RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITÄT BONN Faculty …repositorio.educacionsuperior.gob.ec/bitstream/28000/1995/1/T... · RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITÄT BONN Faculty

RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITÄT BONN

Faculty of Agriculture

M A S T E R T H E S I S

As part of the Master programme

Agricultural Sciences and Resource Management in the Tropics

and Subtropics (ARTS)

Submitted in partial fulfilment of the requirements for the degree of

„Master of Science“

delta-O-18 signature and phosphate pools in selected soil groups

submitted by:

Maria Soledad Ortiz Navarro

2666229

submitted on: 27.08.2015

First examiner: Prof. Dr. Wulf Amelung

Second examiner: Dr. Eva Lehndorff

Main supervisor: Prof. Dr. Wulf Amelung

Co-supervisor: Dr. Eva Lehndorff

Chairperson: Dr. Jürgen Schellberg

Declaration

I hereby affirm that I have prepared the present paper self-dependently, and without the use of

any other tools, than the ones indicated. All parts of the text, having been taken over verbatim or

analogously from published or not published scripts, are indicated as such. The thesis hasn’t yet

been submitted in the same or similar form, or in extracts within the context of another

examination.

Bonn, 27th August 2015

__________________________

Maria Soledad Ortiz Navarro

i

Acknowledgements

I would like to thank the Ecuadorian Government through SENESCYT (Secretaria Nacional de

Ciencia y Tecnología del Ecuador), which funded the entirety of my studies with a full

scholarship.

I would like to express my profound gratitude to Prof. Wulf Amelung, who, before I came to

Germany, helped me and guided me in the admission process of ARTS Program. Furthermore

he opened the doors of Division Soil Science of the Institute of Crop Science and Resource

Conservation (INRES) to do my thesis research.

I would also like to acknowledge the supervision of Sara Bauke M.Sc., who shared her

experience and knowledge without any signs of selfishness or arrogance, I am pretty sure that

without her guidance I would not have been able to finish this research.

I appreciate the help and support of the technical staff and colleagues at INRES, Division of Soil

Science. I do not want list their names in case I forget some of them.

And my infinite thanks to my family and friends who have spurred me on over these 2 years with

their support, prayers and blessings.

ii

Dedication

This thesis is dedicated to my family. My father and mother have always emphasized the

importance of studying along with personal values such as honesty, loyalty, love and passion for

everything that one decides to do. I would also like dedicate this effort to my sister and brother,

who are the most important people in my life. And finally, last but not least, I want to dedicate

this stage of my life to God, who always blesses me and brings me to the correct place in the

precise time and with the suitable people.

iii

Table of contents

Acknowledgements ...................................................................................................................... i

Dedication ................................................................................................................................... ii

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

Abbreviation List ........................................................................................................................ vi

List of tables .............................................................................................................................. vii

List of figures ........................................................................................................................... viii

Abstract...................................................................................................................................... 1

I. Introduction ............................................................................................................................. 2

II. State of the Art ....................................................................................................................... 5

2.1 Phosphorus as an element and nutrient ........................................................................... 5

2.2 Soil Phosphorus cycle ...................................................................................................... 8

2.3 Methods of phosphorus investigation ................................................................................ 9

2.3.1 Phosphorus fractionation ............................................................................................ 9

2.3.2 Analytical Techniques ...............................................................................................11

2.3.3 Stable Isotopes .........................................................................................................12

2.4 Soil classification .............................................................................................................14

2.4.1 Ferralsols ..................................................................................................................15

2.4.2 Andosols ...................................................................................................................16

2.4.3 Luvisols .....................................................................................................................16

2.4.4 Chernozems ..............................................................................................................17

iv

III. Materials and Methods .........................................................................................................18

3.1 Soils ................................................................................................................................18

3.2 Procedures ......................................................................................................................19

3.2.1 Soil Phosphorus (P) Fractionation .............................................................................19

3.2.2 Phosphate determination ..........................................................................................21

3.2.3 δ18OP of inorganic phosphate .....................................................................................24

3.3 Statistical analyses ..........................................................................................................30

IV. Results ................................................................................................................................32

4.1 Phosphate pools in selected soil groups ..........................................................................32

4.2 Observed δ18OP vs δ18OPE ...............................................................................................38

V. Discussion ............................................................................................................................42

5.1 Phosphate pools in selected soil groups ..........................................................................42

5.1.1 Ferralsols ..................................................................................................................43

5.1.2 Andosols ...................................................................................................................44

5.1.3 Luvisols .....................................................................................................................45

5.1.4 Chernozems ..............................................................................................................45

5.2 Observed δ18OP vs δ18OPE ...............................................................................................46

5.2.1 Ferralsols ..................................................................................................................47

5.2.2 Andosols ...................................................................................................................47

5.2.3 Luvisols .....................................................................................................................48

5.2.4 Chernozems ..............................................................................................................49

v

VI. Conclusion - Recommendation ............................................................................................50

VII. References .........................................................................................................................52

vi

Abbreviation List

Al Aluminium

Ca Calcium

ICP-OES Inductively Coupled Plasma Optical Emission Spectroscopy

Pi Inorganic phosphorus

IUSS International Union of Soil Sciences

Fe Iron

Po Organic phosphorus

O Oxygen

P Phosphorus

RSGs Reference Soil Groups

NaHCO3 Sodium bicarbonate

NaOH Sodium hydroxide

TC/EA-IRMS Thermal Conversion Elemental analysis/Isotope Ratio Mass Spectometry.

PT Total phosphorus

VSMOW Vienna Standard Mean Ocean Water

WRB World Reference Base

vii

List of tables

Table 1. Essential macroelement and role in plants ............................................................... 6

Table 2. Sampling sites description. ......................................................................................19

Table 3. Preparation of supernatants to be measured in the photometer. .............................22

Table 4. Preparation of standard solution to be used in the photometer ................................23

Table 5. Selected samples to be processed following Tamburini et al. (2010) method. .........24

Table 6. δ18OW and δ18OPE calculated using the equation suggested by Bowen & Wilkinson

(2002) and Longinelli & Nuti (1973) respectively. ......................................................................38

Table 7. Mean δ18OP values of HCl-extractable P (‰, VSMOW) and standard deviation (SD)

of selected soil groups. .............................................................................................................39

viii

List of figures

Plant acquisition of soil phosphorus. (Schachtman 1998). .................................... 7 Figure 1.

Major processes in the soil phosphorus cycle (Foth 1990). ................................... 8 Figure 2.

Flow chart of the sequential P extraction (Tiessen, H. and Moir, J.O. 1993). .......10 Figure 3.

Location selected soil groups. ..............................................................................18 Figure 4.

Scheme of modified Hedley procedure. ...............................................................20 Figure 5.

Modified Hedley procedure.. ................................................................................21 Figure 6.

P-blue procedure.. ...............................................................................................22 Figure 7.

Scheme of the steps followed to prepare Ag3PO4 for δ18OP analysis. ...................29 Figure 8.

Weight of the analyte plotted against the area of the oxygen yield peak for Figure 9.

selected samples. .....................................................................................................................30

Mean of P content measured from the supernatant I extracted by NaHCO3 in the Figure 10.

selected soil groups. .................................................................................................................33

Mean of P content measured from the supernatant II extracted by NaOH in the Figure 11.

selected soil groups. .................................................................................................................34

Mean of P content measured from the supernatant III extracted by HCl in the Figure 12.

selected soil groups. .................................................................................................................36

Mean of P content measured from the supernatant IV extracted by concentrate Figure 13.

HCl in the selected soil groups.. ................................................................................................37

δ18OP signature vs Pi content for the HCl-extractable P pool in Ferralsols. ...........40 Figure 14.

δ18OP signature vs Pi content for the HCl-extractable P pool in Andosols.............40 Figure 15.

δ18OP signature vs Pi content for the HCl-extractable P pool in Luvisols. .............41 Figure 16.

δ18OP signature vs Pi content for the HCl-extractable P pool in Chernozems. ......41 Figure 17.

Abstract

1

Abstract

Soil P-cycling is an issue that requires a great deal of attention regarding the importance of P in

all living organisms. This study focused its attention in four selected soils: Ferralsols (China) and

Andosols (Indonesia), which are used for paddy rice cultivation; Luvisols (Germany) covered by

winter wheat cultivars and Chernozems (Russia) covered by natural steppe vegetation. The

study hypothesizes that the soil-forming factors and anthropogenic influences determine the

composition of phosphate pools and the δ18OP signature.

The soil samples were taken from different depths and each one was processed according to

Hedley et al. (1982) modified by Tiessen and Moir (1993) and to modified from Tamburini et al.

(2010). Pi concentration was measured by the Molybdenum Blue method of Murphy & Riley

(1962), PT content was determined using ICP-OES, and Po by the difference between PT and

Pi, while δ18OP was measured using TC/EA-IRMS.

As was hypothesized, the enormous differences between soil-forming factors and the

agricultural practices from the selected soils were the main reasons not only for the variety of P

content recorded but also for the observed δ18OP. However it was possible to find a decreasing

trend from the surface to the depth of the soil profile in almost all soils among different P-pools.

The evident exceptions were Luvisols (by HCl extractant) and Ferralsols (by concentrate HCl

extractant) with an accumulation of Pi in the sub-soil due to their mineral composition.

Conversely, an inverse relation was detected between the Pi-content extracted by HCl and the

δ18OP values, when they were below the δ18OPE. Of the selected soil types, only Chernozems

are close to the δ18OPE upto 50 cm depth due to their well-known, huge biologic activity.

Ferralsols are under the equilibrium, apparently due to the effects of its low pH in

microbiological activities. δ18OP in Andosols are above the equilibrium, not only for the presence

of biological activity, but also for the effect of evapotranspiration. Finally, Luvisols upto 60 cm

are above the equilibrium but later on drop below this point.

In summary, the data confirm the effect of soil-forming factors and the cultivated practices which

influence not only in the P content but also in the δ18OP signature. Therefore the thorough study

of these factors should provide a better understanding of this element in the soil-plant system.

Keywords: Andosols, Chernozems, equilibrium isotope, Ferralsols, Luvisols, P-content.

Introduction

2

I. Introduction

Phosphorus (P) is an essential element for plants. It is even considered a “Macro-element”,

meaning the plant is unable to complete its life cycle in the absence of it and the element´s

function cannot be replicated by another mineral element.

P, like other macro-elements, is involved directly in plant metabolism. For instance, Frossard et

al. (2012 pp. 26.1) mentions that “Phosphorus is contained in DNA and RNA and as such is

important with respect to storing and reading genetic information. Phosphorus is also found in

ATP and its derivatives, and is vital for energy storage and transfer in the cell. Finally,

phosphorus is present in phospholipids that are important components of cell membranes”.

It is widely accepted that phosphorus is a finite element and many studies corroborate this;

Sattari et al. (2012) conclude that almost half of the currently available P resources will be

depleted by 2100. Furthermore after analyzing industry data Cordell et al. (2009) conclude that

global phosphorus production will reach its peak in 2033.

It is well known that P shows low availability to plants due to slow diffusion and high fixation in

soils (Shen et al. 2011). In general, either phosphorus scarcity or its own characteristics make it

one of the major limiting factors for plant growth.

Phosphorus is present in the soil either in organic (Po) or inorganic (Pi) forms. The inorganic

phosphorus form of orthophosphate (HPO42− and H2PO4

−) can react with soil particles through

different mechanisms (Frossard et al. 2012).

Plants and microorganisms can take up phosphorus only in the form of free orthophosphate and

incorporate it into stable organic compounds (Frossard et al. 2012; Montalvo et al. 2015).

As has been mentioned by Guo et al. (2000) and Shen et al. (2011) the accessibility of soil P is

complicated. Therefore it is necessary to evaluate it systematically due to its complex chemistry

and spatial variability in soils. There are some methods to characterize soil P availability.

In this study the procedure by Hedley et al. (1982), modified by Tiessen and Moir (1993) was

used to sequentially fractionate soil P; afterwards, spectrophotometry was used to detect Pi

through the formation of blue-coloured phosphomolybdenum complex (Bünemann et al. 2011)

and finally total soil phosphorus (PT) was determined using inductively coupled plasma optical

Introduction

3

emission spectroscopy (ICP-OES). Organic phosphorus (Po) was calculated by the difference

between PT and Pi.

In soils, oxygen (O) surrounds the central P atom in PO4. Oxygen has three stable isotopes 16O,

17O and 18O. The natural abundance of the 18O bound to P is expressed in parts per thousand

(‰) and reported in the conventional delta notation (δ). For oxygen isotopes in phosphate

(δ18OP), the reference material used is the Vienna Standard Mean Ocean Water (VSMOW)

(Frossard et al. 2011; Tamburini et al. 2014).

Many diagenetic reactions involving phosphate are mediated by living organisms;

microorganisms are important in biogeochemical cycling of P not only in aquatic environments

but also in sediments and soils (Blake et al. 1997; Blake et al. 2005).

Under biological activity, following the up-take or ingestion of PO4, oxygen in phosphate is

rapidly exchanged and presumably equilibrated with internal fluids. Meanwhile, in the absence

of biological activity, the exchange between oxygen in Pi and water is slow and negligible (Blake

et al. 1997; Frossard et al. 2011).

Blake et al. (2005) mention that the enzyme that mainly catalyzes temperature-dependent

equilibrium oxygen isotope fractionations between phosphates and water in biological systems

is the inorganic pyrophosphatase. This enzyme seems to dominate the δ18O signature of

dissolved phosphate.

Due to these facts, the use of stable oxygen isotopes in phosphate (δ18OP) has been suggested

either as a tracer to investigate the origin and fate of P in soil-plant systems or to study

biogeochemical cycling of P. (Blake et al. 2005; Tamburini et al. 2014).

The oxygen isotope analysis requires the successful isolation of phosphate from any other

compound containing O and to precipitate it as silver phosphate (Ag3PO4). This compound is

not hygroscopic avoiding the risk of contamination by absorbed water (Frossard et al. 2011;

Tamburini et al. 2014).

Silver phosphate was obtained from the selected soil groups in different depths. The method

followed was slightly modified from the procedure by Tamburini et al. (2010). The samples were

measured by the University of Zürich using a Thermal Conversion Elemental analysis/Isotope

Ratio Mass Spectometry (TC/EA-IRMS).

Introduction

4

Soils are developed under the combination of many factors and their study is quite complex.

Nowadays the World Reference Base (WRB) is widely used as a soil classification system for

naming soils and creating soil map legends (IUSS Working Group WRB 2014).

The WRB is composed of 32 Reference Soil Groups (RSGs) of which four were selected for this

thesis according their importance on the worldwide crop production and the agricultural

practices done on them. Ferralsols: dominance of kaolinite and oxides (China), Andosols:

allophanes or Al-humus complexes (Indonesia), Luvisols: high-activity clays and high base

status (Germany), and Chernozems: blackish topsoil, secondary carbonates (Russia) (IUSS

Working Group WRB 2014).

Although Ferralsols and Andosols are used to cultivate paddy rice they have developed from

different parent material and in contrast, Luvisols and Chernozems were developed from loess

but only the first group shows agricultural practices while the second soil keep its natural

vegetation without any human influence.

Based on the last paragraphs, we hypothesize that the soil factor formation and anthropogenic

influences determine the composition of phosphate pools and the δ18OP signature in different

soils. More precisely:

1) P-pools in soils under the same agricultural management (Andosols and Ferralsols) vary

due to parent material.

2) Soils developed from the same parent material (Chernozems and Luvisols) differ in their

P-pools due to anthropogenic influences.

3) In natural ecosystems the δ18OP signature will approach the equilibrium with the oxygen

in water, while in cultivated soils the equilibrium is disturbed by management practices.

The objectives of the study are:

1) Compare the phosphate pools from selected soil groups.

2) Observe whether or not the δ18OP signature in the selected soil groups approaches the

isotopic equilibrium of the PO4 oxygen (δ18OPE) with the oxygen in water.

State of the Art

5

II. State of the Art

2.1 Phosphorus as an element and nutrient

One of the elements present in the Earth´s crust is phosphorus (P) with an abundance of

around 1.05 ppm by weight (Foth 1990; Chesworth 2008; Baskaran 2011). Three global P

cycles occur on earth, from which two have a clear biological influence; the terrestrial P cycle

(between the soil and its flora) is the second highest flux, while the first occurs in the ocean

(between the sea and marine biota). The third P cycle is the primary inorganic cycle dominated

by chemical and physical reactions for instance: mineral weathering, leaching, erosion transfer

P from land to sediments, etc. (Chesworth 2008).

P has a high reactivity and consequently it does not appear as a free element in nature. Instead

it is found in different minerals such as apatite and many essential organic compounds as

deoxyribonucleic acid (DNA), ribonucleic acid (RNA), adenosintriphosphat (ATP) and

phospholipids (Baskaran 2011).

Pansu & Gautheyrou (2006) mention that there are also many manufactured compounds which

contain P (for example, fertilizers, pesticides, detergents, fuel additives, plastics), which play an

important role in ecology. Additionally, from these compounds it has been identified that at least

220 of them are stable in the geological time scale. These new compounds need to be included

in the P cycle analysis because of the impact of them on arable lands and certain farming

practices. For example the use of sewage sludge adds polyphosphates and organic forms of P

that do not exist in the natural environment.

Phosphorus is one of the macro-elements required for plants due to its major role in plant

growth (Table 1). Plants have an average of phosphorus content of 0.25% of the dry weight

whereas the mean P concentration in the surface layer (from 0 to 15cm) is normally between

380 and 1330 mg.kg-1. This is the lowest amount of the macronutrients in plants.(Vance et al.

2003; Chesworth 2008)

Plants take up phosphorus mainly in the form of free orthophosphate as HPO42− above pH 7.2

and H2PO4- below pH 7.2 (preferably 4.5 - 5) (Foth 1990; Vance et al. 2003). Yet these chemical

forms also rapidly react with cations, particularly aluminum and iron under acid conditions

creating insoluble complexes. Thus, P becomes unavailable for the plants (Vance et al. 2003).

State of the Art

6

Plant P acquisition (Figure 1) is based in the root system, where its geometry and morphology

are important; additionally the use of mycorrhizae are also a key to improving the P uptake by

the plants. Some of the mechanisms of P uptake are: specialized transporters at the root/soil

interface for the extraction of Pi from the soil solution of micromolar concentrations, specific

transport across membranes between intracellular compartments (Schachtman 1998).

Table 1. Essential macroelement and role in plants

Source: Foth (1990)

Phosphorus is a mobile element in plants, when a P deficiency occurs one of its most common

symptoms is the purplish color that moves from the old to the younger leaves into the plant.

Another symptom is the significant reduction of shoots and roots (Foth 1990).

State of the Art

7

Plant acquisition of soil phosphorus. (Schachtman 1998). Figure 1.

Generally, P fertilizers are applied in order to supply the necessary P amount for plant growth;

usually these are produced from calcium phosphate (phosphate rock). Rock phosphate is a

resource located mainly in the Middle East, China, Russia, Morocco and United States of

America (Baskaran 2011).

There are many studies which estimate until when the natural P source is able to cover the

demand for agriculture production and the results are not encouraging. The most optimistic

results show that phosphate rock reserves will only be available for the next 400 years (Sattari

et al. 2012).

Under the facts that P is an indispensable and irreplaceable element in several vital functions in

plants, and that in soil, it has a slow diffusion and high fixation and the natural P sources are

depleted year by year. These convert the P in an element to be researched and try to increase

its use efficiency. One step to do that is to better understand P-cycle through to know how it is

working in soil- plant systems.

State of the Art

8

2.2 Soil Phosphorus cycle

Phosphorus concentration in the surface layer of soils varies considerably among regions of the

world (Chesworth 2008).

Figure 2 shows the different stages of the soil phosphorus cycle. Apatite weathering (step 1)

produces phosphate ion H2PO4-, which accumulates in the soil solution. H2PO4

- is immobilized

either by the micro-organisms or roots, when these convert phosphorus into organic compounds

(step 2) but organic phosphorus can again be released into the soil by mineralization (step 3).

Steps 2 and 3 are influenced by pH, redox potential (Eh), physical and chemical soil properties

and climatic conditions (Foth 1990; Pansu & Gautheyrou 2006; Shen et al. 2011).

Due to the chemical characteristics of the P in the soil solution, this easily reacts with other ions

in the soil solution and is converted to fixed forms (step 4), which the plants are unavailable to

take up. Nevertheless the fixed phosphorus on clays and other soil constituents dissolves slowly

into the soil solution again (step 5). The low P concentration in the soil solution can take two

different ways. Either a very small amount of P is moved to the roots or minimal losses take

place due to leaching (Foth 1990).

Major processes in the soil phosphorus cycle (Foth 1990). Figure 2.

Phosphorus in apatites of soil parent material

Calcium phosphates Clay-adsorved phosphorus iron and aluminium phosphates

Solution phosphorus

H2PO4-

Organic phosphorus

1. Weathering

2. Inmovilization

3. Mineralization

4. Fixation

5. Dissolution

” Leachin

g”

State of the Art

9

However, Frossard et al. (2012) mention that in addition to supply through rock weathering, P

also enters the soil by soil particles deposits, inorganic and organic fertilizers and, in a limited

way, by atmospheric inputs. Conversely, P leaves the soil not only through leaching but also by

harvesting, erosion and runoff.

Pansu & Gautheyrou (2006) mention that the chemical and biochemical changes that occur

during the phosphorus cycle can be measured and possibly controlled and directed.

Furthermore Shen et al. (2011) say that soil P availability is complex and strongly associated

with P dynamics among various P pools thus it needs to be systemically evaluated.

As it was observed above, soil P-cycling includes several chemical transformations which are

influenced by biological activities as well as by the own physical-chemical soil properties. Since

long time ago scientist have developed uncountable procedures to determine and quantify these

events in the soil-plant systems. It will continue with a brief summary of the procedures used in

this study to determine and quantify P in the soil.

2.3 Methods of phosphorus investigation

In summary, soil P is found in two important pools: inorganic and organic. Organic forms are

found from 20% to 80% and inorganic forms usually range from 35% to 70% of total soil P

(Schachtman 1998; Shen et al. 2011).

The major form of the organic P is phytic acid (inositol hexaphosphate) (Pansu & Gautheyrou

2006). The inorganic form is contained in 170 mineral forms (Schachtman 1998), among them

the most important for plants and microorganisms are the free orthophosphates (HPO42− and

H2PO4−) (Frossard et al. 2012).

To determine phosphorus availability it is crucial to do it with respect to an external sink. For

instance in an agronomic context it should be measured as a pool of soil P that is somehow

related to that portion of soil P which is plant available (Tiessen, H. and Moir, J.O. 1993).

2.3.1 Phosphorus fractionation

Over the last 60 years scientists have proposed many chemical methods to extract the amount

of plant-available phosphorus. One of them has been the sequential extraction by Hedley et al.

(1982). This method not only separates the different soil P fractions into six pools (organic and

inorganic forms) but can also identify long-term and small changes that occur in soil P in long

State of the Art

10

fallow system and short-term incubation tests respectively (Hedley et al. 1982; Pansu &

Gautheyrou 2006).

Figure 3 illustrates the individual extractants used to obtain the soil P fractions. Data

interpretation should be based on the action of the sequence of the individual extractants and

their relationship to the chemical and biological properties of the soil (Tiessen, H. and Moir, J.O.

1993).

Flow chart of the sequential P extraction (Tiessen, H. and Moir, J.O. 1993). Figure 3.

Briefly Frossard et al. (2012) describes the sequential extraction by Hedley et al. (1982), where

the resin firstly removes the immediate plant-available form from the soil, then the sediment is

treated with NaHCO3 and NaOH sequentially. These alkaline solutions solubilize phosphorus

bound to aluminium or iron oxides and Po. Subsequently applying HCl to the sediment allows

the extraction of the P bound to calcium. And finally, acid digestion (concentrate HCl) will

release the residual phosphorus.

State of the Art

11

Tiessen, H. and Moir, J.O. (1993) mention that Hedley et al´s (1982) approach has been

moderately successful in being used for the evaluation of available Po. It left between 20 and

60% of the P in the soil unextracted, which contains substantial amounts of Po that occasionally

participated in short-term transformations. Therefore, Tiessen and Moir in 1993 presented a

new protocol that modified the Hedley procedure in order to solve the problems that they found

in it.

2.3.2 Analytical Techniques

The chemical analyses of the soil extracts in this thesis are based on spectroscopic,

chromatographic, spectrocolorimetric techniques. Nevertheless other techniques such as X-ray

fluorescence spectrometry and neutron activation can also be important for sensitive elemental

analyses of soils (Chesworth 2008).

When the extracts or supernatants have been obtained from the P fractionation method, the

following step is the titration of P. Two of the most common spectrocolorimetric absorption

methods used are molybdovanadophosphoric complex (430 nm), or molybdenum blue (650 and

890 nm) (Pansu & Gautheyrou 2006).

The principle of the spectrocolorimetry using Molybdenum Blue is expressed in equation 1. It

consists of the reaction of the molybdic acid with ortho forms of phosphorus. The final blue

coloured complex is a result of the formation of phosphomolybdic anion which is only possible in

an acidic medium with a high ionic force (Pansu & Gautheyrou 2006).

The procedure explained above is used commonly to detect inorganic phosphorus, while

organic forms are unable to form a coloured complex with the molybdate reagent. Po can be

determined as the difference between the total P and inorganic P (Bünemann et al. 2011).

PT is determined by inductively coupled plasma optical emission spectroscopy (ICP-OES).

Nowadays, this method is commonly used and can detect all different forms of P without

distinction in comparison to the molybdenum blue, which detect the inorganic P form. Therefore,

HPO4 2– + 12 MoO4 2– + 23 H+– → [PO4(MoO3)12]3– + 12 H2O (1)

Blue reduced form

State of the Art

12

it is necessary to discern precisely which P forms are determined by each method (Pierzynski &

Kovar 2009).

The spectroscopic source of the ICP-OES is the inductively coupled argon plasma. It uses high

temperatures and does not require a light source. Atoms inside the plasma collide with each

other due to the Ar + ions acceleration (Chesworth 2008).

Chesworth (2008 pp. 106) explains that in ICP “The excited atoms emit their characteristic

radiation as they return to their ground state. A grating monochromator selects and isolates the

specific wavelength of the analyte element. The emission intensity at a characteristic

wavelength of an element is generally proportional to the concentration of the element in the

sample being measured. A photomultiplier detector collects the emission light from an element,

and converts it to an electric current signal. This signal is amplified, electronically processed,

and displayed on a readout device.”

2.3.3 Stable Isotopes

In natural environments phosphorus has eight isotopes, of which only 31P is stable and seven

are radioactive, but only 32P and 33P can be used in soil-plant system studies. Even though the

radioactive isotopes have been used to investigate P dynamics, they are not efficient enough to

be used under field conditions due to their short half-life and their extremely low rates in natural

systems. (Baskaran 2011; Frossard et al. 2011).

However, phosphorus in soils is always bounded to oxygen (O) atoms either in the form of

phosphate (PO43-), phosphonate (C-PO3

2-) or polyphosphate (Frossard et al. 2011), thus

scientists have started using stable oxygen isotopes bound to P in studies of soil P dynamics.

This approach has a big potential and year by year is gaining more support.

Frossard et al. (2011) remarks that oxygen has three stable isotopes 16O (99.759% abundance

in the earth´s atmosphere), 17O (0.037%) and 18O (0.20%) and from them the first and the last

are used to track phosphorus cycling and transformations.

The natural abundance of 18O bound to P is expressed in parts per thousand (‰) relative to the

VSMOW (Vienna standard mean ocean water) and it is calculated by equation 2. Here R is the

ratio between the heaviest and lightest O isotopes in phosphate (18O/16O) (Tamburini et al.

2014).

State of the Art

13

It is important to emphasize that the P-O bound is resistant to inorganic hydrolysis at ambient

temperatures and without biological mediation. This means that in the absence of biological

activity, the isotope exchange between the oxygen present in Pi and water is negligible

(Baskaran 2011; Frossard et al. 2011).

𝛿18𝑂 = (𝑅 𝑠𝑎𝑚𝑝𝑙𝑒

𝑅 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑− 1) 1000 (2)

On the other hand, when biological activity is present, the O exchange between phosphate and

water is considerable and controlled by temperature-dependent equilibrium with ambient water.

This equilibrium is catalyzed by the enzyme pyrophosphatase in a reversible intracellular

process by microorganisms (Frossard et al. 2011). Nevertheless, extracellular processes

(generally irreversible) also play an important role understanding the pathways that δ18O takes

in organic P metabolism in the soil-plant system (Frossard et al. 2011).

Longinelli & Nuti (1973) proposed the equilibrium between δ18OP (P=Phosphate), δ18OW

(W=Water) and temperature in the equation (3). It is shown below:

𝑡 = 111.4 − 4.3 (𝛿18𝑂𝑃 − 𝛿18𝑂𝑊 ) (3)

Where: δ18OP is the oxygen isotope ratio of the dissolved phosphate; t is the water temperature,

and δ18OW is the oxygen isotopic composition of the water in which the phosphate equilibrated.

The oxygen isotope composition of meteoric precipitation (δ18Oppt = δ18OW) is considered in the

equation above and used generally to reconstruct the continental paleoclimate and

paleohydrology. Nevertheless the sparse data does not allow us to estimate modern water

composition in actual geographic variation (Bowen & Wilkinson 2002).

Therefore a model was developed by Bowen & Wilkinson (2002) to describe the 18O in modern

distribution based on geographical parameters, with their method of interpolation is possible to

calculate the δ18O values of meteoric water for a given location. Equation 4 shows the

relationship between the altitude and latitude of a specific location to estimate the δ18Oppt =

δ18OW.

𝛿18𝑂𝑝𝑝𝑡 = −0.0051 (|𝐿𝐴𝑇|)2 + 0.1805(|𝐿𝐴𝑇|) − 0.002(𝐴𝐿𝑇) − 5.247 (4)

Using δ18OP in soil-plant system studies is becoming more popular as it expresses perfectly the

link of the biologic activity and the P-cycling in the soil in a unique value, which it is easy to

State of the Art

14

analyze. Yet some specific procedures need to be solved before considering this approach

reliable at all. For instance Frossard et al. (2011) identifies some points to be considered in

oxygen isotope analysis, some of which are detailed below:

The isolation of phosphate forms from any other compound containing O and to convert

it to silver phosphate (Ag3PO4) for oxygen isotope analysis

The carrying out of the measurements of δ18OP on different phosphorus fractions of

sequential extractions.

The analysis of new techniques able to determine δ18OP of extracted organic P like

ultraviolet digestion.

The effect of microbiological uptake and mineralization processes of soil available and

soil microbial P.

In general, the use of δ18OP in soil-plant system studies as well as the molecular study and

chemical mechanism behind the P-cycle in the pools of soil phosphorus from varied origin and

different land use are not solved yet. That is a clear research gap not only because of the lack

of efficient and effective procedures but also due to it has not considered completely the impact

of soil anthropization.

Based in the research gap between the use of δ18OP in soil-plant system from soils with

anthropological influence and developed under the influence of different soil formation factors,

this research selected four soil groups from which three are cultivated and one not. Below the

main features of the selected soil groups will be described.

2.4 Soil classification

Soil definition and its characterization varies depending on the source, thus, around 40 years

ago a universal soil classification was developed called the World Reference Base (WRB). This

allows the soil scientists to speak the same language, classify soils, and create soil maps (IUSS

Working Group WRB 2014).

The WRB is composed of two levels of categorical detail. The first level has 32 Reference Soil

Groups (RSGs) and the second level is a combination of the name of the RSG with a set of

principal and supplementary qualifiers (IUSS Working Group WRB 2014).

State of the Art

15

This study was focused in 4 RSGs: Ferralsols (China), Andosols (Indonesia), Luvisols

(Germany), and Chernozems (Russia). The selected soil groups were chosen according to their

relevance in the cropland covering. Cereals are the most cultivated crops around the world,

from them wheat, barley and rice are the major crops worldwide (Leff et al. 2004).

Wheat is cultivated in temperate latitudes like Germany therefore a Luvisol where wheat is

cropped was chosen for this study. On the other hand rice has a completely different production

system and it is concentrated in Asia on Ferralsols and Andosols. Furthermore, in order to

analyze the P-dynamics on soils where the human influence is null was decided include a

Chernozem, which is kept under natural vegetation although it has a huge agricultural potential.

2.4.1 Ferralsols

There are about 750 million ha of Ferralsols worldwide. They are located in the humid tropics of

South America, Africa and to a lesser extent in Southeast Asia. On Ferralsols, grazing animals

and the cultivation of a variety of annual and perennial crops is quite common (IUSS Working

Group WRB 2014).

Ferralsols are old soils, which are deeply weathered. In humid tropics, they show reddish

(hematite) and yellowish (goethite) colors. These soils have low-activity clay (mainly kaolinite)

and a high content of sesquioxides (IUSS Working Group WRB 2014).

Major constraints are related to their chemical properties: weak cation retention by the mineral

soil, a low pH, a strong fixation of P, Al toxicity and the available plant nutrients are

concentrated in the organic matter instead of the mineral soil. On the other hand, it has good

physical properties including a large soil depth, good permeability, stable microstructure

(pseudo-sand), and easy to work (IUSS Working Group WRB 2014).

The sampled Ferralsol is used to cultivate paddy rice. Paddy rice is one of the most important

crops around the world. Among the common activities in rice cropping are burning of plant

residuals before the next sow, waterlogged conditions, the use of machinery for agricultural

practices and the application of fertilizers. The soil at the sampled site was developed from

limnic and fluvial sediments coming from conglomerates.

State of the Art

16

2.4.2 Andosols

Andosols are located in volcanic regions worldwide amounting to an area of around 110 million

ha. Its major concentration is in the tropics and around the Pacific rim, however, some are also

located in the arctic region and from rolling hills to mountainous environment with an extensive

range of vegetation (IUSS Working Group WRB 2014).

IUSS Working Group WRB (2014) mentions that this soil group is developed not only in glass-

rich volcanic ejecta but also in other silicate-rich materials under acid weathering in humid and

perhumid climates. Inside of the profile it is possible to find an accumulation of stable organo-

mineral complexes or short-range-order minerals such as allophane, imogolite and ferrihydrite.

Generally they are very fertile soils with a high potential for cultivation; its major constraint is the

strong fixation of P. Nevertheless, Andosols are used for the production of a wide range of crops

such as sugar cane, tobacco, tea, vegetables. It is important to mention that in the lowlands the

major crop is paddy rice and forests remain on the hills (IUSS Working Group WRB 2014).

Paddy rice is also cultivated in the sampled Andosols. As it was mentioned above the common

practices are burning of plant residuals and spreading on the field before the next sow,

waterlogged conditions, the use of machinery for agricultural practices and the application of

fertilizers. The big difference between this soil and the previous one is its parent material.

2.4.3 Luvisols

IUSS Working Group WRB (2014) mentions that over 500-600 million ha are covered by

Luvisols, generally in temperate regions and in Sub-tropical and Tropical regions, occurring

mainly on young land surfaces. Luvisols are widely sown with grains, sugar beet, and in slopes

are used for grazing or tree crops.

The most important characteristic of this soil group is the enrichment of clay in the subsoil (clay

migration leading to an argic subsoil horizon); another feature is the presence of a blenched

eluviation horizon, just between the surface and the argic horizon (IUSS Working Group WRB

2014).

These soils contain high-activity clays and high base saturation, especially from 50 to 100 cm.

The parent material, of this group includes an extensive variety of unconsolidated materials from

colluvial deposits up to glacial tills. (IUSS Working Group WRB 2014).

State of the Art

17

The sampled Luvisols were developed from loess. This soil is also used for crop cultivation but

not under flooding conditions. In this soil common agricultural practices include the application

of fertilizers and the use of heavy machinery .

2.4.4 Chernozems

IUSS Working Group WRB (2014) mentions that around 230 million ha of earth surface are

covered by Chernozems, the majority of which are located in the mid-latitude steppes of

Euroasia and North America, in flat to undulating plains, where the climate consists of cold

winters and hot summers.

Generally, they are blackish soils due to their blackish chernic surface horizon. The dark color of

their superficial horizon is because of a large organic matter content. These soils are very deep

and usually come from Aeolian sediments (loess) (IUSS Working Group WRB 2014).

Chernozems are used for many arable crops mainly wheat and barley (northern temperate belt),

and maize and sunflower (warm temperate belt). Due to their characteristics, they are among

the best soils in the world, however if they are sown it is necessary to apply P fertilizers and

implement some measures to avoid wind and water erosion (IUSS Working Group WRB 2014).

The sampled Chernozems were developed from loess. It has a natural steppe vegetation with

minimal anthropogenic influence.

Materials and Methods

18

III. Materials and Methods

3.1 Soils

Four soils were chosen, which according to World Reference Base (WRB) are characterized as

Ferralsols, Andosols, Luvisols and Chernozems from China, Indonesia, Germany and Russia

respectively (Figure 4). Table 2 summarizes both relevant information of the soils and the

geographic and weather conditions from where the soils were taken.

Location selected soil groups. Figure 4.

Source: jlspedrero.files.wordpress.com/2010/10/robinsonprojection.jpg

The soil from Russia was sampled in 18 layers to a depth of 1.7 m, but for this study only 7 of

samples were taken into account. All other soils were sampled in 7 horizons to approximately

1.3 m depth. The dry soil samples were ground to pass a 2 mm mesh sieve, and stored at room

temperature.

FERRALSOLS

CHERNOZEMS LUVISOLS

ANDOSOLS

Materials and Methods

19

Table 2. Sampling sites description.

3.2 Procedures

3.2.1 Soil Phosphorus (P) Fractionation

The sequential P extraction was carried out according to Hedley et al. (1982) modified by

(Tiessen, H. and Moir, J.O. 1993), but without the initial step of extraction by an anion resin

(hereafter referred to as the modified Hedley procedure). One pair of duplicates for each horizon

of each soil group was fractionated concurrently. Figures 5 and 6 show a summary of the

procedure followed and the extractants sequentially used.

Country, Location

Soil type Parent

material

Practices

MAP (mm) / climate / MAT (°C)

Elevation [m.a.s.l.]

Coordinates

China, Yingtan

Ferralsols

limnic and fluvial

sediments from

conglomerates

Oryza sativa (paddy rice),

Trifolium spec. (clover)

1300-1900 /sub-tropical/

18 45

N 28°14.020´ E

116°53.866´

Indonesia, Sukabumi

Andosols

volcanic ash, intermediate

igneous (andesite)

Oryza sativa (paddy rice), Oryza sativa, Brassica rapa chinensis (pak

choi)

2000-4400 /tropical/

25 870

S 06°52.802‘ E 106°56.457

Germany, Meckenheim*

Luvisols Loess Triticum

aestivum L (winter wheat)

625 /Temperate and humid /

9.6

173 N 50°37.51´ E 6°59.32´

Russia, Kursk**

Chernozems Loess

Stipa pennata L., S.

dasyphylla, and S.

stenophylla Czern. and Festuca

sulcata Hack. (Natural

grasses, typical of Steppe) ***

573 /Temperate/

5.3

219

N 51°45´ E 36°10´

MAP: Mean Annual Precipitation; MAT: Mean Annual Temperature

* (Barej et al. 2014) ** (Rodionov et al. 2006)***(Amelung et al. 2001)

Materials and Methods

20

Scheme of modified Hedley procedure. Figure 5.

The extractant NaHCO3 removes the labile inorganic phosphorus (Pi), and organic phosphorus

(Po), which was sorbed on the soil surface and also a small amount of microbial P. NaOH

solution removes Po and Pi compounds held more strongly by chemisorption to iron and

aluminum components of soil, while the HCl solution removes both apatite-type minerals

(Williams et al., 1971) and extracts occluded P in more weathered soils. Lastly an acid digestion

with hot concentrated HCl extracts Pi and Po from very stable residual pools (Tiessen, H. and

Moir, J.O. 1993)

0.5 g Soil + 30 ml 0.5 M NaHCO3 (pH 8.5)

shake 16 h, centrifuge 20 min (2500 rpm - 20°C), filter supernatant

0.5 g Soil + 30 ml 0.1 M NaOH

shake 16 h, centrifuge 20 min (2500 rpm - 20°C), filter supernatant

0.5 g Soil + 30 ml 1 M HCl

shake 16 h, centrifuge 20 min (2500 rpm - 20°C), filter supernatant

0.5 g Soil + 15 ml concentrate HCl

Water bath with 15 ml concentrate HCl, centrifuge 20 min (2500 rpm -

20°C), washing twice with 10 ml of water + centrifuge 20 min (2500

rpm - 20°C), filter supernatant after each centrifuge

Supernatant II: Pi (associated with iron

(Fe) and aluminum (Al) oxides) and Po

Supernatant III: mineral P (associated with

Calcium (Ca))

Supernatant I: Labile Pi and Po

(sorbed on clays and mineral particles)

Supernatant IV: mineral P

(associated with Fe)

Materials and Methods

21

Modified Hedley procedure. a) 0.5 g of soil; b) Shaker (16 h); c) Centrifuge Figure 6.(2500 rpm, 20 min at 20°C); d) Filtration; e) Supernatants; f) Water bath.

3.2.2 Phosphate determination

Once the supernatants were obtained the Pi concentration was measured by the Molybdenum

Blue method of Murphy & Riley (1962) (hereafter referred to as Mo-blue method) with a

spectrophotometer (SPECORD 205, Analytik Jena, Jena, Germany) (Figure 7).

To obtain the specific conditions required for the molybdenum blue reaction, on one hand, for

the extractants NaHCO3 and NaOH it was necessary to acidify the samples by adding 5 ml and

3 ml of 1N HCl solution respectively, and on the other hand 2 ml of 5N NaOH were added to the

concentrate HCl extractant.

a) b) c)

d) e) f)

Materials and Methods

22

P-blue procedure. a)Sample preparation; b) Standards and samples ready Figure 7.for measurement; c) measurement using photometer.

Tables 3 and 4 show how each supernatant and the standard solution were treated depending

on the extractant solution. For the NaHCO3 solutions, we waited a few hours after adding the

HCl to allow for the outgassing of CO2.

Table 3. Preparation of supernatants to be measured in the photometer.

Supernatant*

ml

1N HCl

ml

Mixing

solution**

ml

Distilled

water

ml

I 10 5 8 27

II 10 3 8 29

III 10 -- 8 32

IV 2 2*** 8 38

*Supernatant I: extractant NaHCO3; II: extractant NaOH; III extractant HCl; IV

concentrade HCl . **Mixing solution: 5n H2SO4; (NH4)6Mo7 O24; K2Sb2C8H4O12· 3 H2O

and ascorbic acid. ***For this supernatant instead to use 1N HCl was used 5N NaOH

a) b) c)

Materials and Methods

23

Table 4. Preparation of standard solution to be used in the photometer

Four measurements were taken for each sample (two per duplicate). The method chosen in the

photometer was “Phil P-blau.par” and the data are represented in µgP.g-1. Afterwards the values

were converted to mg P per kilogram of soil (mg.kg-1)

Total soil phosphorus (PT) was determined using inductively coupled plasma optical emission

spectroscopy (Ultima 2 ICP-OES, HORIBA Jobin Yvon, Longjumeau, France). Only one

measurement was taken for each duplicate.

Organic phosphorus (Po) was calculated by the difference between PT and Pi of the

supernatants, therefore it was necessary to average the Pi measurements of the duplicates to

get only one value per duplicate which was subtracted from the PT measurement.

Standard

µg P ml-1

Matrix

solution

ml

1N HCl

ml

P-Standard

5 µg P ml-1

Mixing

solution

ml

Distilled

water

ml

NaHCO3

0 10 5 0 8 27

20 10 5 4 8 23

50 10 5 10 8 17

NaOH

0 10 3 0 8 29

20 10 3 4 8 25

50 10 3 10 8 19

HCl

0 10 -- 0 8 32

20 10 -- 4 8 28

50 10 -- 10 8 22

HCl (concentrate)

0 2 2* 0 8 38

20 2 2* 4 8 34

50 2 2* 10 8 28

*For this standard instead to use 1N HCl was used 5N NaOH

Materials and Methods

24

3.2.3 δ18OP of inorganic phosphate

The analysis of δ18OP was carried out on the HCl-extract from the modified Hedley procedure. In

order to obtain sufficient amounts for isotope analysis the extract should have a total phosphate

content of at least 30 µmol in, which was again tested by using the molybdenum-blue method.

In order to guarantee at least 30 µmol of total phosphate content in the HCl extractant, two

decisions were taken: first we analyzed only those horizons which have a high P content in HCl-

extractant (based on previous data) (Table 5). Secondly the soil-solution ratio in the modified

Hedley procedure was modified 10 g of soil in 100 ml of extractant.

Table 5. Selected samples to be processed following Tamburini et al. (2010)

method.

Country Soil Group Sample

code Horizon

Depht (cm)

China Ferralsols F1 1 0-8

F2 2 8-15

Indonesia Andosols

A1 1 0-8

A2 2 8-22

A3 3 22-29

A4 4 29-35

Germany Luvisols

L1 1 0-30

L2 2 30-45

L3 3 45-55

L4 4 55-75

L5 5 75-105

L6 6 120-130

Russia Chernozems

Ch1 1 10-20

Ch2 2 40-50

Ch3 3 60-70

Ch4 4 80-90

A special microphotometer cuvette of 2 ml was used for the Mo-blue method to minimize the

amount required from the extractant solution. Here, 0.2 ml of either the supernatant or the

standard plus 0.320 ml of the mixing solution and 1.480 ml of distilled water was added. The

standards were eight with different concentrations of phosphorus (1, 2, 5, 10, 50, 100, 200 and

Materials and Methods

25

300 µmol P.l-1). Due to the use of microphotometer cuvette the method chosen in the

photometer was “P-Blau_ohnesipper.par”.

The method used to prepare silver phosphate (Ag3PO4) for δ18OP analysis was slightly modified

from Tamburini et al. (2010) and is described in detail in figure 8. Once the silver phosphate was

extracted from all samples, it was weighed into silver foil capsules and sent to the University of

Zürich. There, each one was measured twice and denoted in parts per thousand in the δ18O

notation against Vienna Standard Mean Ocean Water (VSMOW) as the reference.

Phosphate extraction

by modified Hedley procedure

PO4 determination

by molybdenum-blue method by

Murphy & Riley (1962).

P-Blau_ohnesipper.par

Supernatant (30 µmol PO4 in 100 ml of

HCl extract)

Da

y 1

Ammonium phospho-molybdate mineral precipitation and dissolution

200 ml Erlenmeyer flask

Add:

Water bath 50°C overnight, shaking

gently

25 ml 35% Ammonium

nitrate + 40 ml 10 %NH4-Mo

Materials and Methods

26

D

ay 2

Filter yellow crystals: 4.5cm Teflon tubes

+ Millipore Express PLUS Membran

filters, 0.22 µm pore size (Merck Millipore,

USA)

Wash:

100 ml 5% Ammonium

nitrate

Collect crystals and filter into 100ml

Erlenmeyer flask. Gently swirl to dissolve

the crystals after removing the filter.

Discard supernatant.

Add:

20 ml NH4-citrate

Magnesium ammonium phosphate mineral precipitation and dissolution

Place the erlenmeyer onto the multiple

magnetic stirrer

25ml Mg-Solution

Cover with parafilm, make small holes, leave

overnight

Slowly 7ml 1:1 ammonia-

solution

Materials and Methods

27

D

ay 3

Filter white crystals: 4.5 cm Teflon tubes

+ Millipore Express PLUS Membrane

filters, 0.22 µm pore size (Merck Millipore,

USA

Collect crystals and filter into 50 ml tubes

and shake to dissolve the crystals.

Discard supernatant

Wash:

100 ml 1:20 ammonia-

solution

Add:

20 ml 0.5 N HNO3

Cation removal

Seal with parafilm and shake overnight 6 ml cation resin slurry (pH 7)

Materials and Methods

28

D

ay 4

Filter the samples: 2.5 cm Teflon tubes +

Isopore Membrane polycarbonate filters,

0.22 µm pore size (Merck Millipore, USA)

Rinse the resin:

1-2 ml distilled water

Collect the supernatant

Place the resin to be reconditioned

Ag3PO4 precipitation

Check if Cl is still present (If after add

AgNO3 the solution becomes white

(AgCl) wait 5 min and filter again)

Add:

little amount AgNO3

When the supernatant is Cl-free

Ag-ammine

Place the tubes open into the oven at

50°C, 24-48 hours

If yellow crystals are not formed after 24-

48 h check the pH and bring it to 7, using

only either HNO3 or NH4OH

Materials and Methods

29

Scheme of the steps followed to prepare Ag3PO4 for δ18OP analysis. The left Figure 8.column explains the procedure and the right, the solutions using in each step.

Da

y 5

Filter the crystals: 4.5 cm Teflon tubes +

membrane filter GTTP 0.2 µm

Wash:

Distilled water

Collect filters and crystals. Discard

supernatant

Place the filters on petri dishes and cover

it, put into the oven at 50°C, for at least 1

day.

Da

y 6

Scrape the filters gently

Collect the dried crystals and storage into little plastic vials. Prepare samples by placing 300 mg of the Ag3PO4 crystals into silver foil capsules.

TC/EA-IRMS (Thermal Conversion Elemental analysis/Isotope Ratio Mass Spectometry) made by University of Zürich. Technical details are available in Tamburini et al. (2010)

Materials and Methods

30

In order to control the reliability of the observed δ18OP values, it was used the method explained

by Tamburini et al. (2010). This method requires plotting the weight of the Ag3PO4 introduced

into the TC/EA-IRMS with the peak area of the oxygen yield (Figure 9), which is proportional to

the oxygen content of the sample. If the values of the samples are located above the regression

line defined by the Ag3PO4 standards indicates an extraneous oxygen contamination and these

values cannot be considered in the discussion.

Weight of the analyte plotted against the area of the oxygen yield peak for Figure 9.selected samples.

3.3 Statistical analyses

This study statistically assessed the effect of selected soil groups and soil depths on soil P

levels using a nonparametric alternative to ANOVA called “Kruskal-Wallis One Way Analysis of

Variance on Ranks” (hereafter referred to as ANOVA on ranks). This statistical analysis was

chosen because almost all data were not normally distributed and the variance varied among

the tested groups.

The One Way Analysis of Variance test was only possibly for the Pi content in Andosols and

Chernozems extracted by NaHCO3. This was because these data passed the Normality Test

(Shapiro-Wilk) and the Equal Variance Test (Brown-Forsythe).

0.00

5.00

10.00

15.00

20.00

25.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60

Peak h

eig

ht

[nA

]

Sample weight [mg]

pure samples

contaminated samples

Standards

Materials and Methods

31

Statistical analysis was conducted with SigmaPlot Version 13.0 from Systat Software, Inc. San

Jose California. USA. SigmaPlot Version 13.0 automatically analyzed the data with a

significance of either P < 0.05 or < 0.01, depending on the data. Tukey and Dunn´s test were

performed for all pairwise comparisons when the treatment group size was equal or unequal

respectively. This test was used to identify different groups among horizons which are shown

from the Figure 10 to 13. Tukey test reports generated using SigmaPlot Version 13.0 are

attached in Annex 1.

Results

32

IV. Results

4.1 Phosphate pools in selected soil groups

After the modified Hedley procedure was run, four supernatants were extracted: I by NaHCO3, II

by NaOH, III by HCl and IV by HCl concentrate. The Pi, PT, and Po were measured for each

supernatant. This measurement was done by using the Mo-Blue method for Pi, through ICP-

OES for PT and finally through subtraction for Po.

Due to the difference between the thicknesses of the horizons of the four soil groups, the P

content was analyzed among horizons of the same soil profile. These results were then visually

compared between the selected soil groups.

Unfortunately some data measured by ICP-OES were unreliable and these were not taken into

account in the data analysis. Thus the following data is missing: PT and Po from the extractant

NaHCO3 and NaOH in the Luvisols and PT and Po from the extractant concentrate HCl in

Ferralsols and Andosols.

The mean concentration of P formed in the selected soil groups after phosphorus fractionation

by modified Hedley procedure are presented in figures 10 to 13. These figures does not include

the standard deviation because in this thesis only was analyzed one profile per soil type.

Nevertheless the significant differences at P < 0.05 is showed by letters. The significant

difference describes the significance only for Pi, because for the others the post-hoc test did not

work properly.

In general, Pi concentration in the supernatant I (NaHCO3; Figure 10) showed a declining trend

from the surface to the depth; although in Chernozems the highest amount of Pi is located at

60-90 cm and not in the surface; nevertheless, the Pi-content at the bottom of the profile

decreases sharply. The highest concentration of Pi in the surface was present in Andosols with

around 200 mg.kg-1, followed by Luvisols (around 100 mg.kg-1).

The average amount of PT (Figure 10) in Ferralsols, Andosols and Chernozems displayed a

declining trend, where the highest PT content (around 230 mg.kg-1) was shown in Andosols

followed, by Chernozems (around 170 mg.kg-1) and lastly Ferralsols (around 100 mg.kg-1).

Meanwhile Po average concentration did not show a clearly decreasing tendency in Ferralsols

and Andosols. The decreasing trend occurred only in Chernozems where it was evident that the

Results

33

high Po value approximately 150 mg.kg-1 was at the surface and decreased to 5.76 mg.kg-1 at

the bottom.

Mean of P content measured from the supernatant I extracted by NaHCO3 in Figure 10.the selected soil groups. Different letters indicate significant differences at P < 0.05 among horizons in that specific P-pool, while without letters mean not significant difference at P < 0.05.

Pi concentration in supernatant II (NaOH; Figure 11) clearly diminished only in Andosols and it

reported the highest Pi concentration (more than 500 mg.kg-1) into its first 35 cm. In the case of

Chernozems, the highest Pi values (around 130 to 200 mg.kg-1) were located within 40 – 90 cm

but afterwards the Pi concentration decreased in the depths.

b

ab

b

ab

ab

a

0 20 40 60 80 100 120

120 - 130

75 - 105

55- 75

45 - 55

30 - 45

0 - 30

P content (mg.kg-1)

Dep

th (

cm

)

Luvisols

Pi

b

b

b

b

ab

a

a

0 20 40 60 80 100 120

60 - 95

42 - 60

28 - 42

20 - 28

15 - 20

8 -15

0-8

P content (mg.kg-1)

Dep

th (

cm

)

Ferralsols

PT

Po

Pi

f

g

e

d

c

a

b

0 50 100 150 200 250 300

75 - 105

50 - 75

35 - 50

29 - 35

22 - 29

8 - 22

0 - 8

P content (mg.kg-1) D

ep

th (

cm

)

Andosols

PT

Po

Pi

c

c

c

a

a

b

b

0 50 100 150 200

160 -170

130 -140

110 - 120

80 - 90

60 - 70

40 - 50

10 - 20

P content (mg.kg-1)

Dep

th (

cm

)

Chernozems

PT

Po

Pi

Results

34

Mean of P content measured from the supernatant II extracted by NaOH in Figure 11.the selected soil groups. Different letters indicate significant differences at P < 0.05 among horizons in that specific P-pool, while without letters mean not significant difference at P < 0.05.

Ferralsols (Figure 11) showed the highest Pi concentrations in its first 15 cm (around 120

mg.kg-1) and quite similar values between 15 – 40 cm (around 70 mg.kg-1) where it starts rising

until 106 mg.kg-1. In contrast, Luvisols presented a similar dynamic to Ferralsols. This dynamic

was high values at the surface (around 120 mg.kg-1), then similar values in the middle of the

profile (around 50 mg.kg-1) after a small increase of 20 units, and finally at the bottom of the

profile, the Pi concentration decreased to around 18 mg.kg-1.

b

ab

ab

ab

ab

a

0 20 40 60 80 100 120 140

120 - 130

75 - 105

55- 75

45 - 55

30 - 45

0 - 30

P content (mg.kg-1)

Dep

th (

cm

)

Luvisols

Pi

a

ab

ab

ab

b

a

a

0 100 200 300 400 500

60 - 95

42 - 60

28 - 42

20 - 28

15 - 20

8 -15

0-8

P content (mg.kg-1)

Dep

th (

cm

)

Ferralsols

PT

Po

Pi

b

b

b

a

a

a

a

0 500 1000 1500 2000 2500

75 - 105

50 - 75

35 - 50

29 - 35

22 - 29

8 - 22

0 - 8

P content (mg.kg-1)

Dep

th (

cm

)

Andosols

PT

Po

Pi

b

b

ab

ab

a

a

ab

0 200 400 600 800 1000 1200

160 -170

130 -140

110 - 120

80 - 90

60 - 70

40 - 50

10 - 20

P content (mg.kg-1)

Dep

th (

cm

) Chernozems

PT

Po

Pi

Results

35

Andosols (Figure 11) reported a clear reduction of its PT content from the surface to the depths

(from around 2000 to 400 mg.kg-1). In Chernozems the highest PT values (around 1000 mg.kg-1)

are located among 40 – 70 cm but afterward the concentration decreased dramatically in the

depths to a value of 75 mg.kg-1.

Andosols and Chernozems presented the high Po concentration in their second horizon with

1400 mg.kg-1 and 860 mg.kg-1 respectively. Within the last horizon, the Po concentration sharply

decreased until it reached around 250 mg.kg-1 in Andosols and 45 mg.kg-1 in Chernozems

(Figure 11).

In Ferralsols the Pi concentration in supernatant III (HCl; Figure 12) was recorded only in the

first three horizons, with a range of values from 2 to 40 mg.kg-1. In the deeper horizons the Pi

concentration was below the detection limit. Meanwhile, concentrations in Andosols and

Chernozems in the second and third horizons showed the highest Pi concentration (around 110

mg.kg-1 and 200 mg.kg-1 respectively) and afterwards then decreased to 20 mg.kg-1 in Andosols

and 40 mg.kg-1 in Chernozems.

In Luvisols the first horizon as well as the last two horizons had the highest Pi content of more

than 230 mg.kg-1. However the concentration in the other horizons was smaller and the

reduction was not exaggerated. The values are in the range of 110 – 150 mg.kg-1 (Figure 12).

PT content in Luvisols, Andosols and Chernozems showed the same trend that was described

for the Pi concentration. Unlike the other soils, the dynamic in Ferralsols was different because

the last 4 horizons have values that oscillate between 7 and 17 mg.kg-1 (Figure 12).

In the case of Po content in Ferralsols, a gradually increase from the surface to the bottom (from

around 2 mg.kg-1 to 17 mg.kg-1) was evident. Meanwhile in Andosols the Po content was the

highest among the soil-selected groups and the values were between 60 mg.kg-1 to 120

mg.kg-1) (Figure 12).

Po content in Luvisols was the smallest among the first horizons of the soil-selected groups.

Nevertheless, the highest content was in its last horizon (29 mg.kg-1). Generally, the Po content

in Chernozems presented a diminishing trend from 41 mg.kg-1 to 19 mg.kg-1 even though the

smallest Po content was located at 60 – 70 cm (about 12 mg.kg-1) (Figure 12).

Results

36

Mean of P content measured from the supernatant III extracted by HCl in Figure 12.the selected soil groups. Different letters indicate significant differences at P < 0.05 among horizons in that specific P-pool, while without letters mean not significant difference at P < 0.05.

Finally, Pi content measured in supernatant IV (concentrated HCl; Figure 13) indicated that in

Ferralsols Pi content increased from the surface (about 50 mg.kg-1) to the bottom where it

reached a value close to 120 mg.kg-1.

0 10 20 30 40 50

60 - 95

42 - 60

28 - 42

20 - 28

15 - 20

8 -15

0-8

P content (mg.kg-1)

Dep

th (

cm

)

Ferralsols

PT

Po

Pi

a

a

ab

b

ab

ab

0 50 100 150 200 250 300

120 - 130

75 - 105

55- 75

45 - 55

30 - 45

0 - 30

P content (mg.kg-1)

Dep

th (

cm

)

Luvisols

PT

Po

Pi

ab

b

b

ab

a

a

ab

0 50 100 150 200 250

75 - 105

50 - 75

35 - 50

29 - 35

22 - 29

8 - 22

0 - 8

P content (mg.kg-1)

Dep

th (

cm

)

Andosols

PT

Po

Pi

b

ab

ab

a

a

a

a

0 50 100 150 200 250

160 -170

130 -140

110 - 120

80 - 90

60 - 70

40 - 50

10 - 20

P content (mg.kg-1)

Dep

th (

cm

)

Chernozems

PT

Po

Pi

Results

37

Mean of P content measured from the supernatant IV extracted by Figure 13.concentrate HCl in the selected soil groups. Different letters indicate significant differences at P < 0.05 among horizons in that specific P-pool, while without letters mean not significant difference at P < 0.05.

In the case of Andosols, although Pi content decreased from the top to the bottom, the highest

content of P was located until 50 cm depth with more than 270 mg.kg-1, while the smallest Pi

content was in the last horizon (75-105 cm) around 130 mg.kg-1 (Figure 13).

Chernozems did show an extreme change among horizons. In almost all of the horizons, Pi

content oscillated between 80 -120 mg.kg-1. Only in its fifth and sixth horizons was Pi content

less than 66 mg.kg-1 (Figure 13).

b

b

ab

ab

ab

a

ab

0 100 200 300 400 500

75 - 105

50 - 75

35 - 50

29 - 35

22 - 29

8 - 22

0 - 8

P content (mg.kg-1)

Dep

th (

cm

)

Andosols

Pi

b

ab

a

b

b

b

0 50 100 150 200

120 - 130

75 - 105

55- 75

45 - 55

30 - 45

0 - 30

P content (mg.kg-1)

Dep

th (

cm

)

Luvisols

PT

Po

Pi

ab

ab

b

ab

ab

ab

a

0 50 100 150 200 250 300 350

160 -170

130 -140

110 - 120

80 - 90

60 - 70

40 - 50

10 - 20

P content (mg.kg-1)

Dep

th (

cm

) Chernozems

PT

Po

Pi

a

a

a

a

b

ab

ab

0 20 40 60 80 100 120 140

60 - 95

42 - 60

28 - 42

20 - 28

15 - 20

8 -15

0-8

P content (mg.kg-1)

Dep

th (

cm

)

Ferralsols

Pi

Results

38

In contrast, Luvisols did not register an evident trend. Pi content was between 45 to 95 mg.kg-1

and its highest content was located within 55 – 105 cm with more than 80 mg.kg-1 (Figure 13).

On the contrary, PT and Po content in Chernozems showed a general a decreasing trend from

the surface to the bottom; Pi content goes from around 280 mg.kg-1 to 122 mg.kg-1. However the

smallest content are located within 110 -120 cm with almost 64 mg.kg-1 and Po content

decreased from 159 mg.kg-1 to 7 mg.kg-1 (Figure 13).

In the case of Luvisols Pi and Po content did not change much among horizons. Pi content

oscillated between 140 to 190 mg.kg-1, meanwhile Po content increased from 90 to 125 mg.kg-1

(Figure 13).

4.2 Observed δ18OP vs δ18OPE

In order to determine the δ18OW and δ18OPE (PE= phosphate in equilibrium) of the sampling sites,

equations described in chapter II were used. Table 6 not only shows the generated values of

δ18OW using equation 4 but also the δ18OPE (δ18OP = δ18OPE) using the equation 5 (from equation

3, as proposed by Longinelli & Nuti (1973), δ18OP (δ18OP = δ18OPE) was isolated)

𝛿18𝑂𝑃𝐸 = 𝛿18𝑂𝑤 + (111.4 − 𝑡)/4.3 (5)

Table 6. δ18OW and δ18OPE calculated using the equation suggested by Bowen &

Wilkinson (2002) and Longinelli & Nuti (1973) respectively.

From the isolated silver phosphate samples taken from the selected horizons, the University of

Zurich measured each sample. The mean of δ18OP denoted in parts per thousands in the δ18O

notation against Vienna Standard Mean Ocean Water (VSMOW), is shown in table 7.

Country δ18

OW δ18

OPE

China -4.31 17.42

Indonesia -5.99 14.11

Germany -9.53 14.15

Russia -10.00 14.67

Results

39

Table 7. Mean δ18OP values of HCl-extractable P (‰, VSMOW) and standard

deviation (SD) of selected soil groups.

Country Soil Group Sample

code Horizon

Depth (cm)

Mean δ

18OP

(‰,VSMOW)

SD

(‰)

China Ferralsols F1 1 0-8 8.23 0.198

F2 2 8-15 12.81 0630

Indonesia Andosols

A1 1 0-8 15.57 0.382

A2 2 8-22 21.78 0.412

A3 3 22-29 17.76 0.339

A4 4 29-35 18.95 0.354

Germany Luvisols

L1 1 0-30 16.33 0.223

L2 2 30-45 NR* NR*

L3 3 45-55 15.39 0.141

L4 4 55-75 13.23 0.260

L5 5 75-105 11.61 0.038

L6 6 120-130 11.46 0.128

Russia Chernozems

Ch1 1 10-20 13.54 0.002

Ch2 2 40-50 13.62 0.116

Ch3 3 60-70 11.29 0.191

Ch4 4 80-90 12.04 0.012

*The measurement is not reliable because the sample was contaminated with some other compounds containing

oxygen according procedure explained in figure 9.

The δ18OP signature for the HCl-extractable P pool and its respective Pi content were compared

by visual examination. Ferralsols, Luvisols and Chernozems show clearly that only below δ18OPE

the Pi content and the observed δ18OP has an inverse relation (Figure 14, 16 and 17). This

means that even though the Pi content has a high value, the signature δ18OP value does not

show a high value and vice versa. In contrast, Andosols (Figure 15) has another trend because

all its δ18OP are above δ18OPE.

Additionally, it is clear to see that in Ferralsols, Andosols and Luvisols the δ18OP (observed) do

not reach the δ18OPE (equilibrium). The first one is under equilibrium; the second soil shows a

signature above the equilibrium line and in the case of Luvisols, the equilibrium value is over the

observed data only until 55 cm. In contrast, although Chernozems has a signature under

equilibrium it is the one that most constantly approaches the δ18OPE at least upto 50 cm.

Results

40

δ18OP signature vs Pi content for the HCl-extractable P pool in Ferralsols. Figure 14.

δ18OP signature vs Pi content for the HCl-extractable P pool in Andosols. Figure 15.

Results

41

δ18OP signature vs Pi content for the HCl-extractable P pool in Luvisols. Figure 16.

δ18OP signature vs Pi content for the HCl-extractable P pool in Chernozems. Figure 17.

Discussion

42

V. Discussion

5.1 Phosphate pools in selected soil groups

Soil P content differs due to many aspect such as the origin and nature of the parent material,

climatic conditions, time scale, biologic activity, erosion, leaching (Pansu & Gautheyrou 2006).

The distribution and dynamic of P soil also has a substantial spatio-temporal variation (Shen et

al. 2011).

A difference between the P content in the selected soil groups in the four analyzed fractions,

was expected as four soils were developed under totally different environments and from

diverse parent material. It was possible to identify some trends of the soil P content among the

studied soils.

Additionally it was assumed in this research that the modified Hedley procedure works in the

same way, even in different soils. Yet, Guo et al. (2000) and other scientists suggests that not

all P fractions that are extracted by Hedley procedure are certain for all soil and under specific

agricultural management.

In addition, it was evident that there was a generated error when Po was calculated. This error

occurred because it was determined only through the difference between PT and Pi as

mentioned by Tiessen, H. and Moir, J.O. (1993).

Another big point that definitely influenced the results, at least up to 30 cm of soil, was the

agricultural system of these sampled soils. In the case of Ferralsols and Andosols the

production systems is based in paddy rice, while on Luvisols the main crop is winter wheat and

obviously not under waterlogged conditions and with regard to Chernozems were found natural

grasses typical of the steppe that had never been cropped.

Paddy soils are extremely modified by anthropogenic activities. Among the common practices

on these soils are puddling (tillage of the wet soil), developing of a plough pan (controlling

flooding and drainage associated with specific redoximorphic features), liming, fertilizer

application, organic manuring. These conditions have a direct influence on the microbial activity

and its function and thus short-term biogeochemical processes (Kögel-Knabner et al. 2010).

Under waterlogged conditions the microbial community remains very high for some time,

nevertheless the reductive character throughout the whole plowed layer affect considerably on

Discussion

43

bacteria, fungi, and archaea and the decomposition of soil organic matter at long term

depended on oxygen supply (Takai et al. 1956; Takai & Kamura 1966; Kögel-Knabner et al.

2010).

On the other hand, the sampled Luvisols has an agricultural system, which does not require

flooding conditions. Among the common agricultural practices are ploughing, liming, organic

manuring and fertilizer application, which change the natural condition of the soil and thus the

cycling of P. That is why the P content extracted by NaHCO3 (labile P) has a big difference

among its other horizons.

In contrast, the sampled Chernozems have not had any kind of human influence. This P-cycle is

in function only of its parent material, its natural enrichment of organic matter and the in situ

development of microorganism, flora and fauna of this region. That is why it could be consider

as the “genuine or natural P-cycle”.

5.1.1 Ferralsols

Ferralsols are highly weathered soils, with a high content of either goethite (yellowish color) or

hematite (reddish pigment) and the high presence of low-activity clays (kaolinite). This could be

a reason for the smallest values of plant P-availability among the different soils, as suggested

by Guo et al. (2000).

In contrast, due to the high content of Fe/Al oxyhydroxides, it was observed that Ferralsols, after

Andosols, have the second highest values of Pi and Po content extracted by NaOH among the

analyzed soils. The presence of this chemical compound is supported by Montalvo et al. (2015),

who states that Andisols1 and Oxisols2 contain a very low concentration of free-orthophosphate

but a high concentration of P, which is strongly associated with Fe/Al oxides.

Organic P in NaOH solution is quite evident in the upper part of the profile because of the

presence of iron oxides. These oxides promote the formation of organo-mineral compounds and

organic matter stabilization, as they do not transform either by mineralization or humification.

They are reflected in this P-fraction (Dalmolin et al. 2006).

1 Andisols: “Orden” name given by the Soil taxonomy System (United States of America) to Andosols

2 Oxisols: “Orden” name given by the Soil taxonomy System (United States of America) to Ferralsols

Discussion

44

In this soil group there is a clear absence of Ca-containing minerals in the sub-soil. The lack of

Ca-containing minerals is why the Pi content decreased from 20 cm to 0 mg.kg-1. This finding

was confirmed by Guo et al. (2000), who found that in highly weathered soils the HCl-Pi content

was generally less than 10 mg.kg-1. However, these 0 values determine the content of Po

because it is calculated by the difference between PT and Pi. Therefore if Pi is zero, it may seem

like the total amount of PT corresponds to Po; this however, is false. The more likely reason

could be the detection limit of the devices used to measure either Pi or PT content, The Pi

concentration was below the detection limit of the photometer, while the ICP was still able to

measure something.

It was quite interesting to see that in the sampled Ferralsols below 20 cm, the residual Pi

content extracted by concentrated HCl had the highest amounts among their horizons. This may

be due to some chemical compounds that include P, but are bound to other elements that could

not be extracted before.

5.1.2 Andosols

This soil group has the highest P content extracted by NaOH among the analyzed soils. This is

because these soils, after the rapid weathering of volcanic glasses, accumulate a large amount

of amorphous structures and Fe/Al complexes (IUSS Working Group WRB 2014), which are

extracted by this alkaline solution. Unfortunately, the large amount of P present in the soil is not

plant-available because it is trapped in these organo-mineral compounds.

In theory, after NaOH extraction was used, the stable organomineral-complexes may remain

unextracted. When HCl is used to extract Ca-associated P, any Po is rarely present in this

extract (Tiessen, H. and Moir, J.O. 1993). However in the Andosols, the Po values were still

quite high. This may be a sign of the large amount of Fe and Al associated P which was not

able to be completely extracted by NaOH. They appear in this extract even though these

molecules apparently are insoluble in acid. The large quantities of Po are because of the

intrinsic error, explained in the point 5.1.

Additionally, in soils with high content of amorphous materials such as Andosols, it is clear to

see a continuous buildup in the residual P, This was observed by Guo et al. (2000) and it is

confirmed with our data. Therefore the horizons with high P content in NaOH extraction have

the same trend in the P content as measured in the hot concentrated HCl.

Discussion

45

5.1.3 Luvisols

The data obtained from Luvisols are similar to other studies carried out in the same location and

under a similar approach.

The high amount of plant available-P (until 30 cm) shows the free orthophosphates in the soil

solution. This is partly attributed to the amount of biological activity (such as the presence of

earthworms) which makes the P more available (Barej et al. 2014).

Furthermore, there is a clear enrichment of Ca-Pi bound in the sub-soil reflecting the Ca-rich

parent material of this soils (Barej et al. 2014). It is for this reason the data for the P content

extracted by HCl was larger than the P extracted by NaOH.

On the other hand, the high content of Ca-P in Luvisols seems to act as a buffer for readily

available P. This means that when plants take up the available phosphorus, this Ca-P in the

residual fraction may be transformed and replace the available P (Guo et al. 2000).

5.1.4 Chernozems

This soil group has an enrichment of high-quality humus. Humus is formed by humic and fluvic

acids. Humic acids intensely compete against Pi molecules for adsorption sites due to their

huge number of negative charges as well as carboxyl and hydroxyl group composition (Shen et

al. 2011). Consequently the Po values are larger than their Pi content in the supernatants

extracted by NaHCO3.

Further, this was concluded by Guo et al. (2000), who stated that when Pi is high, the

contribution of Po is not significant and vice versa. For instance the Pi content extracted by

NaOH in the first 90 cm is around 150 mg.kg-1 while the Po content is more than 400 mg.kg-1.

This high content of Po in this fraction is a result of the stable organo-mineral complexes found

in this soil. However, the organic P present must be mineralized before in order to be available

for the plant. It is for this reason that although the P content is large, it is necessary to apply P

fertilizers to increase yields.

Meanwhile, after further analysis of the results of HCl extracts, it was not a surprise to see quite

high Ca-P content below 90 cm. This is because most Chernozems develop from carbonate-rich

loess of varying thickness (0.4 – 0.8 m), which often could be bounded P (Altermann et al.

2005).

Discussion

46

5.2 Observed δ18OP vs δ18OPE

The equilibrium δ18OPE calculated and shown in table 7 are in the range between 14 – 17‰.

These values are consistent with what was documented by Amelung et al. (in press). They

mention that in terrestrial systems δ18OPE values commonly range between 10 and 20 ‰.

Observed δ18OP values are in the range from 8.23 to 21.78‰. Only the highest value are outside

of the range summarized by Tamburini et al. (2014). This research shows data taken from HCl-

P pools, where the lowest value is 5.6‰ and the highest 21.3‰. Nevertheless, the difference

between the highest δ18OP value is not more than 0.5 delta units.

While it is true that the δ18OP signature is commonly used to understand P-cycling in aquatic

systems, it does not mean that the findings cannot be used to comprehend the origin and fate of

P in soil–plant systems. Young et al. (2009) mention that in open ocean waters, the biological

activity almost completely mediated equilibrium with the surrounding water and have values of

δ18OP, which are close to the expected temperature-dependent equilibrium. This observation is

valid and is evident in soil ecosystems. It is clearly presented by Frossard et al. (2011), who

shows that in sterile soils, there is no any O exchange between H2O and PO4, however under

biological activity (microorganisms) this exchange was tremendously accelerated.

The main drivers of the observed δ18OP are biological processes controlled by soil-moisture.

Four of these drivers include the crucial equilibrium with plants, uptake by plants and microbes,

extracellular mineralization by phosphatases, and equilibrium within the soil microbial

community (Angert et al. 2011). Furthermore Tamburini et al. (2014) recompiles many studies,

that clearly show the soil biological activity is a principal factor determining the δ18OP of

phosphate pools.

Interestingly, the Pi content and the observed δ18OP of Ferralsols, Luvisols and Chernozems

show an inverse relation if the fall below the δ18OPE. In the case of Andosols due to δ18OPE is

below all δ18OP values this inverse relation does not appear.

The inverse relation could be explained by the role of microorganism on P mobilization. It seems

that with sufficient or even surplus quantities of P in the soil, microorganisms do not turnover

completely all the P present in the soil solution. Thereby the δ18OP in the HCl extract rather

reflects the isotopic signature of phosphate of the parent material which has not been

mineralized yet. Although under P-deficiency, microorganisms apparently also rely on P

Discussion

47

resources from the rather stable HCl-extractable pool, thus causing the δ18OP to approach the

δ18OPE. In this case, δ18OP in relation to δ18OPE could be interpreted as an indicator of P-supply

in the respective soils

The temperature-dependent equilibrium oxygen isotope fractionation between phosphates and

water in biological systems is mainly catalyzed by phosphatases (Frossard et al. 2011). These

enzymes are released either by plants or microorganisms to mineralize P (Richardson &

Simpson 2011). Therefore, deviations of δ18OP from δ18OPE in deeper soil horizons could also

indicate a decreasing accessibility of soil P resources to microorganisms or plants, due to

decreasing microbial activity and root density with depth.

This research could have better explained this theory if it had included, in some way, the

measurement of biological activity in the different horizons. As Angert et al. (2011) explain the

varying rates of biological activity inside the soil should explain the differences in the observed

data.

5.2.1 Ferralsols

The two observed δ18OP values 8.23‰ and 12.81‰ at 8 and 15 cm respectively are below the

δ18OPE (17.42 ‰). It can be interpreted that the expected temperature dependent equilibrium

could not be reached because the biological activity did not fully mediate equilibrium with the

surrounding water. This can be explained since Ferralsols commonly present low pH, which can

limit biological activities. Additionally in this specific sampling site, “burning” is a common

practice and this action also can also result in diminished microbial activity (Natural Resources

Conservation Service. NRCS 2004).

It is important to mention that mineralization of Po is associated with kinetic effects and

equilibrium oxygen isotope fractionations between phosphates and water within microorganism

that is why the δ18OP values are lower than the equilibrium (Angert et al. 2011). Then, it is

assumed that the main process within the first 15 cm of this Ferralsols is the mineralization of

Po.

5.2.2 Andosols

This soil group shows the highest δ18OP values and all the cases are over the equilibrium value

(14.11‰). As Mizota et al. (1992) mention it is common for older soils to be formed from

Discussion

48

volcanic ash. For instance 75% of the generated δ18OP values in this study are in the range

(+16.7 to +24.8‰) reported by them for soils in advanced stages of pedogenesis.

The highest values of δ18OP could be also explained by some studies, which have proven the

influence of evapotranspiration processes on the accumulation of isotopically heavier water in

the soil surface. The studies conclude that the δ18OP signatures in the Hedley HCl are affected

by evapotranspiration up to 50 cm depth (Amelung et al., in press). The data observed in these

Andosols, were taken from a tropical place with high temperatures and sun radiation, and with

an annual precipitation range of 2000 – 4400 mm. In this data set, evapotranspiration does

influence these high values above δ18OPE at 35 cm depth, due to the processes in order to reach

the isotopic equilibrium have to deal with heavier soil water that was isotopically enriched by

evaporation.

5.2.3 Luvisols

A previous study of Luvisols from Germany, calculated an isotopic equilibrium value ranged

from +14.8 to +18.3‰, however, generated value for this study was +14.15 ‰; although this

value is smaller, we consider the data to be trustworthy. The difference was dependent on the

input data. For this study, the annual average temperature of the environment was used instead

to the soil temperature. The δ18OW was calculated using the equation proposed by Bowen &

Wilkinson (2002) instead of the equation that was suggested one year later by Bowen &

Revenaugh (2003).

The observed δ18OP was higher than the δ18OPE (14.15‰) until the depth of 60 cm for at least 2

delta units. Afterwards, the data diminishes under the isotopic equilibrium value for almost 3

delta units. The low data at 120 cm can be explained by the amount of mineral-bound

phosphate in the subsoil where the P increases and it is reflected in small δ18OP values. This

evidences the absence of microbial activity at the depth, thus it is reflected the signature of the

parent material rather than the microbial turnover.

However, as Amelung et al. (in press) explain, the high δ18OP values over 60 cm are influenced

by the evapotranspiration process, which deposit isotopically heavier water on the soil surface.

Furthermore, in this specific site, the soil is not covered by vegetation during spring and summer

due to the crop rotation system. It influences on its high evaporation more than in places

covered by natural vegetation along the whole year as is the case of Chernozems.

Discussion

49

Conversely, Amelung et al. (in press) also mention that the small data in the depths should be

influenced by the different origins of the oxygen of the HCl extractable soil phosphates.

5.2.4 Chernozems

From the observed data, the values of this soil group do not differ too much from the equilibrium

(14.67‰); however, it is clear to see that the first 50 cm the δ18OP data are even closer to the

equilibrium than after 60 cm. One of the main characteristics of this soil is the enrichment of

organic matter and the large thickness of the horizons, which is linked with high soil

microbiological activity. This is why in the “top soil” the data almost reach the equilibrium and

also it explain why the δ18OP are so homogenous throughout the profile.

In contrast, although the δ18OP after 60 cm is slightly lower, it shows a diminution of the

biological activity, due to the abiotic conditions. This was also reflected by Altermann et al.

(2005), who observed a decrease of soil enzymes while the depth increases in Haplic

Chernozems. This result supports the idea that the absence of specific soil enzymes limits the

phosphate cycle and does not lead to equilibrium.

Conclusion - Recommendation

50

VI. Conclusion - Recommendation

The modified Hedley procedure applied was reflected in terms of P content in the main

characteristics of the selected soils groups analyzed in this research. These include the

enrichment of the Ca-enriched parent material, presence of Fe/Al complexes, high content of

organic matter, etc.

This study concluded that the differences in the pedogenic factors [parent material, climate,

organisms (vegetation), topography and time] and the anthropogenic influence among the

selected soils are the main drivers of the variation in P content in the pools. Therefore the first

and the second hypothesis of this study are accepted as valid.

The data shows that in general, the concentration of P in almost all analyzed pools from the four

soil groups shows a decreasing trend from the surface to the depth of the soil profile. The

evident exceptions are Luvisols and Ferralsols, where under HCl extraction, they show an

increase of P content in its sub-soil due to the mineral composition in the depth layers.

The highest content of P was found in Andosols and Chernozems. Unfortunately it is not easily

readily available for the plants, because firstly it has to be mineralized before to be taken for the

plants. This makes it, essential to apply complete nutrient management that not only focused on

the yield increment but also on the health of soil and water in the long term.

The data that was obtained would have better analyzed if chemico-physical and mineralogical

characteristics of the sampled soils had been analyzed. Therefore, it is, strongly recommended

to include these factors in further research.

The results from the study concluded that each selected soil groups had different δ18OP

signatures, which differ among the horizons. The differences were driven by the specific

biological and chemical characteristics of each soil group.

The obtained δ18OP signature from the selected soils did not obtain the exact isotopic

equilibrium with soil water in any case. However, Chernozems was the closest to reaching a

complete equilibrium of phosphate oxygen (δ18OPE) with the oxygen in water. This was due to its

evident active soil microbiology and the absence of disturbance by external practices.

Therefore, it is concluded that agricultural management disrupts in some way the isotopic

Conclusion - Recommendation

51

equilibrium; hence we accept the third hypothesis. Nevertheless, more studies in the same soil

with and without crop cultivation are required to support this conclusion.

The obtained δ18OP that is below δ18OPE value clearly shows an inverse relationship with the Pi

content extracted by HCl among the selected soil groups, this reflects that microbial activity

under P-deficiency works intensively to turn over all the P that is present and that provokes to

reach the equilibrium isotope signature. Thereby the analysis between δ18OP and δ18OPE could

be considered as an indicator of P-supply in soils.

Also, it was concluded that the majority of the O exchange between phosphate and water are

mediated by biological activity. If it is possible to quantify the biological activity present in a soil

profile could help to determine which proportion of the various biological activity influence the P-

cycle and the δ18OP signature.

It is recommended to include in future researches, an equilibrium window. This window can be

calculated using ranges of soil temperatures and oxygen isotope values of soil−water recorded

for a period of time as used by Tamburini et al. (2012). As weather conditions are not always

stable, it has a direct influence on the soil environment. For these reasons, the equilibrium

window should provide an indication of the possible values expected in a relatively long period

of time, and not only give a snapshot of the soil at the time of sampling. (Tamburini et al. 2012)

After analyses of our data and the data shown in other publications, it is recommended to use

the soil temperature instead the annual average temperature of the environment in the equation

as suggested by Longinelli & Nuti (1973). This is because the equation refers to the temperature

of the environment to be analyzed, and in this case, it is the soil environment.

References

52

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Annex

Annex 1. Reports of Tukey test of the treatments that show statistical difference either P < 0.05 or

< 0.001 (generated by Sigma Plot Version 13.0)

1.1 NaHCO3 extractant

FERRALSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 14:35:42

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 14:35:42

Data source: CH_NaHCO3 in Notebook1

Group N Missing Median 25% 75%

Hor_1 4 0 66,360 64,485 66,975

Hor_2 4 0 58,680 57,195 58,905

Hor_3 4 0 22,590 21,675 23,190

Hor_4 4 0 7,740 6,540 8,130

Hor_5 4 0 7,830 6,945 8,535

Hor_6 4 0 9,810 9,135 10,620

Hor_7 4 0 11,070 9,855 11,295

H = 25,752 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_4 89,500 5,440 0,002 Yes

Hor_1 vs Hor_5 86,500 5,258 0,004 Yes

Hor_1 vs Hor_6 61,000 3,708 0,119 No

Hor_1 vs Hor_7 51,000 3,100 0,300 Do Not Test

Hor_1 vs Hor_3 32,000 1,945 0,816 Do Not Test

Hor_1 vs Hor_2 16,000 0,973 0,993 Do Not Test

Hor_2 vs Hor_4 73,500 4,468 0,027 Yes

Hor_2 vs Hor_5 70,500 4,285 0,039 Yes

Hor_2 vs Hor_6 45,000 2,735 0,458 Do Not Test

Hor_2 vs Hor_7 35,000 2,127 0,743 Do Not Test

Hor_2 vs Hor_3 16,000 0,973 0,993 Do Not Test

Hor_3 vs Hor_4 57,500 3,495 0,170 No

Hor_3 vs Hor_5 54,500 3,313 0,224 Do Not Test

Hor_3 vs Hor_6 29,000 1,763 0,877 Do Not Test

Hor_3 vs Hor_7 19,000 1,155 0,983 Do Not Test

Hor_7 vs Hor_4 38,500 2,340 0,647 Do Not Test

Hor_7 vs Hor_5 35,500 2,158 0,730 Do Not Test

Hor_7 vs Hor_6 10,000 0,608 1,000 Do Not Test

Hor_6 vs Hor_4 28,500 1,732 0,885 Do Not Test

Hor_6 vs Hor_5 25,500 1,550 0,930 Do Not Test

Hor_5 vs Hor_4 3,000 0,182 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 16:50:13

Data source: PT (Total P)

Normality Test (Shapiro-Wilk): Passed (P = 0,641)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 16:50:13

Data source: CH_NaHCO3 in China_PT

Group N Missing Median 25% 75%

Hor_1 2 0 97,440 97,320 97,560

Hor_2 2 0 90,900 89,040 92,760

Hor_3 2 0 73,740 73,080 74,400

Hor_4 2 0 60,660 59,400 61,920

Hor_5 2 0 47,820 47,520 48,120

Hor_6 2 0 46,800 46,680 46,920

Hor_7 2 0 45,480 45,360 45,600

H = 12,800 with 6 degrees of freedom. (P = 0,046)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,046)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_7 24,000 4,057 0,063 No

Hor_1 vs Hor_6 20,000 3,381 0,203 Do Not Test

Hor_1 vs Hor_5 16,000 2,704 0,472 Do Not Test

Hor_1 vs Hor_4 12,000 2,028 0,784 Do Not Test

Hor_1 vs Hor_3 8,000 1,352 0,963 Do Not Test

Hor_1 vs Hor_2 4,000 0,676 0,999 Do Not Test

Hor_2 vs Hor_7 20,000 3,381 0,203 Do Not Test

Hor_2 vs Hor_6 16,000 2,704 0,472 Do Not Test

Hor_2 vs Hor_5 12,000 2,028 0,784 Do Not Test

Hor_2 vs Hor_4 8,000 1,352 0,963 Do Not Test

Hor_2 vs Hor_3 4,000 0,676 0,999 Do Not Test

Hor_3 vs Hor_7 16,000 2,704 0,472 Do Not Test

Hor_3 vs Hor_6 12,000 2,028 0,784 Do Not Test

Hor_3 vs Hor_5 8,000 1,352 0,963 Do Not Test

Hor_3 vs Hor_4 4,000 0,676 0,999 Do Not Test

Hor_4 vs Hor_7 12,000 2,028 0,784 Do Not Test

Hor_4 vs Hor_6 8,000 1,352 0,963 Do Not Test

Hor_4 vs Hor_5 4,000 0,676 0,999 Do Not Test

Hor_5 vs Hor_7 8,000 1,352 0,963 Do Not Test

Hor_5 vs Hor_6 4,000 0,676 0,999 Do Not Test

Hor_6 vs Hor_7 4,000 0,676 0,999 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

One Way Analysis of Variance Donnerstag, Juli 16, 2015, 09:58:34

Data source: Po (Organic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,826)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Donnerstag, Juli 16, 2015, 09:58:34

Data source: CH_NaHCO3 in China_PO

Group N Missing Median 25% 75%

Hor_1 2 0 31,500 30,750 32,250

Hor_2 2 0 32,640 31,350 33,930

Hor_3 2 0 51,255 50,490 52,020

Hor_4 2 0 53,190 52,200 54,180

Hor_5 2 0 40,050 39,690 40,410

Hor_6 2 0 36,945 36,780 37,110

Hor_7 2 0 34,740 34,350 35,130

H = 12,629 with 6 degrees of freedom. (P = 0,049)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,049)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_4 vs Hor_1 23,000 3,888 0,087 No

Hor_4 vs Hor_2 21,000 3,550 0,156 Do Not Test

Hor_4 vs Hor_7 16,000 2,704 0,472 Do Not Test

Hor_4 vs Hor_6 12,000 2,028 0,784 Do Not Test

Hor_4 vs Hor_5 8,000 1,352 0,963 Do Not Test

Hor_4 vs Hor_3 4,000 0,676 0,999 Do Not Test

Hor_3 vs Hor_1 19,000 3,212 0,258 Do Not Test

Hor_3 vs Hor_2 17,000 2,874 0,394 Do Not Test

Hor_3 vs Hor_7 12,000 2,028 0,784 Do Not Test

Hor_3 vs Hor_6 8,000 1,352 0,963 Do Not Test

Hor_3 vs Hor_5 4,000 0,676 0,999 Do Not Test

Hor_5 vs Hor_1 15,000 2,535 0,553 Do Not Test

Hor_5 vs Hor_2 13,000 2,197 0,712 Do Not Test

Hor_5 vs Hor_7 8,000 1,352 0,963 Do Not Test

Hor_5 vs Hor_6 4,000 0,676 0,999 Do Not Test

Hor_6 vs Hor_1 11,000 1,859 0,846 Do Not Test

Hor_6 vs Hor_2 9,000 1,521 0,936 Do Not Test

Hor_6 vs Hor_7 4,000 0,676 0,999 Do Not Test

Hor_7 vs Hor_1 7,000 1,183 0,981 Do Not Test

Hor_7 vs Hor_2 5,000 0,845 0,997 Do Not Test

Hor_2 vs Hor_1 2,000 0,338 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

LUVISOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 16:11:55

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,109)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 16:11:55

Data source: G_NaHCO3 in Germany

Group N Missing Median 25% 75%

Hor_1 4 0 105,660 97,815 113,325

Hor_2 4 0 28,170 25,500 30,930

Hor_3 4 0 23,700 22,665 24,600

Hor_4 4 0 20,160 19,440 21,195

Hor_5 4 0 26,970 25,800 28,185

Hor_6 4 0 7,620 3,840 8,565

H = 21,779 with 5 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_6 80,000 5,657 <0,001 Yes

Hor_1 vs Hor_4 64,000 4,525 0,017 Yes

Hor_1 vs Hor_3 48,000 3,394 0,156 No

Hor_1 vs Hor_5 25,000 1,768 0,812 Do Not Test

Hor_1 vs Hor_2 23,000 1,626 0,861 Do Not Test

Hor_2 vs Hor_6 57,000 4,031 0,050 No

Hor_2 vs Hor_4 41,000 2,899 0,314 Do Not Test

Hor_2 vs Hor_3 25,000 1,768 0,812 Do Not Test

Hor_2 vs Hor_5 2,000 0,141 1,000 Do Not Test

Hor_5 vs Hor_6 55,000 3,889 0,066 Do Not Test

Hor_5 vs Hor_4 39,000 2,758 0,372 Do Not Test

Hor_5 vs Hor_3 23,000 1,626 0,861 Do Not Test

Hor_3 vs Hor_6 32,000 2,263 0,599 Do Not Test

Hor_3 vs Hor_4 16,000 1,131 0,967 Do Not Test

Hor_4 vs Hor_6 16,000 1,131 0,967 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

CHERNOZEMS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 17:18:32

Data source: PT (Total P)

Normality Test (Shapiro-Wilk): Passed (P = 0,856)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 17:18:32

Data source: R_NaHCO3 in Russia_PT

Group N Missing Median 25% 75%

Hor_1 2 0 169,680 165,840 173,520

Hor_2 2 0 141,540 141,240 141,840

Hor_3 2 0 125,520 116,760 134,280

Hor_4 2 0 106,020 103,440 108,600

Hor_5 2 0 22,200 22,200 22,200

Hor_6 2 0 17,100 16,800 17,400

Hor_7 2 0 14,220 8,520 19,920

H = 12,599 with 6 degrees of freedom. (P = 0,050)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,050)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_7 22,000 3,719 0,117 No

Hor_1 vs Hor_6 22,000 3,719 0,117 Do Not Test

Hor_1 vs Hor_5 16,000 2,704 0,472 Do Not Test

Hor_1 vs Hor_4 12,000 2,028 0,784 Do Not Test

Hor_1 vs Hor_3 8,000 1,352 0,963 Do Not Test

Hor_1 vs Hor_2 4,000 0,676 0,999 Do Not Test

Hor_2 vs Hor_7 18,000 3,043 0,323 Do Not Test

Hor_2 vs Hor_6 18,000 3,043 0,323 Do Not Test

Hor_2 vs Hor_5 12,000 2,028 0,784 Do Not Test

Hor_2 vs Hor_4 8,000 1,352 0,963 Do Not Test

Hor_2 vs Hor_3 4,000 0,676 0,999 Do Not Test

Hor_3 vs Hor_7 14,000 2,366 0,634 Do Not Test

Hor_3 vs Hor_6 14,000 2,366 0,634 Do Not Test

Hor_3 vs Hor_5 8,000 1,352 0,963 Do Not Test

Hor_3 vs Hor_4 4,000 0,676 0,999 Do Not Test

Hor_4 vs Hor_7 10,000 1,690 0,897 Do Not Test

Hor_4 vs Hor_6 10,000 1,690 0,897 Do Not Test

Hor_4 vs Hor_5 4,000 0,676 0,999 Do Not Test

Hor_5 vs Hor_7 6,000 1,014 0,992 Do Not Test

Hor_5 vs Hor_6 6,000 1,014 0,992 Do Not Test

Hor_6 vs Hor_7 0,000 0,000 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

1.2 NaOH extractant

FERRALSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 14:50:33

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,203)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 14:50:33

Data source: CH_NaOH in China

Group N Missing Median 25% 75%

Hor_1 4 0 112,140 110,145 112,785

Hor_2 4 0 134,040 125,730 147,660

Hor_3 4 0 62,160 58,065 64,995

Hor_4 4 0 68,070 65,190 71,400

Hor_5 4 0 68,970 62,100 75,075

Hor_6 4 0 79,410 73,605 83,775

Hor_7 4 0 101,640 93,825 121,875

H = 24,635 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_2 vs Hor_3 92,000 5,592 0,002 Yes

Hor_2 vs Hor_4 73,000 4,437 0,028 Yes

Hor_2 vs Hor_5 70,000 4,255 0,042 Yes

Hor_2 vs Hor_6 49,000 2,978 0,349 No

Hor_2 vs Hor_7 26,000 1,580 0,923 Do Not Test

Hor_2 vs Hor_1 19,000 1,155 0,983 Do Not Test

Hor_1 vs Hor_3 73,000 4,437 0,028 Yes

Hor_1 vs Hor_4 54,000 3,282 0,234 No

Hor_1 vs Hor_5 51,000 3,100 0,300 Do Not Test

Hor_1 vs Hor_6 30,000 1,823 0,858 Do Not Test

Hor_1 vs Hor_7 7,000 0,425 1,000 Do Not Test

Hor_7 vs Hor_3 66,000 4,012 0,069 No

Hor_7 vs Hor_4 47,000 2,857 0,402 Do Not Test

Hor_7 vs Hor_5 44,000 2,674 0,486 Do Not Test

Hor_7 vs Hor_6 23,000 1,398 0,957 Do Not Test

Hor_6 vs Hor_3 43,000 2,614 0,515 Do Not Test

Hor_6 vs Hor_4 24,000 1,459 0,947 Do Not Test

Hor_6 vs Hor_5 21,000 1,276 0,972 Do Not Test

Hor_5 vs Hor_3 22,000 1,337 0,965 Do Not Test

Hor_5 vs Hor_4 3,000 0,182 1,000 Do Not Test

Hor_4 vs Hor_3 19,000 1,155 0,983 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

ANDOSOLS

One Way Analysis of Variance Montag, Juli 27, 2015, 10:39:30

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Montag, Juli 27, 2015, 10:39:30

Data source: I_NaOH_CORR in Indonesia

Group N Missing Median 25% 75%

Hor_1 4 0 656,910 620,025 697,530

Hor_2 4 0 553,440 391,200 687,150

Hor_3 4 0 552,330 455,700 602,970

Hor_4 4 0 512,400 502,500 525,000

Hor_5 4 0 278,100 274,905 280,125

Hor_6 4 0 159,960 143,760 180,660

Hor_7 4 0 148,950 136,335 159,855

H = 23,246 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_7 87,000 5,288 0,004 Yes

Hor_1 vs Hor_6 79,000 4,802 0,012 Yes

Hor_1 vs Hor_5 59,000 3,586 0,147 No

Hor_1 vs Hor_4 28,000 1,702 0,894 Do Not Test

Hor_1 vs Hor_3 26,000 1,580 0,923 Do Not Test

Hor_1 vs Hor_2 22,000 1,337 0,965 Do Not Test

Hor_2 vs Hor_7 65,000 3,951 0,077 No

Hor_2 vs Hor_6 57,000 3,465 0,178 Do Not Test

Hor_2 vs Hor_5 37,000 2,249 0,689 Do Not Test

Hor_2 vs Hor_4 6,000 0,365 1,000 Do Not Test

Hor_2 vs Hor_3 4,000 0,243 1,000 Do Not Test

Hor_3 vs Hor_7 61,000 3,708 0,119 Do Not Test

Hor_3 vs Hor_6 53,000 3,222 0,255 Do Not Test

Hor_3 vs Hor_5 33,000 2,006 0,793 Do Not Test

Hor_3 vs Hor_4 2,000 0,122 1,000 Do Not Test

Hor_4 vs Hor_7 59,000 3,586 0,147 Do Not Test

Hor_4 vs Hor_6 51,000 3,100 0,300 Do Not Test

Hor_4 vs Hor_5 31,000 1,884 0,838 Do Not Test

Hor_5 vs Hor_7 28,000 1,702 0,894 Do Not Test

Hor_5 vs Hor_6 20,000 1,216 0,978 Do Not Test

Hor_6 vs Hor_7 8,000 0,486 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

LUVISOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 16:08:14

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 16:08:14

Data source: G_NaOH in Germany

Group N Missing Median 25% 75%

Hor_1 4 0 126,060 122,760 128,595

Hor_2 4 0 52,470 37,350 60,390

Hor_3 4 0 48,120 32,370 57,030

Hor_4 4 0 60,570 58,095 62,505

Hor_5 4 0 71,730 71,190 71,865

Hor_6 4 0 17,040 16,980 17,865

H = 21,179 with 5 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_6 80,000 5,657 <0,001 Yes

Hor_1 vs Hor_3 59,000 4,172 0,038 Yes

Hor_1 vs Hor_2 49,000 3,465 0,139 No

Hor_1 vs Hor_4 36,000 2,546 0,466 Do Not Test

Hor_1 vs Hor_5 16,000 1,131 0,967 Do Not Test

Hor_5 vs Hor_6 64,000 4,525 0,017 Yes

Hor_5 vs Hor_3 43,000 3,041 0,262 No

Hor_5 vs Hor_2 33,000 2,333 0,565 Do Not Test

Hor_5 vs Hor_4 20,000 1,414 0,918 Do Not Test

Hor_4 vs Hor_6 44,000 3,111 0,238 No

Hor_4 vs Hor_3 23,000 1,626 0,861 Do Not Test

Hor_4 vs Hor_2 13,000 0,919 0,987 Do Not Test

Hor_2 vs Hor_6 31,000 2,192 0,632 Do Not Test

Hor_2 vs Hor_3 10,000 0,707 0,996 Do Not Test

Hor_3 vs Hor_6 21,000 1,485 0,901 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

CHERNOZEMS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:42:16

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,487)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:42:16

Data source: R_NaOH in Russia

Group N Missing Median 25% 75%

Col 1 4 0 106,320 103,605 109,800

Col 2 4 0 139,440 136,530 142,305

Col 3 4 0 199,590 198,390 200,340

Col 4 4 0 133,020 129,360 136,545

Col 5 4 0 34,830 33,795 36,090

Col 6 4 0 29,820 25,020 33,990

Col 7 4 0 31,410 27,510 33,600

H = 25,419 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Col 3 vs Col 6 87,000 5,288 0,004 Yes

Col 3 vs Col 7 86,000 5,227 0,004 Yes

Col 3 vs Col 5 67,000 4,072 0,061 No

Col 3 vs Col 1 48,000 2,918 0,375 Do Not Test

Col 3 vs Col 4 31,000 1,884 0,838 Do Not Test

Col 3 vs Col 2 17,000 1,033 0,991 Do Not Test

Col 2 vs Col 6 70,000 4,255 0,042 Yes

Col 2 vs Col 7 69,000 4,194 0,048 Yes

Col 2 vs Col 5 50,000 3,039 0,324 Do Not Test

Col 2 vs Col 1 31,000 1,884 0,838 Do Not Test

Col 2 vs Col 4 14,000 0,851 0,997 Do Not Test

Col 4 vs Col 6 56,000 3,404 0,196 No

Col 4 vs Col 7 55,000 3,343 0,214 Do Not Test

Col 4 vs Col 5 36,000 2,188 0,716 Do Not Test

Col 4 vs Col 1 17,000 1,033 0,991 Do Not Test

Col 1 vs Col 6 39,000 2,371 0,632 Do Not Test

Col 1 vs Col 7 38,000 2,310 0,661 Do Not Test

Col 1 vs Col 5 19,000 1,155 0,983 Do Not Test

Col 5 vs Col 6 20,000 1,216 0,978 Do Not Test

Col 5 vs Col 7 19,000 1,155 0,983 Do Not Test

Col 7 vs Col 6 1,000 0,0608 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

1.3 HCl extractant

FERRALSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:04:45

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:04:45

Data source: Pi (Inorganic P)

Group N Missing Median 25% 75%

Hor_1 4 0 45,870 39,105 52,635

Hor_2 4 0 36,390 30,450 42,195

Hor_3 4 0 2,460 1,545 3,330

Hor_4 4 0 0,000 0,000 0,000

Hor_5 4 0 0,000 0,000 0,000

Hor_6 4 0 0,000 0,000 0,000

Hor_7 4 0 0,000 0,000 0,000

H = 26,292 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_7 68,000 4,133 0,054 No

Hor_1 vs Hor_6 68,000 4,133 0,054 Do Not Test

Hor_1 vs Hor_5 68,000 4,133 0,054 Do Not Test

Hor_1 vs Hor_4 68,000 4,133 0,054 Do Not Test

Hor_1 vs Hor_3 28,000 1,702 0,894 Do Not Test

Hor_1 vs Hor_2 8,000 0,486 1,000 Do Not Test

Hor_2 vs Hor_7 60,000 3,647 0,133 Do Not Test

Hor_2 vs Hor_6 60,000 3,647 0,133 Do Not Test

Hor_2 vs Hor_5 60,000 3,647 0,133 Do Not Test

Hor_2 vs Hor_4 60,000 3,647 0,133 Do Not Test

Hor_2 vs Hor_3 20,000 1,216 0,978 Do Not Test

Hor_3 vs Hor_7 40,000 2,431 0,603 Do Not Test

Hor_3 vs Hor_6 40,000 2,431 0,603 Do Not Test

Hor_3 vs Hor_5 40,000 2,431 0,603 Do Not Test

Hor_3 vs Hor_4 40,000 2,431 0,603 Do Not Test

Hor_4 vs Hor_7 0,000 0,000 1,000 Do Not Test

Hor_4 vs Hor_6 0,000 0,000 1,000 Do Not Test

Hor_4 vs Hor_5 0,000 0,000 1,000 Do Not Test

Hor_5 vs Hor_7 0,000 0,000 1,000 Do Not Test

Hor_5 vs Hor_6 0,000 0,000 1,000 Do Not Test

Hor_6 vs Hor_7 0,000 0,000 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

ANDOSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:27:51

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:27:51

Data source: I_HCl in Indonesia

Group N Missing Median 25% 75%

Hor_1 4 0 72,840 64,365 77,310

Hor_2 4 0 103,440 63,480 132,150

Hor_3 4 0 109,950 103,140 113,655

Hor_4 4 0 54,690 52,260 56,895

Hor_5 4 0 10,380 7,170 12,825

Hor_6 4 0 8,910 7,365 11,265

Hor_7 4 0 19,380 13,530 24,150

H = 24,680 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_3 vs Hor_6 81,000 4,923 0,009 Yes

Hor_3 vs Hor_5 78,000 4,741 0,014 Yes

Hor_3 vs Hor_7 57,000 3,465 0,178 No

Hor_3 vs Hor_4 40,000 2,431 0,603 Do Not Test

Hor_3 vs Hor_1 19,000 1,155 0,983 Do Not Test

Hor_3 vs Hor_2 5,000 0,304 1,000 Do Not Test

Hor_2 vs Hor_6 76,000 4,620 0,019 Yes

Hor_2 vs Hor_5 73,000 4,437 0,028 Yes

Hor_2 vs Hor_7 52,000 3,161 0,277 Do Not Test

Hor_2 vs Hor_4 35,000 2,127 0,743 Do Not Test

Hor_2 vs Hor_1 14,000 0,851 0,997 Do Not Test

Hor_1 vs Hor_6 62,000 3,769 0,107 No

Hor_1 vs Hor_5 59,000 3,586 0,147 Do Not Test

Hor_1 vs Hor_7 38,000 2,310 0,661 Do Not Test

Hor_1 vs Hor_4 21,000 1,276 0,972 Do Not Test

Hor_4 vs Hor_6 41,000 2,492 0,574 Do Not Test

Hor_4 vs Hor_5 38,000 2,310 0,661 Do Not Test

Hor_4 vs Hor_7 17,000 1,033 0,991 Do Not Test

Hor_7 vs Hor_6 24,000 1,459 0,947 Do Not Test

Hor_7 vs Hor_5 21,000 1,276 0,972 Do Not Test

Hor_5 vs Hor_6 3,000 0,182 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

LUVISOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 16:16:49

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,603)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 16:16:49

Data source: G_HCl in Germany

Group N Missing Median 25% 75%

Hor_1 4 0 229,020 227,280 233,730

Hor_2 4 0 118,710 117,765 123,300

Hor_3 4 0 113,640 107,850 116,595

Hor_4 4 0 151,440 148,470 159,630

Hor_5 4 0 261,780 249,285 265,680

Hor_6 4 0 258,330 241,050 269,265

H = 21,760 with 5 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_5 vs Hor_3 72,000 5,091 0,004 Yes

Hor_5 vs Hor_2 56,000 3,960 0,058 No

Hor_5 vs Hor_4 40,000 2,828 0,342 Do Not Test

Hor_5 vs Hor_1 24,000 1,697 0,837 Do Not Test

Hor_5 vs Hor_6 0,000 0,000 1,000 Do Not Test

Hor_6 vs Hor_3 72,000 5,091 0,004 Yes

Hor_6 vs Hor_2 56,000 3,960 0,058 Do Not Test

Hor_6 vs Hor_4 40,000 2,828 0,342 Do Not Test

Hor_6 vs Hor_1 24,000 1,697 0,837 Do Not Test

Hor_1 vs Hor_3 48,000 3,394 0,156 No

Hor_1 vs Hor_2 32,000 2,263 0,599 Do Not Test

Hor_1 vs Hor_4 16,000 1,131 0,967 Do Not Test

Hor_4 vs Hor_3 32,000 2,263 0,599 Do Not Test

Hor_4 vs Hor_2 16,000 1,131 0,967 Do Not Test

Hor_2 vs Hor_3 16,000 1,131 0,967 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

CHERNOZEMS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:45:58

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,357)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:45:58

Data source: R_HCl in Russia

Group N Missing Median 25% 75%

Hor_1 4 0 134,100 127,140 141,735

Hor_2 4 0 168,240 159,465 176,115

Hor_3 4 0 171,900 161,850 189,150

Hor_4 4 0 147,570 140,415 161,340

Hor_5 4 0 42,750 32,955 48,585

Hor_6 4 0 26,010 24,840 27,225

Hor_7 4 0 10,380 8,040 12,225

H = 25,131 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_3 vs Hor_7 90,000 5,470 0,002 Yes

Hor_3 vs Hor_6 74,000 4,498 0,025 Yes

Hor_3 vs Hor_5 58,000 3,525 0,162 No

Hor_3 vs Hor_1 39,000 2,371 0,632 Do Not Test

Hor_3 vs Hor_4 25,000 1,520 0,936 Do Not Test

Hor_3 vs Hor_2 8,000 0,486 1,000 Do Not Test

Hor_2 vs Hor_7 82,000 4,984 0,008 Yes

Hor_2 vs Hor_6 66,000 4,012 0,069 No

Hor_2 vs Hor_5 50,000 3,039 0,324 Do Not Test

Hor_2 vs Hor_1 31,000 1,884 0,838 Do Not Test

Hor_2 vs Hor_4 17,000 1,033 0,991 Do Not Test

Hor_4 vs Hor_7 65,000 3,951 0,077 No

Hor_4 vs Hor_6 49,000 2,978 0,349 Do Not Test

Hor_4 vs Hor_5 33,000 2,006 0,793 Do Not Test

Hor_4 vs Hor_1 14,000 0,851 0,997 Do Not Test

Hor_1 vs Hor_7 51,000 3,100 0,300 Do Not Test

Hor_1 vs Hor_6 35,000 2,127 0,743 Do Not Test

Hor_1 vs Hor_5 19,000 1,155 0,983 Do Not Test

Hor_5 vs Hor_7 32,000 1,945 0,816 Do Not Test

Hor_5 vs Hor_6 16,000 0,973 0,993 Do Not Test

Hor_6 vs Hor_7 16,000 0,973 0,993 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 17:22:24

Data source: PT (Total P)

Normality Test (Shapiro-Wilk): Passed (P = 0,538)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 17:22:24

Data source: R_HCl in Russia_PT

Group N Missing Median 25% 75%

Hor_1 2 0 176,280 167,160 185,400

Hor_2 2 0 199,620 191,280 207,960

Hor_3 2 0 186,750 184,620 188,880

Hor_4 2 0 163,710 161,220 166,200

Hor_5 2 0 61,860 58,740 64,980

Hor_6 2 0 46,860 44,880 48,840

Hor_7 2 0 29,160 27,780 30,540

H = 12,629 with 6 degrees of freedom. (P = 0,049)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,049)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_2 vs Hor_7 24,000 4,057 0,063 No

Hor_2 vs Hor_6 20,000 3,381 0,203 Do Not Test

Hor_2 vs Hor_5 16,000 2,704 0,472 Do Not Test

Hor_2 vs Hor_4 12,000 2,028 0,784 Do Not Test

Hor_2 vs Hor_1 7,000 1,183 0,981 Do Not Test

Hor_2 vs Hor_3 5,000 0,845 0,997 Do Not Test

Hor_3 vs Hor_7 19,000 3,212 0,258 Do Not Test

Hor_3 vs Hor_6 15,000 2,535 0,553 Do Not Test

Hor_3 vs Hor_5 11,000 1,859 0,846 Do Not Test

Hor_3 vs Hor_4 7,000 1,183 0,981 Do Not Test

Hor_3 vs Hor_1 2,000 0,338 1,000 Do Not Test

Hor_1 vs Hor_7 17,000 2,874 0,394 Do Not Test

Hor_1 vs Hor_6 13,000 2,197 0,712 Do Not Test

Hor_1 vs Hor_5 9,000 1,521 0,936 Do Not Test

Hor_1 vs Hor_4 5,000 0,845 0,997 Do Not Test

Hor_4 vs Hor_7 12,000 2,028 0,784 Do Not Test

Hor_4 vs Hor_6 8,000 1,352 0,963 Do Not Test

Hor_4 vs Hor_5 4,000 0,676 0,999 Do Not Test

Hor_5 vs Hor_7 8,000 1,352 0,963 Do Not Test

Hor_5 vs Hor_6 4,000 0,676 0,999 Do Not Test

Hor_6 vs Hor_7 4,000 0,676 0,999 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

1.4 Concentrate HCl extractant

FERRALSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:07:59

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,191)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:07:59

Data source: CH_HCL(k) in China

Group N Missing Median 25% 75%

Hor_1 4 0 49,710 40,755 56,415

Hor_2 4 0 43,860 40,545 46,815

Hor_3 4 0 33,360 30,480 37,590

Hor_4 4 0 115,680 112,965 116,910

Hor_5 4 0 105,990 104,865 107,160

Hor_6 4 0 86,250 84,660 88,245

Hor_7 4 0 118,020 116,790 118,845

H = 25,923 with 6 degrees of freedom. (P = <0,001)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = <0,001)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_7 vs Hor_3 94,500 5,744 0,001 Yes

Hor_7 vs Hor_2 73,500 4,468 0,027 Yes

Hor_7 vs Hor_1 67,500 4,103 0,057 No

Hor_7 vs Hor_6 46,500 2,826 0,415 Do Not Test

Hor_7 vs Hor_5 30,500 1,854 0,848 Do Not Test

Hor_7 vs Hor_4 13,000 0,790 0,998 Do Not Test

Hor_4 vs Hor_3 81,500 4,954 0,008 Yes

Hor_4 vs Hor_2 60,500 3,677 0,126 No

Hor_4 vs Hor_1 54,500 3,313 0,224 Do Not Test

Hor_4 vs Hor_6 33,500 2,036 0,781 Do Not Test

Hor_4 vs Hor_5 17,500 1,064 0,989 Do Not Test

Hor_5 vs Hor_3 64,000 3,890 0,086 No

Hor_5 vs Hor_2 43,000 2,614 0,515 Do Not Test

Hor_5 vs Hor_1 37,000 2,249 0,689 Do Not Test

Hor_5 vs Hor_6 16,000 0,973 0,993 Do Not Test

Hor_6 vs Hor_3 48,000 2,918 0,375 Do Not Test

Hor_6 vs Hor_2 27,000 1,641 0,909 Do Not Test

Hor_6 vs Hor_1 21,000 1,276 0,972 Do Not Test

Hor_1 vs Hor_3 27,000 1,641 0,909 Do Not Test

Hor_1 vs Hor_2 6,000 0,365 1,000 Do Not Test

Hor_2 vs Hor_3 21,000 1,276 0,972 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

ANDOSOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:32:26

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,990)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:32:26

Data source: I_HCl(konz) in Indonesia

Group N Missing Median 25% 75%

Hor_1 4 0 330,000 300,150 365,925

Hor_2 4 0 416,400 375,600 503,550

Hor_3 4 0 338,400 197,250 465,825

Hor_4 4 0 281,100 260,925 287,550

Hor_5 4 0 313,800 291,675 334,575

Hor_6 4 0 199,950 137,250 268,725

Hor_7 4 0 141,750 72,225 188,325

H = 19,119 with 6 degrees of freedom. (P = 0,004)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,004)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_2 vs Hor_7 84,000 5,106 0,006 Yes

Hor_2 vs Hor_6 71,000 4,316 0,037 Yes

Hor_2 vs Hor_4 51,000 3,100 0,300 No

Hor_2 vs Hor_3 32,000 1,945 0,816 Do Not Test

Hor_2 vs Hor_5 27,500 1,672 0,902 Do Not Test

Hor_2 vs Hor_1 21,500 1,307 0,969 Do Not Test

Hor_1 vs Hor_7 62,500 3,799 0,102 No

Hor_1 vs Hor_6 49,500 3,009 0,336 Do Not Test

Hor_1 vs Hor_4 29,500 1,793 0,867 Do Not Test

Hor_1 vs Hor_3 10,500 0,638 0,999 Do Not Test

Hor_1 vs Hor_5 6,000 0,365 1,000 Do Not Test

Hor_5 vs Hor_7 56,500 3,434 0,187 Do Not Test

Hor_5 vs Hor_6 43,500 2,644 0,501 Do Not Test

Hor_5 vs Hor_4 23,500 1,428 0,952 Do Not Test

Hor_5 vs Hor_3 4,500 0,274 1,000 Do Not Test

Hor_3 vs Hor_7 52,000 3,161 0,277 Do Not Test

Hor_3 vs Hor_6 39,000 2,371 0,632 Do Not Test

Hor_3 vs Hor_4 19,000 1,155 0,983 Do Not Test

Hor_4 vs Hor_7 33,000 2,006 0,793 Do Not Test

Hor_4 vs Hor_6 20,000 1,216 0,978 Do Not Test

Hor_6 vs Hor_7 13,000 0,790 0,998 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

LUVISOLS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 16:19:18

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,338)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 16:19:18

Data source: HCl(konz) in Germany

Group N Missing Median 25% 75%

Hor_1 4 0 51,750 39,225 55,725

Hor_2 4 0 48,900 47,100 50,250

Hor_3 4 0 61,500 46,425 76,800

Hor_4 4 0 95,100 90,825 100,500

Hor_5 4 0 83,850 79,950 88,875

Hor_6 4 0 51,000 23,925 77,625

H = 16,190 with 5 degrees of freedom. (P = 0,006)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,006)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_4 vs Hor_2 61,000 4,313 0,028 Yes

Hor_4 vs Hor_6 56,000 3,960 0,058 No

Hor_4 vs Hor_1 54,000 3,818 0,075 Do Not Test

Hor_4 vs Hor_3 53,000 3,748 0,086 Do Not Test

Hor_4 vs Hor_5 16,000 1,131 0,967 Do Not Test

Hor_5 vs Hor_2 45,000 3,182 0,215 No

Hor_5 vs Hor_6 40,000 2,828 0,342 Do Not Test

Hor_5 vs Hor_1 38,000 2,687 0,402 Do Not Test

Hor_5 vs Hor_3 37,000 2,616 0,434 Do Not Test

Hor_3 vs Hor_2 8,000 0,566 0,999 Do Not Test

Hor_3 vs Hor_6 3,000 0,212 1,000 Do Not Test

Hor_3 vs Hor_1 1,000 0,0707 1,000 Do Not Test

Hor_1 vs Hor_2 7,000 0,495 0,999 Do Not Test

Hor_1 vs Hor_6 2,000 0,141 1,000 Do Not Test

Hor_6 vs Hor_2 5,000 0,354 1,000 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

CHERNOZEMS

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 15:50:10

Data source: Pi (Inorganic P)

Normality Test (Shapiro-Wilk): Passed (P = 0,352)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 15:50:10

Data source: R_HCl(konz) in Russia

Group N Missing Median 25% 75%

Hor_1 4 0 121,200 96,225 162,600

Hor_2 4 0 95,700 90,075 107,400

Hor_3 4 0 80,850 72,525 94,800

Hor_4 4 0 78,600 57,375 108,150

Hor_5 4 0 49,200 45,975 56,250

Hor_6 4 0 64,950 53,325 78,600

Hor_7 4 0 105,300 84,000 154,275

H = 17,890 with 6 degrees of freedom. (P = 0,007)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,007)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_5 80,000 4,863 0,011 Yes

Hor_1 vs Hor_6 61,500 3,738 0,113 No

Hor_1 vs Hor_3 40,000 2,431 0,603 Do Not Test

Hor_1 vs Hor_4 35,000 2,127 0,743 Do Not Test

Hor_1 vs Hor_2 15,000 0,912 0,995 Do Not Test

Hor_1 vs Hor_7 13,500 0,821 0,997 Do Not Test

Hor_7 vs Hor_5 66,500 4,042 0,065 No

Hor_7 vs Hor_6 48,000 2,918 0,375 Do Not Test

Hor_7 vs Hor_3 26,500 1,611 0,916 Do Not Test

Hor_7 vs Hor_4 21,500 1,307 0,969 Do Not Test

Hor_7 vs Hor_2 1,500 0,0912 1,000 Do Not Test

Hor_2 vs Hor_5 65,000 3,951 0,077 Do Not Test

Hor_2 vs Hor_6 46,500 2,826 0,415 Do Not Test

Hor_2 vs Hor_3 25,000 1,520 0,936 Do Not Test

Hor_2 vs Hor_4 20,000 1,216 0,978 Do Not Test

Hor_4 vs Hor_5 45,000 2,735 0,458 Do Not Test

Hor_4 vs Hor_6 26,500 1,611 0,916 Do Not Test

Hor_4 vs Hor_3 5,000 0,304 1,000 Do Not Test

Hor_3 vs Hor_5 40,000 2,431 0,603 Do Not Test

Hor_3 vs Hor_6 21,500 1,307 0,969 Do Not Test

Hor_6 vs Hor_5 18,500 1,124 0,985 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

One Way Analysis of Variance Mittwoch, Juli 15, 2015, 17:24:16

Data source: PT (Total P)

Normality Test (Shapiro-Wilk): Passed (P = 0,833)

Equal Variance Test (Brown-Forsythe): Failed (P < 0,050)

Test execution ended by user request, ANOVA on Ranks begun

Kruskal-Wallis One Way Analysis of Variance on Ranks Mittwoch, Juli 15, 2015, 17:24:16

Data source: R_HCl(konz) in Russia_PT

Group N Missing Median 25% 75%

Hor_1 2 0 286,080 280,320 291,840

Hor_2 2 0 226,980 225,240 228,720

Hor_3 2 0 156,570 155,160 157,980

Hor_4 2 0 117,540 117,000 118,080

Hor_5 2 0 63,990 61,440 66,540

Hor_6 2 0 83,040 77,820 88,260

Hor_7 2 0 122,250 120,900 123,600

H = 12,800 with 6 degrees of freedom. (P = 0,046)

The differences in the median values among the treatment groups are greater than would be expected by chance;

there is a statistically significant difference (P = 0,046)

To isolate the group or groups that differ from the others use a multiple comparison procedure.

All Pairwise Multiple Comparison Procedures (Tukey Test):

Comparison Diff of Ranks q P P<0,050

Hor_1 vs Hor_5 24,000 4,057 0,063 No

Hor_1 vs Hor_6 20,000 3,381 0,203 Do Not Test

Hor_1 vs Hor_4 16,000 2,704 0,472 Do Not Test

Hor_1 vs Hor_7 12,000 2,028 0,784 Do Not Test

Hor_1 vs Hor_3 8,000 1,352 0,963 Do Not Test

Hor_1 vs Hor_2 4,000 0,676 0,999 Do Not Test

Hor_2 vs Hor_5 20,000 3,381 0,203 Do Not Test

Hor_2 vs Hor_6 16,000 2,704 0,472 Do Not Test

Hor_2 vs Hor_4 12,000 2,028 0,784 Do Not Test

Hor_2 vs Hor_7 8,000 1,352 0,963 Do Not Test

Hor_2 vs Hor_3 4,000 0,676 0,999 Do Not Test

Hor_3 vs Hor_5 16,000 2,704 0,472 Do Not Test

Hor_3 vs Hor_6 12,000 2,028 0,784 Do Not Test

Hor_3 vs Hor_4 8,000 1,352 0,963 Do Not Test

Hor_3 vs Hor_7 4,000 0,676 0,999 Do Not Test

Hor_7 vs Hor_5 12,000 2,028 0,784 Do Not Test

Hor_7 vs Hor_6 8,000 1,352 0,963 Do Not Test

Hor_7 vs Hor_4 4,000 0,676 0,999 Do Not Test

Hor_4 vs Hor_5 8,000 1,352 0,963 Do Not Test

Hor_4 vs Hor_6 4,000 0,676 0,999 Do Not Test

Hor_6 vs Hor_5 4,000 0,676 0,999 Do Not Test

Note: The multiple comparisons on ranks do not include an adjustment for ties.

A result of "Do Not Test" occurs for a comparison when no significant difference is found between the two rank

sums that enclose that comparison. For example, if you had four rank sums sorted in order, and found no significant

difference between rank sums 4 vs. 2, then you would not test 4 vs. 3 and 3 vs. 2, but still test 4 vs. 1 and 3 vs. 1 (4

vs. 3 and 3 vs. 2 are enclosed by 4 vs. 2: 4 3 2 1). Note that not testing the enclosed rank sums is a procedural rule,

and a result of Do Not Test should be treated as if there is no significant difference between the rank sums, even

though one may appear to exist.

Curriculum Vitae

Personal information

First name(s) / Surname(s) ORTIZ NAVARRO, Maria Soledad

Nationality Ecuadorian

Date of birth 19.01.1983

Place of birth Ibarra, Ecuador

e-Mail [email protected]

Education and training

Dates September 2000 - December 2006

Title of qualification awarded/Name of the

organisation or institution

Agronomist. Universidad Central del Ecuador. Best graduate 2006-2007

Dates October 2013 – September 2015

Title of qualification awarded/Name of the

organisation or institution

Master of Science in Agricultural science and resource management in

the Tropics and Sub-tropics (ARTS). University of Bonn- Germany

Work experience

Dates From March 2014

Occupation or position held Evaluator of organic agricultural projects from Latin America

Name of employer KIWA - BCS ÖKO Garantie GmbH. Nürnberg-Germany

Dates August 2012 - July 2013 and March 2010 - January 2012

Occupation or position held Geomatic specialist 1 (Public servant 5). Soil survey activities

Name and address of employer

Instituto Espacial Ecuatoriano. Quito, Ecuador

Dates January 2007 - July 2011

Occupation or position held Technical assistant / Inspector of national organic projects / Evaluator of organic agricultural projects from Latin America

Name of employer BCS ÖKO Garantie Cia Ltda. Riobamba - Ecuador

Dates April 2006 - December 2006

Occupation or position held Agronomist technical. Level 2

Name of employer SIGTIERRAS (ex-PRAT). Quito, Ecuador

Hobbies Travelling, dancing and photography