EFFECTS OF LAND-USE CHANGE

ON THE QUALITY OF UPLAND

TROPICAL SOIL

Number of words: 18,328

Reynilda Monteza Stamnummer: 01400898

Promotor: Prof. dr. ir. Wim Cornelis

Master’s Dissertation submitted for obtaining the degree of Master of Science in Physical Land

Resources - main subject Soil Science

Academic Year: 2016 - 2017

i

Copyright

This is an unpublished M.Sc dissertation and is not prepared for further distribution.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,

The Promoter, The Author,

Prof. Dr. ir. Wim Cornelis Reynilda Monteza

ii

Acknowledgement

The success of this book is deeply dedicated to the following individuals who have made this

piece realistic.

First and foremost, I offer my great gratitude to our LORD GOD for giving me something to

hope for and talk with in times of despair and lonesome. For always being there, I know, even

to the point of giving up.

To my family who have always been there, to understand, to support, and to always listen. This

is all for you guys!

I would like to extend my gratitude to my promoter, Prof. Dr. Ir. Wim Cornelis for the guidance

he had given and for extending his patience to understand my situation, as always.

I am also thankful to all lab technicians: Maarten Volckaert for the assistance you have

extended during my analysis in the Soil Physics Laboratory and Annemie Terryn during the

textural analysis.

I would also like to thanks Prof. Dr. Victor B. Asio for all the help during the conduct of my

soil sampling and for allowing me to be accompanied by his researchers for free of charge. I

will surely not forget you guys!

Special thanks to VLIR-UOS not just for the scholarship but for the opportunity to fulfill my

goal and long-time dream of studying abroad.

Great thanks to all the Physical Land Resources’ professors and tutors for additional knowledge

and useful skills they all have thought us.

I would also like to thanks all my classmates who became my friends and companion for two

long years of battling the academe. Truly, we rock the world for coming from different culture

yet managed to become one in our endeavors here. I miss you all guys!

To all my ever supportive Filipino co-scholars here: April, Pao-pau, Bryan, ate Eve, Clod, kuya

Elmer, Nina, ate Marianne, Abz, Jham, Thirdy, Neriza, Hyzel, ate Yhurz, and all the rest I may

have failed to mention here but do know I remember you by heart guys!

To you who have inspired and motivated me to the best that you can. The most persistent guy

I have ever met, ‘Thank You!’ coz you believed I can and I did! ^_^

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Table of Contents

Copyright………………………………………………………………………………. i

Acknowledgement……………………………………………………………………... ii

Table of contents………………………………………………………………………. iii

List of figures………………………………………………………………………….. vi

List of tables…………………………………………………………………………… vii

List of abbreviations and acronyms……………………………………………………. viii

Abstract………………………………………………………………………………… ix

1. Introduction………………………………………………………………………… 1

2. Literature review…………………………………………………………………… 4

2.1 Soils in the tropics……………………………………………………………... 4

2.2 Soil quality……………………………………………………………………... 5

2.3 Soil degradation………………………………………………………………... 7

2.3.1 Soil erosion………………………………………………………………... 8

2.3.2 Chemical degradation………………………………………………...…… 9

2.3.3 Physical degradation……………………………………………………… 9

2.4 Land use change and its impacts…………………………..…………………… 10

2.5 Approach for soil quality assessment………………...……………...………… 12

2.6 Approach for soil quality improvement………………………………………. 13

2.6.1 Organic agriculture………………………………………..………………. 14

2.7 The Philippine scenario……...………………………………...………………. 15

3. Methodology………………………………………………….……………………. 18

3.1 Site characteristics and sampling………………………...……………………. 18

3.2 Infiltration measurement………………………….…………………………… 20

3.3 Visual Evaluation of Soil Structure……………………………………………. 21

3.4 Earthworm Density and Biomass……………………………………………… 22

3.5 Laboratory analyses of disturbed soil samples………………………………… 22

3.5.1 Soil pH…………………………………………………………………… 22

3.5.2 Soil Organic Carbon……………………………………………………... 23

3.5.3 Available Phosphorus……………………………………………………. 23

3.5.4 Soil Texture……………………………………………………………… 23

3.5.5 Particle Density…………………………………………………………... 23

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3.6 Laboratory analyses of undisturbed (core) soil samples……………………….. 23

3.6.1 Soil Water Retention Curve……………………………………………… 23

3.6.2 Bulk Density and Total Porosity…………………………………………. 24

3.6.3 Aggregate Stability……………………………………………….……… 25

3.6.4 Saturated Hydraulic Conductivity…………...…………………………... 25

3.7 Soil Quality Index calculation…………………………………………………. 25

3.8 Data Analyses………………………………………………………………….. 26

4. Results……………………………………………………………………………… 27

4.1 Soil chemical properties and characteristics…………………………………… 27

4.2 Soil physical properties and characteristics……………………………………. 28

4.2.1 Soil Texture……………………………………………………………… 28

4.2.2 Bulk Density and Soil Porosity (Macro and Matrix Porosity) ………….. 28

4.2.3 Wet Aggregate Stability and Structural Stability Index…………………. 29

4.2.4 Saturated Hydraulic Conductivity……………………………………….. 30

4.2.5 Soil water characteristics………………………………………………… 31

4.2.5.1 Soil Water Retention Curve………………………………………….. 31

4.2.5.2 Moisture Content at Field Capacity and Permanent Wilting Point….. 33

4.2.5.3 Plant-Available Water Capacity……………………………...……… 33

4.2.5.4 Soil Water Storage Capacity…………………………….…………… 34

4.2.5.5 Air Capacity……………………………………..…………………… 34

4.3 Soil Biological Property………...……………………………………………... 35

4.4 Visual Evaluation of Soil Structure……………………………………………. 35

4.5 Soil Quality Index……………………………………………………………… 36

5. Discussion………………………………………………………………………….. 39

5.1 Land use impact on soil chemical properties…………………………………... 39

5.2 Land use impact on soil physical properties…………………………………… 40

5.2.1 Bulk density, porosity, aggregate and structural stability………………... 40

5.2.2 Saturated hydraulic conductivity………………………………………… 41

5.2.3 Soil water retention curve and derived parameters………………………. 42

5.3 Land use impact on soil biological properties………………………………… 43

5.4 Soil Quality Index Assessment………………………………………………… 44

6. Conclusions and recommendations………………………………………………... 45

6.1 Conclusions…………………………………………………………………….. 45

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6.2 Recommendations……………………………………………………………… 46

7. References………………………………………………………………………….. 47

8. Appendices…………………………………………………………………………. 54

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List of figures

Figure 1. Global estimated changes in land use from 1700-1990............................................ 11

Figure 2. Hierarchical relationship of soil quality to agricultural sustainability.. ................... 13

Figure 3. Land cover map of the Philippines in 2010. ............................................................. 16

Figure 4. Map of Leyte, Philippines, and the location of the study site (red arrow). .............. 18

Figure 5. Representative pit profile for morphological identification of the soils of (a)

secondary forest (b) rainforestation (c) mahogany plantation (d) coffee plantation and (e)

grassland land uses. .................................................................................................................. 20

Figure 6. A close view of the double ring infiltrometer and measuring infiltration under field

conditions. ........................................................................................................................ ……21

Figure 7. Visual evaluation of soil structure and earthworm counting on field. ..................... 22

Figure 8. Structural stability index (SSI) at 0-20 and 20-40 cm soil depth of soils of the

different land uses. ................................................................................................................... 30

Figure 9. Laboratory saturated hydraulic conductivity of the soils under different land uses

for (a) 0-20cm (b) 20-40cm soil depth ..................................................................................... 31

Figure 10. Fitted and measured water content of the soils under different land uses for (a) 0-

20cm (b) 20-40cm soil depth.. ................................................................................................. 33

Figure 11. VESS score (Visual Evaluation of Soil Structure) of the soils under different land

uses. .......................................................................................................................................... 36

Figure 12. Overall scores of the soil quality indicators at different land uses. ........................ 37

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List of tables

Table 1. Key soil indicators for soil quality assessment. ........................................................... 6

Table 2. Key soil indicators for soil quality assessment. ........................................................... 7

Table 3. Framework for evaluating soil quality. ...................................................................... 12

Table 4. Successful crop diversification patterns in the Philippines. ...................................... 17

Table 5. Brief land-use history of the study sites..................................................................... 19

Table 6. Soil chemical properties of the study sites................................................................. 27

Table 7. Particle size distribution of the soils of the study sites. ............................................. 28

Table 8. Bulk density, soil porosity (macro- and matrix) of the soils of the study sites. ........ 29

Table 9. Mean weight diameter and aggregate stability of the soils of the study sites. ........... 30

Table 10. Field saturated hydraulic conductivity of the soils of the study sites. ..................... 31

Table 11. Derived van Genuchten parameters used for determining the water retention curve.

.................................................................................................................................................. 32

Table 12. Soil physical indicators derived from the water retention curve. ............................ 34

Table 13. Earthworm population density of soils under different land uses. .......................... 35

Table 14. Overall scores for soil functions (f) and soil quality index (SQI) values within the

0-40cm layer in five land uses. ................................................................................................ 37

Table 15. Pearson’s correlation (r) among soil properties and with soil organic carbon (SOC)

in forest, rainforestation, mahogany plantation, coffee plantation, and grassland land uses. .. 38

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List of Abbreviations and acronyms

SQI Soil Quality Index

BD Bulk Density

SOC Soil Organic Carbon

AC Air Capacity

MacPor Macroporosity

MatPor Matrix porosity

FC Field Capacity

PWP Permanent Wilting Point

PAWC Plant Available Water Capacity

SWSC Soil Water Storage Capacity

SSI Structural Stability Index

AS Aggregate Stability

MWD Mean Weight Diameter

Kfs Field Saturated Hydraulic Conductivity

Ks Saturated Hydraulic Conductivity

VESS Visual Evaluation of Soil Structure

Eworm Earthworm Density

SF Secondary Forest

RF Rainforestation demo farm

MP Mahogany Plantation

CP Coffee Plantation

GL Grassland

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Abstract

Conversion of land is a widespread phenomenon not only in temperate but especially in the

tropics. Conversion is mostly done from a native vegetation (like forest) to agroforestry to

agricultural land which, often overlooks the impacts it has on the soil and ecosystems. In the

Philippines, land use change is believed to be the cause of land degradation. In order to

conserve the environment and sustain the demands of increasing agricultural land, a good soil

quality is needed. Assessment of soil quality should be done to gain insights on the possible

ways to improve it if needed. A soil quality index is calculated using a set of soil indicators

that involves chemical, physical, and biological properties. Indicators that are most sensitive

are best used. This study evaluated the effect of land-use change on the chemical, physical and

biological properties of upland tropical soil. One hundred soil core ring samples were collected

from five land uses including secondary forest (SF), rainforestation farming (RF), mahogany

plantation (MP), coffee plantation (CP), and grassland (GL) at varying depths from 0-40cm.

Chemical and physical indicators were determined using standard laboratory methods. Field

experiments were also done for earthworm count, structure, and hydraulic conductivity.

Statistical analysis showed significant difference in bulk density, total porosity, soil

macroporosity, aggregate and structural index, and field saturated hydraulic conductivity.

Parameters derived from the water retention curve showed statistical difference at 20-40cm soil

depth and includes moisture content at field capacity (-100cm), moisture content at permanent

wilting point, air capacity and soil water storage capacity for both pressure heads of -100cm

and -340cm, and plant available water capacity (-340cm). The field evaluation of the soil

structure by Visual Evaluation of Soil Structure (VESS) showed a significant difference as

well. The soil quality index calculation showed that MP improved the field saturated hydraulic

conductivity, soil macroporosity, air capacity, soil structure, earthworm, and bulk density

compared to that of secondary forest. On the other hand, there was significant decrease in field

saturated hydraulic conductivity, soil macroporosity, air capacity, and an increase in bulk

density in CP and GL land uses. The RF land use showed considerable improvement of soil

quality from that of SF.

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1. Introduction

Land conversion and intensification of agricultural activities are widespread throughout the

tropics (Geissen, et al., 2009b). Increasing population growth and food resources demand

resulted in migration of poor and lowland peasants to forested uplands. As the forest cannot

sufficiently sustain the food demand, people start to cultivate the land after deforestation.

Illegal logging is also done for additional profit to meet their basic needs. According to Theng

(2015), conversion of tropical forests to agricultural land occurs at a rate of at least 10 million

hectares per annum. In most countries, continuous alteration of the natural land cover leads to

an increase of soil erosion (Lal, 2001) and landslides, and degradation of soil qualities (Giertz,

et al., 2005; Geissen, et al., 2009a; Navarrete & Tsutsuki, 2008)

The Philippines has a wet tropical climate with high annual precipitation. For the past years,

the country endowed abundant natural resources and great biodiversity. It is geographically

divided into two regions: the upland and the lowland area. Natural resources still exist in the

uplands but are faced with great pressure from the agricultural sector (Shively, 2001).

According to Philippine Forestry Statistics (PFS) of the Forest Management Bureau (FMB,

2014), the forest cover has declined from ~60% of the land area in 1934 to ~28% in 2013.

Conversion of the forest areas into agricultural land, and residential and industrial areas has

been extensive for decades.

Soil degradation is a severe global problem of modern times (Lal, 1998) and is more serious in

tropical than in temperate areas (Asio, et al., 2009). Widespread occurrence of land degradation

in the Philippines is believed to be caused by land-use change (Asio, 1996). Recently, reports

of extensive soil erosion and landslides are increasing. Areas without history of flooding are

now becoming susceptible. Land productivity and crop yield are declining in most areas

especially in the uplands.

Suitable soil management and land use are necessary to alleviate the problem. Farmers should

be aware of the implications of improper use of the soil. In order to do this, soil quality must

be evaluated. Soil quality involves chemical, physical, and the biological properties. Studies

on shifting cultivation reported a decline in some of the soil chemical properties like base

nutrients, soil pH, organic matter (Siebert, 1987; Sanchez, 1976), soil carbon, and nitrogen

content (Navarrete & Tsutsuki, 2008). Abu and Abubakar (2013) reported that conventionally

tilled soil had poorer physical quality compared to non-tilled and minimum-tilled soil. This

2

might result in a reduction of soil macroporosity and permeability which was observed (Giertz

et al., 2005; Opara-Nadi & Lal, 1987) to have a significant influence on the hydrological

processes in the soil. According to Fu, et al. (2000) low soil moisture content is associated more

to forests than to slope farmlands. They observed that agricultural activities in the farmlands

make the soil more porous and friable allowing high infiltration of water. The compact topsoil

and shortage of grass under forest discourage infiltration. However, Neris, et al. (2012) reported

that forest soil has more infiltration rate compared to cropped soils. Land use also affects the

biological quality of soil. A study found that the diversity and species richness of earthworm

were higher in managed soils than in successional forest (Geissen, et al., 2009a). Earthworm

populations changed from native species in forest to exotic species in managed soils.

Alternatively, visual soil assessment methods have been used to evaluate soil quality. Visual

soil assessment is a direct evaluation of morphological structural properties in the field. This

provides a rapid semi-quantitative data on physical quality (Mueller, et al. 2009). These

methods have been developed to give a simple and repeatable methodology for monitoring soil

degradation and soil quality, and to evaluate small areas in detail and large areas quickly

(Moncada et al. 2014a). Visual field assessments can be subdivided into soil profile description

and topsoil examination (Ball, et al., 2007). Three methods that have been widely used and

evaluated on temperate and tropical soils are the soil quality scoring procedure (SQSP), the

visual evaluation of soil structure (VESS), and the visual soil assessment (VSA). Soil structural

quality can also be assessed visually based on type of aggregates, and based on water aggregate

stability (Moncada et al. 2014b).

Therefore a need for evaluating soil quality in tropical areas is necessary since their soils are

more prone to degradation due to their properties and the prevalent climatic conditions (Asio

et al., 2009).

The overall objective of the study is to analyze the effects of land use change on the soil quality

in an upland tropical environment.

Specific objectives are to:

1) Evaluate the chemical, physical and biological properties of upland tropical soil under

different land uses.

2) Elucidate the relationship of soil quality to changes in land use.

3) Identify the most relevant soil quality indicators for the soils of interest.

3

We hypothesize that the soil quality, as reflected by several indicators, is greatly dependent

on land use; and that all the soil quality indicators link well with OC as a central key

indicator.

4

2. Literature Review

2.1 Soils in the tropics

Soil is an essential part of nature and considered the skin of the Earth with interfaces between

lithosphere, hydrosphere, biosphere, and atmosphere (Chesworth, 2008). It is a natural body

that exists as part of the pedosphere and thus plays essential functions such as 1) sustaining

plant and animal life, 2) regulating water, 3) filtering soil pollutants, 4) storing and cycling of

nutrients, and 5) supporting human health and habitation (Seybold et al., 1998). Soil is

described by a large set of soil characteristics that can either be chemical (i.e. pH, organic

matter, carbon content), physical (i.e. porosity, particle size, water and air permeability, water

retention), biological (i.e. roots, macro- and micro-organisms), or morphological (i.e. depth,

colour, horizon thickness). Soils are formed through physical, chemical, and biological

processes as influenced by combination of factors involving climate, organisms, relief, parent

material, and time.

Tropical soils occur over large areas in Africa, Central and South America, and Southeast Asia,

but are not extensive in Australia (Verheye, 2015). The tropics are subdivided into five major

agro-ecological zones: 1) humid tropics, 2) semiarid tropics, 3) acid savannas, 4) tropical

steeplands, and 5) tropical wetlands (Sanchez & Logan, 1992). Humid tropical areas are

characterized by high and constant temperature and significant amount of rainfall. This

condition allows the process of weathering over a long period of time. The native vegetation is

tropical rainforest or semi-deciduous forest with Ultisols or Oxisols type of soil (Verheye,

2015). Semiarid tropics and acid savannas have dry periods of 6-9 months and 3-6 months,

respectively. The majority of the soils in savannas are Alfisols and Ultisols, and Aridisols in

the semiarid tropics. These conditions are found in parts of Western Africa and Brazil. Tropical

steeplands are defined as those regions with slope of >30%, typically of the mountain ranges,

while tropical wetlands have aquic soil moisture regimes. Soils that develop in tropical

steeplands are generally Inceptisols (Shaxson, 1999). In tropical wetlands, most typical soils

are either Entisol, Inceptisol, or Mollisols, and Alfisol or Ultisol in terraces (Kyuma, 1985).

The major arable soils in the tropics have been classified as kaolinitic, oxidic, allophanic and

smectitic according to their dominant clay mineralogy. Kaolinitic soils are the most widely

occurring soils and comprise more than 70% of the arable soils in the tropics (Juo 2003).

5

Tropical rainforests support native vegetation in the majority of Southeast Asia. The climate

in this region is mainly tropical and the majority of the countries are characterized as warm,

humid tropics. Due to abundance in natural resources, those areas are inhabited by people who

practice subsistence agriculture. Human-induced activities like shifting cultivation resulted in

conversion of most parts of forested uplands (Turkelboom et al., 2008). Studies in Thailand

(Aumtong et al., 2009; Turkelboom et al., 2008) and Philippines (Siebert, 1987; Navarrete &

Tsutsuki, 2008) reported an increasing number of forest conversion.

In Africa, researchers noted accelerating soil surface runoff and erosion (Giertz et al., 2005;

Abu & Abubakar, 2013). The same scenarios are also experienced in tropical countries in North

America like in Mexico (Geissen et al. 2009) where a declining percentage of forest cover was

reported and degradation of forested and upland soils is aggravating. Navarrete & Tsutsuki

(2008) reported a decreased of soil carbon and nitrogen after conversion of Philippine forest

soil to rainforestation farming and grassland. A study in Java, Indonesia revealed that

deforestation and land-use change led to a high risk of soil eorion (Rudiarto & Doppler, 2013)

2.2 Soil Quality

Throughout the 1990s, the concept of soil quality evolved in response to the increased global

emphasis on sustainable land use and with a holistic focus emphasizing that sustainable soil

management requires more than soil erosion control (Karlen et al., 2003).

According to the Soil Science Society of America, soil quality is defined as the capacity of a

specific kind of soil to function, within natural or managed ecosystem boundaries, in order to

sustain plant and animal productivity, maintain or enhance water and air quality, and support

human health and habitation (Karlen et al., 1997). It includes an inherent component,

determined by the soil’s physical and chemical properties within the constraints set by climate

and ecosystem (Doran & Zeiss, 2000). This component is also affected by management and

land use decisions.

There are three types of soil quality: 1) soil physical quality, 2) soil chemical quality, and 3)

soil biological quality (Lal & Ratta, 1999). Arshad & Martin (2002) stated that a significant

decline in soil quality has occurred worldwide through adverse changes in its physical,

chemical, and biological properties and contamination by inorganic and organic chemicals.

Impact on soil quality can influence agronomic productivity and the environment as well.

6

Different factors contribute to this adverse change. Soil management practices like tillage,

cropping patterns, and application of pesticides and fertilizers can either improve or degrade

the soil quality. Human alterations of the land can also affect the natural biodiversity, soil

productivity, and the environment.

In order to assess soil quality, certain quality indicators can be determine and compared to

desired values for a specific use in a selected agro-ecosystem. Arshad & Martin (2002) define

soil quality indicators as measurable soil attributes that influence the capacity of soil to perform

crop production or environmental functions. Attributes that are most sensitive to management

are most desirable as indicators (Table 1). Systems that improve performance of the indicators

can be promoted and advanced to assure sustainability.

Table 1. Key soil indicators for soil quality assessment (Arshad & Martin, 2002).

Other authors suggested other indicators for soil quality. Shukla et al. (1996) noted that soil

organic carbon (SOC) is the single most dominant attributes for assessing soil quality. The

Environmental Assessment of Soil for Monitoring (ENVASSO) of the European Commission

Selected indicator Rationale for selection

Organic matter

Defines soil fertility and soil structure, pesticide and water

retention, and is used in process models

Topsoil-depth Estimates rooting volume for crop production and erosion

Aggregation

Soil structure, erosion resistance, crop emergence and early

indicator of soil management effect

Texture Retention and transport of water and chemicals, modeling use

Bulk density Plant root penetration, porosity, adjust analyses to volumetric

basis

Infiltration Runoff, leaching and erosion potential

pH Nutrient availability, pesticide absorption and mobility,

process models

Electrical conductivity

Defines crop growth, soil structure, water infiltration;

presently lacking in most process models

Suspected pollutants Plant quality, and human and animal health

Soil respiration

Biological activity, process modeling; estimate of biomass

activity, early warning of management effect on organic

matter

Forms of N

Availability to crops, leaching potential, mineralization/

immobilization rates, process modeling

Extractable N, P and K Capacity to support plant growth, environmental quality

indicator

7

had identified 20 qualified key indicators in assessing soil quality (Table 2) and the

corresponding procedures for evaluating the parameters of different indicators were also tested

(Kibblewhite et al., 2008).

Table 2. Key soil indicators for soil quality assessment (Kibblewhite et al., 2008).

2.3 Soil degradation

Soil degradation refers to the decline in long-term productive potential (Scherr, 1999), in other

words, to the decline of soil quality, or the reductions of attributes in relation to specific value

to humans (Lal, 2001). It has become a global issue because of its negative impacts on the

environment and socio-economic aspects (Tóth et al., 2008).

Two categories of soil degradation are recognized by the Global Assessment of Soil

Degradation (GLASOD). The first category is soil degradation such as soil erosion by water

Soil quality threat/Issue Indicator Name

Soil erosion Water erosion

Estimated soil loss by rill, inter-rill, and sheet

erosion

Decline in soil organic matter

(status)

Topsoil organic carbon content, Soil organic

carbon stocks

Soil contamination

Diffuse contamination

Local soil contamination

Heavy metal contents in soil,

Critical load exceedance by S and N

Progress in the management of contaminated

sites

Soil sealing

Land consumption

Brownfield re-development

Sealed area

Land take (to urban and infrastructural

development)

New settlement area established on previously

developed land

Soil compaction

Compaction, structural degradation

Causes of compaction

Density

Air-filled pore volume at a specified suction

Vulnerability to compaction

Decline in soil biodiversity

Species diversity

Soil microbial respiration

Earthworm diversity and biomass

Collembola diversity

Microbial respiration

Soil salinization Salt profile (EC), Exchangeable sodium

percentage (ESP), potential salt sources

Desertification Land area at risk of desertification, land area

burnt by wildfire

8

and wind. The second category is in situ soil degradation due to chemical processes like loss

of nutrients and organic matter, salinization, acidification, and pollution, and due to physical

processes such as compaction, waterlogging, and subsidence (Oldeman, 1992).

2.3.1 Soil erosion

Soil erosion is the process of displacement of soil material by wind or water agent. Erosion by

operation processes, like tillage, is also seen as a separate process and is called “tillage erosion”

(Wildemeersch et al., 2014). Erosion removes the fertile topsoil that causes reduction of the

soil quality. Soil degradation by accelerated erosion is a serious problem especially in the

developing countries of the tropics and subtropics (Lal, 2001). In the Philippines, many upland

areas have reddish appearance of the soil due to the exposure of humus-poor iron oxide-rich

subsoil after the dark and humus-rich topsoil have been removed by erosion (Asio et al., 2009).

Removal of soil materials by water depends on factors like rainfall intensity and runoff, soil

erodibility, slope gradient and length, and vegetation cover (Verdoodt, 2016). Sediment

transport and deposition follow the process of displacement either by means of splash erosion,

and sheet, rill, and gully erosion. The distance acquired by the materials before deposition

varies from few millimeters to thousands of kilometer.

Wind erosion is greatly influenced by vegetation and land use (Gay et al., 2009). Decrease in

vegetation cover, either due to overgrazing or removal of vegetation cover for domestic or

agricultural purposes, often resulted in removal of top materials by wind (Oldeman, 1992).

Tillage erosion is a process wherein tillage translocation in one direction is larger than tillage

translocation in the opposite direction and, thus a net tillage translocation occurs and causes

spatial variability on soil quality (Wildemeersch et al., 2014).

Activities such as deforestation and intensified land use in the uplands have led to increased

soil erosion. In the humid tropics of Asia, farmers grow a range of subsistence crops in sloping

and marginal uplands using practices which are often highly erosive (Lapar & Pandey, 1999).

In the Philippines, one third of the total land area has been excessively eroded, and erosion

rates vary considerably with land use systems and across different areas of the country (Asio

et al., 2009). In northern Thailand, Turkelboom et al. (2008) found that gully development at

the Dze Donglo catchment occurred more commonly in fields with significant runoff inflow

9

(run-on caused gullies) than in fields with large in-situ runoff generation (in-situ caused

gullies). They also observed that soil with upland rice cropping system appeared to have high

erosion susceptibility.

2.3.2 Chemical degradation

Chemical degradation refers to processes that cause changes in the chemical environment of

soils adversely impacting their productive capacity (Soil Degradation, 2010). Loss of essential

nutrients can be due to soil erosion, leaching and removal of vegetation (Asio et al., 2009).

Shifting cultivation or type of cropping systems also have an effect on soil nutrient conditions.

Farmers who practice agriculture in low-fertile upland soils often do not apply fertilizer,

possibly because by lack of money or poor knowledge of the soil status. Moreover, erosion and

decomposition play as the major processes of organic matter loss. Forest conversion and

deforestation also play a key role. As the forested areas are converted to agricultural land, the

inherent organic matter of the soil can be lost or depleted in the long run if the landowner does

not replenish it through fertilization.

Inappropriate and imbalanced fertilizer application in agricultural land often brings pollution

and soil acidification. Excess of minerals applied through fertilization is leached out of the soil

and goes down to the groundwater or adjacent bodies of water. This practice often causes

leaching of nitrates as product of nitrification and eutrophication to bodies of water.

Acidification can also occur in coastal areas upon drainage.

Salinization is defined as a change in the salinity status of the soil. This is often caused by

improper management of irrigation schemes, mostly in arid and semi-arid regions (Oldeman,

1992). It can also be observed in the low-lying coastal areas of the Philippines due to salt

intrusion and use of saline water for irrigation (Asio et al., 2009).

2.3.3 Physical degradation

Physical degradation occurs when human activities greatly impact the physical properties of

the soil. Soil compaction, sealing, and crusting are the common processes under this category.

Soil compaction is commonly caused by use of heavy machineries in agricultural field and

overgrazing. Low organic matter also contributes to compaction. Compacted soils are often

characterized by high bulk density and low macro-porosity. Sealing and crusting, on the other

10

hand, are results of deposition of transported fine materials or sediments. During intense

rainfall, under low vegetation cover or bare land conditions, raindrops directly hit the top layer

of the soil. This causes destruction of aggregates and dispersion, and orientation of fine

particles will later cause clogging. When this sealed layer dries, shrinkage occurs. These

processes pose detrimental effects on crops and the environment. Compaction and crusting

causes root impedance and can also result in low water infiltration that triggers surface runoff

(Cornelis, 2014).

2.4. Land-use change and its impacts

In the last decades, severe land use change has become widespread. The spatial reach of human

alterations of the Earth’s land surface to obtain food, fiber, timber, and other ecosystem goods

(DeFries et al., 2004) are unprecedented. Land use is the sum of the proximate causes of land-

cover change, namely, human activities or immediate actions that originate from the intended

manipulation of land cover. Proximate (or direct) causes involve a physical action on land cover

and are usually limited to a recurrent set of activities such as agriculture, forestry, and

infrastructure construction. Underlying (or root, or indirect) causes are fundamental forces that

underpin the more proximate circumstances. They operate more diffusely (i.e., from a

distance), often by altering one or more proximate causes (Geist et al., 2006).

Land use change can either be exogenous or endogenous. Exogenous changes are those that

arise from a depletion of key resources or a decline in economic goods and services. On the

other hand, endogenous changes are driven by economic development, urbanization, or

globalization (Lambin & Meyfroidt, 2010).

Land-use changes are so pervasive that, when aggregated globally (Figure 1), they affect the

Earth system functioning (Lambin et al., 2001). The ecosystems play an important role in 1)

providing goods for plant and animal, 2) climate regulation (energy and water flow), 3)

provision of freshwater, 4) biological diversity, 5) regulation of vectors of diseases, and 6) soil

fertility. The ecosystem responses variably to different land-use changes (DeFries et al., 2004).

11

Land-use change enhances the share of primary production for human consumption, but

decreases the share available for other ecosystem functions. For example, forest transitions

impact hydrological cycles, climate change, biodiversity crises, and soil conservation (Rudel

et al., 2005). Urbanization and land clearing may cause occurrence of flash floods. Conversion

of land may also cause changes in soil physical and chemical characteristics that can affect soil

fertility, increase soil erosion or cause compaction. However, this is not always the case. A

study on banana monoculture and agroforestry systems showed that intensive management of

this land use does not lead to a decrease in nutrient content in comparison with successional

forests (Geissen et al., 2009). On the other hand, Geissen et al. (2009) noted that soils used as

pastureland showed acidification and soil compaction. In contrast with the study of Navarrete

& Tsutsuki (2008), they found that forest conversion into secondary land uses decreased the

carbon, nitrogen, and neutral sugar contents in the soil.

Figure 1. Global estimated changes in land use from 1700-1990 (modified from Goldewijk,

2001)

The effect of land conversion may also vary from different ecological settings. Deforestation

can have different impact in temperate areas compared to tropical areas. In the temperate or

boreal regions, decrease in forest cover means exposure of brighter land surfaces. This can

cause a cooling effect as a result of increase in albedo (Bonan, 1999). On the other hand,

deforestation in the tropics can cause warming of the earth that can cause rise in temperature

and evapotranspiration.

Implementation of different soil management practices or agricultural land use also has impact

on the status of soil. Effects can either be positive or negative. Opara-Nadi & Lal (1987) noted

12

that moisture retention capacity of soil is lesser with conventional tillage than with no-tillage

methods. Their result agrees with reports of Giertz et al. (2005), and Abu and Abubakar (2013)

who observed reduction of macroporosity and permeability.

2.5. Approach for soil quality assessment

Soil quality assessment enables evaluation of management-induced changes in the soil and to

link existing resource concerns to environmentally sound land management practices

(Friedman et al., 2001). When soil quality is assessed over time, it can tell something about the

sustainability of management practices. Assessment is conducted by evaluating indicators.

Indicators can be physical, chemical, and biological properties, processes, or characteristics of

soils. They can also be morphological or visual features of plants. Indicators are measured to

monitor management induced changes in the soil. The methods of assessment can be

categorized into two major groups. The first group includes methods based on analysis in the

field and laboratory. This methods can be done using minimum data set (MDS). MDS is the

smallest set of soil properties or indicators needed to measure or characterize soil quality. A

number of studies proposed several sets of MDS (Doran & Parkin, 1994; Larson & Pierce,

1991). A general framework to evaluate soil quality is shown in table 3 as proposed by Carter

et al. (1997).

Table 3. Framework for evaluating soil quality (Carter et al., 1997).

The second group comprise visual soil assessment methods where soils are scored based on

(morphological) field observations. This provides rapid semi-quantitative data on physical

quality (Mueller et al. 2009). These methods have been developed to give a simple and

Process Attribute (or

property)

Indicators Possible method for

determining attribute

Capacity to accept,

hold, and release

water

Infiltration Infiltration rate,

sorptivity

Tension permeameter

Water-holding

capacity

Desorption curves Tension table,

pressure plate

Permeability Hydraulic

conductivity

Guelph permeameter

Capacity to accept,

hold, and release

energy

Organic matter Organic C Dry combustion

Labile organic

matter

Microbial biomass Chloroform

fumigation

Carbohydrates Acid hydrolysis

Microorganic matter Dispersion/sieving

Particle size Clay Hydrometer / pipette

13

repeatable methodology for monitoring soil degradation and soil quality (Pulido Moncada et

al. 2014a). Visual field assessments can be subdivided into soil profile description and topsoil

examination (Ball et al., 2007). Methods that have been widely used and evaluated on

temperate and tropical soils are the soil quality scoring procedure (SQSP), the visual evaluation

of soil structure (VESS), the visual soil assessment (VSA), visual assessment of aggregate

stability, and visual type of aggregates index (Pulido Moncada et al., 2014b).

2.6. Approach for soil quality improvement

Soil quality appears to be an adequate indicator of sustainable management (Figure 2).

Sustainability refers to the longevity of the health of an agricultural land-use system and hence

the ability of this system to maintain a productive capacity (Zuazo et al., 2011). A good

management system is one that balances the needs for production of food and fiber with those

for maintenance of the environment.

Figure 2. Hierarchical relationship of soil quality to agricultural sustainability. (Zuazo et al.,

2011).

14

Harwood (1990) emphasizes that the development of sustainable land management systems is

complicated by the need to consider their utility to humans, their efficiency of resource use,

and their ability to maintain a balance with the environment that is favorable both to humans

and to most other species. Practices such as conservation tillage, crop rotation, organic farming,

and residue management strongly influence the dynamics of soil quality (Carter, 1994). In

Philippines upland areas, practices which conserve soil and enhance the long-term

sustainability of agricultural production systems have been introduced and adopted by farmers

(Asio et al., 2009). However, Lapar and Pandey (1999) noticed that adoption of conservation

technologies in those areas are rather low. They cited that the problem is often not the lack of

technology but rather the incompatibility of the technology promoted with the socio-economic

conditions under which farming is carried out.

2.6.1 Organic agriculture

Organic agriculture is defined as “a holistic production management system which promotes

and enhances agro-ecosystem health, including biodiversity, biological cycles, and soil

biological activity” (FAO/WHO, 2003). It emphasises the use of management practices in

preference to the use of off-farm inputs and accomplished by using, where possible, agronomic,

biological, and mechanical methods, as opposed to using synthetic materials. The most

important decision to improve the soil quality through organic farming is the adjustment of

C/N ratio and increase the C and N storage (Clark et al., 1999).

Vermicomposting is one of the techniques adopted in this approach. It is used as soil enhancer

and is expected to improve the fertility status of degraded soils especially those low in humus

content (Asio et al., 2009). Other farmers practice ‘biochar’ application and integration of crop

residues or animal manures in the field. Asai et al. (2009) reported that biochar application had

improved the saturated hydraulic conductivity of the topsoil in an upland rice system. They

also observed that biochar application resulted in higher grain yields at sites with low P

availability and improved the response to N and NP chemical fertilizer treatments. Marinari et

al. (2000) found a positive correlations between soil porosity, enzymatic activity and CO2

production in organic treated soil. The increase in macropores, ranging from 50–500 μm, in

soil treated with organic fertilisers was mainly due to an increase in elongated pores, which are

considered very important both in soil–water–plant relationships and in maintaining a good

soil structure. Organic treatments stimulated soil biological activity probably due to an

15

enrichment of soil organic matter. Ferreras et al. (2006) reported that an increase in soil organic

matter by addition of organic fertilisers significantly increased the stable soil aggregates.

2.7. The Philippine scenario

Understanding the land use status in humid tropical agroecosystems may assist in developing

more appropriate farming systems, alleviate deforestation and regenerate degraded land

resources. Land-use change has featured in the development of the Philippine landscape, and

has apparently contributed to the widespread occurrence of degraded land across the country

(Asio, 1996). According to the Philippine Forestry Statistics (PFS), the forest cover from 1934

was estimated to be 60% of the land area, but the acreage of the forest had decreased markedly

to ~28% in 2013 (FMB, 2014). A 2010 land cover map (Figure 3) shows the small percentage

of land area covered with forest. Most of the areas are already converted into cropland either

with annual or perennial crops. The Philippines is largely dependent on agriculture. Its major

commodities include rice, corn, coconut, vegetables, plantation crops (pineapple and banana),

ornamentals, and timber. Among those, rice is considered to be the first staple food followed

by corn. For the past decades, extensive production of rice became the main focus of the

government due to the accelerated food demand. This consequently resulted in expansion of

production area by converting upland areas for upland rice production.

Deforestation and land use change or agricultural intensification are the two major driving

factors of land degradation in the country. Farmers grow a range of subsistence crops in sloping

and marginal uplands using practices which are often highly erosive (Garrity et al., 1993). In

addition, these activities reduce in-situ productivity and sustainability of the land (Lapar &

Pandey, 1999). To alleviate this situation, contour hedgerow technology was actively promoted

in some areas in the Visayas and Mindanao. Hedgerows can be very effective at preventing

loss of soil from fields by acting as a barrier to water-borne run-off. Mulching by use of crop

residues is now being accepted by most farmers. Mulching is done using plastic film and with

continous promotion of the local government units to the farmers it is now practiced in most

areas, specially by vegetable farmers in areas with limited supply of water for irrigation. Crop

residue cover/mulching proves to be beneficiary through an increment in soil moisture,

reduction in soil erosion, and maintenance of soil temperature (Patil et al., 2013).

In lowland ricefield areas, various methods are implemented to reduce the use of machinery in

land preparation. Some farmers practice minimum tillage which means plowing and harrowing

the field just once compared to “1 plow + 2 harrow + levelling” traditional land preparation.

16

The effects of such practice vary depending on the sites. Some reported difficulty in

establishing a ‘good’ field using minimum tillage, as it encourages growth of weeds which later

compete with the main crop. Another practice is leaving the cut rice stubbles in the field as a

surface cover during fallow period and left to decomposed as source of nutrients and organic

matter for the next cropping.

Figure 3. Land cover map of the Philippines in 2010 (FMB, 2013).

The Department of Agriculture has adopted diversification of crops to promote and hasten

agricultural development. It is categorized into two perspectives: one is planting cash crops

17

after the main crop and the other is planting intercrops (permanent or cash crops) in-between

the main crop, usually a permanent crop. This strategy helps to attain the goal of the Department

of Agriculture in increasing productivity and farm income while conserving the environment

(FAO, 2001). Various cropping patterns have been tested, but only eight gave a successful

result in terms of adaptability, crop yield and profit (Table 4).

Table 4. Successful crop diversification patterns in the Philippines (FAO, 2001).

Cropping System Location Total Yield/ha Profit/ha

Rice-onion Talavera,N.E 3.43 6,116

Rice-garlic Laoag, I.N 1.7-2.4 14,006-17,249

Rice-peanut Ilocos region 1.8 25,990

Rice-mungbean Ilocos region 0.88 6,147

Rice-onion Central luzon 10.66 64,380

Coconut+cacao Murcia, Negros - 30,202.50

Coconut+passion fruit Lucban, Quezon - 30,000

Coconut+banana Southern mindanao - ROI* =163-63%

Coconut+pineapple Southern mindanao - ROI =68%

Coconut+pineapple+

cacao+banana

Jaro, Leyte - 18,892

*ROI= return of investment; - means crop rotation and + means intercropping

18

3. Methodology

3.1 Site characteristics and sampling

The study was conducted at the reserved rainforest area on the lower western slope of Mt.

Pangasugan, approximately 8 km north of Baybay City, Leyte, Philippines (10°46’N and

124°50’E) (Figure 4). The study area has a slope of <5% at approximately 73-112 m a.s.l

(Navarrete & Tsutsuki, 2008). The climate is humid tropical monsoon with an average annual

precipitation of 2600 mm and an average air temperature of 27°C. The soil was classified as

Haplic Alfisol (FAO System) or Typic Hapludult (Soil Taxonomy) derived from andesitic

pyroclastic materials of late Quaternary (probably Holocene to upper Pleistocene) origin (Asio,

1996). Five sites with comparable climate, parent material, geology and soil type, but with

different land uses were selected.

Figure 4. Map of Leyte, Philippines, and the location of the study site (red arrow).

The pedological and vegetation studies of the area revealed that the area is under forest until

the 1950s (Navarrete & Tsutsuki, 2008). The land-use types selected were: secondary forest

(reference land use), mahogany plantation, rainforestation farming, coffee plantation, and

grassland (Table 5). Secondary forest was selected since native forest was hard to find at

elevations below 250 m a.s.l because of anthropogenic perturbation. From each site, a total of

10 subsampling plots were randomly selected. Uniform sampling depths was considered from

19

each site: 0-20, 20-40, and 40-60 cm for disturbed samples and 0-20 and 20-40 cm for

undisturbed samples using a core sampler. Each soil samples were properly processed and

stored and later transported to Faculty of Bioscience Engineering, Department of Soil

Management, Gent University for various physical and chemical analysis. Per subsampling

plot the earthworm count and VESS method were applied for visual assessment of soil quality.

A representative pit profile for each site was excavated for soil horizons characterization.

Table 5. Brief land-use history of the study sites (Navarrete & Tsutsuki, 2008)

Site Location Elevation

(m)

Description of land-use history

SF N10°44.905′

E 124°48.262′

112 A secondary forest that was not cultivated at all since

1950s. The area was dominated by tree species such as

Albizia lebbeck, Pterocymbium tinctorium, Artocarpus

blancoi, Barringtonia racemosa and Glochidion album.

Many other tree species can be found.

MP N10°44.753′

E 124°48.229′

99 Intensively used for growing root crops and vegetables

from the 1950s to the early 1970s. The typical land-use

management in the area was burning the crop residue

after each harvest. In the early 1980s, a total of 1.6 ha was

planted with mahogany.

RF N10°44.688′

E 124°48.329′

107 A 14 year-old closed-canopy rainforestation project of the

Visayas State College of Agriculture-German Agency for

Technical Cooperation tropical ecology program. The

area experienced several years of intensive cultivation in

the 1960s, and was converted into bushland in the late

1970s. It was cultivated again in the 1980s and was

abandoned until the establishment of rainforestation

farming in 1992. Imperata cylindrical dominated the area

prior to rainforestation. In 1992, a total of 10, 616 native

tree species and fruit trees comprising 263 plant species

were planted.

CP N10°44.614′

E 124°48.138′

75 Abandoned shifting cultivation field since the 1980s and

in the 1990s was planted with coffee. The area was under

shifting cultivation from the 1950s to the early 1980s. For

the past decade organic manure has been applied to the

coffee trees. The area is subject to manual tillage.

GL N10°44.592′

E 124°48.124′

73 Intensively cultivated with root crops from the 1960s to

1980s. It was abandoned and left for grassland until

recently. The area is sometimes used for grazing pasture

animals.

SF= secondary forest, RF= rainforestation farming, MP= mahogany plantation, CP= coffee

plantation, GL= grassland.

20

Figure 5. Representative pit profile for morphological identification of the soils of (a)

secondary forest (b) rainforestation (c) mahogany plantation (d) coffee plantation and (e)

grassland land uses.

3.2 Infiltration measurement

The infiltration rate was determined in the field using a double ring infiltrometer (Figure 6)

with falling water head method described by Reynolds et al. (2002). On each site, two to three

measurements were done. The rings were hammered into the soil at a depth of 5 cm and both

were filled with water at an equal height. The water level was kept the same for both rings to

allow the vertical infiltration from the inner ring. The drop of water level (cm) was measured

(a) (b) (c)

(d

)

(e)

21

at certain time interval. The cumulative infiltration (I) was computed using the equation of

Philip (1957) and the infiltration rate (i) was derived from the cumulative infiltration.

(1) 𝐼 = 𝑆√𝑡 + 𝐴𝑡 (2) 𝑖 = 𝑆 1

2√𝑡+ 𝐴

The t corresponds to the time of infiltration, A is the hydraulic conductivity and S is the

sorptivity which are both derived from curve fitting.

Figure 6. A close view of the double ring infiltrometer and measuring infiltration under field

conditions.

3.3 Visual evaluation of soil structure

Visual field assessment of the soil morphological structural properties is an alternative for a

detailed and quick assessment of soil condition. Visual assessment of the soil structure was

done using the visual evaluation of soil structure (VESS) following the procedure by Ball et

al. (2007). Ten subsampling plots were randomly sampled for the assessment. In each subplots

a dimension of 25cm x20cm x 30cm were dug using a spade to get a block samples. Each

22

block samples were further divided into two layers (0-10cm and 10-30cm). Each sample was

scored according to (1) ease of break up (2) aggregates shape and size (3) roots activity (4)

and anaerobism. The score ranges from Sq1 to Sq5 with a ‘less is better’ approach. This means

that soil given with a score of Sq1 has the best structure. Afterwards, the same samples were

used for earthworm density and biomass determination.

3.4 Earthworm density and biomass

Earthworm population density was measured at ten subsampling plots under each land uses,

earthworms were extracted by hand from a 25x25cm monolith at depths of 0-10 and 10-30 cm.

The fresh and oven dried weight of all the collected earthworms were measured to determined

the earthworm biomass.

Figure 7. Visual evaluation of soil structure and earthworm counting on field.

3.5 Laboratory analyses of disturbed soil samples

3.5.1 Soil pH

The soil samples were air-dried and sieved (<2 mm). Two extracting solution were used (1)

1:5 soil-water extract ratio and (2) KCl extracting solution with 1:2.5 extract ratio. The soil pH

was measured using a pH meter electrode type that was calibrated with standard solutions of

pH 3 and pH 7.

23

3.5.2 Soil organic carbon

The organic carbon was determined following the Walkley-Black Method (1934). An oxidizing

agent (1N K2Cr2O7) was mixed to a weighed amount of soil samples which was further reduced

by oxidizing the organic matter in the soil. Then the remaining amount of oxidant was measured

by titrating with 1 N FeSO4.

3.5.3 Available phosphorus

It was determined with the Bray and Kurtz method (1945) and was measured using a

spectrometer at a wavelength of 665 nm.

3.5.4 Soil texture

The soil texture was determined by sieving and sedimentation by pipette method (Gee and Or

2002). The analysis was performed on air-dried and sieved soil samples (< 2mm). Good

dispersion of the clay group was achieved by removing all the cementing materials such as

CaCO3, organic matter and oxides. The loam and clay (fine fractions) were separated from the

sand by wet sieving on a sieve with 50 µm aperture. The clay and loam fractions were separated

by pipetting after sedimentation at a constant temperature and sedimentation fixed time. All

fractions were oven-dried at 105°C and weighed.

3.5.5 Particle density

The particle density was determined using the Pycnometer method.

3.6 Laboratory analyses of undisturbed (core) soil samples

3.6.1 Soil water retention curve

The soil water retention curve of 50 samples was determined using the sandbox (Eijkelkamp

Agrisearch Equipment). The core samples were subjected to various pressure heads: -10 cm, -

30 cm, -50 cm, -70 cm and -100 cm inside the sandbox. Each core samples were weighed until

equilibrium was reached prior to changing to a higher pressure head. After the -100 cm head

applied the soil samples were divided into 4 subsamples. Using a pressure plates apparatus

same soils were subjected to higher pressure heads of -340 cm, -1020 cm, and -1540 cm

following the procedure outlined in Cornelis et al. (2005). An undisturbed subsamples of the

core soil samples were used for -340 cm and -1020 cm while the disturbed portion were used

for -1540 cm. Another part of the disturbed subsamples was used for determining the moisture

24

content and volumetric content at -100 cm pressure heads by oven drying and the weights were

measured.

The van Genuchten model (van Genuchten et al., 1980) was used to fit the derived water

retention curve to the measured volumetric water content at each pressure heads:

θ = θ𝑟 + (θ𝑠 − θ𝑟). [1 + (α. h)n] − m (3)

where, θ= volumetric water content, θr= residual volumetric water content, θs= volumetric

water content at saturation, α= parameter (cm-1), n and m are dimensionless parameters (-).

Other soil quality indicators like air capacity (AC), soil water storage capacity (SWSC), plant

available water capacity (PAWC), matric porosity (MatPor), and macroporosity (MacPor)

(Reynolds et al., 2007, 2008, 2009) were derived from the water retention data.

AC = θ𝑠 – θFC (4)

PAWC = θFC – θPWP (5)

SWSC = θFC

θ𝑠 (6)

MacPor = θ𝑠 − MatPor (7)

MatPor = θm (8)

where, θS, θm, θFC , θPWP (m3m-3) are the saturated volumetric water content of the soil (h=0),

the saturated volumetric water content of the soil matrix, volumetric water content at field

capacity (h= -100 cm), volumetric water content of the soil at permanent wilting point (h=-

15300 cm) respectively.

3.6.2 Bulk density and total porosity

The bulk density (BD) was measured using the subsamples from the core samples used for the

sandbox. The samples were oven dried and weighed and BD was calculated using the equation:

𝜌𝑏 = Ms

Vs (9)

The total porosity of the soil will be deduced from the two density measures as:

25

Φ = (1 −𝜌𝑏

𝜌𝑠) ∗ 100 (10)

where, ρb is the bulk density, Ms is the mass of soil, Vs is the volume of soil, φ is the total

porosity, and ρs is the particle density.

3.6.3 Aggregate stability

Aggregate stability of the soil was determined with the Yoder method modified by Kemper

and Rosenau (1986). Fast wetting and slow wetting will be applied to determine the aggregate

stability by using the wet sieving apparatus by Eijkelkamp Agrisearch Equipment (The

Netherlands). The mean weight diameter (MWD) will be calculated using the equation:

MWD =W𝑠d

W𝑡 (11)

where, Ws is the stable soil aggregate fraction, d is the mean diameter of the fraction, and Wt

is the total weight of the sample.

3.6.4 Saturated hydraulic conductivity

The laboratory saturated hydraulic conductivity was measured from the 50 undisturbed core

soil samples by means of a lab permeameter with constant head method (Klute and Dirksen,

1986; Eijkelkamp Agrisearch Equipment) and is calculated using Darcy’s equation:

Q = −Ks ∗ A [∆H

∆z] (12)

where, Q is the discharge (steady state flow), Ks is the saturated hydraulic conductivity, A is

the transect surface area, ∆H is the hydraulic head difference, and ∆z is the length of the soil

sample or vertical distance.

3.7 Soil quality index calculation

A minimum dataset was developed to calculate the SQI. Minimum dataset consisted of 10

indicators that corresponded to important soil functions. The mean data for each parameters

were used. Development of the SQI followed three steps that were outlined by Karlen et al.

(2003). The first step was to select appropriate SQI indicators that represent five soil functions.

These includes f(i) supply nutrients, f(ii) supply water and soil aeration, f(iii) sustain biological

activity, f(iv) sustain plant growth, and f(v) ability to resist erosion and soil degradation. Ten

indicators were selected as the minimum dataset. The second step was interpreting indicators

by transforming them into a unit less value ranging from 0 to 1. The transformation steps

26

followed the linear technique described by Andrews et al. (2002) as cited by Cherubin et al.

(2016). The third step involved the multiplication of transformed indicators into their weight.

The result were summed within each soil functions which were subsequently multiplied by

their weight. Finally the results were summed to calculate the SQI (Appendices 1).

3.8 Data Analyses

Values of chemical properties referred to the composite sample of the 10 subplots while data

on all other properties were taken from each subplots. The data were subjected to the test of

normality (Shapiro-Wilk test) and homogeneity of variances. Analysis of variance (ANOVA)

followed by the least significant difference (LSD) were used to estimate significant differences

between the land use types. Data that did not satisfy the two assumptions of ANOVA even

after transforming the data were tested using nonparametric Kruskal-Wallis. In case of the non-

homogeneity of variances the Dunnet T3 was used. Statistical correlations between quality

indicators were determined using the Pearsons correlation test. All analyses were done at 5%

significance level and performed using the SPSS Statistics 24 software.

27

4. Results

4.1 Soil chemical properties and characteristics

The behavior of soil pH under different land uses is shown in table 5. The table shows that all

soils were slightly acidic with pH (H2O) ranging from 5.23-6.12. The soil pH was higher for

MP site compared to SF site, whereas the rest of the land-use types (CP, RF, and GL) has more

or less similar soil pH with SF land use. The soil organic carbon (SOC) content from all land

uses showed a low percentage which is below the optimal SOC range of 3-5 wt. % as cited by

Reynolds et al. (2008) for medium and fine-textured soils. Moreover, SOC tend to decrease

with depth to almost 50% from the topsoil to the subsoil (20-40 cm depth) for soils of SF, RF,

and MP while CP and GL has almost the same decrease at 40-60 cm depth. Available P was

generally very low with range of 0.71-1.25 mg kg-1 and showed no measurable differences

among different land uses.

Table 6. Soil chemical properties† of the study sites.

Land Use Depth

(cm)

SOC

(%)

CaCO3

(%)

Avail. P

(mg kg-1)

Soil pH

KCl H2O

SF 0-20 1.78 0.00 1.00 4.26 5.41

20-40 0.90 0.00 0.84 4.03 5.36

40-60 0.75 0.00 0.83 4.00 5.30

RF 0-20 1.49 0.00 0.91 4.26 5.61

20-40 0.47 0.00 0.88 4.15 5.62

40-60 0.73 0.00 0.91 4.15 5.66

MP 0-20 1.55 0.25 1.01 4.60 6.12

20-40 0.89 0.19 1.13 4.26 5.95

40-60 0.77 0.47 1.25 4.20 5.84

CP 0-20 1.83 1.07 0.87 4.20 5.38

20-40 1.27 0.22 0.71 4.10 5.29

40-60 0.97 0.00 0.94 4.01 5.23

GL 0-20 1.86 0.05 1.05 4.58 5.75

20-40 1.07 0.27 0.94 4.45 5.63

40-60 0.81 0.00 0.80 4.32 5.50 †Values were from composite sample of 10 subplots. SF= Secondary Forest, RF= Rain

forestation, MP= Mahogany Plantation, CP= Coffee Plantation, GL= Grassland, SOC= Soil

Organic Carbon, CaCO3= Calcium Carbonate content.

28

4.2 Soil physical properties and characteristics

4.2.1 Soil texture

Table 7 shows that all soils from five different land uses have a high clay content of >30% and

were identified as of clay texture with the exception of soils from MP which has clay loam

texture all throughout the soil profile (0-60 cm depth).

Table 7. Particle size distribution of the soils of the study sites.

Land Use Depth

(cm)

Sand Silt Clay Texture

class Ɨ (%)

SF 0-20 19.4 24.4 56.2 C

20-40 16.5 20.0 63.4 C

40-60 14.2 17.7 68.1 C

RF 0-20 27.2 31.5 41.3 C

20-40 23.0 30.2 46.7 C

40-60 20.7 29.6 49.7 C

MP 0-20 31.1 35.0 33.9 CL

20-40 34.3 31.6 34.1 CL

40-60 28.1 32.8 39.0 CL

CP 0-20 22.5 19.6 57.9 C

20-40 14.3 18.4 67.3 C

40-60 10.2 15.5 74.3 C

GL 0-20 23.3 17.1 59.6 C

20-40 11.1 16.6 72.4 C

40-60 9.7 14.4 76.0 C Ɨ Food and Agriculture Organization (2006); C- clay, CL – clay loam, SF= Secondary Forest,

RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee Plantation, GL= Grassland

4.2.2 Bulk density and soil porosity (macro and matrix porosity)

Table 8 shows the bulk density and porosity of the soils under different land uses. Bulk density

showed no significant difference between SF, RF and MP land use but differs significantly

from CP and GL (p=0.03) at the upper soil layer. On the other hand, bulk density was highest

for CP and was significantly greater than SF and MP (p<0.03) at both soil layers. MP had the

lowest bulk density among the land uses for both soil depth.

Porosity decreased significantly from changing the land use to RF, CP and GL. This can be

attributed to the increase in bulk density of the latter land uses as evident of the negative

29

correlation of the two (Table 15). Nevertheless, there was no observed significant change in

the soil matrix porosity and macroporosity among the different land uses. The soil

macroporosity only differs significantly among different land uses at a depth of 20-40 cm

which can be caused by the bioturbation in SF and presence of rock fragments in the subsoil at

MP sites.

Table 8. Bulk density, soil porosity (macro- and matrix) of the soils of the study sites.

Land

Use†

Depth

(cm)

BD

(Mg m-3)

Total

Porosity Soil MatPor Soil MacPor

(m3 m-3)

0-20 cm

SF 0-20 0.99a 0.64a 0.58ns 0.06ns

RF 0-20 1.01ab 0.62ab 0.56ns 0.06ns

MP 0-20 0.98a 0.64a 0.55ns 0.09ns

CP 0-20 1.18b 0.57b 0.53ns 0.03ns

GL 0-20 1.18b 0.56b 0.53ns 0.03ns

20-40 cm

SF 20-40 0.91a 0.66a 0.53ns 0.14a

RF 20-40 1.07ab 0.60b 0.55ns 0.05b

MP 20-40 0.82ac 0.70a 0.55ns 0.14a

CP 20-40 1.17b 0.56b 0.52ns 0.04b

GL 20-40 1.10ab 0.59b 0.50ns 0.09ab

Treatment means within columns followed by the same letter do not differ significantly at 5%

level of significance using Least Significant Difference (LSD); ns- not significant.

†SF= Secondary Forest, RF= Rain Forestation, MP= Mahogany Plantation, CP= Coffee

Plantation, GL= Grassland, BD= Bulk density, MatPor= Matrix porosity, MacPor=

Macroporosity.

4.2.3 Wet aggregate stability and structural stability index

Aggregate stability index showed a lower value under MP and CP land uses (Table 9). At the

upper layer (0-20cm depth), the stability index value of CP was significantly lower (p<0.05)

compared to other land uses. Under CP the topsoil is subjected to manual tillage that can

continuously breakdown the aggregates. However, stability index of CP in the subsoil showed

a higher value. This may indicate the nature of tillage practice done in the area which only

disturbed the topsoil.

All soils of the different land uses were observed to be structurally degraded as indicative of a

low structural stability index (SSI) value (Figure 8). The SSI for all land uses shows a

30

measurable decrease (approximately 50%) with depth and RF having the lowest SI value which

means poorer soil structural stability compared to other land uses at 20-40 cm soil depth.

Table 9. Mean weight diameter and aggregate stability of the soils of the study sites.

Land Use† MWD AS

Index value Classification Ɨ

0-20 cm

SF 1.21 0.809a Stable

RF 1.10 0.736ac Stable

MP 1.02 0.679ac Intermediate

CP 0.74 0.494bc Unstable

GL 1.20 0.800a Stable

20-40 cm

SF 1.31 0.872a Stable

RF 1.06 0.708ac Stable

MP 0.87 0.580bc Intermediate

CP 1.29 0.864a Stable

GL 1.23 0.823ac Stable

Treatment means within columns followed by the same letter do not differ significantly at 5%

level of significance using Least Significant Difference (LSD).

†SF= Secondary Forest, RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee

Plantation, GL= Grassland, MWD= Mean Weight Diameter and AS= Aggregate Stability.

Ɨadapted from Pulido Moncada et al. (2013). AS classification: >0.7= stable; <0.5= unstable.

Figure 8. Structural stability index (SSI) at 0-20 and 20-40 cm soil depth of soils of the different

land uses.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Forest Rainforest Mahogany Coffee Grassland

SS

I (%

)

Land use

0-20 cm 20-40 cm

31

4.2.4 Saturated hydraulic conductivity

Saturated hydraulic conductivity was both measured in the field (Kfs) and in laboratory (Ks).

There was no significant difference of Ks among different land uses (Figure 9). Nevertheless,

a large decrease in Ks with depth was observed especially for soils under SF, RF and MP. On

the other hand, the Kfs among land uses showed a significant difference with each other (Table

10). Ks was significantly greater for MP than CP and GL (p=0.003 and p=0.007, respectively).

Figure 9. Laboratory saturated hydraulic conductivity of the soils under different land uses for

(a) 0-20cm (b) 20-40cm soil depth. SF= Secondary Forest, RF= Rain forestation, MP=

Mahogany Plantation, CP= Coffee Plantation, GL= Grassland, ‘ns’ denotes treatment

Table 10. Field saturated hydraulic conductivity of the soils of the study sites.

Means followed by the same letter are not significantly different according to Dunnet’s test

(p<0.05). Treatment mean is the average of 3 replicates. Kfs= Field saturated hydraulic

conductivity.

4.2.5 Soil water characteristics

4.2.5.1 Soil water retention curve

The water retention curves for different land uses at 0-20cm and 20-40cm soil depth are

presented in Figure 10. The curves determined using the van Genuchten equation fitted the

measured data well as indicated by the high coefficient of determination, R2 in Table 11.

Land Use Kfs (x10-5m s-1) St. Dev.

Secondary forest 4.68a 0.41

Rainforestation 2.21a

Mahogany plantation 10.9ab

Coffee plantation 0.96ac

Grassland 1.38ac

0

0.5

1

1.5

2

SF RF MP CP GL

Ks(

x1

0-4

m s

-1)

Land Use

(a)

ns

0

0.5

1

1.5

2

SF RF MP CP GL

Ks(

x1

0-4

m s

-1)

Land Use

(b)

ns

32

Table 11. Derived van Genuchten parameters used for determining the water retention curve.

Land Use†/Depth 0-20 cm 20-40 cm

SF RF MP CP GL SF RF MP CP GL

Derived ParametersƗ

θr (cm3 cm-3) 0.340 0.261 0.275 0.290 0.000 0.193 0.300 0.302 0.000 0.000

θs (cm3 cm-3) 0.581 0.572 0.553 0.521 0.557 0.535 0.554 0.539 0.517 0.502

α (cm-1) 0.018 0.031 0.026 0.013 0.058 0.013 0.016 0.007 0.003 0.005

n (-) 1.404 1.308 1.358 1.726 1.104 1.171 1.318 2.842 1.067 1.015

m (-) 0.288 0.236 0.264 0.421 0.094 0.146 0.241 0.648 0.063 0.015

R2 0.986 0.979 0.986 0.936 0.935 0.968 0.964 0.970 0.976 0.972

†SF= Secondary Forest, RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee

Plantation, GL= Grassland

Ɨ θr= residual volumetric water content, θs= volumetric water content at saturation, α=

parameter, n and m are dimensionless parameters, R2= coefficient of determination

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1.0 10.0 100.0 1000.0 10000.0 100000.0

Wat

er C

onte

nt

(m3/m

3)

negative matric head (cm)

(a)

Fitted SF Fitted RF Fitted MP Fitted CP Fitted GL

Meas. SF Meas. RF Meas. MP Meas. CP Meas. GL

33

Figure 10. Fitted and measured water content of the soils under different land uses for (a) 0-

20cm (b) 20-40cm soil depth. SF= Secondary Forest, RF= Rain forestation, MP= Mahogany

Plantation, CP= Coffee Plantation, GL= Grassland, Meas.= Measured water content.

4.2.5.2 Moisture content at field capacity and permanent wilting point

The moisture content at field capacity (FC, -100cm) and permanent wilting point (PWP) at 0-

20 cm, both showed a non-significant differences among the different land uses (Table 12).

However, the moisture content at a pressure head of -340cm, and PWP at 20-40cm soil depth

showed a significant difference. Both parameters were significantly lower for MP compared to

other land uses.

4.2.5.3 Plant-available water capacity

Plant-available water capacity (PAWC) did not vary among land uses at both pressure heads

(h=-100 and -300cm) in the 0-20 cm soil depth. In the subsoil, PAWC at h= -100cm showed

a significant variation among land uses with MP having the highest value as shown in Table

12.

0

0.1

0.2

0.3

0.4

0.5

0.6

1.0 10.0 100.0 1000.0 10000.0 100000.0

Wat

er C

on

ten

t (m

3/m

3)

negative matric head (cm)

(b)

Fitted SF Fitted RF Fitted MP Fitted CP Fitted GL

Meas. SF Meas. RF Meas. MP Meas. CP Meas. GL

34

Table 12. Soil physical indicators derived from the water retention curve.

Treatment means within columns followed by the same letter do not differ significantly at 5%

level of significance using Least Significant Difference (LSD); ns- not significant.

†SF= Secondary Forest, RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee

Plantation, GL= Grassland, FC= Field capacity, PWP= Permanent wilting point, AC= Air

capacity, SWSC= Soil water storage capacity, PAWC= Plant-available water capacity.

4.2.5.4 Soil water storage capacity

Soil water storage capacity (SWSC) only showed a significant variation among land uses at

soil depth of 20-40cm for both h values considered (i.e., -100cm and -340cm) There was an

increase in SWSC at the deeper layer except for MP which showed significantly lower SWSC

(p<0.05) than the other land uses. A significant positive correlation was observed between

SWSC and bulk density (BD) and a negative correlation with total porosity (ø).

4.2.5.5 Air capacity

The same trend was observed for air capacity (AC) with that of the other parameters. There is

no significant difference among land uses at 0-20cm soil depth and only at 20-40cm soil depth

a significant variation between land uses was observed for both h. An increase in SWSC results

to a decrease in AC as indicated by the negative correlation of the two (Table 15).

Land

Use†/

Depth

Physical Indicators (m3 m-3)

FC PWP AC SWSC PAWC

(-100) (-340) (-15400) (-100) (-340) (-100) (-340) (-100) (-340)

0-20 cm

SF 0.52ns 0.43ns 0.36ns 0.12ns 0.20ns 0.82ns 0.68ns 0.16ns 0.07ns

RF 0.48ns 0.39ns 0.30ns 0.14ns 0.24ns 0.78ns 0.62ns 0.18ns 0.08ns

MP 0.47ns 0.38ns 0.31ns 0.16ns 0.25ns 0.74ns 0.61ns 0.17ns 0.08ns

CP 0.46ns 0.33ns 0.29ns 0.10ns 0.24ns 0.82ns 0.58ns 0.24ns 0.04ns

GL 0.46ns 0.37ns 0.26ns 0.10ns 0.19ns 0.82ns 0.64ns 0.19ns 0.11ns

St.Dev 0.05 0.08 0.07 0.05 0.07 0.07 0.11 0.05 0.04

20-40 cm

SF 0.51ns 0.42a 0.33a 0.16a 0.24a 0.77a 0.65a 0.24a 0.10ns

RF 0.51ns 0.42a 0.34a 0.09a 0.18ac 0.84a 0.69a 0.18ab 0.08ns

MP 0.50ns 0.34b 0.28ab 0.19b 0.36b 0.72ab 0.49b 0.36ac 0.06ns

CP 0.51ns 0.48a 0.40ac 0.06c 0.08c 0.90c 0.86c 0.08ab 0.09ns

GL 0.49ns 0.46a 0.38ac 0.10ac 0.13c 0.83ac 0.78ac 0.13b 0.08ns

St.Dev 0.04 0.07 0.07 0.07 0.12 0.10 0.16 0.06 0.03

35

4.3 Soil Biological Property

Table 13 shows the earthworm population density per square meter of soils. The data showed

no significant differences among the different land uses. Nevertheless, it was observed that MP

and GL have the highest population of earthworm. MP was observed to have higher porosity

compared to other land uses which give a favorable condition for earthworms. GL also

encourages earthworm activity because of continuous source of food from decomposing

perennial grassland and animal waste from occasional herding.

Table 13. Earthworm population density of soils under different land uses.

Treatment mean is the average of 10 replicates. SF= Secondary Forest, RF= Rain forestation,

MP= Mahogany Plantation, CP= Coffee Plantation, GL= Grassland.

4.4 Visual Evaluation of Soil Structure

A lower VESS score indicates a good quality of structure, hence, less is better for this

parameter. Figure 11 shows the VESS score for each land use. Among the five land uses, MP

had the lowest score value though not significantly different from SF and CP. On the other

hand, GL had a score between Sq2 and Sq3 which means a firm or intact structural quality but

not compact enough to hinder root activity.

Land Use Earthworm density

(individual m-2)

Earthworm

biomass (c)

Secondary forest 70 1.61

Rainforestation 0 0.32

Mahogany plantation 358 0.23

Coffee plantation 94 0.18

Grassland 312 0.31

36

Figure 11. VESS score (Visual Evaluation of Soil Structure) of the soils under different land

uses. SF= Secondary Forest, RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee

Plantation, GL= Grassland. Means followed by the same letter under are not significantly

different (Kruskal-Wallis compare-wise test 5%).

4.5 Soil Quality Index

The overall scores for each soil function and soil quality index (SQI) for each land use are

presented in Table 14. All the land uses have a high SQI value which indicates a good soil

quality. Detailed calculations of the SQI are shown in Appendix 1.

Ten soil quality indicators were chosen to form a minimum dataset that was used to quantify

the soil quality of each land uses. The minimum dataset indicator scores are presented in Figure

12. The change of land use to RF improves the structural quality of the soil as indicated by high

VESS score and earthworm density score. Change of land use from SF to MP improved the

BD, Kfs, AC, and MacPor of the soils while all other indicators remain almost the same for all

land uses.

a

ab

a

a

b

0

0.5

1

1.5

2

2.5

3

SF RF MP CP GL

VE

SS

sco

re (

Sq

)

Land Use

37

Table 14. Overall scores for soil functions (f) and soil quality index (SQI) values within the

0-40cm layer in five land uses.

Land

UseƗ

Soil functions† SQIƗ

f(i) f(ii) f(iii) f(iv) f(v)

SF 0.99 0.64 0.34 0.32 0.99 0.66

RF 1.00 0.60 0.35 0.47 0.98 0.68

MP 0.99 0.84 0.39 0.41 0.96 0.72

CP 0.99 0.74 0.29 0.33 0.98 0.67

GL 1.00 0.72 0.31 0.40 0.98 0.68 Ɨ SF= secondary forest, RF= rainforestation, MP= mahogany plantation, CP= coffee plantation,

GL= grassland, SQI= soil quality index

†f(i) supply nutrients, f(ii) supply water and soil aeration, f(iii) sustain biological activity, f(iv)

sustain plant growth, f(v) ability to resist erosion and soil degradation

Figure 12. Overall scores of the soil quality indicators at different land uses. SF= Secondary

Forest, RF= Rain forestation, MP= Mahogany Plantation, CP= Coffee Plantation, GL=

Grassland. BD=bulk density, Kfs=field saturated hydraulic conductivity, MWD=mean weight

diameter, SSI=structural stability index, SOC=soil organic carbon, MacPor=macroporosity,

AC=air capacity, Eworm=earthworm density, VESS=visual evaluation of soil structure.

0.00

0.10

0.20

0.30

0.40

0.50pH

SOC

Kfs

MacPor

AC

Eworm

VESS

BD

MWD

SSI

SF RF MP CP GL

38

Table 15. Pearson’s correlation (r†) among soil properties and with soil organic carbon (SOC) in forest, rainforestation, mahogany plantation,

coffee plantation, and grassland land uses.

Ɨ P=available phosphorus, BD=bulk density, ø= porosity, MatPor=matrix porosity, MacPor=macroporosity, Kfs=field saturated hydraulic

conductivity, Ks=laboratory saturated hydraulic conductivity, AS= aggregate stability, MWD=mean weight diameter, VESS=visual evaluation of

soil structure, SSI=structural stability index, Eworm=earthworm population density (indiv./m2), FC=field capacity (h=-340), PWP=permanent

wilting point, PAWC=plant-available water capacity, AC= air capacity, SWSC= soil water storage capacity, SOC=soil organic carbon.

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

pH P SOC BD ø MatPor MacPor Kfs Ks AS MWD VESS SSI Eworm FC PWP PAWC AC SWSC Clay

pH 1† 0.687** 0.043 -0.384 0.390 0.016 0.404 0.387 0.342 -0.113 -0.113 0.196 0.219 -0.072 0.184 0.418 -0.305 0.226 -0.167 -,780**

P 1 0.065 -0.311 0.314 -0.071 0.400 -0.090 0.133 0.049 0.049 -0.048 0.189 -0.194 0.042 0.469 -0.453 0.254 -0.232 -,621*

SOC 1 -0.326 0.334 0.165 0.211 -0.093 0.250 -0.270 -0.270 -0.299 ,977** -0.384 -0.024 -0.032 0.016 0.316 -0.278 -0.061

BD 1 -0.997** -0.368** -0.697** -0.299 -0.321* -0.337* -0.337* -0.078 -0.433 -0.267 -0.265 0.051 -0.254 -0.711** 0.590** ,607*

ø 1 0.374** 0.697** 0.299 0.336* 0.341* 0.341* 0.084 0.440 0.279* 0.253 -0.074 0.274 0.722** -0.603** -,604*

MatPor 1 -0.405** 0.610* 0.098 0.071 0.071 -0.260 0.159 0.161 0.747** 0.124 0.390** -0.197 0.293* -0.058

MacPor 1 -0.242 0.255 0.281* 0.281* 0.284* 0.330 0.150 -0.328* -0.169 -0.031 0.864** -0.821** -,595*

Kfs 1 -0.031 0.038 0.038 0.153 -0.034 0.569 0.626* -0.079 0.509 -0.193 0.295 -0.217

Ks 1 0.074 0.074 0.003 0.345 0.094 -0.195 -0.226 0.134 0.442** -0.425** -0.392

AS 1 1.000** -0.006 -0.216 0.225 0.190 0.222 -0.133 0.172 -0.134 -0.285

MWD 1 -0.006 -0.216 0.225 0.190 0.222 -0.133 0.172 -0.134 -0.285

VESS 1 -0.307 0.062 -0.167 -0.013 -0.105 0.195 -0.198 0.228

SSI 1 -0.380 -0.018 0.032 -0.045 0.408 -0.361 -0.259

Eworm 1 0.160 -0.015 0.134 0.137 -0.104 -0.031

FC 1 0.574** 0.024 -0.487** 0.616** -0.059

PWP 1 -0.805** -0.478** 0.530** -0.302

PAWC 1 0.230 -0.200 0.270

AC 1 -0.985** -0.503

SWSC 1 0.449

Clay 1

39

5. Discussion

5.1 Land use impact on soil chemical properties

All land uses were identified to have a slightly acidic soil with pH-H2O ranging from 5.23-6.12

as shown in Table 6. This pH range is within the ideal range for plant growth. Soil pH of RF,

MP, and GL was observed to be higher than that of SF. In contrast, CP showed lower pH

compared to that of SF at all three soil depths. This may imply that farming activities in CP

(e.g tillage and fertilizer application) enhanced the acidification of the soil. Intensified tillage

in the sloping part of the CP site can accelerate soil erosion which could have led to removal

of the original top layer leaving the more acidic subsoil exposed. Among the land uses, MP at

the 0-20cm soil depth showed a higher pH value of 6.12. The increase in soil pH at the top

layer for MP can be attributed to abundance and addition of organic matter, in the form of plant

litter and other decomposing materials as was observed during the field area assessment and

soil morphological characterization. This finding is in agreement with the findings of Navarrete

et al. (2008) and Asio (1996) who studied chemical properties of soil in the area.

There were no observed large differences among land uses in terms of the available phosphorus

(P) and all land uses had generally very low P value with range of 0.71-1.25 mg kg-1. The same

trend was observed for the soil organic carbon (SOC). However, SOC tended to decrease with

depth to almost 50% for SF, RF, and MP. The decrease can be attributed to loss of soil organic

matter due to erosion in SF, RF, and MP sites. These sites were more prone to erosion because

of their hilly topography compared to CP and GL. This was also evident on the thin top layers

of soils and presence of rock outcrops in those sites observed during morphological

characterization. Also, the previous practice of burning crop residue in RF and MP may have

led to decrease in organic carbon. This result is in contrast with the study of Guillaume et al.

(2016) where rainforest yield higher organic carbon compared to plantations land uses. The

higher SOC of CP and GL suggested the continuous addition and accumulation of organic

matter to the soil despite of disturbances from cultivation in CP and low litter production in

GL. In CP, grasses and vines sometimes grow under trees and when cut were left on the field

which led to the same result as GL. The CaCO3 content of all soils was very low to none.

40

5.2 Land use impact on soil physical properties

5.2.1 Bulk density, porosity, aggregate and structural stability

The mean bulk densities of the study area fell within the optimal value of 0.9-1.2 Mg m-3 as

suggested by Reynolds et al. (2007) for fine-textured soils with exception of MP at 20-40cm

which has BD of 0.82 Mg m-3. A bulk density of below 0.9 Mg m-3 can result in inadequate

plant anchoring due to low soil strength. The change of land use from SF to CP and GL showed

a significant increase with the bulk density at both soil layers (Table 7). An increase in BD

means a more compacted soil. This increase can be attributed to lower porosity of the soils in

CP and GL land uses as confirmed by the significant negative correlation between bulk density

and total porosity, matric porosity, and macroporosity (r= -0.997, -0.368, and -0.697,

respectively) shown in Table 15. Consequently, an opposite trend of significance was observed

for total porosity. The lower value of bulk density in SF and MP can be attributed to biological

activity in this areas. During the morphological characterization, bioturbation throughout the

soil profile of SF was observed. In the case of MP, high earthworm activity was observed

especially at the surface soil layer which resulted to more loose and porous soil.

Soil MatPor showed no significant difference among the different land uses for both soil

depths. Soil MacPor, on the other hand differ significantly between land uses at 20-40cm soil

depth. The soil MacPor of SF was significantly higher than that of RF, CP, and GL. The data

also suggests that conversion of land use to RF, CP and GL resulted to a lower MacPor at the

lower soil layer. This can be explained by the regular tillage and cultivation management done

in the CP that rendered the soil to be more compacted resulting to decreased structural porosity.

In GL and RF gravitational compaction was more of the case. GL area was used for grazing

animals like horses and cattle which added to the compaction of soil. Unlike in SF, the RF site

was a continuing project of the university where it is located which means disturbance of the

soil during its establishment and thereafter, frequent activity in the area during monitoring and

other field works.

Land uses showed a significant effect on the aggregate stability of the soils (Table 9). In

particular, MP had a significantly lower stability index (AS) value compared to other land use

forms in both soil layers. All other land use forms were classified to have a stable aggregation

except CP at 0-20cm soil depth. The AS value of 0.494 in CP suggested an unstable aggregation

at the surface soil depth. The farming practices done in CP attributed largely to this result. The

frequent disturbance of the soil through tillage and cultivation management resulted to

41

breakdown of aggregates especially at the top soil. Similar results were reported by Celik

(2005) for cultivated land against forest soil.

The structural score for all land use forms fell below Sq3 which means fair structural quality

for all. Nevertheless, a significant difference was observed between the mean Sq values (Figure

11). The mean Sq for RF and GL was significantly greater than SF, MP, and CP. This implies

that SF, MP, and CP had better structural quality compared to RF and GL. The presence of

high microbial activity in MP resulted to a more loose and porous soil and enhanced the

structure of the soil. The SSI was in contrast with the above finding as the mean values (Figure

8) for all the land use forms were below 5% which indicates a structurally degraded soil based

on Pieri (1992) as cited by Reynolds et al. (2007). This could be caused by the low organic

carbon for all soils (Table 6). It could also be that with VESS the assessment was more of

qualitative approach while SSI was quantitative. Assessment of structural quality using VESS

can also be subjective depending on the person evaluating the soil and scores are estimated,

although according to Ball et al. (2007) the effect of this is limited.

5.2.2 Saturated hydraulic conductivity

Saturated hydraulic conductivity is a good indicator of the soil’s ability to transmit water and

redistribute it for plant roots uptake and also drain excess water out of the root zone (Reynolds

et al. 2007). In this study, saturated hydraulic conductivity was measured in the field (Kfs) by

means of infiltration test and in the laboratory (Ks) using permeameter. The Ks among different

land use forms showed no significant differences (Figure 9). The mean Ks values of RF, CP

and GL for both soil depth fell within the ideal range of Ks which is 5x10-5 – 6x10-6 m s-1 as

suggested by Reynolds et al. (2003). On the other hand, Ks of MP and SF was higher than the

ideal range. This might be due to the presence of biological activity in the soil that made the

soil more porous and resulted to fast flow of water through the soil. This high value of Ks

suggested a favorable condition for plant growth but maybe limiting during dry periods and

may cause drought. However, the thick canopy cover for SF and MP may prevent this from

happening.

The saturated hydraulic conductivity in field condition (Kfs) also showed significant differences

among the land use forms (Table 10). The higher Kfs value for MP might be due to the

preferential flow of water through the worm holes and root canals.

Both Ks and Kfs of the SF was found to be higher than that of RF, CP, and GL though

differences were not significant. This still implies that conversion to this latter land use forms

42

lowers the ability of soil to transmit water and may also cause soil erosion especially for CP

where soil cover is not permanent due to cultivation management. The same result was reported

by Reynolds et al. (2003) and Celik (2005) wherein they found that cultivated land and

conventional tillage lowered Ks of the soil.

According to O’Neal (1949) soils of the study area ranged from being slow to moderate

permeability. MP was the only one to have moderate permeability and all the rest fell to slow

to moderately slow permeability.

5.2.3 Soil water retention curve and derived parameters

Water retention curve shows the relationship between the water content and water potential. It

was used to predict the soil water storage and water available for plants. The van Genuchten

model was used to fit the curve using derived parameters (Table 11). Figure 10 showed the

water retention curve for two soil depths. The curve was well fitted to the measured data as

confirmed from the high coefficient of determination (R2) values. The figure showed that large

amounts of water were retained from the soil even after reaching the permanent wilting point.

This is typical for clay soil because of its stable micro-aggregates, high specific surface area

and charge density (Jury and Horton, 2004) that can hold water even at higher pressure. The

moisture content at field capacity (FC, both pressure heads) and permanent wilting point (PWP)

of all land use forms at 0-20cm depth showed no significant differences. However, at 20-40cm

depth the water content at FC (h=-340cm) and at PWP were significantly lower than the other

land use (Table 12). Since MP has a clay loam texture and its clay content is approximately 10-

30% lower than the other land use, its ability to store water is lowered as well.

Soil air capacity (AC) is a useful indicator for soil aeration (Reynolds et al., 2008). There was

no significant differences among land use forms at 0-20cm depth for both pressure head

of -100cm and -340cm. At 20-40cm depth at both pressure head, MP showed significantly

higher AC than the other land uses. This is evident of the high biological activity in MP as the

soil became favorable for ther soil fauna’s respirative demands. CP had the lowest AC of 0.06

m3 m-3 and 0.08 m3 m-3 at pressure heads of -100cm and -340cm, respectively. This value was

lower than the suggested range value of >0.15 m3 m-3 for fine-textured soils to compensate for

the low gas diffusion rates and the respirative demands of biological activity (Reynolds et al.,

2007).

Soil water storage capacity (SWSC) at the lower layer showed a significant differences among

different land uses at both pressure heads. MP was significantly lower than the other land uses

43

but its plant-available water capacity was higher than the other land uses (0.36 m3 m-3 at

pressure head of -100cm). This is due to the low bulk density of MP which means less

compaction of the soil, thus higher MacPor that retain water for plant uptake. It was observed

that CP and GL land use had higher SWSC than SF (0.90 m3 m-3 and 0.86 m3 m-3, respectively).

However, these high values do not necessarily mean a favorable condition for plant growth.

Conversion of SF to CP and GL resulted to a more compacted soil thereby leaving smaller pore

size which further causes increased water retention at low water potential, thus making the

water unavailable for plant uptake. Furthermore, SWSC exceeded the 0.66 m3 m-3 ideal value

for good balance between water and air in the soil. These results were similar to the study of

Cherubin et al. (2016) where conversion of native vegetation to pasture increased SWSC.

In terms of the plant available water capacity (PAWC), only SF and MP provided the value

(0.24 m3 m-3 and 0.36 m3 m-3, respectively) that fell within the ideal value of >0.20 m3 m-3

proposed by Hall et al. (1977) and Cockcroft and Olsson (1997) as cited by Reynolds et al.

(2007) for maximum root growth and minimum droughtiness in fine-textured soil. As expected,

the SWSC of CP and GL were very low and this can cause serious problem for plant growth

especially dry season.

5.3 Land use impact on soil biological properties

Biological component of the soil is also necessary to evaluate soil quality. Microbial activity

plays an important role in most of the soil processes and functions. However, in this study only

earthworm density and biomass were measured. MP land use provided the largest earthworm

density (358 individual m-2) while RF had none. The high earthworm population in MP

suggested a favorable condition of the soil for microbial activity. High MacPor and lower BD

of MP may have contributed to this. The non-existence of earthworm in RF land use can be

due to its relatively high BD and low MacPor, although the same was true for both CP and GL

but earthworm was found in the latter two land uses. Vice versa, earthworms may increase

MacPor and decrease BD. More than 50% of the earthworms were found in the 0-10cm soil

layer. The earthworm biomass was found to be highest in SF with 1.61 kg m-2 though it ranked

fourth for earthworm density. This can be attributed to the larger and mature earthworms

collected in SF compared to the other land uses where the population mostly consisted of young

and small earthworms. This was in contrast to the study of Geissen et al. (2009) where they

found the larger earthworms in plantations and agroforestry systems while small earthworms

were found in the forest.

44

5.4 Soil quality index assessment

Soils are considered to be good and well-functioning for plant growth when their properties are

optimum or within the required range. In this study indicators were selected as suggested by

Reynolds et al. (2007) and Cherubin et al. (2016). Selected indicators corresponded to certain

functions of the soil. Ten indicators were used to estimate the soil quality index (SQI) (Figure

12). The scores for soil function 1 (supply nutrients to plants) did not vary among land uses

(Table 14). Soil functions 2 (supply water and soil aeration) and 5 (ability to resist erosion and

soil degradation) generated high scores as well. For functions 2 and 3 (sustain biological

activity), MP had the highest scores of 0.84 and 0.39 which were represented by the indicators

Kfs, MacPor, earthworm density and air capacity. With regards to the four indicators

mentioned, the MP excelled on that. Functions 3 and 4 (sustain plant growth) generated the

least scores. This can be explained by the high BD and low Ks that can consequently affect

other properties in the soil. The availability of water for plant growth can also be affected by

the high SWSC and low SAC. The calculated SQI for all land use forms showed a high value

suggesting a good soil quality. The SQI of the SF, RF, CP and GL was almost the same while

MP had the highest SQI of 0.72. The result suggested that conversion of SF into CP did not

improve nor worsen the quality of the soil. Moreover, the conversion of SF to MP had a positive

effect on the soil quality.

45

6. Conclusions and recommendations

6.1 Conclusions

The conversion of SF to other land uses had no significant effects on the chemical properties

of the soils. The soil organic carbon remained low and pH remained slightly acidic. For the

physical properties, most of them showed a significant variation. In particular, conversion of

SF to MP showed to have affected positively most of the physical properties. The BD was

lowered in MP resulting from high MacPor. The aggregate stability did not improve but high

Ks was still recorded. MP showed a lower moisture content at FC and PWP but the AC, SWSC,

and PAWC was good if not excellent. MP was the only one that did not exceed the optimal

value for SWSC of 0.66 at pressure head of -340cm. This suggested a good balance between

water and air necessary for biological activity and supplying water and aeration.

Conversion of SF into CP and GL worsen some of the soil parameters. The soil became more

compacted with tillage and cultivation. MacPor was lower and the PAWC was very low even

with high SWSC for both land uses. This can be a problem for sustaining plant growth and

supplying water and aeration. The Ks was also lower for both and though soil aggregation was

good this can pose problems to areas with sloping topography. Low Ks may hasten soil erosion.

Most of the soil parameters showed significant correlation with each other. However,

hypothesis of soil organic carbon to be the central key indicators was not fulfilled.

Generally, conversion of SF to CP showed no effect on the soil quality as indicated by the SQI.

The SQI of RF and GL did not have a large difference from SF but nevertheless it was higher

than SF. This may suggest that RF and GL may improve the soil quality in the long run.

Conversion of SF to MP improved most of the soil indicators resulting in a higher SQI than

that of SF.

6.2 Recommendations

Research on the effects of land use has been done by many scientists in the past. However,

most of them focus on the intensification of the land and effect of different agricultural farming

technique. Also, little studies were done regarding the land use impacts under the Philippine

conditions. This study focused on the effect of land use change on the soil quality and generated

a considerably good result. Nevertheless, there are still some points that need to be done.

46

(1) Additional soil parameters are suggested to widen the choice of selecting indicators that

are more sensitive to calculating the SQI (i.e other chemical and biological properties).

(2) Larger number of replicates for soil sampling to fully represent the diversity of the study

area (i.e topography).

(3) Further study on the topic is advised so to monitor other land use effects in the long run

(i.e RF and GL).

47

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54

8. Appendices

Appendix 1. Weighting factor for soil functions and indicators.

Land

use

Soil

function

weight

(I) indicator

weight

(II)

transformed

value (III)

score

(IIxIII)

soil function

score IV

[Ʃ(IIxIII)]

weighted

score (IVxI)

SPQI

[Ʃ(IVxI)]

Forest f(i) 0.20 pH 0.50 1.00 0.50 1.00 0.20 0.66

SOC 0.50 1.00 0.50

f(ii) 0.20 Kfs 0.50 0.61 0.31 0.64 0.13

MacPor 0.50 0.67 0.33

f(iii) 0.20 AC 0.50 0.67 0.34 0.34 0.07

Eworm 0.50 0.48 0.24

f(iv) 0.20 VESS 0.50 0.64 0.32 0.32 0.06

BD 0.50 0.86 0.43

f(v) 0.20 MWD 0.50 0.97 0.49 0.99 0.20

SSI 0.50 1.00 0.50

RF f(i) 0.20 pH 0.50 1.00 0.50 1.00 0.20 0.68

SOC 0.50 1.00 0.50

f(ii) 0.20 Kfs 0.50 0.70 0.35 0.60 0.12

MacPor 0.50 0.49 0.25

f(iii) 0.20 AC 0.50 0.70 0.35 0.35 0.07

Eworm 0.50 0.65 0.33

f(iv) 0.20 VESS 0.50 0.93 0.47 0.47 0.09

BD 0.50 0.90 0.45

f(v) 0.20 MWD 0.50 0.96 0.48 0.98 0.20

SSI 0.50 1.00 0.50

MP f(i) 0.20 pH 0.50 1.00 0.50 1.00 0.20 0.72

SOC 0.50 1.00 0.50

f(ii) 0.20 Kfs 0.50 0.96 0.48 0.84 0.17

MacPor 0.50 0.71 0.36

f(iii) 0.20 AC 0.50 0.78 0.39 0.39 0.08

Eworm 0.50 0.40 0.20

f(iv) 0.20 VESS 0.50 0.81 0.41 0.41 0.08

BD 0.50 0.91 0.45

f(v) 0.20 MWD 0.50 0.92 0.46 0.96 0.19

SSI 0.50 1.00 0.50

CP f(i) 0.20 pH 0.50 1.00 0.50 1.00 0.20 0.67

SOC 0.50 1.00 0.50

f(ii) 0.20 Kfs 0.50 0.90 0.45 0.74 0.15

MacPor 0.50 0.59 0.29

f(iii) 0.20 AC 0.50 0.58 0.29 0.29 0.06

Eworm 0.50 0.39 0.20

f(iv) 0.20 VESS 0.50 0.65 0.33 0.33 0.07

BD 0.50 0.90 0.45

f(v) 0.20 MWD 0.50 0.96 0.48 0.98 0.20

SSI 0.50 1.00 0.50

GL f(i) 0.20 pH 0.50 1.00 0.50 1.00 0.20 0.68

SOC 0.50 1.00 0.50

f(ii) 0.20 Kfs 0.50 0.98 0.49 0.72 0.14

MacPor 0.50 0.47 0.24

f(iii) 0.20 AC 0.50 0.63 0.31 0.31 0.06

Eworm 0.50 0.50 0.25

f(iv) 0.20 VESS 0.50 0.80 0.40 0.40 0.08

BD 0.50 0.91 0.45

f(v) 0.20 MWD 0.50 0.97 0.48 0.98 0.20

SSI 0.50 1.00 0.50

55

Appendix 2. Measured water content using the sandbox for different land uses.

Matric

Head

(cm)

Measured Water Content (m3 m-3)

SF RF MP CP GL

0-20cm 20-40cm 0-20cm 20-40cm 0-20cm 20-40cm 0-20cm 20-40cm 0-20cm 20-40cm

10 0.58 0.53 0.56 0.55 0.55 0.55 0.53 0.52 0.54 0.50

30 0.55 0.52 0.52 0.53 0.51 0.52 0.48 0.52 0.50 0.50

50 0.54 0.51 0.50 0.52 0.49 0.51 0.48 0.51 0.48 0.50

70 0.53 0.51 0.49 0.52 0.48 0.54 0.47 0.51 0.48 0.50

100 0.52 0.51 0.48 0.51 0.47 0.50 0.46 0.51 0.47 0.50

340 0.44 0.43 0.39 0.42 0.39 0.34 0.33 0.48 0.38 0.49

1020 0.43 0.43 0.38 0.42 0.37 0.34 0.35 0.48 0.40 0.49

15400 0.36 0.33 0.31 0.34 0.31 0.28 0.29 0.40 0.26 0.47

56

Appendix 3. Fitted water content using van Genuchten model

Fitted water conter (m3 m-3)

SF RF MP CP GL

Head (cm) 0-20 cm 20-40 cm 0-20 cm 20-40 cm 0-20 cm 20-40 cm 0-20 cm 20-40 cm 0-20 cm 20-40 cm

1.00 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.56 0.50

1.26 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

1.58 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

2.00 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

2.51 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

3.16 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

3.98 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

5.01 0.58 0.53 0.57 0.55 0.55 0.54 0.52 0.52 0.55 0.50

6.31 0.58 0.53 0.56 0.55 0.55 0.54 0.52 0.52 0.54 0.50

7.94 0.58 0.53 0.56 0.55 0.54 0.54 0.52 0.52 0.54 0.50

10.00 0.58 0.53 0.56 0.55 0.54 0.54 0.52 0.52 0.53 0.50

12.59 0.57 0.53 0.55 0.55 0.54 0.54 0.52 0.52 0.53 0.50

15.85 0.57 0.53 0.55 0.55 0.53 0.54 0.51 0.52 0.52 0.50

19.95 0.57 0.53 0.54 0.54 0.53 0.54 0.51 0.52 0.52 0.50

25.12 0.56 0.52 0.53 0.54 0.52 0.54 0.51 0.52 0.51 0.50

31.62 0.56 0.52 0.53 0.53 0.51 0.54 0.50 0.51 0.50 0.50

39.81 0.55 0.52 0.52 0.53 0.51 0.54 0.49 0.51 0.50 0.50

50.12 0.54 0.51 0.51 0.52 0.50 0.53 0.49 0.51 0.49 0.50

63.10 0.53 0.51 0.49 0.52 0.48 0.53 0.47 0.51 0.48 0.50

79.43 0.52 0.50 0.48 0.51 0.47 0.52 0.46 0.51 0.47 0.50

100.00 0.51 0.49 0.47 0.50 0.46 0.50 0.44 0.51 0.46 0.50

125.89 0.50 0.49 0.46 0.49 0.45 0.47 0.43 0.51 0.45 0.50

158.49 0.49 0.48 0.45 0.48 0.44 0.44 0.41 0.51 0.44 0.50

199.53 0.48 0.47 0.43 0.47 0.43 0.41 0.40 0.50 0.43 0.50

251.19 0.47 0.46 0.42 0.46 0.41 0.38 0.38 0.50 0.42 0.50

316.23 0.46 0.45 0.41 0.45 0.40 0.36 0.37 0.50 0.41 0.50

398.11 0.45 0.45 0.40 0.44 0.39 0.34 0.36 0.49 0.40 0.49

501.19 0.44 0.44 0.39 0.43 0.39 0.33 0.35 0.49 0.39 0.49

630.96 0.43 0.43 0.39 0.42 0.38 0.32 0.34 0.48 0.38 0.49

794.33 0.42 0.42 0.38 0.41 0.37 0.31 0.33 0.48 0.37 0.49

1000.00 0.41 0.41 0.37 0.40 0.36 0.31 0.33 0.47 0.37 0.49

1258.93 0.41 0.40 0.36 0.40 0.36 0.31 0.32 0.47 0.36 0.49

1584.89 0.40 0.40 0.35 0.39 0.35 0.30 0.32 0.46 0.35 0.49

1995.26 0.40 0.39 0.35 0.38 0.34 0.30 0.31 0.45 0.34 0.48

2511.89 0.39 0.38 0.34 0.38 0.34 0.30 0.31 0.45 0.33 0.48

3162.28 0.39 0.37 0.34 0.37 0.33 0.30 0.31 0.44 0.32 0.48

3981.07 0.38 0.37 0.33 0.37 0.33 0.30 0.30 0.44 0.32 0.48

5011.87 0.38 0.36 0.33 0.36 0.32 0.30 0.30 0.43 0.31 0.48

6309.57 0.38 0.35 0.32 0.36 0.32 0.30 0.30 0.42 0.30 0.48

7943.28 0.37 0.35 0.32 0.35 0.32 0.30 0.30 0.42 0.30 0.47

10000.00 0.37 0.34 0.31 0.35 0.31 0.30 0.30 0.41 0.29 0.47

12589.25 0.37 0.34 0.31 0.35 0.31 0.30 0.30 0.40 0.28 0.47

15848.93 0.36 0.33 0.31 0.34 0.31 0.30 0.30 0.40 0.27 0.47

19952.62 0.36 0.32 0.30 0.34 0.30 0.30 0.29 0.39 0.27 0.47

25118.86 0.36 0.32 0.30 0.34 0.30 0.30 0.29 0.39 0.26 0.47

31622.78 0.36 0.31 0.30 0.33 0.30 0.30 0.29 0.38 0.26 0.46

39810.72 0.36 0.31 0.30 0.33 0.30 0.30 0.29 0.37 0.25 0.46

50118.72 0.36 0.31 0.29 0.33 0.30 0.30 0.29 0.37 0.24 0.46

63095.73 0.35 0.30 0.29 0.33 0.29 0.30 0.29 0.36 0.24 0.46

79432.82 0.35 0.30 0.29 0.33 0.29 0.30 0.29 0.36 0.23 0.46

100000.00 0.35 0.29 0.29 0.32 0.29 0.30 0.29 0.35 0.23 0.46