Improving disease resistance against root knot
nematodes (Meloidogyne spp.) in organic
greenhouse systems in the Netherlands via
integrated approaches with compost, soil
additives and bio-organisms
MSc thesis
Jan-Paul van der Kolk
Farming Systems Ecology
i
Source frontpage photo: http://www.nemgenix.com/pest-focus/plant-parasitic-nematodes/
ii
Improving disease resistance against root knot
nematodes (Meloidogyne spp.) in organic
greenhouse systems in the Netherlands via
integrated approaches with compost, soil
additives and bio-organisms
Registration number student: 800325458010
Code number/name course: FSE-80436
Credits: 36 ECTS
Supervisors: Dr. ing. JM Scholberg (FSE); Dr. AWG van der Wurff (WUR-Greenhouse Horticulture)
Professor/examiner: Dr. ir. E Lantinga (FSE)
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Contents
Preface ..................................................................................................................................... v
Executive summary ................................................................................................................... vi
List of tables, figures and abbreviations ....................................................................................... viii
1 Introduction ........................................................................................................................... 1
1.1 Thesis structure ................................................................................................................. 1
1.2 General context ................................................................................................................. 1
1.2.1 Problem statement ....................................................................................................... 1
1.2.2 Measures for managing RKN .......................................................................................... 1
1.3 Thesis scope and objectives................................................................................................. 4
1.4 Research questions ............................................................................................................ 5
2 Material and Methods ............................................................................................................... 6
2.1 Experimental design ........................................................................................................... 6
2.2 Measurements ................................................................................................................. 10
2.2.1 Soil .......................................................................................................................... 10
2.2.2 Crop ......................................................................................................................... 12
2.2.3. Root knot nematodes ................................................................................................. 13
2.2.4. Environmental conditions ............................................................................................ 13
2.3 Data and statistical analyses ............................................................................................. 14
3 Results ................................................................................................................................ 16
3.2 Soil- and compost properties ............................................................................................. 16
3.3 Results Research Question 1 .............................................................................................. 19
3.4 Results Research Question2............................................................................................... 24
3.4.1 Champost-based stackings .......................................................................................... 24
3.4.2 Woody compost-based stackings .................................................................................. 25
3.4.3 Mature compost-based stackings .................................................................................. 26
3.4.4 Immature compost-based stackings .............................................................................. 27
4 Discussion ............................................................................................................................ 28
4.1 Research Question 1 ........................................................................................................ 28
4.2 Research Question 2. ....................................................................................................... 29
5 Conclusion ........................................................................................................................... 32
6. Recommendations ................................................................................................................ 34
References .............................................................................................................................. 36
Appendices ............................................................................................................................. 40
Appendix A1 Score chart used to determine Root Knot Index (RKI) 1 values .................................. 40
Appendix A2 Experimental map layout ..................................................................................... 41
Appendix A3-1 Temperature and radiations levels in the greenhouse. .......................................... 42
Appendix A3-2 Irrigation realization ......................................................................................... 43
Appendix A4 Overall statistical analyses ................................................................................... 44
Appendix A4-1 RKI ............................................................................................................. 44
iv
Appendix A4-2 Natural logarithm number of juveniles per gram fresh weight root ........................ 47
Appendix A4-3 Above dry weight (gr) .................................................................................... 50
Appendix A4-4 Dry weight roots (gr) ..................................................................................... 53
Appendix A4-5 Length (cm) ................................................................................................. 56
Appendix A4-6 Browning roots ............................................................................................. 59
Appendix A5 Statistical analyses stackings ................................................................................ 62
Appendix A5-1 Champost stacking ........................................................................................ 62
Appendix A5-2 Woody compost stacking ................................................................................ 72
Appendix A5-3 Mature compost stacking ................................................................................ 82
Appendix A5-4 Immature compost stacking ........................................................................... 92
Appendix A6 Missing leaves .................................................................................................. 103
Appendix A7 Fruit weight correction ....................................................................................... 105
Appendix A8 Normal distribution overall ................................................................................. 106
Appendix A9 Normal distribution stackings .............................................................................. 107
Woody stacking ................................................................................................................ 107
Champost stacking ........................................................................................................... 108
Mature stacking ................................................................................................................ 109
Immature stacking ........................................................................................................... 110
v
Preface
This research titled “Improving the disease resistance against root knot nematodes in organic
greenhouse vegetables” was performed at Wageningen UR in Bleiswijk, the Netherlands. I’m grateful
for having had the opportunity to conduct my thesis research in this field. I hope that this research
will bring agriculture one step closer towards a sustainable agricultural system where we can feed,
not only our generation but also the following, while respecting the nature.
My thesis would not have been possible and successful without the enthusiastic and dedicated
supervising by my supervisors Andre van der Wurff (Wageningen UR, Bleiswijk), and Johannes
Scholberg (Farming Systems Ecology (FSE). They helped improving my academic and scientific skills
by being critical and by giving valuable advices. Moreover, they gave me the chance to expand my
knowledge and practical skills as related to my thesis subject. Furthermore I like to thank all the
colleagues from the Wageningen UR Bleiswijk that were involved and helped with my research. I
would like to thank Aat van Winkel, Wim Voogt, Marta Streminska, Astrid de Boer and Jan Janssen
for their substantial contribution. Also I would like to thank Fred van Leeuwen and Kees Scheffers for
their contribution to the operational part of my research, where their critical observations and
actions were essential for the quality of this research.
Finally I like to give my thankfulness to all companies who were involved in this research, in terms of
materials or advice. In special I like to thank Gebr. Verbeek for their contribution, in particular Robert
Berkelmans who substantial contributed to the quality of this research by critical observations and
sharing his research experience.
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Executive summary
In organic greenhouse vegetables, root knot nematodes (Meloidogyne spp., RKN) are a serious
concern. In 2007, root knot nematodes were classified in the top 10 of the most serious diseases and
pests that affect organic greenhouse vegetables in the Netherlands.
To address this concern two main research questions for organic greenhouse tomatoes in the
Netherlands where being formulated. Firstly, can the control of RKN be improved (Meloidogyne spp.)
by addition of compost, additives, antagonists and stackings thereof when compared the untreated
reference in organic greenhouse systems. The second question was if the control of RKN
(Meloidogyne spp.) is being enhanced by combining (stacking) of different measures, i.e., composts,
additives and antagonists when compared to a single or double measure for organic greenhouse
systems. The stackings used in this research were: (i) immature compost with lime and Bacillus firmus
NCCB 48015, (ii) mature compost with chitin and Paecilomyces lilacinus strain 251, (iii) champost and
basaltlavameal, and (iv) woody compost with Trichoderma harzarium strain T22. The soil used was
obtained from an organic greenhouse vegetable grower in the Netherlands.
The woody compost-based stacking had a significant (P<0.05) lower root knot index (RKI) for woody
compost as such and for woody compost stacked with T. harzarium compared to the non-amended
control (reference). The RKI for stacking of champost and basaltlavameal was also significantly lower
(P<0.05) compared to the control. The juveniles (J2) counts per gram fresh weight root (#J2/gr FR)
decreased significantly (P<0.05) with woody compost alone compared to the reference. The decrease
of the RKI and partially of #J2/gr FR in the woody compost stacking may be caused by its relatively
high carbon to nitrate (C/N) ratio and lignin content along with the increase in pore volume and
probable pH of the soil due to a higher pH of the woody compost. For the champost, a high electric
conductivity (EC) and organic matter content (OM%), ammonium to nitrate ratio and level of
ammonium could explain its suppressive effect on RKN.
Furthermore, for the champost treatments, a stacking with basaltlavameal resulted in a synergetic
reducing effect on the RKI compared to the usage of champost or basaltlavameal alone. However,
this synergetic effect was not observed for the second stage juvenile counts. It is concluded that
although stacking may hold promise for more effective control of nematodes, additional research is
needed to verify the findings of the current study.
vii
viii
List of tables, figures and abbreviations
Table 1 Outline of the experimental treatments .............................................................................. 7 Table 2 Application scheme of compost, bio-stimulants, fertilizers and antagonists .............................. 8 Table 3 Soil properties of the unamended soil .............................................................................. 16 Table 4 Compost measurements ................................................................................................ 17 Table 5 Calculated NO3-N/NH4-N ratios ........................................................................................ 17 Table 6 Physical soil properties of pores% and solid parts %. ......................................................... 18 Table 7 Physical soil properties of pF values. ................................................................................ 18 Table 8 Nematode countings ...................................................................................................... 19 Table 9 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the
champost stacking. .................................................................................................................. 24 Table 10 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the woody
stacking.................................................................................................................................. 25 Table 11 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the
mature stacking. ...................................................................................................................... 26 Table 12 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the
immature stacking. ................................................................................................................. 27 Table 13 Hypotheses acception or rejection for the RKI, #J2/gr FR, Dry weight shoot and root ............ 32 Table 14 Conclusions for the effect of the stackings with immature, mature, woody and champost
compost. ................................................................................................................................ 33
Fig. 1 Possible modes of actions as induced by compost addition ....................................................... 4 Fig. 2 Experimental map for Phase 1 ............................................................................................. 9 Fig. 3 Experimental map for Phase 2 ........................................................................................... 10 Fig. 4 Measuring height for weekly measurements ........................................................................ 12 Fig. 5 Visual nematode assessment based on the Root knot index (RKI). .......................................... 20 Fig. 6 Second stage juvenile counts expressed as number per gram fresh weight root (#J2/gr FR). ...... 20 Fig. 7 Above-ground biomass dry matter (DM) accumulation (gr plant-1) .......................................... 21 Fig. 8 Dry matter (DM) root gr plant-1. ........................................................................................ 22 Fig. 9 Correlation between dry weight above biomass (gr) and dry weight roots (gr) n=238 ................ 22 Fig. 10 Browning of tomato roots. .............................................................................................. 23 Fig. 11 Temperature °C realization ............................................................................................ 42 Fig. 12 Artificial lighting realization ............................................................................................. 42
Abbreviations
#J2/gr FR = Number of second stage juveniles per gram fresh weight root
C/N ratio = Carbon to nitrogen ratio
DM = Dry matter
EC= Electrical conductivity
Eh = Redox potential
J2 = Second stage juveniles
OM = Organic matter
pH = degree of acidity
RKI = Root knot index
RKN = Root knot nematodes
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1
1 Introduction
1.1 Thesis structure
The first chapter of this thesis report outlines the background of this research, which is divided in a
literature study, research objectives, and corresponding questions and hypotheses. The second
chapter describes the material and methods, in which the measurements, the methods used, the
treatments and amounts of additives and statistical analyses are being described.
The third chapter presents the results of the research including the outcomes for key measurements
and the calculation of soil parameters. Then, the overall results of the Root knot Index (RKI) and 2nd
stage juvenile counts per unit fresh root mass (#J2/g FR) along with key plant growth parameters are
being described followed by the statistical analyses. In the fourth chapter, a discussion of the results
is presented. In the fifth and sixth chapter, the final conclusions and recommendations are presented
and followed by a list of references and the Appendices.
1.2 General context
1.2.1 Problem statement
In organic greenhouse vegetables, root knot nematodes (Meloidogyne spp., RKN) are a serious
concern. In 2007 root knot nematodes were classified in the top 10 of most serious diseases and
pests that affect organic greenhouse vegetables in the Netherlands. In general, year-round
production systems of fruit-bearing vegetables (tomato, cucumber, bell pepper) encounter the
largest problems with root knot nematodes (van der Wurff et al, 2010). Meloidogyne spp. are well-
adapted to diverse soil environments such as a wide range of soil pH and electrical conductivity (EC),
but their infection and reproduction rates tend to be highest in light (sandy) soils (Oka, 2014).
1.2.2 Measures for managing RKN
In organic greenhouse production settings, there are several different methods that are being used
to improve suppression of RKN, ranging from conventional methods, such as steaming of the soil, to
more bio-based methods, such as the use of compost, additives and antagonists. The use of compost
is a basic requirement for organic vegetable growers. Therefore, the choice of the type of compost is
very important as it influences the level of disease suppression in greenhouse-based organic
vegetable production systems.
Current research by WUR Greenhouse Horticulture shows that six modes of action are involved in
compost driven disease resistance. The addition of compost to the soil may result in enhanced
2
resource competition, plant resistance, improved soil structure, along with a release of toxic
compounds and antibiotics against plant parasitic nematodes or improve the antagonistic potential
of soil (Wurff, PPS, 2014). Although many studies were executed, RKN are still a major concern in
organic greenhouse horticulture and more effective control measures are urgently needed.
Since biological control methods are often less efficient than conventional methods, effective control
of RKN via soil suppression may require combination (stackings) of complementary means and
modes of action. This may increase the chance that a certain mode of action is effective or may
facilitate its efficacy by enhancing overall nematode control via synergistic actions. Trichoderma and
bark material, Paecilomyces lilacinus and chitin, Bacillus firmus in alkine conditions with ammonium
are examples of stackings. These examples are further described in the next paragraphs.
Trichoderma spp. inhibit the reproduction of nematodes via parasitism of larvae and eggs (Sharon et
al, 2001; Suarez et al, 2004) or prevent nematodes to cause damage to the plant by inducing plant
defence mechanism (Sharon et al., 2001). Furthermore, it is known that Trichoderma spp. are
present in tree bark-based composts (Nelson et al, 1983; Hoitink and Boehm, 1999). Thus, multiple
modes of action against RKN may be involved: induction of plant resistance, antagonistic action by
physically protecting the roots, parasitism of nematodes and excretion of antibiotics. Furthermore,
woody material, such as bark, was reported to enhance disease suppression (Stone et al, 2001). In
addition, bark may act as a carrier of Trichoderma spp. and therefore serve as a permanent source,
allowing Trichoderma spp. to persist. Moreover, the addition of bark can improve soil structure by
increasing soil pore volume.
The nematophagous fungus Paecilomyces lilacinus can inhibit damage caused by RKN and represents
another example of the advantage of combining modes of action. The production of chitinases and
proteases was suggested to be a key mode of action for P. Lilacinus strain PL251 against RKN.
Moreover, it is known that P. lilacinus strain 251 produced chitinase in a medium containing chitin
(Kahn et al, 2003). Furthermore, it was shown that the number of galls per gram fresh weight roots
was significantly decreased when soils were amended with chitin and P. Lilacinus compared to a
control treatment (Mittal et al, 1995). In a study using cabbage, the addition of Clandosan (CLA),
which is a chitinious material, led to an increase of the number of chitinolytic bacteria and fungi
(Spiegel, 1988). Thus in these examples various modes of action are involved: (i) chitin serving as a
resource to stimulate the growth of P. Lilacinus, (ii) the increase of P. Lilacinus resulting in enhanced
resource competition, (iii) plant resistance against RKN being improved due to the enhanced growth
of P. Lilacinus.
3
In field trials using tomato plants, it was shown that application of Bacillus firmus (Bionem WP)
resulted in a reduction of the number of galls by 75-84%, 45 days after the plants where treated with
this bacteria (Terefe et al, 2009). It is also known that some strains of B. firmus can tolerate high
alkaline soil conditions (Guffanti et al, 1980). The combination of bacteria that can kill RKN such as B.
Firmus, with alkaline soil conditions may generate alternatives for methods using soil fumigation with
ammonia. Ammonium application in combination with increased pH may reduce galling index due to
a higher conversion rate from ammonium to ammonia which reduces the incidence of nematodes.
Combining ammonium sulphate (AS) and alkaline-stabilised bio-solid (ASB) was shown to significantly
reduced the root galling index compared to individual treatment with either AS and ASB individually
(Oka et al, 2006).
When ammonium is converted to nitrate, acids are released which lowers the pH of the soil
(Sonneveld and Voogt, 2009). A study, in which different combinations of nitrate and ammonium N-
fertilizer applications together with M. Javanica inoculation were evaluated, revealed that, a month
after treatment, root galling and the numbers of nematodes in root knots were lower in the
treatment with a high ammonium/nitrate ratio. However, two months after the treatment, no
differences were observed between the treatments (Spiegel et al, 1982). Another study using M.
incognita demonstrated that ammonium treatments, compared to nitrate treatments, reduced the
numbers of nematodes. However, it is important to note that the duration of the treatment used in
this study was not specified (Oteifa, 1955). Collectively, these studies highlight that the amount and
ratio between nitrate and ammonium may also be an important factor that can control the growth of
RKN. The challenge here is to find the balance between maximizing suppression of RKN without
having a decrease in plant growth due to excessively high soil ammonium contents.
Other soil minerals that also contribute to the suppression of RKN include calcium and magnesium. It
has been shown that increased translocation of calcium and magnesium to the roots occurs when
tomato plants are being infected with M. incognita (Goswami et al, 1976). The fact that calcium plays
a role in plant defence response against Meloidogyne is obvious. It was established that the
resistance gene RMc1(blb) was responsible for mediating plant resistance against Meloidogyne
chitiwoodi in potato (Zhang et al, 2007). It was revealed that Ca2+ channels are involved in mediating
RMc1(blb)-activated hypersensitive response (Davies et al, 2014). In addition to calcium, silicon may
also induce plant resistance. It has been decribed that the infestation by aphid, white fly and spider
mite, was inhibited by applying silicon (Ranger et al, 2009, Gatarayiha et al, 2010). In a greenhouse
study, silicon applied as a root dip reduced nematode reproduction as compared to applying silicon
near to the roots (Miller and Faske, 2011). This indicates there could be a suppressive effect on RKN
of silicon mediated by the plant as well. Silicon may be involved in multiple modes of actions in
4
suppressing RKN infestation and/or subsequent growth reduction. However, knowledge on this topic
still remains scarce.
Although addition of antagonists can be beneficial, the chance that the antagonists will actually
establish and persist in the soil environment depends on different factors such as soil structure and
soil composition. A strategy to increase plant resistance against RKN can be by promoting indigenous
antagonists by changing soil structure, fertilization and/or adding bio-stimulants such as chitin. To
improve the effectiveness of stacking multiple modes of actions against RKN, different stackings need
to be evaluated in a controlled environment. Therefore, this research focussed on the stacking of
nematode control measures where at least three modes of actions i.e., soil structure, plant health
and antagonist are involved as related to the addition of compost, soil additives and selected
microorganisms.
1.3 Thesis scope and objectives
As mentioned in the previous paragraph, this thesis focuses on the advantage of using stackings of
modes of action in order to (i) increase the likeness that at least one certain mode of action is
successful, (ii) facilitate actions or (iii) even enhance effective control via synergistic action. As
discussed in the previous paragraph, for compost six modes of action may be involved in disease
resistance (Wurff, PPS, 2014) which are shown in Fig. 1. The use of compost is a basic requirement
for the growth of organic vegetables and as a mean to stimulate targeted soil biological activity (e.g.
soil suppressiveness). The objective of the thesis is to stack different types of compost-based actions
with supplemental soil additives and antagonists. More details will be discussed in the Chapter 2
(Material and Methods).
Fig. 1 Possible modes of actions as induced by compost addition (Wurff, PPS,2014)
5
1.4 Research questions The main research question of this thesis can be described as follow:
1. Can the control of RKN (Meloidogyne spp.) be improved by adding compost, additives,
antagonists or stackings thereof when compared the untreated reference in organic
greenhouse tomatoes grown in the Netherlands.
a. Which type of compost (mature, immature, champost or mulch) would be the most
effective in controlling RKN?
b. Which soil additives (calciumsulphate, calciumcarbonate, silicon or chitin) could
improve plant resistance to RKN?
c. Do T. hazarium strain T22, P. Lilacinus strain 251 and B. firmus NCCB 48015 decrease
the infection rate of the plants by RKN?
d. Which stackings of compost (A), soil additives and bio-stimulants (B) and soil
organism (C) would most effectively minimize the infestation by RKN?
2. Can the control of RKN (Meloidogyne spp.) be enhanced by a stack of different means, i.e.,
composts, additives and antagonists when compared to a single mean or double mean in
organic greenhouse tomatoes grown in the Netherlands?
More specifically, do the following stackings have synergetic, antagonistic or no effect on the
control of RKN (Meloidogyne spp.):
i. Champost and Basaltlavameal (of which the main content is silicon);
ii. Woody compost and T. hazarium strain T22;
iii. Immature compost, calciumcarbonate and B. firmus NCCB 48015;
iv. Mature compost, chitin and P. lilacinus strain 251.
Hypothesis
- The use of compost, soil additives and bio-organism will enhance or maintain plant growth
while reducing the RKI and/or #J2/gr FR compared to the non-amended group;
- The RKI and #J2/gr FR is higher in singular or double means treatment compared to
treatments that entail a stacking of double or triple means;
6
2 Material and Methods
2.1 Experimental design The aim of this experimental design was to test the effect of three specific treatment combinations
based on specific modes of action on the suppression of RKN when compare to a non-amended
control (Tref). These include the addition of compost (TA), soil additives and bio-stimulants (TB) and
composition of the soil in micro-organism (TC). In context of RKI, the higher the RKI value, the more
severe the nematode infection. Therefore, in this case treatments were considered effective in
controlling nematodes if TA-Tref <0, TB-Tref <0, or TC-Tref <0). In addition, it was evaluated if
stackings of multiple factors resulted in antagonistic (e.g. for RKI TA,B-TA < 0 and/or TA,B-TB > 0),
additive (TA,B - TA -TB = 0), or synergistic (TA,B - TA -TB < 0) effects on the suppression of root
knot nematodes in greenhouse tomatoes. The definition of synergy in this research is the following:
the interaction of elements that when combined produce a total effect that is greater than the sum
of the individual elements (http://dictionary.reference.com, 2015)
The soil used was obtained from the commercial greenhouse cooperation of the Gebr. Verbeek in
Velden, The Netherlands. This soil contained 68% sand 14% silt and 5% clay, while the OM content
was 12%. The soil was extracted from the upper layer (15 cm) of a soil that had a high infection level
of root knot nematodes, since the last crop had a RKI of 8-9 at the end of the growing season of
tomato in November 2014.
This research was performed at the Wageningen UR Glastuinbouw in Bleiswijk, The Netherlands. In
total there were 20 treatments including 11 single-means treatments, 6 double-means treatments, 2
triple-means treatments and 1 reference (control) treatment. Table 1 provides an outline of
experimental treatments. The control treatment consisted of an unamended/untreated soil as it was
derived from Gebr. Verbeek. Each treatment was replicated 8 times, where one repetition consists of
three 5-L PVC pots. This implies that there were 8 * 3 = 24 pots per treatment, with 1 plant per pot.
7
Table 1 Outline of the experimental treatments
Compost type Soil additive Bio-organism Type
1 None None None Control
2 Immature None None Single means
3 Mature None None
4 Woody None None
5 Champost None None
6 None CaSO4 None
7 None CaCO3 None
8 None Si None
9 None Chitin None
10 None None Trichoderma harzianum strain T22
11 None None Paecilomyces lilacinus strain 251
12 None None Bacillus firmus NCCB 48015
13 Mature Chitin None Double means
14 Immature CaCO3 None
15 Champost Si None
16 Mature None Paecilomyces lilacinus strain 251
17 Immature None Bacillus firmus NCCB 48015
18 Woody None Trichoderma harzianum strain T22
19 Mature Chitin Paecilomyces lilacinus strain 251 Triple means
20 Immature CaCO3 Bacillus firmus NCCB 48015
The total duration of the experiment was approximately 12 weeks, and the study consisted of two
phases. During the first phase compost, soil additives/bio-stimulants and antagonist were mixed and
placed in 5L PVC pots. Antagonists were applied three days before planting (DBP) directly in the soil
and at the soil plug of the young plants by dipping the soil plug in water that contained the
antagonist. In Table 2, the amounts and methods of applying the additives are shown.
8
Table 2 Application scheme of compost, bio-stimulants, fertilizers and antagonists
Compost type
1 Volume %
Bio-stimulant and fertilizer
2 gr/plant Antagonist
5 Units
Mulch 14% CaCO3 (lime) 3
20
Trichoderma harzianum strain T22 (trianum-P koppert) 0.06 gr per plant
Immature 11% CaSO4 (gypsum) 3
18.3
Paecilomyces lilacinus strain 251
6
4500 miu7 per
plant
Mature 10% Chitin4 22.5
Bacillus firmus
NCCB 48015
(CBS-KNAW) 2.88 * 10
6 cfu
7
per plant
Champost 18% Basaltlavameal8 (si) 30
1 Applied at wk 49 simultaneously with filling the pots and values were calculated on the basis of 200 ton/ha in a
soil layer of 25 cm 2 Applied at wk 49 simultaneously with filling the pots
3 Productname lime: Dolokal (Ankerport, Maastricht, the Netherlands) which contained 19% MgO.
Productname gypsum: Calcium sulfate dihydrate (Sigma-Aldrich chemie GmbH, Steinheim, Germany) 4 Productname chitin: Nematoden mix from Koppert B.V. biological systems
5 Antagonist was applied to the soil in the pot and plant plug 3 days before planting (DBP)
6 Paecilomyces lilacinus was applied 3 times 3 days DBP, 4 days DAP and 11 DAP with 1500 miu per time
7 mIU means milli-international units and CFU means colony forming unit
8 The main composition of DCM basaltlavameal is CaO 14%, MgO 20%, SiO2 39%, Fe2O3, K2O. Other naturally
common elements are Co, Zn, Bo, Mo, S, Ti, Na and Al.
Pots were filled with soil and treatments approximately 27 days before planting (DBP) in order to
acclimatize, where 4 days were needed to fill all pots. In terms of environmental conditions, at this
stage the set-point for the heating was set at 6 ⁰C while for the ventilation it was 10 ⁰C. This was
done to ensure the mortality rate of nematodes would not be too high as there is a relation between
temperature and life span (Wurff et al. 2010). Furthermore, the soil was kept moist to prevent
nematodes from desiccation. Eight days old tomato seedlings were placed in the greenhouse at 13
DBP. At the moment the seedlings were placed in the greenhouse, the temperature set point was set
to 20 ⁰C. To stimulate plant growth artificial lightning was switched on with an intensity of 117 µmol
m-2 s-1 PAR (Photosynthetically Active Radiation). The set point for artificial lighting was automatically
switched on at sunrise while it was turned off after sunset. The layout of the treatments and
implementation of the trial during Phase 1 is shown in Fig. 2.
9
Fig. 2 Experimental map for Phase 1 of the greenhouse experiments, where the left figure is schematic overview while the right picture shows the actual set-up in the greenhouse.
During the Second Phase, tomato transplants of cultivar Capricia CV (Rijk Zwaan) were planted in 5-L
PVC pots and the plants were grown for 56 days. The plant density was 4.2 plants m-2. Water was
applied daily using micro-emitters, with actual application rates depending on daily plant water
needs, as shown in Appendix 3-2. Since the basal application of fertilizers via composts and soil was
not adequate to meet crop nitrogen (N) and potassium (K) demand, additional N and K was supplied
via continuous fertigation, with Fontana 6N-0.1P-3.5K (Liquid organic fertilizer, Memon BV, Arnhem).
Total fertilizer application amounted approximately to 3.52, 0.12 and 2.64 gram of N, P and K per
plant, respectively.
The temperature was set at heating set-point of day/night 20/20 ⁰C at the beginning of the
experiment. To promote plant growth, artificial lightning was switched on 9 DAP with an intensity of
210 µmol m-2 s-1 PAR and was switched on 1 hour before sunrise and switch off at sunset. When
outside radiation reached a threshold of 260 W/m2 (measured by a Solari meter) the lights were
automatically switched off. During Phase 2, climatic conditions in the greenhouse and irrigation were
adjusted to the needs of the plant to optimize plant growth using standard practices and
recommendations. The actual radiation by additional lighting and temperature values are presented
in Appendix 3-1. At 56 DAP final plant growth and yield measurements were taken. The experimental
map of Phase 2 is shown in Fig. 3.
10
Fig. 3 Experimental map for Phase 2. there were a total of 20 treatments, which were replicated 8 times. Each replicate consisted of 3 pots. In total there were 12 gutters of which 2 were used for border plants. The length of the gutters was 12 m and the width of the greenhouse 12 m which resulted in a total experimental greenhouse area of 144 m
2
2.2 Measurements
In order to address the research questions listed in paragraph 1.3 a number of measurements were
taken during the research. These are being presented in distinct section as measures related to 1)
Soil; 2) Crop; 3) RKN; and 4) Greenhouse environmental conditions.
2.2.1 Soil
- Before starting Phase 1, soil samples of the reference soil (3 replicated samples) were
analyzed by a commercial lab (Blgg AgroXpertus, Wageningen, the Netherlands) using
standard procedures:
o Nutrient content (N, P, K, Ca, Mg, S) and micro-nutrients (B, Cu, Fe, Mo, Mn, Zn) (1:2
extract)
o Organic matter content (NIRS (TSC®))1
o Textural analysis % clay, silt and sand (NIRS (TSC®))1
o C/N ratio (Derived value)1
o EC, pH (1:2 extract)
T19 T10 T13 T3 T2 T18 T13 T5 T16 T1
T16 T17 T5 T2 T20 T11 T17 T8 T7 T2
T20 T16 T1 T4 T8 T20 T9 T17 T20 T3
T11 T14 T12 T9 T6 T2 T6 T10 T14 T4
T2 T18 T6 T5 T16 T13 T5 T9 T11 T5
T10 T19 T4 T19 T17 T14 T7 T4 T15 T6
T17 T3 T15 T14 T1 T6 T8 T13 T18 T7
Border Path Path Path Path Path Path Border
plants plants
T3 T2 T9 T12 T13 T4 T14 T3 T1 T8
T4 T8 T18 T17 T11 T8 T11 T3 T12 T9
T13 T7 T8 T15 T12 T5 T10 T1 T2 T10
T8 T11 T13 T10 T5 T9 T12 T18 T19 T11
T6 T20 T11 T6 T10 T10 T20 T19 T6 T12
T9 T7 T20 T15 T4 T19 T7 T16 T20 T13
T18 T15 T7 T18 T19 T17 T16 T2 T19 T14
T14 T5 T1 T9 T7 T1 T12 T15 T18 T15
T12 T1 T16 T14 T3 T15 T3 T4 T17 T16
11
- Before starting Phase 1, compost types were analysed in duplio for:
o NH4 and NO3 content. The method used is shaking the soil for 2 hours in 0.01 mol
CaCl2. The NH4 and N03 amount were determined by a Techicon segmented flow
analyser (Auto-analyzer II, Technicon Corporation, Oakland, Calafornia, United
States) The values were used to calculate NH4 /NO3 ratios .
o EC/pH (potentiometric using a pH/mV measurer (inoLab® pH/Cond Level 1,
Weilheim, Germany)
o C/N ratio (by the Dumas Method with a CHN1110 Element Analyzer (CE instruments,
Milan, Italy)
o Organic matter content, by gravimetric determination (Dry-ashing of the organic
material in an oven at 500-550 °C).
o Soil structure analyses, pore volume using pF curve determination for the reference
soil (T1) and different compost treatments (T2,T3,T4 and T5). Measurement and
calculations were done according to the European standard ( EN 13041, 1999)
- Soil pH measurement in the first week of Phase 2 (Wk 4) (1:2 extract) of treatment 1 and
14
1 Method developed by BLGG AgroXpertus, Wageningen
12
2.2.2 Crop
Non-destructive crop measurement were taken during the experiment while destructive
measurements were taken at the end of the experiment (56 DAP). The following measurements were
performed:
I) Non-destructive measurements: - Plant height was recorded for 8 plants per
treatment during the experiment while at 56
DAP all 24 plants per treatments were
measured. Plant height was determined by
measuring the length from the bottom till the
growing point as displayed in Fig. 4.
- SPAD readings were taken on 38 and 53 DAP
using a SPAD meter (Minolta SPAD 502 meter,
Konica-Minolta, Tokyo, Japan). The values
were expressed in SPAD units (manual, SPAD
502 plus).
- Fruit set number of the 1st, 2nd and 3th cluster
was determined at 46 DAP and at final harvesting. A fruit was considered to have set if the
fruit primordia exceeded approx. 2mm.
II) Destructive measurements
- Fresh weight (gr) of roots, stem and tomato fruits were determined at 56 DAP. All samples
were weighted including the bag and corrected by subtracting the bag weight which was an
average of 10 bags. Fresh weight (gr) of leaves was not determined because of abortion of
oldest leaves, which were considered not to be representative to give an indication of fresh
leaf weight.
- Dry weight (gr) for shoot was separated in fruits, stem and leaves weights were based on 24
plant per treatment while dry weight (gr) of roots were taken for 12 plants per treatment.
All samples were weighted including the bag, which was corrected by subtracting the bag
weight (average of 10 bags) from the total weight (Bag + plant). For some fruit weight
measurements, fruit size was very small and this resulted in a negative value, these values
were set to zero (0.00) in the subsequent analyses. In Appendix A7 the corrections are
shown.
- Dry weight (gr) of aborted leaves was determined at 49 DAP. In some cases aborted leaves
could not be allocated to a plant. As a consequence the dry weight of such leaves were not
Fig. 4 Measuring height for weekly measurements, the red line shows the measuring point which is at the first visible leaf split off
13
included in dry weight determinations. In Appendix A6 the number and location of this leaves
are shown.
- The total number of leaves per plant where determined also recorded where al leaves where
counted except the lob leaves.
2.2.3. Root knot nematodes
- Determination of number of juveniles (J2) was done at the beginning of Phase 1 from the
reference soil and for treatments amended with champost (T5), mature (T3), immature (T2),
and mulch (T4). Determination was done by extracting 100 ml of the soil. The Saprophage
(non-plant parasitic) and Meloidogyne spp. nematodes were counted with aid of a
microscope. In addition, to identify the species within the genus Meloidogyne, a qPCR tests
were performed. The difference between the identified species and total Meloidogyne spp.
translated to the total amount of tropical Meloidogyne including M. incognita and M.
javanica. Whereas these species cannot be identified by using qPCR. This method was
conducted by BLGG agroXpertus, Wageningen.
- Determination of the root knot index (RKI) started after harvesting of the experiment
(DAP=56) by washing the root systems manually to remove any remaining soil and organic
debris . The roots were cooled at 4 ⁰C to maintain the quality of the roots until roots were
visually rated for nematode infection using the RKI. The size and percentage of the galls were
compared to the reference chart as shown in Appendix A1.
- The second stage juveniles (J2) within the roots expressed as individuals per gram fresh
weight root (#J2/gr FR) were determined after incubation using a mistifiër (Oostenbrink,
1960) for 12 plants per treatment. During four weeks the roots were incubated in a mistifiër,
the nematodes were collected 2 and 4 weeks after placing the roots in the mistifiër. After 4
weeks the number of Meloidogyne spp. was counted.
2.2.4. Environmental conditions
Environmental parameters including air temperature, relative humidity, irrigation application, vapour
deficit, radiation, assimilation lights activation time, energy curtain position were logged at 5 min
intervals using letsgrow (Online logging platform, Vlaardingen, the Netherlands).
14
2.3 Data and statistical analyses Data was entered into a spreadsheet (Excel, MS office) and statistically analyzed using SPSS (22.0.0.0,
IBM Corp ©, New York, United States) and this program also provided average values and standard
deviations for each treatment combination. Data sets were tested for missing values, outliers and
data entry mistakes. In the analyses acephalous plants with a shoot and replaced plants were used in
the statistical analyses. Pots without a plant where not analysed, this were plant number 82 and 382,
in Appendix A2 an overview of plant numbers and treatments is given.
After testing for normality, a General linear model (GLM) was used, for univariate analysis and
Tukey’s-b as post hoc test. During this analyses the treatment was used as a fixed factor and the
replicate as random factor to compensate for the replicate (block) effect. For the statistical model,
treatment and replicate were used individually. It was assumed that all twenty-four pots per
treatments where independent of each other. When data was not normally distributed, a natural
logarithm was used with the formula Ln(2x+1). After performing the statistical analyses the average
values were back transformed by the inverse function of Ln(2x+1). Synergetic and antagonistic
effects were determined by using L-matrix command using contrasts as described by Howell and
Lacroix (2012).
The results of the overall statistical analyses are provided in section 3.3 while the results of the
stackings (synergistic effects) are shown in section 3.4. The data set used in the analyses of the
stackings contained only the values of the treatments in the stacking. This resulted in a different
numeric values for the degrees of freedom (df) compared to the total data set. In some cases this
resulted in significant differences in the stacking analyses (post hoc tukey-b), that where they were
not significant in the total dataset (post hoc tukey-b).
For the RKI the following statistical analyses were executed:
In order to investigate Research Question 1, (see section 1.4) formula Ia, IIa and IIIa were used. It
describes the difference in RKI among treatments i, j, k, where i, j, k refer to individual compost, soil
additives and bio-organism treatments, respectively, while ref refers to the control treatment (e.g.
treatments have lower infection than the control)
Ia = RKIi,j,k-RKIref <0
15
IIa) double factors are less sensitive to nematodes compared to the control
IIa = RKIi+j-RKIref <0 and IIa = RKIi+k-RKIref <0
IIIa) RKIi+j+k-RKIref <0 triple factors are less sensitive to nematodes compared to the control)
IIIa = RKIi+j+k-RKIref <0
Furthermore in order to investigate Research Question 2 (see Chapter 1.4) formula IIb, IIIb and IIIc
were used.
IIb) Double factors are more effective than single factors
IIb = RKI i+j – RKI i,j,k < 0 and IIb = RKI i+k – RKI i,i,k < 0
IIIb) Triple factors are more effective than single factors
IIIb = RKI i+j+k – RKI i,j,k < 0
IIIc) Triple factors are more effective than double factors
IIIc = RKI i+j+k – RKI i+j < 0 and IIIc = RKI i+j+k – RKI i+k < 0
For the second stage juveniles counts, and other parameters where low values are assumed to be
indicative of reduced crop sensitivity to root know nematodes the same formulas were used as
described for the RKI. In terms of plant growth measures, the reverse is true and the “<” sign in the
above equations was replaced by a “>” sign instead.
16
3 Results
This Chapter is divided in paragraphs for soil- and compost properties (3.2), assessment of
treatment performance as related to the reference (Research Question 1, 3.3), and synergistic
effects (Research Question 2, 3.4) as related to the stacking of champost-, woody compost-,
mature/immature compost-based treatments.
3.2 Soil- and compost properties To determine the properties of the soil and pure compost basic biochemical analysis were conducted
as described in the material and methods for which the results are shown in Table 3. The soil which
was sandy and had relatively high soil organic matter content.
Table 3 Soil properties of the unamended soil used in all treatments as obtained from the greenhouse from Gebr. Verbeek, Velden, Limburg.
Name Units Value
C/N-ratio -- 12.7
N supplying capacity kg N/ha 176
P plant available mg P/kg 22
K plant available mg K/kg 396
Ca plant available kg Ca/ha 329
Acidity (pH) 6.5
Organic matter % 11.5
Clay % 5
Silt % 14
Sand % 68
Clay-humus mmol+/kg 263
CEC-occupation % 100
EC(mS/cm) mS/cm 1.0
NH4-N Kg/ha* <5.5
NO3-N Kg/ha* 310 * Values recalculated from mmol/l in a 1:2 extract. Values are based on a growth layer of 25 cm and equations decribed by
Sonneveld, 1990.
For the different compost types the results of the biochemical analysis are shown in Table 4. In this
case the values shown, represent compost material before it was added and/or mixed with the soil
obtained from Gebr. Verbeek.
17
Table 4 Compost measurements of: woody which is the larger fraction of the immature and mature compost, champost, mature (>50 weeks old) and immature ( approximately 10 weeks old) compost.
EC
mS/cm pH Org. Matter
% C % N % C/N NO3-N mg/kg
NH4-N mg/kg
NH4-N/ NO3-N Ratio
Woody 0.8 7.5 31 17.0 0.7 25 0.3 1.5 5.8
Champost 6.4 6.5 61 30.0 2.0 15 3.1 60.1 19.1
Mature 1.4 7.5 20 10.5 0.8 13 352.7 0.9 0.003
Immature 1.9 7.2 21 11.7 0.9 11 423.4 2.5 0.006
After mixing the reference soil with the different compost treatments the NO3-N/NH4-N ratio was
calculated. The results of this calculation are shown in Table 5.
Table 5 Calculated NO3-N/NH4-N ratio for the reference (T1), woody (T4), champost (T5) ,mature (T3) and immature (T2) treatment at the start of phase 1.
Volume compost% Total mg NO3-N /kg soil Total mg NH4-N /kg soil NH4-N/ NO3-N * 10
-3 Ratio
Reference 162.2 0.00 -
Woody 14% 149.0 0.12 0.81
Champost 18% 149.2 4.89 32.77
Mature 10% 177.7 0.07 0.41
Immature 11% 183.4 0.21 1.12
As seen in table 4 the C/N ratio was highest for woody compost. The EC, OM% and NH4 content was
also highest for the champost treatment. Although the NH4-N/ NO3-N ratio was higher in immature
compost compared to the mature compost it was not as high as expected. Additional analyses of the
compost materials were performed by BLGG AgroXpertus in Wageningen. However, for the analyses
solid particles >2mm were sieved out as a standard procedure which poses problems for crude
composts samples. Therefore these results are not shown and used, because the analyses are not
representative of the entire sample and overall compost material. The outcomes of the
measurements on the structure and determination of the pF-curve for the mixtures with compost
are shown in table 6 and 7.
18
Table 6 Physical soil properties, measurements were done according the European standard (EN 13041, 1999). The percentage pores is indicative of the soil porosity with values showing the average of two samples followed by standard errors. The reference treatment is the soil described in Table 3, the compost types are a mix of the reference soil and de compost types where the volumetric mixing percentage (% compost per volume soil) for added compost material was 14, 11, 10 and 18% for woody, immature, mature and champost mixtures, respectively.
Pores % Solid parts %
Reference 64.80 ± 0.30 35.20 ± 0.30
Woody 66.55 ± 0.45 33.45 ± 0.45
Immature 65.60 ± 0.10 34.40 ± 0.10
Mature 64.30 ± 0.30 35.70 ± 0.30
Champost 67.80 ± 0.20 32.20 ± 0.20
Table 7 Physical soil properties including values for pF-curve and volumetric soil moisture content at different soil tension with values showing the average of two samples followed by errors. Measurements were done according the European standard (EN 13041, 1999). Soil moisture tension is expressed as the height of the watercolumn (tension) in –cm. The reference treatment is the soil described in Table 3, the compost types are a mix of the reference soil and de compost types where the volumetric mixing percentage (%compost per volume soil) for added compost material was 14, 11, 10 and 18% for woody, immature, mature and champost mixtures, respectively.
Moisture % -3cm Moisture % -30cm Moisture % -50cm Moisture resaturation
pF 0 pF 1,5 pF1,7 pF 0
Reference 55.45 ± 0.05 51.25 ± 0.35 47.40 ± 0.10 55.95 ± 0.25
Woody 55.55 ± 0.15 50.75 ± 0.45 45.55 ± 0.45 56.80 ± 0.30
Immature 55.60 ± 0.10 51.35 ± 0.35 47.90 ± 0.60 56.35 ± 0.25
Mature 54.80 ± 0.20 51.25 ± 0.15 48.80 ± 0.00 55.90 ± 0.30
Champost 55.90 ± 0.00 52.00 ± 0.50 46.75 ± 0.15 57.45 ± 0.05
Air % -3cm Air % -30cm Air % -50cm Air resaturation
pF 0 pF 1.5 pF1.7 pF 0
Reference 9.35 ± 0.35 13.55 ± 0.65 17.35 ± 0.45 8.90 ± 0.60
Woody 11.00 ± 0.30 15.75 ± 0.85 21.00 ± 0.90 9.75 ± 0.15
Immature 9.90 ± 0.00 14.20 ± 0.20 17.70 ± 0.50 9.25 ± 0.15
Mature 9.50 ± 0.10 13.05 ± 0.45 15.50 ± 0.30 8.40 ± 0.60
Champost 11.90 ± 0.20 15.75 ± 0.65 21.05 ±0.35 10.35 ± 0.25
Although champost had the highest pore volume followed by woody compost differences across
treatments appeared to be relatively small (table 6) The section pressure height showed that the
moisture content in the mature compost treatment was highest for all treatments (48.8%) at -50cm
and while for woody compost it appeared the lowest (45.6%). Which implies that woody compost
releases water more easily. But again differences were relatively small.
To determine the reproduction of nematodes of a baseline assessment for nematode counts for
Treatments 1 through 5 was made. The number of Meloidogyne spp. in the reference soil and the
different compost treatments at the start of the trial are presented in table 8
19
Table 8 Nematode countings for the different groups for reference-, immature-, mature-, woody- and champost treatments . The tropical Meloidogyne spp.are thermoplylic RKN and counts are including m. incognita and m. javanica. Determination was done by counting the juveniles per 100 ml soil.
Sample-description
Meloidogyne fallax
Meloidogyne hapla
Meloidogyne tropical
Meloidogyne total
Saprofage nematodes
Reference 24 72 899 995 2095
Immature 18 76 756 850 2690
Mature 16 137 1052 1205 2320
Champost 32 57 571 660 7460
Mulch/woody 26 44 720 790 4955
From table 8 it is evident that although all treatments had a basic infection of RKN there were some
differences among treatments. More specifically, in terms of m. incognita and m. javanica the
mature compost treatment had relatively high values (1052) as compared to the champost (571)
treatment. The mature compost was checked for contamination of Meloidogyne, however, no
contamination was established.
3.3 Results Research Question 1 In this section the general results are presented for all the treatments as related to Research
Question 1, i.e., can the control of RKN be improved (Meloidogyne spp.) by adding compost,
additives, antagonists or stackings thereof when compared the untreated reference in organic
greenhouse tomatoes grown in the Netherlands?
3.3.1 Nematode assessment
Prior to the analyses, a correlation between the RKI and Fresh root weight was investigated. The
correlation was significant (P<0.05) but with a R2 of 0.034 (data not shown) it was assumed not to
have much influence on the overall RKI determination. Therefore, the analyses was performed
without correcting for root weight. The values of the RKI for all treatments are shown in Fig 5
whereby values for specific treatments where compared using a post hoc tukey-b test with those for
the reference treatment.
20
Fig. 5 Visual nematode assessment based on the Root knot index (RKI). Abbreviations coding sequence is related to composting type, followed by soil additives, and soil organisms based on the following abbreviations: 0 = none; Compost type: Im = immature compost, Ma = mature compost, Wo = woody compost, Ch = champost; Soil addiditives Gy = Gypsum, Li = Lime, Si = Basaltlavameal, Ci = chitine; Soil organisms: Tr = Trichoderma harzianum strain T22., Pe = Paecilomyces lilacinus strain 251, Ba = Bacillus firmus NCCB 48015 . Bars marked in green are indicative of treatments being significantly different (P<0.05) from the non-amended reference treatment (0/0/0) based on a Post-Hoc performed Tukey-B test (n=478).
From Fig. 5 it is evident that treatments Wo/0/0, Ch/Si/0 and Wo/0/Tr were significant different
(P<0.05) compared to the reference treatment. In Appendix A4-1 more detailed tables of the
statistical analyses are shown. In Paragraph 3.4 – 3.7 the results are further analysed in terms of
assessing if there were significant synergetic or antagonistic effects within the used stackings. In Fig
6, the outcomes are shown for the second stage juvenile counts.
Fig. 6 Second stage juvenile counts expressed as number per gram fresh weight root (#J2/gr FR). The formula used for the natural logarithm transformation was LN(2*#J2/gr FR+1) to approximate a normal distribution, the values were back transformed with the inverse function of the logarithm. The X axis shows the treatments and the Y-axis the RKI. Treatment abbreviations are 0 = none, Im = immature compost, Ma = mature compost, Wo = woody compost, Ch = champost, Gy = Gypsum, Li = Lime, Si = Basaltlavameal, Ci = chitine, Tr = Trichoderma harzianum strain T22., Pe = Paecilomyces lilacinus strain 251, Ba = Bacillus firmus NCCB 48015. The bars are marked green when they have a significant difference (P<0.05) with the reference treatment which is 0/0/0. Post-Hoc performed was Tukey-B and n=239
4.0 4.0
4.3
3.4*
3.8
4.2 4.1 4.0
3.8 3.9
4.2 4.1
4.0 4.0
3.5*
4.1 3.9
3.7*
4.0 3.9
3
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5R
KI
(0-1
0)
Treatment
21.0
33.3
53.3
3.9* 6.8
54.1
18.6 25.6
8.4 10.2
27.8
12.0 13.1 13.0 7.7
81.8
23.9
5.8
31.0
9.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
#J2
/gr
FR
Treatment
21
The woody compost treatment (Wo/0/0) had significantly lower nematode counts (P<0.05)
compared to the non-amended reference treatment (Fig. 6). When compared to the results based on
RKI (Fig. 5), there were three treatment that showed significant differences with the references. An
explanation could be the high dispersion in the data of the J2 counts. In Appendix 4-2 the detailed
results for the statistical analyses are shown. It was observed that the correlation between the RKI
and log-transformed second stage juvenile counts per gram fresh weight root showed a R2 of 0.184
which is considered to be rather low.
3.3.2 Crop performance
The overall effect of treatment combinations on both total above-ground biomass is shown in Fig. 7.
Fig. 7 Above-ground biomass dry matter (DM) accumulation (gr plant-1
) which is the sum of fruit, stem and leaf dry weights. The X axis shows the treatments and the Y-axis dry weight above mass (gr). Treatment abbreviations are 0 = none, Im = immature compost, Ma = mature compost, Wo = woody compost, Ch = champost, Gy = Gypsum, Li = Lime, Si = Basaltlavameal, Ci = chitine, Tr = Trichoderma harzianum strain T22., Pe = Paecilomyces lilacinus strain 251, Ba = Bacillus firmus NCCB 48015. The bars are green when they have a significant difference (P<0.05) with the reference treatment which is 0/0/0. Post-Hoc performed was Tukey-B and (n=478).
From Fig. 7 it is evident that the champost, chitin and the triple stacking Im/Li/Ba treatments had a
higher above-ground biomass accumulation compared to the reference treatment. Furthermore,
analyses of plant height (cm) showed that treatment 0/Ci/0, Wo/0/Tr and Im/Li/Ba had a higher total
plant height compared to the reference treatment (P<0.05). The dry weight of the roots is shown in
Fig. 8 with treatment 0/Ci/0 showing a significant difference (P<0.05) compared to the reference
treatment
28.2
32.0 32.6 33.8
42.0*
29.0 29.6
32.6
45.4*
29.3 31.8
30.5
44.0*
33.1
39.8*
34.0 32.4
34.3
43.2*
39.5*
20
25
30
35
40
45
50
Ab
ove
-gro
un
d b
iom
ass
(gr)
Treatment
22
Fig. 8 Dry matter (DM) root gr plant-1
. The X axis shows the treatments and the Y-axis dry weight root (gr). Treatment abbreviations are 0 = none, Im = immature compost, Ma = mature compost, Wo = woody compost, Ch = champost, Gy = Gypsum, Li = Lime, Si = Basaltlavameal, Ci = chitine, Tr = Trichoderma harzianum strain T22., Pe = Paecilomyces lilacinus strain 251, Ba = Bacillus firmus NCCB 48015. The bars are green when they have a significant difference (P<0.05) with the reference treatment which is 0/0/0. Post-Hoc performed was Tukey-B and (n=238)
3.3.3 Correlation crop growth and nematodes
To determine if above-ground total dry weight
and root dry weight were correlated, a
bivariate correlation was performed for which
the R2 value was 0.614. Which is considered to
be relatively high. In terms of this correlation it
implies that the overall shoot root ratio was
somewhat constant.
For the RKI there was also a correlation
(P<0.05) with above-ground biomass dry
weight although the value was only 0.040. For
the second stage juveniles count number his
correlation was also significant (P<0.05) while the R2 value again was low (0.087). Since both values
are considered to be low, it was thus assumed that neither RKI nor the amount of second stage
juveniles affected growth.
3.09 3.64
4.09 3.62
4.26
3.66 3.46 3.72
5.57*
3.20 3.75
3.51
4.48 4.38 4.01
4.41
3.37 3.67
4.71 5.05
0.0
1.0
2.0
3.0
4.0
5.0
6.0D
ry w
eig
ht
roo
t (g
r)
Treatment
Fig. 9 Correlation between dry weight above biomass (gr) and dry weight roots (gr) n=238
23
3.3.4 Root health
To determine if treatments affected overall root health, a visual assessment of the brown color of the
roots was made. The browning was assumed to be linked to incidence of Fusarium but this was not
validated by laboratory analysis via plating techniques. The results for the visual scoring for different
treatment combinations are shown in Fig. 10.
Fig. 10 Browning of tomato roots assumed to be linked to fusarium infection with roots being visually rated from 1= little 2=medium 3=high infection. Treatment abbreviations are 0 = none, Im = immature compost, Ma = mature compost, Wo = woody compost, Ch = champost, Gy = Gypsum, Li = Lime, Si = Basaltlavameal, Ci = chitine, Tr = Trichoderma harzianum strain T22., Pe = Paecilomyces lilacinus strain 251, Ba = Bacillus firmus NCCB 48015. The bars are green when they have a significant difference (P<0.05) with the reference treatment which is 0/0/0. Post-Hoc performed was Tukey-B and n=472.
An overview of the level of Fusarium spp. infection of the root system based on brown discoloration
of the roots systems is shown in Fig. 10 . The reference treatment had the highest score. With
exception of the Im/0/0 treatment, all compost-based treatments had significantly (P<0.05) less
browning of the roots which appears to be indicative of lower potential Fusarium spp. infection
scores compared to the reference treatment.
2.9
2.5
2.1* 2.0* 1.9*
2.4
2.5 2.5
2.3*
2.7 2.6 2.6
2.1* 2.1*
1.8* 1.8*
2.3*
1.8* 1.7*
2.2*
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3
Bro
wn
ing
roo
ts 1
= lit
tle
3 =
mu
ch
Treatment
24
3.4 Results Research Question2
In this paragraph results related to Research Question 2 are being presented. The section is divided in
subsections as related to the stackings for champost-, woody compost, mature- and immature
compost-based treatment combinations.
3.4.1 Champost-based stackings
As discussed in Section 3.3, champost compost-based treatment combined with Basaltlavameal (si)
showed a significant decline in RKI, analysed of all treatments. Results in terms of effects on
nematode incidence and plant growth parameters for champost-based stacking are presented in
Table 9.
Nematode assessment
The champost and basaltlavameal stacking resulted in a synergy among main means as it caused a
significant reduction (F=4.503; P<0.05) in RKI as compared to either single means such as champost
(Ch) or Basaltlavameal (Si) application. In terms of 2nd Stage Juvenile Counts (#J2/gr FR) no synergetic
effect (P>0.05) associated with stacking of treatments was found.
Crop performance
In terms of crop growth the L-matrix analyses showed that the stacking for the above-ground dry
weight resulted in an antagonistic effect (F=4.341; P<0.05) and for dry weight roots there was no
clear stacking effect (P>0.05).
Table 9 Effects of champost-based stacking on Root Knot Index (RKI, 0-10), second stage juvenile counts per fresh weight root mass ([#J2/gr FR], shoot and root dry weight of the champost stacking. Champost (Ch) and Basaltlavameal (Si). Mean separation was based on a Tukey-B Post Hoc performed test.
RKI (0-10)
n=96 [#J2/gr FR]
n=48 Dry weight Shoot (gr)
n=96 Dry weight root (gr)
n=48 Fresh Weight Root (gr)
n=96
0/0/0 4.0bc 21.0b 28.2b 3.1a 32.1b
Ch/0/0 3.8b 6.8a 42.0a 4.3a 43.8a
0/Si/0 4.0c 25.6b 32.6b 3.7a 36.0ab
Ch/Si/0 3.5a 7.7a 39.8a 4.0a 40.9ab
25
3.4.2 Woody compost-based stackings
As discussed in Section 3.3, woody compost-based treatments showed a significant decline in RKI,
analysed of all treatments. The effect of woody compost-based stacking on nematodes and plant
growth are presented in Table 10.
Nematode assessment
Analyses with L-matrix using contrasts resulted in an antagonistic effect of the stacking of woody
compost (Wo) with T. harzianum (Tr) in terms of the RKI values (F=6.797; P<0.05). This implies that
when Tr and Wo are being used simultaneously the effect is not amplified when compared to single
means of either Tr or Wo separate. Similar effects where observed for 2nd stage juvenile densities
(F=4.386; P<0.05)
Crop performance
Based on the L-matrix analyses it also appears that there was no significant effect (P>0.05) of the
stacking on the above-ground dry matter accumulation and the dry weight of the roots (Table 10)
when compared to single means of either Tr or Wo separate.
Table 10 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the woody stacking. Abbreviations are woody (Wo) and Trichoderma harzianum strain T22 (Tr). Post Hoc test performed was Tukey-B
RKI (0-10)
n=96 #J2/gr FR n=48
Dry weight Shoot (gr) n=96
Dry weight root (gr) n=48
0/0/0 4.0c 21.0b 28.2b 3.1a
Wo/0/0 3.4a 3.9a 33.8a 3.6a
0/0/Tr 3.9c 10.2ab 29.3b 3.2a
Wo/0/Tr 3.7b 5.8a 34.3a 3.7a
26
3.4.3 Mature compost-based stackings
The effect of mature compost-based stackings on nematodes and plant growth are presented in
Table 11. In this case there were incidences of both double (two factors) and triple (three factors).
More specifically, mature compost-based systems were compared to those stacked with either chitin
or P. lilacinus. The triple mode action (Mature compost + chitin + P. lilacinus) was compared to
either one of the double mode action (Mature compost + P. lilacinus and Mature compost and chitin)
treatments.
Table 11 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the mature stacking. Treatment abbreviations are Mature (Ma), Chitin (Ci) and Paecilomyces lilacinus strain 251 (Pe). Post Hoc test performed was Tukey-B
RKI (0-10) #J2/gr FR Dry weight Shoot (gr) Dry weight root (gr)
n=166 n=83 n=166 n=83
0/0/0 4.0abc 21.0abc 28.2b 3.1b
Ma/0/0 4.3c 53.3cd 32.6b 4.1ab
0/Ci/0 3.8a 8.4a 45.4a 5.5a
0/0/Pe 4.2bc 27.8bcd 31.8b 3.8ab
Ma/Ci/0 4.0ab 13.1ab 44.0a 4.5ab
Ma/0/Pe 4.1bc 81.8d 34.0b 4.4ab
Ma/Ci/Pe 4.0bc 31.0bcd 43.2b 4.7ab
Nematode assessment
For the RKI there was a synergetic effect (F=6.173; P<0.05) of stacking mature compost with P.
lilacinus compared to applying P. lilacinus separate. But the RKI values were still above the reference
treatment. The #J2/gr FR count did not show any significant stacking effects (P>0.05)
Crop performance
The effect of stacking of mature compost-based with chitin on shoot dry matter accumulation was
antagonistic (F=3.928; P<0.05) since stacking mature compost + chitin resulted in lower shoot dry
weights compared to applying chitin separate. In Appendix A5.3 the statistical analyses of this
synergetic and antagonistic effects can be found. No significant (P>0.05) effects were found.
27
3.4.4 Immature compost-based stackings
The effect of immature compost-based stacking on nematodes and plant growth are presented in
Table 12. In this case there were incidences of both double (two means) and triple (three means).
More specifically, immature compost-based systems were compared to those stacked with either
lime or B. firmus. The triple mode action (Immature compost + lime + B. firmus) was compared to
either of the double mode action (Immature compost with B. firmus and Immature compost with
lime) treatments.
Table 12 Means of the RKI (0-10), #J2/gr FR, dry weight of the shoot and fresh weight root of the immature stacking. Treatment abbreviations are Immature (Ma), Lime (Li) and Bacillus firmus NCCB 48015 (Ba). Post Hoc test performed was Tukey-B
RKI (0-10) #J2/gr FR Dry weight Shoot (gr) Dry weight root (gr)
n=168 n=84 n=168 n=84
0/0/0 4.0ab 21.0a 28.2b 3.1b
Im/0/0 4.0ab 33.3a 32.0b 3.6ab
0/Li/0 4.1b 18.6a 29.6b 3.5ab
0/0/Ba 4.1b 12.0a 30.5b 3.5ab
Im/Li/0 4.0ab 13.0a 33.1b 4.4ab
Im/0/Ba 3.9ab 23.9a 32.4b 3.4ab
Im/Li/Ba 3.9a 9.1a 39.5a 5.0a
Nematode assessment
For the RKI there was a synergetic effect (F=4.020; P<0.05) of stacking immature compost with B.
firmus compared to applying B. firmus separate and for stacking immature compost with lime
compared to applying lime separate. The #J2/gr FR count did not show any significant stacking
effects (P>0.05)
Crop performance
For above-ground dry weight (gr) there was a synergetic effect (F=3.090; P<0.05) of stacking
immature compost + lime + B. firmus compared to immature compost + B. firmus while other
stacking effects were not significant (P>0.05). In Appendix A5-4 more detailed statistical analyses of
synergetic and antagonistic effects can be found.
28
4 Discussion
4.1 Research Question 1 For the statistical analysis conducted to address Research Question 1, a total of twenty treatments
were included which were outlined in Section 2.1. The woody compost-based stackings typically
reduced RKI (P<0.05) both if compost occurred as single means but also when it was stacked with T.
harzarium compared with the reference treatment. The RKI for stacking based on either champost
and basaltlavameal was also significant lower (P<0.05) compared to the reference. Similarly, for 2nd
stage juvenile per gram fresh weight root were significant (P<0.05) lower for woody compost
separate compared to unammended reference treatment.
Result thus revealed that the use of woody compost had a suppressive effect on RKN (Fig. 5 and Fig.
6). This may be attributed to the relatively high C/N ratio, lignin content and pH values of the
compost material. Research on soil suppressiveness and functional diversity of the soil microflora in
organic farming systems in the Netherlands showed that soils with i.a. a high C/N quotient favour
high populations of antagonistic Streptomyces spp. (Postma et al, 2008). Similar observations were
made for microbial communities in boreal forest soils (Hogberg et al, 2006) while there is also a
close correlation between C/N ratio and bacterial and eukaryotic community structures (Marschner
et al, 2003). Therefore, it appears that indigenous antagonistic organisms may be stimulated due to
an increase in C/N ratio of added organic amendment. Furthermore, not only the C/N ratio has effect
on the microbial community since the type of carbon content also has an influence on this
composition as well. For an example, it was shown that lignin amendments changed soil microbial
community structure (Beneden et al, 2010). For the woody material used in this research it is
supposed that it had a relatively high lignin content in comparison to the other compost types.
Another factor governing soil suppressiveness is the pH of either the soil amendment and its effect
on soil pH. The woody compost had a pH of 7.5 (Table 4) and the reference soil a pH of 6.5.
Therefore, it is assumed that there was a pH increase due to the addition of the woody compost. The
fungal to bacteria ratio in soil systems is influenced by pH (Hogberg et al, 2006). Generally it can be
said that fungal pathogens prefer lower pH while bacteria thrive better in high pH environments
(Oka, 2014).
In terms above-ground biomass production, use of woody did not enhance crop growth. This could
be explained by the higher C/N of this material which may increase the potential risk of (initial)
immobilisation of nitrogen compared to the reference treatment. However, over time it is to be
expected that that net mineralization will be relative higher compared to the reference treatment
29
due to the decomposition of the added woody compost material. Which is in agreement with the
observed results since there was no negative effect seen on growth. Another reason why growth
was not stimulated may be related to an increase of pH potentially reducing the availability and
uptake of nutrients as iron (Finck, 1976).
For the pure champost treatment it is noticeable that it had an high EC and OM%, which were shown
to have a suppressive effect on RKN (Wurff, unpublished data 2014) Furthermore, the champost had
a higher ammonium to nitrate ratio compared to the reference treatment. Research showed that use
of N materials with a higher ammonium to nitrate ratio resulted in a reduction in severity of root
knot nematodes one month after application (Spiegel et al, 1982). The measurements of the pure
champost showed a composition of 19 to 1 (N-NH4 to N-NO3). Furthermore, shoot growth was also
stimulated (P<0.05) by the champost addition compared to the reference treatment. Elaborating on
this, it may be argued that the high EC due to higher nutrient amounts, OM% and nitrogen contents
could have had a positive effect on overall plant growth. Also the addition of chitin showed an
increase in above-ground plant dry weight, which could be explained by the addition of nitrogen
within the chitin (Kumar and Majeti, 2000) whereas the extra nitrogen could promote plant growth.
In woody compost and the champost the structure of the soil may have also changed compared to
the reference treatment. Research with the migration of J2 of M. incognita showed that presence of
large macropores prevented J2 juveniles from migrating (Otobe et al, 2004). This may partially
explain why both the champost as the woody compost treatments had lower nematode densities
and scores. In this case an increase of larger sized pores due to the addition of these materials that
still contain a large percentage of rather large/crude components may have cause discontinuities in
the soil matrix which could have hampered to movement and spread of nematodes throughout the
soil matrix.
4.2 Research Question 2. For Research Question 2, four stackings were included in this research: Woody compost and
antagonist T. harzianum, champost with lava basalt meal and silicon, and immature compost with
lime and B. firmus and mature compost with chitin and P. lilacinus.
For the stacking of Woody compost with T. harzianum it was hypnotized that this would enhance the
growth of T. harzianum as suggested by various authors (Nelson et al, 1983; Hoitink and Boehm,
1999). However, based on RKI and #J2/gr FR counts there was no evidence that this treatment
combination resulted in a decline of nematodes as the RKI for this stacking did not decrease
compared to applying woody compost separate.
30
For champost treatments, a stacking with basaltlavameal resulted in a synergetic effect in terms of
reducing RKI values (which is indicative of reduced severity of nematodes infection) compared to the
usage of champost or basaltlavameal alone. The absorbability of silicon by the plants is dependent on
the structure and moisture content of the soil.
It was reported that redoxpotential (Eh) decreases as the soil water content in a Yolo silt loam
increased and anaerobic microsites start dominating the soil system . The decrease of the Eh was not
only high at high saturation levels, but at 58% to 60% of the pores filled with water Eh was already
decreased drastic. It was discussed that this effect occurred because the smaller pores where
completely filled with water and gradually became anaerobic. In this context, it is relevant to note
that microbial activity is often concentrated in micropores (Za´ rate-Valdez, 2006). Moreover, a
decrease in soil Eh could increase the solubility of soil Si (Ponnamperuma, 1965). Although structure
of the soil changed in terms of an increased number pores and less solid parts due to addition of
champost (see Table 6 and 7) no clear effects on soil moisture content could be discerned. More
detailed measurements thus are needed in future studies to determine whether more soluble Si was
available due to champost addition. Furthermore, research with transplanted rice showed that when
applying Si, the uptake of phosphorus, calcium, magnesium and the formation of carbohydrate was
increased but for nutrients as nitrogen, potassium, iron the uptake decreased (Islam and Saha, 1969)
Also ammonium acetates were used as extractant for soil Si (Sauer et al.,2006) and analysis showed
that there is a direct relation between soil nutrient composition and plant available Si.
Although synergetic effects for the RKI (P<0.05) were observed for mature compost-based stacking
with P. lilacinus compared to applying P. lilacinus alone, the RKI was not lower compared to the
reference treatment. The #J2/gr FR values for the mature compost + P. lilacinus was highest of all
treatments within the mature compost stacking. Therefore, it is evident that there was no synergistic
suppressive effect on RKN for this specific stacking combination. Furthermore, nematode counts at
the start of Phase 1 showed a higher number of thermophilic Meloidogyne nematodes in the mature
compost treatment compared to the reference treatment. However, there is no clear explanation for
the increase nematode counts due to the application of mature compost. Therefore, further
biochemical and microbial analyses of the compost material would be needed to explain potential
differences.
31
Immature compost-based stacking had no suppressive effect on RKI or #J2/gr FR values. This is in
contrast with reports in the literature were it was documented that with an increase in the
ammonium to nitrate ratio, the incidence of nematodes per mm root would decrease (Spiegel et al,
1982). However, overall nitrate values were relatively high. Moreover, it should be noted that both
immature and mature compost had a relative low organic matter content which reduces its
effectiveness as a soil amendment as related to soil structure and nematode suppressing properties.
32
5 Conclusion
Before starting this research two hypothesises were formulated. The first hypotheses was that
the use of compost, soil additives and bio-organism will enhance or maintain plant growth and
reduce the RKI and/or #J2/gr FR compared to the non-amended group. Table 13 provides an
overview of the outcomes related to the first hypotheses.
Table 13 Hypotheses acception or rejection for the RKI, #J2/gr FR, Dry weight shoot and root
Significant difference Hypotheses accepted for 1 or more
treatments
RKI Significantly lower (P<0.05) in
Wo/0/0, Ch/0/0 and Wo/0/Tr
Yes
#J2/gr FR Significantly lower (P<0.05) in woody
compost (T4)
Yes
Dry weight shoot Significantly higher (P<0.05) in
treatments Ch/0/0, Ch/Si/0, 0/Ci/0,
Ma/Ci/0, Ma/Ci/Pe and Im/Li/Ba. No
treatments where significantly lower
than the reference treatment 0/0/0.
Yes
Dry weight root Significantly higher (P<0.05) in
treatments Ch/0/0, Ch/Si/0, 0/Ci/0
Yes
As seen in Table 13, none of the treatments showed a decline in shoot or root dry weight compared
to the reference treatment. Thus all amendments sustained or even enhanced overall crop growth.
For the nematodes assessment the hypotheses for the RKI was accepted for treatment Wo/0/0,
Ch/0/0 and Wo/0/Tr while for the second stage juvenile count density the hypotheses was accepted
for Wo/0/0. Furthermore RKI had a correlation (P<0.05) with R-squared value of 0.040 with above-
ground dry weight (gr) while for second stage juvenile count density this correlation (P<0.05) with
above dry weight was this value was not much higher (R2 = 0.079). Both values are considered to be
low which may be related to regression slopes being close to zero. Therefore it is assumed that RKI
and #J2/gr FR did not have a pronounced effect on root growth.
The second hypotheses was based on the stacking of different means and was formulated as follow:
The RKI and #J2/gr FR is higher in singular or double means treatment compared to treatments that
entail a stacking of double or triple means. In Table 14 an overview of the conclusions is given.
33
Table 14 Conclusions and hypotheses acception or rejection for the effect of the stackings with immature, mature, woody and champost compost.
Stacking RKI #J2/gr FR
Immature Hypothesis accepted because stacking immature
compost with B. firmus resulted in a synergetic
effect (F=4.020; P<0.05) in terms of a lower RKI
compared to applying B. firmus separate. This
also complied (F=4.020; P<0.05) for stacking
immature compost with lime compared to
applying lime separate.
Hypothesis rejected as there was
no clear (significant) effect.
Mature Hypothesis accepted because stacking mature
compost with P. lilacinus resulted in a synergetic
effect (F=6.173, P<0.05) in terms of a lower RKI
compared to applying P. lilacinus separate.
Hypothesis rejected as there was
no clear (significant) effect.
Woody Hypothesis rejected as there was an antagonistic
effect Antagonistic effect of the stacking woody
compost with T. harzianum (F=6.797; P<0.05).
Hypothesis rejected as there was
an antagonistic effect of the
stacking woody compost with T.
harzianum (F=4.386; P<0.05)
Champost Hypothesis accepted because a synergetic effect
(F=4.503; P<0.05) of the stacking champost with
basaltlavameal was found.
Hypothesis rejected as there was
no clear (significant) effect.
As seen in Table 14 the immature, mature and champost showed one or more synergetic effects
according to the L-matrix. Although there were synergetic effect found in the immature and mature
compost-based stacking it didn’t result in a decline in RKI or #J2/gr FR. For the champost stacking the
stacking did reduce the RKI compared to applying the means separate.
34
6. Recommendations
In applied research, it is important to be as close as possible to actual practice, i.e., to mimic the
conditions of the greenhouse of organic vegetable growers. Therefore, all means where in
agreement with SKAL regulation for organic agriculture in the Netherlands. One of the aims of this
research was to translate the results to practice. However, a few difficulties arose in translating the
results to practise: This research was performed in pots whereas the organic growers grow their
vegetables in the soil. The use of pots allows for fine-tuning different environmental conditions as
moisture content and to implement many different treatments in a limited amount of space. The
duration of this research was 12 weeks whereas the cultivation of tomatoes is a year. Some of the
measures, such as woody compost, may show good results during the first two months of the
cultivation but have none or even negative effect during the rest of the growing season because for
example of decomposition of material.
The advantage of using commercially available compost is that outcome of the experiments can be
used immediately. For example, the champost is a waste product from mushroom growers, which
presents a win-win situation for mushroom- and vegetable growers. The woody compost is a fraction
of the compost that is normally sieved out to produce, immature and mature compost.
According to this research, the woody and champost compost have the highest potential for further
research. The underlying mechanism is not studied here, however, the high NH4 load might kill
nematodes in soil. Because of the relative high NH4-N/ NO3-N ratio of champost, it would be an idea
to increase the pH in combination with the addition of champost in a next research to stimulate the
conversion of NH4 to NH3. Relating to plant toxicity the threshold value of NH4 and NH3 have to be
determined to maximize RKN decline and minimize plant growth reduction. Applying champost in the
period before planting in a bare soil would have the advantage that a temporally increase in pH and
NH3 would not directly affect plant growth. Further research is also needed to determine the time
span and amount that are needed to supress RKN and prevent plant toxic effects. The amount and
time used in this research didn’t show negative effect on plant growth. Furthermore for woody
compost it would be interesting to study mechanisms behind the suppressive effect of woody
material against RKN as discussed in the discussion chapter. In this study it would be interesting to
make distinction between different types of woody material.
35
At the start of phase 1 many plants were destroyed by wood-lice (Armadillidium spp.). To prevent
this potential problem during subsequent studies, preventive addition of slug pellets may be
advisable. Frequent and careful observation especially on pest and deceases is also needed directly
after planting. In a research with RKN it is important to minimize the effect of others factors outside
the research goals. For example in this research calculations were made to prevent plant to have a
deficiency of nutrients, therefore every week measurements of NO3 in the soil of the border plants
were performed to start with fertilizing before a deficiency of NO3 would occur.
Finally, cautious is needed to use this results directly in practice since this is a preliminary study. It is
recommended to do more research on the arguments discussed in above paragraphs.
36
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Columbia root-knot nematode resistance introgressed from Solanum bulbocastanum. Crop Science
47: 2021–2026 .
Websites and others
1. http://dictionary.reference.com/browse/synergy
2. Konica Minolta. 2009. Manual Chlorophyll meter SPAD 502 Plus
3. Oka, Y. 2014., personal communication
4. Wurff van der, A.W.G. 2014. Unpublished data
5. Wurff van der, A.W.G. 2014. PPS, werkplan duurzame bodem kasteelt. TKI number KV1309
084
40
Appendices
Appendix A1 Score chart used to determine Root Knot Index (RKI) 1 values
1 Source: Wurff1 van der, A.W.G., Janse1, J., Kok2, C.J., Zoon2, F.C. 2010. Biological control of root knot
nematodes in organic vegetable and flower greenhouse cultivation. 1Wageningen UR Glastuinbouw,
Bleiswijk. 2Plant Research International, Wageningen. Wageningen UR Glastuinbouw. Report 321.
41
Appendix A2 Experimental map layout
42
Appendix A3-1 Temperature and radiations levels in the greenhouse.
The next figures show the realized temperature and artificial lightning during the research.
Fig. 11 temperature °C realization during the total research period of phase 1 and 2. At -12 DAP the transplants were placed in the greenhouse and therefore temperature heating set point was set to 20/20 °C Day/Night
Fig. 12 artificial lighting realization during the total research period of phase 1 and 2. Values given are average values in PAR µmol/m
-2/s
-1
As seen in Fig. 11 the temperature realized from -33 dap till -12 dap is colder compared to the period
from -12 DAP till 57 DAP. In this cold period a biological equilibrium was meant to be set without
losing to many nematodes in the soil, because the life cycle period of the RKN is temperature
dependent. For the artificial lighting the length of lighting per day was dependent on the outside
radiation.
0
5
10
15
20
25
-33
-29
-25
-21
-17
-13 -9 -5 -1 3 7
11
15
19
23
27
31
35
39
43
47
51
55
Tem
per
atu
re °
C
DAP
0
20
40
60
80
100
120
-33
-29
-25
-21
-17
-13 -9 -5 -1 3 7
11
15
19
23
27
31
35
39
43
47
51
55
Avg
PA
R µ
mo
l/m
-2/s
-1
DAP
43
Appendix A3-2 Irrigation realization
February March
DAP Day Watering per plant (ml) DAP Day Watering per plant (ml)
13 1 220 41 1 400
14 2 150 42 2 150
15 3 100 43 3 340
16 4 100 44 4 300
17 5 100 45 5 350
18 6 100 46 6 300
19 7 50 47 7 300
20 8 50 48 8 300
21 9 100 49 9 300
22 10 50 50 10 300
23 11 50 51 11 350
24 12 50 52 12 350
25 13 50* 53 13 350
26 14 140 54 14 300
27 15 100 55 15 250
28 16 50
29 17 100
30 18 100
31 19 100
32 20 100
33 21 180
34 22 120
35 23 100
36 24 300
37 25 250*
38 26 100
39 27 300*
40 28 No data * extra watering was performed which differed per plant depending on the plant needs determined by the weight of the pot.
** From < DAP 13 no data is registered
44
Appendix A4 Overall statistical analyses
Appendix A4-1 RKI
Between-Subjects Factors
Name Value Label N
1,00 0/0/0 24
2,00 Im/0/0 24
3,00 Ma/0/0 24
4,00 Wo/0/0 24
5,00 Ch/0/0 24
6,00 0/Gy/0 24
7,00 0/Li/0 24
8,00 0/Si/0 24
9,00 0/Ci/0 24
10,00 0/0/Tr 24
11,00 0/0/Pe 23
12,00 0/0/Ba 24
13,00 Ma/Ci/0 23
14,00 Im/Li/0 24
15,00 Ch/Si/0 24
16,00 Ma/0/Pe 24
17,00 Im/0/Ba 24
18,00 Wo/0/Tr 24
19,00 Ma/Ci/Pe 24
20,00 Im/Li/Ba 24
Repeatment N
1 60
2 59
3 60
4 60
5 60
6 60
7 59
8 60
45
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of
Squares df
Mean
Square F Sig.
Intercept Hypothesis 7409.926 1 7409.926
20098.72
8 .000
Error 2.581 7.000 ,369a
Name Hypothesis 22.214 19 1.169 9.892 .000
Error 53.305 451 ,118b
Repeatmen
t
Hypothesis 2.581 7 .369 3.119 .003
Error 53.305 451 ,118b
a. 1,000 MS(Repeatment) + 3,831E-5 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatme
nt) Var(Error)
Quadratic
Term
Intercept 59.737 1.000
Intercept,
Name
Name 0.000 1.000 Name
Repeatment 59.739 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the
coefficients in the cells times the variance components, plus a quadratic
term involving effects in the Quadratic Term cell.
b. Expected Mean Squares are based on the Type III Sums of Squares.
46
RKI
Tukey Ba,b,c
Name N
Subset
1 2 3 4 5 6 7
Wo/0/0 24 3.3750
Ch/Si/0 24 3.5417 3.5417
Wo/0/Tr 24 3.6667 3.6667 3.6667
0/Ci/0 24 3.7500 3.7500 3.7500
Ch/0/0 24 3.7917 3.7917 3.7917 3.7917
Im/Li/Ba 24 3.8750 3.8750 3.8750 3.8750
Im/0/Ba 24 3.9167 3.9167 3.9167 3.9167 3.9167
0/0/Tr 24 3.9167 3.9167 3.9167 3.9167 3.9167
Ma/Ci/0 23 3.9565 3.9565 3.9565 3.9565 3.9565
Im/Li/0 24 3.9583 3.9583 3.9583 3.9583 3.9583
0/0/0 24 4.0000 4.0000 4.0000 4.0000
Im/0/0 24 4.0417 4.0417 4.0417 4.0417
0/Si/0 24 4.0417 4.0417 4.0417 4.0417
Ma/Ci/Pe 24 4.0417 4.0417 4.0417 4.0417
0/0/Ba 24 4.0833 4.0833 4.0833 4.0833
Ma/0/Pe 24 4.0833 4.0833 4.0833 4.0833
0/Li/0 24 4.1250 4.1250 4.1250
0/Gy/0 24 4.1667 4.1667
0/0/Pe 23 4.1739 4.1739
Ma/0/0 24 4.2500
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,118. a. Uses Harmonic Mean Sample Size = 23,896.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
47
Appendix A4-2 Natural logarithm number of juveniles per gram fresh weight root
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
2,00 Im/0/0 12
3,00 Ma/0/0 12
4,00 Wo/0/0 12
5,00 Ch/0/0 12
6,00 0/Gy/0 12
7,00 0/Li/0 12
8,00 0/Si/0 12
9,00 0/Ci/0 12
10,00 0/0/Tr 12
11,00 0/0/Pe 12
12,00 0/0/Ba 12
13,00 Ma/Ci/0 11
14,00 Im/Li/0 12
15,00 Ch/Si/0 12
16,00 Ma/0/Pe 12
17,00 Im/0/Ba 12
18,00 Wo/0/Tr 12
19,00 Ma/Ci/Pe 12
20,00 Im/Li/Ba 12
Repeatment 1 40
2 20
3 40
4 20
5 40
6 20
7 39
8 20
48
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 2652.009 1 2652.009 660.327 .000
Error 29.485 7.342 4,016a
Name Hypothesis 142.874 19 7.520 7.556 .000
Error 210.983 212 ,995b
Repeatment Hypothesis 30.334 7 4.333 4.354 .000
Error 210.983 212 ,995b
a. ,905 MS(Repeatment) + ,095 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 26.605 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 29.398 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell.
b. Expected Mean Squares are based on the Type III Sums of Squares.
49
Ln_J2_gr_root
Tukey Ba,b,c
Name N
Subset
1 2 3 4 5 6 7
Wo/0/0 12 2.1676
Wo/0/Tr 12 2.5267 2.5267
Ch/0/0 12 2.6748 2.6748 2.6748
Ch/Si/0 12 2.7981 2.7981 2.7981 2.7981
0/Ci/0 12 2.8785 2.8785 2.8785 2.8785
Im/Li/Ba 12 2.9508 2.9508 2.9508 2.9508
0/0/Tr 12 3.0610 3.0610 3.0610 3.0610
0/0/Ba 12 3.2154 3.2154 3.2154 3.2154
Im/Li/0 12 3.2934 3.2934 3.2934 3.2934 3.2934
Ma/Ci/0 11 3.3003 3.3003 3.3003 3.3003 3.3003
0/Li/0 12 3.6405 3.6405 3.6405 3.6405 3.6405
0/0/0 12 3.7628 3.7628 3.7628 3.7628 3.7628 3.7628
Im/0/Ba 12 3.8873 3.8873 3.8873 3.8873 3.8873 3.8873
0/Si/0 12 3.9542 3.9542 3.9542 3.9542 3.9542
0/0/Pe 12 4.0359 4.0359 4.0359 4.0359 4.0359
Ma/Ci/Pe 12 4.1429 4.1429 4.1429 4.1429
Im/0/0 12 4.2141 4.2141 4.2141 4.2141
Ma/0/0 12 4.6781 4.6781 4.6781
0/Gy/0 12 4.6927 4.6927
Ma/0/Pe 12 5.1038
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,995. a. Uses Harmonic Mean Sample Size = 11,946.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
50
Appendix A4-3 Above dry weight (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
2,00 Im/0/0 24
3,00 Ma/0/0 24
4,00 Wo/0/0 24
5,00 Ch/0/0 24
6,00 0/Gy/0 24
7,00 0/Li/0 24
8,00 0/Si/0 24
9,00 0/Ci/0 24
10,00 0/0/Tr 24
11,00 0/0/Pe 23
12,00 0/0/Ba 24
13,00 Ma/Ci/0 23
14,00 Im/Li/0 24
15,00 Ch/Si/0 24
16,00 Ma/0/Pe 24
17,00 Im/0/Ba 24
18,00 Wo/0/Tr 24
19,00 Ma/Ci/Pe 24
20,00 Im/Li/Ba 24
Repeatment 1 60
2 59
3 60
4 60
5 60
6 60
7 59
8 60
51
Tests of Between-Subjects Effects
Dependent Variable:
Source Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 580795.617 1 580795.617 795.894 .000
Error 5108.210 7.000 729,740a
Repeatment Hypothesis 5108.364 7 729.766 13.967 .000
Error 23563.777 451 52,248b
Name Hypothesis 13189.850 19 694.203 13.287 .000
Error 23563.777 451 52,248b
a. 1,000 MS(Repeatment) + 3,831E-5 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term Intercept
59.737 1.000 Intercept, Name
Repeatment 59.739 1.000
Name 0.000 1.000 Name Error 0.000 1.000 a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
52
Total_dry_weight_above_gr
Tukey Ba,b,c
Name N
Subset
1 2 3 4
0/0/0 24 28.2273
0/Gy/0 24 29.0338
0/0/Tr 24 29.2733
0/Li/0 24 29.5933
0/0/Ba 24 30.5471
0/0/Pe 23 31.7600
Im/0/0 24 32.0421
Im/0/Ba 24 32.3692
0/Si/0 24 32.5600
Ma/0/0 24 32.6254
Im/Li/0 24 33.0600 33.0600
Wo/0/0 24 33.7613 33.7613 33.7613
Ma/0/Pe 24 34.0355 34.0355 34.0355
Wo/0/Tr 24 34.3008 34.3008 34.3008
Im/Li/Ba 24 39.5121 39.5121 39.5121
Ch/Si/0 24 39.8213 39.8213
Ch/0/0 24 42.0388
Ma/Ci/Pe 24 43.2279
Ma/Ci/0 23 44.0288
0/Ci/0 24 45.3675
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 52,248. a. Uses Harmonic Mean Sample Size = 23,896.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
53
Appendix A4-4 Dry weight roots (gr)
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
2,00 Im/0/0 12
3,00 Ma/0/0 12
4,00 Wo/0/0 12
5,00 Ch/0/0 12
6,00 0/Gy/0 12
7,00 0/Li/0 12
8,00 0/Si/0 12
9,00 0/Ci/0 12
10,00 0/0/Tr 12
11,00 0/0/Pe 11
12,00 0/0/Ba 12
13,00 Ma/Ci/0 12
14,00 Im/Li/0 12
15,00 Ch/Si/0 12
16,00 Ma/0/Pe 12
17,00 Im/0/Ba 11
18,00 Wo/0/Tr 12
19,00 Ma/Ci/Pe 12
20,00 Im/Li/Ba 12
Repeatment 1 20
2 39
3 20
4 40
5 20
6 40
7 20
8 39
54
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 3580.181 1 3580.181 113.803 .000
Error 223.210 7.095 31,460a
Name Hypothesis 93.992 19 4.947 2.179 .004
Error 479.069 211 2,270b
Repeatment Hypothesis 241.224 7 34.461 15.178 .000
Error 479.069 211 2,270b
a. ,907 MS(Repeatment) + ,093 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 26.544 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 29.273 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell.
b. Expected Mean Squares are based on the Type III Sums of Squares.
55
Dry_weight_root_gr
Tukey Ba,b,c
Name N
Subset
1 2
0/0/0 12 3.0858
0/0/Tr 12 3.2000
Im/0/Ba 11 3.3655
0/Li/0 12 3.4617 3.4617
0/0/Ba 12 3.5058 3.5058
Wo/0/0 12 3.6242 3.6242
Im/0/0 12 3.6383 3.6383
0/Gy/0 12 3.6633 3.6633
Wo/0/Tr 12 3.6742 3.6742
0/Si/0 12 3.7217 3.7217
0/0/Pe 11 3.7527 3.7527
Ch/Si/0 12 4.0067 4.0067
Ma/0/0 12 4.0908 4.0908
Ch/0/0 12 4.2583 4.2583
Im/Li/0 12 4.3833 4.3833
Ma/0/Pe 12 4.4067 4.4067
Ma/Ci/0 12 4.4800 4.4800
Ma/Ci/Pe 12 4.7092 4.7092
Im/Li/Ba 12 5.0475 5.0475
0/Ci/0 12 5.5717
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 2,270. a. Uses Harmonic Mean Sample Size = 11,892. b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
56
Appendix A4-5 Length (cm)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
2,00 Im/0/0 24
3,00 Ma/0/0 24
4,00 Wo/0/0 24
5,00 Ch/0/0 24
6,00 0/Gy/0 24
7,00 0/Li/0 24
8,00 0/Si/0 24
9,00 0/Ci/0 24
10,00 0/0/Tr 24
11,00 0/0/Pe 23
12,00 0/0/Ba 24
13,00 Ma/Ci/0 23
14,00 Im/Li/0 24
15,00 Ch/Si/0 24
16,00 Ma/0/Pe 24
17,00 Im/0/Ba 24
18,00 Wo/0/Tr 24
19,00 Ma/Ci/Pe 24
20,00 Im/Li/Ba 24
Repeatment 1 60
2 59
3 60
4 60
5 60
6 60
7 59
8 60
57
Tests of Between-Subjects Effects
Dependent Variable:
Source Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 8096163.705 1 8096163.705 15054.190 .000
Error 3764.681 7.000 537,801a
Name Hypothesis 7411.615 19 390.085 2.928 .000
Error 60086.945 451 133,230b
Repeatment Hypothesis 3764.718 7 537.817 4.037 .000
Error 60086.945 451 133,230b
a. 1,000 MS(Repeatment) + 3,831E-5 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term Intercept
59.737 1.000 Intercept, Name
Name 0.000 1.000 Name Repeatment
59.739 1.000
Error 0.000 1.000 a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
58
Length_cm
Tukey Ba,b,c
Name N
Subset
1 2 3 4
0/0/0 24 122.1125
0/0/Tr 24 124.4792 124.4792
0/0/Pe 23 126.3826 126.3826 126.3826
0/0/Ba 24 126.7667 126.7667 126.7667 126.7667
Ma/0/Pe 24 127.8946 127.8946 127.8946 127.8946
Ma/0/0 24 128.1792 128.1792 128.1792 128.1792
Im/Li/0 24 128.3333 128.3333 128.3333 128.3333
Im/0/0 24 128.9292 128.9292 128.9292 128.9292
Im/0/Ba 24 129.3792 129.3792 129.3792 129.3792
0/Li/0 24 129.4792 129.4792 129.4792 129.4792
Ch/Si/0 24 130.4125 130.4125 130.4125 130.4125
0/Gy/0 24 130.4458 130.4458 130.4458 130.4458
0/Si/0 24 130.7667 130.7667 130.7667 130.7667
Ch/0/0 24 131.2083 131.2083 131.2083 131.2083
Ma/Ci/0 23 131.3696 131.3696 131.3696 131.3696
Ma/Ci/Pe 24 131.5417 131.5417 131.5417 131.5417
Wo/0/0 24 133.2708 133.2708 133.2708 133.2708
Wo/0/Tr 24 135.7458 135.7458 135.7458
0/Ci/0 24 137.8708 137.8708
Im/Li/Ba 24 138.3458
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 133,230. a. Uses Harmonic Mean Sample Size = 23,896.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
59
Appendix A4-6 Browning roots
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
2,00 Im/0/0 24
3,00 Ma/0/0 21
4,00 Wo/0/0 24
5,00 Ch/0/0 24
6,00 0/Gy/0 24
7,00 0/Li/0 24
8,00 0/Si/0 24
9,00 0/Ci/0 24
10,00 0/0/Tr 24
11,00 0/0/Pe 23
12,00 0/0/Ba 21
13,00 Ma/Ci/0 23
14,00 Im/Li/0 24
15,00 Ch/Si/0 24
16,00 Ma/0/Pe 24
17,00 Im/0/Ba 24
18,00 Wo/0/Tr 24
19,00 Ma/Ci/Pe 24
20,00 Im/Li/Ba 24
Repeatment 1 60
2 59
3 60
4 54
5 60
6 60
7 59
8 60
60
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 2352.169 1 2352.169 423.755 .000
Error 38.861 7.001 5,551a
Repeatment Hypothesis 38.897 7 5.557 17.190 .000
Error 143.843 445 ,323b
Name Hypothesis 53.431 19 2.812 8.700 .000
Error 143.843 445 ,323b
a. ,999 MS(Repeatment) + ,001 MS(Error)
b. MS(Error)
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term Intercept
58.815 1.000 Intercept, Name
Repeatment 58.882 1.000
Name 0.000 1.000 Name Error 0.000 1.000 a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
61
Browning_roots_1little_3much
Tukey Ba,b,c
Name N
Subset
1 2 3 4 5 6
Ma/Ci/Pe 24 1.7083
Ma/0/Pe 24 1.7917
Wo/0/Tr 24 1.7917
Ch/Si/0 24 1.8333 1.8333
Ch/0/0 24 1.9167 1.9167
Wo/0/0 24 1.9583 1.9583 1.9583
Im/Li/0 24 2.0833 2.0833 2.0833 2.0833
Ma/Ci/0 23 2.0870 2.0870 2.0870 2.0870
Ma/0/0 21 2.0952 2.0952 2.0952 2.0952
Im/Li/Ba 24 2.2083 2.2083 2.2083 2.2083 2.2083
0/Ci/0 24 2.2500 2.2500 2.2500 2.2500 2.2500
Im/0/Ba 24 2.2500 2.2500 2.2500 2.2500 2.2500
0/Gy/0 24 2.3750 2.3750 2.3750 2.3750 2.3750
Im/0/0 24 2.5000 2.5000 2.5000 2.5000
0/Si/0 24 2.5417 2.5417 2.5417
0/Li/0 24 2.5417 2.5417 2.5417
0/0/Pe 23 2.5652 2.5652 2.5652
0/0/Ba 21 2.6190 2.6190 2.6190
0/0/Tr 24 2.6667 2.6667
0/0/0 24 2.9167
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,323. a. Uses Harmonic Mean Sample Size = 23,561.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
62
Appendix A5 Statistical analyses stackings
Appendix A5-1 Champost stacking
Natural logarithm J2 per gram fresh weight root
Between-
Subjects
Factors
Value Label N
Name 1,00
0/0/0 12
5,00 Ch/0/0 12
8,00 0/Si/0 12
15,00 Ch/Si/0 12
Repeatment 1 8
2 4
3 8
4 4
5 8
6 4
7 8
8 4
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of
Squares df Mean
Square F Sig.
Intercept Hypothesis 444.678 1 444.678 204.348 .000
Error 16.300 7.490 2,176a
Name Hypothesis 15.419 3 5.140 6.845 .001
Error 27.784 37 ,751
b
Repeatment Hypothesis 16.301 7 2.329 3.101 .011
Error 27.784 37 ,751
b
a. ,903 MS(Repeatment) + ,097 MS(Error)
b. MS(Error)
63
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 5.333 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 5.905 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Ln_J2_gr_root
Tukey Ba,b
Name N
Subset
1 2
Ch/0/0 12 2.6748
Ch/Si/0 12 2.7981
0/0/0 12 3.7628
0/Si/0 12 3.9542
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,751. a. Uses Harmonic Mean Sample Size = 12,000. b. Alpha = 0.05.
64
RKI
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
5,00 Ch/0/0 24
8,00 0/Si/0 24
15,00 Ch/Si/0 24
Repeatment 1 12
2 12
3 12
4 12
5 12
6 12
7 12
8 12
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 1418.344 1 1418.344 8009.471 .000
Error 1.240 7 ,177a
Name Hypothesis 3.781 3 1.260 11.119 .000
Error 9.635 85 ,113b
Repeatment Hypothesis 1.240 7 .177 1.562 .158
Error 9.635 85 ,113b
a. MS(Repeatment)
b. MS(Error)
65
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 12.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 12.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
RKI
Tukey Ba,b
Name N
Subset
1 2 3
Ch/Si/0 24 3.5417
Ch/0/0 24 3.7917
0/0/0 24 4.0000 4.0000
0/Si/0 24 4.0417
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,113. a. Uses Harmonic Mean Sample Size = 24,000.
b. Alpha = 0.05.
66
Dry weight above (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
5,00 Ch/0/0 24
8,00 0/Si/0 24
15,00 Ch/Si/0 24
Repeatment 1 12
2 12
3 12
4 12
5 12
6 12
7 12
8 12
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of
Squares df Mean
Square F Sig.
Intercept Hypothesis 122089.428 1 122089.428 1046.429 .000
Error 816.707 7 116,672a
Name Hypothesis 2948.645 3 982.882 16.573 .000
Error 5041.157 85 59,308b
Repeatment Hypothesis 816.707 7 116.672 1.967 .069
Error 5041.157 85 59,308b
a. MS(Repeatment)
b. MS(Error)
67
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 12.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 12.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Total_dry_weight_above_gr
Tukey Ba,b
Name N
Subset
1 2
0/0/0 24 28.2273
0/Si/0 24 32.5600
Ch/Si/0 24 39.8213
Ch/0/0 24 42.0388
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 59,308. a. Uses Harmonic Mean Sample Size = 24,000. b. Alpha = 0.05.
68
Dry weight roots (gr)
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
5,00 Ch/0/0 12
8,00 0/Si/0 12
15,00 Ch/Si/0 12
Repeatment 1 4
2 8
3 4
4 8
5 4
6 8
7 4
8 8
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 629.043 1 629.043 179.863 .000
Error 28.133 8.044 3,497a
Name Hypothesis 9.179 3 3.060 1.252 .305
Error 90.415 37 2,444b
Repeatment Hypothesis 25.272 7 3.610 1.477 .206
Error 90.415 37 2,444
b
a. ,903 MS(Repeatment) + ,097 MS(Error)
b. MS(Error)
69
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 5.333 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 5.905 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Dry_weight_root_gr
Tukey Ba,b
Name N
Subset
1
0/0/0 12 3.0858
0/Si/0 12 3.7217
Ch/Si/0 12 4.0067
Ch/0/0 12 4.2583
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 2,444. a. Uses Harmonic Mean Sample Size = 12,000. b. Alpha = 0.05.
70
Contrasts
Calculation: 0/Si/0 – 0/0/0 - Ch/Si/0 + Ch/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
RKI L1 Contrast Estimate -.292
Hypothesized Value 0 Difference (Estimate - Hypothesized)
-.292
Std. Error .137 Sig. .037 95% Confidence Interval for Difference
Lower Bound -.565
Upper Bound -.018
a. Based on the user-specified contrast coefficients (L') matrix: ch si interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast .510 1 .510 4.503 .037
Error 9.635 85 .113
71
Contrast Results (K Matrix)a
Contrast
Dependent Variable
Total_dry_weight_above_gr
L1 Contrast Estimate -6.550
Hypothesized Value 0
Difference (Estimate - Hypothesized)
-6.550
Std. Error 3.144
Sig. .040
95% Confidence Interval for Difference
Lower Bound -12.801
Upper Bound -.299
a. Based on the user-specified contrast coefficients (L') matrix: ch si interactie
Test Results
Dependent Variable:
Source Sum of Squares df
Mean Square F Sig.
Contrast 257.435 1 257.435 4.341 .040
Error 5041.157 85 59.308
72
Appendix A5-2 Woody compost stacking
Natural logarithm juveniles per gram fresh weight root
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
4,00 Wo/0/0 12
10,00 0/0/Tr 12
18,00 Wo/0/Tr 12
Repeatment 1 8
2 4
3 8
4 4
5 8
6 4
7 8
8 4
Tests of Between-Subjects Effects
Dependent Variable:
Source Type III Sum of
Squares df Mean
Square F Sig.
Intercept Hypothesis 363.990 1 363.990 326.619 .000
Error 8.950 8.031 1,114a
Name Hypothesis 17.332 3 5.777 7.506 .000
Error 28.478 37 ,770b
Repeatment Hypothesis 8.059 7 1.151 1.496 .199
Error 28.478 37 ,770b
a. ,903 MS(Repeatment) + ,097 MS(Error)
b. MS(Error)
73
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 5.333 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 5.905 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Ln_J2_gr_root
Tukey Ba,b
Name N
Subset
1 2
Wo/0/0 12 2.1676
Wo/0/Tr 12 2.5267
0/0/Tr 12 3.0610 3.0610
0/0/0 12 3.7628
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,770. a. Uses Harmonic Mean Sample Size = 12,000.
b. Alpha = 0.05.
74
RKI
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24 4,00 Wo/0/0 24 10,00 0/0/Tr 24 18,00 Wo/0/Tr 24
Repeatment 1 12 2 12 3 12 4 12 5 12 6 12 7 12 8 12
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 1342.510 1 1342.510 4196.126 .000
Error 2.240 7 ,320a
Name Hypothesis 5.698 3 1.899 15.299 .000
Error 10.552 85 ,124b
Repeatment Hypothesis 2.240 7 .320 2.577 .019
Error 10.552 85 ,124b
a. MS(Repeatment)
b. MS(Error)
75
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 12.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 12.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
RKI
Tukey Ba,b
Name N
Subset
1 2 3
Wo/0/0 24 3.3750
Wo/0/Tr 24 3.6667
0/0/Tr 24 3.9167
0/0/0 24 4.0000
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,124. a. Uses Harmonic Mean Sample Size = 24,000.
b. Alpha = 0.05.
76
Above dry weight (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
4,00 Wo/0/0 24
10,00 0/0/Tr 24
18,00 Wo/0/Tr 24
Repeatment 1 12
2 12
3 12
4 12
5 12
6 12
7 12
8 12
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 94596.025 1 94596.025 841.840 .000
Error 786.577 7 112,368a
Name Hypothesis 685.906 3 228.635 5.140 .003
Error 3780.966 85 44,482b
Repeatment Hypothesis 786.577 7 112.368 2.526 .021
Error 3780.966 85 44,482b
a. MS(Repeatment)
b. MS(Error)
77
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 12.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 12.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Total_dry_weight_above_gr
Tukey Ba,b
Name N
Subset
1 2
0/0/0 24 28.2273
0/0/Tr 24 29.2733
Wo/0/0 24 33.7613
Wo/0/Tr 24 34.3008
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 44,482. a. Uses Harmonic Mean Sample Size = 24,000. b. Alpha = 0.05.
78
Dry weight roots (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 12
4,00 Wo/0/0 12
10,00 0/0/Tr 12
18,00 Wo/0/Tr 12
Repeatment 1 4
2 8
3 4
4 8
5 4
6 8
7 4
8 8
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 523.180 1 523.180 126.564 .000
Error 32.337 7.823 4,134a
Name Hypothesis 3.169 3 1.056 .455 .715
Error 85.889 37 2,321b
Repeatment Hypothesis 30.295 7 4.328 1.864 .104
Error 85.889 37 2,321b
a. ,903 MS(Repeatment) + ,097 MS(Error)
b. MS(Error)
79
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 5.333 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 5.905 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Dry_weight_root_gr
Tukey Ba,b
Name N
Subset
1
0/0/0 12 3.0858
0/0/Tr 12 3.2000
Wo/0/0 12 3.6242
Wo/0/Tr
12 3.6742
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 2,321. a. Uses Harmonic Mean Sample Size = 12,000. b. Alpha = 0.05.
80
Contrasts
Calculation: 0/0/Tr – 0/0/0 - Wo/0/Tr + Wo/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
RKI L1 Contrast Estimate .375
Hypothesized Value 0 Difference (Estimate - Hypothesized)
.375
Std. Error .144 Sig. .011 95% Confidence Interval for Difference
Lower Bound .089
Upper Bound .661
a. Based on the user-specified contrast coefficients (L') matrix: wo tr interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast .844 1 .844 6.797 .011
Error 10.552 85 .124
81
Calculation: 0/0/Tr – 0/0/0 - Wo/0/Tr + Wo/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
Ln_J2_gr_root
L1 Contrast Estimate 1.061
Hypothesized Value 0
Difference (Estimate - Hypothesized)
1.061
Std. Error .507
Sig. .043
95% Confidence Interval for Difference
Lower Bound .035
Upper Bound 2.087
a. Based on the user-specified contrast coefficients (L') matrix: Wo Tr interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast 3.376 1 3.376 4.386 .043
Error 28.478 37 .770
82
Appendix A5-3 Mature compost stacking
Natural logarithm juveniles per gr fresh weight root
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
3,00 Ma/0/0 12
9,00 0/Ci/0 12
11,00 0/0/Pe 12
13,00 Ma/Ci/0 11
16,00 Ma/0/Pe 12
19,00 Ma/Ci/Pe 12
Repeatment 1 14
2 7
3 14
4 7
5 14
6 7
7 13
8 7
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 1138.626 1 1138.626 608.880 .000
Error 14.348 7.672 1,870a
Name Hypothesis 40.559 6 6.760 7.399 .000
Error 63.037 69 ,914b
Repeatment Hypothesis 13.768 7 1.967 2.153 .049
Error 63.037 69 ,914b
a. ,908 MS(Repeatment) + ,092 MS(Error)
b. MS(Error)
83
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 9.269 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 10.208 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Ln_J2_gr_root
Tukey Ba,b,c
Name N
Subset
1 2 3 4
0/Ci/0 12 2.8785
Ma/Ci/0 11 3.3003 3.3003
0/0/0 12 3.7628 3.7628 3.7628
0/0/Pe 12 4.0359 4.0359 4.0359
Ma/Ci/Pe 12 4.1429 4.1429 4.1429
Ma/0/0 12 4.6781 4.6781
Ma/0/Pe
12 5.1038
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,914. a. Uses Harmonic Mean Sample Size = 11,846.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.
c. Alpha = 0.05.
84
RKI
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
3,00 Ma/0/0 24
9,00 0/Ci/0 24
11,00 0/0/Pe 23
13,00 Ma/Ci/0 23
16,00 Ma/0/Pe 24
19,00 Ma/Ci/Pe 24
Repeatment 1 21
2 20
3 21
4 21
5 21
6 21
7 20
8 21
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 2701.409 1 2701.409 11379.412 .000
Error 1.662 7.002 ,237a
Name Hypothesis 3.693 6 .615 5.708 .000
Error 16.390 152 ,108b
Repeatment Hypothesis 1.662 7 .237 2.202 .037
Error 16.390 152 ,108b
a. 1,000 MS(Repeatment) + ,000 MS(Error)
b. MS(Error)
85
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 20.732 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 20.739 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
RKI
Tukey Ba,b,c
Name N
Subset
1 2 3
0/Ci/0 24 3.7500
Ma/Ci/0 23 3.9565 3.9565
0/0/0 24 4.0000 4.0000 4.0000
Ma/Ci/Pe 24 4.0417 4.0417
Ma/0/Pe 24 4.0833 4.0833
0/0/Pe 23 4.1739 4.1739
Ma/0/0 24 4.2500
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,108. a. Uses Harmonic Mean Sample Size = 23,706.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
86
Above dry weight (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
3,00 Ma/0/0 24
9,00 0/Ci/0 24
11,00 0/0/Pe 23
13,00 Ma/Ci/0 23
16,00 Ma/0/Pe 24
19,00 Ma/Ci/Pe 24
Repeatment 1 21
2 20
3 21
4 21
5 21
6 21
7 20
8 21
Tests of Between-Subjects Effects
Dependent Variable:
Source Type III Sum of Squares df Mean Square F Sig.
Intercept Hypothesis 227586.886 1 227586.886 591.415 .000
Error 2693.961 7.001 384,817a
Name Hypothesis 6772.731 6 1128.788 21.168 .000
Error 8105.415 152 53,325b
Repeatment Hypothesis 2694.466 7 384.924 7.218 .000
Error 8105.415 152 53,325b
a. 1,000 MS(Repeatment) + ,000 MS(Error)
b. MS(Error)
87
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 20.732 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 20.739 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Total_dry_weight_above_gr
Tukey Ba,b,c
Name N
Subset
1 2
0/0/0 24 28.2273
0/0/Pe 23 31.7600
Ma/0/0 24 32.6254
Ma/0/Pe 24 34.0355
Ma/Ci/Pe 24 43.2279
Ma/Ci/0 23 44.0288
0/Ci/0 24 45.3675
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 53,325. a. Uses Harmonic Mean Sample Size = 23,706.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
88
Dry weight roots (gr)
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
3,00 Ma/0/0 12
9,00 0/Ci/0 12
11,00 0/0/Pe 11
13,00 Ma/Ci/0 12
16,00 Ma/0/Pe 12
19,00 Ma/Ci/Pe 12
Repeatment 1 7
2 13
3 7
4 14
5 7
6 14
7 7
8 14
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 1488.790 1 1488.790 108.230 .000
Error 100.046 7.273 13,756a
Name Hypothesis 42.539 6 7.090 2.499 .030
Error 195.762 69 2,837b
Repeatment Hypothesis 104.032 7 14.862 5.238 .000
Error 195.762 69 2,837b
a. ,908 MS(Repeatment) + ,092 MS(Error)
b. MS(Error)
89
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 9.269 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 10.208 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Dry_weight_root_gr
Tukey Ba,b,c
Name N
Subset
1 2
0/0/0 12 3.0858
0/0/Pe 11 3.7527 3.7527
Ma/0/0 12 4.0908 4.0908
Ma/0/Pe 12 4.4067 4.4067
Ma/Ci/0 12 4.4800 4.4800
Ma/Ci/Pe
12 4.7092 4.7092
0/Ci/0 12 5.5717
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 2,837. a. Uses Harmonic Mean Sample Size = 11,846.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
90
Contrasts
Calculation: 0/0/Pe – 0/0/0 - Ma/0/Pe + Ma/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
RKI L1 Contrast Estimate .335
Hypothesized Value 0 Difference (Estimate - Hypothesized)
.335
Std. Error .135 Sig. .014 95% Confidence Interval for Difference
Lower Bound .069
Upper Bound .601
a. Based on the user-specified contrast coefficients (L') matrix: Pe Ma interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast .666 1 .666 6.173 .014
Error 16.390 152 .108
91
Calculation 0/Ci/0 – 0/0/0 – Ma/Ci/0 + Ma/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
Total_dry_weight_above_gr L1 Contrast Estimate 5.942
Hypothesized Value 0 Difference (Estimate - Hypothesized)
5.942
Std. Error 2.998 Sig. .049 95% Confidence Interval for Difference
Lower Bound .018
Upper Bound 11.865
a. Based on the user-specified contrast coefficients (L') matrix: Ci Ma interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean Square F Sig.
Contrast 209.448 1 209.448 3.928 .049
Error 8105.415 152 53.325
92
Appendix A5-4 Immature compost stacking
Natural logarithm juveniles per gram fresh weight root
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 12
2,00 Im/0/0 12
7,00 0/Li/0 12
12,00 0/0/Ba 12
14,00 Im/Li/0 12
17,00 Im/0/Ba 12
20,00 Im/Li/Ba 12
Repeatment 1 14
2 7
3 14
4 7
5 14
6 7
7 14
8 7
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of
Squares df Mean
Square F Sig.
Intercept Hypothesis 944.528 1 944.528 476.272 .000
Error 15.610 7.871 1,983a
Name Hypothesis 13.719 6 2.287 1.953 .084
Error 81.965 70 1,171b
Repeatment Hypothesis 14.491 7 2.070 1.768 .108
Error 81.965 70 1,171b
a. ,903 MS(Repeatment) + ,097 MS(Error)
b. MS(Error)
93
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 9.333 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 10.333 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Ln_J2_gr_root
Tukey Ba,b
Name N
Subset
1
Im/Li/Ba 12 2.9508
0/0/Ba 12 3.2154
Im/Li/0 12 3.2934
0/Li/0 12 3.6405
0/0/0 12 3.7628
Im/0/Ba
12 3.8873
Im/0/0 12 4.2141
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 1,171. a. Uses Harmonic Mean Sample Size = 12,000.
b. Alpha = 0.05.
94
RKI
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
2,00 Im/0/0 24
7,00 0/Li/0 24
12,00 0/0/Ba 24
14,00 Im/Li/0 24
17,00 Im/0/Ba 24
20,00 Im/Li/Ba 24
Repeatment 1 21
2 21
3 21
4 21
5 21
6 21
7 21
8 21
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 2688.000 1 2688.000 21952.000 .000
Error .857 7 ,122a
Name Hypothesis 1.167 6 .194 3.002 .008
Error 9.976 154 ,065b
Repeatment Hypothesis .857 7 .122 1.890 .075
Error 9.976 154 ,065b
a. MS(Repeatment)
b. MS(Error)
95
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 21.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 21.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
RKI
Tukey Ba,b
Name N
Subset
1 2
Im/Li/Ba 24 3.8750
Im/0/Ba 24 3.9167 3.9167
Im/Li/0 24 3.9583 3.9583
0/0/0 24 4.0000 4.0000
Im/0/0 24 4.0417 4.0417
0/0/Ba 24 4.0833 4.0833
0/Li/0 24 4.1250
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = ,065. a. Uses Harmonic Mean Sample Size = 24,000. b. Alpha = 0.05.
96
Above dry weight (gr)
Between-Subjects Factors
Value Label N
Name 1,00 0/0/0 24
2,00 Im/0/0 24
7,00 0/Li/0 24
12,00 0/0/Ba 24
14,00 Im/Li/0 24
17,00 Im/0/Ba 24
20,00 Im/Li/Ba 24
Repeatment 1 21
2 21
3 21
4 21
5 21
6 21
7 21
8 21
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of
Squares df Mean
Square F Sig.
Intercept Hypothesis 174113.394 1 174113.394 382.219 .000
Error 3188.732 7 455,533a
Name Hypothesis 1909.655 6 318.276 5.557 .000
Error 8820.960 154 57,279b
Repeatment Hypothesis 3188.732 7 455.533 7.953 .000
Error 8820.960 154 57,279b
a. MS(Repeatment)
b. MS(Error)
97
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 21.000 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 21.000 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Total_dry_weight_above_gr
Tukey Ba,b
Name N
Subset
1 2
0/0/0 24 28.2273
0/Li/0 24 29.5933
0/0/Ba 24 30.5471
Im/0/0 24 32.0421
Im/0/Ba 24 32.3692
Im/Li/0 24 33.0600
Im/Li/Ba 24 39.5121
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 57,279. a. Uses Harmonic Mean Sample Size = 24,000.
b. Alpha = 0.05.
98
Dry weight roots (gr)
Between-Subjects Factors
Value Label N
Name 1,00
0/0/0 12
2,00 Im/0/0 12
7,00 0/Li/0 12
12,00 0/0/Ba 12
14,00 Im/Li/0 12
17,00 Im/0/Ba 11
20,00 Im/Li/Ba 12
Repeatment 1 7
2 14
3 7
4 14
5 7
6 14
7 7
8 13
Tests of Between-Subjects Effects
Dependent Variable:
Source
Type III Sum of Squares df
Mean Square F Sig.
Intercept Hypothesis 1086.552 1 1086.552 76.582 .000
Error 102.437 7.220 14,188a
Name Hypothesis 35.466 6 5.911 2.494 .030
Error 163.522 69 2,370b
Repeatment Hypothesis 107.695 7 15.385 6.492 .000
Error 163.522 69 2,370b
a. ,908 MS(Repeatment) + ,092 MS(Error)
b. MS(Error)
99
Expected Mean Squaresa,b
Source
Variance Component
Var(Repeatment) Var(Error) Quadratic
Term
Intercept 9.269 1.000
Intercept, Name
Name 0.000 1.000 Name
Repeatment 10.208 1.000
Error 0.000 1.000
a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares.
Dry_weight_root_gr
Tukey Ba,b,c
Name N
Subset
1 2
0/0/0 12 3.0858
Im/0/Ba 11 3.3655 3.3655
0/Li/0 12 3.4617 3.4617
0/0/Ba 12 3.5058 3.5058
Im/0/0 12 3.6383 3.6383
Im/Li/0
12 4.3833 4.3833
Im/Li/Ba 12 5.0475
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 2,370. a. Uses Harmonic Mean Sample Size = 11,846.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed. c. Alpha = 0.05.
100
Contrasts
Calculation: 0/0/Ba – 0/0/0 - Im/0/Ba + Im/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
RKI L1 Contrast Estimate .208
Hypothesized Value 0 Difference (Estimate - Hypothesized)
.208
Std. Error .104 Sig. .047 95% Confidence Interval for Difference
Lower Bound .003
Upper Bound .414
a. Based on the user-specified contrast coefficients (L') matrix: Ba Im interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast .260 1 .260 4.020 .047
Error 9.976 154 .065
101
Calculation: 0/Li/0 – 0/0/0 – Im/Li/0 + Im/0/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
RKI L1 Contrast Estimate .208
Hypothesized Value 0 Difference (Estimate - Hypothesized)
.208
Std. Error .104 Sig. .047 95% Confidence Interval for Difference
Lower Bound .003
Upper Bound .414
a. Based on the user-specified contrast coefficients (L') matrix: Li Im interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean
Square F Sig.
Contrast .260 1 .260 4.020 .047
Error 9.976 154 .065
102
Calculation: Im/0/Ba – Im/0/0 – Im/Li/Ba + Im/Li/0
Contrast Results (K Matrix)a
Contrast
Dependent Variable
Total_dry_weight_above_gr L1 Contrast Estimate -6.125
Hypothesized Value 0 Difference (Estimate - Hypothesized)
-6.125
Std. Error 3.090 Sig. .049 95% Confidence Interval for Difference
Lower Bound -12.229
Upper Bound -.021
a. Based on the user-specified contrast coefficients (L') matrix: Ba ImLi interactie
Test Results
Dependent Variable:
Source Sum of
Squares df Mean Square F Sig.
Contrast 225.094 1 225.094 3.930 .049
Error 8820.960 154 57.279
103
Appendix A6 Missing leaves
Plant_number Treatment No.of leaves not present
Location (no.) leaves counted from below excluding the lob leaves
192 13 1 1
204 19 1 1
212 5 1 1
229 2 1 1
235 11 1 1
238 18 1 1
247 19 1 1
252 4 1 1
256 5 1 1
258 5 1 1
260 12 1 1
270 1 1 1
291 14 1 1
305 10 1 1
309 15 1 1
316 14 2 1 and 3
328 4 1 1
329 4 1 3
331 2 1 1
338 16 2 1 and 2
341 1 1 1
344 7 1 1
345 7 1 1
350 11 1 1
351 11 1 1
353 13 1 1
355 8 1 1
357 8 1 1
358 18 1 1
364 15 1 1
375 12 1 4
378 1 2 1 and 2
380 5 1 6
381 5 1 1
383 13 1 4
386 1 1 1
388 5 1 1
389 5 1 1
390 5 2 1 and 3
391 15 1 1
392 15 3 1,?,?
104
Plant_number Treatment No.of leaves not present
Location (no.) leaves counted from below excluding the lob leaves
393 15 1 1
398 20 1 1
403 7 1 4
408 8 1 1
412 3 1 1
416 19 1 1
424 16 1 1
435 12 1 1
437 14 1 1
438 14 1 1
441 18 1 1
448 8 2 4 and 5
454 4 1 1
455 4 1 4
456 4 1 2
462 17 1 1
468 2 1 1
470 11 1 1
471 11 2 1 and 2
472 20 2 1 and 2
474 20 1 1
476 16 2 1 and 2
477 16 2 1 and 2
479 19 1 1
480 19 1 1
105
Appendix A7 Fruit weight correction
Plant_number Treatment Dry weight fruit original value (gr) Dry weight corrected value (gr)
45 2 -0.3 0.00
92 7 -0.04 0.00
103 2 -0.05 0.00
138 17 -0.02 0.00
145 3 -0.04 0.00
148 12 -0.02 0.00
154 7 -0.04 0.00
256 5 -0.04 0.00
288 2 -0.02 0.00
313 12 -0.07 0.00
315 12 -0.07 0.00
383 13 -0.03 0.00
399 20 -0.01 0.00
403 7 -0.03 0.00
430 10 -0.01 0.00
449 8 -0.04 0.00
458 3 -0.03 0.00
462 17 -0.01 0.00
463 10 -0.02 0.00
466 2 -0.06 0.00
467 2 -0.01 0.00
477 16 -0.01 0.00
106
Appendix A8 Normal distribution overall
107
Appendix A9 Normal distribution stackings
Woody stacking
108
Champost stacking
109
Mature stacking
110
Immature stacking
