towards sustainability of land use in a highly vulnerable and degraded

Towards sustainability of land use in a highly
vulnerable and degraded tropical soil landscape
of northern Vietnam - bridging scales



PhD dissertation, 2015, Bui Le Vinh


INSTITUTE OF SOIL SCIENCE AND
LAND EVALUATION
UNIVERSITY OF HOHENHEIM


Soil Science and Petrography Unit

Prof. Dr. Karl Stahr


Towards sustainability of land use in a highly vulnerable and degraded
tropical soil landscape of northern Vietnam – bridging scales

Dissertation
Submitted in fulfillment of the requirements for the degree "Doktor der Agrarwissenschaften"
(Dr.sc.agr. in Agricultural Sciences)

to the



Faculty of Agricultural Sciences



presented by



M.Sc. agr. Bui Le Vinh
Hai Duong, Vietnam
2015
HOHENHEIMER BODENKUNDLICHE HEFTE - NR. 116



Bui Le Vinh

Towards sustainability of land use in a highly vulnerable and degraded
tropical soil landscape of northern Vietnam – bridging scales
Auf dem Weg zu einer nachhaltigen Landnutzung in einer sehr anfälligen und
degradierten tropischen Bodenlandschaft des nördlichen Vietnam – Skalen
Überbrückung
Hướng tới sự bền vững trong sử dụng đất cho vùng đất bị thoái hóa và dễ bị
tổn thương ở vùng đồi núi phía bắc Việt Nam – Mô hình mở rộng

This thesis was accepted as a doctoral dissertation on December 17
th
, 2014 in fulfilment of the
requirements for the degree “Doktor der Agrarwissenschaften” by the Faculty of Agricultural
Sciences at Hohenheim University.

Date of oral examination: March 24
th
, 2015


Examination Committee:
Head of the Committee: Prof. Dr. Jens Wünsche
Supervisor and Review: Prof. Dr. Karl Stahr
Co-Reviewer: Prof. Dr. Joachim Müller
Additional examiner: PD Dr. rer. nat. Daniela Sauer
Table of Contents


i
1. Introduction 1
1.1. Problem setting 1
1.2. Objectives 2
1.3. Hypotheses 3
2. Methodology 5
2.1. SOTER approach 5
2.2. Geographic Information System 8
2.3. Data collection and derivation 9
2.3.1. Secondary data 9

2.3.2. Primary data 10
2.3.3. Derivation of some terrain variables using GIS techniques 14
2.3.3.1. Generation of main slope positions 14
2.3.3.2. Generation of curvatures 16
2.3.3.3. Generation of slope forms based on the main slope positions 17
2.3.4. Soil property calculation for Yen Chau 20
2.3.4.1. Calculation of further physical properties 20
2.3.4.2. Calculation of further chemical properties 21
2.3.4.3. Criteria for soil quality analysis 22
2.4. Soil mapping under fuzzy logic and Soil-Land Inference Model (SoLIM) 23
2.4.1. Limitations of conventional soil mapping under crisp logic and introduction of
fuzzy logic soil mapping 23
2.4.2. Theoretical basis for soil inference using fuzzy logic 26
2.4.2.1. Basic theory 26
2.4.2.2. Expert system approach 27
2.4.2.3. Fuzzy set theory 28
2.4.3. Methodology 29
2.4.3.1. Knowledge acquisition 29
2.4.3.2. Soil-environment key development interview 30
2.4.3.3. Soil-environment description interview 31
2.4.3.4. Optimality curve definition interview 32
2.4.3.5. Knowledge verification interview 33
2.4.3.6. The fuzzy soil inference process 33
3. General description of the study area 35
3.1. Physiography 35
3.2. Geology 37
3.3. Climate 42
Table of Contents
ii
3.4. Soils and Land use 44

3.5. Ethnic groups and land use systems 46
4. Results 49
4.1. Characterization of the SOTER database of Yen Chau district 49
4.1.1. Terrain units 49
4.1.1.1. Characterization of major landforms and terrain units 49
4.1.1.2. Determination of terrain units for each of the parent materials 51
4.1.2. Terrain components 58
4.1.3 Soil components 60
4.2. Soils and soil properties 66
4.2.1. Overview of soils of Yen Chau 66
4.2.2. Major soil properties for soil quality assessment 74
4.2.2.1. Air capacity – AC (%) 75
4.2.2.2. Available water capacity – AWC (l/m
2
) 75
4.2.2.3. Organic matter – OM (kg/m
2
) 78
4.2.2.4. Total nitrogen – N
t
(kg/m
2
) 78
4.2.2.5. Available phosphorous – P
Bray1
(g/m
2
) 79
4.2.2.6. S-value 80
4.2.2.7. The sum parameter N-P-S 80

4.2.2.8. Biological activity 82
4.2.2.9. Human impact 83
4.2.3. Computations of correlation coefficients for soil properties 84
4.2.4. Major soils and their distribution in Yen Chau 93
4.3. Soil mapping model 109
4.3.1. Calibration of the formation of soils in Yen Chau 109
4.3.2. Spatial delineation of the soil map of Yen Chau using SoLIM software 109
4.4. Soil quality mapping model 117
5. Discussion 127
5.1. The Yen Chau SOTER database 127
5.2. The variability of Yen Chau soils 128
5.2.1. Soil pH (H
2
O) 129
5.2.2. The A-horizon thickness 130
5.2.3. Soil organic matter 130
5.2.4. Total nitrogen - N
t
132
5.2.5. Base saturation 134
5.2.6. Cation exchange capacity for clay minerals (CEC
clay
) 136
5.2.7. S-value 138
Table of Contents


iii
5.2.8. Standardized parameter N-P-K 139
5.2.9. Soil forming processes to the variability of soils in Yen Chau 140

5.2.9.1. Clay illuviation 140
5.2.9.2. Clay formation 142
5.2.9.3. Humus accumulation 143
5.2.9.4. Decalcification 144
5.2.9.5. Base leaching 145
5.2.10. Environmental conditions and their role in soil and soil quality mapping 147
5.2.10.1. Climate in association with elevation 147
5.2.10.2. Parent material 149
5.2.10.3. Relief 151
5.2.10.4. Vegetation 154
5.2.10.5. Biological activity 155
5.2.10.6. Human impact 155
5.2.10.7. Time 156
5.3. Validation of the soil and soil quality maps 158
5.3.1. Validation of the soil map 159
5.3.2. Validation of the soil quality map 159
6. Conclusions 163
6.1. General conclusions 163
6.2. Specific conclusions 163
7. Summaries 165
7.1. Summary 165
7.2. Zusammenfassung 168
7.3. Tóm tắt 171
8. References 175
8.1. Literature 175
8.2. Other information sources 188
9. Appendix 191
9.1. Abbreviations 191
9.2. Description of reference soil profiles 192
9.3. Soil properties calculations for the Yen Chau SOTER database 217

Acknowledgement 223
Table of Contents
iv














Introduction


1
1. Introduction
Northwestern Vietnam is a mountainous region and home to almost three million people from
many different ethnic minorities. The region has remained the poorest over the whole country
for many years with the highest poverty rate (Phan, 2008).
The region has a wide range of elevations, strong relief variations, land use patterns, climatic
patterns, land cover, and petrography. The geological patterns of the area are ophiolite
complex, granitic intrusion complexes, volcanic rocks, terrigenous and carbonate sedimentary
rocks ranging from Proterozoic age till today (Hung, 2010b).
Swiddening and slash-and-burn agriculture had a long cultivation history of people in

mountainous regions and they had been claimed to be sustainable (Dao, 2000). These
cultivation practices worked very well in providing efficient subsistence to local people and
sustaining the land use systems of the area (Vien et al., 2004). However, due to population
growth and immigration happening over decades, like many other mountainous regions, the
population in the northwest of Vietnam has risen remarkably, creating a severe stress on
cultivated soils in covering the food demand for the growingpopulation. Moreover, upland
agriculture has been shifted towards meeting markets’ demands, or market-orientation, i.e.
intensification of cash crops like maize as a leading income crop (Clemens et al., 2010). This
has promoted more intensive uses of soils and deforestation to widen arable land on hill
slopes. Consequences have then quickly arrived represented by serious flooding due to
increasing deforested area, increasing soil degradation due to soil erosion and soil nutrient
depletion because of overuse of agro-chemicals and intensive use of agricultural land, and,
therefore, decreasing productivity of the cultivated soils (Toan et al., 2001, Wezel et al. 2002).
The demand to mitigate these facts is to drive cultivation practices towards sustainability.
Some solutions to achieving the sustainable development goal are to recover the lost forested
area and importantly to adapt new suitable crops that not only bring high income to the local
people but also function to mitigate soil degradation and recover soil quality. To do so, a good
knowledge of soil resources and soil quality must be necessarily achieved (Igué, 2000).
1.1. Problem setting
A common database about different soils, their properties and distribution for the whole
country has not been completed. Soil information achieved so far is very sparse. And not an
exception, the northwest of Vietnam does not have a good soil database necessary for related
research or applications (Bo et al., 2002; Tin, 2005).
The ministry of Natural Resources and Environment (Circular 28
th
, 2010) of Vietnam defined
a set of technical and economic requirements for land surveys, soil mapping and evaluation of
soil quality. For soil mapping, soil sampling in mountainous regions is carried out within a
minimum areal unit of 30 ha with 5 soil profiles studied, about 1 profile for 5 ha on average.
Physical properties are described for all 5 profiles and only 1 of them is sampled for chemical

analyses. Therefore, there maximum 5 different soils can be classified for an area of 30 ha.
Introduction
2
With 6 soil profiles studied for one catena, Clemens et al. (2010) found 4 soil types that likely
have been formed by different distinctive combinations of slope position, slope inclination,
elevation, and period of cultivation. According to our soil survey experience, an average
catena has an area of 3-10 ha. Thus, with an area of 30 ha we can have at least three adjacent
catenas which could end up with having more than 5 soil types found when applying the
method of Clemens et al. (2010). Therefore, the sampling method of the Circular 28
th

potentially will produce a huge overgeneralization of soil information. It means a lot of soil
information in a huge area will be lost, if this method is applied. Therefore, it is necessary to
have another soil mapping approach that can better achieve soil knowledge for a mountainous
region of Vietnam.
Modern soil mapping methods have focused on studying relationships between soils and their
forming factors (Odeh et al., 1991; Zhu et al., 1996, 1997a; Batjes et al., 1997, 2000, 2008;
Schuler et al., 2008; Qin et al., 2009, 2012). Jenny (1941) mentioned five main soil-forming
factors: parent material, climate, organisms, topography, and time. In a study about the
development of soils on hill slopes in a South Australian subcatchment related to the
environment, Odeh et al. (1991) emphasized the importance of soil-landform interactions in
the local pedogenesis. They concluded that landform features such as slope gradient, plan
convexity, profile convexity contribute remarkably to spatial variations of soils. The
combinations of profile-plan curvatures result in different slope forms (surface forms) which
will be discussed in details in section 2.3.3. Slope forms were taken into account in studying
soil variations at large scales (subcatchment scale to regional scale) in some other studies
(Schuler et al., 2008; Qin et al., 2009, 2012; Cong, 2011). Qin et al. (2009) developed an
eleven slope position system for an area of 60 km
2
with low relief energy (average slope

gradient of 2
0
, elevation difference between the lowest and highest points of 120 m). Cong
(2011) and Schuler et al. (2008) also considered slope forms in mapping soils at subcatchment
and catchment scales in mountainous areas having much stronger relief conditions. However,
they simply described some major slope forms, for example straight-straight, convex-convex,
concave-concave, etc., that were observed in their soil observations, rather than trying to
capture an overall image of slope forms for the whole areas. Therefore, this information might
not be sufficient in drawing a good picture of soil-landscape relationships. Especially when
soils of a larger area (ex. 1:25.000 or smaller scale) having strong relief conditions are to be
mapped from catena units.
1.2. Objectives
In the last 6 years, several studies were made within the SFB 564 (Sonderforschungsberich 564)
Programme (or the Uplands Program) in Vietnam on soil quality (Clemens et al., 2010; Häring
et al., 2010), land evaluation (Cong, 2011) in northwestern Vietnam. Clemens et al. (2010) and
Häring et al. (2010) studied soil quality at catchment scale. Cong (2011) implemented a land
evaluation research at two small catchments. Starting from a sketch, he developed a Soils and
Introduction


3
Terrrain Digital Database (SOTER) for the catchments as the basis for the implementation of
his land evaluation work.
The overall goal of this study over the last 4 years was to develop a detailed soil information
system for the northwestern mountainous district of Yen Chau using SOTER database
upscaled from Cong’s work (2011). From this upscaled SOTER database, a soil map and a soil
quality map will be generated for Yen Chau district using Soil and Landscape Index Model
(SoLIM). To achieve this goal, the following objectives have been identified:
 Derivation of a detailed slope form system for the area. The result will be a map consisting
of all possible slope forms that will be used later as one of the soil-forming environmental

parameters in the SoLIM model in mapping the variations of different soils and the quality
of these soils.
 Development of a SOTER database for Yen Chau district that rules the formation of soils
through unique combinations between soils and environmental conditions such as
landforms, slope forms, slope gradients, parent materials.
 Application of SoLIM in deriving the soil map for Yen Chau based on the relationships
between soils and terrain characteristics from the SOTER database.
 Calculation of soil quality indices and studying their relationship with the environmental
parameters for the development of a soil quality map for Yen Chau applying SoLIM.
1.3. Hypotheses
In the efforts to the mapping of soils and their quality in Yen Chau district, the following main
hypotheses need to be proven.
a) Different rock types have certain influence on soil occurrences and soil quality.
b) Slope inclination affects the degree of soil loss due to erosion, therefore, the soil quality,
and leads to transformations to new soils in relation to human impacts through agricultural
activities.
c) Elevation plays an important role in the occurrences of different soils and their quality
degrees.
d) Slope forms are very important in capturing spatial variations of soils and if they are
correctly spatially delineated, loss of soil information can be minimized and spatial soil
gradation can be better seen as a continuum.
e) The age of cultivation in relation to the major slope positions (crest, upper-, middle-, foot
slope, and valley) has influences on soil distribution and soil quality.

Introduction
4
Methodology


5

2. Methodology
2.1. SOTER approach
Oldeman (1993) stated that degradation and pollution of land and water resources under
pressure of increasing population had led to a need of having a system, which was capable of
managing natural resources data so they could be accessed, combined and analyzed. This
management manner must serve potential use in the sense of meeting food requirements,
mitigating environmental impacts, and maintaining environmental conservation. From this
fact, a Methodology for a World Soils and Terrain Digital Database (SOTER) was developed
at the International Soil Reference and Information Centre (ISRIC), based in Wageningen, the
Netherlands, in 1987.
The original idea and premise of SOTER were developed in Russia and Germany to map land
characteristics based on interactions among physical, chemical, biological and social
phenomena over time (van Engelen, 1995). SOTER studies land through distinctive
combinations of soils and terrain characteristics such as landform, surface form, slope, and
parent material. Each combination represents a SOTER unit. Data in the SOTER database are
organized in:
 A relational data base, which consists of attribute data of soil-related information, and
 A Geographic Information System, which stores geometrical data that can be used for
different spatial representations of soil information.
In addition, the SOTER database is incorporated with a set of rules, formulas, and models,
which can be used to produce new maps, make scenario predictions, and derive new data.
SOTER allows developing databases on different mapping scales and it is possible for
individual databases on different scales later to be merged into a global database (van Engelen,
1995). Beyond the existing map of establishing SOTER databases of the world, other studies
have been carried out at different scales. Oliviera and van den Berg (1992) first implemented a
SOTER database in São Paulo State of Brazil at a scale of 1:100.000. Dobos et al. (2005)
carried out the development of a SOTER database at scales of 1:1 and 1:5 million for Europe.
Our working group at the Institute of Soil Science and Land Evaluation, Hohenheim
University, Germany, has produced different SOTER databases at various scales. Graef et al.
(1999) carried out a land evaluation study using SOTER at regional and village scale. Gaiser

et al. (1999) applied the SOTER approach for estimating yield potentials at regional scale in
Brazil. Graef and Stahr (2000) used the SOTER approach for management of soil, terrain, and
land use and vegetation data at regional scale. Herrmann et al. (2001) applied SOTER as a tool
for land use planning in West Africa at a scale of 1:200.000. Graef et al. (2002) developed a
SOTER database in Niger for improving soil and water conservation measures. Igué (2000)
and Igué et al. (2004) successfully developed a SOTER database for Central Benin at a scale
of 1:50.000. Schuler et al. (2010) and Cong (2011) created SOTER databases for small
catchments with area ranging from 4-10 km
2
at a scale of 1:10.000. Igué et al. (2014) studied
Methodology
6
the variability of physical and chemical soil characteristics in relation with landscape in Benin
using the SOTER approach. This research will develop a SOTER database for a mountainous
district of northern Vietnam, at a scale of 1:25.000.
SOTER units reveal unique combinations of soils and terrain characteristics and they can be
mapped. The information of these units can be stored in two ways: geometry and attribute data
in which an attribute characterizes an object or a geometric shape. Geometry data are stored in
a Geographic Information System (GIS). Attribute data are structured progressively in the
order of terrain units, terrain components and soil components, known as SOTER
differentiating criteria (van Engelen, 1995; Oldeman and van Engelen, 1993). The structure of
a SOTER database is well illustrated in Figure 2.1, in which:
 Terrain units are general description of physiography and parent material. Physiography
represents landforms of the earth’s surface. When observing terrain characteristics, one
should be able to capture as fully as possible the major landforms. The major landforms
can then be subdivided in combination with parent material or lithology. Terrain units
characterize an area through combinations of landforms and lithology. A terrain unit can
have one or more terrain components.
 Terrain components are the subdivisions of terrain units by taking into account different
parameters like surface forms, slope categories, mesorelief, surface drainage, ground

water, etc. A terrain component can have one or more soil components.
 Soil components contain information of soils of the area and are characterized by soil
profiles. Every soil component has one or more fully described and analyzed reference soil
profiles. One soil profile should have maximum five subjacent horizons to the depth of at
least 150 cm. A soil horizon should not exceed 50cm in depth. In the soil database, each
horizon must be characterized with physical and chemical properties. Reference profiles are
represented on maps as points given by unique coordinates.
Methodology

7
Figure
2.1. SOTER data structure combined from Weller and Stahr (1995) and van Engelen
(1995) 1:M = one
to many, M:1 = many to one relations
, 1:1 = one to one

Methodology
8
2.2. Geographic Information System
Geographical Information Systems (GIS) is a computer-based information system, which
allows representing, manipulating, storing, and analyzing features on the Earth’s surface, that
are geographically and spatially referenced. Since originated, it has been defined in many
different ways by different authors and some of the definitions are as below with which a GIS is
defined as:
 “a special case of information systems where the database consists of observations on
spatially distributed features, activities, or events, which are definable in space as
points, lines, or areas. A GIS manipulates data about these points, lines, and areas to
retrieve data for ad hoc queries and analyses” (Dueker, 1979). “a powerful set of tools
for collecting, storing, retrieving at will, transforming and displaying spatial data from
the real world” (Burrough, 1986).

 “a database system in which most of the data are spatially indexed, and upon which a
set of procedures operated in order to answer queries about spatial entities in the
database” (Smith et al., 1987).
 “a system that consists of computer software, hardware, and peripherals that
transform geographically referenced spatial data into information on the locations,
spatial interactions, and geographic relationship of the fixed and dynamic entities that
occupy space in the natural and built environments” (Weller, 1993).
 “automated systems for the capture, storage, retrieval, analysis, and display of spatial
data” (Clarke, 1997).
GIS has become a huge interest worldwide and has been applied in many different areas
(Maguire et al., 1991). In agriculture, GIS was used in studying hot spots to deforestation (van
Laake, 2004), planning manure application (Basnet et al., 2002), identifying potential irrigated
agriculture in Western Desert of Egypt (Ismail et al., 2012), risk assessment of agricultural
chemicals (Verro et al., 2002), modelling of soil contamination (Kumar and Vaani, 2008), or
mapping soil nutrients (Yang and Zhang, 2008), etc.
In military, GIS can be used in modeling of military operations such as movement of military
personnel, detection of enemy (Pincevičius et al., 2005); studying the mobility of military
vehicles on the ground through a relief analysis (Pahernik et al., 2006); tracking down a target
in an area where the target is likely to be located by using a fusion-based approach (Bardford
et al., 2011); etc.
In health, GIS studies have been taken in public health (Xiaolin, 2006), studying contributing
factors to malaria prevalence in Thailand (Jeefoo et al., 2009), modelling drinking water wells
(Dursun et al., 2009), in health care analysis (Barnes and Peck, 1994), etc.
In economic research, GIS has been used in selecting shopping mall location in Australia
(Cheng et al., 2007), in mapping urban economic analysis (Clapp, 1997), using cost
effectiveness analyses and cost benefit analyses to assess avalanche hazard mitigation
Methodology


9

strategies (Fuchs et al., 2007), or in analyzing geo-demographic using a fuzzy clustering
method (Son et al., 2012), etc. GIS has a wide range of applications and can be used in many
other different fields as well.
According to van Engelen and Wen (1995), the soils and terrain database consists of
information of soils and terrain characteristics in a Relational Database Management System
(RDBMS) linked with a Geographic Information System (GIS). SOTER is where information
of terrain and soils is stored, and where each map unit representing a distinctive combination
of landform/terrain, parent materials and soil information is defined. The GIS package is a tool
to spatially deal with questions that involve input data from the SOTER database (Burrough,
1986). Batjes (1990) said “the type of questions that can be asked is primarily determined by
the type, format and manner according to which attributes are stored in the database”. To
provide answers to these questions, attribute and spatial data must be studied and analyzed
thoroughly.
The SOTER approach can be applied in many different aspects. A detailed SOTER database
can be used very well for implementations of land-use planning (Igué et al, 2004) and land
evaluation (Cong, 2011). SOTER can be used in different modelling purposes, such as
modelling of soil information (Schuler et al., 2010), hydrological processes and sediment yield
(Bossa et al., 2012); calculating stocks of carbon and nitrogen in soils (Batjes et al., 1999;
Batjes, 2000), mapping soil carbon stocks (Batjes, 2008), predicting soil organic carbon stocks
and changes over a certain time (Batjes et al., 2007; Cerri et al., 2007).
2.3. Data collection and derivation
2.3.1. Secondary data
Before carrying out a research, some necessary information must be gathered. A topographic
map at the scale of 1:25.000 and a 5m digital elevation model of Yen Chau district were
collected. A 10 m DEM was then produced based on the 5m one to match with the research
purposes of this study area. The projected coordinate system of the maps is WGS 1984 UTM
Zone 48N. The DEM was made from an aerial photo taken in 2004 having VN2000 coordinate
system and center meridian of 104
0
.

The geological map of Yen Chau district is part of the Vạn Yên geology and mineral
resources sheet (F-48-XXVII) which covers part of the northwestern Vietnam (Bao, 2004).
The map has a scale of 1:200.000. According to the Van Yen sheet, the geology of Yen Chau
originally has 18 units. Due to many similarities in properties and characteristics as well as
minor differences of the rocks, they were grouped into 8 different geological units.
According to the Circular 19
th
(2009) of the Ministry of Natural Resources and Environment
of VietNam, land-use planning at district and provincial level is implemented every 5
consecutive years. The land-use map of Yen Chau was obtained on the completion for 2011
from the Department of Natural Resources and Environment of Son La province. Most land-
use maps in Vietnam were created from aerial photos and get updated periodically by the
Methodology
10
network of cadastre offices and officers down to village level. When updating a map, cadastre
officers at lower levels gather information of changes of land uses and submit it to officers at
higher levels, who are responsible for updating the changes at higher administrative levels
(district, province).
2.3.2. Primary data
The maps mentioned in 2.3.1 and related information were very necessary for studying the
area and making plans for field surveys based on altitude patterns, geological units, and major
land use types. Maps were studied and farmer meetings and interviews were carried out before
soil investigation was taken into implementation in order to have some overview about the
local soils through indigenous knowledge. Soil surveys were then implemented very
specifically for every single geological unit/rock type. In each unit of geology, soils were
studied at catena scale at five major slope positions (crest, upper slope, middle, foot slope, and
valley) with an assumption that soils differ at different slope positions. There were 5 to 7 soil
profiles studied along a catena covering the 5 slope positions. In total, there were 124 soil
profiles described and analyzed for the Yen Chau SOTER database. Furthermore, hundreds of
auger drillings were taken as reference information to fulfill the soil database of Yen Chau.

The SOTER database was built by 3 PhD, 7 MSc, 3 BSc, and 4 internship students in the
period of 2007-2012 within the SFB 564 Research Program.
The soil profiles and auger drillings were described according to the Field Guide for Soil
Description, Soil Classification and Soil Evaluation (Jahn et al., 2003) and the World
Reference Base for Soil Resources 2006 (FAO, 2006). In every soil horizon, physical
properties such as soil colour, soil structure, soil texture, stone content, root density,
biological activity, etc. were carefully described in the field. Soil samples were then taken for
laboratory analyses.
Almost 600 soil horizon samples were taken. The samples were taken to the lab in Yen Chau
for being oven-dried (for Bulk Density measurement) and air-dried (for nutrient analyses).
Air-dried samples were then ground, sieved to pass a 2 mm sieve and weighed. Every sample
bag weighed at least 400g and was packed so it remained dry when it got to the labs in Hanoi
and Hohenheim University for further chemical analyses. The analyses were accomplished
for soil texture, soil pH (H
2
O and KCl), total carbon (C
t
), total nitrogen (N
t
), exchangeable
cations (Na
+
, Ca
2+
, K
+
, and Mg
2+
), cation exchange capacity (CEC), available phosphorus, and
available potassium.

There were two automatic weather stations modeled CR 800 produced by Campbell
Scientific Ltd installed in Yen Chau district. One station was installed in Muong Lum
commune and the other one was set in Chieng Khoi commune. The both weather stations
recorded data of average air temperature, average relative humidity, average solar radiation,
wind direction, wind velocity, and total rainfall. A record was taken every two minutes.
The achievement of physical and chemical properties (eg. Table 2.1) is discussed as follows:
Methodology


11
 Physical properties
Bulk Density (g/cm
3
): five cylinders with each being of approximately 95 cm
3
in volume were
used to sample soil for every horizon. These core samples were then dried in an oven of 105
0
C
for 24 hours and their dry weights were taken. Bulk Density of these five core samples was
calculated by dividing the dry soil weights by the volumetric cylinder. The final Bulk Density
value for the soil horizon was taken by the average number of the five values.
Stone content (%): was estimated in the field using FAO guidelines for soil description and
soil classification (FAO, 2006).
Soil colour: soil colour was determined in the field under a moist condition using on a Munsell
colour chart (Oyama and Takehara, 1967).
Soil texture: soil texture was first carried out in the field under a suitably moist condition
followed with procedures described in Guidelines for Soil Description (FAO, 2006). Soil
texture was then determined in a soil lab in Vietnam. First soil organic matter was destructed
(if organic matter content exceeds 1%) and soil materials were dispersed with 0.05M NH

4
OH
solution and sieved into the fractions of: coarse sand (630m – 2mm), middle sand (200m –
630m), and fine sand (63m – 200m), coarse silt (20m – 63m), middle silt (6.3m –
20m), fine silt (2m – 6.3m), clay (< 2m).
Soil structure: was determined in the field based on Guidelines for Soil Description (FAO,
2006).
 Chemical properties
Extraction of available P (P
Bray1
_mg/kg): dissolve 11.11 g of reagent-grade ammonium fluoride
(NH
4
F) in about 9 L of distilled water. Add 250 mL of previously standardized 1M HCL and
make to 10L volume with distilled water. Mix thoroughly. The pH of the resulting solution
should be pH 2.6 ± 0.05. The adjustments to pH are made using HCL or ammonium hydroxide
(NH
4
OH) (Herrmann, 2005).
Carbonate carbon (C
carb
_%): C
carb
was determined using a carbonate detector (Woesthoff
Carmhograph C12S). A surplus of phosphoric acid was added to the sample in order to release
carbonate C as CO
2
. The amount of CO
2
was conductometrically determined after a reaction

with 0.1M NaOH solution (Herrmann, 2005).
Exchangeable cations of Ca, Mg, K, and Na (mmol
c+
/kg): Exchangeable cations were
extracted with NH
4
-acetate at pH 7. Mg was measured with an AAS while for Ca, K, and Na a
flame photometer was used (Herrmann, 2005).
pH (H
2
O): was determined in a supernatant solution of a 1:2.5 soil-water mixture (FAO 1995).
The measurement was carried out with a WTW pH/mV Hand-Held Meter pH 330.
pH (KCl): was determined in a supernatant solution of a 1:2.5 soil-1 M KCl mixture (FAO
1995). The measurement was carried out with a WTW pH/mV Hand-Held Meter pH 330.
Methodology
12
Potential cation exchange capacity (CEC_cmol
c+
/kg): The soil was treated with Na-acetate in
order to exchange all cations. Afterwards the sample was cleaned with ethanol. To extract Na-
cations, samples were treated with NH
4
-acetate. The Na concentration was measured in
propane activated flame photometer at 589m (Schlichting et al., 1995).
Total carbon (C
t
_%) and total nitrogen (N
t
_%): the content of C
t

and N
t
was determined with
a C/N analyser (LECO CN-2000). For samples without carbonate the soil organic matter
(SOM) content was calculated by multiplying C
org
with the factor 1.724 (Schlichting et al.,
1995).
Methodology


13

Table
2.1. An example of the organization of primary physical and chemical properties of soils in Yen Chau

Methodology
14
2.3.3. Derivation of some terrain variables using GIS techniques
2.3.3.1. Generation of main slope positions
There are 5 main slope positions: ridge, upper-, middle-, foot slope, and valley in which ridge
and valley were extracted first as the basis for deriving the three slope positions in between.
a) Gully (valley) position
A cell is considered a gully when its two opposite neighboring cells are higher in elevation and
when its two orthogonal neighboring cells have one being lower and one being higher in
elevation (Figure 2.2a) (Skidmore, 1990).

Figure 2.2. A stream line with raised channel sides and draining in a north-south direction (a).
Two cell wide stream line along and cross the drainage channel (b) (Skidmore, 1990)
The generation of a gully can be obtained by applying a function called Extract Drainage
Networks in the SimDTA software written by Qin et al. (2009). This software was provided
personally by the author. To operate this function, a corrected DEM file with 10m resolution
of the area was needed. The function applies Peucker and Douglas (1975) algorithm.
b) Ridge position
A cell is defined as a ridge when its two opposite neighboring cells are lower and its two
orthogonal neighboring cells are either both being lower or have one being lower and one
being higher in elevation (Figure 2.3a) (Skidmore, 1990). If the orthogonal neighboring cells
have a lower cell and a higher cell in elevation, respectively, then the ridge is extracted with
two cells equal in elevation (Figure 2.3b).
elevation
north
east
raised
channel sides
drainage
direction
test cell
(a)
higher
channel
sides
drainage

direction
(b)
test cell
Methodology


15
The generation of a ridge is obtained by applying a function called Extract Ridge in the
SimDTA software. To operate this function, a corrected DEM file with 10m resolution of the
area is needed. The function applies Peucker and Douglas (1975) algorithm.









Figure 2.3. Ridge surrounded by lower cells (a). Ridge that drains in three directions which is
more than two cells long (b) (Skidmore, 1990)
c) Interpolating mid-slope positions
To create a map with every cell determined with a position relative to the ridge and to the
valley of the slope containing that cell, Euclidean distance algorithm is applied. To prepare for
the interpolation of mid-slope positions (upper-, middle-, and foot slope), a valley and ridge
must first be obtained. The SimDTA software has a function called Relative Position Index
which calculates the relative position of every cell to the ridge and valley of every slope. This
function applies the Relative Position Index (RPI) algorithm proposed by Skidmore (1990) as
follows:

If a cell is not marked as a valley or ridge, the Euclidean distance from the cell to the nearest
valley and the Euclidean distance from the cell to the nearest ridge are calculated. These two
distances are then summed up to calculate the ratio between the Euclidean distance of the cell
to the nearest valley and this summed distance to determine the relative position of the cell.
The calculation produces binary values within the range of [0, 1] in which 0 and 1 represented
the lowest and highest positions along a catena, i.e. valley and ridge, respectively. From this
range, smaller value ranges were divided to define the three mid-slope positions: upper-,
Orthogonal
to ridge
Direction
of ridge
(b)
note that
ridge drains
in three
directions
ridgeline more
than two cells
long
(a)
Ridge
lower
cells
P
ij
=
Euclidean (E) distance to the nearest valley

(E distance to the nearest valley + E distance to the nearest ridge)

where P
ij
is relative position of a cell having (i,j) location.

Methodology
16
middle-, and foot slope. For the study area, the value assignment to characterize different
slope positions is as follows:
Slope position
RPI value
Ridge
0.99 - 1
Upper slope
0.7 – 0.99
Middle slope
0.3 – 0.7
Foot slope
0.01 – 0.3
Valley
0 – 0.01
Assigning RPI values to define slope position is completely dependent on the conceptual
perception and experience of the user about an area, and can be adjusted any time to better
characterize terrain units. The reason we chose this value allocation is that our study area is
located in a mountainous region which has extremely high relief conditions. The average slope
length is often longer than areas that have lower relief conditions, and slopes are normally
steeper. Ridge and valley along a transect should not spread over a wide range of PRI values,
therefore, their ranges must be small, i.e. [0.99, 1] and [0, 0.01], respectively. Middle slope
tends to spread over a longer distance than the other position categories, therefore, it should
have the longest value range, i.e. [0.3, 0.7]. Upper slope and foot slope were supposed to be
shorter than the middle slope, having the value ranges of [0.7, 0.99] and [0.01, 0.3],

respectively.
2.3.3.2. Generation of curvatures
Curvature is defined as “the second derivative of a surface or the slope of the slope”
(Muehrcke et al., 2009). In other words, curvature redraws the general shape of the land
surface, or slope form, which is one of the very important soil formative factors (Odeh et al.,
1992; Shary et al., 2002). Slope form is the description of the general shape of the slope in
vertical and horizontal directions (FAO, 2006). There are two curvatures that characterize a
slope form: profile (vertical) curvature and planform (horizontal) curvature.
Profile curvature is parallel to the slope and indicates the direction of maximum slope and is
the rate of change of gradient. It affects the acceleration and deceleration of flow across the
surface and hence influences soil aggradation or soil loss (Odeh et al., 1991) (Figure 2.4a).
Planform curvature is defined as the rate of change of aspect being perpendicular to the
direction of the maximum slope and affects the convergence and divergence of flow across the
surface (Odeh et al., 1991) (Figure 2.4b).
Methodology


17








Figure 2.4. Profile (a) and Planform (b) curvatures (Aileen Buckley, Mapping Center Lead-
ESRI) (FAO, 2006)
For both profile and planform curvatures, negative (-) values represent concave and positive
(+) values represent convex forms; the value zero indicates a plane surface of that cell (Qin et

al., 2009). Profile and planform curvatures can be computed by a function called curvatures in
SimDTA software using Shary et al. (2002) algorithm with a corrected DEM input file.
2.3.3.3. Generation of slope forms based on the main slope positions













Figure 2.5. Nine major slope forms (redrawn from
Schoeneberger et al., 2002)
The Guidelines for Soil Decription
(FAO, 2006) mentioned nine major
slope forms proposed by
Schoeneberger et al. (2002) as can be
seen in Figure 2.5. A question is then
asked “How to characterize slope
forms of an area using these nine
major forms?”
+
-
0
A

B
C
(a)
+
-
0
A
B
C
(b)
Methodology
18
Qin et al. (2009) proposed a methodology in quantifying spatial gradation of slope positions.
In this study, 11 slope positions were achieved and considered as a basic component of a
landform. The way to achieve this eleven slope position system is summarized in Figure 2.6.
This two-tier hierarchical system of slope positions starts with the five major slope positions:
Ridge, shoulder slope (upper slope), back slope (middle slope), foot slope, and valley. The
second tier is a subdivision of the first tier by taking into account the convexity and concavity
of the surface shape along a contour line.










This system was applied for a low relief mountainous area of a 60 km

2
in northern China in
which the difference between the lowest point (233.6 m asl) and highest point (352.6 m asl)
was just nearly 120 m. The average slope gradient of this area was only 2
0
.
However, the mountainous region of Yen Chau has extremely high relief conditions with
elevation difference between the lowest and highest points of over 1400 m and highest slope
gradient reaching 78
0
. These resulted in longer slope lengths and more variations of surface
forms, which were observed during field surveys. Hence, the 11 slope position system
proposed by Qin et al. (2009) would apparently not be appropriate for the situation of Yen
Chau. There must be another system that is more suitable in this case.

First tier
Second tier
Ridge (summit)
(SMT)
Shoulder slope
(SHD)
Backslope
(BKS)
Footslope
(FTS)
Valley
(VLY)


Contour curvature

convex
planar
concave
Profile curvature

convex
Divergent SHD
(DSHD)
Planar SHD
(PSHD)
Convergent SHD
(CSHD)
planar
Divergent BKS
(DBKS)
Planar BKS
(PBKS)
Convergent BKS
(CBKS)
concave
Divergent FTS
(DFTS)
Planar FTS
(PFTS)
Convergent FTS
(CFTS)

Figure 2.6. System of slope positions (Qin et al., 2009)