INTERUNIVERSITY PROGRAMME
IN
PHYSICAL LAND RESOURCES
Ghent University
Vrije Universiteit Brussel
Belgium
Analysing Land Use Effects on Soil Development at
Lembang, Bandung District, Indonesia
Promoter(s) :
Master dissertation submitted in partial
fulfillment of the requirements for the
Prof. Peter A. Finke
Mathijs Dumon M.Sc.
degree of Master of Science in Physical
Land Resources
by Sastrika Anindita (Indonesia)
Tutor(s) :
«Promotor_1»
«Promotor_2»
Academic Year 2015 - 2016
This is an unpublished M.Sc dissertation and is not prepared for further distribution.
The author and the promoter give the permission to use this Master dissertation for
consultation and to copy parts of it for personal use. Every other use is subject to the
copyright laws, more specifically the source must be extensively specified when
using results from this Master dissertation.
Gent, August 2016
The Promoter(s),
The Author,
Prof. Peter A. Finke
Sastrika Anindita
Mathijs Dumon M.Sc.
Acknowledgement
This achievement is the result of kindness, encouragement, direction, and support from promoters,
peers, friends, and family. In this opportunity, I would like to thank them.
First of all, I would like to express my deepest sense of gratitude to Prof. Peter Finke for your
guidance, advice, support, and kindness during thesis period. My sincere gratitude goes to my copromoter, Mathijs Dumon M.Sc. for your guidance and assistance during mineralogical analysis.
Secondly, I would like to give my special thanks to Indonesian Forest Enterprises, Indonesian
National Institute of Aeronautics and Space, and Geophysics Station Class I Bandung (Indonesia
Agency for Meteorology, Climatology, and Geophysics) for getting permission to access the data,
and to farmer in Lembang, Mr. Rohman, who allowed me to take the samples in his field and
provided the field data regarding with this thesis. Furthermore, I would also like to give my sincere
thanks to Veerle Vandehelde for your assistance during analysis and to M. Fahmy Nugraha, Aldi
Putra Guntara, M. Ichsan Zainal, and Mahar Maulana for your help and kindness during field
sampling.
Last but not least I would like to thank my lovely family, especially my parents and my sisters who
always pray, love, and support me. Thank you to my amazing classmate in Physical Land Resources
and Indonesian Students Association in Ghent and Belgium who always entertain and create a warm
environment.
Gent, August 2016
Sastrika Anindita
Table of Content
Table of Content ...................................................................................................................................... i
List of Tables .......................................................................................................................................... iii
List of Figures ......................................................................................................................................... iv
Abstract ................................................................................................................................................... v
I. Introduction ......................................................................................................................................... 1
1.1 Background ................................................................................................................................... 1
1.2 Research Question ........................................................................................................................ 3
1.3 Hypothesis..................................................................................................................................... 3
II. Literature Review ................................................................................................................................ 4
2.1 Soil formation in volcanic area...................................................................................................... 4
2.1.1 Influence of pedogenetic factors to soil formation in volcanic area ..................................... 4
2.1.2 Morphology and Soil Properties in Volcanic Soil ................................................................... 6
2.2 Volcanic soils in the study site ...................................................................................................... 8
2.3 Land use ...................................................................................................................................... 10
2.3.1 History and characteristic of land use.................................................................................. 10
2.3.2 Land use effects on soil development processes................................................................. 11
2.4 SoilGen Model ............................................................................................................................. 12
2.4.1 Chemical weathering ........................................................................................................... 15
2.4.2 Cation exchange capacity..................................................................................................... 17
2.4.3 Vegetation, carbon cycling, and plant uptake processes .................................................... 17
III. Materials and Methods .................................................................................................................... 19
3.1 Field site information .................................................................................................................. 19
3.2 Survey and field sampling ........................................................................................................... 20
3.3 Laboratory analysis ..................................................................................................................... 20
3.4 Pedogenetic simulation using SoilGen ........................................................................................ 21
3.5 Land use scenario ........................................................................................................................ 22
IV. Result and Discussion ...................................................................................................................... 24
4.1 Analysis of effects of land use change on soils using analytical data ......................................... 24
4.1.1 Physico-chemical properties ................................................................................................ 24
4.1.2 Mineralogical Properties ...................................................................................................... 30
4.1.2 Limitation of the use of analytical data ............................................................................... 33
4.1.3 Conclusion ............................................................................................................................ 33
i
4.2 Analysis of future effects land use change using soil modelling................................................. 34
4.2.1 Calibration ............................................................................................................................ 34
4.2.2 Scenario output.................................................................................................................... 39
4.2.3 Limitation of the model approach ....................................................................................... 48
4.2.4 Conclusion ............................................................................................................................ 49
V. Conclusions and Recommendation .................................................................................................. 50
5.1 Conclusions ................................................................................................................................. 50
5.2 Recommendation........................................................................................................................ 50
References ............................................................................................................................................ 51
Appendix 1. Plot data ............................................................................................................................ 61
Appendix 2a. Soil description (Forest Plantation) ................................................................................ 62
Appendix 2b. Soil description (Grassland) ............................................................................................ 63
Appendix 2c. Soil description (Cultivated Land) ................................................................................... 64
Appendix 3. Precipitation...................................................................................................................... 65
Appendix 4. Water composition ........................................................................................................... 66
Appendix 5. Climate data ...................................................................................................................... 67
Appendix 6. Weathering rates .............................................................................................................. 68
Appendix 7. Timing C input of vegetation ............................................................................................ 69
Appendix 8. Bioturbation and Event ..................................................................................................... 70
Appendix 9. Fertilization ....................................................................................................................... 71
Appendix 10. Conversion andesine and bytownite to albite and anorthite ......................................... 72
Appendix 11. Recalculation of quantified minerals as applied in SoilGen model ................................ 74
Appendix 12. Graphic of chemical properties as function of depth ..................................................... 75
Appendix 13. Mineral composition of soil profiles under different land use ....................................... 77
Appendix 14. Simulated exchangeable Mg........................................................................................... 78
Appendix 15. Simulated exchangeable Na ........................................................................................... 79
ii
List of Tables
Table 1. Stratigraphy of Tangkuban Parahu complex ............................................................................. 8
Table 2. Data requirement to run the SoilGen 2 model ....................................................................... 13
Table 3. Factors of soil formation and their link to the soil forming processes simulated in the SoilGen
model ...................................................................................................................................... 14
Table 4. Morphological and Physical Characteristic of soil profiles under three different land uses .. 24
Table 5. Chemical properties of soil profiles in three different land uses ............................................ 25
Table 6. Mineralogical composition of soil profiles in different land uses ........................................... 30
iii
List of Figures
Figure 1. Location of the study site and its lithology ........................................................................... 19
Figure 2. Simulated amorphous mineral (kg m-2) ................................................................................ 35
Figure 3. Comparison weathering rate of several minerals at 25˚C .................................................... 35
Figure 4. Simulated organic carbon (top) and total reserve base (bottom) with mineral factor
1/60000 in the forest plantation .......................................................................................... 36
Figure 5. Simulated clay fraction with reducing splash release of clay ............................................... 37
Figure 6. Comparison of pH between the estimated and measured value ......................................... 37
Figure 7. Comparison of CEC (left) and base saturation (right) between estimated and measured
value ..................................................................................................................................... 38
Figure 8. Simulated organic carbon (mass% solid fraction) in forest plantation (top), grassland
(middle), and cultivated land (bottom) with scale of 0-19.214% ........................................ 40
Figure 9. Simulated organic carbon in cultivated land with TRB (top) and non TRB (bottom)............ 40
Figure 10. Simulated exchangeable Ca in forest plantation (top), grassland (middle), and cultivated
land (bottom). The upper scale (0 – 50.130 mmol+ kg-1 soil) is used for forest plantation
and grassland and bottom scale (0 – 235.712 mmol+ kg-1 soil) is used for cultivated land . 41
Figure 11. Simulated exchangeable K in forest plantation (top), grassland (middle), and cultivated
land (bottom). The upper scale (0 – 21.715 mmol+ kg-1 soil) is used for forest plantation
and grassland and bottom scale (0 – 48 mmol+ kg-1 soil) is used for cultivated land .......... 42
Figure 12. Simulated exchangeable Al in forest plantation (top), grassland (middle), and cultivated
land (bottom) with scale of 0 – 50 mmol+ kg-1 soil............................................................... 43
Figure 13. Simulated Effective CEC in forest plantation (top), grassland (middle), and cultivated land
(bottom). The upper scale (0 – 76.864 mmol+ kg-1 soil) is used for forest plantation and
grassland and bottom scale (0 – 280 mmol+ kg-1 soil) is used for cultivated land ............... 44
Figure 14. Simulated amorphous mineral in forest plantation (top), grassland (middle), and cultivated
land (bottom) with scale of 0 – 31.785 kg m-2 ..................................................................... 45
Figure 15. Simulated kaolinite mineral in forest plantation (top), grassland (middle), and cultivated
land (bottom) with scale of 0 – 34.797 kg m-2 ..................................................................... 46
iv
Abstract
Land use conversion from forest plantation to grassland and cultivated land can affect the soil
development process and soil properties. In this study, the effects of land use conversion were
assessed using analytical data to evaluate different soil properties at the present time and the
SoilGen model to simulate soil development process and soil properties over next 250 years. Three
soil profiles were excavated as representative of three different land uses, forest plantation,
grassland, and cultivated land. Soil samples were taken in all horizons and were analyzed. Analytical
data showed that there were large differences between soil properties under forest plantation and
after conversion of forest plantation to cultivated land, such as pH, soil texture, bulk density, organic
carbon, exchangeable basic cations, ECEC, and base saturation. Meanwhile, the effects of land
conversion from forest plantation to grassland on those soil properties were small. However, this
study showed that, beside land use, minerals also have effect on the difference of soil properties.
In the simulation using the SoilGen model, calibration was done in advance. Dealing with
volcanic soils, the weathering rate of amorphous minerals and decay of organic carbon were two
variables that were calibrated. The proposed weathering rate which was determined based on the
weathering rate between basaltic glass and muscovite/quartz, resulted in quite stable amorphous
minerals. The role of total reserved of base (TRB) using a “mineral factor” effectively controlled the
loss of humus, especially in the sub soils. The simulation showed that there were differences of soil
properties among land uses in the top soils, and the discrepancies became larger after 250 years,
particularly for organic carbon. The differences of soil properties over 250 years is mainly due to the
influence of vegetation, such as cation uptake by plant roots, agricultural activities, and mineral
composition.
Keywords: land use conversion, SoilGen model, volcanic soils, amorphous minerals, organic carbon,
agricultural activities, soil properties
v
I. Introduction
1.1 Background
Human activities play an important role in managing land. One such activity is the conversion of
land from forest to other land uses, such as grassland and cultivated area. Gibs et al. (2010) reported
that 55% new agricultural land came from intact forest and 28% came from disturbed forest during
1980 – 2000 in tropical regions, meanwhile annual deforestation of forest in Indonesia reached up to
800 000 ha in 1990 (Trexler and Haugen, 1994). However, land use change followed by improper
land management will lead to negative environmental impact. Soil as foundation of the terrestrial
ecosystem (Yaalon, 2000) and non-renewable resources in human time frame should be protected in
order to fulfill its function providing nutrient and water to sustain agriculture and other ecosystem
services.
Land use conversion can affect soil properties and also soil development processes for long years
after. The effect of various types of management on the soil was illustrated using the concepts of
genoform and phenoform by Bouma and Droogers (2007). Genoform is defined as the genetic soil
type and phenoform is the effect of various type of management in a certain genoform. In addition,
the concept of soil as human-natural body also indicates the role of humans to transform soil’s
physical, chemical, and biological properties and processes (Richter and Yaalon, 2011). These
authors also reviewed one of the evidences of human-soil interaction namely the change of the
average decomposition rates in soil organic carbon in response to cultivation, manure amendments,
reforestation (Post and Kwon, 2000; West and Post, 2002). These rates were higher than the rates in
natural soil formation (Schlesinger, 1990). Quantification of soil forming processes related with
human-soil interaction is required to evaluate the future of soil.
Processes of quantification of soil formation can be studied by using pedogenesis models. Such
models have been well recognized as a useful tool to understand the soil system (Stockmann et al,
2011), to quantify soil forming processes as the result of interaction of all soil forming factors (Jenny,
1941) i.e. climate, organisms, relief, parent material, and time (McBratney et al., 2003; Goddéris et
al., 2010; Finke, 2012), to evaluate the reconstructed soil based on soil data in the present time
(Finke, 2012), and so forth. The SoilGen model (Finke, 2012) is one pedon scale soil evolution model
that can simulate major soil forming processes and also simulate the impact of human activity on soil
formation by taking into account the effect of fertilization and plowing over multi-millennia time
scale (Opolot, 2014).
Lembang, located in Bandung district West Java, has Andosol soil type (Yatno and Zauyah, 2008;
Devnita et al., 2010) with andesitic parent material of Holocene age (Silitonga, 2003; Devnita et al.,
2010). Andosols are amongst the most productive soils (Shoji et al., 1993). They have good physical
properties, high organic matter content (Nanzyo, 2002) and rapid release of nutrients (Fiantis et al.,
2005). However, due to its fertility, the land in this area is often converted to other land use forms.
1
Ruswandi et al. (2007) reported that forest, in Lembang and Parongpong Sub-District, experienced
significant decline in area from 5,470 ha in 1992 to 1,746 ha in 2002, equivalent to a reduction by
68%. Information about the impact of land use change to the soil properties and their processes is
still lacking in this area, so there is a need to conduct this research.
The main pedogenic processes in volcanic materials are the formation of non-crystalline
materials and the accumulation of organic matter (Shoji et al., 1993). Non-crystalline materials, such
as allophane and imogolite are minerals formed from weathering in volcanic soil (Shoji and
Takahashi, 2002), while stabilization of organic matter may be due to formation of Al-humus
complexes and sorption to allophane, imogolite, and ferrihydrite (Ugolini and Dahlgren, 2002).
Moisture regime, chemical and mineralogical properties of the parent material and the rate of
organic matter decomposition are important factors controlling formation of mineral-humus
complexes in volcanic materials (Wada, 1987). Furthermore, interaction of all soil forming factors
can drive transformation, translocation, addition, and losses of soil constituents providing the
mechanism of soil development that can be seen from soil horizon (Simonson, 1959).
Humification and mineralization processes occur very rapid in the tropical region (Last et al.,
1982). It is usually most clearly shown in the top soil horizons and is caused by organisms such as
earthworms that can homogenize upper soil profiles (Amundson, 2004) or by human activities
causing decline of soil organic matter due to tillage process (McLauchlan, 2006). In addition, the
climate factor and organism activities can enhance weathering of minerals. Formation of noncrystalline minerals will occur when Al and Si co-precipitate. Conversely, the presence of humus can
limit the formation of allophane or imogolite due to strong bonding Al to humus, thus Si which are
not leached initiate the formation of opaline silica (Wada, 1987). Buried soils observable in
subsurface horizons were subject to surface weathering in the past and to subsurface weathering
after the soils are buried, hence more intense weathering might be observable in these horizons. If
the weathering intensity increases, metastable non-crystalline materials can transform to more
stable crystalline materials (Ugolini and Dahlgren, 2002).
This study was conducted in three different land use types, forest plantation, grassland, and
cultivated land. Forest plantation dominated by pine trees mixed with coffee plantation
characteristically has a deep rooting system. Leaves and roots litter from pine and coffee contribute
to continuous supply of organic matter through the depths. Grassland also has continuous supply of
organic matter in the top soil horizons. Decomposition from litter is closely related with nutrient
cycling in these land use types (Wang et al., 2015). In contrast, tillage processes can significantly
reduce the amount of organic matter and organisms in the top horizons while intensive fertilizer
applications can enhance the amount of cations in the soil. In addition, soils in cultivated land are
also not entirely protected by crop cover so the soils are vulnerable toward external impacts, such as
rain. These conditions can cause erosion and will then lead to the change of some soil properties,
such as reduction of the carbon stock and increase of soil compaction (Shete, et al., 2015).
Fertilization will increase Ca, K, and pH in mixed cropping land (Bertin et al., 2015). Velde and
Meunier (2008) reported that agricultural activities can enhance the weathering process so soils in
the cultivated land is expected to have more weathered minerals.
2
Understanding the influence of land use change to the soil is important as a basis for making
decisions on land management. This research aims to analyze the evolution of some soil properties
after the conversion of land use. Analytical data is used to evaluate different soil properties in the
present time and the SoilGen model is used for future development. Before model projections can
be made, calibration has to be done of the SoilGen model in order to have a plausible quality of this
model for simulating the processes and some properties of soils in this volcanic region over the next
two hundred fifty years.
1.2 Research Question
Questions of this research are:
1. Are there any differences in soil properties and soil development under different land use,
based on soil analysis in three phenotypes in volcanic soils?
2. How will the soils under different land use develop over the next two hundred fifty years
and will any differences develop in soil properties?
1.3 Hypothesis
Hypothesis of this research are:
1. The differences in soil properties between profiles under different land use can entirely be
attributed to the land use differences
2. There are differences in soil properties and soil development, especially, in the upper
horizons due to external influences. Large differences of soil properties are expected
between cultivated land and other land uses, forest plantation and grassland.
3. If the land uses are still the same over the next hundred years, the discrepancies of soil
development among land use types will be higher and also their characteristics will show
stronger differences
3
II. Literature Review
2.1 Soil formation in volcanic area
Soils derived from volcanic materials are globally distributed nearby active and recently active
volcanoes. These soils approximately cover 124 million ha or 0.84% of the world land surface
(Leamy, 1984) and approximately 60% of those soils occur in tropical countries (Shoji et al., 1993).
Volcanic soils are concentrated in the circum-Pacific Ring of Fire where there are many volcanoes,
such as New Zealand, Melanesia, the Phillippines, Japan, Kamchatka, Aleutian, Alaska, and the
western coast of North America to South America (Bullard, 1984). The age of these volcanoes,
geologically, is still young, and volcanic mountains are still growing and show high explosivity (Katsui,
1978).
Andosols are a typical soil type formed from volcanic ejecta. The soils are subjected to have
intermittent deposition of volcanic ash which can rejuvenate the development processes of soil.
However, Andosols can rapidly alter to other soil orders under humid and warm climate, such as
Inceptisols, then Oxisols on a stable landscape (Chadwick et al., 1994). “Andosolization” was
introduced by Duchaufour (1982) to describe the process of weathering volcanic ash or tephra which
results in the formation of large amount of non-crystalline materials. A large amount of volcanic
glass, which shows the least resistance to chemical weathering, is commonly founded in volcanic ash
or tephra.
Weathering of volcanic ash is rapid. There are two trends: (1) volcanic ash rich in basic cations
and/or slow removal of weathering products will follow pattern of volcanic ash  2:1 mineral + free
Fe constituent  1:1 mineral + free Fe constituent  free Al + Fe constituent. (2) volcanic ash with
rapid removal of weathering products will follow pattern of volcanic ash  Al-rich constituent
(allophane) + free Fe constituent  1:1 mineral + free Fe constituent  free Al+ Fe constituent
(Qafoku et al., 2004). The rate of weathering will depend on the amount, size, porosity, chemical,
and mineralogical composition of the weatherable primary minerals, especially volcanic glass. Other
factors of influence are time since deposition and climate (as they determine the rate export of
constituents released during weathering), composition of the deeper layers (which determines rate
of transport of the soil solution), and the presence or absence of a water table.
2.1.1 Influence of pedogenetic factors to soil formation in volcanic area
Interaction of pedogenetic factors as Jenny (1941) stated can be a foundation to explain the
formation of soil. It is also applied in the volcanic region. Genesis of Andosols is strongly related to
the properties of parent materials (Shoji et al, 1993). Furthermore, the environment and time are
important factors that influence the formation and transformation of clay minerals (Lowe, 1986).
The effects of each pedogenetic factor to soil formation in volcanic regions will be explained in the
following paragraphs.
4
Parent material could be considered as the initial state of the system, including its chemical,
physical, and mineralogical properties (Schaetzel and Anderson, 2005). Characteristics of parent
materials in volcanic areas, such as small particle size, the glassy nature of the particles, high
porosity, and high permeability can increase the rate of chemical weathering (Lowe, 1986; Dahlgren
et al., 1999). This weathering releases some elements, such as Si, Al, and Fe, which is faster than
crystalline minerals can form, thus the soil solutions become over-saturated with respect to
metastable non crystalline minerals such as allophane, imogollite, opaline silica, and ferrihydrite
(Ugolini and Dahlgren, 2002). Several studies conducted in andesitic tephras under humid and
temperate climate showed that stable allophane and imogolite form rapidly in 300 years from
weathering glass and pumice, and persist for long period (Russell et al., 1981; Violante and Wilson,
1983). Gibbsite can form rapidly, coexisting with allophane and imogolite in deposits as young as
3000 years, but in general in older deposits (Wada, 1977).
Relief influences soil development through its control of the hydrological conditions (Schaetzel
and Anderson, 2005). The slope gradient is responsible for the differences in potential energy of
water and may thus drive erosion and deposition. In the volcanic region, this factor can affect the
accumulation of tephras or burial of a soil. Buried surface horizons have reduced organic matter
input, thus it allows for precipitation of secondary minerals (Lawrence et al., 2015). Studies from
previous research reported that allophane and traces of gibbsite are found in well-drained deposits
while poorly drained deposits are dominated by halloysite or smectite minerals (Chadwick et al.,
2003).
Climate is one of the most important factors in soil formation causing weathering and
translocation of materials. Solar radiation, temperature, and rainfall are the primary variables of
climate. Temperature has effect on the activity and diversity of biota and on the speed of chemical
reactions (Van Breemen and Buurman, 2002). An increase of mean temperature can increase the
weathering rate of tephras (Lulli and Bidini, 1980). Meanwhile, rainfall is well-known as a factor that
can cause leaching of cations, thus it also determines the loss rate of cations and acidification
(Chadwick et al., 2003). In volcanic soils, rapidly lost cations under moderate to high rainfall will lead
to the formation of non-crystalline minerals (Parfitt et al., 1984). Furthermore, gibbsite may be
present under high leaching conditions. This mineral is formed when the Si content is low in the
solution thus preventing aluminum-silicate formation (Violante and Tait, 1979).
The contribution of organisms to the soil development processes involves the role of animals
and vegetation. Bioturbation is a term to describe activities of animal and plants which affect the soil
development (Minasny et al., 2008). Soil fauna activities such as by earthworms (Buchan, 2010) can
result in the translocation, sorting, and deposition of materials which lead to the mixing of materials
between surface and subsurface horizons. However, this activity is important because it can obscure
the tephra stratigraphy, especially where deposits are thinly bedded, and bring other minerals from
another layer as “contamination” materials (Dudas and Harward, 1975). On the other hand,
vegetation is a main factor supplying organic matter to the soil.
Time is related with the age of soil or the degree of maturity. Evaluating the effect of time as
independent factor to soil formation processes is difficult because other pedogenetic factors, such as
5
macro and micro climate, vegetation, tephra thickness or depth of buried soil vary with time.
However, soil age has significant impacts in controlling mineralogical evolution in andesitic lava
(Nieuwenhuyse et al., 2000). The age of tephras are broadly correlated with the amount of clay-sized
materials (Lowe and Nelson, 1983). They reported that under temperate humid climate in New
Zealand, tephras with an age of 3000 years contain < + 5% clay; tephras 3000 – 10,000 years old
contain 5 – 10%; tephras 10,000 – 50,000 years old contain 15-30% clay; while tephras older than
50,000 years old contain > 60% of clay. However, this relationship is only valid if the tephras are
weathering under similar conditions and the rate of clay formation can be different depending on
the environment.
2.1.2 Morphology and Soil Properties in Volcanic Soil
2.1.2.1 Morphological characteristics
Soils formed in the volcanic materials, Andosols, may have AC, ABC, or multisequum of these
horizon sequences (Shoji, et al., 1993). He reported that these horizon sequences are the result of
intermittent tephra deposition. Surface horizons of these soils usually have thick humus horizon (Ah)
which is related to the high organic carbon content with dark color. In Andosols, the soil color
depends on the type of tephra, the amount of soil organic matter, and the composition of
weathering products. Dark colors are related to the organic carbon content while yellow or red
colors usually appear in the non-humus horizons when they are subjected to weathering under udic
moisture regime. Andosols have a stable structure (Hoyos and Comeford, 2005). The common
structure that is usually present, is a granular structure in top horizons and a sub angular blocky in
the Bw horizon or under cultivation. Meanwhile, soil texture in Andosols shows a wide variation
depending on the particle size of tephras and the degree of weathering.
2.1.2.2 Mineralogical properties
Mineral properties are important as a basis to understand physical, chemical, and biological
properties between new tephra and matured Andosols. There are two types of minerals, primary
minerals and secondary minerals. Volcanic glass, plagioclase, quartz, pyroxenes opaque, hornblende,
biotite, olivine, and so on are the main primary minerals in fresh tephra (Nanzyo, 2002). Feldspar
and heavy minerals tend to be more or less high in the fraction coarser than 0.1 mm in diameter
while volcanic glass is likely to be high in fraction finer than 0.1 mm in diameter (Yamada and Shoji,
1975). The glass component in volcanic ash is abundant. There are two types of volcanic glass
namely non-colored and colored volcanic glass. Colored tephras belong to basaltic andesite and
basalt while non colored tephras contain rhyolite, dacite, and andesite. Non-colored tephras often
weather to form nonallophanic soils (Shoji et al., 1983; Shoji and Fujiwara, 1984).
Reactive components in Andosols consist of allophane, imogolite, Al-humus complex,
ferrihydrite, and KCl-extractable Al (Nanzyo, 2002). Allophane or amorphous clay is a product of
weathering of volcanic ash and consists of “hollow spherules” with diameter 3.5-5 nm (Wada, 1987).
The atomic ratio of Si/Al in this mineral is between 0.5 and 1 but a Si/Al ratio of 0.5 is more common
6
(Shoji et al., 1993). The spherule structure has several entry holes in which water molecules and
some cations, such as NH4 and Ca2+ can freely pass inside of the spherules (Wada, 1987). Allophane
is commonly present as a mixture with imogolite. Imogolite has a nesosilicate structure with Si/Al
ratio of 0.5 (Nanzyo, 2002) and is less reactive to phosphate compared to allophane (Henmi et al.,
1982). Another characteristic is opaline silica which is detected in the A horizons of Andosols
younger than several thousand years (Shoji et al., 1993). Meanwhile, a group of 2:1 minerals that
have polymerized hydroxyl Al ions and exchangeable Al ions in their interlayers can be grouped as
chloritized 2:1 minerals (Nanzyo, 2002). The presence of these minerals is low compared to the Alhumus complex.
2.1.2.3 Physical properties
Shoji et al. (1993) reviewed that the structure of Andosols usually reflects the great amount of
non-crystalline materials and organic materials which are related to low bulk density. The bulk
density of Andosols is 0.9 g cm-3 or lower. Nanzyo (2002) explained that the low bulk density of
Andosols is because of well-developed aggregate structures which causes high porosity and high
amount of humus content. In addition, Andosols also contain less macropores and more micropores
as formation of non-crystalline materials increases (Nanzyo, 2002). These physical properties
contribute to the good air permeability (Hoyos and Comeford, 2005) and high capacity to retain
water which creates favorable condition for plant growth.
2.1.2.4 Chemical properties
Non-crystalline secondary minerals and Al-humus complexes are main factors in determining
chemical properties of Andosols. Short range order minerals that are formed during soil
development have large and highly reactive surface area which results in an excellent capacity to
sequester carbon (Kleber et al., 2005). The soils also have high phosphate fixation and weak
retention of exchangeable bases which is approximately less than 10% (Wada, 1987), strong acidity,
and presence of Al toxicity in the increase of weathering intensity (Ugolini and Dahlgren, 2002)
Andosols contain twelve major elements namely C, N, Na, Mg, Al, Si, P, K, Ca, Ti, Mn, and Fe
(Nanzyo, 2002). Si and Al are the main elements in this soil. During the weathering process, Al and Si
are released from volcanic ash. Secondary minerals in Andosols, allophane or imogolite, and Alhumus complex have variable charge characteristics. They can be positive or negative depending on
pH and salt concentration. Variable negative charges are due to carboxyl groups of humus and
silanol groups of allophane and imogolite while variable positive charges are caused by protonated
hydroxyl groups bound to alluminum of allophane and imogolite and to iron minerals (Nanzyo,
2002). The increase of pH can increase the amount of variable negative charge conversely the
amount of variable positive charge will increase if the pH decreases.
Accumulation of humus is crucial for the stabilization of humus by complexation with Al. The
accumulation is usually located in the A and buried A horizons. Continuous supply of organic matter
usually occurs in the surface A horizons while decomposable organic matter is less in the buried A
horizons, especially if the fresh tephra layers are thick and root penetration cannot reach this depth.
7
Consequently, humus in buried A horizons is fully complexed with Al (Nanzyo, 2002) while this is not
the case in surface A horizons.
2.2 Volcanic soils in the study site
The Alpine-Himalayan volcanic belt is traced in some islands in Indonesia such as Sumatra, Java,
and the Lesser Sunda islands (Bullard, 1984). There are approximately 400 volcanoes scattered in
Indonesian archipelago and twenty one of them are located in West Java (Van Bemmelen, 1970).
Various substances are produced from the activities of these volcanoes, such as tuff, pumice,
cinders, lahars, and other volcanic ejecta. These materials are important as parent materials of soils.
The development of soils originated from parent ash materials in Indonesia varies but all of them
originate from recent Pleisto-Holocene eruptions (Whitford, 1975; Utami, 1998; Van Ranst et al.,
2002). Further, the effect of volcanic ash on soil properties depends on the age and depth of the ash
deposit, land form, and lithology. Andosols are the common soil types in the young volcanic area of
the Indonesia archipelago (Dudal and Soepraptohardjo, 1979).
Tangkuban Parahu is an active well-preserved stratovolcano mountain located in Sunda-Banda
volcanic arc (Van Bemmelen, 1970). The formation of this volcano is a part of volcano activities
series in the Mt. Sunda complex. Table 1 shows there are three main activities in this complex
namely eruptions of Pre-Sunda Volcano, Sunda volcano, and Tangkuban Parahu volcano. In the first
series, Batunyusun Andesite is a representative formation which unconformably overlies
sedimentary deposits. In the second series, Sunda volcano deposits consist of Sunda Andesite and
Sunda Pyroclastics. Sunda Pyroclastics are related to the collapse of Sunda caldera due to the
eruption. Sunda volcano deposits contain many andesitic lavas, pyroclastic flows, and mudflows
(Silitonga, 1973; Hadisanto, et al., 1986). Furthermore, Tangkuban Perahu is the third series which
consists of Old Tangkuban Perahu and Young Tangkuban Perahu. Old Tangkuban Perahu deposits
comprise Tangkuban Parahu Andesite and Tangkuban Parahu Pyroclastics. Tangkuban Perahu
volcano started its activity around 0.18 Ma - 0.05 Ma (Sunardi and Kimura, 1998).
Table 1 Stratigraphy of Tangkuban Parahu complex
Major unit
Post-caldera
Young
Tangkuban
Perahu tephra
Old
Tangkuban
Perahu
Origin of
eruption
Phreatic
MagmaticPhreato
Magmatic
Stratigraphy
Age (Ma)
 Baru
 Ciater
 Siluman
 Ratu
 Doman
Tangkuban Perahu
Tephra
Tangkuban Perahu
Pyroclastics
Tangkuban Perahu
Andesite
0.00383-0.00144
0.00998-0.00945
0.040-0.022
0.062-0.040
8
Syn-caldera
Sunda
Volcanics
Pre-caldera
Pre-Sunda
Volcanics
Tertiary Sedimentary Basement
Magmatic
Magmatic
Sunda Pyroclastics
Sunda Andesite
Batunyusun Andesite
0.205-0.180
0.56-0.205
1.1
Sand and clay stones
>3.0
Source: Nasution et al (2004)
The main products of Tangkuban Parahu volcano are Andesitic rocks with calc-alkaline lavas
(Nasution et al., 2004), pyroclastic flows, and air-fall deposits of basaltic-andesitic composition
(Neumann van Padang, 1951). Quartz, feldspar, cristobalite, trydymite, gibbsite, goethite, hematite,
mica, and some other layer silicates are minerals that can be found in volcanic soils in Indonesia
(Devnita et al, 2010). The origin of 2:1 and hydroxyl-Al interlayered 2:1 silicates in the well-drained
volcanic ash are still controversial. Several hypothesis according to the origin 2:1 silicate such as
neoformation in specific microenvironments, eolian dust contamination, and inheritance from
parent material through hydrothermalism or incorporation of lithic fragments into tephra during
eruption are reviewed by Van Ranst et al. (2008).
Study of lava composition in Tangkuban Perahu volcano series showed that Tangkuban Parahu
Andesite contains basaltic to andesitic lava comprising plagioclase, pyroxene, and minor amounts of
olivine in the fine-grained mineral groundmass; meanwhile Tangkuban Parahu Pyroclastics contain
pyroclastic fall and flow (Nasution et al., 2004). Scoria, pumice, sand and andesite, or basalt lithic
fragments of lapilli to pebble sizes are found in pyroclastic fall. These materials are well stratified,
weathered, and have a brownish yellow color. On the other hand, less well sorted pumice clasts,
sands, lithics and ashes are detected in the pyroclastic flow. In addition, Kartadinata et al (2002)
presented
14
C dating of charcoal in the east and west of Tangkuban Parahu’s pyroclastic flow,
cropping out on the upper part of the Sunda volcano, which showed ages of 40,750 + 270 and
22,380 + 80 year B.P, respectively. Both of them are categorized as old Tangkuban Parahu tephra
and the origin of eruption is phreatomagmatic origin. Meanwhile, 14C dating from young Tangkuban
Parahu tephra showed age of 9,445 + 50 and 9,980 + 50 years B.P, and the origin of eruption is
phreatic.
Studies about mineralogical, chemical and physical properties of volcanic soils in pine forest,
Lembang have been conducted. Analysis of mineral composition in sand fraction, such as noncolored volcanic glass, colored volcanic glass, feldspar, quartz, hornblende, augite, and hypersthene
were detected (Devnita et al., 2010). They also reported that soils in this area show high organic
carbon contents which can reach up to 9% in some layers, are acidic with pH around 5, have
phosphate retention of more than 90%, and Alox + ½ Feox more than 2%. In addition, low bulk
density, domination of silt followed by clay and sand, high available water are characteristic physical
properties (Yatno and Zauyah, 2008). According to the Soil Survey Staff (2010), classification of soils
in this area is Andisols with (family) classification of Typic Hapludand, medial, amorph,
isohyperthermic (Devnita et al., 2010).
9
2.3 Land use
2.3.1 History and characteristic of land use
The study areas are dominated by three types of land use, namely forest plantation (pine forest
mixed with coffee plantation), grassland (Imperata cylindrical, shrub, herbaceous plants, and
Eucalyptus trees), and cultivated land (vegetables). However, the areas were deciduous forest in the
past. The existence of deciduous forest was recorded from the pollen records found at intermediate
altitudes (1300-1500 m) in Sumatra and West Java. The pollen records showed an indication of
lowering forest altitudinal zone and/or greater abundance of gymnosperms in Late Pleistocene
forest than in those of the Holocene period which are related with cooler climate in Late Pleistocene
period. Furthermore, abundance pollen of Altingia, Castanopsis, and Quercus were present in the
early Holocene, indicating a warmer climate (Stuijts et al., 1988). Previous study reviewed by
Kershaw et al., (2007) reported that Bandung basin was a rainforest around 6,000 years ago and
forest recession was recorded to occur in many areas in Java around 2,000 – 1,500 years ago in Java
(Lavigne and Gunnell, 2006; Sémah and Sémah, 2011).
Forest management in the study site has been documented when the Dutch government
encouraged to plant teak trees in mountain areas in 1900 and other timber trees in 1916; meanwhile
pine (Pinus merkusii) trees were widely planted in 1962 (Perhutani, 2014). Pinus merkusii is the only
Pinus species in Indonesia, it often occurs in the volcanic soils and is a pioneer in successions (Stuijts
et al., 1988). In addition, the occurrence of coffee plantation in the pine forest has not been well
documented. Based on field observation, the quantity of coffee plants is much less compared to pine
and coffee plantation are planted randomly depending on farmers. Coffee plantation in this area is
not planted for commercial production and farmers only applied urea fertilizer at the beginning of
planting. Besides pine and coffee, grasses or shrub are also found on the surface soils but not all
surface soils are covered by these vegetation.
Based on the interview with the local forest guardian in the study site area, some of the areas in
the pine forest were converted to grassland around 1987. Dominant vegetation in this area is
Imperata cylindrical grass, but shrub, herbaceous plants and Eucalyptus trees are also present. This
land conversion was done by cutting down the trees. Meanwhile, conversion of forest to agricultural
land, in Lembang, widely occurred around 1990 (Ruswandi et al., 2007). Cultivated land in the study
site was converted from pine forest in 1996. These farmers apply an intensive vegetable cropping
system with 3-4 crop rotations in a year with cabbage, tomatoes, broccoli, and cauliflower as the
main crops. In addition, agricultural activities, such as tillage and fertilization, are applied during the
crop cycle. Tillage is usually done each time before planting using traditional hoe. This equipment
can completely mix the soils in the surface layer, approximately 25-30 cm. Organic fertilizer, chicken
manure, is applied once a year. Inorganic fertilizers, such as NPK and ATS, are also applied up to four
times for one crop cycle. The majority of cultivated land in Lembang area is on terraces. The height
of the terrace is covered by grass and boards to prevent soil erosion from above and the width of
terrace is about 10 – 15 m.
10
2.3.2 Land use effects on soil development processes
Organic matter (humus) content is one of the important parameter to evaluate effect of land use
on soil development processes. The availability of humus depends on the supply of organic matter.
Furthermore, climate, biochemical composition of organic matter, soil fauna, and physical protection
of organic matter will determine the rate of decomposition, and stabilization of organic matter in
volcanic soils is hypothesized to be related secondary Al and Fe (Rasmussen et al., 2005). Deciduous
forest in the tropical region generally has diverse flora that without any disturbance, supplies fresh
organic matter continuously. Conversely, land use change from deciduous forest to pine forest,
grassland, as well as cultivated land can limit the supply of organic matter as the diversity of flora is
reduced. This conversion also alters the micro climate and biological activity in this area.
Temperature influences the accumulation of organic matter (Jenny, 1941). Warm climates
generally enhance the decomposition process so soils contain less accumulation of organic matter.
Decomposition rates become more rapid in cultivated land as the soils are not entirely covered by
vegetation compared to other land uses. On the other hand, biochemical composition, such as high
lignin content in needles from pine trees, may slow down decomposition (McTiernan et al., 2003).
This component requires strong oxidation agents to decompose and only limited soil
microorganisms are able to mineralize lignin (Hammel, 1997), thus accumulation of organic matter is
expected to be higher in pine forest than in other land uses. Another factor influencing the rate of
decomposition is the quantity of microorganisms. Abundance of microorganisms can be found in
warm-temperate climate, with high humidity, at high rates of crop residue application, and at higher
proportion of readily available C in the substrate, as these are favorable conditions for soil fauna and
microorganisms (Gregorich and Janzen, 2000). Microorganisms also prefer soil near neutral pH (67.5). Organic material decomposes more slowly in strongly acid than in neutral soils, but this effect is
only important in the early stage of decomposition and will disappear over time (Gregorich and
Janzen, 2000).
Stabilization of soil organic carbon by physical protection is usually related to the formation of
soil structure that may limit the diffusion of enzymes (Blanco-Qancui and Lal, 2004; Rasmussen et al.,
2005). Elliott (1986) reported that release of CO2 when the aggregates are crushed can be an
evidence of the physical protection. The role of aggregates in stabilization of organic carbon was also
reported by Richard et al. (2009). Chemical analysis showed that some of the organic materials
located within aggregates are relatively undecomposed (Gregorich and Janzen, 2000). However,
physical protection in soils is limited and may occur in soils that have not been cultivated or that
have been under grass for a long time. Physical protection may occur in the young volcanic soils as it
possesses abundant micropores (Huang et al., 2002).
Stabilization of organic carbon can occur through organo-metal association and organo-mineral
association. In volcanic soils, short range order minerals are well-known to have considerable
reactive surface area and microporosity that may contribute to the adsorption of organic matter
(Huang et al., 2002). Conversely, McCarthy et al. (2008) reported that organic carbon was
encapsulated rather than adsorbed by minerals because organic carbon was held within pores. Al in
11
monomers or polymeric forms interacts with carboxyl groups of organic matter which may play an
important role in stabilizing organic matter. Fe3+ and Al3+ are likely the most important cations for
bridging the negative charge of mineral and organic surfaces in well drained, neutral to acidic soils
(Rasmussen et al., 2005). Doetterl et al., (2015) reported that organo-mineral interactions can act as
a protection of soil organic carbon source.
Interaction between humic acids and allophane may likewise be described in terms of ligand
exchange, the requirement for balance of surface charge, and the law of mass action (Yuan et al.,
2000). Formation of surface complexes, explained by ligand exchange between a hydroxyl group
attached to Al in the allophane structure and carboxylate group of the humic acid ((allophane-Al)-OH
+ -OOC-HA-), creates a surface negative charge which can be compensated by uptake cation such as
Na+ or Ca2+ ((allophane-Al)--OOC-HA - Ca2++OH-). Negative surface charge resulting from interaction
of allophane and humic substance can influence management of allophanic soils. Allophane, by
itself, only carries small negative charge at pH < 6 thus will be ineffective to retain cations. By
developing an excess of surface negative charge, the formation of complex with humus can enhance
the capacity to retain and immobilize cations.
In cultivated land, stabilization of organic matter that is already present in the volcanic soils can
be disturbed. Management practices such as tillage, selection of crops and cropping sequence, and
fertilization can alter decomposition rates, physical and chemical properties of young volcanic soils.
Tillage can accelerate the rate of C biodegradation due to disturbance of physical protection
(Balesdent et al., 2000). In a study of the effect of land use on mineral-bound organic matter
(Doelsch et al., 2009), it was found that cultivation may potentially modify the physicochemical
stability of the mineral phase, lead to vertical migration of organo-mineral complexes and their
accumulation in the deeper profile, and modify the amount of organic compounds linked to minerals
with decrease in topsoil and increase in subsoil. In addition, fertilizer application in acid volcanic soils
may be ineffective. Soils with low pH tend to have more positive variable charge so cations added
cannot be retained and under humid tropical conditions, cations are highly leached. The best way to
decrease pH with zero charge, thus increase CEC is by increasing the amount of organic matter.
2.4 SoilGen Model
Soils formation naturally occurs over geologic time scales nevertheless short term external
forces, such as environmental change and human factors, can enhance the processes and thus have
major influence in the development of characteristics and properties of the soils. Using a
pedogenesis model is one of the methods to accommodate all the factors and to understand their
effects on the soils. The development of pedogenesis models has been reported in previous studies
(Minasny et al., 2008; Samouëlian and Cornu, 2008; Stockmann et al., 2011; Samouëlian et al., 2012)
and one of the most complete pedogenesis models identified by Samouelian et al (2012) is the
SoilGen model (Finke and Hutson, 2008; Finke, 2012).
SoilGen model is a pedon scale pedogenesis model that takes into account all soil forming
factors and describes various interacting processes of soil formation i.e. biological, geochemical, and
physical processes simultaneously, hence the model is able to simulate alteration of soil properties
12
over millennium time scales (Opolot, 2014). The model uses a 1D solute transport model based on
Richard’s equation for unsaturated water flow, the convection dispersion equation for solute
transport, and the heat flow equation for dynamic simulation of soil temperature (Finke et al., 2015).
In addition, the model also use the concept of the RothC26.3 model (Jenkinson and Coleman, 1994)
to simulate organic carbon cycle (Finke and Hutson, 2008; Finke et al., 2015). Major soil forming
processes simulated in SoilGen are physical and chemical weathering, clay migration, cation
exchange, bioturbation, chemical equilibria, carbon cycling, soil phase redistribution processes, and
the effect of slope/exposition on precipitation and evapotranspiration. Calibration and application of
this model in a number of field case studies also have been reviewed in some literatures (Finke,
2012; Sauer et al, 2012; Yu et al, 2013; Opolot and Finke, 2015; Finke et al, 2015). In some of the
above studies, root mean square error (RMSE) (Eq.1), bias (Eq.2), and the dissimilarity statistics are
used to evaluate how well simulations can reproduce measured parameters.
1
RMSE = √
𝑛
Bias =
1
𝑛
∑𝑛𝑖=1(𝑒𝑖 − 𝑚𝑖 )2
(1)
∑𝑛𝑖=0(𝑒𝑖 − 𝑚𝑖 )
(2)
where e is estimated, m is measured, and n is number of observation. Meanwhile, the scaled
dissimilarity is the ratio of unscaled dissimilarity (Eq.3) over maximum and minimum of parameter
found in particular profile. Unscaled dissimilarity is defined by comparing measured (m) and
simulated (s) at individual soil layers. The scaled dissimilarity ranges from 0 (perfect) to 1 (very poor)
if the simulated content is within the range of measured content
1
unDISprofile = 𝑘 ∑𝑘𝑘=1 𝑎𝑏𝑠 (𝑥𝑚,𝑘 − 𝑥𝑠,𝑘 )
(3)
Several input data and factors of soil formation related with the soil formation processes
simulated in the SoilGen are provided in Table 2 and 3. For detailed explanation of the processes
simulated in SoilGen, referenced is made to Finke and Hutson (2008) and Finke (2012). Chemical
weathering processes, cation exchange, carbon cycling and plant uptake processes, and soil phase
redistribution processes as the main processes of this study are explained in detail in the subsequent
subsections.
Table 2. Data requirement to run the SoilGen 2 model
Data group
Sub data
Essential plot data
Slope angle, upslope bearing, downwind bearing, degrees
latitude
Starting depth and ending depth
cm
Initial clay, silt, sand, and organic carbon
mass% solid
fraction, should
sum to 100%
Initial bulk density
kg dm-3
Initial moisture content
dm3 dm-3
Initial temperature
˚C
Initial CEC
mmol-1 kg-1
Initial amount at exchange complex
mmol-1 kg-1
Amount of precipitation
mm
Essential soil data
Precipitation data
Units
13
Evaporation and air
temperature data
Water composition (Ca, Mg, Na, K, Cl, SO4, alkalinity, Al)
Potential evapotranspiration
Mean daily temperature
Mean daily amplitude
Climate and
vegetation data
Annual precipitation
Timing for C-input
per vegetation type
in a typical year
Mineral (per layer)
Bioturbation history
intensity
Fertilization history
Event history
Annual potential evapotranspiration
Average January temperature
Average July temperature
Annual C-input (root and leaf litter or crop residue) of
plants
Annual C-input as organic manure
Vegetation type
Initial profile data: distribution C over various pools (%)
(all soil compartments)
Per month in a typical year for each one of 4 vegetation
types
 Percentage of the plant residues (litter) input to the
soil
 Percentage of the manure input to the soil
Per vegetation type
 Maximum rooting depth
Albite, K-feldspar, Muscovite, Biotite, Quartz, Chlorite as
Clinochlore,
Anorthite,
Fosterite,
Augite
as
Orthoferrosilite, Kaolinite, Illite, Montmorillonite,
Hornblende, Fayalite, Gibbsite, Amorphite, Otherite
Upper depth of bioturbation
mmol dm-3
mm (sum of the
week)
˚C (calculated
over the week)
˚C (calculated
over the week)
mm
mm
˚C
˚C
kg.1000ha-1y-1
kg.1000ha-1y-1
%
%
%
mm
mass fraction
mm
Depth maximal of bioturbation
mm
Lower depth of bioturbation
mm
Magnitude of (maximal) bioturbation at depth of
maximal bioturbation
Magnitude of bioturbation at lower depth of
bioturbation (usually 0)
Ca, Mg, Na, K, Cl, SO4, HCO3, CO3 (year before present)
mol m-2
Simulation year, plowing number of compartment
involved, mass fraction mixed with plowing
Source: Adapted from Finke (2014)
Table 3. Factors of soil formation and their link to the soil forming processes simulated in the SoilGen model
Factor of soil formation
SoilGen governing processes
Climate
Heat flowa
Temperature
14
Organisms
Relief
Parent material
Time
Water flowa
Solute flowa
Evapotranspirationa
C-cyclingb, CO2 production and diffusion,
cation uptake and release, root distribution.
Fauna
Bioturbation
Human Influence
Fertilizationa, Plowing/Tillage
Slope
Runoffa
Erosion/sedimentation
Removal or addition of top layers
Local variants of temperature, Heat/water/solute flow with precipitation
precipitation, and evaporation and evaporation as f (exposition)
Texture
Dissolution/precipitationa, Bioturbation, Ccycling, Physical weathering, Clay migration,
CEC as an f(clay, OC)
Mineralogy
Cation release from chemical weatheringc
Solute and exchange
Chemical equilibriaa
Chemistry of Ca, Al, Mg, and Cation exchange equilibriaa, Arrhenius
Na
temperature
correction,
Al-Gibbsite
equilibrium, Exchangeable acidity, Base
Saturation
Change
of
boundary
conditions
Precipitation: water
Precipitation: solute
Evapotranspiration
Vegetation
Source: Opolot (2014)
a Based on LEACH code (Hutson, 2003)
b Based on RothC 26.3 (Coleman and Jenkinson, 2005)
c Opolot and Finke (2015)
2.4.1 Chemical weathering
Chemical weathering in the SoilGen2.25 is based on the transition state theory (Opolot and
Finke, 2015). The previous SoilGen model (2.24) only considered four common minerals such as
Anorthite, Chlorite, Microcline, and Albite (Opolot et al., 2014). The new version of SoilGen (2.25)
can simulate the weathering of primary minerals such as anorthite, albite, potassium feldspar,
biotite, muscovite, quartz, hornblende, fayalite, forsterite, augite, and secondary minerals such as
(Ca) montmorillonite, chlorite, illite, gibbsite, gypsum, and calcite. Regarding the volcanic soil in this
study, two additional minerals are simulated namely “amorphite” and “otherite” which correspond
to x-ray amorphous and diopside. These minerals are user defined, which means that weathering
parameters and stoichiometric composition of the mineral are input.
2.4.1.1 Weathering of non-crystalline minerals
In this study, SoilGen model is able to simulate the weathering of parent material from volcanic
ash. The composition of amorphous mineral of the Tangkuban Perahu volcanic deposit is derived
from lava flows in the late Pleistocene period (Sunardi and Kimura, 1998). However, the formation of
secondary mineral such as allophane or imogolite as an indicator to estimate the amount of
weathering cannot be simulated in this model yet and it still needs to be implemented in the future.
15
Determination the value of weathering rate of this mineral is a part of this study and will be
explained in the chapter of result and discussion.
2.4.1.2 Weathering of silicate minerals
The release of cations is a quantitative indicator of silicate weathering. It can be computed as:
r i,k = ∑𝑁
𝐾=1 𝐴𝑘 𝑉𝑖,𝑘 𝑟𝑘 𝑚𝑘 t
(4)
-2
-1
2
-1
where ri,k is the release of cation from all the k minerals (mol m s ), Ak (m mol ) is the specific
surface area of kth mineral, vi,k (-) is the stoichiometric number of the ith elements in mineral k, and
rk (mol m-2 s-1) is the dissolution rate constant of the kth mineral. mk is the amount of the kth mineral
in the parent material expressed in (mol m-3 soil) and t (m) is the thickness of soil compartment (0.05
m in SoilGen).
The percentage fractions of sand, silt, and clay are used to derive the total surface area of soil
minerals, Aj (m2g-1) (Sverdrup and warfvinge, 1993), meanwhile the individual reactive area Ak is
derived as a product of weight composition of kth (kcomp) mineral and Aj. Ak is multiplied by the
relative formula mass of the mineral (kRFM) in (gmol-1)
Aj = 8 xclay + 2.2 xsilt + 0.3 xfine sand + 0 xcoarse sand
(5)
Coefficients of clay, silt, and fine sand in the formula represent the specific surface areas (m2g-1).
Ak = Aj x kcomp x kRFM
(6)
In the above formula, particle size fractions of clay, silt, and fine and coarse sand are assumed to
add up to 1 i.e 100% (Sverdrup and Warvinge, 1995). Based on the review by Opolot and Finke
(2015) from previous study, this formula is still reasonable and widely used to assess mineral surface
area in the natural environment.
Dissolution rates of most silicate minerals are calculated based on the laboratory kinetic laws
obtained from the concept of transition state theory (Eyring, 1935; Brantley et al., 2008).
𝑚
rk = KH𝑎ℎ𝑛 + kH2O + kOH𝑎𝑂𝐻
(7)
where rk is dissolution mineral rate, at far from equilibrium conditions is calculated as a function
of pH. KH and kOH are mineral dissolution rate at acidic and alkali sites, respectively and they need to
be corrected for temperature. 𝑎𝐻+ and 𝑎𝑂𝐻− are activities of H+ and OH- respectively and
superscripts n and m represent the reaction order. KH2O is parameter representing the dissolution
rate at neutral pH and is not applied because the dissolution rate of silicates is very slow at neutral
conditions, hence the contribution is insignificant (Brantley, 2003)
𝐾𝐻
𝐾𝐸𝑎𝐻
1
1
= exp [ 𝑅 (298.15 − 𝑇)]
𝐾𝐻25
𝐾𝑂𝐻
𝐾𝐸𝑎𝑂𝐻
1
1
= exp [ 𝑅 (298.15 − 𝑇)]
𝐾𝑂𝐻25
(8)
(9)
Where kH25 and kOH25 are measured dissolution rate constants at 25˚(298.15 K), kEaOH, kEaH (KJ mol1
K-1) are the activation energies of a kth mineral at acid and basic condition, respectively, and R is
the gas constant (0.00831446 KJ mol-1K-1). T is absolute soil temperature and it is simulated in the
model.
16
2.4.2 Cation exchange capacity
SoilGen model is able to simulate dynamics of cation exchange capacity related with clay
migration and variation in organic matter content using a 2-domain CEC model. At the initial
condition, total CEC is split in a part linked to the mineral fraction and a part linked to soil organic
carbon (the amount of carbon in parent material at the start of the simulation). By using the
regression equation of Foth and Ellis (1996), total CEC (mmol+ kg-1) can be calculated from the
contribution of organic carbon (%) and clay (%).
CEC = f*(32 + 36.7*OC + 1.96*Clay)
(10)
Where f is a factor matching the empirical CEC after Foth and Ellis (1996) to the initial CEC in the
simulated pedon and the constant in the formula accounts for cation exchange sites at particles
larger than 2 μm.
The equation above is an approach which is used without considering the effect of pH change on
CEC in soils (Finke, 2012). Meanwhile, one of the volcanic soil characteristics is variable charge.
Formation of Al-humus complex can reduce the amount of negative charge although soils contain a
lot of humus. In order to cope with this problem, effective CEC (ECEC) is the best approach to
estimate CEC, hence the values of ECEC are used as input in the SoilGen model in this study. Thus, it
is assumed that soil pH is not changing dramatically.
2.4.3 Vegetation, carbon cycling, and plant uptake processes
Annual litter input, carbon cycling, and ion uptake are parameters to link the relation of
vegetation and soils in SoilGen model. These parameters are influenced by the vegetation type
namely grass/scrub, agriculture, conifers, and deciduous wood. Each of this vegetation types has
different characteristics such as carbon decomposition rates, cation uptake, annual leaf and litter
input, and unique rooting density function (Finke and Hutson, 2008).
SoilGen simulates the carbon cycling based on the concepts of the RothC 26.3 model (Jenkinson
and Coleman, 1994). In this model, dead plant materials, consisted of leaf litter (ectorganic) and root
litter (endorganic), are split into resistant plant material (RPM) and decomposable plant material
(DPM). Fractions of RPM and DPM will degrade into humus, microbial biomass, and CO2.
Decomposition rates are determined by the amount present in each fraction and environmental
factors, such as soil temperature, soil moisture deficit, soil cover fraction, and time increment
determine degradation rate of RPM and DPM. Furthermore, the model can produce CO2 at daily
time interval. This gas goes to the gas regime equation and give pCO2 value at the end of the day for
the chemical equilibria in this day.
The cations measured in the plants are assumed as a reflection of cation (Al, Ca, Mg, Na, and K)
uptake by vegetation via transpiration stream. In the model, each vegetation type is characterized
based on relative cation concentration uptake (Finke and Hutson, 2008). These cations are stored in
the carbon pools and eventually pass through the mineralized (CO2) pool where after they enter soil
solution.
17
2.4.4 Soil phase redistribution processes
Bioturbation and tillage are two processes considered by SoilGen as factors that lead to
redistribution of soil phases in the soil profile. Bioturbation is an incomplete mixing process by
organisms. This process produces a new vertical distribution of all soil properties in two steps,
vertical and horizontal mixing. Vertical mixing describes the amount of mass fraction subject to
vertical redistribution by soil meso- and macro-fauna in each compartment (each compartment
thickness is set to 50 mm). Further, the resulting mass in each compartment which contains partly of
bioturbated mixing and partly by the original content, is horizontally (1 x 1 m area) mixed within the
same compartment (Finke and Hutson, 2008). In addition, the tillage process is considered as
extreme form of bioturbation. In this process, mass fraction turbation can be set up to 95% over the
plowing depth depending on the magnitude of plowing.
18
III. Materials and Methods
3.1 Field site information
This study was conducted in the upland hillslope of Tangkuban Parahu mountain region located
in Lembang, Bandung district, West Java (Fig.1). Three sites were selected representing different
types of land use namely forest plantation (S 06˚47’21.51” & E 107˚37’21.61”), grassland (S
06˚46’54.00” & E 107˚37’31.02”), and cultivated land (S 06˚47’56.58” & E 107˚38’36.15”). Forest
plantation and grassland are situated in Jayagiri village with altitude 1,563 m asl and 1,597 m asl,
respectively. On the other hand, cultivated land is located in Cikole village with altitude 1,320 m asl.
The annual precipitation of these sites are between 2000 – 2500 mm/year and monthly average
temperature is between 19-20˚C. Forest plantation and grassland have slope 20˚ and 25˚,
respectively, while cultivated land has slope 12˚. According to the soil map scale of 1:50 000, the soil
type in these sites is Andosols. These sites have the same parent materials (symbolized by Qyd)
namely brownish sandy tuff from Mt. Dano and Mt. Tangkuban Parahu (eruption “C” of Van
Bemmelen, 1934), which are very porous and contain very coarse hornblende crystals and also red
weathered lahar, lapilli layers, and breccia (Fig.1) (Silitonga, 1973). Forest plantation is dominated by
pine plantation (Pinus merkusii), other vegetations in this site are coffee and grass. Vegetation in the
grassland are Imperata cylindrica, Eucalyptus, and shrub. Meanwhile, the main crops of cultivated
land are broccoli (Brassica oleracea var. italica), cauliflower (Brassica oleracea var. botrytis), cabbage
(Brassica oleracea var. capitate), and tomatoes (Solanum lycopersicum).
Figure 1. Location of the study site and its lithology (contour interval of the elevation map is 25 meters; Qyl
means young lava flows and in general has basaltic composition and scoriaceous; Qyt means tuffaceous sand,
lapilli bombs, scoriaceous lava, angular fragments andesite-basalt) (Silitonga, 1973)
19
3.2 Survey and field sampling
Survey and field sampling were conducted in August 2015. The research uses a survey method
with physiographic approach. Pre-survey was done to determine the profile locations. Pre-survey
consists of several activities such as (i) collecting maps (land use map, geology map, soil map,
precipitation, topographic map, contour map, and administration map in scale 1:50,000), (ii) making
delineated areas of unique combinations of land use, geology, soil unit, and precipitation to
determine profile locations and field sampling.
Three profiles were selected and excavated which correspond to the forest plantation,
grassland, and cultivated land. These profiles have size 2 m x 2 m with 2 m depth. Observations
about soil morphology and pH measurement were conducted in the field, then undisturbed and
disturbed soil samples were taken from each horizon to be analyzed in the laboratory. Twin soil
meter was used to measure pH in the field. The pH measurement was done by placing the meter in
the horizons of soil profile until the brass ring was fully covered by soils, waiting about 20 seconds
and reading the pH or pressing the button to see humidity, then wiping the meter with cloth to take
another measurement. Observations and collection of data at the sites such as climate, geology, land
use history, and management practices by institution and local people were also conducted to
support observations and analytical data.
3.3 Laboratory analysis
Analysis of organic carbon, exchangeable bases, cation exchange capacity (CEC), exchangeable
acidity, and texture were done in Indonesian Vegetable Research Institute, Indonesia while bulk
density was analyzed in Laboratory of Soil Physics, Faculty of Agriculture, Padjadjaran University,
Indonesia. Meanwhile, quantitative analysis of mineralogy was done in the Laboratory of Soil Science
in Ghent University, Belgium.
Analytical procedures for the determination of organic carbon, CEC, exchangeable bases,
exchangeable acidity, and texture were done according to Balittanah (2005). Organic carbon was
analyzed using the Walkley and Black method and measured colorimetrically using
spectrophotometer (Balittanah, 2005). Colorimetry was used to measure the color change resulted
from the presence of Cr3+ in solution (Schumacher, 2002). CEC measurement was done by
ammonium acetate method at pH 7 and quantitative analysis by distillation. Since the samples were
derived from volcanic soils which have variable charge, effective cation exchange capacity (ECEC)
was calculated. ECEC is summation of exchangeable base (K, Mg, Ca, Na) and acidity (Al, H).
Exchangeable acidity (Al, H) was examined by potassium chloride extraction. Exchangeable bases
(Ca, Mg, Na, K) were extracted from the soils using ammonium acetate buffer. Ca and Mg were
determined by atomic absorption spectrophotometry while exchangeable K and Na by flame atomic
emission spectrophotometry. Furthermore, base saturation was calculated from the sum of
exchangeable base divided by ECEC and multiplied by 100% (Van Reeuwijk, 2002). In addition,
analysis of texture was done using pipette method and analysis of bulk density was measured by
calculating dry weight of bulk sample divided by volume of soil core (USDA, 2014).
20
Quantification of mineral composition was done using X-ray diffraction and interpreted
quantitatively using the BGMN Rietveld model and the Profex user interface (Bergmann et al., 1998;
Döbelin and Kleeberg, 2015). Samples preparation was done by several steps. First, organic carbon
had to be removed using sodium hypochlorite method (Anderson, 1963). Second, coarser samples
were pulverised (using pestle and mortar or 2 minutes 300 RPM in a Retsch agate ball mill) until all
grains passed a 500 µm mesh sieve. A 5 g sub-sample spiked with 5% zincite internal standard was
then micronized to a grain size below 10 µm using wet grinding in a McCrone Micronizing mill. The
obtained slurries were spray dried to obtain spherical aggregates. Third, these powders were then
analysed using X-ray diffraction in Bruker D8 ECO Advance, equipped with a Cu-anode (40 kV, 25mA)
and an energy-dispersive position sensitive LynxEye XE detector. The incoming beam was
automatically collimated to a fixed beam length of 17 mm. The obtained patterns were then
interpreted qualitatively and quantitatively using the BGMN Rietveld model and the Profex user
interface.
The amount of amorphous material was estimated in several steps. Principally, the values were
derived from the mismatch between the obtained amount internal standard and the real amount
internal standard (5% of zincite) which later allow us to down-scale all obtained amounts, so the
internal standard became 5% again and the missing amount was the amorphous content.
Furthermore, the internal standard was taken out the calculation (0%) and then all the minerals
were re-scaled to 100%.
3.4 Pedogenetic simulation using SoilGen
Data obtained from the field and laboratory were converted to SoilGen input data. These consist
of plot (Appendix1), soil (Table 4 and 5; Appendix 2), precipitation (Appendix 3), water composition
(Appendix 4), climate (Appendix 5), weathering rate of minerals (Appendix 6), timing for C-inputs per
vegetation (Appendix 7), bioturbation and plowing (Appendix 8), fertilization (Appendix 9) data. The
SoilGen 2.25 model comprises four input files, namely soil data input files, chemical equilibrium
constant, parameters to describe C-dynamics, and chemical weathering parameters. In addition, the
boundary conditions over time are also specified in four input files such as bioturbation time series,
climate and vegetation evolution, time series of fertilization during possible agricultural periods, and
pedogenically relevant events. To do the simulation, data obtained were converted to the format of
the input files of SoilGen. Detailed input protocols can be found in Finke (2014). Preparation of
mineralogical input data was conducted, so the minerals observed in the laboratory analysis can be
used in SoilGen. In this preparation, alunite and rutile minerals were neglected because the
occurrence was in small amount and high stability. Rutile minerals are stable during weathering and
not dissolving (Figlia et al., 2007). Conversion from bytownite and andesine to albite and anorthite,
respectively, also was done with smallest possible difference of the number of moles per element
between these values (method described in Appendix 10). The conversion is based on the
consideration that bytownite, andesine, albite, and anorthite are plagioclase feldspar. After this
modification, the amount of the minerals were re-scaled to 100% (Appendix 11).
21
Simulation was done in three steps. First, a set of simulations was done for the period 300 BC –
2015 AD. This period of simulation was done in forest plantation. This land use was chosen because
the input of the boundary condition is assumed to be more closely with the actual condition, thus
the calibration of important parameters (weathering rate and mineral factor) can be a basis data for
other land use. Also, volcanic ash deposit during this period can be considered negligible. The aim of
the first simulation was (1) to evaluate the weathering rate of amorphous minerals, from the range
of the most likely minerals in this non-XRD determinable group, (2) to calibrate the organic matter
sub model so that present day values can be reconstructed. According to Doetterl et al. (2015),
organic carbon breakdown as a function of “protection” by soil mineral has relation with total
reserve of bases (TRB). Total reserve of bases is the sum of exchangeable bases plus mineral Ca, Mg,
K, and Na expressed in cmol+kg-1. They reported that soil compartments with a high TRB have faster
decomposition. This mechanism, furthermore, was implemented in SoilGen for calibration of organic
matter in the humus and the biomass pool. A ”mineral factor” is added to determine how sensitive
this factor is to a change in TRB.
LossBIO = - BIOpool * (1 - 𝑒𝑥𝑝(−𝑘_𝑏𝑖𝑜 ∗ 𝑥2 ∗ 𝑥3) )
(11)
LossHUM = - HUMpool * (1 - 𝑒𝑥𝑝(−𝑘_ℎ𝑢𝑚 ∗ 𝑥2 ∗ 𝑥3) )
(12)
Where k_bio and k_hum are the default of degradation rates, x2 is a degradation rate modifier
expressing mainly the effect of temperature and soil moisture (degradation goes fast with high
temperature and more moisture), and x3 is extra modifier expressing the effect of the base status.
X3 = TRB*mineral factor.
Some modifications were also done in the input of SoilGen model in order to represent
characteristics of Andosols and actual conditions in the field. The modifications include reducing
splash release of clay and decreasing the value of interception evaporation of coniferous forest.
Reducing the splash release of clay is due to characteristic of non-crystalline minerals which are
difficult to disperse (Ugolini and Dahlgren, 2002). Meanwhile, decreasing the value of interception is
because, in the equatorial region with low wind speed, interception loss from forest and grassland
has only small difference (Lockwood and Sellers, 1982). Furthermore, the bias statistic as described
in the chapter 2.4 was used to select the best result.
Second, simulations for the future were conducted in all land use to see how soil properties
would change over time in the future for defined land use scenarios
3.5 Land use scenario
Evaluation of the effect of land use on soil properties for future time was simulated over 250
years from present time. This period was selected to see if there are any differences in the soil
characteristic to be expected over short time period. Three sets of different land use (forest
plantation, grassland, and cultivated land) were prepared. The input which include soil data,
minerals, climate, and vegetation were based on the measurement data and situation at the present
time. Boundary conditions such as input carbon from vegetation, fertilization, bioturbation, and
plowing were assumed to be the same as at present time, and the author assumed there will be no
event such as eruption or land slide which can have huge effect to the land use change. The amount
22
of fertilization was based on the interviews with the farmers. After the simulation, a comparison was
made of soil development processes and some physical, chemical, and mineralogical properties
under three different land uses.
23
IV. Result and Discussion
4.1 Analysis of effects of land use change on soils using analytical data
Forest plantation, grassland, and cultivated land have different parent materials based on the
morphological observation (Appendix 2), chemical (Table 5), and mineralogical (Table 6) analysis in
soil profiles. The amount of amorphous minerals in the depth of 156-200 cm and 125-200 cm in
cultivated land and grassland, respectively, were much lower than the above horizons (Table 5). This
condition leads to the assumption that the soils in these depth have older age than the above
horizons. According to the stratigraphy of Mt. Tangkuban Parahu complex, Mt. Tangkuban Perahu
comprises old Tangkuban Perahu tephra and young Tangkuban tephra which overlies the large scale
ignimbrite erupted during the formation of Sunda caldera (Table 1). Formation of Sunda caldera was
taken place in the early Pleistocene period, before the formation Tangkuban Perahu which occurred
in Holocene and late Pleistocene period. Mt. Putri in which its location is not too far from the study
site can be the reason of the presence of older soils in the deeper depth. Mt. Putri is a part of Sunda
Volcanic and has Sunda Andesite stratigraphy (Nasution et al., 2004). It is coded Qvu in the
lithological map (Fig.1), indicating undifferentiated old volcanic products which consist of volcanic
breccia, lahar, and lava repeatedly interlayered (Silitonga, 1973). Stratigraphy of Sunda Andesite has
age around 0.56-0.205 Ma which is older than Tangkuban Perahu tephras (Table 1), so it may
possible that the weathering and transformation of non-crystalline to crystalline mineral had already
taken place for a long time resulting in low content in amorphous minerals, high kaolinite content,
and the presence of gibbsite.
4.1.1 Physico-chemical properties
Table 4. Morphological and Physical Characteristic of soil profiles under three different land uses
Horizon
Depth
Color
Texture
Sand
cm
Ap1
Ap2
Ap3
AB
BC
2AB1
2AB2
2Bw1
2Bw2
0-16
16-29
29-38
38-60
60-78
78-101
101-125
125-170
170-200
10 YR 4/4
10YR 6/6
10 YR 6/8
10 YR 5/8
10 YR 4/6
10 YR 3/1
10 YR 1.7/1
10YR 3/3
10 YR 4/6
Silt
%
Forest Plantation
24.99
58.64
19.21
64.36
24.02
53.82
20.02
64.81
32.25
54.74
21.44
64.31
15.18
67.85
15.91
65.51
16.36
67.35
Grassland
Clay
Bulk
Density
Class
kg dm-3
10.57
11.53
18.26
10.48
8.80
7.46
6.25
12.17
12.51
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
0.700
1.130
1.150
0.910
0.870
0.880
0.800
0.840
1.050
24
Ap1
Ap2
BC
2AB1
2AB2
2AB3
3Bw1
3Bw2
0-21
21-30
30-60
60-71.5
71.5-107
107-125
125-166
166-200
7.5 YR 4/3
10 YR 4/6
10 YR 5/6
10 YR 2/3
10 YR 1.7/1
10 YR 2/3
10 YR 3/3
10 YR 4/6
Ap
AB1
AB2
2AB1
2AB2
0-23
23-45
45-60
60-91
91-127
10 YR 4/4
10YR 3/4
10 YR 4/6
10 YR 3/4
10 YR 2/2
2Bw1
2Bw2
127-156
156-170
3Bw1
170-200
10 YR ¾
10 YR 3/4 and
10 YR 2/2
7.5 YR 3/1
27.50
38.49
26.29
56.34
23.44
57.20
26.48
61.48
21.89
59.28
13.76
67.89
8.48
75.53
17.46
68.89
Cultivated Land
30.19
33.11
21.56
33.33
26.50
39.25
17.97
52.95
8.64
60.48
25.66
11.27
13.13
6.62
10.03
10.09
12.25
10.67
Loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
0.690
0.940
0.840
0.930
0.800
0.750
0.690
0.910
34.09
43.13
32.38
24.58
24.96
0.880
1.360
1.240
1.090
1.130
6.62
28.87
67.11
54.86
20.80
12.51
Clay loam
Clay
Clay loam
Silt loam
Silty clay
loam
Silt loam
Silt loam
9.46
54.87
30.27
Silty clay
loam
0.980
1.000
1.000
Table 5. Chemical properties of soil profiles in three different land uses
Hor
Depth
pH
Org. C
Basic cation
Ca
Unit
Ap1
Ap2
Ap3
AB
BC
2AB1
2AB2
2Bw1
2Bw2
cm
%
0-16
16-29
29-38
38-60
60-78
78-101
101-125
125-170
170-200
5.3
5.2
5.2
5.4
5.6
5.5
5.8
6.0
5.7
5.80
4.90
3.90
4.69
4.21
6.79
10.72
6.41
3.78
0.33
0.12
0.16
0.34
0.25
0.59
0.21
7.47
0.45
Ap1
0-21
Ap2
21-30
BC
30-60
2AB1 60-71.5
2AB2 71.5-107
2AB3 107-125
3Bw1 125-166
5.4
5.5
5.4
5.5
5.1
5.5
5.5
8.35
6.10
6.24
5.42
8.80
8.26
5.73
0.29
0.23
0.16
0.10
0.13
0.19
0.17
Exch.
acidity
Mg
K
Na
Al
H
-1
cmol+ kg
Forest Plantation
0.11 0.12 0.08 0.39 0.05
0.03 0.06 0.06 0.40 0.05
0.04 0.07 0.06 0.44 0.05
0.05 0.09 0.11 0.48 0.06
0.03 0.07 0.06 0.38 0.05
0.09 0.11 0.09 0.97 0.08
0.05 0.07 0.07 1.21 0.35
0.10 0.10 0.11 0.38 0.09
0.06 0.09 0.07 0.00 0.12
Grassland
0.11 0.16 0.08 1.48 0.55
0.05 0.08 0.06 0.73 0.18
0.04 0.09 0.07 0.00 0.29
0.03 0.12 0.12 0.27 0.16
0.03 0.09 0.06 0.48 0.25
0.06 0.22 0.13 0.53 0.27
0.04 0.15 0.20 0.36 0.08
CEC
ECEC
BS*
%
18.09
13.75
11.69
15.60
19.54
30.86
42.78
27.81
31.56
1.08
0.74
0.83
1.13
0.85
1.95
1.96
8.26
0.79
4
2
3
4
2
3
1
28
2
22.58
20.81
23.18
30.12
36.16
37.96
32.12
2.67
1.33
0.65
0.80
0.14
1.40
1.00
3
2
2
1
1
2
2
25
3Bw2
166-200
5.6
2.98
0.10
Ap
AB1
AB2
2AB1
2AB2
2Bw1
2Bw2
3Bw1
0-23
23-45
45-60
60-91
91-127
127-156
156-170
170-200
5.8
6.5
6.5
6
5.8
5.6
5.4
5.8
2.61
1.98
1.86
4.50
5.91
5.47
3.75
5.39
3.07
11.1
12.55
21.75
25.59
22.54
13.92
16.76
0.02 0.09 0.07
Cultivated land
0.59 1.58 0.19
2.01 2.06 0.49
1.94 1.25 1.03
2.05 0.28 1.06
2.54 0.20 0.49
2.13 0.20 0.66
2.03 0.21 0.46
2.74 0.42 0.64
0.00
0.12
26.13
0.40
1
2.18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.08
0.10
0.10
0.09
0.10
0.10
0.10
0.12
22.64
26.58
26.75
38.37
45.52
42.66
35.08
43.60
7.69
15.76
16.87
25.23
28.92
25.63
16.76
20.68
24
59
63
66
63
60
47
47
*Base saturation was calculated using CEC
Table 4 shows morphological and physical properties of three different land uses. The colors of
soil are yellowish brown to brownish black with light color (brown) in the surface horizon, reflected
in the value of chroma (moist) more than 2 in the Munsell Soil Color. Devnita (2012) reported that
the soil profile in pine forest, Lembang has fulvic Andosols. Fulvic Andosols has characteristic low
humic acid to fulvic acid ratios with a low degree of humification (Honna et al., 1988). A horizons
have darker color compared to B horizons both in young and buried horizons as reflected by high
amount of organic carbon in A horizon (Table 4). Nearly all soil horizons are dominated by silt loam
texture class in forest plantation and grassland. On the other hand, soils in cultivated land are more
varying with clay loam and clay texture class in young soil while buried horizons have silt loam and
silty clay loam. In Andosols, type and particle size of tephra, degree of weathering, and so forth
determine the soil texture (Shoji et al., 1993). Physical disturbance in cultivated land, such as tillage
and precipitation, and warmer climate can enhance the weathering. Soil particles become smaller as
the weathering continues. Therefore, the increase of clay fraction in upper horizons can be an
indication that soils in upper horizons (depth of 0-60 cm) experienced more intense weathering than
soils in sub horizons. Typical bulk density of Andosols to have Andic properties is lower than 0.9 kg
dm-3 (Soil Survey Staff, 2010). However, some of the horizons in the study site are slightly higher
than this value. This was detected in Ap2, Ap3, AB and 2Bw2 horizons in forest plantation, Ap2, 2AB1,
3Bw2 in grassland, and most of horizons except surface horizon in cultivated land. Bulk density in
cultivated land is higher than forest plantation and grassland which ranges from 0.880-1.360 kg dm-3.
This indicates that agricultural practices, such as frequent tillage, increase the bulk density. Another
factor that can increase bulk density in Andosols is mixing of parent materials of tephra and nontephra deposits (Takahashi and Shoji, 2002). High bulk density (>0.9 kg dm-3) in cultivated land due
to soil compaction as a result of tillage process and in natural forest was also reported by Getachew
et al (2012).
Analysis of chemical properties are provided in (Table 5) and (Appendix 12). Measurement of pH
in the field (Table 5) shows that pH in forest plantation and grassland ranges from 5 to 6 while pH in
cultivated land ranges from 5.4 to 6.5. High pH value was found in the upper horizons (0-60 cm) of
cultivated land which ranged from 5.7 to 6.5. The low basic cation contents in the forest plantation
and grassland can be the reason of low pH. Reuss and Johnson (1986) stated that removal of base
26
cations, precipitation, and any processes that develop negative charge can reduce the pH. Another
factor that can decrease pH is the acidifying effect of pine, which has been reported by Sanchez
(1985) and Ariksson and Erikkson (1998). On the other hand, high pH in upper horizons (0-60 cm) of
cultivated land is due to application of organic and inorganic fertilizers. These fertilizers contain basic
cations that may able to compensate removal cations by leaching. In forest plantation and grassland,
variation of pH among land use is mainly due to nutrient dynamics by vegetation. Soil organic matter
and its relation with nutrient content, decomposition, cations uptake and allocation to biomass pool
will determine the availability of nutrients (exchangeable basic cations) and can also determine the
variability of pH (Bertin et al., 2015).
Organic carbon content in the study site is relatively high (Table 5), especially in the surface
layers of forest plantation and grassland where it is higher than 5%. In contrast, upper horizons (0-60
cm) of cultivated land showed less organic carbon content with value of 1.86%-2.61%. Organic
carbon content ranges between 3.78%-10.72%, 2.98%-8.80%, and 1.86%-5.39% in forest plantation,
grassland, and cultivated land, respectively (Table 5). Higher values were found in surface layers of
young soils and buried soils, and the amount decreases with increasing depths. Furthermore, organic
carbon content in buried soils was higher compared to young overlying soils in all land use.
Characteristic of Andosols which consist of short range order mineral or Al-humus complex are
thought to enhance carbon accumulation. In addition, location of the study site in the humid climate
is favorable for the formation and preservation of short range order mineral and Al-humus complex
(Peña-Ramírez et al., 2009).
Buried soils containing more organic carbon than surface soils may be related to the
accumulation of organic matter from previous and current land use, and stabilization of organic
carbon with minerals. The former land use in the study site was deciduous forest and was shifted to
the forest plantation dominated by pine trees. These land use can contribute to the high input of
organic matter. The roots that are still present up to depth of 2 m due to the vegetation type in
current land use, forest plantation and grassland (Appendix 2), can also increase the supply of
organic carbon in the deeper depth. Root turnover increase with depth can cause higher carbon
inputs per unit of standing root biomass in deep soil layers (Jobbagy and Jackson, 2000).
Furthermore, minerals in buried soils have been weathered longer time than young soils. Allophane,
imogolite, and ferrihydrite were found higher in the subsoils than in surface soils of Andosols
(Ugolini and Dahlgren, 2002; Nanzyo, 2002) and the amount is linear with time (Egli et al., 2008). The
importance of non-crystalline minerals to the stabilization of total soil carbon in sub soils of volcanic
region has been studied by Gamboa and Galicia (2012). Previous studies also found that organic
carbon in the deeper layers was high due to clay and slower cycling of soil organic carbon at depth
(Jobbagy and Jackson, 2000; Trumbore, 2000). Mechanism of controlling organic carbon by mineral
is also related to the TRB (Doetterl et al., 2015). Their study in non-tropical volcanic soils suggested
that (1) if the amount of TRB is high (many primary minerals), there is little stabilization of organic
carbon and the decay is high, (2) if the amount of TRB is intermediate (many secondary minerals),
the organic carbon will strongly stabilize with mineral and the decay is low, (3) if the amount of TRB
is low (almost no primary minerals and secondary minerals are very aged), stabilization of organic
27
carbon with mineral is weak and the decay is high. The soils in the study site have many secondary
minerals (Table 6), so high organic carbon in buried soils can be mainly due to stabilization with
minerals.
Soil organic matter dynamics in the young soils are influenced by vegetation type, litter quality,
influence of soil fauna, and land management. Low organic carbon content in the cultivated land use
is mainly due to land management. Location of cultivated land with slope facing east and low canopy
cover create a warm micro climate. Agriculture activities have huge effects to the soil properties
such as tillage that can breakdown physical protection, removal of organic matter that can reduce
organic carbon supply, and application of N and P fertilizer which can increase rate of decomposition
(Liu et al., 2005). These combination factors can reduce the amount of organic carbon in the upper
horizons (Post and Kwon, 2000). Similar study also found that conversion of forest to croplands leads
to a loss of soil carbon (Murty et al., 2002). On the other hand, the difference amount of organic
carbon in forest plantation and grassland is mainly due to vegetation and soil fauna that are present.
In forest plantation dominated by pine trees, needle is the main litter. Needle consist of lignin as
the main component, other components are cellulose, hemicelluloses, non-structural compound,
and nitrogen (McTiernan, et al., 2003). Meanwhile, in the grassland, Imperata cylindrica leaves have
high lignin and polyphenols (Haartemink and O’Sulllivan, 2001). Ratio of C/N and L+PP/N
(lignin+polyphenol / nitrogen) are parameters to predict decomposition rate. High C/N and L+PP/N
in litter of both land uses lead to slow decomposition. However, the result shows that organic
carbon content in upper horizons of grassland is higher than in forest plantation. The increase of soil
organic carbon when forest is converted to pasture is possible for tropical moist and wet forest life
zone depending on specific circumstances (Post and Kwon, 2000) and might be explained by the high
root litter input in the soil surface in the pasture compared to that in the forest. Van Dam et al.
(1997) also reported that conversion forest to pasture in volcanic soil rich with Andic properties can
increase soil organic carbon. In the study site, higher amount soil organic carbon in the upper
horizons of grassland may be due to higher supply of organic carbon input from the vegetation, such
as Imperata cylindrical, herbaceous plants, and shrubs since these vegetation fully cover the surface
soil.
Nearly all horizons have exchangeable basic cations less than 1 cmol+ kg-1 in forest plantation
and grassland while exchangeable cations in cultivated land ranges from 5.43 – 28.82 cmol+ kg-1
(Table 5). In forest plantation and grassland, the low value can be due to several reasons. First, high
intensity of rainfall in humid climate which causes removal of basic cations and results on low basic
cations (Saigusa et al., 1980). Second, low nutrient content that can contribute to the low basic
cations. Litter of pine forest and grassland exhibit low nutrient concentrations. Low nutrient
availability in grassland ecosystem was reported by Bond (2008) while needle of Pine merkusii, Java
contains 9.2, 1.9, 2.5 mg/g of calcium, magnesium, and potassium, respectively (Bruijnzeel, 1985).
The amount of Ca tends to be higher than other basic cations and may due to high concentration of
Ca in needles litter, plant tissue, and leaves of grassland (Jobbagy and Jackson, 2004) and weathering
of minerals such as plagioclase feldspar (andesine and bytownite), hornblende, and diopside. The
28
result also shows that here is a significant high exchangeable Ca content in the depth of 125-170 cm
in the forest plantation.
In forest plantation and grassland, concentration of cations in soil profile is relatively similar
through the depth. Previous study reported that distribution of exchangeable K, Mg, Ca, and Na can
be concentrated in certain depth due to plant nutrient uptake (Jobbági and Jackson, 2001). Another
study also found that conversion of Acacia plantation to Imperata grasland can increase
exchangeable cations due to translocation of exchangeable basic cation from tree biomass in
plantation to soil in grassland (Yamashita et al., 2007). In this case, the result does not show
significant different in exchangeable cations after conversion or their distribution through the depth
due to plant cycling. The author assumes that leaching is the main factor to the distribution of
exchangeable cations in soil profile. This reason is supported from study by Porder and Chadwick
(2009) which stated that at high rainfall (> 1500 mm/year), the effect of plants on nutrient
distribution can be diminished with soil age due to leaching.
High amounts of exchangeable basic cations in cultivated land are mainly due to huge
applications of fertilizer during the crop cycle. The increase of exchangeable basic cations due to
fertilizers in cultivated land after conversion from natural land use was also reported in previous
studies (Braihmoh and Vlek, 2003; Carvalho et al., 2009). Furthermore, distribution of exchangeable
basic cations in cultivated land is more variable than in other land uses. The result (Table 5) shows
that exchangeable K is high in the top soil while exchangeable Ca, Mg, and Na start to increase at
depth of 23-45 cm. This result is in line with vertical distribution of nutrient which ranks in the order
of P > K > Ca > Mg > Na from shallowest to deepest layers (Jobaggy and Jackson, 2001). Higher
concentration of Ca indicates preference of soils to adsorb Ca, thus Ca is dominant cation in the
exchange site. Previous studies reported that Japanese Andosol has a preference to adsorbed Ca
rather than K (Nakahara and Wada, 1995; Yoshida, 1961).
The exchangeable acidity (Al and H) ranges from 0.20-1.56 cmol+ kg-1, 0.10-2.26 cmol+ kg-1, and
0.12-2.03 cmol+ kg-1 for forest plantation, cultivated land, and grassland, respectively. (Table 5;
Appendix 12) shows that concentration of exchangeable acidity increases in the buried horizons of
forest plantation and grassland. The increase of exchangeable Al in surface soil and buried horizon
was also found in the European volcanic areas studied by Madeira et al (2007). On the other hand,
exchangeable Al in cultivated land is only present, 2.18 cmol+ kg-1, in the surface soil (0-23 cm) and it
is absent in other horizons. Application of ammonium fertilizer in the cultivated Andosols, which can
increase acidity, can be the reason of the increase of exchangeable Al in the exchange surface
(Matsuyama et al., 2005). The absence of Al in most of horizons in cultivated land is related to the
pH. As the pH increases, Al is in the insoluble form. However, the result (Table 5) shows that the
exchangeable Al3+ is absent in the sub soils of cultivated land when the pH is low. Pansu and
Gautheyrou (2007) reported that in some tropical soil pH near 5 can contain more exchangeable Al3+
than at pH 4. This result indicates that pH water is not always correlated with Al3+ extractable KCl.
Cation exchange capacity in the study site ranges from 11.69 to 45.52 cmol+ kg-1 (Table 5), thus it
is consistent with relative amount of CEC in Andosols, 17-38 cmol+ kg-1 (Schulte and Ruhiyat, 1998).
In all land uses, buried soils have relatively higher CEC than young soils, which is mainly due to the
29
high amount of organic carbon and short range order minerals. Similar result were also found by
Madeira et al. (2007). However, measurement of CEC using ammonium acetate method
overestimates the CEC in Andosols as the measurement pH is higher that pH in the field, so ECEC is
used for better estimation of CEC at field pH. (Table 5) shows that there is a large difference
between measured CEC and ECEC. The amount of ECEC in forest plantation and grassland is very
low, 0.40 - 2.67 cmol+ kg-1, while ECEC in the cultivated land ranges 7.69-28.92 cmol+ kg-1. However,
in Andosols, total C and pH (CaCl2) in high organic C (>8%) explain 44% of the variation in ECEC and
only explain around 22% in soil layers with organic carbon < 8% (Seybold and Grossman, 2006). The
amount of exchangeable bases and acidity in forest plantation and grassland influences the ECEC.
Previous study found that some profiles in volcanic soils also had very low amounts of basic cations
and exchangeable Al, thus they assume a large number of exchangeable H-ions present (Madeira et
al., 2007). However, in this study, the amount of exchangeable H is also low. Abreu Jr et al. (2003)
reported that hydrogen retained by covalent bound is a non-exchangeable cation, when associated
to the variable negative charge of organic matter, allophane, kaolinite, and iron and aluminum
oxides. Therefore, the present of hydrogen in non-buffered KCl extract cannot represent the soil
exchangeable acidity, but low stability of hydroxy-Al forms, except if soils contain high amounts of
organic matter.
4.1.2 Mineralogical Properties
Table 6. Mineralogical composition of soil profiles in different land uses
Hor.
Amor
phous
Quartz
group
Horn
blen
de
Diop
side
Ap1
Ap2
Ap3
AB
BC
2AB1
2AB2
2Bw1
2Bw2
55.20
54.50
52.24
61.30
52.61
64.64
77.38
64.76
53.36
21.85
22.18
25.41
13.99
13.47
14.04
11.08
13.47
10.11
1.48
1.66
0.85
3.46
8.88
7.20
1.20
2.14
0.00
4.90
5.34
5.22
7.44
4.25
2.34
3.40
5.55
2.64
Ap1
Ap2
BC
2AB1
2AB2
2AB3
3Bw1
3Bw2
50.69
54.50
51.64
42.66
55.32
62.03
2.91
24.36
25.98
21.85
20.18
18.88
19.39
19.31
19.53
18.52
1.31
2.59
2.74
10.85
8.48
2.27
0.00
0.00
5.67
6.19
6.93
4.72
3.27
4.32
6.15
3.12
Mus
covite
Kaoli
nite
Forest plantation
1.84
2.63
1.98
2.89
2.57
3.54
2.09
1.34
1.67
1.34
2.09
1.51
1.37
1.46
0.00
2.15
0.00
22.29
Grassland
2.54
3.22
2.47
2.18
2.37
2.14
1.57
2.00
2.51
1.88
1.66
1.64
0.00
49.43
0.00
29.52
Cultivated land
Aluni
te
Ruti
le
Ande Bytow
sine
nite
Gibb
site
4.05
4.35
4.41
2.74
2.05
0.98
1.17
0.00
0.91
2.58
1.64
1.60
0.52
1.03
1.96
1.11
2.54
1.30
5.48
4.90
4.17
6.38
13.29
3.65
1.01
1.20
1.68
0.00
0.56
0.00
0.74
1.41
1.58
0.81
8.18
2.54
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.17
3.67
3.32
4.49
2.54
1.33
2.15
2.48
3.84
1.36
2.44
2.78
3.30
2.29
3.13
3.94
1.98
5.56
4.46
5.02
10.60
3.29
1.69
5.74
3.13
0.00
0.00
1.71
2.87
2.25
1.79
4.46
2.78
0.00
0.00
0.00
0.00
0.00
0.00
5.35
12.77
30
Ap
AB1
AB2
2AB1
2AB2
2Bw1
2Bw2
3Bw1
20.00
22.00
23.20
36.39
44.32
46.58
19.61
3.29
11.49
7.72
9.37
7.89
14.27
6.58
7.79
11.51
6.56
6.68
10.44
8.06
2.57
4.59
10.38
2.05
1.97
2.74
1.50
3.25
2.61
2.64
5.21
2.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
45.63
44.74
36.16
30.45
28.98
31.79
49.77
75.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.76
1.37
1.98
2.61
1.03
0.97
6.99
10.20
13.05
7.21
1.27
0.83
2.97
1.81
2.82
4.72
4.70
4.04
1.91
1.27
2.48
0.76
4.54
1.21
0.81
1.34
2.10
3.12
0.76
0.85
The result of semi-quantitative analysis of mineral composition can be seen in (Table 6) and is
provided in the diagram in (Appendix 13). The analysis shows the dominance of amorphous minerals
in forest plantation and grassland which is above 50%. On the other hand, in cultivated land,
amorphous minerals are dominant in the horizons 2AB1, 2AB2, and 2Bw1 with values of 36.39%,
44.32%, and 46.58%, respectively, while other horizons are dominated by kaolinite. This study did
not examine the mineralogical composition of amorphous mineral. However, as most of the horizons
have pH between 5 and 7, formation of allophane and imogolite is likely (Shoji et al, 1993; Ugolini
and Dahlgren, 2002). The presence of short range order mineral is quite stable and climate is the
most important factor determining it. Moist conditions can inhibit the process of clay newformation
and slow down mineralization of organic compound in forest plantation and grassland (Duchaufour,
1982). Therefore, it is possible that short range order minerals are still present in soil profiles in high
amount.
An abrupt change in the amount of amorphous mineral was found in the 3Bw1 horizon (depth of
125-166 cm) in grassland (Table 6; Appendix 13) which has an older age than the above horizons
and, in the same horizon, the amount of kaolinite significantly increases (from 1.64% in the above
horizon to 49.43%) and gibbsite is present. The increase of kaolinite was also detected in 2Bw2
horizon of forest plantation. The amount rises from 2.15% to 22.29%. Cultivated land has a higher
amount of kaolinite compared to forest plantation and grassland. The highest amount is located in
the 3Bw1 horizon (75.90%) where the amorphous mineral is only 3.29%. Parfitt et al. (1983) stated
that kaolinite is the product of weathering and it has sequence of primary minerals (ash) 
allophane  halloysite  kaolinite. Therefore, higher amount of kaolinite can be an indication that
the soils experienced more intense weathering. Higher amounts of kaolinite in the deeper horizons
are most likely due to the factor time, as these horizons are older deposits (Churchman and Lowe,
2012). The presence of dominant kaolinite in the upper horizons of cultivated land can be the
evidence that agricultural activities can alter primary mineral given enough time. This result is in line
with previous work by Velde and Meunier (2008). Duchaufour (1982) reported that increasing the
speed of humus turnover by cultivation can enhance the speed of clay newformation. In addition, a
warmer and dryer climate is one of the important factors in the formation of crystalline minerals
(Ugolini and Dahlgren, 2002). Kaolinite start to appear as the weathering intensified up the profile
(Churchman and Lowe, 2012).
Gibbsite is mostly present in small amounts, less than 5%. The occurrence of gibbsite in sub soil
of buried horizons of forest plantation (2Bw2), grassland (3Bw1, 3Bw2), and also in all horizons of
31
cultivated land is related to the present of kaolinite. Disintegration of kaolinite under strong leaching
can result in the formation of gibbsite (Churchman and Lowe, 2012). The occurrence of gibbsite can
also be limited due to high levels of organic carbon that preferentially absorb aluminum (Shoji et al.,
1993). Gibbsite as weathering product of volcanic materials was also found in northern California
which has andesitic and basaltic input (Takahashi et al., 1993). The presence of gibbsite in the
deeper horizons of forest plantation and grassland indicates that these horizons experience more
weathering, even though the present amount is generally less than 5% (Table 6). On the other hand,
the presence of gibbsite in all horizons of cultivated land may link to the vegetation and land
management. Tillage, low canopy cover, and a fallow period between crops cycling without cover
crop can increase the amount and the rate of water percolating through the soils. This process will
decrease the Si concentration/activity leading to the formation of gibbsite. The presence of gibbsite
and kaolinite in upper horizons was also found in andesitic parent material studied by Nieuwenhuyse
et al. (2000).
Besides dominant amorphous and kaolinite minerals, the minerals in the quartz group which
consist of trydimite, quartz, and cristobalite minerals are present in notable quantity. In forest
plantation and grassland, the sum of these minerals ranges from 10.11 – 25.98%. The amounts are
higher in the upper horizons and decrease in the sub soil. On the other hand, in the cultivated land,
the amount of these minerals are less than in other land use (6.58-11.49%). Lower amounts of
quartz group minerals in cultivated land than other land use may be due to the increase of pH
because cristobalite, tridymite, and quartz are stable in acid environment (Mizota and Aomine,
1975). They also reported that cristobalite and tridymite are formed rapidly solidified from magma in
a high temperature while the origin of quartz is still a problem. Study of composition of quartz in
volcanic ash in Japan showed an intimate association of hornblende with abundance fine quartz
indicating that some soil quartz was inherited from primary constituents in parent volcanic ashes
(Mizota et al., 1990). However, some studies on volcanic soils in Indonesia showed there was no
significant relationship between sand mineralogical composition and parent materials (Syarif, 1990).
Other minerals, except amorphous, kaolinite, and quartz groups, are mostly present in low
amounts. Hornblende ranges from 0.00-10.85%, diopside ranges from 1.50-6.93%, muscovite ranges
from 0.00-2.57%, alunite ranges from 0.00-4.49%, rutile ranges from 0.00-3.30%, andesine ranges
from 0.83-13.29%, and bytownite ranges from 0.00-8.18% for all land uses. These minerals and also
quartz group such as cristobalite, tridymite, and quartz, are commonly present in soils derived from
recent volcanic ash in Indonesia (Fiantis, 2004). Study by Cortes and Franzmeier (1972) in Cordillera
of Colombia, South America also found that feldspar, hornblende, and quartz were present in
notable quantity while mica was in low content. The most notable one is the absent of muscovite
and alunite in the all horizons of cultivated land while these minerals are still present in the very low
amount in forest plantation and grassland. This result may be due to soil environment that is more
favorable for the K release.
32
4.1.2 Limitation of the use of analytical data
In the aforementioned results, there are some limitations in the analysis that may contribute to
the accuracy of the data. Measurement of pH will be more accurate by analyzing pH H2O and pH KCl,
instead of quick pH measurement in the field. In Andosols soil with variable negative charge, the
difference value between measurement pH H2O and pH KCl can give an indication about the
development of charge in soils whether positive or negative. Information about weathering of
secondary mineral can be predicted by analyzing the amount of allophane or ferrihydrite. The usual
way to determine allophane and ferrihydrite content is by measuring extractable Fe, Al, and Si with
axid oxalate and pyrophosphate. This measurement can extract Al from oxides and humus complex.
Therefore, with this measurement, the process which involved the formation of Al-humus complex
or allophane in each horizons can be derived. Furthermore, to measure the better ECEC of variable
charge soils, the sum of basic cations using the BaCl2-compulsive exchange procedure (Gillman,
1979) and exchangeable acidity using extraction with 1 m KCl is suggested. BaCl2-compulsive
exchange procedure determines the soil’s capacity to retain nonacid cation at the pH and ionic
strength of the soil (Gilman and Sumpter, 1986).
4.1.3 Conclusion
Semi quantitative analysis of mineral composition shows that the parent material in three land
use was not the same. Older parent material, indicated by low amorphous mineral content, was
detected in the deeper horizons of grassland and cultivated land use while forest plantation has high
amorphous mineral in all horizons. In general, conversion of forest to cultivated has large difference
in physical, chemical, and mineralogical properties than forest to grassland.
Analysis of physical, chemical, and mineralogical properties shows that conversion of forest
plantation to grassland after 28 years without slash and burn event does not make any significance
change in the soil properties. Different type of vegetation gives small difference in the change on the
soil properties, except the increase of organic carbon content in the upper layer of grassland.
Organic carbon content is generally higher in these land uses (>5%). These land uses have silt loam
as dominated texture, low bulk density (0.690-1.150 kg dm-3), low pH (5.1-6.0), low exchangeable
basic cations (0-0.33 cmol+ kg-1) except high amount Ca in the 2Bw1 horizon in the forest plantation,
low exchangeable acidity (0-1.48 cmol+ kg-1), low ECEC (0.40 – 8.26 cmol+ kg-1), and low base
saturation (1-4%) except in the 2Bw1 horizon in the forest plantation. On the other hand, conversion
of forest plantation to cultivated land after 23 years shows large differences. This is mainly due to
agricultural activities. Cultivated land has more variation in texture class, high bulk density (0.8801.360 kg dm-3), medium pH (5.4-6.5), high exchangeable basic cations (0.20-25.59 cmol+ kg-1) with
exchangeable Ca as the dominant exchangeable basic cation, absence of exchangeable Al, except in
the top layer (2.18 cmol kg-1), and high ECEC (7.69-28.92 cmol kg-1), and high base saturation (2466%) compared to other land uses. The results also show that there is a large different value
between CEC and ECEC. Low amount of exchangeable basic cations using ammonium acetate at pH 7
and exchangeable acidity using 1 M KCl extraction leads to very low amount of ECEC. The proposed
33
calculation of ECEC using exchangeable basic cations by BaCl2-compulsive exchange procedure and
exchangeable acidity by 1 M KCl may give better estimation to measure CEC at soil pH.
Amorphous mineral is the dominant mineral in the forest plantation, grassland, and sub soils of
cultivated land (>50%). Kaolinite is present in the high amount in the deeper depth of all land use
and in the upper layers of cultivated land. The high amount of kaolinite mineral in cultivated land is
mainly due to agricultural activities meanwhile the high amount kaolinite mineral in deeper depth of
forest plantation and grassland is due to older parent material. The presence of gibbsite, even
though in low amount, in the deeper depth of forest plantation and grassland and all horizons of
cultivated land can indicate intensive weathering. Another mineral such as quartz group mineral is
present in notable quantity (6.45-11.49%) in all horizons. Other minerals, such as hornblende,
diopside, muscovite, bytownite, andesine, alunite, and rutile are present in low amount (0 – 13.29%)
4.2 Analysis of future effects land use change using soil modelling
4.2.1 Calibration
The calibration was done to estimate to what model parameters the output of selected variables
is more sensitive, and then to tune these parameters so that measurements are most accurately
reproduced. There are two variables which will be calibrated, the weathering rate of amorphous
minerals and organic carbon content. In the simulation, the measurement of present time condition
is used as the input data in SoilGen model. This implies that the comparison between measured and
simulated finite state will be based on few data locations but several horizons.
4.2.1.1 Specification of amorphous mineral by evaluating option
This step was done by determining the weathering rate of amorphous minerals in soils.
Determination the rate of weathering amorphous minerals is important as it is the main mineral in
soil profile of study site and its relation to the release of elements. In Andosols, fragmental tephra
components, such as vesicular glass and pumice, possess a greater surface area and high porosity, so
these compounds are easily to break down (Wolff-Boenisch et al., 2004). Nevertheless secondary
minerals such as short range order minerals can persist for a long time (Duchaufour, 1982). Analysis
of mineral composition in present time shows high amount of amorphous minerals (Table 6)
indicating that this minerals are relatively stable, in other words the weathering rate is slow.
34
Figure 2. Simulated amorphous mineral (kg m-2)
Figure 2 shows the amorphous minerals in soil profile of forest plantation starting from 300 BC –
until present time (2015 AD). This figure displays that amorphous mineral content slightly decrease
indicated by small change in color through the time, so the proposed weathering rate value of
amorphous mineral seems satisfied (Appendix 6). Determination of weathering rate of amorphous
mineral is in between volcanic glass and muscovite/quartz which correspond to the most and least
easily weatherable minerals, respectively, according to transition state theory with parameters given
by Goddéris et al. (2006) (Fig. 3). Rasmussen and Matsuyana (2007) reported that time span for the
transformation of short range order material to thermodynamically stable kaolin minerals takes 10100,000 years. Chemical and mineralogical changes between 15,000 and 115,000 years in the A and
B horizons were small (Egli et al., 2008).
1.0E-08
log rate (mol m-2 s-1)
4
5
6
7
8
9
10
1.0E-09
1.0E-10
1.0E-11
1.0E-12
1.0E-13
1.0E-14
pH
Basaltic glass
Biotite
Muscovite
Amorphous
Quartz
Figure 3. Comparison weathering rate of several minerals at 25˚C
35
4.2.1.2 Calibration for organic carbon using total reserve of bases
Figure 4. Simulated organic carbon (top) and total reserve base (bottom) with mineral factor 1/60000 in the
forest plantation
Figure 4 shows the evolution of soil organic carbon and total reserve of bases using mineral
factor of 1/60000. Soil organic carbon tends to gradually increase through the time in upper horizons
(0-50 cm) while soil organic carbon content in the sub soils slightly decrease. The increase of organic
carbon in the upper horizons is mainly due to supply of organic matter from the past land use.
Alteration of deciduous forest to coniferous forest in 1962 formed high accumulation of organic
carbon near last years of simulation. The effect of bioturbation in the upper horizon was also shown
in the diagram indicated by mixing layer (depth of 0 – 45 cm). The stable amount of organic carbon
in the sub soil confirms that mineral factor plays a role in the sensitivity of TRB, thus it controls the
loss of organic carbon. In the simulation, the amount of TRB also slightly decreases as the effect of
weathering. This mechanism can be assumed as stabilization of organic carbon. Mineral factor of
1/60000 was chosen as the best option to represent the alteration of soil organic carbon storage in
the study site. By using this mineral factor value, comparison of organic carbon between measured
value and estimated value in the last year simulation by SoilGen shows mean bias statistic of 0.10532% of OC. It means that the simulated values only slightly underestimate the measured
values.
36
Figure 5. Simulated clay fraction with reducing splash release of clay
Figure 6. Comparison of pH between the estimated and measured value
37
Figure 7. Comparison of CEC (left) and base saturation (right) between estimated and measured value
The result of reducing splash release of clay particles can be seen in the (Fig.5). The simulation
shows that the clay in the depth near 50 cm start to decrease in the early years. It may due to the
effect of bioturbation. Figure 5 also shows that reducing clay dispersion has effect on the limited clay
migration as can be expected in volcanic ash soils. The clay input value in this depth is actually only
slightly higher than in other horizons. However, the increase of clay particles at certain depths due
to clay migration is uncommon in Andosols soil. In addition, the increase or decrease of clay particles
in the soil profile also does not change the textural class as all the horizons are dominated by silt
loam (Table 4), so there is not much evidence for clay migration. The simulated clay fraction
underestimates the measured value with mean bias -0.47160% of clay.
Measurement of pH (Fig. 6) shows large difference of pH distribution between measured value
and simulated value at the end of last simulated year with mean bias 1.39442. This means that the
model overestimates the measured value. The value of pH is related to the interaction between soil
organic carbon and the release of basic cations through mineralization, leaching from precipitation,
uptake cations by plant, and also weathering of minerals that release basic cation, thus increase the
pH value. At the end of first simulated year, upper layers show higher pH than other horizons and at
the end of last simulated year, upper layers have lower pH than the layers below depth of 60 cm.
This can be due to leaching that transport basic cations to the lower layers. Nevertheless, the
difference between simulated and measured value is still high. Weathering of minerals seems to
have small effect because the main component is amorphous mineral and weathering of these
minerals was limited. The incorrect initial water content in the soil profile can be the reason. The soil
profile may actually contain much water and additional high water from precipitation through the
years can dilute more basic cations. It is also noted that the measurement of pH was not really
precise.
38
Figure 7 shows the comparison between simulated and measured value of CEC (left) and base
saturation (right). Simulated CEC in the SoilGen model is using ECEC value since SoilGen model
cannot calculate pH dependent-CEC and the same case with base saturation which use ECEC in the
calculation. The simulated CEC underestimates the measured value with mean bias -4.33732 mmol+
kg-1 while simulated base saturation is highly overestimating the measured one by mean bias of
38.92755%. Low input ECEC in the beginning of simulation, except a high ECEC at the depth of 125170 cm, causes low amount of CEC at the end of first simulated year. The effect of high organic
matter input and bioturbation can be seen in the upper layer indicated by the amount of ECEC which
is slightly higher than sub soils. In the sub soils, the difference of ECEC in the first and last simulated
year is small. It is due to the simulated TRB and the associated (calibrated) mineral factor that the
loss of organic carbon is reduced. The role of clay is small because the contribution of clay for CEC in
the formula (Eq.10) used by SoilGen is limited and OC has a stronger contribution. On the other
hand, by using ECEC value in the base saturation calculation, the simulated value is highly
overestimating the measured value. Very low ECEC input, 0.74-1.96 cmol+ kg-1, except in the depth
of 125-170 cm which is 8.26 cmol+ kg-1 (Table 5), make basic cations easily saturated the exchange
capacity, so it raises the base saturation value.
4.2.2 Scenario output
For each scenario, a selection of the output variables was showed in time-depth diagrams (Fig.815) and is discussed hereafter. The simulation started from left to the right of time depth diagram
which correspond to present time and future time (250 years later), respectively.
39
Figure 8. Simulated organic carbon (mass% solid fraction) in forest plantation (top), grassland (middle), and
cultivated land (bottom) with scale of 0-19.214%
Figure 9. Simulated organic carbon in cultivated land with TRB (top) and non TRB (bottom)
40
Figure 10. Simulated exchangeable Ca in forest plantation (top), grassland (middle), and cultivated land
(bottom). The upper scale (0 – 50.130 mmol+ kg-1 soil) is used for forest plantation and grassland and bottom
scale (0 – 235.712 mmol+ kg-1 soil) is used for cultivated land
41
Figure 11. Simulated exchangeable K in forest plantation (top), grassland (middle), and cultivated land
(bottom). The upper scale (0 – 21.715 mmol+ kg-1 soil) is used for forest plantation and grassland and bottom
scale (0 – 48 mmol+ kg-1 soil) is used for cultivated land
42
Figure 12. Simulated exchangeable Al in forest plantation (top), grassland (middle), and cultivated land
(bottom) with scale of 0 – 50 mmol+ kg-1 soil
43
Figure 13. Simulated Effective CEC in forest plantation (top), grassland (middle), and cultivated land (bottom).
The upper scale (0 – 76.864 mmol+ kg-1 soil) is used for forest plantation and grassland and bottom scale (0 –
280 mmol+ kg-1 soil) is used for cultivated land
44
Figure 14. Simulated amorphous mineral in forest plantation (top), grassland (middle), and cultivated land
(bottom) with scale of 0 – 31.785 kg m-2
45
Figure 15. Simulated kaolinite mineral in forest plantation (top), grassland (middle), and cultivated land
(bottom) with scale of 0 – 34.797 kg m-2
Figures 8 depicts the dynamic of soil organic carbon among land use. The time-depth diagram
shows that upper horizons of all land uses have more OC-dynamic than the sub soils. Organic carbon
content is quite stable due to the influence of TRB as described in the chapter 4.2.1.2. In forest
plantation and grassland, there is a slight increase of organic carbon in the surface layers and
gradual increase of organic carbon below the surface layers up to 50 cm. This is due to bioturbation
which plays a major role in the mixing soil of upper layers (depth of 0-45 cm). As reported by
Wilkinson et al. (2009), bioturbation has big influence in the alteration of fundamental soil
properties such as organic carbon, nutrient, particle-size distribution, and porosity. Lavelle et al.,
(2006) also found that bioturbation played a role in soil carbon dynamic. In the early years, the effect
of decomposition and bioturbation cannot be seen directly due to limited addition of organic matter
every year. As time goes, more organic matter is decomposed and, with bioturbation, the mixing
layers become more obvious after several years. On the other hand, in cultivated land, organic
carbon content in the upper layers start to gradually increase and reach 19% after 250 years. High
46
input value of manure fertilizer, approximately, 30 ton ha-1 year-1, is the main reason such a high
accumulation in the upper horizons compared to other land uses. This assumption is supported with
previous study by Ren et al. (2014). They reported that application chicken manure with N fertilizer
can significantly increase soil labile carbon fraction in the high-input cropping system. This high input
may compensate the loss of organic carbon which usually occur due to plowing or high
decomposable material of plant residue. However, in the actual condition, the amount of the
accumulation seems too high. Considering the mineral properties, dominant mineral in upper layers
of cultivated land is kaolinite. Lawrence et al. (2014) reported that kaolinite provides long-term
stability of soil organic carbon but with limited amount. As the input is very high, the storage
capacity may become saturated with carbon. Previous studies reviewed by Six et al. (2002) showed
that there is no increase in soil carbon content with a two or three folds increase in C input.
Therefore, a large number of organic carbon accumulation in cultivated land scenario may
overestimate the actual condition. In order to estimate organic carbon in upper horizons, the
simulations with TRB and non TRB modifier were conducted (Fig.9). The estimated value for organic
carbon in the upper layers should be in the range between these simulations.
The exchangeable Ca content (Fig. 10) and exchangeable Mg (Appendix 14) show the same
pattern in the simulation, despite the high exchangeable Ca at the depth 125-170 cm in the forest
plantation. The difference is that the exchangeable Mg occurs slightly in lower amounts than
exchangeable Ca. In the forest plantation, the gradual increase of exchangeable Ca occur around the
depth of 20-120 cm and at the end of simulation the exchangeable Ca in this depth is higher than in
the upper layers. The similar pattern also occur in the grassland scenario, nevertheless the
exchangeable Ca in the upper layers in grassland is still high compared to the sub soils. This is due to
the preference for Ca-uptake and root distribution. According to the nutrient cycling in SoilGen
model, Ca uptake for grassland is 0.27 while coniferous forest is 0.77 (Thompson et al., 1997;
Navrátil, 2003). However, the presence of roots and organic carbon in grassland are concentrated in
the upper layers, hence the nutrient cycling mostly occur in the upper layers. On the other hand, in
forest plantation scenario, exchangeable Ca is slightly higher in depth of 70-120 cm than in upper
layers may be due to high organic carbon content in this depth (Fig. 8). In the cultivated land,
exchangeable Ca and Mg are concentrated at the sub soils while the amount is less in the upper
layers. Plowing causes severe leaching of these cations in the upper layers and later these cations
distribute through the depth. High fertilizer input may compensate the loss of cations by leaching, so
the concentration of these cations are still high in soil profile. In general, the amount of
exchangeable Ca and Mg in the cultivated land scenario is higher than other land use scenarios.
The exchangeable K (Fig.11) and exchangeable Na (Appendix 15) have different pattern with
exchangeable Ca and Mg, especially in the cultivated land scenario. In the scenario of forest
plantation and grassland, the exchangeable K and Na content are already low since the start of
simulation, and after 250 years of simulation, these exchangeable cations are in very low content.
On the other hand, cultivated land scenario shows that the exchangeable K is always concentrated in
the upper layers while exchangeable Na mostly concentrates in the sub soils. This result is in line
with previous study by Jobbagy and Jackson (2001). They suggested that K was enriched surface soils
47
while Na usually concentrated at depth. Low exchangeable K concentration in the sub soils is mainly
due to little K uptake below this depth. As the maximum of vegetable rooting depth in cultivated
land scenario is around 60 cm, there will be small amount exchangeable K content below this depth.
Figure 12 shows the pattern of distribution of exchangeable Al content over 250 years. The initial
condition of exchangeable Al among land uses is already different. Generally, the depth where the
exchangeable Al is present decreases in function with time in the forest plantation and grassland
scenarios. Therefore, at the end of simulation, exchangeable Al only exists in the depth of 120-200
cm. The reason can be due to leaching of exchangeable Al. The figure also shows that exchangeable
Al content in forest plantation is slightly higher than grassland. According to the Thompson et al
(1997) and Navrátil (2003), relative concentration of exchangeable Al in coniferous forest is higher
than in grassland, 0.023 and 0.001, respectively. On the other hand, exchangeable Al in the
cultivated land gradually increase and accumulate in the top layers (0-20 cm) after 150 years of
simulation, then the exchangeable Al decrease after this year. High exchangeable Al after 150 years
can be due to high initial input in top layers and the nutrient cycle accumulate after several years.
The decrease of exchangeable Al in the last fifty years is due to a respond of change of hydraulic
characteristic according to the pedotransfer function used in SoilGen model. In these years, organic
matter content is very high and it passes the threshold (more than 18% organic carbon), hydraulic
conductivity changes to organic soils and it affects to the low infiltration. As the infiltration is low,
basic cations may stay in the top layers, thus the pH increases, concentration of Al in solution
decreases, and as a consequence exchangeable Al decreases. On the other hand, the ECEC in the all
land use scenario is quite stable especially in the sub soil (Fig.13). As it describes in the chapter of
4.2.1.2, it is due to stabilization of organic carbon. The differences occur in the upper layers where
the ECEC tends to slight decrease in the forest plantation and grassland scenario while the ECEC
gradually increase in the upper layers of cultivated land. It should be note that with high input of
organic fertilizer that is applied every year, high ECEC is achieved after around 200 years.
Amorphous (Fig.14) and kaolinite (Fig.15) are two dominant minerals in the all land use scenario.
In the simulation, it shows that the amount of minerals located in the sub soils are quite stable after
250 years, only slightly decrease in the upper layers. The decrease can be due to factor of climate,
vegetation, or land management (in the cultivated land). As the weathering of minerals has a
geologic time scale, the decrease will be more obvious if the simulation takes long period of time. It
should be noted, however, that the new formation of kaolinite is not simulated by the present
model version.
4.2.3 Limitation of the model approach
The aforementioned studies demonstrate the potential field applications of SoilGen in
simulating soil development processes in tropical volcanic soils. However, results of these studies
have also shown some limitations and major discrepancies between model simulated and
measurements. The limitations can be partly attributed to formation of non-crystalline minerals and
the ability of model to work in pH-dependent charged soils. The limitations of accurate data
according to the condition in tropical soil also creates discrepancies i.e. the typical value for
48
interception evaporation in coniferous forest in tropical region. In this study, the model version also
has not yet simulated the new formation of kaolinite mineral.
4.2.4 Conclusion
Weathering rate of amorphous minerals and organic carbon are assumed to be the most
sensitive variables that need to be calibrated in order to simulate future effects of land use change in
volcanic soils. The proposed weathering rate for amorphous minerals, which is determined based on
the weathering rate between basaltic glass and muscovite/quartz, results in quite stable amorphous
minerals. Furthermore, the role of TRB in controlling loss of humus is worked. A “mineral factor” of
1/60000 that was used to determine the sensitivity of TRB gave the best value for TRB in controlling
loss of humus with the mean bias statistic of -0.10532% of OC. Reducing splash release of clay
particles results in the absence of clay migration in the soil profile. Analysis statistics to compare
simulated and measured values in some soil properties, such as pH, ECEC, and base saturation shows
large difference with mean bias statistic 1.39442, -4.33732 mmol+ kg-1, and 38.92755%, respectively.
In pH simulation, incorrect initial water content might be the reason of overestimation of the
simulated value relative to the measured value. On the other hand, very low amounts of basic and
acidic cations contribute to the large discrepancy between simulated and measured values of ECEC
and base saturation.
Scenario outputs of some parameters, such as organic carbon, exchangeable Ca, Mg, K, Na, Al,
ECEC, and amorphous minerals were obtained over the next 250 years. In the simulation of organic
carbon, mineralization of organic carbon and bioturbation, plays a role in the upper layers of forest
plantation and grassland scenario. There is a slight increase of organic carbon in the upper layer
after 250 years meanwhile organic carbon in the subsoil is quite stable due to TRB factor. On the
other hand, accumulation of organic carbon content in the upper layers of cultivated land scenario is
very high, which is seems impossible in the actual condition. In this case, the high input value seems
to saturate the storage capacity with carbon. Kaolinite as the dominant mineral in the upper layers
of cultivated land only stabilizes limited amounts of organic carbon, so there will be no increase in
soil carbon after it passes a threshold amount because the soil carbon above the threshold will be
unprotected and will mineralize quickly. Furthermore, exchangeable basic cations in cultivated land
scenario are higher than in other scenarios which is mainly due to high input of fertilizer. In the
forest plantation and the grassland scenario, the exchangeable Ca has the same pattern as
exchangeable Mg, nevertheless concentration of exchangeable Ca in the upper layers of grassland is
higher than in forest plantation. This is mainly due to distribution of roots in grassland. Exchangeable
K and Na in the cultivated land scenario concentrate in the top soils and sub soils, respectively. This
is due to K uptake by plants in the top soils while Na is less cycling, so it tends to be concentrated in
the sub soils. The depth where exchangeable Al is present decreases in function with time in forest
and grassland while exchangeable Al content in upper layers of cultivated land accumulates after
several years. Simulated ECEC is more varying in the upper layers of all land use scenario.
Furthermore, amorphous mineral as the dominant mineral in forest plantation and grassland
scenario is still quite stable in the sub soils but gradually decreases in the top soils.
49
V. Conclusions and Recommendation
5.1 Conclusions
Land use conversion has effect on soil properties and soil development processes. Analysis of
physical, chemical, and mineralogical properties shows that there are differences between soil
properties under forest plantation and after conversion of forest plantation to other land uses.
Conversion of forest plantation to cultivated land results in large differences in soil properties, such
as pH, soil texture, bulk density, organic carbon, exchangeable basic cations, ECEC, base saturation,
amorphous and kaolinite minerals. Meanwhile, conversion of forest plantation to grassland results in
small differences in those soil properties. The differences were mostly detected in the upper
horizons and were mainly due to vegetation and land management. However, characteristics of soil
properties are not entirely attributed due to land use differences. Interaction between amorphous
minerals and organic carbon has effect on the soil development process within soil profiles which
also determines the soil properties. Analysis of future effects of land use change using SoilGen model
also shows the difference of soil properties among land uses in the top soils, and the discrepancies
become larger over next 250 years, particularly for organic carbon. On the other hand,
characteristics of soil properties in sub soils are relatively stable over 250 years. The distribution of
soil properties through the depths over 250 years in the simulation is mainly due to the influence of
vegetation, such as cation uptake by plant roots, agricultural activities, and mineral composition.
5.2 Recommendation
Minerals play an important role in determining characteristics of soil properties in volcanic
region. In the future, detailed analysis of mineral components, such as allophane, imogolite,
ferrihydrite will produce the more comprehensive description about the development of soils in this
region. Dealing with soils which have variable charge, measurement of exchangeable basic cations
using BaCl2 and exchangeable acidity using KCl extraction is suggested to estimate better ECEC and
soil pH. Furthermore, SoilGen model has been adapted and applied for the simulation in tropical
volcanic region. Some parameters need to be adapted and improved in order to have better
simulation, such as allophane chemistry, pH-dependent charge, and parameters of C-cycling for the
tropical region. In addition, capacity of minerals to store organic carbon can be considered for the
development of SoilGen model in the future.
50
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60
Appendix 1. Plot data
Slope bearing (degrees)
Upslope bearing (degrees)
Downwind bearing (degrees)
Latitude (degrees)
Forest plantation
20
20
215
-6
Grassland
25
349
215
-6
Cultivated land
12
278
215
-6
61
Appendix 2a. Soil description (Forest Plantation)
Location
: Jayagiri village, Lembang, Indonesia
Elevation
: 1563 asl
Coordinate
: S 06˚47’21.51” & E 107˚37’21.61”
Physiographic position: middle slope
Vegetation
: Pine trees and coffee
Slope
: 20˚
Drainage
: well drained
Rock outcrops : Erosion
:Parent material : Andesitic from Holocene age
Hor.
Ap1
Depth
(cm)
0 - 16
Ap2
16 - 29
Ap3
29 - 38
AB
38 - 60
BC
60 - 78
2AB1
78 - 101
2AB2
101 - 125
2Bw1
125 – 170
2Bw2
170 - 200
Description
Brown (10 YR 4/4); silt loam; friable (moist) consistency; granular
structure; very few fine rounded soft iron nodules ; many coarse, fine,
and very fine roots; common coarse voids; clear smooth boundary; (pH
5.3)
Bright brown (10 YR 6/6); silt loam ; slightly firm (moist) consistency;
crumb structure; common fine roots; common very fine voids; gradual
wavy boundary; (pH 5.2)
Bright brown (10 YR 6/8); silt loam; friable (moist) consistency; crumb
structure; few very fine and fine roots; few medium voids; clear smooth
boundary; (pH 5.2)
Yellowish brown (10 YR 5/8); silt loam; slightly firm (moist) consistency;
weak granular structure; few fine and very fine roots, open and infilled
large burrow that makes some parts have friable (moist) consistency; few
fine voids; gradual smooth boundary; (pH 5.4)
Brown (10 YR 4/6); silt loam; friable (moist) consistency; weak granular
structure; very few prominent black (10 YR 2/1) mottles; few fine and
very fine roots, few fine voids; gradual smooth boundary; (pH 5.6)
Brownish black (10 YR 3/1); silt loam; friable (moist) consistency; weak
sub angular blocky structure; many prominent bright brown (10 YR 6/8)
mottles; very few fine roots; few fine voids; gradual wavy boundary; (pH
5.5)
Black (10 YR 1.7/1); silt loam; slightly firm (moist) consistency; weak sub
angular blocky structure; very few fine rounded soft of iron concretion;
few fine and very fine roots; common fine voids; gradual wavy boundary;
(pH 5.8)
Brownish black (10 YR 2/3); silt loam; firm (moist) consistency; moderate
sub angular blocky structure; very few very fine roots; common very fine
voids; gradual wavy boundary; (pH 6.0)
Brown (10 YR 4/6); silt loam; firm (moist) consistency; moderate sub
angular blocky structure; few faint black (10 YR 2/1) mottles; common
very fine voids; very few very fine roots; (pH 5.7)
62
Appendix 2b. Soil description (Grassland)
Location
: Jayagiri village, Lembang, Indonesia
Elevation
: 1597 asl
Coordinate
: S 06˚ 46’ 54.00” & E 107˚ 37’ 31.00”
Physiographic position: middle slope
Vegetation
: Grass and Eucalyptus
Slope
: 25˚
Drainage
: well drained
Rock outcrops : Erosion
:Parent material : Andesitic from Holocene age
Hor.
Ap1
Depth
(cm)
0 – 21
Ap2
21 – 30
BC
30 - 60
2AB1
60 - 71.5
2AB2
71.5- 107
2AB3
107– 125
3Bw1
125 – 166
3Bw2
166 – 200
Description
Brown (7.5 YR 4/3); loam; friable (moist) consistency; coarse to medium
granular structure; few coarse and common fine and very fine roots; many
medium voids; clear smooth boundary; (pH 5.4)
Brown (10 YR 4/6); silt loam; friable (moist) consistency; crumb structure;
many very fine and fine roots, few ant burrows; few medium voids; clear
smooth boundary; (pH 5.5)
Yellowish brown (10 YR 5/6); silt loam; slightly firm (moist) consistency;
many prominent brownish black (7.5 YR 2/1) mottles; crumb structure; very
few very fine rounded soft of iron nodules; many very fine and fine roots,
few ant burrows; common fine voids; clear smooth boundary; (pH 5.4)
Brownish black (10 YR 2/3); silt loam; friable (moist) consistency; weak
granular structure; very few coarse and common fine roots; few ant
burrows; few fine voids; clear smooth boundary; (pH 5.5)
Black (10 YR 1.7/1); silt loam; friable (moist) consistency; few distinct
yellowish brown (10 YR 5/8) mottles; weak granular structure; few medium
rounded soft of iron nodules; very few coarse and common fine roots; few
fine voids; gradual wavy boundary; (pH 5.1)
Brownish black (10 YR 2/3); silt loam; slightly firm (moist) consistency; very
few medium rounded of iron nodules; granular structure; very few coarse
and many very fine roots; few fine voids; gradual wavy boundary; (pH 5.5)
Dark brown (10 YR 3/3); silt loam; more firm (moist) consistency rather than
upper layer; very few distinct yellow orange (10 YR 7/8) mottles; weak sub
angular blocky structure; very few coarse and few fine roots; few fine voids;
gradual wavy boundary; (pH 5.5)
Brown (10 YR 4/6); silt loam; firm (moist) consistency; many prominent
black (10 YR 1.7/1) mottles; weak sub angular blocky structure; few fine
voids; very few fine roots; (pH 5.6)
63
Appendix 2c. Soil description (Cultivated Land)
Location
: Cikole, Lembang, West Jave Indonesia
Elevation
: 1320 asl
Coordinate
: S 06˚47’ 56.58” & E 107˚38’36.15”
Physiographic position: upper slope
Vegetation
: broccoli, cauliflower, cabbage, tomatoes
Slope
: 12˚ (terrace)
Drainage
: well drainage
Rock outcrops : Erosion
:Parent material : Andesit from Holocene age
Hor.
Ap
Depth
(cm)
0 - 23
AB1
23 - 45
AB2
45 - 60
2AB1
60 - 91
2AB2
91 - 127
2Bw1
127 - 156
2Bw2
156 - 170
3Bw1
170 - 200
Description
Brown (10YR 4/4); clay loam; weak granular structure; friable (moist)
consistency; few fine roots, few medium voids; abrupt smooth boundary;
(pH 5.8)
Dark brown (10YR 3/4); clay; moderate to weak medium granular
structure; firm (moist) consistency; many very fine roots; few fine voids;
few fine voids; abrupt smooth boundary; (pH 6.5)
Brown (10 YR 4/6); clay loam; weak granular structure; firm (moist)
consistency; few fine roots, very few medium roots; few medium voids;
gradual smooth boundary; (pH 6.5)
Dark brown (10YR 3/4); silt loam; weak granular structure and some parts
are crumb; firm (moist) consistency; very few faint yellowish brown (10 YR
5/6) mottles; few very fine roots, an open infilling burrow; gradual wavy
boundary; (pH 6)
Brownish black (10YR 2/2); silt clay loam; weak granular structure; slightly
firm (moist) consistency; very few distinct bright brown (7.5 YR 5/8)
mottles; very few very fine roots; few fine voids; diffuse wavy boundary;
(pH 5.8)
Dark brown (10YR 3/4); silt loam; weak sub angular blocky structure; firm
(moist) consistency but some parts are friable; very few very fine roots;
few fine voids; clear wavy boundary; (pH 5.6)
Dark brown and black (10 YR 3/4 and 10 YR 2/1); silt loam; weak sub
angular blocky structure; firm (moist) consistency; no root; few fine voids;
gradual wavy boundary; (pH 5.4)
Brownish black (7.5 YR 3/1); silt clay loam; weak sub angular blocky
structure; firm (moist) consistency; no root; few fine voids; (pH 5.8)
64
Appendix 3. Precipitation
Date
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
1
5.9
4.5
1.4
19.5
2
8.3
3.2
50.5
0.3
1
3
3.9
19
21
1.5
5.6
4
10.4
41.1
0.3
0.9
24.5
5
1.7
49
35
6.4
1.6
5.7
40
6
5.2
10.5
2.8
39.5
17
3.7
4.7
1
7
42
4
42
0.5
1.7
9.3
8
27
23.1
21
1.5
0.7
9
8.1
7.1
18.8
0.1
39.8
31.6
45.7
0.7
10
5.9
7.7
27.9
11.5
11.4
11
6.2
13.8
20.4
36.1
0.1
2.6
12
12
7.7
0.3
13
25.1
24
4.1
6.2
2.8
1
14
0.2
18.4
47.8
17.9
15
6.2
52.3
12.8
8.8
16
0.2
19
10.6
8.2
4.7
13.8
3
17
5
25.5
6.1
3.2
15.2
2.9
0.2
18
15.1
2.8
28.3
6.8
1
19
40.1
10
12.6
7.5
20
26.5
4.1
49.5
10.5
45.7
19.2
21
0.2
8.9
17.6
0.1
66.8
25.1
22
0.2
28.6
23
0.6
8.5
23
3.3
4
42
3.2
24
0.6
48
0.2
5.9
25
22.9
2
3
5
26
0.6
7.8
0.6
4.7
2.8
27
1
3
0.3
3.8
28
29
15
17
18.2
11.1
29
10.6
9.3
6
15.1
30
17.4
69.7
0.1
3.6
1.6
31
8.4
6.8
19.5
Source: Stasiun Geofisika kelas I Bandung. Indonesian Agency for Meteorology, Climatology, and Geophysics (2010)
10.2
1.8
7.3
7.1
16.4
18
22
2.9
9.6
4.5
5.1
0.8
16.1
2.1
60.6
0.4
24.4
0.5
17.2
22.8
25
63
6.7
-
29
2.3
1.3
13.6
5.4
0.6
8.6
1.9
0.2
0.2
14.3
14.3
19.6
60.7
17.6
11.8
11.8
2.5
30.9
9.7
4.6
15.5
1.5
12.7
19.2
16.6
9
4.8
1.6
18.3
26.1
18.5
9.2
4.3
0.8
1.7
20.8
1.1
23.6
9.8
12.3
32.3
32.3
30.6
28.3
26.3
53.7
15.4
62.6
0.1
21
7.2
16.1
25.3
22.8
23.1
6
26.3
0.1
1
0.1
0.4
1.7
2.7
26.7
0.1
5
0.1
5.8
17.3
1.1
2.3
1.6
mm
65
Appendix 4. Water composition
Month
SO42-
Ca2+
January
February
March
April
May
June
July
August
September
October
November
December
0.0096
0.0129
0.0183
0.0164
0.0153
0.0194
0.019
0.0166
0.0093
0.0145
0.0275
0.0129
0.0092
0.0148
0.0185
0.1095
0.0221
0.0104
0.0095
0.0104
0.0068
0.0072
0.0089
0.0076
Mg2+
K+
mmol dm-3
0.0019
0.0039
0.0034
0.003
0.003
0.0031
0.011
0.0129
0.0027
0.0062
0.0022
0.0041
0.0033
0.0059
0.0023
0.0037
0.0016
0.0019
0.0021
0.0038
0.0021
0.0031
0.0031
0.0061
Cl-
Na+
0.0031
0.0039
0.0053
0.0151
0007
0.0089
0.0076
0.0082
0.0037
0.0059
0.0073
0.0056
0.0017
0.005
0.0087
0.016
0.007
0.0083
0.0069
0.0098
0.0034
0.0053
0.0058
0.0065
Source: Indonesian National Institute of Aeronautics and Space (2010)
66
Appendix 5. Climate data
Latitude & Longitude
: 06˚49’35.6”S & 107 37’03.6”E
Week no
Potential ET
Mean Temperature
Mean Amplitude
1
31.83
20.3
5.59
2
30.39
19.3
5.39
3
31.45
19.4
5.73
4
32.51
20.5
5.67
5
33.06
20.4
5.92
6
30.43
20.0
5.06
7
31.02
20.0
5.32
8
31.25
20.4
5.50
9
32.16
20.6
5.16
10
30.51
20.3
5.56
11
31.31
20.0
4.64
12
28.84
20.8
5.19
13
29.85
20.4
5.67
14
31.14
21.0
5.96
15
31.42
21.0
5.75
16
29.77
20.5
5.66
17
29.67
21.2
5.00
18
27.44
21.3
5.64
19
28.17
20.8
4.87
20
25.85
21.1
4.03
21
25.61
21.3
3.93
22
25.37
21.0
5.69
23
25.57
20.1
5.20
24
25.04
19.9
5.30
25
21.84
20.3
4.66
26
21.34
19.9
4.61
27
25.64
19.7
5.91
28
24.67
19.5
4.69
29
25.22
19.7
5.14
30
23.73
19.4
4.90
31
24.44
19.6
4.66
32
27.34
19.0
4.61
33
25.57
20.2
4.69
34
27.84
20.3
5.14
35
27.42
19.9
4.90
36
26.21
19.7
4.40
37
27.30
19.3
4.71
38
27.93
19.7
4.71
39
27.53
20.0
4.39
40
28.84
20.5
4.60
41
32.60
20.2
5.90
42
29.52
20.1
4.84
43
32.65
19.9
5.97
44
31.22
20.5
5.34
45
30.58
20.1
5.16
46
29.70
19.9
4.96
47
28.43
20.4
4.43
48
33.27
20.1
6.23
49
27.79
20.0
4.99
50
32.72
19.5
6.23
51
33.21
19.4
6.46
52
31.58
19.7
5.69
Source: Geophysics station Class I Bandung, Indonesian Agency for Meteorology, Climatology, and Geophysics (2010)
67
Appendix 6. Weathering rates
Mineral
Albite
Amorphous
Anorthite
Gibbsite
Hornblende
Kaolinite
Muscovite
Otherite/
diopside
Quartz
KH
Log kH25
mol ms-1
-9.70
-10.0
-5.90
-7.65
-10.20
-12.45
-11.8
-9.85
No
effect
EaH
KJ mol-1 K-1
66.9
38.75
79.5
47.5
50.0
50.0
54.4
42.0
Source
kOH
n
(-)
0.50
0.32
0.90
0.99
0.55
0.28
0.14
0.14
Surface
area
Log kOH25
mol ms-1
-9.95
-10.8
No effect
-16.65
No effect
-10.74
-11.7
No effect
EaOH
KJ mol-1 K-1
60
65.05
m
(-)
0.50
0.16
80.1
-0.78
1
4
1
2
40.0
22
0.73
0.16
1
1
311
0.68
-11.0
85
0.25
3
0.07
m2 g-1
0.17
1.28
1Goddéris
et al. (2006)
as reported in Palandri and Kharaka (2004) (T=25˚C; pH=0)
3Violetta et al., 2002
4Modification from the author based on Goddéris et al. (2006)
2Data
68
Appendix 7. Timing C input of vegetation
Forest plantation1
Month
1
Plant residue (%)
8
Manure (%)
0
Max. rooting depth (mm)
Plant in C (t ha-1 y-1)
Manure in C (t ha-1 y-1)
2
8
0
3
4
9
8
0
0
2000
3.47
0
5
8
0
6
9
0
3
4
9
8
0
0
2000
1.44
0
5
8
0
6
9
0
7
8
0
8
8
0
9
9
0
10
8
0
11
8
0
12
9
0
8
8
0
9
9
0
10
8
0
11
8
0
12
9
0
Grassland2
Month
1
2
Plant residue (%)
8
8
Manure (%)
0
0
Max. rooting depth (mm)
Plant in C (t ha-1 y-1)
Manure in C (t ha-1 y-1)
7
8
0
Cultivated land (Cauliflower, broccoli, tomatoes)3
Month
1
Plant residue (%)
0
Manure (%)
100
Max. rooting depth (mm)
Plant in C (t ha-1 y-1)
Manure in C (t ha-1 y-1)
2
0
0
3
4
33 0
0
0
600
0.6
17.4
5
0
0
6
33
0
1
Data obtained from Lasco (2002)
2
Data obtained from Long et al. (1992)
Data obtained from Kong et al. (2004) (with modification from the author)
3
7
0
0
8
0
0
9
33
0
10
0
0
11
0
0
12
34
0
69
Appendix 8. Bioturbation and Event
Upper depth bioturbation (mm)
Depth of max. bioturbation (mm)
Lower depth of bioturbation (mm)
Magnitude of bioturbation at upper
depth (fraction of soil mass)
Magnitude of maximal bioturbation
(fraction of soil mass)
Magnitude of bioturbation at lower
depth of bioturbation (fraction of
soil mass)
Forest plantation
0
250
450
0.015
Grassland
0
250
450
0.015
Cultivated land
0
250
450
0.015
0.015
0.015
0.015
0.000
0.000
0.000
Land use
Event
Cultivated land
Plowing
Depth of plowing
(mm)
300
Magnitude
0.95
70
Appendix 9. Fertilization
Application of manure and inorganic fertilizer
Fertilizer
Application
Manure
30 000 kg OM ha-1 year-1 = 17 647 kg C ha-1 year-1
NPK
12 000 kg ha-1 year-1
ATS
4 500 kg ha-1 year-1
Calculation of anion and cation in fertilizer
Fertilizer
Cation/anion
Manure Fertilizer
K2O
Content
(%)
1.57
Ca
1.57
Mg
1.44
Inorganic Fertilizer K
(NPK)
16
Inorganic fertilizer
24
S
Calculation
 K (%) as K:
1.57 * 10000 = 15700 (ppm K as K2O)
15700 / 1.23 = 12764.23 (ppm K as K)
12764.23 * 0.0001 = 1.28 (% K as K)
 K (mol m-2)
0.0128 * 30000 = 384 kg ha-1 = 384000 g ha-1 =
384000/39.096 = 9821.98 mol ha-1 = 0.9822 mol
m-2
0.0157 * 30000 = 471 kg ha-1 = 471000 g ha-1 =
471000/40.078 = 11752.08 mol ha-1 = 1.1752 mol
m-2
0.0144 * 30000 = 432 kg ha-1 = 432000 g ha-1 =
432000/24.305 = 17774.12 mol ha-1 = 1.7774 mol
m-2
 K(%) as K:
16 x 10000 = 160000 (ppm K as K2O)
160000 / 1.23 = 130081.30 (ppm K as K)
130081.30 * 0.0001 = 13.01 (% K as K)
 K (mol m-2)
0.1301 * 12000 = 1560.98 kg ha-1 = 1560975.61 g
ha-1 = 39924.69 mol ha-1 = 3.9925 mol m-2
 S (%) as SO4
24 * 10000 = 240000 (ppm S as S)
240000 * 3 = 720000 (ppm S as SO4)
720000 * 0.0001 = 72 (%S as SO4)
 SO4 mol m-2
0.72 x 4500 = 3240 kg ha-1 = 3240000 g ha-1 =
3240000/96.06 = 33728.92 mol ha-1 = 3.3729 mol
m-2
*Data obtained from Wiryanta and Bernardinus (2002)
71
Appendix 10. Conversion andesine and bytownite to albite and anorthite
Conversion of andesine and bytownite to albite and anorthite minerals, respectively, consist of
several steps:
1. Finding formula of each minerals and their molecular weight
Minerals in XRD:
Andesine = Na0.5Ca0.5Al1.5Si2.5O8
Bytownite = Na0.15Ca0.85Al1.8Si2.2O8
Minerals in SoilGen: Albite = NaAlSi3O8
Anorthite = CaAl2SiO8
2. Calculating the number of moles from the mass percentage of minerals (andesine and
bytownite) derived from the XRD analysis. This calculation was done by dividing mass
percentage of minerals with their molecular weight. For example :
Mass percentage of andesine = 5.476803 %
5.476803 / (0.5*22.98977 + 0.5*40.078 + 1.5*26.981538 + 2.5*28.0855 + 8*15.9994) = 0.020268
moles
3. Calculating the number of moles of each element by multiplying the resulting “mole number”
with number of atoms in the formula of minerals. For example:
Andesine: moles Na = 0.20268*0.5 = 0.0101
moles Ca = 0.20268*0.5 = 0.0101
moles Al = 0.20268*1.5 = 0.0304
moles Si = 0.20268*2.5 = 0.0507
4. Determining the mass percentage of Albite and Anorthite minerals by trying to find the value for
which the difference between total moles of andesine-bytownite and albite-anorthite is
minimal. The calculation process to determine moles of Na, Ca, Al, and Si of albite and anorthite
is similar as that for andesine and bytownite. For example :
Mass
moles Na
percentage
5.476803
0.0101
0
0.0000
0.0101
moles Ca
moles Al
moles Si
0.0101
0.0000
0.0101
0.0304
0.0000
0.0304
0.0507
0.0000
0.0507
+
2.65
2.82
0.000
0.0101
0.0101
0.0101
0.0203
0.0304
0.0303
0.0203
0.0506
+
(XRD) – (SoilGen)
0.0000
0.0000
Difference = sum of ((XRD) – (SoilGen)) = 0.0001
0.0000
0.0001
Andesine
Bytownite
(from XRD)
Albite
Anorthite
(used in
SoilGen)
0.0101
0.0000
0.0101
72
Horizons
Andesine
Ap1
Ap2
Ap3
AB
BC
2AB1
2AB2
2Bw1
2Bw2
5.476803
4.901761
4.165871
6.377361
13.28816
3.648912
1.013598
1.203143
1.682197
Ap1
Ap2
BC
2AB1
2AB2
2AB3
3Bw1
3Bw2
5.558724
4.462187
5.02118
10.60353
3.290454
1.687725
5.742332
3.134578
Ap
AB1
AB2
2AB1
2AB2
2Bw1
2Bw2
3Bw1
6.9888
10.2015
13.05412
7.214655
1.27239
0.825092
2.965825
1.805124
Mass percentage (w%)
Albite
Bytownite
Forest plantation
2.650
0.000
2.450
0.557134
2.021
0.00
3.200
0.73773
6.650
1.414648
2.010
1.58147
0.607
0.810297
1.800
8.183838
1.220
2.538968
Grassland
2.695
0.000
2.165
0.000
2.700
1.709661
5.600
2.870742
1.928
2.24898
1.090
1.788289
3.480
4.462846
1.960
2.778192
Cultivated land
3.830
2.816
5.716
4.717359
7.050
4.699156
4.148
4.038826
0.920
1.908585
0.588
1.267105
1.835
2.477235
1.116
1.57056
Difference
Anorthite
2.820
3.000
2.131
3.919
8.050
3.234
1.218
7.620
3.020
0.0001
0.0003
0.0002
0.0003
0.0005
0.0005
0.0003
0.0028
0.0008
2.860
2.300
4.050
7.920
3.635
2.390
6.770
3.990
0.0001
0.0001
0.0005
0.0009
0.0008
0.0006
0.0014
0.0009
6.020
9.250
10.750
7.165
2.280
1.498
3.648
2.285
0.0009
0.0015
0.0015
0.0012
0.0006
0.0005
0.0008
0.0005
73
Appendix 11. Recalculation of quantified minerals as applied in SoilGen model
Hor
Amor
phous
Quartz
Cristo
balite
Tridy
mite
Ap1
Ap2
Ap3
AB
BC
2AB1
2AB2
2Bw1
2Bw2
59.13
57.98
55.59
63.37
54.28
66.59
79.18
66.42
54.55
8.94
8.75
9.52
7.34
0.99
3.95
2.87
4.65
2.69
2.52
2.76
2.64
1.76
2.30
3.47
3.36
3.29
4.04
Ap1
Ap2
BC
2AB1
53.37
57.84
55.69
45.31
9.42
9.55
9.04
6.04
2.90
2.59
2.26
1.84
Horn
Mus
blende covite
Weight (%)
Forest plantation
11.94
1.58
1.97
12.09
1.76
2.10
14.88
0.90
1.74
5.37
3.57
2.16
6.75
9.16
1.73
7.05
7.42
2.15
5.11
1.23
1.40
5.88
2.19
0.00
3.61
0.00
0.00
Grassland
15.04
1.38
2.68
11.04
2.74
2.62
10.47
2.95
2.56
10.17 11.52
1.67
2AB2
2AB3
3Bw1
3Bw2
57.40
65.49
3.11
25.86
6.34
5.86
4.22
3.97
4.55
2.07
8.41
6.53
9.23
12.46
8.24
9.16
Ap
AB1
AB2
2AB1
2AB2
2Bw1
2Bw2
3Bw1
19.99
21.99
23.36
36.87
45.21
47.83
19.81
3.32
3.14
3.12
2.64
2.84
2.15
1.28
1.07
3.14
2.94
2.77
3.20
4.22
3.71
4.47
3.70
5.20
5.41
1.82
3.59
0.94
8.70
0.00
2.89
5.35
8.80
2.61
2.40
1.75
0.00
0.00
0.00
0.00
Cultivated land
6.56
0.00
6.67
0.00
10.52
0.00
8.17
0.00
2.62
0.00
4.71
0.00
10.49
0.00
2.07
0.00
Kaoli
nite
Albi
te
Anor
thite
Diops
ide
Gibb
site
2.81
3.08
3.76
1.38
1.38
1.56
1.50
2.21
22.79
2.84
2.61
2.15
3.31
6.86
2.07
0.62
1.85
1.25
3.02
3.19
2.27
4.05
8.31
3.33
1.25
7.82
3.09
5.25
5.68
5.55
7.70
4.39
2.41
3.48
5.69
2.70
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.28
3.39
2.31
2.30
2.13
0.00
0.00
1.84
3.05
5.97
6.57
7.47
5.01
0.00
0.00
0.00
0.00
1.95
1.74
52.82
31.34
5.85
4.74
5.41
11.2
6
3.41
1.78
6.14
3.33
2.33
1.89
4.77
2.95
3.39
4.56
6.57
3.31
0.00
0.00
5.72
13.55
45.61
44.72
36.43
30.85
29.56
32.64
50.27
76.63
3.83
5.71
7.10
4.20
0.94
0.60
1.85
1.13
6.02
9.25
10.82
7.26
2.33
1.54
3.68
2.31
1.97
2.74
1.51
3.30
2.66
2.71
5.27
2.08
4.54
1.21
0.82
1.36
2.14
3.20
0.77
0.86
74
Appendix 12. Graphic of chemical properties as function of depth
pH
5
6
7
0
0
50
50
depth (cm)
0
100
150
200
200
forest
cultivated land
5
grassland
forest
Exchangeable Ca (cmol kg-1)
10
20
30
0
0
0
50
50
100
150
200
200
cultivated land
grassland
forest
Exchangeable K (cmol kg-1)
0
0.5
1
1.5
2
2.5
0
50
50
depth (cm)
0
100
1
2
3
cultivated land
grassland
150
200
200
grassland
0.5
1
1.5
100
150
cultivated land
grassland
Exchangeable Na (cmol kg-1)
0
forest
cultivated land
100
150
forest
15
Exchangeable Mg (cmol kg-1)
depth (cm)
depth (cm)
0
10
100
150
Depth (cm)
depth (cm)
4
Organic carbon (%)
forest
cultivated land
grassland
75
Exchangeable Al (cmol kg-1)
1
2
0
3
50
50
depth (cm)
0
0
100
150
200
200
forest
cultivated land
grassland
forest
ECEC (cmol kg-1)
0
10
0.4
0.6
cultivated land
grassland
Base saturation (%)
20
30
40
0
0
50
50
depth (cm)
0
100
150
200
200
cultivated land
20
40
60
80
100
150
forest
0.2
100
150
grassland
forest
cultivated land
grassland
CEC (cmol kg-1)
0
10
20
30
40
50
0
50
depth (cm)
depth (cm)
depth (cm)
0
Exchangeable H (cmol kg-1)
100
150
200
forest
cultivated land
grassland
76
Appendix 13. Mineral composition of soil profiles under different land use
0%
20%
40%
60%
80%
100%
0-16
Forest plantation
16-29
29-38
38-60
60-78
78-101
101-125
Amorphous
125-170
Quartz
Cultivated land
170-200
Cristobalite
0-23
23-45
Tridymite
45-60
Hornblende
60-91
Diopside
91-127
Muscovite
127-156
156-170
Kaolinite
Alunite
170-200
Grassland
0-21
Rutile
21-30
Andesine
30-60
Bytownite
60-71.5
Gibbsite
71.5-107
107-125
125-166
166-200
77
Appendix 14. Simulated exchangeable Mg
Simulated exchangeable Mg in forest plantation (top), grassland (middle), and cultivated land. The upper scale (0 - 19.479)
mmol+ kg-1) is used for forest plantation and grassland and bottom scale (0 – 40.00 mmol+ kg-1) is used for cultivated land
78
Appendix 15. Simulated exchangeable Na
Simulated exchangeable Na in forest plantation (top), grassland (middle), and cultivated land. The upper scale (0 – 6.75681
mmol+ kg-1) is used for forest plantation and grassland and bottom scale (0 – 13 mmol+ kg-1) is used for cultivated land
79