Conosciamo più il movimento dei corpi celesti che il …sdeneve/PhD_Bram_Moeskops.pdfConosciamo...

Conosciamo più il movimento dei corpi celesti

che il terreno su cui camminiamo

We know more about the movement of celestial bodies

than about the soil underfoot

Leonardo da Vinci

Promoter:

Prof. dr. ir. Stefaan De Neve

Department of Soil Management, Ghent University

Dean:

Prof. dr. ir. Guido Van Huylenbroeck

Rector:

Prof. dr. Paul Van Cauwenberge

ir. Bram Moeskops

Biochemical and microbial indicators of

soil quality in contrasting agro-ecosystems

Thesis submitted in fulfillment of the requirements for the

degree of Doctor (PhD) in Applied Biological Sciences

(land and forest management)

Dutch translation of the title:

Biochemische en microbiële indicatoren voor bodemkwaliteit in

contrasterende agro-ecosystemen

Cover illustration:

Woman at work at the organic farm Bina Sarana Bakti in Cisarua

(Ilona Plichart)

To refer to this thesis:

Moeskops B (2010) Biochemical and microbial indicators of soil quality in

contrasting agro-ecosystems. PhD thesis, Ghent University, Gent, 225 p.

ISBN number:

The author and the promoter give the authorization to consult and to copy

parts of this work for personal use only. Every other use is subject to the

copyright laws. Permission to reproduce any material contained in this work

should be obtained from the author.

Acknowledgements - Dankwoord

1st of December 2010

Suddenly winter has started. Belgium is caught by cold and snow. It reminds

me of November 2008 when Ilona, newborn Arune and I arrived home from

tropical Indonesia in winter Belgium. This PhD has been quite an

undertaking, not only for me but for the whole family. Therefore I dedicate

this thesis to my wife, Ilona, my daughter, Arune, and our coming baby.

Particular thanks go to my promoter, Stefaan De Neve, who offered me the

opportunity to conduct this research and always remained confident of its

importance. In particular, I want to thank Stefaan for allowing me to change

the subject of my thesis in spite of the additional work and uncertainty this

caused. Finally, I want to thank Stefaan for all the reading of my papers and

manuscripts. I also would like to thank the jury members for their useful

comments on my thesis.

This thesis could not have been written without the support of the

researchers of the Indonesian Institute of Soil Research. In the first place I

need to thank pak Kris for his invaluable support with the organization of the

field work. I also want to thank ibu Neno, ibu Rini, ibu Sri, ibu Rasti, pak Edi

Husen and especially ibu Erny for resolving smaller or greater problems. Ibu

Lenita helped me wonderfully with the PLFA-extractions during her stay in

Belgium and became a good friend during our stay in Bogor. Thanks also to

all field workers for their help with the sampling and obviously also to all

farmers who allowed me to do research on their fields. Terima kasih atas

semua. I would also like to thank some other Indonesian friends: pak Bona

Acknowledgements - Dankwoord

and his family for welcoming us so hearty in their neighbourhood and Leny

who taught me a lot about life in the megapolis of Jakarta.

Sommige collega’s van de Vakgroep Bodembeheer waren verbaasd dat het

einde van mijn mandaat er zo snel was. “Dat komt omdat jij altijd in

Indonesië was”. Bovendien heb ik de laatste maanden vaak thuis gewerkt. Ik

was geen trouwe collega, toch heb ik graag met jullie samengewerkt. Dave,

bedankt voor alle aangename gesprekken en voor de onschatbare hulp bij

de PLFA-extracties en het tellen van de nematoden. Sara, het was leuk om

met jou het SOCO-project te doen, bedankt ook om de goede orde in het

labo te bewaken. Steven, de senior onder ons, bedankt voor al je wijze raad.

Liesbeth en Nele, bedankt voor de aanmoedigingen. Tina, Sophie, Mathieu

en Luc bedankt voor alle analyses die jullie voor mij gedaan hebben.

Bedankt ook Luc voor de hulp met het veldwerk.

Ik moet ook Sarah en Ilse van de Vakgroep Gewasbescherming bedanken

voor hun hulp en advies bij de ziektewerendheidstesten.

Tot slot wil ik ook alle thesisstudenten bedanken die de voorbije vier jaar

meewerkten aan mijn onderzoek: Linca, Jelke, Suzana en Lieven, en de

Indonesische studenten, Budi, Hary, Irfan, Deni, Emma, Winny, Nina en Yuli.

Tot slot een laatste woord van dank voor broer, ouders, schoonouders,

familie en vrienden voor hulp bij babysitten als ik weer eens moest

doorwerken en voor hun begrip als ik er humeurig of slecht uitgeslapen

bijliep.

Bram

Table of contents

List of symbols and abbreviations iii

List of tables v

List of figures vii

Chapter 1 Background and objectives 1

Chapter 2

Intensive organic and conventional vegetable farming

in West Java, Indonesia (2007) 27

Chapter 3


in West Java, Indonesia (2008) 59

Chapter 4 Organic and conventional paddy fields in Central Java, Indonesia 103

Chapter 5 The impact of exogenous organic matter on biological soil quality

and soil processes 121

Chapter 6 Final discussion and conclusions 151

i

Table of contents

ii

Summary 165

Samenvatting in het Nederlands 173

References 183

Curriculum Vitae 215

List of symbols and abbreviations

AMF arbuscular mycorrhizal fungi

amsl above mean sea level

CDA canonical discriminant analysis

CF conventional farming

CI channel index

C/N carbon to nitrogen ratio

CSL cattle slurry treatment

EI enrichment index

F/B fungi to bacteria ratio

FCP1 treatment with farm compost with C/N ratio of 20-50

FCP2 treatment with farm compost with C/N ratio of 10-20

FYM farmyard manure treatment

G+/G- Gram-positive to Gram-negative bacteria ratio

MBC microbial biomass carbon

MI maturity index

MIN N mineral nitrogen treatment

NF- no fertilizer, no crop treatment

NF+ no fertilizer, crop treatment

NLFA neutral lipid fatty acid

OF organic farming

PLFA phospholipid fatty acid

PNP p-nitrophenol

PPI plant-parasite index

RDA redundancy analysis

SI structure index

SOC soil organic carbon

SOM soil organic matter

iii

List of symbols and abbreviations

iv

TN total nitrogen

TPF triphenyl formazan

VFG vegetable, fruit and garden waste compost treatment

WFPS water-filled pore space

List of tables

Table 1.1: Overview of nutrient cycling processes in which microorganisms

are involved. 6

Table 2.1: Selected crops and management data. 33

Table 2.2: Physical soil properties of research sites. 36

Table 2.3: Chemical soil properties. 44

Table 2.4: Concentrations of marker PLFAs. 48

Table 2.5: Pearson correlation coefficients between PLFAs (mol%)

and first dimension of CDA. 50

Table 2.6: Parameters retained by stepwise CDA, raw canonical coefficients

and Pearson correlation coefficients with soil quality index scores. 51

Table 2.7: Soil quality index scores. 51

Table 2.8: Pearson correlation coefficients between biochemical and

chemical soil properties. 54

Table 3.1: Management data of selected fields. 67

Table 3.2: Physical soil properties of research sites. 71


Table 3.4: Basal respiration rates. 83

Table 3.5: Soil suppressiveness against R. solani. 84

Table 3.6: NLFA/PLFA ratios of 16:1 5c. 86

Table 3.7: Concentrations of marker PLFAs. 88

Table 3.8: Pearson correlation coefficients between mol% of PLFAs and

CDA dimensions. 90

Table 3.9: Shannon diversity indices, cy17:0/16:1 7c and F/B ratios. 91

Table 3.10: Discriminant index scores. 91

Table 3.11: Maturity indices, PPI/MI ratios and channel indices. 93

v

List of tables

vi

Table 3.12: Coefficients of model for basal respiration. 99

Table 4.1: Location and management data of selected fields. 108


Table 4.3: Dehydrogenase activity and aerobic respiration rates. 112

Table 4.4: cy17:0/16:1 7c ratios. 113

Table 5.1: Applied amounts of organic matter, its C/N ratio

and the additional amounts of mineral N applied (2008 and 2009). 128

Table 5.2: SOC, TN, C/N ratios and net N mineralization. 134

Table 5.3: Concentrations of marker PLFAs, F/B ratios

and P-values of ANOVA. 136

Table 5.4: Averages, medians and coefficients of variation of

suppressiveness against Rhizoctonia solani. 139

Table 5.5: Parameters retained by stepwise CDA, raw canonical

coefficients of the first dimension and Pearson correlation coefficients

with scores of the first dimension. 141

Table 6.1: Soil quality index of chapter 6 applied on data of chapter 2. 160

Table 6.2: Soil quality index of chapter 2 applied on data of chapter 6. 160

List of figures

Fig. 2.1: Microbial biomass C contents. 45

Fig. 2.2: Enzyme activities; a. dehydrogenase activity, b. -glucosidase

activity, c. -glucosaminidase activity, d. acid phosphomonoesterase

activity. 46

Fig. 2.3: Specific dehydrogenase activity. 47 Fig. 2.4: Scatter plots of the first two dimensions of the CDAs on PLFAs;

a. CDA including secondary forest, b. CDA on OF and CF data only. 49

Fig. 3.1: Dehydrogenase activity. 81

Fig. 3.2: -glucosidase activity. 82

Fig. 3.3: Ergosterol contents. 85

Fig. 3.4: NLFA/PLFA ratios plotted against the amount of PLFA.;

a. Cisarua1, b. Cisarua2. 87

Fig. 3.5: Scatter plot of CDA on PLFAs. 89

Fig. 3.6: Profiles representing the nematode community structure;

a. cp-triangle, b. faunal profile with structure and enrichment axis. 92

Fig. 4.1: -glucosidase activity. 112

Fig. 4.2: Biplot of RDA on PLFAs. 114

Fig. 5.1: Layout of the field experiment. 127

Fig. 5.2: Microbial biomass C contents. 135

Fig. 5.3: Enzyme activities; a. dehydrogenase activity, b. -glucosidase

activity, c. -glucosaminidase activity. 138

Fig. 5.4: Scatter plots of the first two dimensions of the CDAs; a. CDA on

PLFAs, b. stepwise CDA. 140

vii

Chapter 1: Background and objectives

Illustration:

View at the valley from Bukit Organik in Ciwidey (Bram Moeskops)

Chapter 1: Background and objectives

1.1. Soil quality Society at large expects farmers to produce affordable, high quality food to

satisfy the demands of an ever increasing world population. This food also

needs to be produced safely and with a minimum impact on the

environment. During the last century agricultural productivity has increased

exponentially, but the environmental sustainability of conventional

agricultural practices is increasingly being questioned. Prominent concerns

are environmental pollution (Horrigan et al., 2002), reduction in biodiversity

(Lupwayi et al., 2001; Oehl et al., 2004) and soil erosion (Reganold et al.,

1987). The FAO (1989) defines sustainable agricultural production as “a

practice that involves the successful management of resources for

agriculture to satisfy human needs, while maintaining or enhancing the

quality of the environment and conserving natural resources”. Soil should be

considered as the central resource of agriculture. It is not merely a physical

support for crops, but is in itself a whole ecosystem. Besides being essential

for crop nutrition and crop health, soils affect air and water quality, play a

role in climate change and support biodiversity (Mulier et al., 2005). Soil is,

however, affected by many agricultural practices, inter alia tillage,

fertilization, pesticide application, crop rotation and crop residue

management. From the necessity to evaluate and monitor the status of soils,

the concept of soil quality emerged in the early 1990s (Janvier et al., 2007).

Karlen et al. (1997) defined soil quality as “the capacity of a soil to function,

within natural or managed ecosystem boundaries, to sustain plant and

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

human health and habitation”. The framework of soil quality is focused

towards better management of the soil resources. Carter and MacEwan

3

Chapter 1

(1996) remarked that although soil quality describes an objective state or

condition of the soil, it also is subjective, i.e. evaluated partly on the basis of personal and social determinations. Further, it should be recognized that soil

quality always is a combination of two factors: inherent soil quality and

dynamic soil quality (Karlen et al., 2001). Inherent soil quality results from

differences in parent material and soil forming factors such as climate, time,

topography and vegetation. Differences in inherent quality between soils can

hardly be influenced by management. Dynamic soil quality, on the other

hand, is the result of decisions taken by people about the use and

management of the soil (Karlen et al., 2001).

As pointed out in the definition, soils may perform many functions. Biomass

production is one of the five soil functions defined by Mulier et al. (2005) and

was taken as central function for this thesis. Mulier et al. (2005) list six key

soil processes for the production of biomass:

enabling root growth

good oxygen supply

sufficient supply of water

adequate nutrient supply

degradation of pollutants that may harm plant growth

biological equilibrium, stimulation of plant growth and disease

suppressiveness

Root growth and oxygen and water supply are mainly physical processes. In

this thesis, however, biochemical and microbial soil properties will be

investigated which are mainly (but not only) related to disease

suppressiveness, nutrient supply and degradation of pollutants. Nutrient

supply and degradation of pollutants are linked to each other because they

both are mainly determined by the catabolic activity of the soil biosphere.

Considering the enormous potential impact of global warming on

ecosystems, one more soil function will be discussed in this introduction,

namely that of sink and source of greenhouse gases (CO2, CH4, N2O).

4

Background and objectives

1.2. Soil microorganisms and soil quality Soil organisms contribute to the maintenance of soil quality in that they

control many key processes. Soil microorganisms are responsible for

beneficial processes such as organic matter decomposition, humus

formation, nutrient cycling and methane oxidation. Microbes also play a

major role in the formation of good soil structure. Bacterial mucigel and

hyphal threads produced by fungi and actinomycetes bind the soil particles

together. Microbial activity helps to aggregate the soil. Microbes also have

the potential to be used for biological control: to control insects, pathogens,

and weeds as a result of their ability to either lower the population of the pest

or reduce the pest’s impact. At the same time, however, soil microorganisms

may have negative effects on plant production, including pathogenic activity,

production of phytotoxins, and loss of plant-available nutrients (Kennedy and

Papendick, 1995).

Soil microbial communities are continually changing and adapting to

changes in their environment by varying individual activity, by increasing

reproduction of species with favourable abilities, and by spreading new

capabilities via horizontal gene transfer. Microorganisms respond sensitively

to changes and environmental stress because they have intimate relations

with their surroundings due to their high surface-to-volume ratio (Winding et

al., 2005). The dynamic nature of this group makes them a sensitive

indicator to assess changes in soil resulting from management changes.

Although soil organic matter (SOM) is often regarded as a key indicator for

the integrated assessment of soil quality (Carter, 2002; Reeves, 1997), the

microbial community may, because of its faster turnover time (1-5 years

compared to <15 years for fast C pools and 20-300 years for medium and

slow C pools; Bol et al., 2009; Winding et al., 2005), provide evidence of

subtle changes in soil long before it can be accurately measured by changes

in organic matter.

5

Chapter 1

The next paragraphs will further elaborate on the importance of the soil

microbial community for plant nutrient supply, disease suppressiveness and

exchange of greenhouse gases.

1.2.1. Nutrient cycles Photosynthesis, i.e. the fixation of inorganic to organic C, is considered the

primary process of terrestrial ecosystems. Nevertheless, its mirror image,

namely the degradation of organic to inorganic C is of equal importance

(Brussaard et al., 2004). Nutrients are recycled in the soil with repeated

mineralization and immobilization during organic matter degradation. Soil

organisms decompose, but also re-synthesize organic compounds, thereby

contributing to humification of organic matter (Brussaard et al., 2004).

Further, microorganisms can alter nutrient solubility making otherwise

unavailable nutrients available to the plant. Nitrogen-fixing bacteria transform

N2 gas to plant-available nitrogen. Table 1.1 summarizes the nutrient cycling

processes in which microorganisms are involved.

Table 1.1: Overview of nutrient cycling processes in which microorganisms are involved (combined from Giller et al., 1997; Kennedy and Papendick, 1995 and Rutgers et al., 2009).

Process Responsible microorganisms

decomposition of crop residues and manure litter- and dung-related fungi, bacteria

recycling of nutrients (mineralization, immobilization) bacteria, fungi, protozoa

nitrification nitrifying bacteria (e.g. Nitrosomonas, Nitrobacter), nitrifying archaea

denitrification denitrifying bacteria (mainly heterotrophic, but also autotrophic)

carbon sequestration mainly fungi

nitrogen fixation free (e.g. cyanobacteria) and symbiotic (e.g. Rhizobia) nitrogen-fixing bacteria

increase plant nutrient availability mycorrhizal fungi, phosphate solubilising bacteria

6


The fungi that are probably most abundant in agricultural soils are

arbuscular mycorrhizal fungi (AMF) (phylum Glomeromycota). They account

for 5–50% of the biomass of soil microbes (Olsson et al., 1999). Most plant

species form beneficial association with AMF. Only a few families and

genera of plants do not generally form arbuscular mycorrhizae. These

include Brassicaceae, Cyperaceae, Chenopodiaceae, and Amaranthaceae,

although each of these families has some representatives that are usually

colonized by AMF (Cardoso and Kuyper, 2006). The ability of AMF to

enhance host-plant uptake of relatively immobile nutrients, in particular P,

and several micronutrients (e.g. Cu, Zn), has been the most recognized

beneficial effect of mycorrhizae (Cardoso and Kuyper, 2006; Munyanziza et

al., 1997). Rhizosphere interactions between AMF and other soil

microorganisms, such as nitrogen-fixing bacteria, also influence plant

nutrient balances (Cardoso and Kuyper, 2006). 1.2.2. Soil disease suppressiveness Suppressive soils have been defined by Baker and Cook (1974) as soils in

which disease severity or incidence remains low, in spite of the presence of

a pathogen, a susceptible host plant, and climatic conditions favourable for

disease development. Both the abiotic characteristics of a soil and its

biological properties can be responsible for disease suppression. However,

in most cases suppressiveness is fundamentally microbial in nature

(Alabouvette et al., 2004). Soil microorganisms contribute to the

suppressiveness through four principal mechanisms of biological control: (1)

competition for nutrients and energy, (2) parasitism/predation, (3) antibiosis

and (4) systemic induced resistance of plants (Hoitink and Boehm, 1999).

Antibiosis in the broad sense is the result of specific or non-specific microbial

metabolites that are harmful for other organisms, e.g. lytic agents, enzymes,

volatile compounds or other toxic substances (Jackson, 1965). Induced

systemic resistance of plants occurs when root colonization by certain non-

7

Chapter 1

pathogenic bacteria or fungi stimulates defence-related plant genes

(Cardoso and Kuyper, 2006; van Peer et al., 1991). AMF are a special kind

of root-colonizing microorganisms. They also play a role in the prevention of

pathogenic damage by fungi and bacteria. Besides nutritional mechanisms

(plants with good P status are less sensitive to pathogens), AMF contribute

to disease suppression by activating plant defence systems, by changing

exudation patterns which results in changed mycorrhizosphere populations,

by increasing lignification of cell walls, and by competing for infection sites

(Cardoso and Kuyper, 2006). Soil disease suppressiveness may also be subdivided into general and

specific suppression. General suppression depends on overall diversity and

activity of the soil biota and acts against a broad range of pathogens. Not a

single microorganism or specific group of microorganisms is responsible by

itself for general suppression. Specific suppression on the other hand is the

result of antagonistic effects of individual or selected groups of organisms

against single pathogens. However, specific suppression always operates

against a background of general suppression (Cook and Baker, 1983).

Intense general suppression enhances the specific interactions between

pathogens and antagonists. In the search for biological control agents, many

researchers focus on specific antagonists, but this approach has still not

been as successful as expected and therefore general suppression

deserves again more attention as a mechanism for attaining healthy soils

(Alabouvette et al., 2004).

1.2.3. Source and sink of greenhouse gases Soils are both sources and sinks for mainly three greenhouse gases, namely

carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). The

exchange of CO2 is part of the decomposition and sequestration of organic

matter and is hence governed by a broad range of processes. Exchange of

CH4 and N2O, on the other hand, entails rather specific processes. Methane

8


production occurs under highly anaerobic conditions and as a result flooded

rice fields are a large anthropogenic source of atmospheric CH4 with an

estimated contribution of 16% of the global CH4 emissions (Komatsuzaki

and Ohta, 2007). Management options for mitigating CH4 emission from

paddy fields include mid-season drainage, intermittent irrigation, improved

infiltration, sulphate fertilizer application and application of well-composted

organic matter in stead of fresh organic matter and green manure. The

primary removal of methane from the atmosphere is by chemical oxidation to

CO2. A small portion of CH4 is converted to CO2 via methanotrophic

bacteria, mainly aerobically (Shively et al., 2001). Methanotrophs utilize

methane as their sole source of carbon and energy. They can be divided into

two distinct physiological groups that utilize different assimilation pathways.

Type I methanotrophs belong to the gamma-proteobacteria, type II

methanotrophs belong to the alpha-proteobacteria (Hanson and Hanson,

1996). Methanotrophs in aerobic soils oxidize methane present in the

atmosphere. In flooded fields, methane may be oxidized in the interface

between anoxic and oxic sites where concentration gradients of CH4 and O2

overlap. According to Seghers et al. (2003), the function and composition of

the methanotrophic community in arable soils are altered in soils amended

with mineral fertilizer with increased nitrate concentrations slowing down low

affinity methane oxidation. Boeckx et al. (1998) found that several pesticides

reduce methane oxidation in arable soils.

The production of N2O in soils is mainly due to nitrifying and denitrifying

microorganisms. During nitrification, N2O can be formed by the oxidation of

nitroxyl (NOH) or the reduction of nitrite (NO2–) under low oxygen

concentration. N2O is also produced as an intermediate or end product of

denitrification, which is the anaerobic reduction of nitrate (NO3–)

(Komatsuzaki and Ohta, 2007).

9

Chapter 1

1.3. Biochemical and microbial indicators of soil quality Notwithstanding the importance of the soil microbial community for soil

functioning and soil quality, little is known about how it is influenced by

production methods. In developing our knowledge of the microbial

component of soil ecosystems, the identification of suitable microbial soil

quality indicators that assist in determining best management practices is

necessary. A microbial indicator of soil quality should represent integrated

properties of the environment, which can be interpreted beyond the

information that the measured or observed parameter represents by itself.

Microbial indicators can be based on the size and activity of microbial

communities, on several diversity measures (functional, taxonomic, genetic),

on processes and their contributions to soil functions, on measures for

resilience, resistance, robustness and stability, and/or on the trophic

structure in relation to the soil food web (Winding et al., 2005).

1.3.1. Microbial biomass

Given the importance of soil microorganisms for so many soil processes,

their total biomass is a key parameter in any ecosystem. Determination of

microbial biomass is an essential baseline parameter in many national soil

monitoring programs (e.g. in Germany, The Netherlands, United Kingdom,

New Zealand) (Winding et al., 2005).

Microbial biomass can be directly estimated by microscopic counts using

conversion factors. This is a very laborious technique, except when

combined with automated image analysis as used in the Dutch Soil Quality

Network (Bloem and Breure, 2003). Different soil preparation and staining

techniques allow to differentiate between bacterial and fungal biomass.

Chloroform fumigation is the most commonly used indirect method of

microbial biomass determination. The chloroform vapour kills and lyses the

microorganisms in the soil. Subsequently the size of the killed biomass is

10


estimated either by quantification of respired CO2 over a specified period of

incubation (Jenkinson and Powlson, 1976) or by direct extraction from the

soil immediately after the fumigation (Vance et al., 1987). Extraction also

allows for analysing microbial N and P besides microbial C. Another

common indirect measure of microbial biomass is substrate-induced

respiration. After addition of an easily decomposable substrate (e.g.

glucose), the measurement of the initial change in the soil respiration rate is

related to the metabolically active part of the soil microbial biomass

(Anderson and Domsch, 1978). Less common methods include extraction

and measurement of ATP (Jenkinson and Oades, 1979), total adenylates

(Dyckmans et al., 2003) and total DNA (Marstorp and Witter, 1999). All

methods give similar estimates of microbial biomass if appropriate

conversion values are used (Dyckmans et al., 2003).

Finally, the abundance of particular groups of microorganisms can be

estimated by determining specific biomarkers, provided that they do not

accumulate in the SOM pool. For this reason, the value of ergosterol as an

indicator of fungal biomass requires more investigation. Several researchers

found that ergosterol did not accumulate in the SOM pool in the long-term

(Nylund and Wallander, 1992; Engelking et al., 2008), but others found that

a significant amount of ergosterol is accumulated for certain periods in

incubation experiments (Mille-Lindblom et al., 2004; Zhao et al., 2005). The

amino sugars glucosamine and muramic acid have successfully been used

as measures for fungi and bacteria respectively, but because they

accumulate in the SOM pool they are indicative of microbial residues rather

than of living biomass (Joergensen and Wichern, 2008).

1.3.2. Microbial activity

Organic matter serves as a source of energy and carbon for heterotrophic

organisms and the release of organically bound nutrients such as nitrogen,

sulphur, and phosphorus is first of all dependent on the degradation of

11

Chapter 1

organic matter by the action of microorganisms. Soil respiration provides a

general estimate of microbial activity and is directly linked to organic matter

degradation. N mineralization and denitrification are two important microbial

processes of the N cycle. All these activities involve the action of one or

more enzymes. The procedures for measuring these individual enzyme

activities are often more standardized than those of the overall processes. 1.3.2.1. Soil respiration

The oxidation of organic compounds to CO2 by aerobic heterotrophic

microorganisms is a key process in the carbon cycle of all terrestrial

ecosystems. Soil respiration is positively correlated with SOM content, and

often with microbial biomass and activity (Alef and Nannipieri, 1995). Soil

fauna respiration constitutes only a minor fraction of the total respiration

(Winding et al., 2005). Soil respiration can be quantified by measuring either

CO2 production or O2 consumption, or both. Measurement of CO2

concentration is the most sensitive method, due to the low atmospheric

concentration of CO2 compared with O2 (Winding et al., 2005). Respiration

is highly influenced by temperature, soil moisture content, and availability of

nutrients and soil structure. Hence, field measurements are highly variable

and are less frequently used. Preconditioning and standardization of the soil

before measurement of respiration is often considered necessary to

minimize the effect of climatic variables (Winding et al., 2005). Joergensen and Emmerling (2006) listed various stress factors that reduce

soil respiration such as salinization, heavy metals and pesticides. Soil

respiration measurements have been found to discriminate between different

management intensities within the Dutch Soil Quality Network (Bloem and

Breure, 2003). Castillo and Joergensen (2001) reported that soil basal

respiration was higher under organic compared to conventional agriculture in

Nicaragua, while Tirol-Padre et al. (2005) found that 40 years of

incorporation of rice straw compost increased aerobic soil respiration

compared to urea fertilized soils in Japanese paddy rice fields. The

12


metabolic quotient, the ratio of basal respiration to microbial biomass qCO2,

is a measure for the maintenance requirement of microbial biomass. It is one

of the most widely used indices of stress. For example, sublethal stress in

polluted soils lowers the efficiency of substrate use, i.e. more substrate must

be catabolised to CO2 and less substrate can be incorporated into the

microbial biomass which increases the qCO2 (Chander et al., 2001; Frische

and Höper, 2003). Several researchers reported lower qCO2 values in

organic systems than in conventional ones (Fließbach et al., 2007;

Lagomarsino et al., 2009).

1.3.2.2. N mineralization

N Mineralization is the general term for the conversion of organic to

inorganic N. The paradigm of N mineralization as it developed in the late

1990s recognizes two steps. The first and critical point is the

depolymerization of N-containing compounds. Polymers are not immediately

bioavailable because they are too large. They are cleaved by extracellular

enzymes to release monomers (amino acids, amino sugars, nucleic acids,

etc.) that are broadly bioavailable and may be used by either plants or

microorganisms (Schimel and Bennett, 2004). The second step is the

conversion of organic N to NH4+-N and is called ammonification.

Concomitantly with mineralization, the opposite process, assimilation or

immobilization of inorganic N into microbial biomass, also takes place. In

agricultural systems, with often high N availability, the N economy is NO3-

dominated and plants rely mainly on NO3- for their N need (Schimel and

Bennett, 2004). In those systems, the measurement of net N mineralization

(gross mineralization — immobilization) by controlled incubation experiments

(e.g. De Neve and Hofman, 1996) provides information about plant N

availability and the activity of the N mineralizing soil microorganisms.

However, it should be kept in mind that even in NO3- dominated systems,

depolymerisation and the release of N containing monomers by enzymes

still regulates the overall rate of N cycling (Schimel and Bennett, 2004). N

13

Chapter 1

mineralization has been found to be higher in organic than in conventional

systems due to the higher use of organic amendments (Monokrousos et al.,

2006; Tu et al., 2006). Conventional and organic systems receiving similar

amounts of farmyard manure have, however, similar N mineralization rates

(Birkhofer et al., 2008).

1.3.2.3. Denitrification

As denitrification is an anaerobic process it is very dependent on abiotic

factors such as precipitation, flooding, soil compaction and infiltration rates.

Thus, soil management practices readily influence the amount of

denitrification occurring in agricultural fields. Measurement of denitrification

is usually carried out by the acetylene inhibition technique (Smith and Tiedje,

1979), in which the reduction of N2O to N2 is inhibited by acetylene and

accumulated N2O is measured by gaschromatography. This method is

practical, but has the disadvantage that production of N2O and N2 cannot be

assessed separately. Furthermore, acetylene does not diffuse through larger

soil cores. Therefore other methods have been developed such as the use

of an artificial soil atmosphere of He and O2 and/or the use of isotopes (Bol

et al., 2003; Dhondt et al., 2003).

1.3.2.4. Enzyme activities

The many reactions of organic matter turnover and cell maintenance that

support soil life are catalyzed by enzymes. Soil enzymes may be produced

by animal and plant cells, but are mainly of microbial origin (Winding et al.,

2005). They may be located in the cytoplasm, in the periplasm of Gram-

negative bacteria or attached to the outer surface of cells. Enzymes may be

present in proliferating and non-proliferating cells (microbial spores or

protozoan cysts), in entirely dead cells or in cell debris. Enzymes may also

be present as extracellular soluble molecules, temporarily associated in

enzyme-substrate complexes, adsorbed to clay minerals or associated with

humic colloids (Alef and Nannipieri, 1995).

14


Many soil enzymes are involved in the decomposition of lignocellulose (e.g.

-glucosidase, -glucosaminidase, cellulase and phenol oxidase). In this

thesis -glucosidase and -glucosaminidase will be measured. -

glucosidase is an enzyme involved in the C cycle that catalyses the

conversion of disaccharides into glucose (Alef and Nannipieri, 1995), while

N-Acetyl- -D-glucosaminidase plays an important role in both C and N

cycling because it hydrolyzes N-acetyl- -D-glucosamine residues from the

terminal non-reducing ends of chitooligosaccharides. Other enzymes

immediately release plant-available nutrients, e.g. arylsulphatase, amidases

and phosphatases). The latter are important enzymes of the P cycle and

catalyse the hydrolysis of organic phosphoesters to inorganic phosphorus.

Some enzyme activities, finally, may provide a general measure of microbial

activity, e.g. dehydrogenase and fluorescein diacetate hydrolase. Especially

dehydrogenase activity is often used for this purpose. Dehydrogenase is an

intracellular enzyme participating in the processes of oxidative

phosphorylation of microorganisms (Alef and Nannipieri, 1995) and is thus

linked with microbial respiratory processes.

Dick (1994) was one of the first to propose soil enzyme activities as

indicators of soil quality, based on their relationship to soil biology and soil

functioning, rapid response to changes in soil management and their ease of

measurement. Soil enzyme activities have successfully been used to

discriminate between a wide range of soil management practices, such as

different crop rotations (Chander et al., 1997), conventional and no-tillage

(Balota et al., 2004) or organic and conventional cultivation (Lagomarsino et

al., 2009; Mäder et al., 2002). Measurements of soil enzyme activities are

usually based on the addition of an artificial, soluble substrate, and represent

the maximum potential activities rather than the actual enzyme activity

because the incubation conditions of enzyme assays are chosen to ensure

optimum rates of catalysis. The concentration of substrate is in excess,

optimal values of pH and temperature are selected, and the volume of the

15

Chapter 1

reaction mixture is such that it allows free diffusion of substrate (Alef and

Nannipieri, 1995).

1.3.3. Microbial community profiling Overall indicators of the soil microbial community (e.g. microbial biomass or

activity) are not always sensitive enough to detect negative impacts of

particular treatments or management options (Fließbach and Mäder, 2004;

Widenfalk et al., 2008). Investigating shifts in the microbial community may

be more promising. Three major groups of methodologies to characterize

microbial communities have evolved: (1) substrate utilization methods

usually called community level physiological profiling (CLPP), (2) extraction

of phospholipid fatty acids (PLFAs) and sometimes also neutral lipid fatty

acids (NLFAs) from cell membranes of living microorganisms, and (3)

determination of nucleic acid profiles by polymerase chain reaction (PCR). In

a review of 53 studies, Ramsey et al. (2006) concluded that PLFA analysis is

the most powerful approach to demonstrate changes in the total microbial

community structure. While they found no studies where CLPP- or PCR-

based methods differentiated treatments that were not also differentiated by

PLFA, in 14 of 32 studies PLFA differentiated treatments that were not

resolved by CLPP analysis, and in 5 of 25 studies PLFA differentiated

treatments that were not resolved by PCR-based methods. However, PLFA

profiles offer limited insight into changes in specific microbial populations

compared to PCR-based methods (Ramsey et al., 2006). While certain

PLFAs can be used as biomarkers for specific populations (e.g. Kozdrój and

van Elsas, 2001), the resolution of population level change within

communities is coarse due to several factors: (1) overlap exists in the PLFA

composition of microorganisms, (2) determination of signature PLFAs for

specific microbes requires their isolation in pure culture, and (3) PLFA

patterns for individual populations can vary in response to environmental

stimuli (Ramsey et al., 2006). But in contrast to PCR-based methods, PLFA

16


measurements can also be used to estimate the living biomass of the total

microbial community (e.g. Joergensen and Emmerling, 2006) or of specific

groups such as fungi (e.g. Joergensen and Wichern, 2008; Zelles, 1999),

bacteria (Kozdrój and van Elsas, 2001) or methanotrophs (Bossio and Scow,

1998; Seghers et al., 2003). Anyhow, it should be kept in mind that all

profiling techniques present a limited view of microbial communities, as the

number of species in environmental samples often is orders of magnitude

greater than what can be analyzed practically (Torsvik et al., 1990).

PLFA profiling has been used e.g. to investigate shifts of the soil microbial

community in forests due to pH changes (Bååth et al., 1995; Frostegård et

al., 1993), to examine the impact of different cropping and tillage regimes

(Minoshima et al., 2007) or to distinguish between organically and

conventionally managed soils (Petersen et al., 1997).

1.4. Soil management and soil quality in agro-ecosystems Basically, three aspects of the agricultural practice affect the soil microbial

community: (1) the use of chemical fertilizers and organic amendments, (2)

the application of pesticides, and (3) tillage. The list of human impacts on the

soil ecosystem is, however, much longer and includes artificial drainage,

salinization as a result of irrigation, and contamination by heavy metals or

organic pollutants in agricultural fields near or on former industrial areas.

Possibly also transgenic plants may affect the soil microbial community

(Milling et al., 2004; Sessitsch et al., 2004). In this paragraph, the discussion

will be limited to the use of chemical and organic fertilizers and the

application of pesticides.

1.4.1. Pesticide use

Many inconsistent findings about the effects of pesticides on soil

microorganisms have been reported, not at least because of the enormous

17

Chapter 1

diversity in molecular structure of pesticides, their mode of action and

application rate. Whereas a certain pesticide may well be toxic to non-target

organisms, some microbial groups will be able to use it as a source of

energy and nutrients. As the soil microbial community is a complex of

interwoven relationships between organisms of different trophic levels, this

will lead to many indirect effects (Johnsen et al., 2001). Singh et al. (2002)

reported that the fungicide chlorothalonil (10 mg active ingredient kg-1 dry

soil) negatively affected total microbial biomass, and phosphatase and

dehydrogenase activity, but the insecticides fenamiphos and chlorpyrifos did

not. Application of the fungicide mefenoxam increased the total population of

bacteria, but reduced the amount of free N fixing bacteria (Monkiedje et al.,

2002). Fließbach and Mäder (2004) conducted a controlled experiment in

which potato plants were sprayed with a range of herbicides, insecticides

and fungicides at recommended rates. Dehydrogenase activity, basal

respiration and microbial biomass were lower than control values 21 days

after the last pesticide application, but values returned to normal 135 after

the last application. The structure of the microbial community, however,

measured by CLPP, appeared to be changed on the long-term. Fließbach

and Mäder (2004) therefore agree with Engelen et al. (1998) and Johnsen et

al. (2001) that microbial community analysis is a useful or even better tool

than overall metabolism indicators (e.g. microbial biomass or activity) for

assessing pesticide side-effects. Shifts in community structure may have

important consequences on soil fertility and soil functioning if persistent

microorganisms cannot compensate for biogeochemical functions normally

carried out by the eliminated microbial groups (Johnsen et al., 2001;

Widenfalk et al., 2008).

1.4.2. Organic amendments and mineral fertilizers

Research into the side-effects of pesticides is usually carried out in the

laboratory, while field trials are scarce. Much more field experiments are

18


carried out that compare chemical fertilizers and different types of organic

amendments. A good example is the field trial at Bad Lauchstädt (Germany)

established in 1902. Böhme et al. (2005) reported that plots only receiving

farmyard manure had lower qCO2 values and a higher microbial biomass

and -glucosidase, protease and alkaline phosphatase activity compared to

plots only receiving inorganic fertilizer. Likewise, microbial biomass was

higher in dairy manure (Peacock et al., 2001) and poultry litter (Jangid et al.,

2008) amended plots than in plots fertilized with only mineral fertilizer.

However, the positive impacts of organic amendments apparently cannot be

generalized to all soils and climates. In a Hungarian field experiment started

in 1963 farmyard manure treated plots and plots receiving only chemical

fertilizer had comparable microbial biomass and enzyme activities (Böhme et

al., 2005).

A field trial comparing different fertilization strategies, including five different

types of organic amendments, was started in 2005 at the experimental farm

of Ghent University. Leroy (2008) investigated the impact of the different

treatments on PLFA profiles. In the short-term, no significant differences

between the treatments were observed. However, after one and a half year

plots amended with compost showed higher fungi to bacteria (F/B) ratios

compared to plots amended with farmyard manure and cattle slurry,

although these differences were not significant (P>0.05). Further evidence of

increased F/B ratios in the compost plots could, however, be found in the

nematode populations. Compost plots had significantly lower populations of

bacterivore nematodes than the manure and slurry plots, while the

fungivorous nematodes tended to be more abundant. Higher F/B ratios are

suggested to be indicative for more sustainable agroecosystems with lower

impact on the environment (de Vries et al., 2006). Also the bacterial

community itself changes due to differences in fertilization regime. Jangid et

al. (2008) found that bacterial diversity was higher in poultry litter amended

soils than in soils receiving inorganic fertilizer, whereas the latter soils

harboured a bigger population of oligotrophic Acidobacteria. Peacock et al.

19

Chapter 1

(2001) reported that manured soils were relatively enriched with Gram-

negative bacteria, while the use of chemical fertilizer resulted in relatively

more Gram-positive bacteria. Gram-negative bacteria are fast growers that

take advantage of increases in the availability of organic substrates (Burke

et al., 2003). Gram-positive bacteria on the other hand have slower growth

rates and are able to degrade complex substrates (Burke et al., 2003), for

example those of the recalcitrant organic matter still present in organic

matter depleted soils.

Finally, organic amendments, especially composts, have repeatedly been

reported to control soil-borne pathogens such as Fusarium spp. (Szczech,

1999), Phytophthora spp. (Szczech and Smoli ska, 2001), Pythium spp.

(Veeken et al., 2005) and Rhizoctonia solani (Diab et al., 2003).

Unfortunately, the disease suppressiveness of organic amendments is often

inconsistent. Amendments that are suppressive to some pathogens may be

conducive to others (Bonanomi et al., 2010). After an extensive review of

252 articles, Bonanomi et al. (2010) concluded that organic matter

decomposition is a crucial process determining disease suppressiveness.

However, during decomposition disease suppressive properties may either

increase, decrease or even show complex responses. Nevertheless,

Bonanomi et al. (2010) stick to the conclusion earlier made (Bonanomi et al.,

2007) that more decomposed materials (e.g. mature composts) are in

average more suppressive than fresh crop residues or animal manure.

1.4.3 Organic and conventional agriculture

Organic farming methods rely on organic inputs and recycling for nutrient

supply, emphasize cropping system design and soil biological processes for

pest management, and ban applications of synthetic fertilizers and

pesticides (Rigby and Cáceres, 2001). Field experiments comparing

conventional and organic agriculture therefore allow to assess

20


simultaneously the impact of chemical fertilizers and pesticides versus that

of only organic inputs.

The most famous long-term field experiments comparing organic and

conventional agriculture is the DOK-trial established in 1978 in Switzerland.

In 2002 Mäder et al. (2002) summarized the major findings of the trial

obtained so far in Science. Organically managed soils exhibited greater

biological activity than the conventionally managed soils. In contrast,

chemical and physical parameters showed fewer differences. Soil microbial

biomass was lowest in conventional soils receiving only chemical and no

organic fertilizer, while it was highest in bio-dynamically managed soils

receiving composted farmyard manure. In soils of the organic systems

(organic with slightly rotted farmyard manure and bio-dynamic with

composted farmyard manure), activities of the enzymes dehydrogenase,

protease, and phosphatase were higher than in those of the conventional

systems. Also microbial diversity, measured by CLPP, was higher under

organic management, while the metabolic quotient qCO2 followed the

opposite trend, with lowest qCO2 values in the bio-dynamic system. Mäder

et al. (2002) hence concluded that organic cultivation fosters microbial

communities with increased diversity that transform carbon at lower energy

costs and build up a higher microbial biomass.

Another example of a long-term field experiments is the Sustainable

Agriculture Farming Systems Project in California, established in 1989 and

stopped in 2001. Results from this field-experiment, obtained in 1994, were

very similar to the findings of the DOK-trial. Microbial biomass, basal

respiration and the ratio of microbial biomass C (MBC) to soil organic C

(SOC) were higher under organic than under conventional management,

while qCO2 was lower (Lundquist et al., 1999). The inverse relationship of

the MBC/SOC ratio and qCO2 reveal the interdependence of catabolism and

anabolism (Anderson, 2003). If the efficiency of substrate use is higher, i.e.

less substrate must be catabolised to CO2, more substrate can be

incorporated into microbial biomass (Joergensen and Emmerling, 2006).

21

Chapter 1

Lundquist et al. (1999) also observed a higher F/B ratio in the organically

managed field.

1.5. Conclusions The soil microbial community is to a large extent responsible for the cycling

of nutrients and is assumed to be essential for sustainable crop production.

AMF represent a particular group of microorganisms because of their close

association with plants and play a role in both plant nutrient supply and

disease suppression. Many studies, mainly carried out in Europe or North

America, report positive effects of organic amendments on the soil microbial

community, but the relation between organic matter additions and disease

suppressiveness remains unclear.

A number of biochemical and microbial indicators were reviewed. Microbial

biomass, enzyme activities and PLFA profiles appeared to be particularly

sensitive to differences in soil quality. Enzyme activities have a direct link

with microbial activity and nutrient turnover. A quite extensive list of marker

PLFAs exist for a range of microbial groups. In this thesis, we will focus on

Gram-positive, Gram-negative and total bacteria, actinomycetes, fungi and

AMF. One should bear in mind that there are several other microbial groups

that may be investigated by PLFA biomarkers, such as the methanotrophs.

However, the six groups selected here can be considered as the most

informative ones in terms of nutrient turnover and disease suppressiveness.

PLFA measurements can also be used to calculate informative ratio’s such

as the Gram-positive to Gram-negative ratio and the fungi to bacteria ratio.

Additionally, a number of other specific bio-indicators were discussed. The

amino sugars glucosamine and muramic acid appeared to be reliable

indicators for fungi and bacteria respectively, but rather of residues in the

SOM pool than of living biomass. To which extent ergosterol does

accumulate in the SOM pool is not yet clear. Therefore, it was decided to

include ergosterol measurements in this study but not amino sugars. Finally,

22


three important general microbial processes were reviewed, namely soil

respiration, N mineralization and denitrification of which soil respiration and

N mineralization will be measured in this thesis.

1.6. Objectives and outline of the thesis Despite growing knowledge about the impact of agricultural inputs

(fertilizers, pesticides) on the soil microbial community, important knowledge

gaps remain, two of which will be addressed in this thesis: (1) soil quality

under tropical conditions, and (2) a direct comparison of the specific effects

of different kinds of organic amendments. By comparing the findings of the

different agro-ecosystems investigated in this thesis, we expect to be able to

draw general conclusions about the use of biochemical and microbial

measurements for the assessment of soil quality.

1.6.1. Soil quality under tropical conditions

As indicated earlier, relationships between soil management and soil

microorganisms established in one region are not necessarily valid for other

soils and climates. Relationships obtained in one region, should therefore be

validated in other parts of the world. Most of soil microbiological research

has been carried out in Europe and North America, while the tropics have

until now only received limited attention. However, the continuous and strong

increase in population pressure in many tropical regions, including Java

(Indonesia), has caused agricultural land use to expand and intensify

(Verburg et al., 1999). Incited by the Green Revolution, this expansion has

often been accompanied by the introduction or the multiplication of inputs,

i.e. chemical fertilizers and pesticides, which may potentially have negative

impacts on soil quality and soil functioning. The first part of this thesis is

therefore dedicated to soil quality in vegetable production and rice cultivation

systems (the two most intensive crop systems of Southeast Asia) in Java.

23

Chapter 1

Chapters 2 and 3 compare microbial and enzyme activity, microbial biomass

and PLFA profiles under organic and conventional vegetable production in

the fully humid equatorial climate of West Java. Based on the measurements

presented in chapter 2 a soil quality index will be developed that will be

validated using the results of chapter 3. In addition, chapter 3 presents

measurements of the fungal biomarker ergosterol and results of a disease

suppressiveness assay. Chapter 3 also provides a discussion of different

PLFA indices and explores the value of nematode research for assessing

soil quality. Chapter 4 deals with differences in microbial soil quality between

organic and conventional paddy rice production in the monsoonal equatorial

climate of Central Java.

1.6.2. Impact of different organic amendments on soil quality In most of the studies on the use of exogenous organic matter only a few

amendments are investigated. Direct comparisons of several kinds of

exogenous organic matter are scarce. As a result, the question which

organic amendment is the best for improving or maintaining soil quality

remains unresolved. In particular, there are still many questions about

organic fertilizers and disease suppressiveness. Chapter 5 reports

measurements from the field trial of Ghent University mentioned earlier. This

trial is conducted on an arable field in a fully humid temperate climate with

warm summers. Results from a disease suppressiveness assay will be

presented and an index will be calculated based on measurements of

microbial biomass, enzyme activity and PLFAs that assesses the impact of

exogenous organic matter on soil quality. The relation between this index

and N mineralization will be discussed.

24


25

1.6.3. General discussion

In the final chapter, chapter 6, results obtained in the three agro-

ecosystems, namely vegetable production, paddy rice cultivation and arable

agriculture in their respective climate, will be brought together. The use of

enzyme activities, PLFA measurements and disease suppressiveness

assays for evaluating soil quality will be evaluated. Finally, the significance

and scope of application of the developed soil quality indices will be

discussed and suggestions for further research will be given.

Chapter 2


in West Java, Indonesia (2007)

Redrafted after: Moeskops B, Sukristiyonubowo, Buchan D, Sleutel S, Herawaty L, Husen E,

Saraswati R, Setyorini D, De Neve S (2010) Soil microbial communities and

activities under intensive organic and conventional vegetable farming in

West Java, Indonesia. Applied Soil Ecology 45: 112-120.

Illustration:

Harvest at the organic farm Bina Sarana Bakti in Cisarua (Bram Moeskops)

Chapter 2



2.1. Introduction

The continuous and strong increase in population pressure in many tropical

regions, including Java (Indonesia), has caused agricultural land use to

expand and intensify (Verburg et al., 1999). Incited by the Green Revolution,

this expansion has often been accompanied by the introduction or the

multiplication of inputs, i.e. chemical fertilizers and pesticides. The Green

Revolution especially affected rice cultivation (Martawijaya and Montgomery,

2004) and vegetable production (Rerkasem, 2005), the two most intensive

crop systems in Southeast Asia. Throughout tropical Asia vegetables are

generally overfertilized (Rerkasem, 2005). Poudel et al. (1998) report

application rates up to 211 kg inorganic N ha-1 growth cycle-1 for cabbage in

the Philippines. A survey of vegetable farms in Thailand found up to 600 kg

N and 250 kg P ha-1 applied per year (Phupaibul et al., 2002). Even more

serious are reports about pesticide overuse. Farmers in Myanmar were

found to apply 15 to 60 times recommended rates of some insecticides to

tomatoes (Rerkasem, 2005). Also in the Cameron Highlands, Malaysia’s

vegetable production region, pesticides are heavily used (Mazlan and

Mumford, 2005). Conventional farming practices and associated chemical

inputs thus increasingly raise environmental and public health concerns

(Horrigan et al., 2002). Prominent among these are environmental pollution

(Horrigan et al., 2002), reduction in biodiversity (Lupwayi et al., 2001; Oehl

et al., 2004) and soil erosion (Reganold et al., 1987). As a result of these

concerns the long-term sustainability of conventional production methods is

29

Chapter 2

questionable, and the potential for organic farming receives increasing

attention. Organic farming methods rely on organic inputs and recycling for

nutrient supply, emphasize cropping system design and soil biological

processes for pest management, and ban applications of synthetic fertilizers

and pesticides (Rigby and Cáceres, 2001). They may thus reduce negative

effects attributed to conventional farming (Mäder et al., 2002; Oehl et al.,

2004; Reganold et al., 1987). However, in Indonesia also organic vegetable

cultivation is very intensive. Up to 190 Mg compost ha-1 y-1 is applied on

organic vegetable farms in Java.

As indicated in chapter 1, the decline in soil quality in Indonesia, and in the

tropics in general, has not been well documented. How production methods

influence the microbial community in tropical soils remains almost

unexplored. Chapter 2 and 3 therefore examine the effect of organic

vegetable production on soil microbial community composition and on soil

enzyme activities, as compared to conventional production systems in the

humid tropical climate of West Java. We expected that the large differences

in management methods would allow the identification of clear indicators of

differences in soil quality. In chapter 2 results from 2007 are reported, while

chapter 3 deals with results from 2008. Following parameters were

measured in 2007: microbial biomass C (MBC), phospholipid fatty acid

(PLFA) profiles and the activity of the enzymes dehydrogenase, -

glucosidase, -glucosaminidase and acid phosphomonoesterase.

2.2. Materials and Methods

2.2.1. Experimental set-up At three locations in West Java an organic vegetable farm (OF) and two

conventional vegetable fields (CF), within less than 800 m from the organic

farm, were selected. Two of these locations were situated in the Cisarua

district (Bogor regency), further referred to as Cisarua1 (06° 41’ S, 106° 57’

30

Vegetable farms (2007)

E) and Cisarua2 (06° 41.5’ S, 106° 57’ E), and the third one in the Ciwidey

district (07° 08’ S, 107° 29.5’ E) (Bandung regency). We selected a

secondary forest to provide reference values for the parameters measured,

situated in the Ciwidey district at about 1 km from the farming sites there.

The OF in Cisarua1 adopted organic principles in in 1999, that of Ciwidey in

2002. At the OF in Cisarua2, a distinction was made between plots that had

been organically cultivated since 1984 (long-term) and plots that had been

converted from conventional management in 2005 (short-term). At the two

organic farms in Cisarua vegetables are cultivated on small beds of 10 m2.

Following the principle of intercropping, the same crop is never planted on

adjacent beds. At the OF of Ciwidey crops are grown in groups of 3-15 beds

of 8 m2 each. On all three organic farms, generally a second vegetable is

intercropped between the rows of the main crop. Conventional vegetable

production is also small-scale. The area of a single field, with one main crop

and sometimes an intercrop, ranges between 500 and 2000 m2.

For each location enzyme activities, MBC and PLFAs were determined

under two crops on both organic and conventional farms. We selected crops

that suffer recurrently from pests and diseases and/or for the production of

which conventional farmers rely heavily on pesticides and mineral fertilizers.

Selected crops and their management are specified in Table 2.1. Whereas

the organic farms applied a uniform fertilization rate for all crops, the

conventional farmers applied variable rates of fertilizer (and pesticides)

according to the crop grown. Hence, the rates given for the conventional

farms only apply to the crops grown at the moment of sampling. The organic

farms in Cisarua applied smaller amounts of compost to each newly

transplanted crop, while at the organic farm in Ciwidey higher compost

doses were applied, but less frequently so. Conventional famers generally

applied purchased dried chicken manure mixed with rice husks as organic

fertilizer. The conventional cauliflower farmer in Cisarua2 mixed this chicken

manure with excreta from his own goats and horse. Organic fertilizers

applied at the organic farms were more diverse but always consisted of

31

Chapter 2

32

composted crop residues and animal manure (chicken and goat in Cisarua1,

chicken in Ciasura2, cattle and chicken in Ciwidey).

2.2.2. Climate of the research sites The climate in the research area is still fully humid equatorial according to

the Köppen-Geiger classification, but approaching the monsoonal equatorial

climate (Kottek et al., 2006). This means the climate is characterized by two

seasons: a rainy season from October to April with about 80% of the annual

precipitation and a dry season from May to September. Total annual

precipitation ranges between 2380 mm and 3690 mm in Cisarua, and

between 1990 mm and 3240 mm in Ciwidey. The research sites in Cisarua

are located at an average altitute of 960 m amsl and those in Ciwidey at

1360 m amsl. Average monthly temperatures in the highlands of Cisarua

and Ciwidey range between 20.5°C and 22°C, allowing vegetables to be

grown continuously. Fallow periods are restricted to a few weeks only and

cultivation of 4-6 crops per year on the same field is not uncommon.

Tabl

e 2.

1: S

elec

ted

crop

s an

d m

anag

emen

t dat

a.

Loca

tion

Cro

ps

Con

v ent

iona

l man

agem

ent

O

rgan

ic m

anag

emen

t

scal

lion

(Alli

um

fistu

losu

m L

.)

man

ure:

5 M

g =

70 k

g N

ur

ea: 1

15 k

g N

in

terc

ropp

ing

with

lettu

ce (L

actu

ca s

ativ

a L.

)

Cis

arua

1 ca

bbag

e (B

rass

ica

oler

acea

L.)

man

ure:

4 M

g =

56 k

g N

(N

H4)

2SO

4: 6

0 kg

S a

nd 5

3 kg

N

phos

phat

e: 3

6 kg

P2O

5 K

Cl:

60 k

g K

2O

pest

icid

es: p

ropi

neb

(988

mg

l-1) a

nd p

rofe

nofo

s (1

000

mg

l-1) a

re a

pplie

d ev

ery

9 da

ys

inte

rcro

ppin

g w

ith s

calli

on

dolo

mite

enr

iche

d co

mpo

st:

14 M

g =

69 k

g N

bi

o-pe

stic

ide:

ext

ract

from

toba

cco

leav

es (N

icot

iana

taba

cum

L.)

tom

ato

(Sol

anum

ly

cope

rsic

um L

.)

man

ure:

4.9

Mg

= 69

kg

N

urea

: 100

kg

N

phos

phat

e: 1

39 k

g P

2O5

KC

l: 65

kg

K2O

N

PK

: 4 k

g N

, 4 k

g P

2O5

and

4 kg

K2O

pe

stic

ides

: pro

pine

b (6

59 m

g l-1

) and

pro

feno

fos

(706

mg

l-1) a

re a

pplie

d ev

ery

wee

k be

fore

ha

rves

t per

iod,

and

two

times

a w

eek

durin

g ha

rves

t per

iod

Cis

arua

2

broc

coli/

caul

iflow

er

(Bra

ssic

a ol

erac

ea L

.)

man

ure:

3.5

Mg

= 49

kg

N

(NH

4)2S

O4:

48

kg S

and

42

kg N

K

Cl:

30 k

g K

2O

pest

icid

es: e

mam

ectin

ben

zoat

e (9

4 m

g l-1

) is

appl

ied

ever

y 8-

9 da

ys

com

post

: 18

Mg

= 94

kg

N

Fert

iliza

tion

rate

s ar

e gi

ven

per h

a an

d pe

r gro

wth

cyc

le (8

5 da

ys).

Loca

tion

Cro

ps

Con

v ent

iona

l man

agem

ent

O

rgan

ic m

anag

emen

t

pota

to (S

olan

um

tube

rosu

m L

.)

man

ure:

100

Mg

= 14

00 k

g N

(N

H4)

2SO

4: 8

40 k

g S

and

735

kg

N

phos

phat

e: 5

60 k

g P

2O5

and

175

kg S

pe

stic

ides

: end

osul

fan

(294

mg

l-1) a

nd

chlo

roth

alon

il (1

103

mg

l-1) a

re a

pplie

d ev

ery

10 d

ays

Ciw

idey

cabb

ageB

rass

ica

oler

acea

L.)

man

ure:

10

Mg

= 14

0 kg

N

NP

K: 2

40 k

g N

, 240

kg

P2O

5 an

d 24

0 kg

K2O

pe

stic

ides

: em

amec

tin b

enzo

ate

(29

mg

l-1) i

s ap

plie

d ev

ery

wee

k

lim

e en

riche

d co

mpo

st: 1

88 M

g =

1391

kg

N p

er y

ear

bio-

pest

icid

e: e

xtra

ct fr

om w

ild p

lant

s (T

oona

sur

eni (

B.l)

Mer

r., A

cmel

la

pani

cula

ta (W

all.e

x D

C) R

.K. J

anse

n,

Muc

una

prur

iens

(L.)

Urb

an, D

atur

a m

etel

L.,

Tith

onia

div

ersi

folia

(Hem

sl.)

A. G

ray)

Fert

iliza

tion

rate

s ar

e gi

ven

per h

a an

d pe

r gro

wth

cyc

le (8

5 da

ys) u

nles

s ot

herw

ise

stat

ed.

Tabl

e 2.

1: S

elec

ted

crop

s an

d m

anag

emen

t dat

a (c

ontin

ued)

.


2.2.3. Soil sampling Soil samples were taken in triplicate plots for each crop. Because the

research sites were each differently organized, selection of the three plots

differed from site to site. At the OF in Cisarua1 and Cisarua2 three separate

beds of 10 m2 spaced approximately 5-20 m apart were chosen for each

selected crop. At the OF in Cisarua2, this was done both for fields under

short- and long-term organic management. At the OF in Ciwidey three

adjacent beds of 8 m2 were selected for each crop. On the conventional

fields, three plots of 10 m2 were selected spaced approximately 5-10 m

apart. In all plots 15 samples were taken from the 0-15 cm soil layer and

bulked into one composite sample per plot. All sites were sampled twice

during the dry season of 2007: in July, shortly after the transplanting of

crops, and at the beginning of September, around harvest. Because of

practical reasons, the activities of the enzymes -glucosidase and

dehydrogenase were measured on the first series, while MBC, PLFAs, and

acid phosphomonoesterase and -glucosaminidase activity as well as

general soil properties were determined on the second series of samples.

2.2.4. General soil properties Determination of general soil properties was carried out on air-dried and

sieved (2 mm) soil. pH-KCl was measured in 1N KCl extracts (soil:KCl ratio

of 1:2.5). Total C and N contents were measured with a Variomax CNS

elemental analyzer (Elementar GmbH, Hanau, Germany) applying the

Dumas method. Since pH-KCl values were acidic (less than 6.5), free

carbonates were assumed not to be present and total carbon contents were

considered equivalent to organic carbon contents. Texture was determined

by the combined sieve and pipette method according to Gee and Bauder

(1986). Physical soil properties are summarized in Table 2.2. All soils were

Andisols.

35

Tabl

e 2.

2: P

hysi

cal s

oil p

rope

rtie

s of

rese

arch

site

s.

Loca

tion

Cro

p M

anag

emen

t 50

-200

0 μm

(%)

2-50

μm

(%)

0-2 μm

(%)

US

DA

te

xtur

e Bu

lk d

ensi

ty

(g c

m-3

)

Cis

arua

1 sc

allio

n or

gani

c 50

.0*

28.5

21

.7

loam

0.

78 (0

.05)

conv

entio

nal

36.3

30

.2

33.5

cl

ay lo

am

0.84

(0.0

6)

ca

bbag

e or

gani

c 50

.0*

28.5

21

.7

loam

0.

70 (0

.03)

conv

entio

nal

30.2

32

.5

37.3

cl

ay lo

am

0.73

(0.0

2)

Ciw

idey

po

tato

or

gani

c 44

.3

54.0

1.

8 si

lt lo

am

0.65

(0.0

9)

conv

entio

nal

66.8

31

.5

1.7

sand

y lo

am

0.71

(0.0

1)

ca

bbag

e or

gani

c 36

.3

62.5

1.

2 si

lt lo

am

0.61

(0.0

5)

conv

entio

nal

56.0

42

.9

1.1

sand

y lo

am

0.65

(0.0

7)

se

cond

ary

fore

st

38

.7

34.3

27

.1

loam

0.

70 (0

.11)

Cis

arua

2 to

mat

o or

gani

c-23

y 30

.7*

33.4

36

.0

clay

loam

0.

76 (0

.25)

orga

nic-

2y

36.3

* 29

.5

34.4

cl

ay lo

am

0.78

(0.1

5)

conv

entio

nal

37.3

30

.7

32.0

cl

ay lo

am

0.79

(0.0

4)

or

gani

c-23

y 30

.7*

33.4

36

.0

clay

loam

0.

74 (0

.21)

broc

coli/

ca

ulifl

ower

or

gani

c-2y

36

.3*

29.5

34

.4

clay

loam

0.

64 (0

.17)

conv

entio

nal

38.0

33

.5

28.4

cl

ay lo

am

0.71

(0.1

1)

* Par

ticle

siz

e di

strib

utio

n is

ave

rage

d fo

r bot

h cr

ops

at th

at s

peci

fic lo

catio

n.

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

.


2.2.5. Soil biochemical and microbial analyses Determination of dehydrogenase and -glucosidase activity, and extraction

of MBC was done on fresh soil stored at 4°C. For the activities of the

enzymes -glucosaminidase and acid phosphomonoesterase air-dried,

sieved soil (2 mm) was pre-incubated at 35 w% moisture content

(approximately 50% WFPS) and 25°C during one week before analysis. Soil

samples for PLFA analysis were freeze-dried and sieved (2 mm) after

sampling and subsequently stored at -18°C until extraction.

2.2.5.1. -glucosidase

-glucosidase is an enzyme involved in the C cycle that catalyses the

conversion of disaccharides into glucose (Alef and Nannipieri, 1995). -

glucosidase hence plays a role in the decomposition of lignocellulose. The

activity of -glucosidase was measured according to a procedure modified

from Eivazi and Tabatabai (1988; cited in Alef and Nannipieri, 1995). One

gram of moist soil was weighed in glass vials. Four ml Modified Universal

Buffer pH 6.0 and 1 ml 25 mM p-nitrophenyl- -D-glucoside were added. Soil

suspensions were incubated for 1 h at 37°C. After incubation, 1 ml of 0.5 M

CaCl2 and 4 ml Tris buffer pH 12 were added. To make concentrations of p-

nitrophenol (PNP) fit within the range of the standard series filtrates were

diluted 10 times using Tris buffer pH 10. Colour intensity of the filtrates was

measured at 400 nm with a Hitachi 150-20 spectrophotometer (Hitachi Ltd.,

Tokyo, Japan). All measurements were carried out in triplicate with one

blank.

2.2.5.2. Dehydrogenase

Dehydrogenase is an intracellular enzyme participating in the processes of

oxidative phosphorylation of microorganisms (Alef and Nannipieri, 1995) and

is thus linked with microbial respiratory processes. It is often used as

measure for microbial activity (Alef and Nannipieri, 1995). The procedure for

37

Chapter 2

dehydrogenase activity was modified from Casida et al. (1964). Five gram

moist soil was weighed in glass vials, and 2 ml 3% solution of

triphenyltetrazolium chloride and 2 ml Tris buffer pH 7.8 were added. Soil

suspensions were incubated in the dark for 24 h at 37°C. After incubation,

18 ml of methanol was added to each vial and the vials were shaken in the

dark for 2 h with a linear shaker (125 rev min-1). Filtrates were collected in 50

ml volumetric flasks. To extract all produced triphenyl formazan (TPF), the

remaining soil in the vials was washed twice with methanol, following which

filter papers were also washed twice. Filtrates in the volumetric flasks were

made up to 50 ml with methanol. The colour intensity of the filtrates was

measured at 485 nm with a Hitachi 150-20 spectrophotometer. All

measurements were carried out in triplicate with one blank.

2.2.5.3. -glucosaminidase

N-Acetyl- -D-glucosaminidase is the enzyme that hydrolyzes N-acetyl- -D-

glucosamine residues from the terminal non-reducing ends of

chitooligosaccharides and plays an important role in both C and N cycling in

soils. -glucosaminidase has an optimum pH value of around 5.5. Therefore,

activities of -glucosaminidase might be of particular importance for N

transformations in acidic soils, like the soils of this study, because most

other enzymes known to be involved in N transformations in soil have pH

optima in the alkaline pH range (Parham and Deng, 2000). The activity of -

glucosaminidase was measured according to the method of Parham and

Deng (2000), which is analogous to the method for -glucosidase described

above. Four ml acetate buffer pH 5.5 and 1 ml 10 mM p-nitrophenyl-N-acetyl-

-D-glucosaminide were added to 1 gram of moist soil. After incubation (1 h)

and extraction of PNP by Tris buffer pH 12 colour intensity of the filtrates

was measured at 405 nm with a Cary 50 UV-Visible spectrophotometer

(Varian Inc., Palo Alto, USA). All measurements were carried out in duplicate

with one blank.

38


2.2.5.4. Acid phosphomonoesterase

Phosphomonoesterases catalyse the hydrolysis of organic

phosphomonoesters to inorganic phosphorus. Acid phosphatase is

predominant in acid soils (Eivazi and Tabatabai, 1977). The analysis of acid

phosphomonoesterase was based on the method of Tabatabai and Bremner

(1969) which is similar to the methods of -glucosidase and -

glucosaminidase. Four ml Modified Universal Buffer pH 6.5 and 1 ml 115 mM

p-nitrophenyl-phosphate solution were added to one gram of moist soil. After

incubation (1 h) and extraction of PNP by Tris buffer pH 12, filtrates were

diluted 10 times using Tris buffer pH 10 and finally colour intensity of the

filtrates was measured at 400 nm with a Cary 50 UV-Visible

spectrophotometer. All measurements were carried out in duplicate with one

blank.

2.2.5.5. Microbial biomass C

MBC was determined using the fumigation-extraction technique (Vance et

al., 1987). Both fumigated soil and unfumigated controls (25 g) were

extracted in duplicate with 50 ml 0.5 M K2SO4. Extracts were stored at -18°C

until analysis. Organic carbon contents of the extracts were determined with

a TOC analyser (TOC-VCPN, Shimadzu Corp., Kyoto, Japan). For conversion

from organic C contents in the extracts to MBC in the soil a kEC value of 0.45

was assumed (Joergensen, 1996).

2.2.5.6. PLFA analysis

The structure of the microbial community was described by the fatty acid

composition of the phospholipids in the soil. PLFAs were extracted using a

modified Bligh and Dyer technique (1959). Four gram freeze-dried soil was

weighed in glass tubes. Then, 3.6 ml phosphate buffer pH 7.0, 4 ml

chloroform and 8 ml methanol were added. The tubes were shaken for 1 h

and afterwards centrifuged for 10 min (1258xg). The supernatant was

decanted in new glass tubes and 3.6 ml phosphate buffer and 4 ml

39

Chapter 2

chloroform were added. Samples were left overnight for phase separation.

The next day, the lipid layer was transferred to new tubes. The remaining

phase was washed with 3 ml chloroform to remove any remaining lipids. The

combined lipid fraction was dried under N2 and re-dissolved in chloroform.

Phospholipids were separated from the lipid extracts by solid phase

extraction, using silica columns (Chromabond, Macherey-Nagel GmbH,

Düren, Germany). After discarding neutral and glycolipids by chloroform and

acetone respectively, phospholipids were eluted using methanol. The

methanol fraction was dried under N2. The dried phospholipids were then re-

dissolved in 1 ml methanol:toluene (1:1 v/v) and 1 ml 0.2 M methanolic KOH.

Samples were incubated at 35°C for 15 min to allow transesterification to

methyl esters. After cooling to room temperature, 2 ml hexane:chloroform

(4:1 v/v), 1 ml 1 M acetic acid and 2 ml water were added to the tubes. After

vortexing, the samples were centrifuged for 5 min (805xg). The hexane

layer, containing the methylated PLFAs, was transferred to pointed tubes.

The aqueous phase was washed twice with hexane:chloroform. The

combined hexane phase was dried under N2. The fatty acid methyl esters

were finally re-dissolved in 0.3 ml of hexane containing methyl

nonadecanoate fatty acid (19:0) as an internal standard. PLFAs were

determined by GC-MS on a Thermo Focus GC coupled to a Thermo DSQ

quadrupole MS (Thermo Fisher Scientific Inc., Waltham, USA) in electron

ionization mode. Samples were chromatographically separated with a Varian

capillary column CP Sil 88 (100 m x 0.25 mm i.d., 0.2 μm film thickness;

Varian Inc., Palo Alto, USA). Following Bossio and Scow (1998) and Kozdrój

and van Elsas (2001), the sums of marker fatty acid concentrations for

selected microbial groups were calculated. For Gram-positive bacteria the

sum of i15:0, a15:0, i16:0, i17:0 and a17:0 was used. The fatty acids

16:1 7c, 18:1 7c and cy17:0 were considered to be typical for Gram-

negative bacteria. The sum of 10Me16:0 and 10Me18:0 was regarded as a

reliable indicator for the actinomycetes. The total bacterial community was

assumed to be represented by the sum of the marker PLFAs for Gram-

40


positive and Gram-negative bacteria, and 15:0, 17:0 and cy19:0. The fatty

acid 18:2 6,9c was used as a signature fatty acid for fungi, and 16:1 5c as

a signature fatty acid for arbuscular mycorrhizal fungi (AMF). The fungi to

bacteria ratio (F/B) and the Gram-positive to Gram-negative bacteria ratio

(G+/G-) were calculated by dividing the respective sums of marker PLFAs.

2.2.6. Data processing The design of the experiment was a randomized complete block design with

nested blocking factors and subsampling (Hinkelmann and Kempthorne,

2008). Results were statistically treated accordingly using SPSS (version

15.0, SPSS Inc., Chicago, USA). The blocking factor crop (2 levels) was

nested within the factor location (3 levels). Two management systems (OF

and CF) were applied on the resulting 6 separate blocks. This lead up to 12

experimental units, each one split up in three plots which corresponded to

three subsamples. While there are differences in soil texture between

organic and conventional sites, the organic and conventional fields still are

comparable from a soil physical and mineralogy point of view. Indeed, the

main characteristics of these soils are their andic properties which largely

override other differences in soil properties. Andic properties determine

dynamics of water and organic matter in soils to a large extent (Chorover,

2002; Maeda et al., 1977). All of the selected soils exhibit such typical andic

properties. The secondary forest was excluded from these statistical

analyses. For Cisarua2 long-term organic management was compared to 2

years organic management in a separate ANOVA for randomized complete

block designs with subsampling (Hinkelmann and Kempthorne, 2008).

To compare the relative composition of the microbial community in the

different soil samples, PLFA concentrations were calculated as percentages

of the total PLFA pool of the respective soil sample. Fisher’s canonical

discriminant analysis (CDA) was applied on this percentage distribution

using SPSS. Fisher’s CDA transforms data in order to discriminate between

41

Chapter 2

predefined groups (Huberty, 1994). In our analysis four groups were

considered: CF, long-term OF, 2-year OF and secondary forest. The fatty

acid 16:0 was disregarded in the analysis since it is ubiquitous throughout

the microbial community (Herrmann and Shann, 1997). All fatty acids

contributing less than 1% to the pool of fatty acids were also removed before

analysis. In total 18 fatty acids were included in Fisher’s CDA.

Finally, the OF and CF data presented in this chapter were used to calculate

a soil quality index that will be validated using the data of chapter 3. The

index was developed in SPSS by stepwise CDA, which is a technique that

allows to select the variables with the highest power to discriminate between

predefined groups or treatments from a more extended data set (Puglisi et

al., 2005; Puglisi et al., 2006). The algorithm is comparable to that of

stepwise linear regression. In the case of stepwise CDA, the variable that

minimizes the overall Wilks’ Lambda is entered into the model at each step

of the algorithm. Maximum significance of F to enter was set to 0.1, minimum

significance of F to remove was 0.25. Before performance of the stepwise

CDA, the data were scaled in order to base the CDA on correlation

coefficients in stead of variance. In total 17 parameters and ratios between

parameters were considered in calculating the index: SOC and TN content,

pH-KCl, the concentration of PLFA 16:0, dehydrogenase and -glucosidase

activity, the proportions of the 6 sums of marker PLFAs to the total PLFA

pool, F/B, G+/G-, SAT/MONO, cy17:0/16:1 7c and the Shannon diversity

index (Shannon and Weaver, 1949) of PLFAs. MBC and acid

phosphomonoesterase and -glucosaminidase activity were not considered,

because they were not measured in 2008. Inclusion of these parameters into

the model would therefore prevent further use of the index. In stead of MBC,

the PLFA 16:0, the most ubiquitous PLFA, was used as a measure for

microbial biomass. The ratio of cy17:0 to 16:1 7c is a measure of

physiological stress in the bacterial community (Bossio and Scow, 1998;

Petersen and Klug, 1994; see also chapter 3), while the ratio of saturated to

monounsaturated PLFAs (SAT/MONO) is considered as an index for nutrient

42


43

limitation (Moore-Kucera and Dick, 2008; see chapter 5 for details about

calculation).

Pearson correlations mentioned in the text were calculated with SPSS.

2.3. Results

2.3.1. Chemical soil properties Andisols are characterized by a high soil organic matter content (Galindo

and Bingham, 1977) and also the fields in this study had high soil organic C

(SOC) and total N (TN) contents (Table 2.3). Because of a significant

management x location interaction statistical analysis of SOC and TN

contents was carried out for each location separately. In Cisarua1 and

Ciwidey SOC and TN contents were higher under OF compared to CF, but

only in Cisarua1 these differences were significant (P<0.01 for SOC, P<0.05

for TN). In Cisarua2, on the other hand, SOC and TN contents of OF and CF

were comparable. In Ciwidey, C/N ratios of OF were significantly higher than

those of CF (P<0.05). Under secondary forest C/N ratios were the highest.

Despite the relatively high pH value of the conventional cabbage field in

Ciwidey, overall ANOVA showed a significantly higher pH (P<0.05) under

organic vegetable production compared to conventional.

Tabl

e 2.

3: C

hem

ical

soi

l pro

pert

ies.

Loca

tion

Cro

p M

anag

emen

t pH

-KC

l S

OC

(%)

Tota

l N (%

) C

:N ra

tio

Cis

arua

1 sc

allio

n or

gani

c 4.

91 (0

.06)

3.

81 (0

.71)

0.

40 (0

.08)

9.

5 (0

.2)

conv

entio

nal

4.17

(0.0

5)

2.32

(0.0

2)

0.26

(0.0

0)

9.0

(0.1

)

ca

bbag

e or

gani

c 5.

02 (0

.23)

3.

80 (0

.33)

0.

43 (0

.04)

8.

8 (0

.2)

conv

entio

nal

4.00

(0.0

5)

2.29

(0.0

7)

0.27

(0.0

1)

8.6

(0.4

)

Ciw

idey

po

tato

or

gani

c 5.

29 (0

.12)

6.

19 (0

.38)

0.

61 (0

.04)

10

.1 (0

.2)b

conv

entio

nal

4.71

(0.0

5)

3.41

(0.0

5)

0.40

(0.0

1)

8.6

(0.2

)

ca

bbag

e or

gani

c 5.

37 (0

.06)

6.

08 (0

.38)

0.

60 (0

.04)

10

.1 (0

.1)

conv

entio

nal

5.75

(0.0

9)

4.01

(0.1

8)

0.46

(0.0

1)

8.7

(0.3

)

se

cond

ary

fore

st

5.

17(0

.35)

6.

96 (3

.50)

0.

52 (0

.26)

13

.4 (0

.9)

Cis

arua

2 to

mat

o or

gani

c-23

y 5.

50 (0

.16)

3.

55 (0

.34)

0.

40 (0

.04)

9.

0 (0

.8)

orga

nic-

2y

5.29

(0.1

1)

3.27

(0.0

4)

0.35

(0.0

0)

9.4

(0.1

)

conv

entio

nal

4.11

(0.0

4)

3.63

(0.0

8)

0.42

(0.0

1)

8.6

(0.0

)

or

gani

c-23

y 5.

42 (0

.08)

3.

06 (0

.35)

0.

34 (0

.02)

9.1

(0.4

)

broc

coli/

ca

ulifl

ower

or

gani

c-2y

5.

36 (0

.12)

3.

10 (0

.24)

0.

33 (0

.03)

9.3

(0.1

)

conv

entio

nal

4.34

(0.0

8)

3.27

(0.3

0)

0.39

(0.0

4)

8.4

(0.2

)

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

.


2.3.2. Enzyme activities and microbial biomass C There was a significant positive impact of organic vegetable production on

MBC contents compared to conventional vegetable production (P<0.05), but

there was no significant difference in MBC content between short-term OF

and long-term OF in Cisarua2 (P>0.05) (Fig. 2.1). For Cisarua1 and for the

cabbage fields in Ciwidey MBC contents were 1.4 times higher under OF.

The organically managed potato field in Ciwidey had a 2.2 times higher MBC

content than the conventional potato field. The natural reference value was

between 1.5 and 2.4 times higher than the values under OF in Ciwidey.

Fig. 2.1: Microbial biomass C contents. Error bars indicate standard deviations. Except for acid phosphomonoesterase, enzyme activities were strongly

depressed under CF compared to OF (P<0.01 for dehydrogenase, P<0.05

for -glucosidase and -glucosaminidase) (Fig. 2.2a-2.2d). Under OF

dehydrogenase activity was 3.8-6.4 times higher compared to CF, while -

glucosidase activity was 1.6-2.9 times higher. -glucosidase activities under

OF were even close to activities under natural conditions. In the organically

managed potato beds -glucosidase activity was higher than under

secondary forest. -glucosaminidase activities were 1.7-4.9 times higher

45

Chapter 2

under OF than under CF. In Cisarua2, two years after conversion to organic

vegetable production enzyme activities were not significantly different

compared to those after 23 years of organic production (P>0.05), indicating

that the microbial activity recovered fast after conversion.

Fig. 2.2: Enzyme activities; a. dehydrogenase activity, b. -glucosidase activity, c. -glucosaminidase activity, d. acid phosphomonoesterase activity. Error bars indicate standard deviations.

We also calculated specific dehydrogenase activity (i.e. expressed per unit

of MBC) (Fig. 2.3), which appeared to be significantly higher under OF than

under CF (P<0.01). No significant difference in specific dehydrogenase

46


47

activity was, however, found between 23-year OF and 2-year OF in Cisarua2

(P>0.05).

Fig. 2.3: Specific dehydrogenase activity. Error bars indicate standard deviations.

2.3.3. Phospholipid fatty acids Based on the amounts of marker fatty acids, all microbial groups considered

(i.e. Gram-positive, Gram-negative bacteria, actinomycetes, total bacteria,

AMF and fungi) were significantly higher represented under OF than under

CF (P<0.01, except for fungi: P<0.05) (Table 2.4). Analogous to the enzyme

activities and MBC, signature fatty acid concentrations two years and 23

years after conversion were not significantly different in Cisarua2 (P>0.05).

The largest marker fatty acid concentrations were found under secondary

forest, except for AMF which were present in slightly higher amounts in the

organic potato beds than under forest.

Tabl

e 2.

4: C

once

ntra

tions

of m

arke

r PLF

As

(nm

ol g

-1 d

ry s

oil).

Loca

tion

Soi

l cov

er

Man

agem

ent

Gra

m-

posi

tive

Gra

m-

nega

tive

Act

inom

ycet

es

Tota

l bac

teria

A

MF

Fung

i

Cis

arua

1 sc

allio

n or

gani

c 12

.88

(3.5

3)

6.69

(0.2

0)

3.09

(0.3

2)

21.4

5 (0

.29)

2.

40 (0

.59)

2.

24 (0

.77)

conv

entio

nal

6.38

(0.5

6)

3.64

(0.3

0)

1.72

(0.1

2)

12:5

6 (0

.86)

1.

04 (0

.06)

0.

90 (0

.18)

orga

nic

13:1

3 (1

.72)

8.

52 (1

:34)

3.49

(0.1

4)

26.1

7 (3

.33)

2.

76 (0

.59)

2.

30 (0

.68)

cabb

age

conv

entio

nal

6.23

(0.3

5)

3.37

(0.5

6)1.

54 (0

.04)

12

.30

(1.0

4)

0.90

(0.0

3)

1.62

(0.7

1)

orga

nic

13.4

1 (1

.22)

10

.58

(0.9

8)2.

96 (0

.10)

28

:90

(2.2

0)

3.84

(0.3

2)

2.10

(0.2

9)

pota

to

conv

entio

nal

5.54

(0.5

7)

3.46

(0.2

3)1.

32 (0

.09)

11

.44

(1.1

8)

0.91

(0.1

0)

1.05

(0.3

7)

orga

nic

10.6

7 (0

.65)

7.

68 (0

.49)

2.98

(0.1

9)

22.5

0 (1

.22)

2.

68 (0

.16)

1.

56 (0

.18)

ca

bbag

e

conv

entio

nal

6.17

(0.7

8)

5.37

(0.5

6)1.

84 (0

.14)

14

.10

(1.7

4)

1.14

(0.1

3)

1.15

(0.3

3)

Ciw

idey

fore

st

20

.73

(6.0

5)

16.4

7 (4

.49)

7.12

(2.5

8)

46.8

4 (1

2.85

) 3.

81 (0

.87)

3.

95 (0

.68)

orga

nic-

23y

14.7

4 (1

.55)

10

.51

(0.7

5)3.

62 (0

.54)

30

.21

(3.0

7)

3.62

(0.5

6)

3.30

(0.4

5)

orga

nic-

2y

12.5

0 (1

.11)

9.

24 (0

.54)

3.09

(0.1

8)

25.9

5 (1

.77)

2.

82 (0

.30)

3.

45 (0

.41)

tom

ato

conv

entio

nal

9.00

(1.2

0)

3.22

(0.6

0)1.

91 (0

.14)

15

.81

(2.0

2)

1.52

(0.2

7)

1.10

(0.2

8)

orga

nic-

23y

13:0

3 (0

.59)

9.

85 (0

.57)

3.41

(0.3

9)

27.5

2 (1

.80)

2.

73 (0

.37)

2.

79 (0

.51)

orga

nic-

2y

12.4

2 (1

.55)

9.

10 (0

.45)

1.90

(0.0

2)

25.7

5 (2

.10)

3.

03 (0

.54)

3.

36 (0

.52)

Cis

arua

2

broc

coli/

ca

ulifl

ower

conv

entio

nal

7.16

(0.6

7)

3.67

(0.2

6)3.

19 (0

.20)

14

.05

(1.2

2)

1.11

(0.0

9)

1.70

(1.0

3)

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

.


Fisher‘s CDA of relative PLFA concentrations resulted in a clear

discrimination of forest, OF and CF with respect to microbial community

structure (Fig. 2.4a). There was no difference in PLFA composition between

recently converted and long-term organically managed soil in the overall

analysis. However, a second Fisher’s CDA without the forest soil data

yielded a differentiation between 2-year and long-term OF (Fig. 2.4b), but

this difference was smaller than that between OF and CF. The first

dimension of this second CDA, explaining 92% of variance, strongly and

negatively correlated with cy19:0, but positively with 18:1 7c (Table 2.5).

Both are marker PLFAs for bacteria, but their proportions depend upon the

growth conditions. Under conditions of stress, 18:1 7c is transformed into

cy19:0 (Petersen and Klug, 1994). This would suggest that the bacterial

community experiences more physiological stress under CF than under OF.

Further, the first dimension was positively correlated with 16:1 5c, indicating

that AMF were relatively more abundant under OF. Finally, a strong and

negative correlation was observed with 10Me18:0, which could point to a

relatively higher abundance of actinomycetes under CF than under OF. But

on the other hand, no significant correlation was observed between the first

dimension and 10Me16:0, the other actinomycetes marker PLFA (P>0.05).

Fig. 2.4: Scatter plots of the first two dimensions of the CDAs on PLFAs; a. CDA including secondary forest, b. CDA on OF and CF data only.

49

Chapter 2

Table 2.5: Pearson correlation coefficients between PLFAs (mol%) and first dimension of CDA (on OF and CF data only) with P<0.001.

PLFA Biomarker for correlation

i16:0 Gram-positive -0.691

i17:0 Gram-positive -0.687

a17:0 Gram-positive -0.668

16:1 5c AMF 0.761

18:0 - -0.583

10Me18:0 actinomycetes -0.900

18:1 7c Gram-negative 0.608

cy19:0 bacteria -0.790

20:4 (protozoa) 0.524

2.3.4. Soil quality index Three parameters were retained by the stepwise CDA (Table 2.6). Of these

three parameters PLFA 16:0 and dehydrogenase activity were strongly

correlated with the index scores and hence these are the most important

parameters for the discrimination between OF and CF. Soil quality index

scores clearly separated OF from CF (Table 2.7). Index scores were

significantly higher under OF than under CF (P<0.01). No significant

difference was found between short-term and long-term OF in Cisarua2

(P>0.05). The index may possibly be used in future to assess soil quality of

vegetable production systems in the humid tropics. However, the index first

needs to be validated, which will be done using the data collected in 2008.

50


Table 2.6: Parameters retained by stepwise CDA, raw canonical coefficients and Pearson correlation coefficients with soil quality index scores. Parameters are listed in order of entrance into the model.

Parameter can. coeff. correlation

PLFA 16:0 2.577 0.952 ***

rel. actinomycetes 0.828 -0.212

dehydrogenase 1.283 0.922 ***

*** Correlation significant at the 0.001 level. Table 2.7: Soil quality index scores.

Location Soil cover Management Score

Cisarua1 scallion organic 1.92 (1.36)

conventional -3.76 (0.57)

organic 2.76 (0.26) cabbage


Ciwidey organic 3.75 (0.57)

potato


organic 1.83 (0.60)

cabbage


organic-23y 4.61 (1.31)

organic-2y 2.61 (0.17)

tomato


organic-23y 2.52 (0.83)

organic-2y 2.70 (0.70)

Cisarua2

broccoli/ cauliflower


Values in parentheses indicate standard deviations.

2.4. Discussion

Except for acid phosphomonoesterase, enzyme activities under OF were

clearly higher than under CF in the humid tropical climate of West Java,

which corroborates studies carried out in other climates and soils, like those

51

Chapter 2

from Marinari et al. (2006) and Monokrousos et al. (2006) in a Mediterranean

climate and Fließbach et al. (2007) in temperate conditions. Marinari et al.

(2006) attributed the increase in enzyme activity mainly to the steady use of

animal manure on organic farms. The conventional soils in this study,

however, also receive large amounts of manure. Our results therefore seem

to confirm the findings of Fließbach et al. (2007) that manure application rate

is not as important for biological soil quality as the farming system, of which

manure quality is an important aspect besides mineral fertilizer and pesticide

use. As in the study of Fließbach et al. (2007) manure quality also differed

between the organic and conventional farms in this study. The organic farms

use compost, while the conventional ones apply dried manure. Cooper and

Warman (1997) found that chicken compost treatments produced higher

dehydrogenase activities than fresh chicken manure in a Canadian silty clay

soil.

Analyses were only conducted once: dehydrogenase and -glucosidase

were measured after the transplanting of crops, while -glucosaminidase,

phosphatase, MBC and PLFAs were determined on soil sampled around

harvest. Dehydrogenase and -glucosidase activity could potentially be

affected by recent application of organic fertilizer. However, because of the

short crop cycles (1-3 months) the fields are repeatedly fertilized throughout

the year which renders the moment of sampling less relevant. The fact that

the results of -glucosaminidase activity are similar to those of

dehydrogenase and -glucosidase activity further substantiates this.

The results of the enzyme activities, and also of the PLFAs, showed no

effect of soil texture. While differences in soil texture between paired organic

and conventional sites were not systematic, e.g. there were organic sites

lower (Cisarua1) and organic sites higher in clay content (Cisarua2) than

conventional sites, the large differences in soil biological parameters were

consistent over all locations.

Dehydrogenase and -glucosidase activities were significantly correlated

with SOC content (Table 2.8). Several authors reported higher enzyme

52


53

activities in soils richer in organic matter (e.g.: Aon and Colaneri, 2001;

Balota et al., 2004). However, the relationship between SOC content and

enzyme activity in this study was not straightforward. In Cisarua2, SOC

contents of organically managed soils and conventionally managed soils

were comparable. Yet, dehydrogenase and -glucosidase activity were

much higher in the organically managed soils. Furthermore, no significant

correlations were found between SOC content and -glucosaminidase and

acid phosphomonoesterase (P>0.05).

Tabl

e 2.

8: P

ears

on c

orre

latio

n co

effic

ient

s be

twee

n bi

oche

mic

al a

nd c

hem

ical

soi

l pro

pert

ies.

SO

C

pH-K

Cl

MBC

D

ehyd

roge

nase

-g

luco

sida

se

-glu

cosa

min

idas

e

Deh

ydro

gena

se

0.69

4**

0.60

0*

0.80

0**

-glu

cosi

dase

0.

681*

* 0.

640*

0.

699*

* 0.

878*

*

-glu

cosa

min

idas

e 0.

480

0.49

2 0.

755*

* 0.

825*

* 0.

760*

*

Phos

phat

ase

0.37

3 -0

.352

0.

657*

* 0.

289

0.30

6 0.

416

* C

orre

latio

n si

gnifi

cant

at t

he 0

.05

leve

l. **

Cor

rela

tion

sign

ifica

nt a

t the

0.0

1 le

vel.


Conventional management resulted in acidification of the soil (with the

exception of the conventional cabbage field in Ciwidey). In general, pH was

too low for optimal vegetable production. While pH-KCl under secondary

forest was 5.17 ± 0.35, pH-KCl values of the conventional fields were 4.51 ±

0.62 on average. Van Ranst et al. (2002) reported pH-KCl values of 4.8-5.2

for Andisols under forest in West Java. The acidification of conventional

fields is attributed to the intensive application of mineral fertilizers, mainly

ammoniacal N (urea and (NH4)2SO4) and superphosphate. On the other

hand, the pH-KCl on organic farms was on average 5.27 ± 0.22, probably as

a result of the intensive use of compost. Compost increases the cation

exchange capacity and base saturation of the soil (Ulrich, 1987), which

results in a higher buffering capacity (Stamatiadis et al., 1999). Enzyme

activities tend to increase with soil pH (Ekenler and Tabatabai, 2003). In this

study, both dehydrogenase and -glucosidase activity were significantly

correlated indeed with pH, but -glucosaminidase and acid

phosphomonoesterase were not (Table 2.8).

Although higher pH and higher SOC contents can potentially explain

increases in enzyme activities, the differences between OF and CF remain

surprisingly high, certainly when compared to studies conducted in

temperate or Mediterranean climates. E.g. Fließbach et al. (2007) and

Marinari et al. (2006) found dehydrogenase activities under organic

agriculture that were around 1.5 times higher and 1.6-3.9 times higher

respectively, while dehydrogenase activities here were 3.8-6.4 times higher

on organic farms. Benitez et al. (2006) reported 1.2 times higher -

glucosidase activities under organic olive orchards compared to

conventional orchards in Spain, while the ratios of organic to conventional

farming were 1.6-2.9 in this study. As regards -glucosaminidase,

Lagomarsino et al. (2009) found equal to 1.9 times higher activities in

organic fields in Italy, while in this study ratios from 1.7-4.9 were found.

The main reasons for the large differences between OF and CF can

probably be found in the use of mineral fertilizers, but certainly also in the

55

Chapter 2

use of pesticides on the conventional fields. In Indonesia, pesticide use is

poorly regulated, and existing regulations are not enforced, resulting in

excessive pesticide use. For example, in Ciwidey potatoes are sprayed with

3.6 kg ha-1 chlorothalonil per application, mainly against late blight

(Phytophthora infestans (Mont.) de Bary), and with 0.96 kg ha-1 endosulfan

per application to control insect pests. Both pesticides are applied at 10-day

intervals in the dry season but up to every other day in the rainy season. In

European temperate climates, for example, the authorities recommend 1.13-

1.5 kg ha-1 chlorothalonil per application as a reasonable dose; to be applied

at the most nine times per year and only if the responsible authority warns

for a plague of late blight. Singh et al. (2002) reported that chlorothalonil

adversely affects dehydrogenase activity and microbial biomass.

Endosulfan, on the other hand, is banned in more than 62 countries,

including the European Union and several Asian and West African countries,

because of its high toxicity and bioaccumulation potential. Herbicides, on the

other hand, are generally considered to have much less impact on soil life,

but are hardly used in Indonesia because of cheap labour cost. In summary,

management differences between organic and conventional farming are

much more extreme in Indonesia, and in Southeast Asia in general, than in

many developed countries, hence the very large differences in enzyme

activity.

Differences between organic and conventional vegetable production were

most pronounced for dehydrogenase activity and the AMF marker fatty acid.

These parameters seem therefore particularly suited as indicators for

(microbial) soil quality. -glucosidase and -glucosaminidase activity also

had a strong discriminating power, but because of the high correlation

between dehydrogenase activity on the one hand and -glucosidase and -

glucosaminidase activity on the other hand (Table 2.8), they seem to be

redundant parameters in this case. Dehydrogenase activity also had the

strongest correlation with MBC. MBC, however, seems to have a smaller

value as indicator for soil quality, since the resulting P-value of the ANOVA

56


was higher than that of dehydrogenase activity (0.036 vs. 0.006). Acid

phosphomonoesterase activity appeared of no value as indicator of the

differences in soil quality that where evident from all other biochemical

parameters measured. Likewise, Lagomarsino et al. (2009) did not consider

acid phosphatase as an effective indicator for determining differences in soil

quality between organic and conventional agriculture. On the contrary,

Monokrousos et al. (2006) did report significant differences in acid

phosphatase. The second CDA (OF and CF data only) indicated that AMF

were relatively more abundant under OF than under CF. This was confirmed

by a formal ANOVA test. PLFA 16:1 5c had as only marker PLFA a

significantly larger proportion of the total PLFA pool under OF compared to

CF (P<0.05). The susceptibility of AMF to agricultural management is

corroborated by studies reporting a clear difference in the colonization

potential of AMF between organically and conventionally managed fields

(Bending et al., 2004) or reporting less inoculum of AMF in conventional

relative to organic systems (Mäder et al., 2002). Communities of AMF are

highly influenced by management, and may be reduced by mineral fertilizer

application, cultivation and pesticides (Kurle and Pfleger, 1994). AMF

communities are diverse, with great differences between species and strains

in habitat and functional interactions with their host. These characteristics,

together with the obligate symbiotic nature of AMF and their susceptibility to

perturbation make AMF important potential indicators of soil fertility in

sustainable agricultural systems (Bending et al., 2004).

F/B ratios did not differ between OF and CF in this study (data not shown).

The proportion of Gram-negative marker PLFAs to the total PLFA pool, on

the other hand, was higher under organic agriculture compared to

conventional agriculture (not significantly, P = 0.056). However, this seems

rather to be caused by differences in pH than by a direct negative impact of

conventional vegetable production. Indeed, the field with the highest pH and

the highest proportion of Gram-negative marker PLFAs was a conventional

one (conventional cabbage in Ciwidey) and pH and proportion of Gram-

57

Chapter 2

58

negative marker PLFAs were strongly correlated (correlation: 0.906,

P<0.01). Several authors reported a shift to more Gram-negative bacteria at

higher pH, e.g. Arao (1999) on Andisols.

Forest soil and organically managed fields had comparable activities of -

glucosidase. Several researchers have found that cultivated soils in tropical

regions that received substantial organic inputs maintained similar or higher

activities of -glucosidase and of several other hydrolytic enzymes

compared to uncultivated soils (Dick et al., 1994; Waldrop et al., 2000).

Activities of the intracellular enzyme dehydrogenase, and MBC and PLFA

contents, however, remained higher under secondary forest. This suggests

that dehydrogenase activity and microbial biomass indicators are more

sensitive to disturbance by cultivation than are hydrolytic enzymes.

2.6. Conclusions

Very few studies have been conducted on the impact of different cultivation

systems on soil microbial properties in the tropics. The extreme differences

in management practices between organic and conventional fields were

reflected in very strong differences in enzyme activities. However, two years

after conversion to organic management microbial biomass and enzyme

activities were comparable to long-term organic management. Higher

microbial activity is a clear indication of improved soil quality, and probably

will affect important soil processes for crop growth such as carbon and

nitrogen cycling. The composition of the soil microbial community strongly

differed between forest and cultivated soil, and a clear difference was

observed as well between conventional and organic farming.

Dehydrogenase activity and 16:1 5c, marker PLFA for AMF, appeared

particularly suited to highlight the impact of management on the soil

microbial community.

Chapter 3



Redrafted after: Moeskops B, Buchan D, Sukristiyonubowo, De Gusseme B, Setyorini D, De

Neve S. Soil quality indicators for intensive vegetable production systems in

West Java, Indonesia. Ecological Indicators. Submitted.

Illustration:

The nursery at the organic farm Permata Hati in Cisarua (Bram Moeskops)

Chapter 3



3.1. Introduction In chapter 2, large differences between organic and conventional

management were observed. However, measurements were only done

during one growth cycle on a limited set of fields. In order to test whether

they can generally be used as indicators of soil quality for the vegetable

production systems of Java, PLFAs, dehydrogenase and -glucosidase

activity were measured again in 2008 on the same organic farms as in 2007,

but different conventional ones. Dehydrogenase activity was selected again

as it represents a general measure of microbial activity that sensitively

distinguished organic from conventional management in 2007. -glucosidase

was preferred to -glucosaminidase as a lignocellulose degrading enzyme,

because in 2007 variability on -glucosaminidase activity was rather high at

some sites. Using the data of 2008, the soil quality index developed in

chapter 2 will be validated in this chapter. Further, the fatty acid analysis is

more extended in chapter 3. Besides the microbial groups already presented

in chapter 2, we will also discuss the indicator value of neutral lipid fatty acid

(NLFA) 16:1 5c, of the ratio of PLFA cy17:0 to PLFA 16:1 7c and of the

Shannon diversity index of PLFAs.

Although the PLFA 16:1 5c is often used as a biomarker for AMF, it also

occurs in Gram-negative bacteria (Zelles, 1997). Therefore Olsson (1999)

proposed the ratio between NLFA and PLFA 16:1 5c to distinguish between

AMF and Gram-negative bacteria as this ratio is high in AMF (1–200) and

low in bacteria (<1). Energy in AMF is mainly stored in neutral lipids, of which

61

Chapter 3

NLFA 16:1 5c comprises more than 60% (Olsson, 1999), while bacteria do

not store energy in the form of lipids. “Bacterial” NLFAs found in soil are

actually phospholipids of which the phosphate group has been cleaved as

the first step in the decomposition process (Bååth, 2003). Analysis of NLFAs

has, to the best of our knowledge, not yet been undertaken in the tropics.

As (Gram-negative) bacteria enter the stationary growth phase, monoenoic

7 PLFAs are transformed into cyclopropyl fatty acids and hence the ratios

of cy17:0 to 16:1 7c and of cy19:0 to 18:1 7c have been proposed as

indicators of stress in the bacterial community (Bossio and Scow, 1998;

Petersen and Klug, 1994). Bossio and Scow (1998) and Petersen and Klug

(1994) mention anaerobic conditions, low pH, high temperature and

starvation as possible stress factors. The analysis was limited to the ratio of

cy17:0 to 16:1 7c, because PLFAs cy19:0 and 18:2 6,9c co-eluted,

preventing chromatographic separation and accurate quantification of these

biomarkers.

Shannon’s diversity index (Shannon and Weaver, 1949) is a common index

for the assessment of heterogeneity in a system. However, the index does

not provide direct information of system function.

Finally, a number of additional analyses, namely a disease suppressiveness

assay, measurements of ergosterol content and basal respiration and a

nematode community analysis were carried out in 2008. Soil disease

suppressiveness and basal respiration have already been discussed in

chapter 1. Ergosterol is the predominant sterol in fungal cell membranes,

and is specific to higher fungal phyla. It has therefore been proposed as a

biomarker to estimate soil fungal biomass (Joergensen and Wichern, 2008;

West et al., 1987). Castillo and Joergensen (2001) found that ergosterol

contents significantly increased under organic management in tropical

Nicaragua. Minoshima et al. (2007) reported the positive impact of no-tillage

and the use of cover crops on ergosterol contents.

The most abundant metazoa on earth are nematodes (Bongers and

Bongers, 1998). Nematodes have successfully adapted to nearly every

62


ecosystem from the polar regions to the tropics. They are ubiquitous in

freshwater, marine, and terrestrial environments. The study of human and

animal parasites, usually larger than soil and aquatic forms, was the first to

develop. In the middle of the 20th century, agronomists started to research

plant parasites such as root-knot nematodes (Meloidogyne sp.) and potato

cyst nematodes (Globodera sp.). Compared to the extended knowledge

concerning plant-parasitic nematodes, still little is known about free-living

non-parasitic soil nematodes. However, the composition of the soil

nematode community has emerged as a useful indicator of environmental

conditions and soil ecosystem functioning (Bongers and Ferris, 1999).

Bongers and Ferris (1999) consider that nematodes make good

bioindicators because of the following characteristics:

they occur under all climatic conditions, in every soil type and in

habitats that vary from pristine to extremely polluted

their permeable cuticle provides direct contact with their

microenvironment

they do not rapidly migrate from stressful conditions; the nematode

community is indicative of the conditions in the soil horizon that it

inhabits

nematodes occupy key positions in the soil food web; besides the

plant feeding species, many bacterivorous and fungivorous

nematodes exist, some are animal predators or are omnivorous

because nematodes are transparent, their internal features can be

seen without dissection which makes identification easier

the feeding behavior of nematodes is easily deduced from the mouth

structure

nematodes respond rapidly to disturbance, stress and enrichment

To be useful as bioindicator the structure of the soil nematode community

needs to be summarized in indices. Besides the general diversity indices

63

Chapter 3

(e.g. Shannon’s and Simpson’s diversity index), more specialized indices

have been developed. Bongers (1990) introduced the maturity index (MI)

and plant-parasite index (PPI). In a study of de Goede (cited in Bongers et

al., 1997) the MI and PPI could still demonstrate the effects of N fertilization

on the soil food web 19 years after fertilization was ceased. Ferris et al.

(2001) proposed the enrichment index (EI), structure index (SI) and an index

distinguishing between food webs dominated by either fungal or bacterial

decomposition channels, called the channel index (CI). Ferris et al. (2001)

demonstrated the use of these indices by comparing organically and

conventionally managed grassland.

Nematode community indices for assessing soil quality are, however, still

developing. Neher et al. (2005) demonstrated that the MI responds

inconsistently to disturbance depending on the ecosystem (wetland, forest or

agriculture), while Ruess (2003) noted that CI values were affected more by

soil and climate factors than by differences among forest, grassland and

agriculture. Neher et al. (2005) therefore suggested that interpretation of

nematode index values should be based according to region or ecosystem

type. Research into the soil nematode communities of the tropics is

exceedingly scarce. However, Blanchart et al. (2006), for example,

compared corn (Zea mays L.) intercropped with velvet bean (Mucuna

pruriens (L.) DC.) and corn fertilized with chemical fertilizer in Benin and

found a higher density of bacterivorous and predatory nematodes under

intercropped corn. Pattison et al. (2008) reported a higher CI and lower EI

under organic than under conventional banana (Musa AAA) cultivation in

tropical and subtropical Australia.

Assessment of the community structure of free-living soil nematodes seems

to have high potential for the evaluation of agricultural soil quality.

Nevertheless, nematode community analysis needs to be extended to more

soil types, climates and ecosystems to increase understanding of the

behaviour of the different indices.

64


3.2. Materials and methods

3.2.1. Experimental set-up In 2008 the same organic vegetable farms were chosen as in 2007, but

different conventional ones. Within 1 km from each organic farm (OF), one

conventional farm (CF) was selected. The newly selected conventional fields

were also Andisols. At the OF in Cisarua2 a distinction was made again

between the long-term organic site and the site now converted from

conventional management three years before sampling. Two distinct sites

were also considered at the OF in Ciwidey in 2008. At the first site,

vegetable production was started in 1992 with organic principles adopted in

2002. The second site was overgrown with brushwood until the beginning of

2008 when it was cleared for organic vegetable production. This second site

will be referred to as ‘OF-cleared site’. Finally, the secondary forest in

Ciwidey was selected again and served in particular to compare the values

of the OF-cleared site to natural reference values. Lay-out of the organic and

conventional farms remained the same as described in chapter 2. In contrast

to 2007, it was not possible to select organic and conventional fields with the

same crops. Although different crops may have different effects on the

microbial community, such crop dependent effects were not apparent from

the data collected in 2007. Furthermore, a wide range of vegetables is

cultivated in a rapid and continuous succession at both the organic and

conventional farms, which reduces the possibility of microbial communities

being adapted to any specific crop. Management of the fields selected in

2008 is specified in Table 3.1. Whereas the organic farms applied a uniform

fertilization rate for all crops, the conventional farmers applied variable rates

of fertilizer (and pesticides) according to the crop grown. Hence, the rates

given for the conventional farms only apply to the crops grown at the

moment of sampling. The organic farms in Cisarua applied smaller amounts

of compost to each newly transplanted crop, while at the organic farm in

65

Chapter 3

66

Ciwidey higher compost doses were applied, but less frequently so. The

conventional farmers in Cisarua1 and Cisarua2 purchased dried poultry litter

and mixed this with excreta from their own goats. The conventional farmer in

Ciwidey only applied chemical fertilizer. Organic fertilizers applied at the

organic farms were more variable in composition but always consisted of

composted crop residues and animal manures (chicken and goat in

Cisarua1, chicken in Ciasura2, cattle and chicken in Ciwidey).

3.2.2. Soil sampling Because the research sites were differently organized, the soil sampling

strategy was designed to be site-specific. At the organic farms in Cisarua1

and Cisarua2 six separate beds of 10 m2, spread evenly over the site, were

selected as replicates to cover the variation of crops grown at these farms.

At the organic farm in Cisarua2, this was done for both the sites under

organic management since 24 years and 3 years. At the organic farm in

Ciwidey two times three adjacent replicate beds of 8 m2 were selected: three

beds at the older organic site and three at the OF-cleared site. On the

conventional fields, three plots of 10 m2 were selected, spaced

approximately 5-10 m apart. In all replicates 15 samples were taken from the

0-15 cm soil layer and bulked into one composite sample per plot. All sites

were sampled twice during the dry season of 2008, in July (shortly after the

transplanting of crops) and in September (around harvest). The activities of

the enzymes -glucosidase and dehydrogenase were measured on both

series of samples. Because of practical constraints, general soil properties

and basal respiration were measured on the first series of samples only,

while PLFAs, NLFAs, ergosterol and nematodes were determined on the

second series only.

Ta

ble

3.1:

Man

agem

ent d

ata

of s

elec

ted

field

s.

Loca

tion

Man

agem

ent

Cro

p Fe

rtiliz

atio

n P

estic

ides

orga

nic

Sol

anum

lyco

pers

icum

L. (

tom

ato)

B

rass

ica

oler

acea

L. (

kai-l

an, b

rocc

oli)

Bra

ssic

a ra

pa L

. (bo

k ch

oy, c

hoy

sum

) B

rass

ica

junc

ea (L

.) C

zern

. (le

af m

usta

rd)

Am

aran

thus

hyb

ridus

L. (

smoo

th a

mar

anth

)

dolo

mite

enr

iche

d co

mpo

st:

14 M

g =

69 k

g N

extra

ct fr

om to

bacc

o le

aves

(N

icot

iana

taba

cum

L.)

Cis

arua

1

conv

entio

nal

Sol

anum

lyco

pers

icum

L. (

tom

ato)

C

apsi

cum

frut

esce

ns L

. (ch

illi)

Bra

ssic

a ol

erac

ea L

. (br

occo

li)

man

ure:

47

Mg

= 65

9 kg

N

(NH

4)2S

O4:

5.0

kg

N, 5

.8 k

g S

ph

osph

ate:

21

kg P

2O5

KC

l: 14

kg

K2O

prof

enof

os (1

94 m

g l-1

), m

anco

zeb

(177

8 m

g l-1

) and

del

tam

ethr

in a

pplie

d on

ce

a w

eek

Fert

iliza

tion

rate

s ar

e gi

ven

per h

a an

d pe

r gro

wth

cyc

le (8

5 da

ys).

Tabl

e 3.

1: M

anag

emen

t dat

a of

sel

ecte

d fie

lds

(con

tinue

d).

Loca

tion

Man

agem

ent

Cro

p Fe

rtiliz

atio

n P

estic

ides

orga

nic

- 24

yea

rs

Cap

sicu

m fr

utes

cens

L. (

chill

i) D

aucu

s ca

rota

L. (

carr

ot)

Bra

ssic

a ol

erac

ea L

. (br

occo

li, c

aulif

low

er)

Lact

uca

sativ

a L.

(let

tuce

) A

mar

anth

us h

ybrid

us L

. (sm

ooth

am

aran

th)

Ara

chis

hyp

ogae

a L.

(pea

nut)

Pha

seol

us v

ulga

ris L

. (Fr

ench

bea

n)

Cro

tala

ria ju

ncea

L. (

gree

n m

anur

e)

orga

nic

- 3 y

ears

Cap

sicu

m fr

utes

cens

L. (

chill

i)

Sol

anum

lyco

pers

icum

L. (

tom

ato)

A

llium

fist

ulos

um L

. (sc

allio

n)

Vig

na a

ngul

aris

(Will

d.) O

hwi &

H. O

hash

i (a

zuki

bea

n)

Lact

uca

sativ

a L.

(let

tuce

) B

rass

ica

oler

acea

L. (

broc

coli,

kai

-lan)

O

cim

um b

asili

cum

L. (

basi

l)

Cis

arua

2

conv

entio

nal

Bra

ssic

a ol

erac

ea L

. (ca

bbag

e)

Cap

sicu

m fr

utes

cens

L. (

chill

i) A

llium

fist

ulos

um L

. (sc

allio

n)

man

ure:

41

Mg

= 57

4 kg

N

urea

: 197

kg

N

NP

K: 2

3 kg

N, 2

3 kg

P2O

5, 2

3 kg

K2O

emam

ectin

ben

zoat

e (2

5 m

g l-1

), pr

opin

eb

(210

0 m

g l-1

) and

man

coze

b ap

plie

d on

ce a

w

eek

com

post

: 18

Mg

= 94

kg

N

no

app

licat

ion

of p

estic

ides

Fert

iliza

tion

rate

s ar

e gi

ven

per h

a an

d pe

r gro

wth

cyc

le (8

5 da

ys).

Loca

tion

Man

agem

ent

Cro

p Fe

rtiliz

atio

n P

estic

ides

orga

nic

Sol

anum

lyco

pers

icum

L. (

tom

ato)

La

ctuc

a sa

tiva

L. (l

ettu

ce)

Bra

ssic

a ra

pa L

. (bo

k ch

oy)

extra

ct fr

om w

ild p

lant

s: T

oona

sur

eni (

B.l)

M

err.,

Acm

ella

pan

icul

ata

(Wal

l. ex

DC

) R

.K. J

anse

n, M

ucun

a pr

urie

ns (L

.) U

rban

, D

atur

a m

etel

L.,

Tith

onia

div

ersi

folia

(H

emsl

.) A

. Gra

y

orga

nic

- cl

eare

d si

te

Zea

may

s L.

(bab

y co

rn)

no

app

licat

ion

of p

estic

ides

Ciw

idey

conv

entio

nal

Zea

may

s L.

(sw

eet c

orn)

ur

ea: 1

77 k

g N

m

anco

zeb

appl

ied

two

times

du

ring

grow

th c

ycle

Fert

iliza

tion

rate

s ar

e gi

ven

per h

a an

d pe

r gro

wth

cyc

le (8

5 da

ys) u

nles

s ot

herw

ise

stat

ed.

lime

enric

hed

com

post

: 18

8 M

g =

1391

kg

N p

er

year

Tabl

e 3.

1: M

anag

emen

t dat

a of

sel

ecte

d fie

lds

(con

tinue

d).

Chapter 3

3.2.3 Soil analyses

3.2.3.1 General soil properties

Determination of general soil properties was carried out on air-dried and






considered equivalent to organic carbon contents. Texture (Table 3.2) was

determined by the combined sieve and pipette method according to Gee and

Bauder (1986).


The activity of -glucosidase was measured according to a procedure

modified from Eivazi and Tabatabai (1988; cited in Alef and Nannipieri,

1995) in which p-nitrophenyl- -D-glucoside is degraded to p-nitrophenol

(PNP) during a 1 h incubation. Dehydrogenase activity was determined as

the reduction rate of triphenyltetrazolium chloride to triphenyl formazan

(TPF) during a 24 h incubation as described by Casida et al. (1964). Both

enzyme activities were measured in triplicate with one blank on fresh soil

stored at 4°C. Concentrations of PNP and TPF were determined with a

Hitachi 150-20 spectrophotometer (Hitachi Ltd., Tokyo, Japan). More

detailed procedures of the enzyme activity measurements are given in

chapter 2.

70

Ta

ble

3.2:

Phy

sica

l soi

l pro

pert

ies

of re

sear

ch s

ites.

Loca

tion

Man

agem

ent

50-2

000 μm

(%)

2-50

μm

(%)

0-2 μm

(%)

US

DA

text

ure

Bul

k de

nsity

(g c

m-3

)

Cis

arua

1 or

gani

c 50

.4

24.5

25

.2

sand

y cl

ay lo

am

0.75

(0.0

4)

co

nven

tiona

l 54

.2

24.4

21

.5

sand

y cl

ay lo

am

0.73

(0.0

3)

Cis

arua

2 or

gani

c - 2

4 ye

ars

30.7

33

.5

35.9

cl

ay lo

am

0.70

(0.0

3)

or

gani

c - 3

yea

rs

34.2

30

.8

35.0

cl

ay lo

am

0.78

(0.0

8)

co

nven

tiona

l 37

.5

34.1

28

.4

clay

loam

0.

79 (0

.05)

Ciw

idey

or

gani

c 56

.8

42.5

0.

8 sa

ndy

loam

0.

67 (0

.02)

or

gani

c - c

lear

ed s

ite

40.4

58

.9

0.7

silt

loam

0.

70 (0

.01)

conv

entio

nal

61.3

26

.212

.5

sand

y lo

am

0.74

(0.0

3)

se

cond

ary

fore

st

40.9

33

.825

.4

loam

0.

70 (0

.01)

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

.

Chapter 3

3.2.3.3. Fatty acid analysis

Soil samples for fatty acid analysis were freeze-dried and sieved (2 mm)

after sampling and subsequently stored at -18°C until extraction. In 2008, a

second extraction cycle was added to the method as described in chapter 2.

Some additional minor modifications were made as well. The new extraction

procedure is described below.

Four gram freeze-dried soil was weighed in glass tubes. Lipids in the soil

samples were extracted twice by adding 3.6 ml phosphate buffer (pH 7.0), 4

ml chloroform and 8 ml methanol. Suspensions were shaken for 1 h and

afterwards centrifuged for 10 min (1258xg). The supernatants of both

extraction cycles were collected in separatory funnels and 8 ml phosphate

buffer and 8 ml chloroform were added to enhance phase separation. The

next day, the lipid layers were transferred to new tubes, dried under N2 and

re-dissolved in chloroform. The lipid extracts were separated into neutral,

glyco- and phospholipids by chloroform, acetone and methanol respectively

using SPE silica columns (Chromabond, Macherey-Nagel GmbH, Düren,

Germany). Chloroform and methanol fractions were dried under N2. The

dried lipids were then re-dissolved in 1 ml methanol:toluene (1:1 v/v) and 1

ml 0.2 M methanolic KOH. Samples were incubated at 35°C for 15 min to

allow transesterification to methyl esters. After cooling to room temperature,

2 ml hexane:chloroform (4:1 v/v), 1 ml 1 M acetic acid and 2 ml water were

added. After vortexing, the samples were centrifuged for 5 min (805xg). The

hexane layer, containing the methylated fatty acids, was transferred to

pointed tubes. The aqueous phase was washed twice with

hexane:chloroform. The combined hexane phase was dried under N2. The

fatty acid methyl esters were finally re-dissolved in 0.3 ml of hexane

containing methyl nonadecanoate fatty acid (19:0) as an internal standard.

PLFAs and NLFAs were determined by GC-MS on a Thermo Focus GC

coupled to a Thermo DSQ quadrupole MS (Thermo Fisher Scientific Inc.,

Waltham, USA) in electron ionization mode. Samples were

72


chromatographically separated with a Restek capillary column Rt-2560 (100

m x 0.25 mm i.d., 0.2 μm film thickness; Restek, Bellefonte, USA).

Following Bossio and Scow (1998) and Kozdrój and van Elsas (2001), the

sums of marker PLFA concentrations for selected microbial groups were

calculated. For Gram-positive bacteria the sum of i15:0, a15:0, i16:0, i17:0

and a17:0 was used. The PLFAs 16:1 7c, 18:1 7c and cy17:0 were

considered to be typical for Gram-negative bacteria. The sum of 10Me16:0

and 10Me18:0 was regarded as indicator for the actinomycetes. The total

bacterial community was assumed to be represented by the sum of the

marker PLFAs for Gram-positive and Gram-negative bacteria, and 15:0 and

17:0. PLFAs cy19:0 and 18:2 6,9c co-eluted, preventing chromatographic

separation and accurate quantification of these biomarkers. Instead of

18:2 6,9c 18:1 9c was used as a fungal biomarker (Joergensen and

Wichern, 2008; Kozdrój and van Elsas, 2001). The ratio between NLFA and

PLFA 16:1 5c was used to distinguish between AMF and Gram-negative

bacteria, but we did not take the threshold value proposed by Olsson (1999),

namely 1, as an absolute limit. According to Bååth (2003), PLFA 16:1 5c is

indicative of AMF if the NLFA/PLFA ratio of 16:1 5c is higher than the

NLFA/PLFA ratios of bacterial fatty acids with similar PLFA concentrations

as PLFA 16:1 5c. In our study bacterial PLFAs i17:0 and a17:0 had similar

concentrations as PLFA 16:1 5c. Finally, The sum of PLFAs

20:4 6,9,12,15c and 20:5 3,6,9,12,15c was used as an indicator for

protozoa.

The ratio of PLFAs cy17:0 to 16:1 7c was calculated and served as an

index for physiological stress in the bacterial community (Bossio and Scow,

1998; Petersen and Klug, 1994). The Shannon diversity index, as a measure

of general diversity (Shannon and Weaver, 1949), was obtained considering

only the data of these PLFAs that contributed more than 1% to the total

PLFA pool. Finally, F/B was calculated as the ratio of PLFA 18:1 9c to the

sum of bacterial marker PLFAs.

73

Chapter 3

3.2.3.4. Ergosterol

Extraction and quantification of ergosterol was based on the method

developed by Gong et al. (2001). In a glass vial, 2 g freeze-dried and sieved

(2 mm) soil was mixed with 4 g glass beads (2 g of 290-420 μm and 2 g of

850-1230 μm). After the addition of 6 ml methanol, the vial was vortexed and

subsequently shaken intensively for 1 h on a linear shaker. The soil mixture

was then allowed to precipitate for 15 min, and a 1.5 ml aliquot of the

supernatant was transferred into an Eppendorf microfuge tube. After

centrifugation for 10 min at 10.000xg the supernatant was loaded for

analysis on a Dionex HPLC (P580 pump, TCC-100 column oven; Dionex

Corp., Sunnyvale, USA) equipped with a C18 reversed-phase column

(Allsphere ODS-2 5 μm, 250 x 4.6 mm; Grace, Deerfield, USA). Ergosterol

could be measured after a retention time of 8.46 min at 282 nm using a

UVD340S detector (Dionex Corp.). Methanol was used as the mobile phase

at a flow rate of 1.5 ml min-1. The column temperature was kept at 30°C. An

additional spike experiment resulted in an average recovery of 98.6% for the

soils in this study. As a consequence, no corrections for incomplete

extraction were necessary.

3.2.3.5. Basal respiration

Because of practical constraints, basal respiration rates were determined for

only four of the six replicates of the OF treatments in Cisarua and for two of

the three replicates of the other treatments. Air-dried and sieved (2 mm) soil,

corresponding to an oven-dry weight of 150 g, was brought to a gravimetric

water content of 35% (approximately 50% WFPS) in PVC tubes (7.5 cm

diameter). Bulk densities were adjusted to approximate those in the field.

Soils were incubated at 25 ± 1 C in airtight closed jars (of 1.5 l) during 6

weeks. Amounts of evolved CO2, captured in 0.2 M NaOH, were regularly

(every 2 days at the start of the experiment up to every 6 days towards the

end of the experiment) measured by titration of the NaOH with 0.2 M HCl to

pH 8.3 in the presence of BaCl2 (Anderson, 1982). After removal of the vials

74


containing NaOH, the glass jars were left open for minimum 3 hours to allow

replenishment of oxygen. Soil moisture content was adjusted, fresh vials

containing NaOH were added, and the jars were sealed again to continue

the respiration measurements. A linear model was fitted to the cumulative

respiration data, expressed in terms of mg C 100 g-1 soil (the data of the first

ten days was omitted because of rewetting effects during that period).

3.2.3.6. Disease suppressiveness

Soil disease suppressiveness against the fungal pathogen Rhizoctonia

solani Kühn (teleomorph Thanatephorus cucumeris (Frank) Donk) was

tested on the organically and conventionally cultivated soils, but not on the

forest soil. The procedure was taken from Postma et al. (2008). The disease

spread of R. solani (AG 2-2 IIIB, isolate M001-1-1) in the soil was measured

as the infection rate of sugar beet seedlings. R. solani causes damping-off,

black root and crown rot in sugar beet (Bakker et al., 2005). Sugar beet is

not cultivated in Indonesia, but it was used in this study as a model plant that

allows to easily measure the spread of R. solani in the soil. Besides sugar

beet, the AG 2-2 IIIB group of R. solani affects (inter alia) rice and soy bean

(Pannecoucque, 2009), two economically very important crops in Indonesia.

The test was performed in a growth chamber at 23.6 ± 0.3°C with a

day/night regime of 16h light and 8h dark. For each field replicate of the

organic and conventional farms, one tray with a size of 20 x 13 x 5 cm was

filled with soil up to 2 cm from the top. After slight compaction, gravimetric

soil water content was adjusted to approximately 60% WFPS. Untreated

Rhizoctonia susceptible sugar beet seeds (Beta vulgaris L., cv. Vedeta HI

0553, Syngenta Seeds B.V., Enkhuizen, The Netherlands) were sown in two

rows of 10 seeds at a depth of 2 cm and at 2 cm intervals. After one week,

the soil in each tray was inoculated with wheat kernels colonised with R.

solani prepared following the method described by Scholten et al. (2001).

Briefly, water-soaked and double autoclaved wheat kernels were infected

with three potato dextrose agar plugs of 4-day-old cultures of R. solani. The

75

Chapter 3

infected wheat kernels were then incubated at 20°C for 10 days in the dark

and shaken every 3-4 days. The soil in the trays was inoculated by placing

two kernels in front of each seedling row at 2 cm distance and at 1 cm depth.

Disease spread was determined 3 weeks after inoculation by counting the

number of seedlings per row displaying damping-off or black lesions on the

stem at soil level. Values of disease spread were transformed into disease

suppressiveness values according to the formula:

disease suppressiveness = 1 - disease spread / maximum disease spread.

Maximum disease spread was 10.

Following Pannecoucque et al. (2008), we isolated the fungal pathogens

from a number of infected plants to control whether R. solani was indeed the

pathogen responsible for the damping-off of seedlings. Plant tissue from

infected plants was surface-sterilized in 1% NaOCl, rinsed and placed on

water agar amended with antibiotics (50 μg ml-1 streptomycin). The water

agar plates were incubated for five days at 20°C. Subsequently, the fungal

colonies were purified on potato dextrose agar plates and identified when

sufficiently developed.

3.2.3.7. Nematode community analysis

In total, 10 plots were sampled for nematode analysis. In Cisarua1, two beds

of the organic farm and one plot of the conventional farm were analysed. In

Cisarua2, one bed under 24-year organic management, one bed under 3-

year organic management and two conventional field replicates were

considered. In Ciwidey, one plot was sampled for every treatment (OF, OF-

cleared and CF). Soil samples were stored at 4°C until extraction of

nematodes.

Active nematodes were extracted from 100 g fresh soil using Cobb’s

method, described in detail by van Bezooijen (2006). Briefly, the soil

samples were suspended and successively sieved at 500 μm, 355 μm, 180

μm, 106 μm and 45 μm. The debris remaining on the 500 μm sieve was

discarded, while the debris remaining on the other sieves was collected and

76


transferred to a nematode extraction sieve placed in a tray containing

around 100 ml water. The extraction sieve was composed of two Rapid

cotton filters between two Favorit milk filters (Nifa Instrumenten BV,

Leeuwarden, The Netherlands). Nematodes were allowed to migrate through

the extraction sieve into the tray during a 24 hour period. The nematode

suspensions thus obtained were concentrated to a few ml and nematodes

were fixed by a formalin-glycerine mixture to ensure proper conservation

until identification.

For the preparation of mass slides for identification, the fixed nematodes

were first concentrated into a small volume of liquid inferior to 1 ml.

Following homogenisation, an aliquot of fixed nematodes was aspirated with

a Pasteur pipette and for each sample 4 slides were prepared. Nematodes

were identified to family level following Bongers (1988) using a binocular

compound microscope. In order to be representative of the sampled

population, identification of slides was continued until a minimum of 100

nematodes – excluding plant-parasitic nematodes and dauer larvae – were

determined for each sample.

3.2.4. Data processing

The discriminant index, as developed in chapter 2, was calculated from the

dehydrogenase activity in July, the absolute content of PLFA 16:0 and the

relative amount of actinomycetes marker PLFAs for the organic and

conventional farms.


different soil samples, PLFA concentrations were converted to percentages

of the total PLFA concentration of the respective soil sample. Fisher’s

canonical discriminant analysis (CDA) was applied to this percentage

distribution using correlation coefficients with Tibco Spotfire S+ (version 8.1,

TIBCO Software Inc., Palo Alto, USA). After removal of all PLFAs that

contributed less than 1% to the total pool of PLFAs, 20 PLFAs were retained

77

Chapter 3

for CDA. Fisher’s CDA transforms data in order to discriminate between

predefined groups (Huberty, 1994). In our analysis three groups were

considered: CF, OF and secondary forest.

Because of a significant management x location interaction, statistical

comparison between treatments was carried out for each location separately

(at the 0.05 level of significance) using SPSS (version 15.0, SPSS Inc.,

Chicago, USA), except for the nematode community analysis (vide infra). For

Cisarua1, T-tests were used to compare OF and CF. For Cisarua2, ANOVA

was applied to compare CF and long-term and 3-year OF. For Ciwidey,

ANOVA was performed to compare secondary forest, the OF-cleared site,

the older organic site and CF. Significant differences between means were

determined by Tukey’s post-hoc test. Other T-tests and Pearson’s

correlations coefficients mentioned in the text were also calculated with

SPSS.

The identified nematodes were assigned to one of the five feeding groups

(herbivore, bacterivore, fungivore, omnivore, predator) following Yeates et al.

(1993) and classified along a colonizer-persister scale (cp-scale) from 1 to 5

as devised by Bongers (1990). Colonizer nematodes, at the lower end of the

cp-scale, are considered enrichment opportunists and therefore indicate

resource availability, while persister nematodes, at the high end of the scale,

indicate system stability, food web complexity and connectance (Ferris and

Bongers, 2009). Based on both nematode classifications, the MI, PPI, SI, EI

and CI were calculated according to the definitions given by Ferris and

Bongers (2009). The MI is defined as the weighted mean cp-value of the

nematodes in a sample, excluding the plant feeders and dauer larva. Low MI

values indicate a disturbed and/or enriched environment, while high MI

values indicate a stable environment (Ferris and Bongers, 2009). The PPI is

comparable to the MI but computed only for the plant-feeding nematodes

(Ferris and Bongers, 2009). Bongers et al. (1997) demonstrated that the MI

and PPI are often inversely related and proposed the PPI/MI ratio as a

measure for nutrient availability and nutrient use efficiency. Higher PPI/MI

78


values indicate higher nutrient surpluses. For the calculation of the EI, SI

and CI feeding group classification and life strategy classification are

integrated into so-called functional guilds. The EI is calculated as the

weighted proportion of cp 1 bacterivores and cp 2 fungivores and assesses

the resources available to the soil food web and the response by primary

decomposers to those resources (Ferris and Bongers, 2009). The SI is the

weighted proportion of cp 3 to cp 5 nematodes. A higher SI value suggests

greater connectance in the soil food web and a higher potential for top-down

regulation by predators. The CI is the weighted proportion of cp 2 fungivores

(Ferris and Bongers, 2009). Differences in nematodes distribution and in

index values between organic and conventional management were

statistically tested by T-tests at the 0.05 level of significance with SPSS. For

these T-tests, it was assumed that the sampled plots were independently

and at random selected from all organic and conventional fields in West

Java respectively.

3.3. Results

3.3.1. Chemical soil properties In Cisarua1 and Cisarua2, pH-values and SOC and TN contents of the

organic farms were comparable to the measurements of 2007. In Ciwidey,

the OF-cleared site had similar SOC and TN contents as well as pH-values

as the organic beds measured in 2007, but the older organic site had a

higher pH and lower SOC and TN contents.

In Cisarua1, pH and SOC and TN content did not differ between OF and CF

in 2008 (Table 3.3). In Cisarua2 on the other hand, pH and TN content were

significantly higher under 3-year and long-term OF than under CF, and TN

content was significantly higher under long-term than under 3-year OF. SOC

content was comparable under 3-year OF and CF, but significantly higher

under long-term OF. In Ciwidey, the OF-cleared site and the secondary

79

Chapter 3

forest had a comparable pH that was significantly lower than that of the older

organic site. SOC content was significantly higher at the OF-cleared site and

under secondary forest than under CF. The OF-cleared site had also a

significantly higher TN content than CF. C/N ratios did not significantly differ

between OF and CF in Ciwidey. In Cisarua1 and Cisarua2 however, C/N

ratios were significantly higher under CF than under OF.

Table 3.3: Chemical soil properties.

Location Management pH-KCl SOC (%) Total N (%) C/N ratio

Cisarua1 organic 5.02 (0.26) 4.09 (0.26) 0.42 (0.04) 9.8 (0.4)a

conventional 5.05 (0.16) 4.21 (0.30) 0.40 (0.02) 10.6 (0.4)b

Cisarua2 organic - 24 years 5.59 (0.21)b 3.90 (0.56)b 0.38 (0.05)c 10.1 (0.3)a

organic - 3 years 5.53 (0.23)b 3.25 (0.50)a 0.32 (0.06)b 10.4 (0.4)a

conventional 4.85 (0.35)a 2.96 (0.34)a 0.25 (0.01)a 12.0 (1.2)b

Ciwidey organic 5.92 (0.13)b 4.80 (0.24)ab 0.43 (0.02)a 11.3 (0.1)a

organic - clrd. site 5.06 (0.06)a 6.80 (0.39)c 0.65 (0.05)b 10.5 (0.4)a

conventional 5.25 (0.11)ab 3.59 (0.24)a 0.37 (0.04)a 9.8 (0.5)a

secondary forest 4.92 (0.59)a 6.00 (1.21)bc 0.40 (0.02)a 14.5 (1.9)b

Values in parentheses indicate standard deviations. Significant differences are indicated by different letters per location (P<0.05); no letters if no significant differences were found.

3.3.2. Enzyme activities In Cisarua1, enzyme activities were not significantly different between OF

and CF in July, right after transplantation (Fig. 3.1, Fig. 3.2). By September

however, enzyme activities had decreased more strongly in the conventional

field than in the organic beds, which resulted in significant differences

between both treatments for both dehydrogenase and -glucosidase activity.

In Cisarua2, dehydrogenase and -glucosidase activities were significantly

higher under 3-year and long-term OF than under CF in both July and

September. Enzyme activities under 3-year and long-term OF were

80


comparable. In Ciwidey, lowest activities were found under CF and at the

older organic site. Because of excessive variability, dehydrogenase activity

in July under secondary forest was removed from all statistical analyses.

Paired T-tests showed that dehydrogenase and -glucosidase activities

were significantly higher in July than in September in Cisarua1 (P<0.01) and

Ciwidey (P<0.05, excluding secondary forest). In Cisarua2 only

dehydrogenase activity was significantly higher in July (P<0.05). -

glucosidase activities were comparable at both times.

Fig. 3.1: Dehydrogenase activity. Error bars indicate standard deviations. Significant differences are indicated by different letters per location and sampling occasion (P<0.05); no letters if no significant differences were found.

81

Chapter 3

Fig. 3.2: -glucosidase activity. Error bars indicate standard deviations. Significant differences are indicated by different letters per location and sampling occasion (P<0.05); no letters if no significant differences were found

3.3.3. Basal respiration Basal respiration was higher under OF than under CF, significantly so in

Cisarua2 (Table 3.4). Long-term and 3-year OF had comparable basal

respiration rates. In Cisarua1 the difference between OF and CF was not

significant. In Ciwidey only the older organic site had a significantly higher

basal respiration than CF. Basal respiration at the OF-cleared site

approached that under CF. The highest basal respiration rate was found

under secondary forest.

82


Table 3.4: Basal respiration rates.

Location Management Basal respiration (mg CO2-C kg-1 dry soil day-1)

Cisarua1 organic 16.08 (2.63)

conventional 12.26 (3.64)

Cisarua2 organic - 24 years 18.55 (1.96)b

organic - 3 years 16.15 (2.33)b

conventional 9.85 (0.15)a

Ciwidey organic 15.60 (0.31)bc

organic - cleared site 11.95 (0.21)ab


secondary forest 19.34 (0.21)c

Values in parentheses indicate standard deviations. Significant differences are indicated by different letters per location (P<0.05); no letters if no significant differences were found. 3.3.4. Disease suppressiveness Isolation of pathogens from the infected plants confirmed that the inoculated

R. solani was indeed responsible for the observed damping-off, except for

two individual trays. In these trays, R. solani from a different anastomosis

group was identified. Plants infected by this different R. solani strain were

not taken into account in the calculations. Remarkably, CF soils were at all

locations more suppressive against R. solani than OF soils (Table 3.5).

However, only in Cisarua2 this difference was significant.

83

Chapter 3

Table 3.5: Soil suppressiveness against R. solani.

Location Management Suppressiveness

Cisarua1 organic 0.783 (0.108)


Cisarua2 organic - 24 years 0.283 (0.207)a

organic - 3 years 0.392 (0.163)a

conventional 0.717 (0.115)b


organic - cleared site 0.633 (0.289)



3.3.5. Ergosterol In Cisarua1 and Cisarua2 ergosterol contents were higher under OF than

under CF, but this difference was significant only in Cisarua1 (Fig. 3.3). In

Ciwidey, ergosterol content was significantly higher under secondary forest

than under agriculture, but no significant differences were found between

agricultural treatments.

84


Fig. 3.3: Ergosterol contents. Error bars indicate standard deviations. Significant differences are indicated by different letters per location (P<0.05); no letters if no significant differences were found.

3.3.6. Fatty acids As was also reported by Bååth (2003), NLFA 16:1 5c measurements varied

considerably in our study and as a result so did NLFA/PLFA ratios (Table

3.6). In Cisarua1, the NLFA/PLFA ratio was significantly higher under CF

compared to OF. In Ciwidey, the NLFA/PLFA ratio was remarkably high at

the OF-cleared site (more than five times higher than at the older organic

site). In Ciwidey, all ratios were higher than 1 and thus certainly for this

location there was little doubt about the AMF origin of PLFA 16:1 5c. In Fig.

3.4 the logarithm of the NLFA/PLFA ratios of the bacterial fatty acids i17:0

and a17:0 is plotted against the logarithm of the amount of PLFA. A

significant linear decrease could be observed in the log ratio with increasing

log PLFA amounts both for Cisarua1 and Cisarua2. This means that the

main reason for different NLFA/PLFA ratios was variable amounts of PLFA

85

Chapter 3

with a constant background amount of NLFA (Bååth, 2003). Both in

Cisarua1 and Cisarua2, the fatty acid 16:1 5c had higher NLFA/PLFA ratios

than would be expected for bacterial fatty acids (Fig. 3.4), indicating that also

at those locations it was indeed indicative of AMF. In the remaining part of

this chapter PLFA 16:1 5c will therefore be considered as a marker PLFA

for AMF.

Table 3.6: NLFA/PLFA ratios of 16:1 5c.

Location Management NLFA/PLFA 16:1 5c

Cisarua1 organic 0.89 (0.36)a

conventional 1.50 (0.21)b

Cisarua2 organic - 24 years 1.06 (0.59)

organic - 3 years 1.70 (0.73)


Ciwidey organic 2.85 (0.07)a

organic - clrd. site 14.80 (3.66)b


secondary forest 3.20 (1.22)a


86


87

Fig. 3.4: NLFA/PLFA ratios plotted against the amount of PLFA. Regression for i17:0 and a17:0 includes all field replicates. For 16:1 5c site averages and standard deviations are shown; a. Cisarua1, b. Cisarua2.

Compared to 2007, absolute PLFA contents were higher both for OF and CF

(Table 3.7). This is probably due to the improved fatty acid extraction

procedure. Absolute contents of marker PLFAs for protozoa did not

significantly differ between OF and CF and are therefore not presented. In

Cisarua1 no significant differences were found in absolute marker PLFA

concentrations between OF and CF, although concentrations were always

higher under OF. In Cisarua2, all microbial groups considered were

significantly more abundant under long-term OF than under 3-year OF and

CF. No significant differences were found between 3-year OF and CF. In

Ciwidey, all microbial groups considered were significantly more abundant

under secondary forest than in agricultural soil. The OF-cleared site had

significantly higher marker PLFA concentrations of Gram-positive bacteria,

actinomycetes and total bacteria than CF. The older organic site only had a

significantly higher marker PLFA content than CF for AMF.

Tabl

e 3.

7: C

once

ntra

tions

of m

arke

r PLF

As

(nm

ol g

-1 d

ry s

oil).

Loca

tion

Man

agem

ent

Gra

m-p

os.

Gra

m-n

eg.

Act

inom

yc.

Tot.

bact

eria

A

MF

Fung

i

Cis

arua

1 or

gani

c 24

.06

(2.7

6)

10.7

1 (1

.76)

5.

96 (0

.64)

36

.38

(4.3

1)

2.76

(0.4

6)

3.12

(0.7

3)

co

nven

tiona

l 21

.40

(0.8

7)

8.56

(0.9

2)

4.96

(0.5

3)

31.3

6 (1

.81)

2.

36 (0

.20)

2.

36 (0

.32)

Cis

arua

2 or

gani

c - 2

4 ye

ars

30.6

2 (4

.81)

b 16

.09

(2.7

8)b

7.61

(0.8

1)b

48.9

2 (7

.05)

b 4.

46 (1

.01)

b 4.

68 (0

.63)

b

or

gani

c - 3

yea

rs

23.1

6 (3

.26)

a 12

.49

(1.4

6)a

6.26

(0.4

8)a

37.3

9 (4

.85)

a 3.

35 (0

.54)

a 3.

74 (0

.53)

a

co

nven

tiona

l 22

.68

(3.3

8)a

11.9

1 (1

.90)

a 6.

19 (0

.63)

a 36

.26

(5.3

0)a

3.04

(0.4

7)a

3.36

(0.4

9)a

Ciw

idey

or

gani

c 18

.81

(2.1

4)a

12.5

8 (1

.49)

a 4.

96 (0

.35)

ab

32.6

6 (2

.87)

a 3.

65 (0

.48)

b 3.

01 (0

.36)

a

or

gani

c - c

lrd. s

ite

27.5

4 (1

.15)

b 12

.99

(1.4

4)a

7.39

(0.9

5)b

42.0

3 (2

.27)

b 3.

34 (0

.33)

ab

3.65

(0.5

8)a

co

nven

tiona

l 18

.00

(2.5

7)a

9.25

(1.3

0)a

3.80

(0.4

3)a

29.2

9 (2

.39)

a 1.

93 (0

.26)

a 2.

59 (0

.48)

a

se

cond

ary

fore

st

64.8

8 (2

.55)

c 36

.43

(1.6

2)b

20.4

1 (2

.80)

c 10

4.25

(4.2

1)c

8.65

(1.0

6)c

9.86

(0.6

0)b

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

. Si

gnifi

cant

diff

eren

ces

are

indi

cate

d by

diff

eren

t let

ters

per

loca

tion

(P<0

.05)

; no

sign

ifica

nt d

iffer

ence

s in

Cis

arua

1


The first dimension of the CDA discriminated between forest and agriculture,

while the second dimension separated organic management from

conventional management (Fig. 3.5). The first dimension strongly and

positively correlated with PLFA 18:1 7c, but negatively with cy17:0. This

would mean that relatively less bacteria are in the stationary growth phase in

the forest soil compared to cultivated soils. The first dimension was further

strongly and negatively correlated with PLFA 20:5 (Table 3.8), a marker

PLFA for protozoa. This indicates that there were proportionally less

protozoa under secondary forest than in agricultural soil. Burke et al. (2003)

also found that protozoa are relatively more important members of the

microbial community in agricultural sites than in forest habitats. The second

dimension of the CDA was most strongly correlated with PLFA 18:1 5c.

Monounsaturated PLFAs, such as 18:1 5c, have been considered as

indicators for high substrate availability (Bossio and Scow, 1998; Moore-

Kucera and Dick, 2008).

Fig. 3.5: Scatter plot of CDA on PLFAs.

89

Chapter 3

Table 3.8: Pearson correlation coefficients between mol% of PLFAs and CDA dimensions with P 0.001.

First dimension Second dimension

PLFA Biomarker for Corr. coeff. PLFA Corr. coeff.

17:0 bacteria -0.587 18:1 5c 0.475

cy17:0 Gram-negative -0.591

18:0 - -0.519

18:1 7c Gram-negative 0.780

24:0 protozoa -0.476

20:5 3,6,9,12,15 protozoa -0.521

OF exhibited lower cy17:0 to 16:1 7c ratios than CF at all sites, but not

always significantly so (Table 3.9). In Cisarua2, differences in

cy17:0/16:1 7c were significant between long-term OF and CF, while 3-year

OF took an intermediate position. In Ciwidey, secondary forest and the older

organic site had a significantly lower cy17:0/16:1 7c ratio than the OF-

cleared site and CF. The low cy17:0/16:1 7c ratio found under secondary

forest corroborated the results of the CDA. OF and CF had similar Shannon

indices at all three locations (Table 3.9). In Ciwidey, secondary forest had a

significantly lower PLFA diversity than CF and the OF-cleared site. At none

of the locations, significant differences in F/B ratio were found (Table 3.9).

3.3.7. Discriminant index As for the absolute PLFA contents, discriminant indices were higher both for

OF and CF compared to 2007 (Table 3.10). This is mainly due to the higher

amounts of PLFA 16:0 that were extracted. In Cisarua1, the discriminant

index was significantly higher under OF compared to CF. In Cisarua2, the

discriminant index was significantly higher under long-term OF than under

CF, while 3-year OF took an intermediate position. In Ciwidey, the OF-

90


cleared site had a significantly higher discriminant index than the older OF

site and CF.

Table 3.9: Shannon diversity indices (H), cy17:0/16:1 7c and F/B ratios.

Location Management H cy17:0/16:1 7c F/B (x 1000)

Cisarua1 organic 2.65 (0.03) 0.652 (0.072) 87.9 (14.2)

conventional 2.66 (0.02) 0.724 (0.050) 75.0 (6.5)

Cisarua2 organic - 24 years 2.67 (0.04) 0.556 (0.084)a 103.6 (14.3)

organic - 3 years 2.67 (0.03) 0.678 (0.110)ab 104.8 (12.9)

conventional 2.66 (0.03) 0.728 (0.122)b 93.0 (6.8)

Ciwidey organic 2.65 (0.01)ab 0.645 (0.026)a 92.0 (4.8)

organic - clrd. site 2.67 (0.02)b 0.786 (0.025)b 86.7 (11.1)

conventional 2.68 (0.03)b 0.828 (0.082)b 98.4 (16.1)

secondary forest 2.60 (0.02)a 0.635 (0.014)a 97.4 (8.8)

Values in parentheses indicate standard deviations. Significant differences are indicated by different letters per location (P<0.05); no letters means if no significant differences were found. Table 3.10: Discriminant index scores.

Location Management Discrim. index

Cisarua1 organic 6.96 (2.02)b


Cisarua2 organic - 24 years 9.59 (2.13)b

organic - 3 years 8.27 (2.50)ab


Ciwidey organic 3.56 (1.02)a

organic - cleared site 9.31 (2.36)b


Values in parentheses indicate standard deviations. Significant differences are indicated per location by different letters (P<0.05).

91

Chapter 3

3.3.8. Nematode community analysis

The proportion of cp 1 nematodes to the total population of nematodes

(excluding plant-parasitic nematodes and dauer larva) was significantly

lower under organic agriculture (P<0.05), while the percentage of cp 3-5

nematodes was in general higher under organic management (Fig. 3.6a). As

a result, MI values were higher under OF than under CF, not only when

compared per location but also when all locations were considered together

(Table 3.11). Nevertheless, this difference was not significant.

Fig. 3.6: Profiles representing the nematode community structure; a. cp-triangle, b. faunal profile with structure and enrichment axis. Filled symbols represent organic management, open symbols conventional management.

Ferris et al. (2001) found that cp-triangles as depicted in Fig. 3.6a do not

provide satisfactory resolution to changes in nematode fauna. Furthermore,

the enrichment and structure axes are not independent in cp-triangles. An

increase in enrichment opportunists (cp 1) results in an apparent decrease

of the structure of the system (cp 3-5). In the faunal profile (Fig. 3.6b), the

enrichment and structure index are calculated independently. Although all

plots had a high EI, conventional fields still had a significantly higher EI than

organic ones (P<0.05). SI values were highly variable.

92


When compared per location, CI and PP/MI values were higher under

organic than under conventional agriculture (Table 3.11).

Table 3.11: Maturity indices, PPI/MI ratios and channel indices.

Location Management MI PPI/MI CI

Cisarua1 organic 1.83 1.60 12.0

1.61 1.84 10.9

conventional 1.51 1.93 9.7

Cisarua2 organic - 24 years 1.81 1.30 29.0

organic - 3 years 2.04 1.40 28.0


1.28 1.71 4.8

Ciwidey organic 2.99 0.98 18.6

organic - cleared site 2.13 1.19 10.5


3.4. Discussion

3.4.1. Enzyme activities

Enzyme activities were always higher under OF than under CF in Cisarua1

and Cisarua2, but in July not significantly in Cisarua1. In Ciwidey,

dehydrogenase activity, but not -glucosidase activity, was always higher

under OF than under CF. Nevertheless, only in July and only between the

OF-cleared site and CF this difference was significant. In 2007 differences in

dehydrogenase and -glucosidase activity were more pronounced than in

2008, but in both years dehydrogenase activity was the most sensitive

parameter of the two.

Many authors have reported seasonal variation in enzyme activities (e.g.

Aon and Colaneri, 2001). In our study the higher enzyme activities in July

were probably caused by: (i) higher soil moisture contents in July than at the

93

Chapter 3

end of the dry season in September, and (ii) application of organic matter

just before or at the same time as crop transplantation in July. But although

enzyme activities were significantly lower in September than in July (except

for -glucosidase in Cisarua2), they generally decreased in a proportional

manner, hence differences between treatments remained similar. Thus, the

possible impact of sampling time on enzyme activity seems to be limited. In

chapter 2 we already attributed this to the typical vegetable production

system of West Java which involves repeated fertilization throughout the

year.

3.4.2. Biomarkers Ergosterol content ranged between 0.99 and 1.90 μg g-1 dry soil under

agriculture, and reached 3.99 μg g-1 dry soil under secondary forest. These

concentrations are higher than those reported for tropical Andisols under

agriculture in Nicaragua: 0.08-0.49 μg g-1 dry soil for CF and 0.14-0.69 μg g-1

dry soil for OF (Castillo and Joergensen, 2001; Joergensen and Castillo,

2001). Ergosterol contents reported by Turgay and Nonaka (2002) for

Japanese Andisols under vegetable production (0.79-1.55 μg g-1 dry soil)

and young forest (1.90-2.39 μg g-1 dry soil) were however comparable to

those found in our study. As already explained, chromatographic separation

of PLFA 18:2 6,9c from PLFA cy19:0 was not successful, except for 10

samples. Pearson’s correlation coefficient between PLFA 18:1 9c and

PLFA 18:2 6,9c calculated for those 10 samples was 0.747 (P<0.05).

Together with the sizeable amount of reports found in the literature (e.g.

Bååth, 2003; Joergensen and Wichern, 2008; Kozdrój and van Elsas, 2001),

this confirms PLFA 18:1 9c as a suitable fungal biomarker. Surprisingly,

results for PLFA 18:1 9c and ergosterol did not correlate (r = 0.304, P =

0.464). The only significant difference in ergosterol content between OF and

CF was found in Cisarua1, while no significant difference could be found for

18:1 9c at that location. In Cisarua2, on the other hand, significant

94


differences were found for 18:1 9c but not for ergosterol. The lack of

correlation between PLFA 18:1 9c and ergosterol thus contrasts with the

results of Klamer and Bååth (2004) who found a good correlation between

ergosterol and PLFA 18:2 6,9c. Högberg (2006) suggested that ergosterol

is only a reliable biomarker for fungi in relatively undisturbed soils, which the

vegetable cultivated soils of West Java are obviously not. Helfrich et al.

(2008) and Zhao et al. (2005) reported that ergosterol could not capture the

considerable decrease in fungal biomass following fungicide application.

Mille-Lindblom et al. (2004) showed that the decomposition of ergosterol, in

soil without living fungi, is a rather slow process with a half-life of ca. 3-5

months. We therefore conclude that ergosterol cannot be considered a

reliable indicator of fungal biomass for the intensive vegetable production

systems in West Java. The indicator value of ergosterol is further impaired

by the fact that coefficients of variation in this study were higher for

ergosterol than for PLFA 18:1 9c, even though both compounds were

extracted from the same freeze-dried, sieved and homogenized samples.

However, ergosterol may still be useful as an indicator when larger

differences in fungal biomass are expected such as those between

secondary forest and agriculture.

Bossio and Scow (1998) and Petersen and Klug (1994) mention among

others decreasing pH and starvation as possible factors that cause the

cy17:0/16:1 7c ratio to increase. No correlation between pH and

cy17:0/16:1 7c was found in this study, but the positive correlation between

the second dimension of the CDA and PLFA 18:1 5c pointed to lower

substrate availability under conventional vegetable production. Nevertheless,

it is not probable that soil microbial communities under conventional

management were resource limited as application rates of organic matter

are high in both the organic and conventional systems (also at the CF in

Ciwidey, although no manure was applied during the sampled growth cycle).

Furthermore, there was no correlation between cy17:0/16:1 7c and SOC

content (r = 0.052, P>0.05). Higher physiological stress under CF compared

95

Chapter 3

to OF is more probably due to the intensive use of pesticides of which the

negative impacts on the soil microbial community have already been

discussed in chapter 2. Further, the high stress signature of the OF-cleared

site in Ciwidey is remarkable. It is possible that the soil microbial community

was severely disturbed by the clearance of the brushwood and subsequent

cultivation and had not yet reached a new stage of equilibrium.

The high NLFA/PLFA ratio of 16:1 5c at the OF-cleared site was probably

also a consequence of the conversion of brushwood into farmland. As

indicated by PLFA 16:1 5c, AMF biomass was reduced to an amount

comparable with the older organic site, but storage structures, apparently not

fully decomposed after six months, still contained large amounts of the NLFA

16:1 5c, resulting in high NLFA/PLFA ratios. The NLFA/PLFA ratio of

16:1 5c was further significantly higher under CF than under OF in

Cisarua1. This again points to a relatively high presence of storage

structures compared to the amount of hyphae. In a field study by Gryndler et

al. (2006), AMF spores were also significantly more abundant, while AMF

hyphal length was significantly lower, when manure was combined with

mineral fertilizer than when manure alone was applied.

The low diversity of the soil microbial community in the secondary forest,

may be an indication of resource limitation. Jangid et al. (2008) found that

oligotrophic bacteria outcompeted copiotrophic bacteria in forest soil in

Georgia (USA) resulting in lower bacterial diversity than in agricultural soils.

Manure and compost are indeed much richer nutrient sources than forest

litter. Upchurch et al. (2008) further proposed that the higher bacterial

diversity observed in managed agricultural soils results from greater

(seasonal) variation in the plant community and increased immigration of

wind or animal borne bacteria to open cropland.

96


3.4.3. Disease suppressiveness Contrary to the hypothesis that compost application at the organic farms

would lead to improved suppressiveness against R. solani, the reverse was

true. Hoitink and Boehm (1999) reviewed evidence that control of R. solani

by composts is very variable. While biological control of oomycete

pathogens (Pythium, Phytophtora) depends on the overall diversity and

activity of the soil biota (general suppression), R. solani is controlled by a

much narrower spectrum of biocontrol agents and these microflora do not

consistently colonize composts (Hoitink and Boehm, 1999). Long-term

curing of composts may, however, increase their suppressive capacity,

whereas high amounts of cellulosic substrates in compost stimulate R. solani

(Hoitink and Boehm, 1999). Possibly, the crop residues in the compost of the

organic farms, especially that of Cisarua2, were not fully decomposed yet

and provided a cellulose source for R. solani.

3.4.4. Correlations between parameters measured Dehydrogenase and -glucosidase activity correlated strongly (0.925,

P<0.001). This indicates the relationship between both enzymes is similar in

both conventional and organic agriculture, and even secondary forest, as

well as between texture classes and sampling periods. Absolute

concentrations of marker PLFAs for Gram-positive and Gram-negative

bacteria, actinomycetes, total bacteria, AMF and fungi were all positively

correlated with dehydrogenase activity in September (P<0.05). -

glucosidase activity in September was positively correlated with Gram-

negative and total bacteria and fungi (P<0.05). These correlations confirm

the functional link between microbial biomass, microbial activity and organic

matter turnover (as measured by -glucosidase activity). Fungi are

considered to be the main actors in the process of organic matter

decomposition (Killham, 1994). In our study higher -glucosidase activities in

97

Chapter 3

September corresponded to larger relative amounts of the PLFA 18:1 9c

(0.825, P<0.01).

Basal respiration correlated with -glucosidase activity (0.768, P<0.05) and

to a lesser extent with dehydrogenase activity (0.703, P = 0.052). Green et

al. (2007) also reported a significant correlation of basal respiration with -

glucosidase activity. Basal respiration was determined on previously air-

dried and sieved soil, but this did not seem to affect the correlation between

basal respiration and -glucosidase activity. The cy17:0/16:1 7c ratio was

strongly correlated with basal respiration (-0.872, P<0.01). A linear model

that combined -glucosidase activity in July and cy17:0/16:1 7c could even

predict 95.6% of the variability of basal respiration (Table 3.12). Basal

respiration is often used as an overall index for assessing microbial

functions. In their review, Joergensen and Emmerling (2006) listed various

stress factors that reduce basal respiration such as salinization, heavy

metals and pesticides.

The cy17:0/16:1 7c ratio was negatively correlated with absolute

concentrations of the AMF marker PLFA 16:1 5c (-0.771, P<0.05, excluding

secondary forest) indicating that AMF are negatively affected by conditions

that are as well stressful for the bacterial community, like conventional

management. The negative impact of conventional agricultural management

on AMF has been reported by several other studies (e.g. Bending et al.,

2004; Kurle and Pfleger, 1994; Mäder et al., 2002). Together with their

obligate symbiotic nature, the susceptibility to disturbance makes AMF

important potential indicators of soil fertility in sustainable agricultural

systems (Bending et al., 2004).

Finally, a positive correlation was observed between pH and the relative

amount of Gram-negative bacteria (0.739, P<0.05, excluding secondary

forest). A shift to more Gram-negative bacteria at higher pH was already

observed in chapter 2.

98


Table 3.12: Coefficients of model for basal respiration (R2 = 95.6%, P<0.001).

Parameter Coefficient Standard Error

Standardized coefficient

P

Constant 27.004 3.555 0.000

cy17:0/16:1 7c -28.119 3.989 -0.667 0.000

-glucosidase (July) 0.054 0.010 0.487 0.002

3.4.5. Nematode community analysis

The lower MI values under OF than under CF indicate that the natural

succession of nematode communities in conventionally managed soils is

continuously disturbed by the application of pesticides and the heavy

amendments of goat and chicken manure. The organic fields also received

considerable amounts of organic matter, but manure and crop residues were

composted before being applied which makes nutrient availability more

gradual. The high nutrient input associated with vegetable production

systems in West Java was reflected in high PPI/MI ratios. According to

Bongers et al. (1997) soil ecosystems with PPI/MI ratios of 1.6 or higher are

severely nutrient enriched and in these systems resource utilization by

plants is far from optimal. PPI/MI values higher than 1.6 were found at all

conventional farms and at the organic farm of Cisarua1. Nevertheless,

nutrient disturbances are also likely at the two other organic farms, since

PPI/MI ratios in habitats where plants make optimal use of nutrient

resources do not exceed 0.9 (Bongers et al., 1997). Even if the threshold

values provided by Bongers et al. (1997) cannot be generalized to tropical

climates, we may conclude that nutrient use efficiency was lower under

conventional than under organic management.

Besides the large inputs of organic matter in the vegetable production

systems of West Java, the high EI values found may also be a consequence

of the tropical climate. High rainfall and soil temperatures stimulate biological

activity and nutrient recycling which makes more resources available to the

soil food web (Pattison et al., 2008). Despite the variability of the SI values

99

Chapter 3

there was a trend towards more structured food webs under organic

agriculture. According to Ferris et al. (2001), food webs in the upper right

quadrant are maturing, while food webs of the upper left quadrant are

disturbed.

Surprisingly, the correlation between CI and F/B calculated from the PLFA

data was low (r = 0.502, P>0.05). This could partly be due to the low amount

of samples. But also the complete PLFA data did not show a clear trend in

F/B ratio between organic and conventional agriculture. It therefore seems

that nematode community analysis is more sensitive than PLFA profiling for

detecting changes in the soil food web. One reason for this could be that the

CI includes weighting parameters for the metabolic rates of the nematodes,

while PLFA data do not provide information about the turnover of bacteria

and fungi.

Neher (1999) found that maturity and diversity indices did not differ between

organic and conventional managed soils (except PPI), because differences

due to different crops grown in the organic and conventional fields were

greater. We sampled soil under a variety of crops, yet differences between

organic and conventional management were still apparent. Taking into

account a masking effect caused by the different crops grown, differences

between organic and conventional management could possibly be large.

3.4.6. Comparison of indicators In Cisarua1, most microbial parameters pointed to increased soil quality

under OF compared to CF. Enzyme activities, basal respiration, marker

PLFA contents and the discriminant index were higher, while the

cy17:0/16:1 7c ratio was lower under OF. However, only dehydrogenase

activity in September and the discriminant index showed significant

differences. In Cisarua2, enzyme activities and basal respiration under long-

term and 3-year OF were comparable and significantly higher than under

CF. In contrast to the findings of 2007, marker PLFA contents were

100


significantly lower under 3-year OF than under long-term OF. This would

suggest that the microbial community actually had not yet fully recovered

from the conventional farming methods. The intermediate position of the soil

microbial community after three years of organic farming was reflected in the

cy17:0/16:1 7c ratio and the discriminant index. Only few significant

differences were found between the older OF site and CF in Ciwidey. Basal

respiration and the AMF marker PLFA were significantly higher at the older

OF site, while the cy17:0/16:1 7c ratio was significantly lower. A negative

effect of cultivation on the microbial community could be noticed in Ciwidey

since the total amount of bacteria marker PLFAs was significantly lower at

the older OF site than at the OF-cleared site, which was until recently

overgrown with brushwood. Also enzyme activities were lower at the older

OF site than at the OF-cleared site (but not significantly). On the other hand

basal respiration was higher at the older organic site (but not significantly

either). Also in Ciwidey, the information obtained from the individual

microbial indicators was adequately summarized in the discriminant index.

The discriminant index was much higher at the OF-cleared site than at the

older OF site and under CF. The discriminant index was again slightly higher

at the older OF site than under CF, but this difference was not significant.

3.5. Conclusions

Although differences were less pronounced than in 2007, organic vegetable

production was again found to have a positive impact on enzyme and

microbial activity. On the other hand, organic cultivation seemed to

negatively affect suppressiveness against R. solani. This indicates that

compost should not only be considered as a source of nutrients for crops

and soil life, but also other impacts, like the effect on soil-borne pathogens,

should be regarded. Ergosterol appears not to be universally applicable as

an indicator for fungi and in this respect seems to be inferior compared to

PLFA markers (18:1 9c or 18:2 6,9c). NLFA 16:1 5c may provide

101

Chapter 3

102

additional information on AMF, but its high variability complicates the

interpretation of data. Nevertheless, the present study confirmed arbuscular

mycorrhizal fungi as sensitive microorganisms and potential indicators of

environmental stress. The ratio of cy17:0 to 16:1 7c was effectively applied

as an indicator of physiological stress experienced by the bacterial

community. The discriminant index developed in the previous chapter was

successfully validated and summarized the information obtained from the

individual parameters and indices rather well. The index may likely be used

to assess soil quality of vegetable production systems in the humid tropics,

but we recommend further testing to asses its range of application.

Based on the soil nematode analysis, we may conclude that organic

vegetable production systems in West Java have more mature soil food

webs than conventional systems. Succession of food webs in conventionally

managed vegetable fields, on the other hand, is continuously set back by the

intensive use of pesticides, mineral fertilizers and fresh manures. Although

both organic and conventional systems are nutrient enriched, nutrient use

efficiency is higher in organic systems. Indeed, mature soil food webs are

indicative for closed ecosystems.

Chapter 4

Organic and conventional paddy fields in Central Java,

Indonesia

Redrafted after: Moeskops B, Buchan D, Sukristiyonubowo, Sleutel S, De Neve S. Microbial

activity and phospholipid fatty acid profiles under organic and conventional

paddy fields in Central Java, Indonesia. Pedosphere. Submitted.

Illustration:

Soil sampling in the rice fields of farmer group Sri Makmur (Bram Moeskops)

Chapter 4

Organic and conventional paddy fields in Central Java,

Indonesia

4.1. Introduction

The Green Revolution has been an important step in helping feed the

world’s hungry, allowing food production to be increased significantly from

1964 onwards. In Indonesia, which for decades has been the largest rice

importer in the world, the Green Revolution has also been a success

enabling the country to become self-sufficient in rice in 1984 (Martawijaya

and Montgomery, 2004). But the Green Revolution has also been criticized,

primarily because of associated environmental pollution. The higher crop

yields achieved required a 20- to 30-fold increase in the worldwide

consumption of agricultural chemicals, mainly fertilizers and pesticides

(Pimentel, 1996). In Indonesia specifically, total inorganic fertilizer

consumption increased more than nine-fold between 1975 and 2002 (FAO,

2005). Environmental effects of modern agriculture include loss of

biodiversity, eutrophication and ground and surface water contamination

(Horrigan et al., 2002; Pimentel, 1996). Soil organisms (microorganisms and

invertebrates), essential to proper functioning of the agro-ecosystem, are

also negatively affected by pesticides and chemical fertilizers (Horrigan et

al., 2002; Pimentel, 1996). As a result of rising concerns about the long-term

sustainability of conventional production methods, the potential for organic

farming has received increasing attention, also from paddy rice growers in

Indonesia.

Paddy rice fields represent a particular kind of soil ecosystem because they

are anoxic during the period of plant development. Research into the effect

of organic paddy rice production on soil biochemical and microbiological

105

Chapter 4

properties is extremely scarce. Most of soil research on organic rice

cultivation focused on plant nutrient supply and soil organic matter stocks

(e.g. Hasegawa et al., 2005; Komatsuzaki and Syuaib, 2010). In this

chapter, we therefore compare the composition of the soil microbial

community (using PLFA profiles) and discuss microbial and enzyme activity

(aerobic respiration, dehydrogenase and -glucosidase activity) under

organic and conventional paddy rice cultivation in Central Java, Indonesia.


4.2.1. Experimental set-up

The impact of organic and conventional rice (Oryza sativa L.) production was

investigated in two soil types, namely Vertisols and Inceptisols, in Sragen

regency (Central Java, Indonesia, 7.5° S and 111° E), resulting in 4

management-soil type combinations. The organic site on Inceptisol was

managed fully organically since 2001. The organic paddy field on Vertisol

was not yet fully converted at the moment the research took place. At this

site, the use of chemical fertilizers had been reduced since 1999, but still

amounted to 30-50 kg urea ha-1 growth cycle-1. Pesticides, however, had

been completely banned since 2007. Management practices as well as

coordinates of the different sites are specified in Table 4.1. The application

rates of fertilizers and pesticides pertain to the period the research took

place, but are representative for the long-term management at the selected

fields. Rice was grown in monoculture at all fields, except for the

conventional field on Inceptisol where a rice - watermelon (Citrullus lanatus

(Thunb.) Matsum. & Nakai) rotation was practised. The area of a single field

ranged between 1500 and 3500 m2.

The climate of the research area is monsoonal equatorial according to the

Köppen-Geiger classification (Kottek et al., 2006). This means the climate is

characterized by two seasons: a rainy season from November to April with

106

Paddy rice fields

107

about 80% of the annual precipitation and a dry season from May to

October.

4.2.2. Soil sampling At each paddy field three separate plots of 10 m2, spread evenly over the

site, were selected as replicates. In all replicates 15 samples were taken

from the 0-15 cm soil layer and mixed to obtain one composite sample per

plot. All sites were sampled twice during the dry season of 2008: in June and

in September. -glucosidase activity was measured on both sample series.

Because of practical constraints, general soil properties, aerobic respiration

and dehydrogenase activity were determined on the first series of samples

only, while PLFAs were analyzed only on the second series of samples.

4.2.3. General soil properties

Determination of general soil properties was carried out on air-dried and






considered equivalent to organic carbon contents. Texture was determined

by the combined sieve and pipette method according to Gee and Bauder

(1986). All soils were clay soils, with percentages of clay ranging between

46.8 and 52.1%.

Tabl

e 4.

1: L

ocat

ion

and

man

agem

ent d

ata

of s

elec

ted

field

s.

Soi

l typ

e M

anag

emen

t Fe

rtiliz

atio

n P

estic

ides

C

oord

inat

es a

nd

altit

ude

(m a

msl

)

Ince

ptis

ol

orga

nic

com

post

ed m

anur

e an

d cr

op re

sidu

es:

7 M

g =

57 k

g N

per

yea

r pl

ant e

xtra

ct (A

zadi

rach

ta in

dica

A.

Juss

., M

elia

aze

dara

ch L

., D

erris

sp

., N

icot

iana

taba

cum

L.,

Cur

cum

a lo

nga

L., C

urcu

ma

xant

horh

iza

L., C

urcu

ma

solo

ensi

s V

al.,

Dio

scor

ea s

p.)

07°

31’ S

, 111

° 09

’ E44

6 m

conv

entio

nal

Pet

rorg

anik

(org

anic

ferti

lizer

): 26

7 kg

= 1

.9 k

g N

ur

ea: 1

53 k

g N

N

PK

: 25

kg N

, 25

kg P

2O5,

25

kg K

2O

delta

met

hrin

: 15-

20 g

07

° 32

’ S, 1

11°

01’ E

261

m

V

ertis

ol

orga

nic

com

post

ed c

attle

man

ure

and

crop

resi

dues

: 1.

8 M

g =

13.3

kg

N

Pet

rorg

anik

: 500

kg

= 3.

5 kg

N

urea

: 18

kg N

plan

t ext

ract

07

° 24

’ S, 1

11°

06’E

106

m

conv

entio

nal

urea

: 132

kg

N

phos

phat

e: 1

54 k

g P

2O5

NP

K: 2

1 kg

N, 2

1 kg

P2O

5, 2

1 kg

K2O

carb

ofur

an: 2

200

g fe

nval

erat

e: 2

25 g

07

° 24

’ S, 1

11°

06’ E

106

m

Rat

es a

re g

iven

per

ha

and

per g

row

th c

ycle

(3 g

row

th c

ycle

s pe

r yea

r) u

nles

s ot

herw

ise

stat

ed.

Paddy rice fields

4.2.4. Biochemical analyses

The activity of -glucosidase was measured according to a procedure

modified from Eivazi and Tabatabai (1988; cited in Alef and Nannipieri,

1995) in which p-nitrophenyl- -D-glucoside is degraded to p-nitrophenol

(PNP) during a 1 h incubation. Dehydrogenase activity was determined as

the reduction rate of triphenyltetrazolium chloride to triphenyl formazan

(TPF) during a 24 h incubation as described by Casida et al. (1964). Both

enzyme activities were measured in triplicate with one blank on fresh soil

stored at 4°C. Concentrations of PNP and TPF were determined with a

Hitachi 150-20 spectrophotometer (Hitachi Ltd., Tokyo, Japan). More

detailed procedures of the enzyme activity measurements are given in

chapter 2.

Soil samples for PLFA analysis were freeze-dried and sieved (2 mm) after

sampling and subsequently stored at -18°C until extraction. PLFAs were

extracted using a modified Bligh and Dyer technique (Bligh and Dyer, 1959)

described in detail in chapter 3 (without the part about NLFAs, which were

not retained in this study).

4.2.5. Aerobic respiration

Although anaerobic respiration (denitrification, methanogenesis) prevails in

paddy fields, we decided to measure respiration under aerobic conditions

because of two reasons. Firstly, anaerobic respiration is correlated to

aerobic respiration (D’Angelo and Reddy, 1999). Secondly, we did not

necessarily want to measure actual field respiration rates, but rather wanted

to find sensitive and relatively practical indicators of soil quality. Because of

practical constraints, aerobic respiration rates were determined on only two

of the three replicates of each treatment. Air-dried and sieved (2 mm) soil,

corresponding to an oven-dry weight of 150 g, was transferred to PVC tubes

(7.5 cm diameter) and brought to approximately 50% WFPS (30%

109

Chapter 4

gravimetric water content for the organic field on Inceptisol, 25% for the

other fields). Soils were incubated at 25 ± 1 C in airtight closed jars (of 1.5 l)

during 6 weeks. CO2-evolution was measured as described in chapter 3. A

linear model was fitted to the cumulative respiration data, omitting the data

of the first ten days because of rewetting effects during that period.

4.2.6. Data processing Treatments and soil types were statistically compared by full factorial two-

way ANOVA using SPSS (version 15.0, SPSS Inc., Chicago, USA), except

for pH-KCl and dehydrogenase activity. For these two parameters organic

and conventional management were compared by separate one-way

ANOVAs for each soil type because of a significant management x soil type

interaction.



of the total PLFA concentration of the respective soil sample. After removal

of all PLFAs that contributed less than 1% to the total pool of PLFAs, 20

PLFAs were retained. Redundancy analysis (RDA) based on correlation

coefficients was applied on this percentage distribution with the package

vegan (version 1.17-0) in R (version 2.10.1, free software). RDA is the

constrained form of Principal Component Analysis (ter Braak, 1995). This

means ordination axes are constrained to be linear combinations of

environmental variables. Two factor variables, management and soil type,

were considered in the RDA.

Finally, the ratio of cy17:0 to 16:1 7 was calculated and served as an index

for physiological stress in the bacterial community (Bossio and Scow, 1998;

Petersen and Klug, 1994).

110

Paddy rice fields

4.3. Results

4.3.1 Chemical soil properties

Both SOC and TN contents were significantly higher in organic paddy rice

fields compared to conventional fields (P<0.01) (Table 4.2). Organic and

conventional fields had comparable C/N ratios, but Vertisols had significantly

higher C/N ratios than Inceptisols (P<0.01). All fields had comparable pH-

KCl values, except the conventional field on Vertisol which had a

significantly higher pH than the organic field on Vertisol (P<0.01).

Table 4.2: Chemical soil properties.

Soil type Management pH-KCl SOC (%) TN (%) C/N

Inceptisol organic 4.67 (0.22) 1.90 (0.22) 0.15 (0.03) 13.0 (1.1)

conventional 4.70 (0.07) 1.42 (0.12) 0.10 (0.01) 14.4 (0.6)

Vertisol organic 4.90 (0.09) 1.71 (0.05) 0.11 (0.01) 16.1 (1.7)

conventional 5.38 (0.02) 1.36 (0.16) 0.08 (0.01) 17.2 (1.0)

Values in parentheses indicate standard deviations 4.3.2 Enzyme activities and aerobic respiration

Both in June and in September, -glucosidase activities were significantly

higher under organic compared to conventional agriculture (P<0.05) (Fig.

4.1). In September, -glucosidase activities were significantly higher in the

Inceptisols compared to the Vertisols (P<0.05). Dehydrogenase activity was

significantly higher under organic compared to conventional paddy rice

cultivation in the Inceptisols (P<0.001) (Table 4.3). In the Vertisols variability

of dehydrogenase activity was too high to give statistically significant

differences (P>0.05), although there was a tendency towards higher

dehydrogenase activities under organic rice production.

111

Chapter 4

Finally, also aerobic respiration was significantly higher under organic rice

production compared to conventional (P<0.01), both when expressed per

mass unit soil and when expressed per mass unit SOC (Table 4.3). No

significant differences were found between soil types (P>0.05).

Table 4.3: Dehydrogenase activity (DHA) and aerobic respiration rates.

Soil type Management DHA (μg TPF g-1 dry soil.24 h-1)

Resp. (mg CO2-Ckg-1 dry soil day-1)

Resp. (mg CO2-C g-1 SOC day-1)

Inceptisol organic 514 (27) 10.94 (0.44) 0.551 (0.075)

conventional 146 (47) 5.31 (0.94) 0.370 (0.022)

Vertisol organic 287 (380) 9.30 (1.20) 0.536 (0.054)

conventional 85 (10) 5.51 (0.47) 0.387 (0.008)


Fig. 4.1: -glucosidase activity. Error bars indicate standard deviations.

112

Paddy rice fields

4.3.3 PLFA profiles The ratio of cy17:0 to 16:1 7c was significantly (P<0.01) lower under

organic than under conventional rice cultivation (Table 4.4). RDA of PLFA profiles clearly separated the four different sites (Fig. 4.2). The

first dimension of the biplot discriminated between organic and conventional

rice cultivation, while the second dimension made a distinction between the

two soil types. Organically managed soils were particularly characterized by

a higher relative abundance of PLFAs 16:0 and 16:1 7c (correlation with

RDA1 >0.850, P<0.001), while PLFAs with 10Me-branched and PLFA

cy17:0 were more prevalent under conventional cultivation (correlation with

RDA1 <-0.850, P<0.001). Separation between soil types could be less well

attributed to certain PLFAs. However, there was an indication that Vertisols

contained relatively more monounsaturated PLFAs (16:1 5c, 18:1 7c).

Table 4.4: cy17:0/16:1 7c ratios.

Soil type Management cy17:0/16:1 7c

Inceptisol organic 0.309 (0.066)


Vertisol organic 0.360 (0.029)


113

Chapter 4

Fig. 4.2: Biplot of RDA on PLFAs. Solid vectors for PLFAs that are correlated with RDA1 (P<0.05), dashed vectors for PLFAs that are correlated with RDA2 (P<0.05), grey dashed vectors for PLFAs that are not significantly correlated with any of the axes. Ellipses group samples from the same site.

4.4. Discussion

4.4.1 Organic and conventional paddy rice cultivation Organic paddy rice cultivation increased organic matter contents and had a

positive impact on aerobic respiration and enzyme activity. Furthermore,

organically and conventionally managed paddy soils clearly differed in the

composition of the microbial community. The organic and conventional rice

production systems in Central Java differ in three important aspects. First,

organic systems rely on compost for plant nutrient supply, or in some cases

(conversion to fully organic) combine compost and a limited amount of

chemical N fertilizer. Conventional systems, on the other hand, almost

exclusively apply inorganic fertilizer. Secondly, commercial pesticides are

114

Paddy rice fields

completely banned in organic systems. Instead, extracts from a wide range

of plants are used to protect rice plants against pest attacks. Thirdly, the

source of irrigation water is different between both systems. Organic farmers

do not use the communal irrigation system as water flowing from

neighbouring conventional fields is likely to be contaminated by pesticides

and fertilizers. Because of its higher altitude, spring water is available for the

organic field on Inceptisol. The organic field on Vertisol, which is located in

the lowland, is irrigated with water supplied by a deep well. Due to the

experimental design, it is not possible to asses the separate impact of each

of these three aspects. However, in the next paragraphs we will discuss the

possible impacts of compost, inorganic fertilizer and pesticides on organic

matter stocks, the soil microbial community and its activity.

Komatsuzaki and Syuaib (2010) determined SOC contents of organic fields

in West Java five years after conversion and found significantly higher

contents compared to conventional fields. Tirol-Padre et al. (2005) reported

that 40 years of incorporation of rice straw compost brought about significant

increases in SOC, TN and aerobic soil respiration compared to urea

fertilized soils. Describing results from a 41-year old field experiment, Lee et

al. (2009) reported decreasing SOC contents in rice fields only receiving

chemical fertilizer, while SOC contents had increased in rice straw compost

amended fields. Finally, Nayak et al. (2007) found that the application of

compost in a field experiment of more than 30 years resulted in increased

dehydrogenase activities compared to fields that received only inorganic

fertilizer. -glucosidase activities, however, were lower in compost amended

soils than in chemically fertilized soils, but higher when compost and

chemical fertilizer were combined. We may conclude that scientific

consensus exists about the positive impact of organic matter amendments

on soil organic matter stocks and microbial and enzyme activities in paddy

rice soils.

Reports about the impact of pesticides on paddy soil are less unanimous.

Das et al. (2003a, 2003b) reported that the insecticides carbofuran and

115

Chapter 4

fenvalerate increased the populations of bacteria, actinomycetes and fungi in

the rhizosphere of paddy rice and concluded that both insecticides served as

a nutrient and energy source for these microorganisms. Nevertheless, some

bacteria (e.g. Pseudomonas sp., Micrococcus sp.) and fungi (e.g. Rhizopus

sp.) were negatively affected by carbofuran and fenvalerate. Although

several laboratory studies reported that carbofuran was toxic for N fixing

cyanobacteria like Anabaena doliolum, Nostoc linckia or Synechococcus

elongatus (Hammouda, 1999; Megharaj et al., 1989), a field study by

Megharaj et al. (1988) found that carbofuran application at common field

rates (up to 2 kg ha-1) significantly increased the total population of

cyanobacteria and chlorophyta. Nevertheless, also at those rates Nostoc

linckia and Synechococcus elongatus were negatively affected. Deltamethrin

reduced bacterial activity in freshwater sediments at a predefined maximum

permissible concentration (1.3 μg kg-1), but no negative effects could be

observed at 100 times the maximum permissible concentration (Widenfalk et

al., 2004). These studies show that the interactions between pesticides and

microbes in submerged soils can be highly complex and do not always allow

for straight-forward interpretations. The many inconsistent findings about the

effect of pesticides on the soil microbial community made several

researchers (e.g. Johnsen et al., 2001; Widenfalk et al., 2008) conclude that

overall community metabolism (e.g. microbial activity) is not a suitable

response variable for detecting toxic effects of pesticides. Investigating

microbial community shifts by PLFA analysis or PCR-based methodology

appears to be more promising (Widenfalk et al., 2008). Indeed, the RDA

revealed clear differences in PLFA composition between organic and

conventional rice production in our study. Furthermore, PLFA 16:1 7c

strongly and positively correlated with RDA1, while cy17:0 was strongly

negatively correlated with RDA1 (Fig. 4.2), and the ratio of cy17:0 to

16:1 7c was significantly (P<0.01) lower under organic than under

conventional rice cultivation (Table 4.2). This indicates that growth

conditions for the microbial community were less favourable under

116

Paddy rice fields

conventional than under organic rice production, possibly because of toxic

effects of pesticide residues. Caution should however be taken when

interpreting the cy17:0/16:1 7c ratio. Anaerobic conditions may also

increase the cy17:0/16:1 7c ratio (Bossio and Scow, 1998; Petersen and

Klug, 1994). In general, cyclopropyl PLFAs are widely used as anaerobic

biomarkers (Gu et al., 2008; Guckert et al., 1985; Vestal and White, 1989),

while monounsaturated PLFAs serve as indicators for aerobic conditions

(Bossio and Scow, 1998). Differences in aeration status between the fields

could hence confound the interpretation of this ratio. In our study, however,

biplot vectors of the monounsaturated PLFAs did not jointly point to the

same direction (Fig. 4.2), indicating there was no particular trend in aeration

status. Furthermore, Bossio and Scow (1998) questioned the use of

cyclopropyl PLFAs as anaerobic biomarkers, as in their study cy17:0 and

cy19:0 did not respond to flooding. Hence, it can be concluded that the

higher physiological stress experienced by the bacterial community under

conventional rice production was related to differences in management

rather than by differences in aeration status. The relatively lower abundance

of 10Me16:0 and 10Me18:0, signature PLFAs for actinomycetes (Kozdrój

and van Elsas, 2001), under organic rice cultivation agrees with the results

of Bossio and Scow (1998) who reported a 10% decrease in mol% of

10Me18:0 if rice straw was incorporated. Shifts in community structure may

have consequences on ecosystem functioning if the persistent

microorganisms cannot compensate for biogeochemical functions normally

carried out by the eliminated microbial groups (Widenfalk et al., 2008). This

could be the case in our study as well since shifts in community structure

were accompanied by reduced -glucosidase activity and respiration, two

important indicators for C metabolism. We therefore may conclude that

conventional rice production had a negative impact on soil functioning

compared to organic production.

The impact of organic and conventional production on soil biochemical and

microbial properties has more comprehensively been documented for arable

117

Chapter 4

and vegetable farming than for paddy rice production. Higher enzyme

activities were for example reported under organic management than under

conventional management at the DOK-trial established in Switzerland

(Fließbach et al., 2007; Mäder et al., 2002) and at different sites in Central

Italy (Lagomarsino et al., 2009; Marinari et al., 2006). In West Java,

intensive chemical fertilizer and pesticide use in conventional vegetable

production systems negatively affected soil enzyme activities (chapter 2 and

3). Furthermore, like the organic and conventional rice fields in this chapter,

organic and conventional vegetable production had also different PLFA

profiles. It therefore seems there are consistent differences in soil microbial

functioning between organic and conventional systems throughout the world

and in widely differing climates and soils.

4.4.2 Dehydrogenase activity Dehydrogenase activity was much more variable than -glucosidase activity.

Within composite soil samples, coefficients of variation were on average

15% for -glucosidase in June, but 35% for dehydrogenase. Variability in

dehydrogenase activity across replicates was particularly high for the

organically managed Vertisol (Table 4.3). Dehydrogenase activity

measurements in vegetable producing soils in West Java (chapter 2 and 3)

did not show such high variability. Being an intracellular enzyme, high

variability in dehydrogenase activity may indicate a high spatial

heterogeneity of the microbial community in paddy fields. Previous research

(Nayak et al., 2007; W odarczyk et al., 2002) suggested that soil aeration

status is the major factor determining dehydrogenase activity. This seems to

hold true in our study as well since spatial heterogeneity of aerated micro-

sites in paddy field soil will result in scattered microbial hotspots and hence

in variability in dehydrogenase activity. We may conclude that although

dehydrogenase activity is a valuable and sensitive indicator of soil microbial

118

Paddy rice fields

119

activity in vegetable farming (chapter 2 and 3), its high variability under

flooded conditions makes it less useful for paddy rice systems.

4.5. Conclusions

Remarkably little research has been carried out concerning the effects of

organic and conventional paddy rice production on soil chemical and

microbiological properties. This study on a limited number of paddy fields in

Central Java was explorative in nature. However, organic farming seemed to

create more favourable growth conditions for the soil microbial community

compared to conventional cultivation which resulted in better soil functioning.

These findings are similar to conclusions reached for arable and horticultural

systems in temperate and tropical climates which indicates there are

consistent differences in soil microbial functioning between organic and

conventional systems throughout the world.

Chapter 5

The impact of exogenous organic matter on biological

soil quality and soil processes

Redrafted after: Moeskops B, Buchan D, Van Beneden S, Fievez V, D’Hose T, Gasper MS,

Sleutel S, De Neve S. The impact of exogenous organic matter on biological

soil quality and soil processes. Applied Soil Ecology. Submitted.

Illustration:

Overview of the experimental field

Chapter 5

The impact of exogenous organic matter on biological

soil quality and soil processes

5.1. Introduction In Western Europe, the transition towards modern agriculture during the last

century and especially after the Second World War, with the adoption of

short crop rotations or monoculture, deep tillage operations, and the

declining use of manure or other organic fertilizers, has resulted in drastic

reductions of soil organic matter levels (Gardi and Sconosciuto, 2007). Soil

organic matter is, however, a key attribute of soil quality (Gregorich et al.,

1994). Soil organic matter is crucial to soil fertility as it represents an

important pool of plant nutrients and increases the cation exchange capacity

of the soil (Rhoton et al., 1993; Riffaldi et al., 1994). Soil physical properties

inextricably linked to soil organic matter are plant available water (Hudson,

1994), infiltration (Boyle et al., 1989; Pikul and Zuzel, 1994), aggregate

formation and stability (Oades, 1984; Tisdall and Oades, 1982) and bulk

density (Ekwue and Stone, 1995; Thomas et al., 1995). Finally, because soil

organic matter serves as a nutrient and energy source for a diverse

population of bacteria and fungi (Birkhofer et al., 2008; Bünemann et al.,

2004) and invertebrates such as earthworms (Hendrix et al., 1992; Leroy,

2008), soil organic matter is indiscernible from biological soil functioning

(Robert et al., 2004).

Given the importance of soil organic matter for soil quality, organic

fertilization is indispensable in sustainable crop production. Many diverse

organic materials, e.g. crop residues, manures, peat and composts, are

used, but they each have specific effects on soil organic matter stocks and

123

Chapter 5

soil functioning. However, there is little knowledge about these specific

effects. While positive effects of organic matter application on microbial

biomass, compared to mineral fertilization, have extensively been

documented (e.g. Kandeler et al., 1999; Peacock et al., 2001), the impact of

organic amendments on the composition of the microbial community is less

clear. Several studies have found that Gram-negative marker PLFAs

relatively increased with the availability of organic substrates (Burke et al.,

2003; Peacock et al., 2001), while the application of mineral N resulted in a

higher proportion of Gram-positive bacteria (Peacock et al., 2001). However,

Marschner et al. (2003) found that the ratio of Gram-positive to Gram-

negative bacteria was higher in organically than in inorganically fertilized

plots. The effect of exogenous organic matter on enzyme activity depends

on which amendment is applied and which enzyme is considered, although

in general organic amendments stimulate enzyme activity (e.g. Kandeler et

al., 1999; Ros et al., 2006). Finally, many questions still remain regarding the

relation between organic amendments and suppression of soil-borne

diseases. Organic amendments, especially composts, have repeatedly been

reported to control soil-borne pathogens, but amendments that are

suppressive to some pathogens may well be conducive to others (Bonanomi

et al., 2010).

In 2005 a field experiment was started at the experimental farm of Ghent

University to compare the impact of eight different fertilizations strategies,

including five different types of organic amendments, on a wide range of soil

properties. In this chapter, we will evaluate whether after five growing

seasons these eight treatments resulted in differences in a number of soil

quality parameters. We measured SOC and TN contents, disease

suppressiveness, soil microbial biomass, enzyme activities, net N

mineralization and did a PLFA analysis.

124

Exogenous organic matter


5.2.1. Experimental set-up We sampled a field trial established in 2005 at the experimental farm of

Ghent University in Melle (Belgium, 50° 59’ N, 03° 49’ E, 11 m amsl) on an

Alfisol with a silt loam texture (USDA; 10.4% 0-2 m, 52.4% 2-50 m and

37.2% > 50 m). Climate in Belgium is fully humid temperate with warm

summers according to the Köppen-Geiger classification (Kottek et al., 2006).

Prior to the establishment of the field experiment, corn (Zea mays L.) had

been cultivated at the site for eight years without application of organic

fertilizer. The field experiment was a randomized complete block design with

four replicates comparing eight treatments (Fig. 5.1): cattle farmyard manure

(FYM), cattle slurry (CSL), vegetable, fruit and garden waste compost (VFG),

two types of farm compost (FCP1 and FCP2), only mineral fertilizer (MIN N),

and two treatments without fertilization, one with a crop (NF+) and one

without (NF-). The two types of farm compost differed in the composition of

the starting materials. FCP1 was composed of mostly woody, C rich

materials resulting in a final C/N ratio of 20-50, while FCP2 was made of

green, N rich materials resulting in a final C/N ratio of 10-20 (Table 5.1).

These two composts were primarily chosen because of their expected

difference in bacteria to fungi ratio. Leroy (2008) tested the PLFA

composition of the farm composts applied in September 2006 and May 2007

and found indeed a lower bacteria to fungi ratio in FCP1 than in FCP2 (not

significantly different in September). Both farm composts as well as the

farmyard manure were obtained from the Institute for Agriculture and

Fisheries Research (ILVO, Merelbeke, Belgium). Cattle slurry was available

at the experimental farm of Ghent University itself. VFG was produced at the

industrial composting plant of Verko (Dendermonde, Belgium) from the

selectively collected organic fraction of household wastes. All plots were 8 x

6 m2. The cropping history was the following: fodder beet (Beta vulgaris L.)

125

Chapter 5

was cultivated from spring to summer 2005, winter wheat (Triticum aestivum

L.) from autumn 2005 to summer 2006, Phacelia (Phacelia tanacetifolia

Benth.) from autumn 2006 to spring 2007, red cabbage (Brassica oleracea

L. var. rubra) in summer 2007, perennial ryegrass (Lolium perenne L.) from

spring to autumn 2008, and corn (Zea mays L.) from spring to autumn 2009.

Fertilization rates were adjusted in order to supply each plot with equal

amounts of organic C: 4000 kg C ha-1 in April and October 2005, 1500 kg C

ha-1 in September 2006, 2000 kg C ha-1 in May 2007, 1100 kg C ha-1 in May

2008 and 3260 kg C ha-1 in May 2009. At the first two applications, part of

the organic C of the CSL treatment was given as crop residues to avoid

excessive application of mineral N. In April 2005, 46% of the organic C was

applied as straw, while in October 2005 63% of the organic C was applied as

beet leaves. Taken over the six applications, 28% of the organic C was thus

given as crop residues in the CSL treatment. At each application, the organic

amendments were incorporated to a depth of 20 cm using a rotary tiller. All

aboveground crop residues, except the stubble, were removed from the

experimental field prior to tillage. Mineral N (as NH4NO3) was applied to

correct for differences in plant available N content of the organic

amendments. Besides N release from the organic amendments, N

mineralization from soil organic matter and mineral N (NO3- and NH4

+) still

available in the soil profile were taken into account in the calculation of

inorganic N needs. Finally, all fertilized plots also received equal minimum

amounts of plant-available P2O5 and K2O: 100 kg P2O5 ha-1 and 300 kg

K2O ha-1 for fodder beet, winter wheat and red cabbage; 75 kg P2O5 ha-1

and 150 kg K2O ha-1 for corn. The MIN N plots received these amounts of

P2O5 and K2O entirely as triple superphosphate 45% and KCl respectively,

while the organic amendments were only supplemented by mineral P2O5

and K2O if their plant-available P2O5 and K2O content did not reach the

required minimum. Fertilization details for the period 2005-2007 can be

found in Leroy (2008). Fertilization data for the years 2008-2009 are detailed

in Table 5.1.

126


Fig. 5.1: Layout of the field experiment. 5.2.2. Soil sampling The field plots were sampled three times at a depth of 0-20 cm during the

winter of 2009-2010. At the first (beginning of October 2009) and second

sampling occasion (end of February 2010) 10 samples were taken per plot,

whereas the third time (end of March 2010) 40 samples per plot were taken

(around 5 kg of soil was needed for the disease suppressiveness test). In

October, dried corn stalks were still standing on the field, except for the inner

6 m2 of each plot, which had already been harvested and was hence

covered by stubble. As the remaining stalks were harvested in December,

the whole field was covered by stubble at the second and third sampling.

Larger pieces of organic material were removed from the soil surface before

collecting the samples. In the laboratory, soil samples were mixed

127

Chapter 5

thoroughly per plot in order to obtain homogeneous and representative

composite samples. Stones and visible plant material were removed.

Because of practical constraints, the analyses were not repeated for each

sample series. N mineralization and SOC and TN contents were determined

on the October samples; microbial biomass C was analyzed on the February

and March samples; PLFA analysis was done on the February samples;

enzyme activities and soil disease suppressiveness were determined on the

March samples.

Table 5.1: Applied amounts of organic matter, its C/N ratio and the additional amounts of mineral N applied (2008 and 2009).

Treatment Organic fertilizer (Mg ha-1)

C/N Mineral N (kg ha-1)

21/05/08 1100 kg C ha-1

MIN N - - 109

CSL 32.221 7.6 0

FYM 14.49 12.9 66

VFG 8.44 14.6 50

FCP1 8.80 26.6 97

FCP2 8.45 14.0 125

11/05/09 3260 kg C ha-1

MIN N - - 232

CSL 93.871 8.9 0

FYM 43.89 26.7 190

VFG 24.96 14.6 164

FCP1 26.20 26.4 235

FCP2 38.47 9.1 248

1 in 1000 l ha-1

128


5.2.3. Soil analyses 5.2.3.1. Soil organic C and N contents

SOC and TN contents were measured on air-dried and sieved (2 mm) soil

with a Variomax CNS elemental analyzer (Elementar GmbH, Hanau,

Germany) applying the Dumas method. Since pH-KCl values were acidic

(less than 6.5) (Leroy, 2008), free carbonates were assumed not to be

present and total carbon contents were considered equivalent to organic

carbon contents. 5.2.3.2. N mineralization

N mineralization was assayed in a 14-week laboratory experiment adapted

from De Neve and Hofman (1996). For every field replicate an oven-dry

equivalent of 221 g air-dried and sieved (2 mm) soil was placed in a plastic

tube with an internal diameter of 46 mm. The soil was compacted to a height

of 10 cm in order to achieve a bulk density of 1.33 g cm 3. The moisture

content of the soil was then adjusted to 50% WFPS by the addition of

distilled water. The tubes were covered with a layer of pin-holed gas-

permeable Parafilm in order to allow gas exchange but to minimize

evaporative water loss. The tubes were incubated in the dark at an average

temperature of 19.6 ± 1.1 °C. Moisture content was kept constant during the

incubation period by regularly weighing the tubes and adding distilled water

as required. Initial mineral N contents and mineral N contents after 14 weeks

of incubation were determined by extraction with 1 M KCl (30 g soil : 60 ml

KCl) and subsequent analysis with a SA4000 continuous flow auto-analyzer

(Skalar B.V., Breda, The Netherlands) applying the Griess reaction for NO3-

(after reduction to NO2- by Cd) and a modified Berthelot reaction for NH4

+.

Net N mineralization was calculated as the difference between the mineral N

content at the end of the incubation experiment and the initial mineral N

content.

129

Chapter 5


Determination of dehydrogenase and -glucosidase activity was done on

fresh soil stored at 4°C. For -glucosaminidase activity air-dried, sieved soil

(2 mm) was pre-incubated again at 50% WFPS and 20°C during one week

before analysis. The activity of -glucosidase was measured according to a

procedure modified from Eivazi and Tabatabai (1988; cited in Alef and

Nannipieri, 1995) in which p-nitrophenyl- -D-glucoside is degraded to p-

nitrophenol (PNP) during a 1 h incubation. The activity of -glucosaminidase

was measured according to the method of Parham and Deng (2000) in

which PNP is produced from p-nitrophenyl-N-acetyl- -D-glucosaminide

during a 1 h incubation. Dehydrogenase activity was determined as the

reduction rate of triphenyltetrazolium chloride to triphenyl formazan (TPF)

during a 24 h incubation as described by Casida et al. (1964). All enzyme

activities were measured in duplicate with one blank. Concentrations of PNP

and TPF were determined with a Cary 50 UV–Visible spectrophotometer

(Varian Inc., Palo Alto, USA). More detailed procedures of the enzyme

activity measurements are given in chapter 2.

5.2.3.4. PLFA analysis

Soil samples for PLFA analysis were freeze-dried and sieved (2 mm) after

sampling and subsequently stored at -18°C until extraction. PLFAs were

extracted using a modified Bligh and Dyer technique (Bligh and Dyer, 1959)

described in detail in chapter 3 (without the part about NLFAs, which were

not retained in this study).

Following Bossio and Scow (1998) and Kozdrój and van Elsas (2001), the

sums of marker PLFA concentrations for selected microbial groups were

calculated. For Gram-positive bacteria the sum of i15:0, a15:0, i16:0, i17:0

and a17:0 was used. The PLFAs 16:1 7c, 18:1 7c and cy17:0 were

considered to be typical for Gram-negative bacteria. The sum of 10Me16:0

and 10Me18:0 was regarded as an indicator for the actinomycetes. The total

bacterial community was assumed to be represented by the sum of the

130


marker PLFAs for Gram-positive and Gram-negative bacteria, and 15:0, 17:0

and cy19:0. The PLFA 18:2 6,9c was used as a fungal indicator. PLFA

16:1 5c was taken as a marker for arbuscular mycorrhizal fungi (AMF).

Additionally, a number of ratios were calculated. The fungi to bacteria ratio

(F/B) and the Gram-positive to Gram-negative bacteria ratio (G+/G-) were

obtained by dividing the respective sums of marker PLFAs. The ratio of

cy17:0 to its precursor 16:1 7 and the ratio of cy19:0 to 18:1 7 were

calculated as indices of physiological stress in the bacterial community

(Bossio and Scow, 1998; Petersen and Klug, 1994). Finally, the ratio of

saturated PLFAs (14:0, 15:0, 16:0, 17:0, 18:0) to monounsaturated fatty

acids (16:1 7, 18:1 5, 18:1 7) (SAT/MONO) was considered as an index

for nutrient limitation (Bossio and Scow, 1998; Moore-Kucera and Dick,

2008). The Shannon diversity index, as a measure of general diversity

(Shannon and Weaver, 1949), was obtained considering only the PLFAs

contributing more than 1% to the total PLFA pool of any soil sample. These

ratios and the Shannon index were used for the calculation of a discriminant

index (see section data processing).

5.2.3.5. Microbial biomass carbon

MBC was determined on fresh soil stored at 4°C using the fumigation-

extraction technique described by Vance et al. (1987). Both fumigated and

unfumigated soil were extracted in duplicate with 0.5 M K2SO4 (30 g soil : 60

ml K2SO4). The mixtures were shaken for 1h on a rotational shaker, and

then filtered with Whatman filter paper no 5. Extracts were stored at -18°C

until analysis. Organic carbon contents of the extracts were determined with

a TOC analyser (TOC-VCPN, Shimadzu Corp., Kyoto, Japan). For conversion

from organic C contents in the extracts to MBC in the soil a kEC value of 0.45

was assumed (Joergensen, 1996).

131

Chapter 5

5.2.3.6. Disease suppressiveness

Soil disease suppressiveness against the fungal pathogen Rhizoctonia

solani Kühn (teleomorph Thanatephorus cucumeris (Frank) Donk) was

analyzed similarly to the method described in chapter 3. The test was

performed in a growth chamber at 18.7 ± 0.4°C with a day/night regime of

16h light and 8h dark. For each field replicate, one tray with a size of 25 x 25

x 8 cm was filled with soil up to 3 cm from the top. After slight compaction,

gravimetric soil water content was adjusted to 22.5%, corresponding to

approximately 60% WFPS. Untreated Rhizoctonia susceptible sugar beet

seeds (Beta vulgaris L., cv. Vedeta HI 0553, Syngenta Seeds B.V.,

Enkhuizen, The Netherlands) were sown in three rows of 10 seeds at a

depth of 2 cm and at 2 cm intervals. After one week, the soil in each tray

was inoculated with wheat kernels colonised with R. solani prepared

following the method described in chapter 3. The soil in the trays was

inoculated by placing one kernel in front of each seedling row at 2 cm

distance and at 1 cm depth. Disease spread was determined 3 weeks after

inoculation by counting the number of seedlings per row displaying damping-

off or black lesions on the stem at soil level. Values of disease spread were

transformed into disease suppressiveness values according to the formula:

disease suppressiveness = 1 - disease spread / maximum disease spread.

Maximum disease spread was 10.

Fungal pathogens were isolated from a number of infected plants, as

described in chapter 3, to control whether R. solani was indeed the pathogen

responsible for the damping-off of the seedlings.

5.2.4. Data processing Data were subjected to two-way ANOVA using SPSS (version 15.0, SPSS

Inc., Chicago, USA). Significant differences between means were

determined by Tukey’s post-hoc test at the 0.05 level of significance. T-tests

132


and calculation of Pearson’s correlation coefficients mentioned in the results

and discussion were also performed in SPSS.



of the total PLFA pool of the respective soil sample. After removal of all

PLFAs that contributed less than 1% to the total pool of PLFAs, 20 PLFAs

were retained. Fisher’s canonical discriminant analysis (CDA) was applied to

this percentage distribution using correlation coefficients with Tibco Spotfire

S+ (version 8.1, TIBCO Software Inc., Palo Alto, USA). Fisher’s CDA

transforms data in order to discriminate between predefined groups

(Huberty, 1994). Eight groups were considered in the analysis,

corresponding to the eight treatments.

Finally, an index was calculated that should discriminate between the eight

treatments taking into account only a limited number of the determined

parameters. This index was developed by stepwise CDA using correlation

coefficients in SPSS. At each step in the development, the variable that

minimized the overall Wilks’ Lambda was entered into the model. Maximum

significance of F to enter was set to 0.1, minimum significance of F to

remove was 0.25. In total 27 parameters and ratios between parameters

were considered in the construction of the index: SOC and TN content, C/N,

N mineralization, MBC in February and in March and the respective ratios to

SOC content, the 3 enzyme activities and their respective ratios to MBC in

March, the proportions of the 6 sums of marker PLFAs to the total PLFA

pool, F/B, G+/G-, SAT/MONO, cy17:0/16:1 7c, cy19:0/18:1 7c, the

Shannon diversity index and disease suppressiveness.

133

Chapter 5

5.3. Results 5.3.1. Soil organic carbon and nitrogen NF-, NF+ and MIN N clearly had the lowest SOC content (Table 5.2). The

SOC content of these plots was significantly lower than those of FCP2, FYM

and VFG, with VFG having the highest content. CSL and FCP1 had

intermediate SOC concentrations. The differences in SOC content were

reflected in the TN contents and hence the different treatments had

comparable C/N ratios. Nevertheless, the lowest and highest C/N ratios

observed, for VFG and NF+ respectively, were significantly different. The

C/N ratio of FCP1 was comparable to that of NF+.

Table 5.2: SOC, TN, C/N ratios and net N mineralization (N min.).

Treatment SOC (%) TN (%) C/N N min. (μg N kg-1 dry soil day-1)

NF- 1.00 (0.06)a 0.077 (0.002)a 13.1 (1.0)ab 274 (49)

NF+ 1.11 (0.07)ab 0.081 (0.006)a 13.8 (0.4)b 357 (30)

MIN N 1.05 (0.03)ab 0.081 (0.004)a 13.0 (0.3)ab 312 (37)

CSL 1.25 (0.07)bc 0.098 (0.004)b 12.8 (0.6)ab 403 (84)

FYM 1.38 (0.14)c 0.104 (0.010)bc 13.3 (0.2)ab 421 (144)

VFG 1.46 (0.14)c 0.117 (0.012)c 12.5 (0.4)a 354 (80)

FCP1 1.26 (0.05)bc 0.092 (0.005)ab 13.6 (0.1)ab 336 (49)

FCP2 1.37 (0.09)c 0.106 (0.007)bc 12.9 (0.3)ab 403 (46)

Values in parentheses indicate standard deviations. Significant differences are indicated by different letters (P<0.05); no significant differences for N mineralization.

5.3.2. Microbial biomass MBC contents were significantly higher at the end of March compared to the

end of February (paired T-test, P<0.001), and in both months highest

contents were found for VFG and lowest for NF- (Fig. 5.2). At the end of

134


135

February, MBC contents of NF- were significantly lower than those of FCP1,

CSL, FYM and VFG. In March, differences between treatments were more

pronounced than in February. All treatments, except MIN N, had a

significantly higher MBC content than NF-. CSL, FYM, and FCP2 had similar

MBC contents as VFG.

Fig. 5.2: Microbial biomass C contents. Significant differences are indicated by different letters per sampling occasion (P<0.05).

Marker PLFA contents of VFG and FCP1 were significantly higher than

those of NF-, except for actinomycetes where only FCP1 was significantly

higher than NF-, and fungi where no significant differences were found

(Table 5.3). Further, CSL had significantly higher marker PLFA contents

than NF- for total bacteria and AMF. The lowest P-value was observed for

the AMF marker PLFA. The F/B ratio was not significantly different among

treatments.

Tabl

e 5.

3: C

once

ntra

tions

of m

arke

r PLF

As

(nm

ol g

-1),

F/B

ratio

s, a

nd P

-val

ues

of A

NO

VA.

Trea

tmen

t G

ram

-pos

itive

G

ram

-neg

ativ

e A

ctin

omyc

etes

To

tal b

acte

ria

AM

F Fu

ngi

F/B

(x 1

000)

P

0.01

6 0.

036

0.04

5 0.

019

0.00

2 0.

676

0.10

1

NF-

8.

19 (1

.36)

a 5.

29 (0

.95)

a 1.

87 (0

.24)

a 14

.99

(2.4

4)a

1.97

(0.3

3)a

0.68

(0.3

3)

45.1

(19.

6)

NF+

10

.15

(0.8

3)ab

7.

49 (0

.23)

ab

2.30

(0.1

7)ab

19

.60

(1.2

4)ab

2.

89 (0

.08)

abc

0.58

(0.0

4)

31.6

(4.8

)

MIN

N

10.1

7 (2

.55)

ab

7.92

(3.1

3)ab

2.

47 (0

.71)

ab

20.1

4 (6

.03)

ab

2.20

(0.4

3)ab

0.

89 (0

.44)

42

.1 (1

0.4)

CSL

12

.31

(0.2

2)ab

9.

64 (1

.41)

ab

2.85

(0.1

4)ab

24

.26

(1.6

5)b

3.60

(0.4

8)bc

0.

81 (0

.17)

33

.3 (4

.8)

FYM

11

.59

(0.8

8)ab

8.

78 (1

.75)

ab

2.79

(0.3

0)ab

22

.62

(2.8

3)ab

3.

17 (0

.70)

abc

0.65

(0.1

8)

28.4

(4.1

)

VFG

12

.69

(1.7

5)b

9.92

(1.7

1)b

3.04

(0.5

9)ab

24

.90

(3.5

7)b

3.84

(0.8

4)c

0.77

(0.1

6)

30.9

(2.6

)

FCP

1 12

.91

(2.9

6)b

9.92

(2.9

1)b

3.26

(1.0

0)b

25.1

6 (6

.33)

b 3.

63 (1

.03)

bc

0.80

(0.3

4)

33.0

(5.5

)

FCP

2 10

.77

(0.7

8)ab

7.

83 (0

.70)

ab

2.55

(0.2

1)ab

20

.69

(1.6

4)ab

2.

78 (0

.25)

abc

0.58

(0.1

2)

28.1

(4.3

)

Valu

es in

par

enth

eses

indi

cate

sta

ndar

d de

viat

ions

. Si

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5.3.3. Enzyme activities and N mineralization With respect to dehydrogenase activity, the treatments were divided into

three groups. NF-, NF+ and MIN N had the lowest activity (Fig. 5.3a). Plots

amended with farm compost had intermediate activities. VFG, CSL and FYM

had the highest activities. With respect to -glucosidase, CSL and FYM had

again the highest activity, while NF- and NF+ again had the lowest activity

(Fig. 5.3b). In contrast to its high -glucosidase activity, CSL only had an

intermediate -glucosaminidase activity, comparable to that of FCP2 and

MIN N (Fig. 5.3c). Highest -glucosaminidase activities were found for FYM,

FCP1 and VFG. Lowest -glucosaminidase activities were observed in the

non-fertilized plots.

No significant differences could be detected for N mineralization (Table 5.2).

Nevertheless, the lowest net release of mineral N was observed for NF-,

followed by MIN N. FCP1, VFG and NF+ had intermediate N mineralization

rates, whereas highest rates were found for CSL, FCP2 and FYM. N

mineralization rate was significantly correlated with -glucosaminidase

activity (r = 0.416, P<0.05), but not with the other enzyme activities.

5.3.4. Disease suppressiveness Suppressiveness against R. solani was highly variable (Table 5.4).

Furthermore, in some cases seedlings at the end of the row became infected

by soil-borne Fusarium, before the inoculum of R. solani could even reach

them. This interference was taken into account when calculating the

suppressiveness values. Notwithstanding these complications, disease

suppressiveness was consistently high in all VFG plots. Plots treated with

high C/N farm compost (FCP1) were highly conducive to R. solani.

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Chapter 5

Fig. 5.3: Enzyme activities; a. dehydrogenase activity, b. -glucosidase activity, c. -glucosaminidase activity. Error bars indicate standard deviations. Significant differences are indicated by different letters (P<0.05).

138


Table 5.4: Averages, medians and coefficients of variation (%CV) of suppressiveness against R. solani.

Treatment Average Median %CV

NF- 0.459 0.484 30.5

NF+ 0.508 0.533 48.6

MIN N 0.442 0.550 51.7

CSL 0.342 0.367 37.7

FYM 0.379 0.325 102.0

VFG 0.650 0.667 8.9

FCP1 0.283 0.283 68.3

FCP2 0.450 0.484 60.0

5.3.5. Canonical discriminant analyses Two CDAs were performed. The first one was applied on the proportional

PLFA data (Fig. 5.4a), the second CDA was constructed stepwisely and

served to construct a discriminant index calculated from a limited number of

variables (Fig. 5.4b).

While the discrimination according to the PLFA data could not be attributed

to a particular group of microorganisms, the CDA clearly demonstrated that

MIN N and NF- each differed in PLFA composition compared to the six other

treatments. On the other hand, CSL, FYM and VFG plots had similar PLFA

patterns.

The first dimension of the stepwise CDA explained 81.1% of the correlations,

while the second dimension only accounted for 9.3%. As such only the first

dimension was necessary to separate the treatments. Two-way ANOVA

showed that the treatments could be divided into 5 groups with significantly

different discriminant scores (Fig. 5.4b). FYM had the highest scores,

followed by CSL and VFG in a second group. FCP1 had scores intermediate

between CSL/VFG and FCP2. MIN N and NF+ belonged together in a fourth

group, while NF- constituted a (fifth) group on its own. Nine parameters and

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Chapter 5

ratio’s were retained by the stepwise CDA of which -glucosaminidase

activity, TN content and -glucosidase activity were the first three

parameters to be included (Table 5.5). These three parameters had a strong

correlation (P<0.001) with the first dimension. MBC in March was also

strongly correlated with the first dimension, but the sign of this correlation

was opposite to the sign of the raw canonical coefficient. Therefore, the

strong correlation between MBC and the first dimension is rather an

indication of the correlation between MBC and enzyme activity than of the

importance of MBC in the canonical function itself. It can therefore be

concluded that TN content and -glucosaminidase and -glucosidase activity

are the most important parameters for separating the eight treatments.

Fig. 5.4: Scatter plots of the first two dimensions of the CDAs; a. CDA on PLFAs, b. stepwise CDA. Significant differences in the scores of the first dimension are indicated by different letters (P<0.05).

140


Table 5.5: Parameters retained by stepwise CDA, raw canonical coefficients of the first dimension and Pearson correlation coefficients with scores of the first dimension. Parameters are listed in order of entrance into the model.

Parameter can. coeff. correlation

-glucosaminidase 3.101 0.870 ***

TN 0.468 0.722 ***

-glucosidase 12.937 0.831 ***

Shannon 1.812 -0.160

MBC March -12.050 0.806 ***

rel. actinomycetes -0.238 0.079 -glucosidase /

MBC March -9.585 -0.044

rel. AMF 2.747 0.353 *

rel. fungi -1.259 -0.501 **

* Correlation significant at the 0.05 level. ** Correlation significant at the 0.01 level. *** Correlation significant at the 0.001 level.

5.4. Discussion

5.4.1. Soil organic carbon and nitrogen content The SOC content of the CSL plots increased from 1.01% before the start of

the experiment to 1.09% in October 2007 (Leroy, 2008). In October 2009 the

SOC content of the CSL plots was 1.25%. The greatest increase in SOC

content hence took place when cattle slurry was applied without crop

residues. We therefore may conclude that the characteristics of the soil

organic matter in the CSL plots are primarily determined by the properties of

the cattle slurry and not by the crop residues applied in 2005. We therefore

may reliably ascribe the results of the microbial parameters reported in this

study for the CSL plots to the application of cattle slurry.

A particular point of discussion in fertilizer research is the impact of inorganic

N fertilizer on SOC stocks. See e.g. the discussion provoked by the paper of

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Chapter 5

Mulvaney et al. (2009), in which the authors concluded that inorganic N

fertilizer promotes N mineralization and depletes soil organic matter stocks in

the course of time. In a comment on this paper however, Powlson et al.

(2010) defended the viewpoint that because of increased plant growth as a

result of inorganic N use (compared to no fertilization), organic matter inputs

(stubble, roots and root exudates) are greater and hence soil organic matter

stocks increase. While the experimental period of our study was relatively

short in terms of soil organic matter research, clear trends could already be

observed. Before the start of the experiment, the SOC content of the plots

was 1.01%, while the TN content was 0.086% (Leroy, 2008). SOC and TN

contents thus increased for all treatments applying organic matter. The SOC

and TN contents of the MIN N plots, on the other hand, remained basically

unchanged and were even similar to those of NF+ plots, despite greater

plant growth on the MIN N plots than on the NF+ plots (Leroy, 2008; D’Hose,

unpublished results). Our results therefore do not provide evidence for a

positive effect of mineral N fertilizer on SOC or TN stocks as advocated by

Powslon et al. (2010). On the contrary, it rather seemed that in the MIN N

treatment the increased organic matter inputs in the form of stubble and

roots were not sequestered into the soil organic matter stock.

Organic amendments with a high C/N ratio are in general more resistant to

decomposition and therefore have a higher C sequestration potential.

Contrary to this expectation, plots amended with compost from woody

materials (FCP1) had a relatively small SOC content. The SOC content of

the FCP1 plots was comparable to that of the plots amended with cattle

slurry, which is the most labile organic amendment with the lowest C/N ratio.

This probably indicates that the high C/N compost was not yet fully mature

and stabilized. The application of FCP1 nevertheless increased the C/N ratio

of the soil organic matter. The highest C/N ratio was observed for NF+,

which may be explained by the fact that N taken up by the cultivated crops is

not replenished by any kind of fertilization.

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Finally, it may be concluded that VFG has the highest organic carbon

sequestration potential.

5.4.2. Disease suppressiveness Of the five organic amendments tested in the field experiment, only VFG

suppressed R. solani compared to MIN N or NF+. Hoitink and Boehm (1999)

reviewed evidence that control of R. solani by composts is very variable.

While biological control of oomycete pathogens (Pythium, Phytophtora)

depends on the overall diversity and activity of the soil biota (general

suppression), R. solani is controlled by a much narrower spectrum of

biocontrol agents and these microflora do not consistently colonize

composts (Hoitink and Boehm, 1999). Long-term curing of composts may,

however, increase their suppressive capacity (Hoitink and Boehm, 1999).

The SOC and TN contents already indicated that VFG was the most

stabilized soil amendment, which seems to be confirmed by its capacity to

suppress R. solani. On the other hand, the SOC and TN contents indicated

that the disease-conducive woody compost (FCP1) was not fully mature and

stable yet. The research reviewed by Hoitink and Boehm (1999)

demonstrated that fresh bark rich in cellulosic substrate was unable to

control R. solani or even stimulated the pathogen, despite the concurrent

stimulation of antagonistic Trichoderma. This lack of control is most likely

explained by the fact that production of lytic enzymes (chitinases) by

Trichoderma is repressed in the presence of the more highly favoured

cellulosic substrate. Long-term curing of compost, on the other hand,

diminishes the concentration of readily available cellulose and increases

saprophytic competitiveness. As a result, Trichoderma’s chitinase genes are

stimulated, which results in parasitism of R. solani. Our results thus

emphasize the importance of a long-term and well performed curing,

certainly when composting woody materials.

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Chapter 5

5.4.3. Enzyme activity Application of organic matter increased enzyme activities compared to

MIN N, except for the farm composts which did not improve -glucosidase

activity. However, only FYM raised the activity of all three enzymes

significantly compared to MIN N. FYM plots also had the highest N

mineralization rate. FCP2, on the other hand, did not significantly increase

the activity of any of the three enzymes compared to MIN N. Relatively few

studies compare the impact of different organic amendments on enzyme

activities and results are often inconsistent. Ros et al. (2006) measured

several enzyme activities in a 12-year field experiment with different kinds of

compost, but did not provide arguments as to why a particular compost

increased the activity of one enzyme but not of another. Chang et al. (2008)

found that four years of soybean meal application generally decreased

enzyme activities compared to several kinds of compost. But none of these

composts systematically yielded high activities of all enzymes tested. In our

experiment, enzyme activities were determined after 6 fertilizer applications

and 10 months after the last application. Our results seem to suggest that

FYM, which consists of partly rotted straw and manure, increases both

general microbial activity (dehydrogenase activity) and the activity of

enzymes involved in the decomposition of lignocellulose ( -glucosidase, -

glucosaminidase) compared to mineral fertilizer, while the behaviour of fresh

cattle slurry and composted plant residues is more complicated. The overall

positive impact of FYM on enzyme activities, in particular -glucosidase and

-glucosaminidase, was confirmed by the stepwise CDA.

5.4.4. Microbial biomass Two reasons may explain the significantly higher MBC contents in March

compared to February. First, soil moisture content was lower in March than

in February (on average 71.5% WFPS in March compared to 77.4% WFPS

144


in February) and hence oxygen supply was improved. Secondly, soil

temperature was higher in March.

Highest MBC contents were found for VFG. Consequently, also marker

PLFA contents were high in the VFG plots, although only highest for the

AMF biomarker (Table 5.3). PLFA 16:1 5c contents were even significantly

higher in VFG plots than in MIN N plots, which was the only significant

difference in marker PLFA content between MIN N and any of the other

treatments. Besides their much documented importance for the release of

inorganic P (e.g. Cardoso and Kuyper, 2006), AMF may also benefit their

host plants by capturing N from decomposing organic material or even by

stimulating N mineralization (Atul-Nayyar et al., 2009; Hodge, 2003; Hodge

et al., 2001). If mineral N availability increases due to inorganic fertilization,

plants respond by reducing C allocation to AMF. As a result, the abundance

of AMF hyphae decreases (Bradley et al., 2006). This may explain why the

16:1 5c content in the MIN N plots was low. However, each treatment

(except NF+ and NF-) was designed to provide sufficient N to the crops. It is

unclear whether under conditions of high overall N availability AMF are still

important for N uptake as Hodge (2003) suggested that plant roots are more

important for N uptake than AMF hyphae when roots may easily access

organic matter. But even when AMF do not stimulate plant growth when

organic matter is applied, AMF may benefit from the organic matter because

of their saprotrophic capability (Hodge et al., 2001). The low ANOVA P-value

found for AMF further provided evidence that AMF sensitively reacted to

changes in soil management. Earlier it was already observed that AMF are

negatively affected by mineral fertilizer and pesticides (chapter 2 and 3;

Bending et al., 2004).

The F/B ratio was negatively correlated with SOC and TN content (r = -0.477

and -0.464 respectively, P<0.01) indicating that increased organic matter

content mainly stimulated the growth of bacteria. In particular Gram-negative

bacteria were stimulated as the proportion of Gram-negative marker PLFAs

to the total pool of PLFAs was positively correlated with SOC content (r =

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Chapter 5

0.380, P<0.05), but the proportion of Gram-positive marker PLFAs was not.

Furthermore, microbial biomass was positively correlated with the proportion

of Gram-negative marker PLFAs (r = 0.512, P<0.01, correlation for MBC in

February). Finally, the relative proportion of Gram-negative marker PLFAs

appeared to be significantly higher in VFG and CSL plots than in NF- plots

(P<0.05) (data not shown). We hence may conclude that the increase in

microbial biomass as a result of organic matter application can mainly be

attributed to an increase in Gram-negative bacteria. This conclusion agrees

with Burke et al. (2003) and Peacock et al. (2001). Gram-negative bacteria

have a high intrinsic growth rate and as a result primarily benefit from

increases in organic substrates. In contrast, many Gram-positive bacteria

have slower growth rates (Burke et al., 2003). Marschner et al. (2003),

however, found that the G+/G- ratio was higher in organically than in

inorganically fertilized plots. The different finding of Marschner et al. (2003)

may probably be explained by their different choice of marker PLFAs.

Marschner et al. (2003) selected only cy17:0 as a marker for Gram-negative

bacteria, while Peacock et al. (2001) considered the monounsaturated

PLFAs as indicative for Gram-negative bacteria. As explained earlier cy17:0

may be associated with nutrient stress, while monounsaturated PLFAs are

indicative of nutrient enrichment (Bossio and Scow, 1998). In our study, as

well as that of Burke et al. (2003), both cy17:0 and monounsaturated PLFAs

were taken as Gram-negative markers. Nevertheless, in our study the size of

the Gram-negative pool was mainly determined by 16:1 7c and 18:1 7c as

the contribution of cy17:0 to the total Gram-negative PLFA pool (cy17:0,

16:1 7c and 18:1 7c) was only around 15%.

5.4.5. Soil quality In the following paragraph, we will give an overview of the impact of the

different treatments on the most important aspects of soil quality. Three soil

processes (sensu Mulier et al., 2005) were explicitly measured in this study,

146


namely sequestration of organic matter, N mineralization and disease

suppressiveness. Further, we discussed the biomass, activity and

composition of the microbial community. Measurements of yield -obviously

an important aspect of soil quality- were not included in this study, but yield

data were obtained from Leroy (2008) and D’Hose (unpublished). Prevention

of eutrophication of water bodies is also an important aspect of soil quality in

Flanders (northern region of Belgium) as agricultural fields have been

overfertilized for a long time.

Application of organic matter increased SOC and TN contents, microbial

biomass and enzyme activity compared to MIN N. The major purpose of the

field experiment is, however, not to compare organic amendments with

mineral fertilizer, but to allow comparisons among the organic amendments

themselves. No single amendment consistently scored better than the others

on each parameter determined. The stepwise CDA was therefore a useful

approach that clarified to what extent organically fertilized plots differ from

each other - considering all parameters and ratios calculated from them

together. The first dimension of the stepwise CDA may be interpreted as an

index of soil quality. However, it should be kept in mind that this index was

constructed to discriminate between treatments without considering an a

priori conceptual link with soil processes. But as TN content and -

glucosidase and -glucosaminidase activity are the most important

parameters of the index, the index has a clear link with organic matter

sequestration and N mineralization. N mineralization is indeed primarily

regulated by the enzymatic release of N containing monomers (Schimel and

Bennett, 2004). According to the stepwise CDA, FYM increased TN content

and enzyme activity the most. The stepwise CDA further suggested that

differences in enzyme activity and TN (and SOC) content between the

treatments are more distinct than differences in microbial community

composition. Indications of increased F/B ratios in compost amended plots

compared to plots treated with farmyard manure or cattle slurry, as reported

by Leroy (2008), were not confirmed in the current study.

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Chapter 5

The soil quality index has no direct link with disease suppressiveness. In our

study, VFG plots were most suppressive against R. solani. On the other

hand, Leroy (2008) reported that the abundance of plant-parasite nematodes

was lowest in FYM plots.

Leroy (2008) recorded yield data for fodder beet and red cabbage. VFG and

FYM plots yielded similar amounts of beet and cabbage and the yield on

these plots was not significantly different from the other fertilized plots. In

2008, no significant differences were found between the fertilized plots in the

yield of perennial ryegrass (D’Hose, unpublished results). Clear differences

were, however, found in the yield of the corn. The highest yield was obtained

on FYM plots. Cob yield was significantly higher on FYM plots than on VFG,

CSL and NF+ plots, while total yield (cob+plant) on FYM plots was

significantly higher than on CSL and NF+ plots (D’Hose, unpublished

results).

Flanders has been entirely designated as a nitrate vulnerable zone under

the European nitrate directive (91/676/CEE), meaning N fertilization is strictly

controlled. In the field experiment, fertilization rates were designed to

provide equal amounts of plant available N. However, the total dose of N

differed between the treatments. In the first two years of the field experiment

(2005 and 2006), fertilization rates were not representative for Flanders.

Therefore, we will not examine the fertilization rates of those years. Of the

five organic treatments, only FYM, VFG and FCP1 did not exceed the

allowed doses of N in 2007. In 2008, all organic treatments complied with

the legislation. In 2009, only FYM and FCP1 did not exceed the allowed

dose of organic N. Nevertheless, the dose of mineral N was still too high in

both treatments. In the FCP1 treatment 85 kg mineral N ha-1 was applied in

excess of the permitted amount, while in the FYM treatment only 40 kg

mineral N ha-1 was applied in excess.

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149

5.5. Conclusions Based on the soil processes, microbial properties and yield data measured

in this and previous studies on the experimental field in Melle, farmyard

manure seems to be the preferred organic amendment for maintaining soil

quality in arable fields under temperate climatic conditions. However, only a

limited amount of farmyard manure is produced in Flanders since barns are

designed to produce slurry manure in order to ensure prompt discharge of

dung. Conversion of the barns to produce farmyard manure is probably not

economically feasible.

Chapter 6

Final discussion and conclusions

Illustration:

Banana trees at the organic farm Permata Hati in Cisarua (Bram Moeskops)

Chapter 6

Final discussion and conclusions

6.1. Introduction

In this thesis, we investigated soil quality under three different agro-

ecosystems, namely vegetable production in a fully humid equatorial climate,

paddy rice cultivation in a monsoonal equatorial climate and arable farming

in a fully humid temperate climate with warm summers. In each agro-

ecosystem we tried to identify biochemical and/or microbial parameters (or

indices calculated from them) that could classify different management

systems as more or less sustainable, i.e. having a positive or rather negative

impact on soil quality. In this chapter, we will bring together the findings

obtained in the different agro-ecosystems. Further, we will assess to what

extent the different parameters and indices can be linked with soil processes

and soil quality, and we will conclude with suggestions for further research.

6.2. Enzyme activity

Although differences between organic and conventional vegetable

production were less pronounced in 2008 than in 2007, dehydrogenase

activity appeared to be a sensitive enzyme in both years. In the paddy rice

systems, however, -glucosidase activity appeared to be more useful to

discern organic from conventional management, because of the high

variability of dehydrogenase activity under flooded conditions. In the

experiment with organic amendments (chapter 5), -glucosidase and -

glucosaminidase activity appeared to be more important for separating the

treatments than dehydrogenase activity.

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Chapter 6

-glucosaminidase activity has a clear link with N mineralization as this

process is primarily regulated by the enzymatic release of N containing

monomers (Schimel and Bennett, 2004). Compared to the measurement of

N mineralization the assessment of -glucosaminidase activity can be

performed much faster and is much easier to standardize. As a result, -

glucosaminidase activities were less variable than N mineralization rates

(chapter 5). Based on work in Iowa (USA), Ekenler and Tabatabai (2004)

suggested that -glucosaminidase activity could be used as an index of N

mineralization. In chapter 5, we found a significant correlation between N

mineralization and -glucosaminidase activity as well, and Dhollander (2010)

found a significant correlation between both parameters in vegetable soils of

Central Java. Besides -glucosaminidase activity, there are several other

enzymes known to be involved in N transformations (e.g. glutaminase,

aspartase, amidase), but most of them have pH optima in the alkaline pH

range (Parham and Deng, 2000). Since all our soils had pH-KCl values of

less than 6.5, -glucosaminidase activity thus seems to be the most relevant

index for N mineralization for the soils investigated in this thesis.

Dehydrogenase and -glucosidase activity do not have a direct link with the

release of plant nutrients. Dehydrogenase activity is, however, a measure for

microbial activity (Alef and Nannipieri, 1995). -glucosidase activity is

important for the C supply to soil microorganisms. Consequently, -

glucosidase activity was correlated with basal respiration (chapter 3).

Dehydrogenase and -glucosidase activity are hence related to the vitality of

the soil microbial community which impacts on the resilience of the soil

ecosystem.

6.3 Composition of the microbial community

Organic and conventional cultivation are very different management

systems. Organic farming methods rely solely on organic inputs for nutrient

supply and ban applications of synthetic fertilizers and pesticides. As a result

154

Conclusions

PLFA profiles clearly differed between organic and conventional vegetable

production as well as between organic and conventional paddy rice

cultivation. In the experiment with organic amendments, pesticides were

applied in all treatments and inorganic fertilizers in all treatments except NF+

and NF-. Consequently, PLFA profiles could not clearly separate the five

organic amendments.

In this thesis, certain PLFAs were considered as markers for particular

microbial groups. However, changes in PLFA patterns could not be

attributed in a straightforward manner to certain groups of microorganisms.

Proportions of marker PLFAs to the total pool of PLFAs were often not

significantly different among treatments. Furthermore, marker PLFAs for the

same microbial group did not always behave similarly in the canonical

analyses. For example, in chapter 2, 10Me18:0 was strongly correlated with

the first dimension of the CDA, while the other actinomycetes marker PLFA,

10Me16:0, was not. Furthermore, the use of marker PLFAs was complicated

by the fact that also marker PLFAs are not always specific. PLFA 16:1 5c is

widely used as a marker PLFA for AMF (e.g. Denef et al., 2009; Unger et al.

2009), although it also occurs in Gram-negative bacteria (Zelles, 1997).

Many researchers consider PLFA 18:1 9c as mainly of fungal origin (Bååth,

2003; Joergensen and Wichern, 2008; Kozdrój and van Elsas, 2001), but

some add it to the other monoenoic PLFAs that are indicative for Gram-

negative bacteria (e.g. Aciego Pietri and Brookes, 2009). Thus, it is not

always easy to interpret PLFA community data, as discussed by Zelles

(1999). Another problem is in distinguishing whether certain PLFAs indicate

the presence of specific taxa or rather physiological changes within the

same taxa (Bossio and Scow, 1998). PLFAs cy17:0 and 16:1 7c provide an

example. Both are marker PLFAs for Gram-negative bacteria, but their

relative proportion depends on the physiological state of the bacteria. This

should be kept in mind when selecting marker PLFAs. The choice for one or

the other Gram-negative PLFA marker may indeed lead to different

155

Chapter 6

conclusions, such as those of Marschner et al. (2003) and Peacock et al.

(2001) (see chapter 5).

To overcome the difficulties in assigning PLFAs to certain microbial groups,

one may prefer to directly link PLFAs to a particular condition of the soil

microbial community (e.g enriched, nutrient limited, disturbed) which may

indeed be more informative than the mere abundance of a particular group

of organisms. The ratio of cy17:0 to 16:1 7c was successfully applied as an

indicator of physiological stress in chapter 3 when comparing organic and

conventional vegetable production and in chapter 4 when comparing organic

and conventional paddy rice cultivation. On the other hand, PLFA 16:1 5c

may be used as an indicator for practices that stimulate the microbial

community -even if one cannot hold with absolute certainty that it is a

biomarker for AMF. In chapter 2, PLFA 16:1 5c had a significantly higher

proportion of the total PLFA pool under organic compared to conventional

vegetable production. In chapter 3, absolute contents of PLFA 16:1 5c were

negatively correlated with cy17:0/16:1 7c, while in chapter 5 absolute PLFA

16:1 5c contents were negatively affected by the exclusive use of mineral

fertilizer. The 10Me-branched PLFAs (generally considered as markers for

actinomycetes) seemed to be associated with conventional management

practices as evidenced by CDA on organic and conventional vegetable

production in chapter 2 and by RDA on organic and conventional paddy rice

cultivation in chapter 4. Other studies reported a relatively increase of 10Me-

branched PLFAs in lower quality soils as well. Potthast et al. (2010) showed

that in the mountains of Ecuador the massive displacement of Setaria-grass

by bracken after pasture abandonment was characterized by decreased pH

values accompanied by a lower microbial biomass and activity as well as a

higher relative abundance of 10Me16:0 and 10Me18:0. According to

Waldrop et al. (2000), conversion from forest to pineapple plantation

decreased microbial biomass and -glucosidase activity, and increased the

relative amount of actinomycetes PLFA markers in Tahiti.

156

Conclusions

Compared to PLFA analysis, the development of indices that provide

information about the condition of the soil food web has made more progress

in soil nematode research. For example, the channel index applied in

nematode research includes weighting parameters for the size and

metabolic rates of the nematodes, while in PLFA research fungi to bacteria

ratios are calculated without considering the different PLFA content of fungi

and bacteria (e.g. Aciego Pietri and Brookes, 2009; Bååth and Anderson,

2003). Nevertheless, PLFA analysis lends itself much more for routine

assessment of soil quality as it can be performed faster and is easier to

standardize. We therefore may conclude that PLFA analysis remains a

powerful tool to detect changes in the microbial community, but it may

benefit from a standardized use of marker PLFAs and from the development

of more informative indices such as the ones that are available in nematode

research.

6.4. Disease suppressiveness

In both disease suppressiveness tests (chapter 3 and 5) counterintuitive

results were obtained. Suppressiveness against R. solani appeared to be

higher in conventional vegetable fields than in organic fields, while FCP1

resulted in higher infection rates than MIN N. The reason may be that R.

solani is controlled by only a narrow spectrum of biocontrol agents of which

presence and activity strongly depend on the quality of the applied organic

matter (Hoitink and Boehm, 1999). This indicates that maintaining soil quality

entails more than merely the use of organic amendments or the

abandonment of agro-chemicals, although both management options may

stimulate microbial biomass and activity. Care should be taken that

composts are fully mature before being applied in order to prevent

stimulation of pathogens.

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Chapter 6

6.5. Soil quality indices As soil quality entails so many aspects, the use of a single parameter as

ecosystem indicator is not possible. In this thesis, we calculated soil quality

indices using stepwise CDA for organic and conventional vegetable soils in

West Java (chapter 2) and for comparing organic amendments in Belgium

(chapter 5). This approach was based on Puglisi et al. (2005) and Puglisi et

al. (2006). The advantage of this method is that it selects the most sensitive

parameters (or ratios) from a wide range of variables, but it does not a priori

consider a particular concept of soil quality. The soil quality index for organic

and conventional vegetable production was correlated with PLFA 16:0 and

dehydrogenase activity, i.e. with microbial biomass and activity. The soil

quality index for the experiment with organic amendments had a clear link

with organic matter sequestration and N mineralization. This difference may

be explained by the different aim of the two studies. The major difference

between organic and conventional management was the use or not of agro-

chemicals. In chapter 5, on the other hand, agro-chemicals were applied in

all treatments and the major difference between treatments was the quality

of the applied organic matter. The intensive use of agro-chemicals hence

primarily seems to affect microbial biomass and general microbial activity,

while differences in the quality of organic amendments mainly affect the

lignocellulose degrading enzymes and the organic matter pool.

Notwithstanding the differences between the two soil quality indices, the

question remains whether it is possible to generalize the use of one or both

indices to other agro-ecosystems, climates and soil types. Therefore we

applied the index of the experiment with organic amendments to the organic

and conventional vegetable farms of 2007. The index of chapter 5 could not

be applied to the organic and conventional vegetable farms of 2008,

because not all required parameters were measured. Further, we calculated

the index of chapter 2 for the treatments compared in chapter 5. No soil

quality index was calculated for the paddy rice fields, because the available

158

Conclusions

data set was not large enough for stepwise CDA. We also did not apply the

soil quality indices of the other ago-ecosystems to the paddy rice fields. The

index of chapter 2 was not useful for these fields because of the large

variability of dehydrogenase activity, whereas the index of chapter 5 could

not be calculated because not all required parameters were measured.

The index of chapter 5 successfully distinguished between the organic and

conventional vegetable farms of 2007 (Table 6.1). Soil quality scores were

significantly higher under organic than under conventional management

(P<0.05, ANOVA as described in chapter 2). Since differences between

organic and conventional vegetable production were large in 2007, it is not

surprising that the index of chapter 5 could also distinguish between both

management systems although the index was not specifically developed for

it. On the other hand, the index of chapter 5 probably contained more

parameters than needed for the vegetable farms of 2007 (9 compared to

only 3 in the index of chapter 2).

According to the index of chapter 2, NF-, NF+ and MIN N resulted in the

lowest soil quality, which agrees with the results of chapter 5. However, the

index was not sensitive enough to demonstrate significant differences

between the eight treatments (Table 6.2). Only one significant difference

was found, namely between NF- and FCP1 (ANOVA as described in chapter

5).

We may conclude that the soil quality indices developed in this thesis may

be valuable in other agro-ecosystems as well, although specifically adapted

indices should always be preferred.

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Chapter 6

Table 6.1: Soil quality index of chapter 5 applied on data of chapter 2.

Location Soil cover Management Score

Cisarua1 scallion organic -27.9 (15.5)


organic -5.8 (37.8) cabbage



potato


organic -0.5 (2.5)

cabbage


organic-23y -9.2 (29.0)

organic-2y 1.9 (22.7)

tomato


organic-23y -10.6 (10.1)

organic-2y 20.1 (15.2)

Cisarua2

broccoli/ cauliflower



Table 6.2: Soil quality index of chapter 2 applied on data of chapter 5.

Treatment Score

NF- -3.81 (0.29)a

NF+ -3.15 (0.98)ab

MIN N -2.85 (1.27)ab

CSL -2.00 (0.22)ab

FYM -1.88 (0.46)ab

VFG -1.44 (1.11)ab

FCP1 -1.18 (1.84)b

FCP2 -2.55 (0.23)ab

Values in parentheses indicate standard deviations. Significant differences are indicated by different letters (Tukey’s post-hoc test, P<0.05).

160

Conclusions

When drawing up environmental protection schemes, decision makers ask

for threshold values that guide policy. For example, the European nitrate

directive (91/676/CEE) sets the limit for nitrate concentrations at 50 mg l 1

for drinking water, which led to the definition of nitrate vulnerable zones

where the application of animal waste is restricted. Such threshold values

are scarce in soil quality research. Unfortunately, no threshold values could

be determined for our soil quality indices within the scope of this thesis.

Determination of threshold values would involve extended testing of the

relation between possible indicators and relevant soil processes. Darby et al.

(2006) conducted four bioassays for damping-off of cucumber and root rot of

bean and corn spread over two years to find suppressive thresholds of free

particulate organic matter, microbial biomass and fluorescein diacetate

(FDA) hydrolase. Only the FDA threshold (2.88 FDA μg g dry soil-1 min-1)

held up over all sampling times. Anyhow, an additional standardization of our

measurements should be carried out before threshold values could be

considered, as soil quality indices for vegetable soils were higher in 2008

than in 2007 just because the PLFA extraction method had been changed.

As a result one cannot classify the quality of a soil as good or bad based on

our soil quality indices, but they do allow relative classification in terms of

better or worse.

The final appraisal of a soil quality index, certainly from a farmer’s

perspective, depends on whether or not it has a relation with yield. In the

arable field experiment, FYM plots had the highest soil quality, as assessed

in the winter of 2009-2010, and these plots also had the highest yield in

2009 (yield of cobs significantly higher than VFG, CSL and NF+, yield of

cobs+plants significantly higher than CSL and NF+). Yields of the vegetable

fields in West Java were not measured. Farmers provided limited yield data,

but comparison between organic and conventional production was

hampered by the varying intercropping patterns of both systems. However,

several conventional farmers reported their yields kept on declining in spite

of increasing use of chemical fertilizers and pesticides. Rice yields in Central

161

Chapter 6

Java were neither measured. According to interviews with farmers, rice

yields dramatically dropped (-70%) after conversion from conventional to

organic cultivation, but after two years of organic production yields started to

increase again (Sukristiyonubowo, unpublished results). Especially since

2006, yields increased steeply and in 2008 rice yields were only around 15%

lower than under conventional management (Sukristiyonubowo, unpublished

results). This increase after a period of low yields is probably due to the

build-up of soil organic matter and the increase of microbial biomass and

activity. Also learning effects, i.e. the farmers increasingly understood how to

cultivate their fields organically, may have played a role.

We may hence conclude that increased soil quality, as measured by the soil

quality indices and indicators proposed in this thesis, may be linked with

better yields.

6.6 Outlook for further research

Four enzyme activities were examined in this thesis, namely

dehydrogenase, -glucosidase, -glucosaminidase and acid

phosphomonoesterase activity, but only in the vegetable production systems

of West Java all four enzyme activities were measured. Although acid

phosphomonoesterase activity did not discern organic from conventional

vegetable production, it may still be worthwhile to test its indicator value for

paddy rice cultivation and for the experiment with organic amendments

because of its role in P mineralization. Maybe also the determination of

arylsulphatase should be considered since it is involved in S mineralization.

According to Scherer (2001), areas of S deficiency are becoming

widespread throughout the world. Especially in Western Europe incidence of

S deficiency has increasingly been reported in Brassica. Finally, the potential

of -glucosaminidase activity as an index for N mineralization seems

promising and should be further examined. For the vegetable soils of West

Java, this would require an adapted protocol for measuring N mineralization.

162

Conclusions

Because of the high contents of mineral N in these soil, the evolution of

mineral N could not be monitored reliably. Research into the relation

between enzyme activity and N mineralization in paddy rice fields is

extremely scarce and was also not part of this thesis. Findings from other

agro-ecosystems are not necessarily valid in paddy rice fields because they

represent a particular kind of soil ecosystem with anoxic conditions during

the period of plant development, Therefore, the link between enzyme activity

and N mineralization in paddy rice fields certainly deserves more attention in

further research.

As indicated in the discussion above, the use of marker PLFAs is not

standardized yet. It seems that each author uses his or her own set of

marker PLFAs, which complicates comparison of results. More efforts are

needed to establish reliable marker PLFAs. Only a limited number of articles

reports original research about the PLFA composition of particular

microorganisms. An extensive review that identifies these articles and

compares their results would be helpful. Anyhow, additional fundamental

research would be required as well. Especially, more quantitative data about

the content of marker PLFAs in microorganisms are needed. These

quantitative data would allow the calculation of correction factors and the

development of more sensitive indicators.

More detailed information about the composition of the composts

used at the organic vegetable farms in West Java is needed to explain why

these composts promoted infection by R. solani. Likewise, more research is

required to identify the properties of the VFG compost that are responsible

for its suppressive capacity. Suppressiveness against only one pathogen

was tested in this thesis. However, mechanisms of suppression depend on

the pathogen being studied (Bonanomi et al., 2010; Hoitink and Boehm,

1999; Termorshuizen et al., 2006). A soil suppressive to a given type of

disease may be conducive to other types of disease (Alabouvette et al.,

2004). Therefore more pathosystems with other plant-pathogen

combinations need to be tested. Damage by Fusarium oxysporum in tomato

163

Chapter 6

164

and chilli was reported several times by farmers in West Java. These

pathosystems hence certainly deserve more attention. Also in temperate

climates Fusarium oxysporum is an important pathogen, e.g. in onion, flax

and several vegetables.

Two promising soil quality indices were developed in this thesis.

These indices could be linked with soil processes, but only in a qualitative

way. Further research should focus on the quantitative relation of both

indices with soil processes. Not only the processes discussed in this thesis

(N mineralization and disease suppression) should be considered, but also

physical aspects of soil quality, like aggregate stability or resistance against

erosion, should be investigated. Finally, validation of both indices in

additional sites would be required as well.

Summary

Illustration:

Woman at work at the organic farm Bina Sarana Bakti in Cisarua

(Ilona Plichart)

Summary

Soil should be considered as the central resource of agriculture. It is not

merely a physical support for crops, but is in itself a whole ecosystem. From

the necessity to evaluate and monitor the status of soils, the concept of soil

quality emerged. The framework of soil quality identifies a range of

processes that are essential for a well-functioning soil. This thesis focused

on the processes nutrient supply and disease suppressiveness, two

processes that are mainly controlled by soil microorganisms. Despite

growing knowledge about the impact of agricultural inputs (fertilizers,

pesticides) on the soil microbial community, important knowledge gaps

remain. In this thesis two of them were addressed: (1) soil quality under

tropical conditions, and (2) a comparison of the specific effects of different

kinds of organic amendments. By comparing the findings of the different

agro-ecosystems investigated in this thesis, we draw some general

conclusions about the use of biochemical and microbial measurements for

the assessment of soil quality.

Chapters 2 and 3 compared intensive organic and conventional vegetable

production on Andisols in the fully humid equatorial climate of West Java. A

secondary forest was each time included to obtain natural reference values.

Chapter 2 reported results obtained in 2007. A strong negative impact of

intensive chemical fertilizer and pesticide use on dehydrogenase, -

glucosidase and -glucosaminidase activity was found. Microbial biomass C

(MBC) content and concentrations of marker phospholipid fatty acids

(PLFAs) were also significantly lower under conventional management. Acid

phosphomonoesterase activity was, however, not depressed under

conventional management. Dehydrogenase and -glucosidase activities

were correlated with soil organic C (SOC) content and pH. -glucosidase

activity under organic management approached that under secondary forest,

167

Summary

while MBC and dehydrogenase activity remained higher under forest. The

composition of the soil microbial community, measured by PLFA analysis,

strongly differed between forest and cultivated soil, a clear difference in

composition was also observed between conventional and organic farming.

Finally, the PLFA biomarker for arbuscular mycorrhizal fungi (AMF),

16:1 5c, had a significantly higher proportion of the total PLFA pool under

organic compared to conventional vegetable production

In order to test the reproducibility of the results of 2007 new measurements

were done in 2008. The same organic farms as in 2007 were sampled, but

different conventional ones. These results and those of a number of

additional parameters were reported in chapter 3. In 2008, differences

between organic and conventional management were less pronounced than

in 2007. Nevertheless, conventional vegetable production again was found

to have a negative impact on dehydrogenase activity, but not always on -

glucosidase activity. Basal respiration was also negatively affected by

conventional management. On the other hand, composts used at the organic

farms seemed to negatively affect soil suppressiveness against Rhizoctonia

solani. As in 2007, the composition of the microbial community, measured by

PLFA analysis, differed between secondary forest and vegetable production

and between organic and conventional management. Also a positive

correlation between pH and the relative amount of marker PLFAs of Gram-

negative bacteria was again observed. Measurements of ergosterol

indicated that this fungal sterol is not universally applicable as a fungal

biomarker and in this respect ergosterol seems to be inferior compared to

PLFA fungal markers (18:1 9c or 18:2 6,9c). Chapter 3 further explored

the value of neutral lipid fatty acid (NLFA) 16:1 5c as an indicator for AMF.

NLFA 16:1 5c may provide additional information on AMF, but its high

variability complicated the interpretation of data. The ratio of PLFAs cy17:0

to 16:1 7c, on the other hand, was effectively applied as an indicator of

physiological stress experienced by the bacterial community. Conventional

vegetable production resulted in higher cy17:0/16:1 7c ratios. Further,

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Summary

linear regression showed that cy17:0/16:1 7c and -glucosidase activity

could together predict 95.6% of the variability of basal respiration. The

cy17:0/16:1 7c ratio was also negatively correlated with absolute

concentrations of the AMF marker PLFA 16:1 5c. A soil quality index,

developed by stepwise canonical discriminant analysis (CDA) using the data

collected in 2007, was successfully validated in chapter 3. This index,

calculated from the absolute amount of PLFA 16:0, the relative amount of

10Me16:0 and 10Me18:0, and dehydrogenase activity summarized the

information obtained from the individual parameters and indices

satisfactorily. Finally, the value of nematode research for assessing soil

quality was examined in chapter 3. From the soil nematode community, it

appeared that organic vegetable production systems in West Java have

more mature soil food webs than conventional systems. Although both

organic and conventional systems were nutrient enriched, nutrient use

efficiency seemed to be higher in organic systems.

Chapter 4 dealt with differences in soil quality between organic and

conventional paddy rice production on Vertisols and Inceptisols in the

monsoonal equatorial climate of Central Java. SOC and total N (TN)

contents were significantly higher in organic paddy rice fields compared to

conventional rice fields. -glucosidase and dehydrogenase activities were

higher under organic compared to conventional paddy rice cultivation, but for

dehydrogenase activity this difference was only significant in the Inceptisols.

Also aerobic respiration was significantly higher under organic rice

production compared to conventional. Redundancy analysis (RDA) of PLFA

profiles clearly separated organic from conventional management and

Inceptisols from Vertisols. The ratio of cy17:0 to 16:1 7c was significantly

higher under conventional than under organic cultivation which indicated that

growth conditions for microorganisms were less favourable under

conventional paddy rice production.

In chapter 5, eight fertilization strategies were compared in a field trial on

Alfisol in a fully humid temperate climate with warm summers: cattle slurry

169

Summary

(CSL); farmyard manure (FYM); vegetable, fruit and garden waste compost

(VFG); high C/N farm compost (FCP1); low C/N farm compost (FCP2);

exclusively mineral fertilizer (MIN N); no fertilization (NF+), no fertilization

and no crop (NF-). SOC and TN contents increased in all treatments

applying organic matter, but VFG resulted in the highest increase. SOC and

TN contents of the MIN N plots, on the other hand, remained unchanged and

were even similar to those of NF+ plots, despite greater plant growth on the

MIN N plots than on the NF+ plots. Application of organic matter increased

dehydrogenase, -glucosidase and -glucosaminidase activity compared to

MIN N, except for the farm composts which did not improve -glucosidase

activity. However, only FYM raised the activity of all three enzymes

significantly compared to MIN N. FYM plots also had the highest N

mineralization rate. Of the five organic amendments tested, only VFG

suppressed Rhizoctonia solani compared to MIN N or NF+. Plots treated

with FCP1, on the other hand, were highly conducive to R. solani.

Suppressiveness against R. solani probably depended on the maturity and

cellulose content of the organic amendments. Highest MBC contents were

found in the VFG plots. Consequently, also marker PLFA contents were high

in VFG plots, although only highest for the AMF biomarker (16:1 5c). PLFA

16:1 5c contents sensitively reacted to the different treatments and were

significantly higher in VFG plots than in MIN N plots. The increase in

microbial biomass as a result of increased soil organic matter content

appeared mainly to be attributed to an increase in Gram-negative bacteria.

Finally, a soil quality index was developed by stepwise CDA. According to

this index, FYM resulted in a significantly higher soil quality than the other

treatments. -glucosaminidase and -glucosidase activity, and TN content

were the most important parameters of the index. Hence, the index had a

clear link with N mineralization. Based on the measurements in this and

previous studies of the field trial, farmyard manure seemed to be the

preferred organic amendment for maintaining soil quality in arable fields

under temperate climatic conditions.

170

Summary

In chapter 6, the results obtained in the previous chapters were brought

together and suggestions for further research were given. Dehydrogenase

appeared to be a sensitive enzyme in the vegetable soils of West Java, both

in 2007 and 2008. In the paddy rice fields, however, -glucosidase activity

appeared to be more useful to discern organic from conventional

management, because of the high variability of dehydrogenase activity

under flooded conditions. In the arable field experiment, -glucosidase and

-glucosaminidase activity appeared to be most important for separating the

treatments. In literature, -glucosaminidase activity has been proposed as

an index for N mineralization. However, the link between both could not be

elaborated in this thesis. The relation between -glucosaminidase and N

mineralization hence deserves more attention in further research, especially

with regard to paddy rice fields.

Organic and conventional cultivation are very different management systems

and PLFA profiles indeed clearly differed between organic and conventional

vegetable production as well as between organic and conventional paddy

rice cultivation. The five treatments with organic amendments compared in

chapter 5 differed less from each other and so did their PLFA profiles. The

ratio of cy17:0 to 16:1 7c was successfully applied as an indicator of

physiological stress in chapter 3 and in chapter 4. On the other hand, PLFA

16:1 5c may be used as an indicator for practices that stimulate the

microbial community. The 10Me-branched PLFAs (generally considered as

markers for actinomycetes) seemed to be associated with conventional

management practices as evidenced by CDA on organic and conventional

vegetable production (2007) and by RDA on organic and conventional paddy

rice cultivation. Nevertheless, the use of marker PLFAs is not standardized

yet. More efforts are needed to establish reliable marker PLFAs. Especially,

more quantitative data about the content of marker PLFAs in

microorganisms are needed.

In both disease suppressiveness tests (chapter 3 and 5) counterintuitive

results were obtained. Therefore more detailed information about the

171

Summary

172

composition of the applied organic amendments is needed to explain their

suppressive or conducive behaviour with regard to R. solani. We also

recommend that other plant-pathogen combinations are tested as

mechanisms of suppression depend on the pathogen being studied. The

disease suppressiveness tests indicate that maintaining soil quality entails

more than merely the use of organic amendments or the abandonment of

agro-chemicals. Care should be taken that composts are fully mature before

being applied in order to prevent stimulation of pathogens.

The soil quality index for organic and conventional vegetable production was

linked with microbial biomass and activity, while the soil quality index for the

experiment with organic amendments had a clear link with organic matter

sequestration and N mineralization. The intensive use of agro-chemicals, as

in the conventional vegetable systems of chapter 2, hence primarily seems

to affect microbial biomass and general microbial activity, while differences

in the quality of organic amendments, as in chapter 5, mainly affect the

lignocellulose degrading enzymes and the organic matter pool.

Notwithstanding the differences between the two soil quality indices, the

index of chapter 5 could also distinguish between the organic and

conventional vegetable farms of 2007, but it probably contained more

parameters than needed for that purpose. The other way round, the index of

chapter 2, was not sensitive enough to demonstrate significant differences

between the treatments of chapter 5, although treatments without organic

matter application had lower scores, which agrees with the results of chapter

5. We may conclude that the use of the developed soil quality indices may

be extended to other agro-ecosystems, although specifically adapted indices

should always be preferred. Both soil quality indices developed in this thesis

could be linked with soil processes, but only in a qualitative way. Further

research should focus on the quantitative relation of both indices with soil

processes. Finally, validation of both indices in additional sites would be

required as well.

Samenvatting in het Nederlands

Illustration:

At work in the laboratory of the Indonesian Soil Research Institute

(Bram Moeskops)

Samenvatting

Eén van de belangrijkste hulpbronnen van de landbouw is de bodem. De

bodem is veel meer dan enkel een houvast voor planten, het vormt een

volledig ecosysteem op zich. Vanuit de noodzaak de staat van de bodem te

evalueren en te monitoren, ontstond het concept bodemkwaliteit. Binnen dit

concept kunnen verschillende processen geïdentificeerd worden die

essentieel zijn voor een goed functionerende bodem. Deze thesis richt zich

op de processen nutriëntenvoorziening en ziektewerendheid, twee

processen die voornamelijk door micro-organismen gereguleerd worden.

Microbiële en biochemische indicatoren kunnen dus erg nuttig zijn voor het

meten van de kwaliteit van deze processen. Dit werd getest in twee

deelstudies: (1) bodemkwaliteit in de tropen, en (2) de specifieke effecten

van verschillende organische meststoffen op de bodemkwaliteit. Door de

bevindingen van de verschillende agro-ecosystemen die in deze thesis

onderzocht werden, met elkaar te vergelijken konden algemene conclusies

genomen worden over het gebruik van biochemische en microbiële

parameters voor de bepaling van de bodemkwaliteit.

Hoofdstukken 2 en 3 vergeleken intensieve biologische en gangbare

groenteteelt op Andisols in het vochtig equatoriaal klimaat van West Java. In

beide hoofdstukken werd een secundair bos opgenomen als natuurlijke

referentie. In hoofdstuk 2 werden resultaten uit 2007 besproken. Er werd

een sterke negatieve impact van intensief kunstmest- en pesticidengebruik

op dehydrogenase-, -glucosidase- en -glucosaminidase-activiteit

gemeten. Het microbiële biomassa C (MBC) gehalte en de concentraties

van merker fosfolipide vetzuren (PLFA’s) waren ook significant lager onder

gangbare groenteteelt. Dit gold echter niet voor de activiteit van het enzym

zure fosfomonoesterase. De activiteiten van dehydrogenase en -

glucosidase waren gecorreleerd met het gehalte aan bodem-organische C

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Samenvatting

(SOC) en de pH. -glucosidase-activiteit onder biologisch beheer benaderde

dat onder secundair bos, terwijl MBC en dehydrogenase-activiteit hoger

waren onder bos. De samenstelling van de microbiële gemeenschap in de

bodem, bepaald door de PLFA-analyses, verschilde sterk tussen het bos en

de groentevelden, en tussen de biologische en gangbare groenteteelt. De

verhouding van PLFA 16:1 5c, een biomerker voor arbusculaire

mycorrhizale fungi (AMF), tot de totale PLFA pool was significant hoger

onder biologische groenteteelt dan onder gangbare groenteteelt. Tot slot

werd op basis van de data van 2007 een bodemkwaliteitsindex ontwikkeld

met behulp van stapsgewijze canonische discriminantanalyse (CDA). Drie

parameters werden opgenomen in de index, namelijk de absolute

hoeveelheid PLFA 16:0, de relatieve hoeveelheid van PLFA’s 10Me16:0 én

10Me18:0, en de dehydrogenase-activiteit.

Om de herhaalbaarheid van de resultaten uit 2007 na te gaan werden in

2008 nieuwe metingen gedaan. Dezelfde biologische bedrijven werden

bemonsterd, maar andere gangbare. Bovendien werden een aantal

bijkomende parameters onderzocht. De resultaten van dit onderzoek werden

in hoofdstuk 3 behandeld. In 2008 waren de verschillen tussen biologisch en

gangbaar beheer minder uitgesproken dan in 2007. Toch kon opnieuw een

negatief effect van gangbare groenteteelt op de dehydrogenase-activiteit

aangetoond worden, maar dit negatief effect was er niet altijd voor de -

glucosidase-activiteit. Ook respiratie bleek negatief beïnvloed te zijn door

gangbaar beheer. Daartegenover stond dat de compost die op de

biologische bedrijven gebruikt werd infectie door Rhizoctonia solani leek te

bevorderen. Net als in 2007 was de samenstelling van de microbiële

gemeenschap, bepaald met een PLFA-analyse, verschillend tussen

secundair bos en de groentevelden, evenals tussen biologische en

gangbare groenteelt. Ook kon, zoals eerder in hoofdstuk 2, een positieve

correlatie gevonden worden tussen de relatieve hoeveelheid merker PLFA’s

voor Gram-negatieve bacteriën en de pH. Metingen van ergosterol toonden

aan dat dit schimmelsterol niet universeel bruikbaar is als biomerker voor

176

Samenvatting

schimmels. Daarom lijkt ergosterol minder geschikt dan de PLFA

schimmelindicatoren (18:1 9c of 18:2 6,9c). In hoofdstuk 3 werd ook de

bruikbaarheid van het neutraal lipide vetzuur (NLFA) 16:1 5c als indicator

voor AMF onderzocht. NLFA 16:1 5c kan bijkomende informatie leveren

over de AMF, maar de hoge variabiliteit van dit vetzuur bemoeilijkt de

interpretatie van de data. De verhouding van PLFA’s cy17:0 en 16:1 7c

bleek daarentegen een goede indicator voor de fysiologische stress van de

bacteriële gemeenschap. Gangbare groenteteelt resulteerde in hogere

cy17:0/16:1 7c ratios. Bovendien toonde lineaire regressie aan dat

cy17:0/16:1 7c en -glucosidase-activiteit samen 95.6% van de variabiliteit

in respiratie konden voorspellen. De cy17:0/16:1 7c ratio was ook negatief

gecorreleerd met de absolute concentraties van de PLFA-merker voor AMF,

16:1 5c. De bodemkwaliteitsindicator die in hoofdstuk 2 ontwikkeld werd,

werd succesvol gevalideerd in hoofdstuk 3 en vatte de informatie verkregen

met de individuele parameters voldoende samen. Tot slot werden ook de

mogelijkheden die nematodenonderzoek kan bieden voor de bepaling van

de bodemkwaliteit, nagegaan in hoofdstuk 3. De nematodenanalyse leerde

dat de biologische groenteteeltsystemen op West Java meer mature

bodemvoedselwebben hebben dan de gangbare systemen. Hoewel zowel

biologische als gangbare systemen aangerijkt zijn met nutriënten, bleek de

nutriëntengebruiksefficiëntie hoger te zijn in de biologische systemen.

In hoofdstuk 4 werden verschillen in bodemkwaliteit onderzocht tussen

biologische en gangbare rijstteelt op Vertisols en Inceptisols in het moesson

equatoriaal klimaat van Centraal Java. SOC and totale N (TN) gehaltes

waren significant hoger onder biologische rijstvelden vergeleken met

gangbare rijstvelden. -glucosidase- en dehydrogenase-activiteit waren ook

hoger onder biologische paddy-rijstteelt, maar voor dehydrogenase- activiteit

was dit verschil enkel significant in de Inceptisols. Ook aerobe respiratie was

significant hoger onder biologische rijstteelt. Redundantie analyse (RDA)

van de PLFA profielen onderscheidde duidelijk biologisch van gangbaar

beheer en Inceptisols van Vertisols. De verhouding van cy17:0 tot 16:1 7c

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Samenvatting

was significant hoger onder gangbare dan onder biologische teelt, wat

aantoont dat de groeiomstandigheden voor micro-organismen minder

gunstig zijn onder gangbare paddy-rijstteelt.

In hoofdstuk 5 werden acht verschillende bemestingsstrategieën met elkaar

vergeleken in een veldproef op Alfisol in een vochtig gematigd klimaat met

warme zomers: drijfmest (CSL); stalmest (FYM); groente-, fruit- en

tuinafvalcompost (VFG); boerderijcompost met hoge C/N (FCP1);

boerderijcompost met lage C/N (FCP2); uitsluitend kunstmest (MIN N); geen

bemesting (NF+), geen bemesting én geen gewas (NF-). SOC en TN

gehaltes namen toe in alle behandelingen waarbij organisch materiaal werd

toegediend, maar VFG resulteerde in de hoogste toename. De SOC en TN

gehaltes van de MIN N plots daarentegen bleven ongewijzigd en waren zelfs

gelijk aan die van de NF+ plots, ondanks de hogere opbrengsten op de MIN

N plots dan op de NF+ plots. Toediening van organisch materiaal deed de

dehydrogenase-, -glucosidase- en -glucosaminidase-activiteit toenemen

vergeleken met MIN N, behalve voor de boerderijcomposten die de -

glucosidase-activiteit niet verbeterden. Hoe dan ook kon enkel FYM de

activiteit van alle drie de enzymen significant verhogen vergeleken met

MIN N. De FYM plots hadden ook de hoogste N-mineralisatiesnelheid. Van

de vijf organische meststoffen die onderzocht werden, kon alleen VFG

Rhizoctonia solani onderdrukken. De plotjes bemest met FCP1 waren

daarentegen erg vatbaar voor infectie door R. solani. Onderdrukking van R.

solani in de bodem werd waarschijnlijk bepaald door de rijpheid en het

cellulosegehalte van de toegediende organische meststoffen. De hoogste

MBC-gehaltes werden teruggevonden in de VFG plotjes. Bijgevolg waren

ook de PLFA-merkergehaltes hoog in de VFG plots, maar alleen het hoogst

voor de AMF-biomerker (16:1 5c). De PLFA 16:1 5c gehaltes reageerden

zeer gevoelig op de verschillende behandelingen en waren significant hoger

in de VFG plots dan in de MIN N plots. De toename in microbiële biomassa

als een gevolg van het toegenomen gehalte aan organisch materiaal in de

bodem bleek vooral te wijten aan een toename aan Gram-negatieve

178

Samenvatting

bacteriën. Tot slot werd in hoofdstuk 5 een bodemkwaliteitsindex ontwikkeld

met behulp van stapsgewijze CDA. Volgens deze index resulteerde FYM in

een significant hogere bodemkwaliteit dan de andere behandelingen. -

glucosaminidase- en -glucosidase-activiteit, en TN-gehalte waren de meest

belangrijke parameters van de index. Bijgevolg had de index een duidelijke

link met N-mineralisatie. Gebaseerd op de metingen in deze en vorige

studies van de veldproef, werd besloten dat stalmest de meest geschikte

organische meststof is voor het behoud van de bodemkwaliteit in akkerland

onder een gematigd klimaat.

In hoofdstuk 6 werden de resultaten van de verschillende hoofdstukken met

elkaar vergeleken en werden suggesties gemaakt voor verder onderzoek.

Dehydrogenase bleek een gevoelig enzym te zijn in de groentevelden van

West Java, zowel in 2007 als in 2008. In de paddy-rijstvelden daarentegen

bleek -glucosidase-activiteit bruikbaarder om biologisch van gangbaar

beheer te onderscheiden, omwille van de hoge variabiliteit van de

dehydrogenase-activiteit in de onder water staande bodems. In het

experiment van hoofdstuk 5 waren -glucosidase- en -glucosaminidase-

activiteit het meest belangrijk om de verschillende behandelingen van elkaar

te onderscheiden. In de literatuur werd -glucosaminidase-activiteit

voorgesteld als index voor N-mineralisatie. De link tussen beide activiteiten

kon echter niet verder onderzocht worden in deze thesis. Deze relatie

verdient daarom meer aandacht in verder onderzoek, in het bijzonder wat

betreft de paddy-rijstvelden. Biologische en gangbare landbouw zijn zeer

uiteenlopende systemen. Bijgevolg waren de PLFA-profielen duidelijk

verschillend tussen biologische en gangbare groenteteelt en tussen

biologische en gangbare rijstteelt. De vijf behandelingen met organische

meststoffen die in hoofdstuk 5 vergeleken werden, verschilden minder van

elkaar en bijgevolg ook hun PLFA-profielen niet. De verhouding van cy17:0

tot 16:1 7c werd succesvol toegepast als indicator van fysiologische stress

in hoofdstuk 3 en 4. PLFA 16:1 5c kan daarentegen gebruikt worden als

indicator voor landbouwpraktijken die de microbiële gemeenschap

179

Samenvatting

stimuleren. De 10Me-vertakte PLFA’s (algemeen beschouwd als merkers

voor de actinomyceten) bleken geassocieerd te zijn met gangbare

landbouw, zoals aangetoond door de CDA op biologische en gangbare

groenteteelt (2007) en door de RDA op biologische en gangbare paddy-

rijstteelt. Toch is het gebruik van merker-PLFA’s nog niet gestandaardiseerd.

Meer onderzoek is nodig om betrouwbare merker-PLFA’s aan te duiden. In

het bijzonder zijn meer kwantitatieve gegevens over het gehalte aan

specifieke merker-PLFA’s in micro-organismen vereist. In beide

ziektewerendheidstesten (hoofdstuk 3 en 5) werden tegenintuïtieve

resultaten bekomen. Meer gedetailleerde informatie is dus nodig over de

samenstelling van de toegediende organische meststoffen om hun

onderdrukkend of bevorderend effect t.o.v. R. solani te verklaren. Het is ook

aangeraden dat andere plant-pathogeen combinaties worden getest, omdat

mechanismen van onderdrukking pathogeneenafhankelijk zijn. De

ziektewerendheidstesten toonden aan dat het behoud van de bodemkwaliteit

meer inhoudt dan enkel de toepassing van organische meststoffen of het

stopzetten van het gebruik van agro-chemicaliën. Om stimulatie van

pathogenen te voorkomen, moet er zorg voor gedragen worden dat de

toegediende composten voldoende rijp zijn. De bodemkwaliteitsindex voor

biologische en gangbare groenteteelt werd voornamelijk bepaald door

microbiële biomassa en microbiële activiteit, terwijl de bodemkwaliteitsindex

voor het experiment met de organische meststoffen een duidelijke link had

met de opbouw van organisch materiaal in de bodem en met N-

mineralisatie. Dit betekent dat het intensief gebruik van agro-chemicaliën,

zoals in de gangbare groenteteeltsystemen van hoofdstuk 2, een negatief

effect had op de totale microbiële biomassa en de algemene microbiële

activiteit en dat de verschillen tussen de organische meststoffen van

hoofdstuk 5 zich uitten in verschillen in de lignocellulose-afbrekende

enzymen en het gehalte aan organisch materiaal. Niettegenstaande de

verschillen tussen beide bodemkwaliteitsindices kon de index van hoofdstuk

5 ook het onderscheid maken tussen de biologische en gangbare

180

Samenvatting

181

groentebedrijven van 2007, hoewel de index waarschijnlijk meer parameters

bevatte dan hiervoor nodig was. Andersom was de index van hoofdstuk 2

niet gevoelig genoeg om significante verschillen aan te tonen tussen de

behandelingen van hoofdstuk 5, hoewel de behandelingen waarbij geen

organisch materiaal werd toegediend lagere scores hadden, wat

overeenkomt met de resultaten van hoofdstuk 5.

We kunnen besluiten dat het gebruik van de ontwikkelde

bodemkwaliteitsindices mag uitgebreid worden naar andere agro-

ecoystemen, hoewel specifiek aangepaste indices altijd de voorkeur

genieten. De beide bodemkwaliteitsindices van deze thesis konden gelinkt

worden aan belangrijke bodemprocessen, maar enkel op een kwalitatieve

wijze. Verder onderzoek moet de kwantitatieve relatie van deze indices met

bodemprocessen uitklaren. Tot slot moeten beide indices bijkomend

gevalideerd worden op nieuwe proefvelden.

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Illustration:

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Curriculum Vitae

Illustration:

Field workers team in Ciwidey (Pak Agus)

Curriculum Vitae

Personal particulars Home address: Spillemansstraat 26, B-2140 Borgerhout, Belgium

Telephone: ++ 32 3 236 96 02

Mobile Phone: ++ 32 487 90 59 35

E-mail: [email protected]

Nationality: Belgian

Place of Birth Antwerpen

Date of Birth: 30th of March, 1982

Civil state: married with Ilona Plichart

Children: daughter Arune Lenita (°15th of August, 2008)

Current Position Employed at the Flemish Platform on Sustainable Development (VODO vzw)

to organize the international conference ‘Future Farms and Food in Europe’

about the transition towards sustainable food production and consumption

which will take place in the European Parliament at the 3rd of February 2011.

Education Master of International Relations and Diplomacy, degree obtained

with distinction in July 2006 at Antwerp University

Bio-engineer Land and Forest Management, degree obtained with

great distinction in July 2005 at Ghent University

Thesis: The effect of soil tillage and cover on the carbon cycle in

soils of the Chinese Loess Plateau

Candidate Bio-engineer, degree obtained with greatest distinction in

July 2002 at Ghent University

217

Curriculum Vitae

Certificate secondary education obtained in June 2000 at the Royal

Athenaeum of Berchem

Study stays abroad July 2009: PhD research at the Indonesian Soil Research Institute,

Bogor.

June-November 2008: PhD research at the Indonesian Soil

Research Institute, Bogor.

June-September 2007: PhD research at the Indonesian Soil

Research Institute, Bogor.

April 2006: study tour to the institutions of the United Nations in

Geneva.

July-September 2005: voluntary work at the Vicaría de Medio

Ambiente (VIMA, www.vima.org.pe). VIMA supports farmer

communities in their struggle against mining projects in northern

Peru (Cajamarca, Piura). I made an inventory of orchids and

medicinal plants in the cloud forests of the region. Together with

other volunteers I prepared the English and Spanish website.

July-September 2004: thesis research in Luoyang (Henan Province)

and Beijing, China.

February-June 2004: study stay at the University of Natural

Resources and Applied Life Sciences, Vienna, Austria

218

Curriculum Vitae

Publications A1

Published

Verspecht A, Vandermeulen V, De Bolle S, Moeskops B, Vermang J, Van

den Bossche A, Van Huylenbroeck G, De Neve S (2010) Integrated policy

approach to mitigate soil erosion in West-Flanders. Land Degradation &

Development, DOI: 10.1002/ldr.991.

Moeskops B, Sukristiyonubowo, Buchan D, Sleutel S, Herawaty L, Husen

E, Saraswati R, Setyorini D, De Neve S (2010) Soil microbial communities

and activities under intensive organic and conventional vegetable farming in

West Java, Indonesia. Applied Soil Ecology 45: 112-120.

Sleutel S, Vandenbruwane J, De Schrijver A, Wuyts K, Moeskops B,

Verheyen K, De Neve S (2009) Patterns of dissolved organic carbon and

nitrogen fluxes in deciduous and coniferous forests under historic high

nitrogen deposition. Biogeosciences 6: 2743-2758.

Jin K, De Neve S, Moeskops B, Lu JJ, Zhang J, Gabriels D, Cai DX, Jin JY

(2008) Effects of different soil management practices on winter wheat yield

and N losses on a dryland loess soil in China. Australian Journal of Soil

Research 46: 455-463.

Sleutel S, Moeskops B, Huybrechts W, Vandenbossche A, Salomez J, De

Bolle S, Buchan D, De Neve S (2008) Modeling soil moisture effects on the

net nitrogen mineralization in loamy wetland soils. Wetlands 28: 724-734.

219

Curriculum Vitae

Submitted

Moeskops B, Buchan D, Van Beneden S, Fievez V, D’Hose T, Gasper MS,

Sleutel S, De Neve S. The impact of exogenous organic matter on biological

soil quality and soil processes. Applied Soil Ecology.

Moeskops B, Buchan D, Sukristiyonubowo, De Gusseme B, Setyorini D, De

Neve S. Soil quality indicators for intensive vegetable production systems in

West Java, Indonesia. Ecological Indicators.

Moeskops B, Buchan D, Sukristiyonubowo, Sleutel S, De Neve S. Microbial

activity and phospholipid fatty acid profiles under organic and conventional

paddy fields in Central Java, Indonesia. Pedosphere.

A3 Moeskops B, Sukristiyonubowo, Husen E, Herawaty L, De Carvalho Franca

S, Buchan D, De Neve S (2009) Soil microbial properties under intensive

organic and conventional vegetable production in West Java.

Communications in Agricultural and Applied Biological Sciences, Ghent

University 74: 89-94.

C1 Moeskops B, Sukristiyonubowo, Herawaty L, Husen E, Saraswati R,

Buchan D, De Neve S (2009) Soil microbial communities and activities under

intensive organic and conventional vegetable farming in West Java,

Indonesia. Proceedings of Tropentag 2009. 6-8 October 2009, Hamburg,

Germany. http://www.tropentag.de/2009/abstracts/full/638.pdf

220

Curriculum Vitae

Moeskops B, Sukristiyonubowo, Buchan D, Sleutel S, Herawaty L, Husen

E, Saraswati R, Setyorini D, De Neve S (2009) Soil microbial communities

and activities under organic and conventional vegetable farming in West

Java, Indonesia. Proceedings of the 2nd Scientific Conference within the

framework of Bioacademy 2009. 24-26 June 2009, Lednice na Morav ,

Czech Republic, pp. 58-61.

Moeskops B (2006) Mijnbouw in Peru: op zoek naar de waarheid. IPIS

dossier 148. Ipis Research, Antwerpen, 32 pp.

C2

Moeskops B, Sukristiyonubowo, Herawaty L, Husen E, Saraswati R,

Buchan D, De Neve S (2009) Soil microbial communities and activities under

intensive organic and conventional vegetable farming in West Java,

Indonesia. Book of Abstracts Tropentag 2009. 6-8 October 2009, Hamburg,

Germany, p. 206.

Moeskops B, Sukristiyonubowo, Herawaty L, Anggria L, Husen E,

Saraswati R, Buchan D, De Neve S (2009) Effect of organic and

conventional farming on soil microbiological and N dynamics in Java,

Indonesia. Proceedings of the 16th Nitrogen Workshop. 28 June – 1 July

2009, Torino, Italy, p. 69.

Moeskops B, Sukristiyonubowo, Herawaty L, Anggria L, Husen E,

Saraswati R, Buchan D, De Neve S (2009) Effect of organic and

conventional farming on soil microbiology in Java, Indonesia. Proceedings of

the Day of Young Soil Scientists. 25 February 2009, Brussel, Belgium, p. 11.

221

Curriculum Vitae

Moeskops B, Sukristiyonubowo, Lenita H, Husen E, Saraswati R, Setyorini

D, Rachman A, De Neve S (2008) Soil microbial communities and microbial

activity in organic and conventional horticultural fields in Java, Indonesia.

Proceedings of the 6th International AgroEnviron symposium. 28 April – 1

May 2008, Antalya, Turkey.

Moeskops B, Jin K, De Neve S, Gabriels D, Cai DX (2007) Effect of tillage

and cropping system on carbon storage in soils of the Chinese Loess

Plateau. Proceedings of the Day of Young Soil Scientists. 21 February 2007,

Brussel, Belgium, p. 26.

Presentations

15th PhD Symposium on Applied Biological Sciences, K.U.Leuven, Leuven,

Belgium, 6 November 2009. Soil microbial properties under intensive organic

and conventional vegetable production in West Java. Oral presentation.

Tropentag 2009, International Research on Food Security, Natural Resource

Management and Rural Development, Hamburg, Germany, 6-8 October

2009. Soil microbial communities and activities under intensive organic and

conventional vegetable farming in West Java, Indonesia. Oral presentation.

16th Nitrogen Workshop, Torino, Italy, 28 June – 1 July 2009. Effect of

organic and conventional farming on soil microbiological and N dynamics in

Java, Indonesia. Poster presentation.

2nd Scientific Conference within the framework of Bioacademy 2009, Lednice

na Morav , Czech Republic, 24-26 June 2009. Soil microbial communities

and activities under organic and conventional vegetable farming in West

Java, Indonesia. Oral presentation.

222

Curriculum Vitae

Day of Young Soil Scientists, Brussel, Belgium, 25 February 2009. Effect of

organic and conventional farming on soil microbiology in Java, Indonesia.

Oral presentation.

Day of Young Soil Scientists, Brussel, Belgium 21 February 2007. Effect of

tillage and cropping system on carbon storage in soils of the Chinese Loess

Plateau. Poster presentation.

Teaching experience

2007-2008:

teaching the practical lessons Stereographic Projections as part of

the subject Earth Sciences instructed by Dr. Joost Salomez

2006-2007:

teaching the practical lessons Stereographic Projections as part of

the subject Earth Sciences instructed by Prof. Georges Hofman

Student supervision Ghent University

Mbwambo Suzana Gasper: Msc. thesis ‘Measurement of

(bio)chemical indicators for soil quality under contrasting soil

management practices’ for Advanced Studies in Physical Land

Resources

Lieven Dhollander (University College Ghent): Msc. thesis ‘Nitrogen

balances in vegetable production in Central-Java: a tool for

improving nitrogen use efficiency for smallholder farmers’ for Master

of Biosciences: Agriculture

223

Curriculum Vitae

Ibrahim A. Sipahutar (Indonesian Soil Research Institute): practical

work in the framework of the VLIR-EI project ‘Nitrogen balances in

vegetable production in Central-Java: a tool for improving nitrogen

use efficiency for smallholder farmers’

Mbwambo Suzana Gasper: literature study ‘Importance of

arbuscular mycorrhizal fungi for organic carbon and nutrient cycling

in agro-ecosystems’

Céline De Caluwé, Ruben Eeckhout, Nina Kerkhove: Bsc. thesis

‘Biologische landbouw in de tropen: luxe of noodzaak?’ for Bachelor

in Bioscience Engineering

Jelke Backeljau: Msc. thesis ‘Bepaling van glomaline en ergosterol

als maat voor arbusculaire mycorrhiza en fungi onder biologische en

gangbare landbouw op Java, Indonesië’ for Master in Environmental

Sanitation

Mario Marquez (Benguet State University): practical work in the

framework of the international cooperation between Ghent

University, K.U.Leuven and Benguet State University (Philippines)

Lenita Herawaty (Indonesian Soil Research Institute): practical work

in the framework of the VLIR-EI project ‘Nitrogen balances in

vegetable production in Central-Java: a tool for improving nitrogen

use efficiency for smallholder farmers’

Linca Anggria (Indonesian Soil Research Institute): Msc. thesis

‘Potential N2O and N2 emissions from horticultural soils from Java,

Indonesia’ for Advanced Studies in Physical Land Resources

Indonesian Soil Research Institute

Supervision of Bsc. students: Budiriza Putra, Harry Noviardi, Irfan,

Deni, Emma, Winny, Nina, Yuli

224

Curriculum Vitae

225

Knowledge of languages Dutch: mother tongue

English: spoken and written language very good

German: spoken and written language good

French: spoken and written language good

Spanish: basic knowledge

Indonesian (Bahasa Indonesia): basic knowledge

Particular interests Until 2007 I was very active in the youth organisation Jeugd, Natuur en

Milieu (JNM, www.jnm.be), both in the local branch and the central board. I

was national treasurer in 2005.

I represented JNM in the executive board of Bond Beter Leefmilieu, the

umbrella organization of the Flemish environmental movement from 2004

until 2006.

After my stay in Peru (2005), I helped to establish the organization Catapa

(www.catapa.be). Catapa supports communities affected by mining projects

in Bolivia, Peru and Guatemala. I was a member of Catapa until 2008.

Since 2008 I am a member of Terra Reversa (www.terrareversa.be), a think

tank for social-ecological change. Since September 2010, I am a member of

the executive committee. Besides our academic work, we also organize

evening classes for a non-specialist audience.

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