevented

the traverses being linear, and site placement was based on

desktop planning, surveyor experience and landscape

observations to ensure that sites represented the major

landforms and soil types along the traverse.

Soils were described according to the standards of the

United States Department of Agriculture – Natural Resource

Conservation Service (Soil Survey Division Staff, 1993;

Schoeneberger et al., 2002). Small representative soil samples

were collected in chip-trays as described in Fitzpatrick et al.

(2010). Soils were classified using the ninth edition of Keys

to Soil Taxonomy (Soil Survey Staff, 2003) as this was the

edition available at the time of the field survey. However,

review of the current eleventh edition (Soil Survey Staff,

2010) indicates classifications would not likely change.

Classifications were determined based on data from previous

soil survey reports and the current field investigations.

A simple soil identification key was developed for the range

of identified soils. The key was based on the presence or

absence of particular soil profile features that could be easilyFigure 1 Study area locations.


Assisting nonsoil specialists to identify soils 253

observed by nonexperts. From a series of questions, the soil is

allocated at the first question with a positive answer, even

though the answers to later questions may also be positive.

Each question required a ‘Yes’ or ‘No’ response to move

through the key, as this was easier for nontechnical users to

follow compared with providing multiple alternatives. For

some questions, the key ends with a ‘No*’ meaning restart or

consider that a new soil has been identified. This is necessary

to recognize that the key was based on the available

information and that there is potential for new soils to be

identified and included at a later date.

Results

Soils identified

Soils classified into two orders, Ultisols and Alfisols, from

seven commonly occurring subgroups: Typic Kandihumults,

Oxyaquic Palehumults, Typic Palehumults, Oxyaquic Haplo-

humults, Typic Haplohumults, Aeric Epiaqualfs and Typic

Epiaqualfs. Two other soils, Aquic Kandihumults and Aquic

Palehumults, occurred infrequently. Profile descriptions for

representative soil types are presented in Table 1.

The soils were dominantly clay textured with subsoils

often containing >35% clay (Table 2). Some areas, usually

on upper hillslopes, had sandy loam or loamy sand subsoils

where there was sandstone substrate. The pH values ranged

from 4.4 to 4.9 for both surface and subsoil horizons, with

the Epiaqualfs tending to be at the upper end of that range.

Electrical conductivity was low and usually <0.1 dS/m;

cation exchange capacities and potassium content were low.

The soils were highly weathered.

Toposequences

The Tutong District hillslope areas were characterized by

Ultisols occurring on the summits to lower slopes and Alfisols

on the lower slopes and flats. An example from a simple

traverse in the Birau Penyelidikan study area shows the change

in soil type with landscape position (Figure 2), and chip-tray

soil samples show morphology and colour differences. The

subsoil colours are a noticeable feature with bright yellows in

the upper slope profiles (i.e. more freely drained) to the greys in

the lower slope profiles (i.e. poorly drained).

Other traverses throughout the Tutong district study areas

presented similar combinations of soil, with some soil types

absent and others present, but they all occurred in the same

relative positions to each other. For example, Typic

Kandihumults always occurred upslope of Oxyaquic Haplo-

humults and these both occurred above Aeric Epiaqualfs on

the lower slopes and flats. Combining knowledge from studied

profiles along a number of traverses allowed a conceptual

toposequence model to be constructed for the seven commonly

occurring hill country soils (Figure 3a).

For the Temburong district, a different conceptual topo-

sequence was prepared as there was a need to include

alluvial terraces that were part of the landscape due to the

larger river systems with wider valleys (Figure 3b). Typic

Haplohumults were found on the upper slopes with

Oxyaquic Haplohumults occurring throughout the slope

areas. Oxyaquic Palehumults occurred on the better drained

soils of the upper terraces, and Typic Epiaqualfs on the

poorly drained soils of the lower terrace flats.

The reliability of the conceptual soil toposequence models

was evaluated by considering as a whole all traverses in the

study areas and comparing the relationship between soil type

and slope position (Figure 4a,b). The figures verify that soil

classes generally occurred on one slope position and in the

same relative position to each other. The collective information

from all traverses was used to generate the conceptual

toposequence models because none of the 35 traverses covered

the complete range. The soil sequences of the 35 traverses were

then reviewed individually against the conceptual toposequence

and none were considered inconsistent. While the Oxyaquic

Haplohumults were dominant in a number of slope positions,

they were considered to be appropriately located with the

conceptual toposequences as they occurred in the same relative

position to the other soils in the hillslope sequence.

Soil identification key

The soil identification key was required to be complementary

to and based on the Soil Taxonomy relationships of the soil

types and used three easily recognizable soil features, subsoil

texture, soil depth and soil colour, to identify each soil

type (Table 3), allowing the diagnostic criteria from Soil

Taxonomy to be ignored.

A collection of plain language soil type and subtype names

were developed corresponding to the major Soil Taxonomy

suborder and subgroup classes. These names are intended to

provide assistance in understanding the general nature of the

soil types. The three soil types in the key are determined

based on soil depth and subsoil colour: (i) very deep yellow

soils, (ii) yellow soils, and (iii) brown over grey soils. These

are further subdivided into nine subtypes based on broad

soil texture categories and the occurrence of redoximorphic

depletions (described in the key as colour spots). At the

request of the local users, soil drainage condition was linked

to soil colour in the key as this gave more meaning in terms

of soil condition for land use. Naming a soil as, for example,

a ‘moderately well-drained, clayey, very deep yellow soil’ has

more meaning for local users than ‘Oxyaquic Palehumult’.

The soil identification key was frequently trialled, tested and

refined by conducting field training with local farmers and

agricultural advisers. The training provided guidance at open

pits on how to describe soil features and use the identification

key and toposequence models to determine soil types and their

distribution. Trainees were challenged to go independently to



selected locations and identify soils using these tools and

training. Afterwards, trainees were asked to comment on the

usability of the soil identification key and their understanding of

used terms. Where necessary, sections of the key were reworded

with common language terms that could be easily understood or

translated for non-English speaking users, for example,

Table 1 Selected soils showing morphological characteristics, with the soil type in bold and Soil Taxonomy class in brackets

Site no.

Horizon Depth (cm) Colour moist Texture class Mottles quantity, colour Structure type Consistence moist

17 0011 Well-drained sandy very deep yellow soil (Typic Kandihumult)

A 0–5 10YR 3/3 FSL SBK FR

AB1 5–10 10YR 4/3 FSL SBK FR

AB2 10–25 10YR 4/3 FSL SBK FR

Bw1 25–50 10YR 5/6 LFS MA VFR

Bw2 50–100 10YR 5/8 LFS MA FI

BC 100–150 10YR 6/8 LFS MA FI

BC1 150–190 10YR 6/6 LFS f 5YR 6/8 MA FI

14 0018 Somewhat poorly drained clayey very deep yellow soil (Aquic Palehumult)

A 0–15 10YR 4/4 SCL SBK FI

Bt 15–70 10YR 5/8 CL c 5YR 6/8 MA EF

BCgt 70–100 10YR 5/4 CL m 10YR 6/1 MA FI

21 0019 Well-drained clayey very deep yellow soil (Typic Palehumult)

A 0–15 CL SBK FI

Bt1 15–80 10YR 3/3 C SBK FI

Bt2 80–120 10YR 5/6 C c 2.5YR 5/8 SBK VFI

Bt3 120–170 7.5YR 5/8 C c 2.5YR 5/8 MA VFI

BC 170–250 5YR 6/8 SCL m 2.5YR 5/8 MA VFI

17 0015 Moderately well-drained yellow soil (Oxyaquic Haplohumult)

A 0–10 10YR 3/3 C GR FR

AB 10–30 10YR 5/4 C SBK FI

Bt1 30–50 10YR 5/4 C SBK FI

Bt2 50–70 10YR 5/4 C SBK FI

BC1 70–90 10YR 5/4 C c 10YR 6/6 MA EF

BC 90–100 10YR 5/4 C c 10YR 6/6 MA EF

25 0009 Well-drained yellow soil (Typic Haplohumult)

Ap 0–5 10YR 5/4 CL SBK FR

AB 5–15 10YR 5/4 C SBK FR

Bw 15–35 10YR 6/6 C SBK FI

BC1 35–70 10YR 5/6 C SBK FI

BC2 70–100 10YR 5/6 C c 10YR 5/8 MA FI

15 0001 Somewhat poorly drained brown over grey soil (Aeric Epiaqualf)

A 0–3 10YR 4/4 SCL SBK FR

AB 3–20 10YR 5/2 C m 10YR 5/8 MA FI

Bgt1 20–35 10YR 5/2 C m 10YR 5/6 MA FI

Bgt2 35–90 10YR 7/1 C m 10YR 5/8 MA EF

Bg 90–100 10YR 7/1 C MA EF

28 0008 Poorly drained brown over grey soil (Typic Epiaqualf)

Ap 0–5 10YR 3/3 CL CDY VFI

ABp 5–20 10YR 5/3 C f 5Y 4/6 CDY VFI

Bg1 20–30 10YR 5/2 C c 5Y 4/6 MA VFI

Bg2 30–60 10YR 4/3 C c 10YR 5/8 MA VFI

Bg3 60–90 10YR 5/2 C m 7.5YR 5/8 MA VFI

Bg4 90–100 10YR 5/2 C m 7.5YR 5/8 MA VFI

LFS, loamy fine sand; FSL, fine sandy loam; SCL, sandy clay loam; C, clay; GR, granular; SBK, subangular blocky; MA, massive; CDY, cloddy;

VFR, very friable; FR, friable; FI, firm; VFI, very firm; EF, extremely firm. Mottles quantity: f, few (<2%); c, common (2 to <20%); m, many (≥20%).



Table

2Soilchem

icalandphysicalcharacteristics

SiteNo.

Horizon

Depth

(cm)

EC

(dS/m

)pH

OC

(%)

Exch.CationsNH

4OAcpH

7.0

cmol(+)/kg

AlKClext.a

Clay(%

)Silt(%

)Sand(%

)Ca

Mg

Na

KTotal

170011

Well-drained

sandyvery

deepyellow

soil(Typic

Kandihumult)

Bw2

50–100

0.03

5.1

0.4

0.3

<0.1

<0.05

<0.05

0.3

0.8

––

–

BC

100–150

0.03

4.9

0.2

0.2

<0.1

<0.05

<0.05

0.3

0.91

––

–

BC1

150–190

0.03

4.8

0.1

0.2

<0.1

<0.05

<0.05

0.3

1.75

––

–

140018

Somew

hatpoorlydrained

clayey

very

deepyellowsoil(A

quic

Palehumult)

A0–15

0.16

4.6

2.1

0.5

0.5

0.22

0.62

1.8

1.79

26.1

30.8

43.1

Bt

15–70

0.06

4.5

0.5

<0.1

0.2

0.19

0.11

0.6

3.62

35.1

27.1

37.8

BCgt

70–100

0.006

4.9

0.5

<0.1

0.8

0.32

0.13

1.3

3.46

––

–

210019

Well-drained

clayey

very

deepyellowsoil(Typic

Palehumult)

A0–15

0.07

4.2

2.0

<0.1

0.3

0.09

0.19

0.6

8.54

42.9

20.4

36.7

Bt1

15–80

0.03

4.4

0.5

<0.1

<0.1

0.07

0.17

0.4

8.98

53.8

19.4

26.8

Bt2

80–120

0.03

4.5

0.5

<0.1

<0.1

0.08

0.19

0.3

9.65

57.4

25.6

17.0

BC

170–250

0.03

4.7

0.2

<0.1

<0.1

0.09

0.11

0.3

4.89

28.2

13.7

58.1

170015

Moderately

well-drained

yellowsoil(O

xyaquic

Haplohumult)

A0–10

0.18

4.7

4.7

3.7

2.9

0.07

0.59

7.2

1.70

39.6

37.0

23.4

AB

10–30

0.08

4.4

1.1

0.5

0.5

0.07

0.21

1.3

5.01

49.7

30.8

19.5

Bt1

30–50

0.06

4.5

0.8

0.4

0.5

0.08

0.14

1.1

5.50

51.7

30.8

17.5

250009

Well-drained

yellowsoil(Typic

Haplohumult)

Ap

0–5

0.11

4.4

2.6

0.7

0.9

0.20

0.30

2.1

5.06

43.5

42.4

14.2

AB

5–15

0.06

4.4

1.8

0.3

0.5

0.17

0.24

1.2

7.61

45.8

43.5

10.8

Bw

15–35

0.06

4.4

0.7

0.2

0.2

0.18

0.19

0.7

9.26

44.1

35.0

20.9

BC1

35–70

0.04

4.5

0.6

<0.1

0.2

0.21

0.20

0.7

9.55

51.3

34.6

14.1

150001

Somew

hatpoorlydrained

brownovergreysoil(A

eric

Epiaqualf)

A0–3

0.15

4.7

6.0

2.4

1.9

0.07

0.28

4.6

1.74

AB

3–20

0.06

4.6

0.8

0.7

1.0

0.08

0.14

1.9

4.00

38.8

20.2

41.0

Bgt1

20–35

0.07

4.7

0.7

0.6

2.2

0.08

0.16

3.0

2.88

48.7

26.5

24.8

Bgt2

35–90

0.06

4.9

0.4

0.5

1.9

0.09

0.13

2.6

1.91

42.9

29.0

28.2

280008

Poorlydrained

brownovergreysoil(Typic

Epiaqualf)

Ap

0–5

0.10

4.8

2.6

1.1

0.6

0.09

0.21

2.0

2.15

43.7

31.1

25.2

ABp

5–20

0.07

4.9

1.7

1.3

0.6

0.07

0.18

2.2

1.73

68.0

27.6

4.4

Bg2

30–60

0.09

5.2

0.9

1.8

2.3

0.07

0.11

4.3

0.45

44.0

28.2

27.8

Bg3

60–90

0.06

4.9

0.5

0.7

1.5

0.15

0.14

2.4

2.53

––

–

EC,electricalconductivity;OC,organic

carbon;–,

data

notavailable.a1

MKClext.Al(c

mol(+)/kg).See

Table

1,fortheusageofbold

andbrackets.



changing the word ‘mottles’ to ‘spots’. They had capability and

commonality in recognition of soil colours, colour pattern,

depth and broad descriptive texture groups which provided

confidence that outcomes were reproducible, although not

formally tested. Subsequent field visits with different groups and

updates led to the final soil identification key (Table 3).

Discussion

Soil identification key

The rigours of Soil Taxonomy as a technical soil classification

system are necessary for ordering the soils and allocating a

scientific name to facilitate transfer of knowledge about the

soils and how crops perform on similarly classified soils.

Using a well-established soil classification also provides links

to land suitability assessments based on such classifications, in

this case the FCC. Once a local soil has been classified, the

complexities of the classification can be distilled down to a

soil identification key using plain language that describes the

local soils in a way that nonsoil specialist users can readily

understand and use (Table 3).

Developing the local soil identification key required good

pedological knowledge and the ability to understand soil

classification and its intent, along with testing and updating

to simplify the questions to direct users to the correct soil

type (Table 3). The small field handbook in English and

Malay (Grealish et al., 2008a,b) contained guidance on

identifying soil features, the soil identification key,

0–5 cm 0–5 cm 0–5 cm

5–20 5–10 5–10

0–5 cm

5–10

20–30

30–50

50–60

60–120

120–130

10–30

15–30

30–50

50–80

80–100

10–25 10–15

30–50

50–85 50–100

85–110

25–50

100–150

150–190

190–200

Profile 17 0013 Profile 17 0012 Profile 17 0011 Profile 17 0010

SPDbrown over

grey soil(Aeric Epiaqualf)

MWDdeep yellow soil

(Oxyaquic)Haplohumult)

WDvery deep yellow soil(Typic Kandihumult)

SPD = somewhat poorly drainedMWD = moderately well drainedWD = well drained

Figure 2 Photograph of a simple hillslope traverse in the Birau

Penyelidikan study area. Chip-tray samples show soil colour and

morphology trends with depth and slope position.

PDbrown over

grey soil(Typic

Epiaqualf)

SPDbrown over

grey soil(Aeric Epiaqualf)

MWDclayey

very deepyellow soil(Oxyaquic

Haplohumult)

WD clayey

very deepyellow soil

(TypicPalehumult)

WDsandy

very deepyellow soil

(TypicKandihumult)

MWDyellow soil(Oxyaquic

Haplohumult)

WDyellow soil

(TypicHaplohumult)

Yellowishbrownwith

>50%red/

orangespotsovergrey

Yellowishbrownwith

<50%red/

orangespotsovergrey

Yellowishbrownwithred/

orangespots

Yellowishbrownwithred/

orangespots

Uniformyellow

orbrown

Uniformyellow

orbrown

Uniformbrightyellow

Yellowovergrey

Subsoil descriptivetextures:

Soil depth(cm):

Subsoilcolour:

Soi

l cla

ssifi

catio

n:Landscapeposition:

< 150< 150 < 150 <150> 150 > 150 > 150 >150

Clayey or loamyClayey Sandy

Summit

Flat & toeslopeFootslope

Backslope

Shoulder

Shoulder&

backslope

PD = poorly drainedSPD = somewhat poorly drainedMWD = moderately well drainedWD = well drained

WDyellow soil

(TypicHaplohumult)

MWDclayey

very deepyellow soil(Oxyaquic

Palehumult)

PDbrown over

grey soil(Typic Epiaqualf)

MWDyellow soil(Oxyaquic

Haplohumult)

Yellowish brownwith red/orange

spots

Yellowish brownwith red/orange

spots

Uniformyellowish

brown

Yellowish brownwith > 50%

red/orange spotsover grey

Subsoildescriptivetextures:

Soil depth (cm):

Subsoil colour:

Soilclassification:

Landscapeposition: Hillslope

< 150< 150 > 150 > 150

ClayeyClayey Loamy orclayey

Clayey

TerraceFlat

PD = poorly drainedSPD = somewhat poorly drainedMWD = moderately well drainedWD = well drained

(a)

(b)

Figure 3 Conceptual toposequence models showing landscape

position and key soil identification features for the major soil types.



toposequences, simple soil descriptions and photographs of

the soils and landscape and a link to land suitability

assessment. Success required that local users could easily

obtain the information to answer the questions and progress

through the key using only the field manual and a spade to

excavate soil pits.

2

3

4

5

6

Slo

pe p

ositi

on

Tutong District

0

1

Typ

ic E

piaq

ualf | | | ~

Aer

ic E

piaq

ualf | | | | | | | | | ~

Aqu

ic P

aleh

umul

tO

xyaq

uic

Pal

ehum

ult | ~

Oxy

aqui

c H

aplo

hum

ult | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ~

Typ

ic H

aplo

hum

ult ~

Typ

ic P

aleh

umul

t | | | | | ~T

ypic

Kan

dihu

mul

t | | | | | ~

Soil types arranged from flat to summit

6

4

5

Temburong District

3

2

Slo

pe p

ositi

oin

0

1

| | | | | | ~ | | | | | | | | | ~ | | | | | ~ | | | | | | | | | | ~

Typ

ic E

piaq

ualf

Aqu

ic P

aleh

umul

t

Typ

ic H

aplo

hum

ult

Oxy

aqui

c P

aleh

umul

t

Oxy

aqui

c H

aplo

hum

ult

Soil types arranged from flat to summit

(a)

(b)

Figure 4 Relationship between soil type and slope position, with each column representing a site showing their frequency. There are (a) 133 soil

observations and (b) 39 observations displayed. Slope position is shown on the x axis with 1 = flat, 2 = toeslope, 3 = footslope, 4 = backslope,

5 = shoulder, 6 = summit.



Table

3Soilidentificationkey

forthehillslopesoils

IdentifyingfeaturesforSoilType

SoilType

IdentifyingfeaturesforSoilSubtype

SoilSubtype

Priordecisionsin

key

(not

presentedhere)

No↓

Does

theupper

subsoilhave

adominantlyyellowish

orbrownishcolour

AND

isthesoildepth

greaterthan150cm

?

No↓Yes

?

Verydeepyellow

soil

(Humult)

Does

thesubsoilhaveasandytexture?

No↓Yes

?Sandyvery

deepyellow

soil(K

andihumult)

Isthelower

part

ofthe

subsoilagreyishcolour

(somew

hatpoorlydrained)?

No↓Yes

?

Somew

hatpoorlydrained

sandy

very

deepyellow

soil(A

quic

Kandihumult)

Isthesubsoilauniform

bright

yellowishcolourthroughout

(welldrained)?

No*Yes

?

Well-drained

sandyvery

deep

yellow

soil(Typic

Kandihumult)

Does

thesubsoilhavealoamy

orclayey

texture?

No*Yes

?

Clayey

very

deepyellow

soil(Palehumult)

Isthelower

part

ofthesubsoil

agreyishcolour(somew

hat

poorlydrained)?

No↓Yes

?

Somew

hatpoorlydrained

clayey

very

deepyellow

soil(A

quic

Palehumult)

Isthesubsoilyellowishbrown

withred/orangespots

(moderately

welldrained)?

No↓Yes

?

Moderately

well-drained

clayey

very

deepyellow

soil(O

xyaquic

Palehumult)


yellowishorbrownish

colour(w

elldrained)?

No*Yes

?

Well-drained

clayey

very

deep

yellow

soil(Typic

Palehumult)

Does

thesubsoilhave

adominantlyyellowish

orbrownishcolour

AND

isthesoildepth

less

than150cm

?

No↓Yes

?

Yellow

soil

(Haplohumult)

Isthesubsoilyellowishbrownwithred/orange

spots

(moderately

welldrained

orsomew

hat

poorlydrained)?

No↓Yes

?

?Moderately

well-drained

yellow

soil(O

xyaquic

Haplohumult)


yellowishorbrownish

colour(w

elldrained)?

No*Yes

?

?Well-drained

yellow

soil(Typic

Haplohumult)

Does

thesubsoilhave

abrownishcolouredlayer

withred/orangespots

overlyinga

greylayer

thathasitsupper

boundary

within

50cm

of

thesoilsurface?

No↓Yes

?

Brownover

greysoil(A

qualf)

Does

thesoilhavegreaterthan50%

brown

colourbetween25and75cm

ofthesoilsurface?

No↓Yes

?

?Somew

hatpoorlydrained

brown

overgreysoil(A

eric

Epiaqualf)

Does

thesoilhaveless

than50%

browncolour

between25and75cm

ofthesoilsurface?

No*Yes

?

Poorlydrained

brownovergrey

soil(Typic

Epiaqualf)

Ongoingdecisionsin

key

(notpresentedhere)

Thesoildescriptivenameisshownin

bold

andthecorrespondingSoilTaxonomyclassificationisbracketed.A

‘No*’

indicatesto

restart

thekey

orconsider

thatanew

soilhasbeen

identified

thatisnotclassified

intheidentificationkey.



Soil depth and soil texture. All of the Humults had similar

soil colours making them difficult to distinguish using colour

alone. Soil depth could easily be measured or estimated and

was used to separate the Kandihumults and Palehumults

(>150 cm) from the Haplohumults (<150 cm). Broad

categories of soil texture were then used to separate the

Kandihumults that were sandy from the Palehumults that

were clayey. While trainees failed to recognize the many

subtle texture classes a soil surveyor would use, they could

readily discriminate the broader texture categories. The

categories were clayey soils that were generally sticky and

moulded with a bit of working, sandy soils that felt gritty

and did not hold together very well when worked, and

loamy that was moulded easily in the hand and felt neither

gritty nor sticky.

Soil colour and colour pattern. Soil colour is usually the first

property recorded in a morphological description and may be

the only feature of significance to a nonexpert. Parent

material was reasonably consistent across the study areas and

from our observation not considered significant in influencing

soil colour variation between soils. Therefore colour was

related to the soil’s position in the landscape as this

influenced soil aeration and organic matter content

(Fitzpatrick, 1988; Bigham et al., 2002). Uniform red and

yellow colours indicated oxidizing conditions and well-

drained soils in the upper parts of landscapes (Figures 2 and

3), followed down slope to moderately well-drained soils

indicated by distinctive yellow or brown colours with red or

yellow spots (mottles), to reduced or waterlogged conditions

indicated by low chroma grey and blue colours (Vepraskas

et al., 1994). Munsell colour assessment was not required, as

soil colour simplified into six dominant colours (black, white,

red, yellow, brown and grey), and examples of the colours

could be printed on a site description sheet to allow general

matching. Soil colour and the depth location of the colour in

the profile differentiated very clearly the identified soil types.

Soil toposequences

The 16 study areas are widely distributed throughout the hill

country, and the consistent pattern of soils provided

confidence that the conceptual soil toposequence models

were appropriate. Landscape position played an important

role in the prediction of soil type, and farmers and

agricultural advisors were readily able to identify terrain

differences and determine what part of the hillslope they

were interested in. The conceptual soil toposequence models

presented as simple visual graphic were readily accepted by

local users, and the soil type could then be verified by

digging the soil and using the soil identification key.

There are other hillslope landscapes higher up in the

forested hinterland that were not encountered in this survey

because they were not being considered for agricultural

development at the time, but this approach could be extended

to include these areas by conducting soil investigations and

updating the toposequences and key if necessary. Presenting

information in this way is specific to the area it was designed

for, but the approach is flexible and can be updated or a new

model and key established for another area.

Assisting with land-use decisions

The focus of this work was to assist with identifying soil

types and where they occur. Land users are more concerned

about the outcome of soil survey information that

determines suitability and management requirements

appropriate for the area, but recognize identification of soils

is a first step. Associated work by Ringrose-Voase et al.

(2008) presents a land suitability assessment for 27 crop

groups using FCC. A simple representation of this can be

linked to the soils and toposequence as shown in Figure 5.

Conclusions

The approach using conceptual soil toposequence models

and a soil identification key to convey soil survey

information, interpreted legacy data and/or newly acquired

data could be applied to any location in the world. The

details presented for this case study are not likely to be

applicable elsewhere but the approach provides guidance on

the structure, process and type of outputs. New areas will

require soil survey experts to develop an understanding of

soil distribution and soil classification that can then be

distilled in collaboration with local users to ensure the level

of detail and its application is understood. There is also the

opportunity for nonsoil specialists to independently

determine soil distribution over an area with limited expert

supervision, and in the process covering more ground than

would be possible given the limited availability of

experienced soil surveyors.

Acknowledgements

The data presented from a larger project funded by the

Department of Agriculture, Negara Brunei Darussalam, and

conducted by CSIRO Land and Water and URS staff. Dr

Anthony Ringrose-Voase provided valuable input to the

earlier technical reports, discussions and review of

manuscripts. Dr Hutson, Mr Rinder and Mr Grigg provided

assistance during the project. We extend our appreciation to

Hajah Suria binti Zanuddin and Dr Thippeswamy from the

Soil Science and Plant Nutrition Unit for administrative

assistance and technical advice on local agriculture.

Anonymous journal referees are thanked for their critical

input to improve the manuscript.



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Conceptual Soil-Regolith Toposequence Models to Support Soil Survey and Land Evaluation

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