SOIL QUALITY ASSESSEMENT OF

SOUTH AFRICAN HOME GARDENS

THE CASE OF THE VILLAGES MUTSHENZHENI, TSHIDZINI AND

DZINDI

Word count: 19.872

Elien Haverbeke Student number: 01405565

Promotors: Prof. dr. Geert Baert, Prof. dr. Wim Van Averbeke A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master

of Science in Bioscience Engineering Technology: Agriculture and Horticulture (Tropical Plant Production)

Academic year: 2017-2018

SOIL QUALITY ASSESSEMENT OF

SOUTH AFRICAN HOME GARDENS THE CASE OF THE VILLAGES MUTSHENZHENI, TSHIDZINI AND

DZINDI

Word count: 19.872

Elien Haverbeke Student number: 01405565

Promotors: Prof. dr. Geert Baert, Prof. dr. Wim Van Averbeke

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of

Master of Science in Bioscience Engineering Technology: Agriculture and Horticulture (Tropical Plant

Production)

Academic year: 2017-2018

“The author and the promoter give the permission to use this thesis for consultation and to

copy parts of it for personal use. Every other use is subject to the copyright laws, more

specifically the source must be extensively specified when using the results from this thesis”.

Promotor 1 Promotor 2 Author

Prof. Dr. G. Baert Prof. Dr. W. Van Averbeke Elien Haverbeke

Preface

I would like to express my gratitude to the following:

The VLIR-UOS travel grant, who supported me financially and gave me the opportunity. It was

an amazing experience to participate in the extension of a former project entitled Improving

home garden soil fertility management to enhance nutritional security among rural homesteads

in Vhembe (Limpopo, South Africa), that was done in collaboration between Ghent University

and the Crop Sciences Department of Tshwane University of Technology.

My promotors Prof. Dr. G. Baert and Prof. Dr. W. Van Averbeke, for guiding me patiently

through the process of writing a thesis and for providing me the required equipment to see this

project through.

More specific I want to thank Prof. W. Van Averbeke for guiding me through the villages of

Thulamela and for his help collecting the samples. I am deeply grateful for allowing me to stay

at his home as a guest when I arrived. I could not have accomplished this without his support.

Gumani, Patho and others who assisted me in the field. They made the fieldwork a lot easier

and helped me to understand the interviewed people. I would like to thank Gumani for teaching

me a lot about her culture and life. I am also grateful for the gardeners who let us take samples

and provided the needed information.

Lastly, I would like to thank my parents, family and friends, who supported my choices and

made me the person I am this day. They taught me how to be independent and gave me the

love and encouragement to accomplish this work.

Abstract

In the context of the growing concern about global food security and inequality in South Africa,

soil quality of South African home gardens in Limpopo province was evaluated. Animal manure

is not fully utilized by home gardeners while often financial resources aren’t available to use

chemical fertilizers. Different soil fertility management practices (application of chemical

fertilizer, animal manure, a combination of animal manure and chemical fertilizer and no

application of fertilizer or manure) were compared. 68 samples were taken from 45 gardens.

This means that in multiple gardens, 2 samples were taken to use for pairwise comparison.

Samples were analyzed on: pH(H2O), EC(1:5), ECe, NO3-N, NH4-N, total mineral N, Olsen-P,

exchangeable K and Ca, total C, total N and C/N-ratio. A soil quality index was compiled to

construct an overall opinion about the soil quality but did not give an adequate insight. This

was due to high variability in the data as a result of differences in soil types. Exchangeable K,

exchangeable Ca, EC and ECe were better when animal manure was applied. Mineral N and

P levels in soils that received animal manure were comparable with soils that received a

combination of chemical fertilizer and animal manure or chemical fertilizer alone. Total N and

C levels were higher in soils that received animal manure than in soils that received chemical

fertilizer. Animal manure is an important source of organic matter and is a better soil fertility

management practice than chemical fertilizer for home gardens in South Africa.

Key words: South Africa, animal manure, chemical fertilizer, chemical soil properties, home

gardens

Samenvatting

In het kader van de groeiende bezorgdheid rond globale voedselzekerheid en ongelijkheid in

Zuid-Afrika, werd het thema rond bodemkwaliteit in Zuid-Afrikaanse moestuinen in de Limpopo

provincie behandeld. Door gebrek aan financiële middelen voor chemische meststoffen bij

particulieren, is dierlijke mest vaak het enige alternatief. Dierlijke mest wordt echter niet ten

volle benut. Verschillende toepassingen ter verbetering van de bodemvruchtbaarheid werden

vergeleken. Deze toepassingen waren toediening van chemische mest, dierlijke mest, geen

mest of een combinatie van dierlijke en chemische mest. Er werden 68 stalen genomen uit 45

verschillende tuinen. In meerder tuinen werden 2 stalen genomen die paarsgewijs vergeleken

werden. Alle stalen werden geanalyseerd op: pH(H2O), EC(1:5), ECe, NO3-N, NH4-N, totale

minerale N, Olsen-P, uitwisselbare K en Ca, totale C, totale N en C/N-ratio. Er werd een

bodemkwaliteitsindex samengesteld om de toepassingen te beoordelen. De

bodemkwaliteitsindex bleek echter geen nuttige manier om uitsluitend chemische parameters

te beoordelen. Hoge variabiliteit in de data, veroorzaakt door verschillen in de bodemtypes,

maakte deze resultaten twijfelachtig. De chemische parameters werden afzonderlijk bekeken.

Uitwisselbare K en Ca, EC(1:5) en ECe waren beter wanneer dierlijke mest werd toegediend.

Mineral N en P in bodems waar dierlijke mest werd gebruikt waren gelijkwaardig aan bodems

waar chemische mest of een combinatie van chemische en dierlijke mest werd toegediend.

Totale N en C waren hoger in bodems waar dierlijke mest werd toegediend dan in bodems met

chemische bemesting. Dierlijke mest is een belangrijke bron van organisch materiaal en is een

betere bemestingsvorm dan chemische mest voor moestuinen in Zuid-Afrika.

Kernwoorden: Zuid-Afrika, dierlijke mest, chemische mest, chemische bodemparameters,

moestuinen

1

Table of contents

List of abbreviations .............................................................................................................. 3

List of figures ......................................................................................................................... 5

List of tables .......................................................................................................................... 6

1 Introduction .................................................................................................................... 7

1.1 Background of the study .......................................................................................... 7

1.2 Overview ................................................................................................................. 8

2 Literature review ............................................................................................................ 9

2.1 Actual use and availability of fertilizers .................................................................... 9

2.1.1 Chemical fertilizer use and availability .............................................................. 9

2.1.2 Animal Manure use and availability .................................................................10

2.2 Composition of fertilizers ........................................................................................11

2.2.1 Organic fertilizers ............................................................................................12

2.2.2 Inorganic fertilizer ............................................................................................15

2.3 Positive effects of organic fertilizers .......................................................................16

2.3.1 Organic matter ................................................................................................16

2.3.2 Function of organic matter ...............................................................................16

2.4 Recommended application rates ............................................................................20

2.5 Soil quality assessment ..........................................................................................22

2.5.1 Selection of the minimum dataset....................................................................23

2.5.2 Scoring of the indicators ..................................................................................23

2.5.3 Soil quality index (SQI) ....................................................................................23

3 Methods and materials .................................................................................................25

3.1 Overall approach ....................................................................................................25

3.2 Description of the study area ..................................................................................25

3.3 Field work ...............................................................................................................26

3.3.1 Interviews ........................................................................................................26

3.3.2 Soil sampling ...................................................................................................26

3.4 Analysis ..................................................................................................................27

3.4.1 Preparation of the soil samples .......................................................................27

3.4.2 Physical characteristics ...................................................................................27

2

3.4.3 Chemical characteristics .................................................................................27

3.5 Statistical analysis ..................................................................................................30

3.5.1 Interviews ........................................................................................................30

3.5.2 Chemical analysis ...........................................................................................30

3.5.3 Soil quality index .............................................................................................30

4 Results and discussion .................................................................................................32

4.1 General findings interviews ....................................................................................32

4.1.1 Use of home garden in summer and winter .....................................................32

4.1.2 Soil fertility management practices ..................................................................34

4.1.3 Application rates ..............................................................................................35

4.1.4 Common practices ..........................................................................................36

4.2 Evaluation of the soil chemical properties ...............................................................36

4.2.1 pH(H2O) ..........................................................................................................38

4.2.2 EC(1:5) and ECe..............................................................................................39

4.2.3 Total mineral N, nitrate and ammonium ...........................................................40

4.2.4 P(Olsen) ..........................................................................................................41

4.2.5 Exchangeable K ..............................................................................................42

4.2.6 Exchangeable Ca ............................................................................................44

4.2.7 Total C, total N and C/N ratio ..........................................................................45

4.3 Soil quality determination .......................................................................................47

4.3.1 Compilation of the minimum dataset ...............................................................47

4.3.2 Scoring of the indicators ..................................................................................50

4.3.3 Soil quality index .............................................................................................51

5 Conclusion ....................................................................................................................55

6 References ...................................................................................................................57

7 Appendix ......................................................................................................................64

3

List of abbreviations

% Percent

°C Degree Celsius

ADD Additive

CA Cattle

Ca

ce

Calcium

Chemical fertilizer

CE Chemical

CH

ce/m

Chicken

Combination of chemical fertilizer and animal manure

CO Compost

Cu Copper

DM Dry Matter

DSS Decision Support System

EC Electrical Conductivity

ECe Electrical Conductivity of a saturated soil Extract

FAO Food and Agriculture Organization

Fe Iron

g Gram

GO Goat

ha Hectare

K Potassium

kg Kilogram

LAN Limestone Ammonium Nitrate

M Molar

m Manure

MDS Minimum Data Set

meq Milliequivalents

mg Milligram

Mg Magnesium

ml Milliliter

mm Millimeter

Mn Manganese

Mo Molybdenum

n Number of samples

N Nitrogen

N1 Reagent

N2

nf

Reagent

No fertilizer

NH4+-N Ammonium Nitrogen

4

nm Nanometer

NO3--N Nitrate Nitrogen

NPK Nitrogen-Phosphorus-Potassium Fertilizer

P Phosphorus

PCA Principle Component Analysis

pH Acidity

ppm Parts Per Million

R Remove

rpm Rounds Per Minute

S Stay

SD

SPSS

Standard deviation

Statistical Package for the Social Sciences

SSF Standard Scoring Functions

SQI Soil Quality Index

t Tonne

WHO World Health Organization

WTD Weighted Additive Score

Zn Zinc

5

List of figures

Figure 1: Composition dry matter of plant residues (Yerima and Van Ranst, 2005) ..............12

Figure 2: Decomposition relationships (Lavelle and Spain, 2005) .........................................17

Figure 3: Model indexing dynamic soil quality (Wienhold et al., 2004) ..................................23

Figure 4: Soil map sample locations Mutshenzheni, Tshidzini and Dzindi.............................26

Figure 5: Sketch soil sampling ..............................................................................................27

Figure 6: Soil sampling .........................................................................................................27

Figure 7: Plots used in winter and summer ...........................................................................32

Figure 8: Example of a home garden ....................................................................................33

Figure 9: Soil fertility management practices in summer and winter ......................................34

Figure 10: Period of the same soil fertility management practices ........................................35

Figure 11: application rates per management practice .........................................................35

Figure 12: Boxplot pH(H2O) - fertilizer category ....................................................................38

Figure 13: Boxplot pH - fertilizer category (Haplic Lixisols) ...................................................39

Figure 14: Boxplot ECe - fertilizer category ...........................................................................40

Figure 15: Boxplot total mineral N - fertilizer category ...........................................................41

Figure 16: Boxplot P(Olsen) - fertilizer category ...................................................................42

Figure 17: Boxplot exchangeable K - fertilizer category ........................................................43

Figure 18: Boxplot exchangeable K - fertilizer category (Haplic Lixisol) ................................44

Figure 19: Boxplot exchangeable Ca - fertilizer category ......................................................45

Figure 20: Boxplot total C - fertilizer category .......................................................................46

Figure 21: Boxplot total N - fertilizer category .......................................................................47

Figure 22: Boxplot SQI - fertilizer category (based on all data) .............................................52

Figure 23: Boxplot ADD SOI - Fertilizer category (based on pairwise samples) ....................53

Figure 24: Boxplot ADD SQI - Fertilizer category (Haplic Lixisol) ..........................................54

6

List of tables

Table 1: Comparison fertilizer use (FAOSTAT, 2017) ............................................................ 9

Table 2: NPK values of cattle manure ..................................................................................13

Table 3: NPK values of sheep manure .................................................................................13

Table 4: NPK values of goat manure ....................................................................................14

Table 5: NPK values of chicken manure ...............................................................................14

Table 6: Analysis results poultry manure on dry basis (van Ryssen et al., 1993) ..................15

Table 7: Recommended application rates (Grubben and Denton, 2004) ..............................21

Table 8: Application rates of animal manure for vegetables (Okorogbona and Adebisi, 2012)

.............................................................................................................................................22

Table 9: Crops grown in winter and summer ........................................................................33

Table 10: Mean, max. and min. application rate per soil fertility management practice .........36

Table 11: mean values and significant differences all chemical soil properties .....................37

Table 12: pairwise comparison animal manure - no fertilizer ................................................37

Table 13: mean values Haplic Lixisols ..................................................................................38

Table 14: Principal components ...........................................................................................48

Table 15: Correlation matrix (p ≤ 0.05) .................................................................................49

Table 16: Optimum ranges scoring indicators .......................................................................50

Table 17: Mean value scores ................................................................................................51

Table 18: SQI split two soil types ..........................................................................................54

7

1 Introduction

To provide a clear view on the meaning of this research, food security and the role of home

gardens will be clarified in paragraph 1.1, an overview of the thesis will be given in paragraph

1.2.

1.1 Background of the study

Food security is a growing worldwide problem due to the growing population, climate change,

water scarcity and socio-economic problems. South Africa is featured as a food secure nation

but the number of stunted children under the age of 5 was 23.9% in 2008 according to the

WHO (2016). Recent studies show that in the context of both over- and under-nutrition, the

adult and child malnutrition rates have deteriorated in South Africa. Based on a study on food

security in rural areas of Limpopo province, 53% of the households are severely food insecure

(De Cock et al., 2013). This study also mentions that only 14.8% of the households in the

Limpopo province is food secure. The main reason for this situation can be situated in the

context of structural unemployment, but also lack of knowledge about nutrition and the high

HIV prevalence. Home-grown or small-scale food production represents a feasible contributor

to a more diverse diet for rural people that can provide essential vitamins and minerals, as

shown by Faber et al. (2011).

Home gardens are defined in different ways. But the most useful description is given by

Drescher et al. (2006) who defines home gardens as a cropping system that is maintained

typically, but not always, near homes by individuals or households who have access to land,

either customary or legal, which they have arranged for themselves. Often the home gardens

are described as an agroforestry land-use system, however there are also home gardens

which are only used to produce annual crops without mimicking a multilayered ecosystem. A

home garden can help to reduce vulnerability and aids in ensuring food security. The products

can provide supplementary household income and in this way improve the status of women in

the household. Apart from all these direct benefits for the households, there is also a big

ecological advantage. As mentioned before home gardens are sometimes described as an

agroforestry land-use system. In the tropics they show a wide range of diversified perennial

and semi-perennial crops, trees and shrubs. These tropical home gardens are reported as

highly sustainable both biophysically and socioeconomically (Adekunle, 2013; Kumar and Nair,

2006).

Taking into account the importance of the homegrown fruits and vegetables, it is important to

optimize the yields under suboptimal conditions. These negative influences are mainly based

on nutrient depletion, combined with the limited use of fertilizers. These fertilizers are needed

to compensate the nutrients taken up by vegetation, nutrients that are geographically

redistributed by runoff, nutrients lost by volatilization, fixation and leaching (FAO, 1998).

8

1.2 Overview

This thesis was preceded by Prudence Dimakatso Ramphisa who wrote about the same

subject but in a different village with fewer samples. It was concluded that gardens receiving

animal manure had better chemical soil properties than gardens receiving a combination of

chemical fertilizer and manure. Moreover, the chemical soil properties of gardens receiving

only chemical fertilizer did not differ significantly with gardens receiving no fertilizer. Physical

soil properties were not affected by the soil fertility management practices. Lastly, it was

concluded that the soil quality of home gardens receiving animal manure or a combination of

animal manure with chemical fertilizer was superior to the soil quality of gardens who only

received chemical fertilizer or no fertilizer. These conclusions are based on the analysis results

of 24 samples. To confirm the significant effects of soil fertility management practices on the

chemical soil properties and the soil quality, a higher number of samples (68) were taken in

three different villages in the same region. The physical soil properties were no longer

measured because no significant differences were found in the previous research and the

current research is done in the same region. Knowing that even in the same region soil types

can differ and so influence chemical soil properties, paired samples were taken when possible.

Paired samples were taken when the garden was split up in multiple zones that were treated

differently. In winter a small part of the garden is often used to grow green vegetables with

application of animal manure, while in summer every piece of land available was planted with

maize, without use of any fertilizer.

The main research question is thus the following. Is application of animal manure significantly

better or at least a valuable equivalent for application of chemical fertilizer in South African

home gardens?

To provide some information on the current situation of fertilizer use, the literature review starts

with a sketch of the use of chemical fertilizer and animal manure in South Africa and the

availability of these nutrient sources. This is followed by the composition of different types of

manure and fertilizer based on multiple researches. Hereafter the positive effects of organic

matter are highlighted and recommended application rates are shown. Finally, a short resume

is given about soil quality assessment. Following the literature review, the material and

methods are given. Next, the results are reviewed and a general conclusion is made.

9

2 Literature review

2.1 Actual use and availability of fertilizers

Comparison of actual and recommended application rates of chemical fertilizers and animal

manure is important to improve the current fertility management practices to improve yields in

home gardens. Too little will not give the potential yield and will not solve the problem of

declining soil fertility. Too much can cause leaching and pollute the environment. The current

situation concerning application rates are discussed in paragraphs 2.1.1.1 and 2.1.2.1. The

main issue is the poor nutrient status of the soil, due to poor living conditions and

overpopulation in the region and shortage on fertilizers seems to be the case. That’s why the

availability of both organic and inorganic fertilizer will be illustrated in 2.1.1.2 and 2.1.2.2

(Odhiambo and Magandini, 2008; van Niekerk, 1995).

2.1.1 Chemical fertilizer use and availability

Current use of chemical fertilizers will be illustrated, followed by the state of availability of these

fertilizers.

2.1.1.1 Application rates

The application rate of nitrogen (N), phosphorus (P) and potassium (K) nutrients in kilogram

per hectare arable land in South Africa can be found in Table 1 and was respectively 33.87,

14.92 and 9.88 in 2014. When compared with the average fertilizer use in Africa and the least

developed countries in the same year, South Africa used more than double. 95% of the

agricultural output in South Africa is produced by large commercial farms and only 5% by

smallholders. This means that the application rates of nutrients are mainly caused by big

producers and the use by smallholders who often don’t have the resources can’t be deduced

by these numbers. The average intensity of fertilizer use in the world is more than twice as

much as the use in South Africa (FAO, 2005; FAOSTAT, 2017).

Table 1: Comparison fertilizer use (FAOSTAT, 2017)

South Africa Africa World Least developed

countries

N kg.ha-1 33.87 14.45 68.75 14.23

P2O5 kg.ha-1 14.92 5.52 29.47 5.54

K2O kg.ha-1 9.88 2.25 23.76 2.81

According to research by Sheahan and Barrett (2017) concerning agricultural input (e.g.

machinery, fertilizers, improved seed) in Sub-Saharan Africa, a strong inverse relationship

exists between the size of a farm or even a plot and the input use intensity. Mandiringana et

al. (2005) and Roberts et al. (2003) also observed that small scaled fields, close to the

homestead and home gardens are more intensively managed and are more fertile. Besides

10

the input rates, some other factors also influence a successful yield. Sheahan and Barrett

(2017) mention that different modern agricultural inputs often miss their potential effect

because they are not combined. Different agronomical inputs can have a synergetic effect

when these are used at the same time. For example, the effect of using chemical fertilizers in

arid regions will be stronger when a sufficient irrigation system is used. Knowing this, the risk

of not getting any rainfall and not having an irrigation system, keeps farmers and home

gardeners from investing in fertilizers because sufficient water is needed for crop response.

Lastly, the uncertainty in market prices for the products as a result of defective regional

transport and storage facilities, also discourages smallholders to invest (Odhiambo and

Magandini, 2008; Sanchez and Swaminathan, 2005).

2.1.1.2 Availability

When it comes to agricultural input in its entirety, the variation in use is mainly caused by the

different policies in regional government or variations in price of these inputs. Several fertilizer

support programs were introduced in Sub-Saharan Africa during the 1970’s and 1980’s. The

difference with South Africa is that they were only supporting white farmers. As in other Sub-

Saharan African countries, the South African government suffered from the high costs of the

subsidies and reduced the state intervention. Without governmental support, it is hard for small

scale farmers and individuals to obtain sufficient amounts of expensive inorganic fertilizers. In

the research of Odhiambo and Magandini (2008) about smallholder farmers in Vhembe district

it is determined that 75% of these farmers could not afford enough fertilizers due to lack of

financial resourses. Sanchez and Swaminathan (2005) state that high fertilizer prices are a

result of poor infrastructural development in rural areas. Another point of view is put forward

by Minot and Benson (2009) who say there was some controversy about the state intervention

programs in the past, which involved state monopolies in fertilizer marketing, because they

had a negative influence on the expansion of an efficient distribution network. It can be

concluded that the main problem is the lack of a well thought-out systematic solution in the

past. A more general problem the fertilizer industry is facing is the high energy requirement for

nitrogen fertilizer synthesis and the finality of phosphorus and potassium sources (Barker and

Pilbeam, 2015; Hanekom, 1998; Kherallah et al., 2002).

2.1.2 Animal Manure use and availability

The current use of animal manure is illustrated by different studies conducted with South

African small-scale farmers. In the subsequent paragraph the availability of the animal manure

is discussed.

2.1.2.1 Application rates

Okorogbona and Adebisi (2012) mention that animal manure is the most common organic

nutrient source used in Africa as a result of high costs, poor marketing and insufficient

distribution of chemical fertilizers as mentioned before. They also state that to improve soil

fertility, organic nutrients on their own are inadequate to produce enough crops to meet the

increasing demand for food. This can be explained by the fact that animal manure contains all

11

the nutrients needed by plants but not in the right proportions required for optimal plant growth

(Okorogbona and Adebisi, 2012; Okwu and Ukanwa, 2007; Van Averbeke and Yoganathan,

1997).

Based on a descriptive analysis of small-scale irrigation farmers from upper Gxulu in

Keiskammahoek District, South Africa, by Yoganathan et al. (1998), manure application rates

varied from 0.3 to 182 tonnes per hectare with village means between 2 to 18 tonnes per

hectare. In the article of Materechera (2010), about animal manure use by small scale farmers

in North West Province in South Africa, 66% used animal manure and the application rate

varied from 0.3 to 22 tonnes per hectare and with a mean of 2.5 tonnes per hectare. Important

to note here is that different types of animal manure can’t be regarded as the same due to the

different composition and quality. The research of Materechera (2010) also found that cattle

manure was most commonly used, followed by sheep and goat manure. Chicken manure was

the least used animal manure (Okorogbona and Adebisi, 2012).

2.1.2.2 Availability

Odhiambo and Magandini (2008) found that due to the limited number of animals kept by the

smallholder farmers, the quantity of manure produced is never sufficient and that because of

high transportation costs the required quantities are not met. But when smallholder farms are

mentioned, the size of the production plots are often way bigger than home garden areas.

Home gardens are also defined to be close to the homestead while for the bigger production

plots this is not always the case. If a home gardener has livestock, these issues are generally

not the problem.

The article by Okorogbona and Adebisi (2012) mentions that the Fertilizer Society of South

Africa stated that 3 million tonnes of animal manure was produced in South Africa originating

from intensive animal farming in 1989. This amount of manure should have been sufficient to

meet respectively 13.3%, 9.9% and 27.6% of the soil requirements for N, P an K nutrients.

However, only 25% of these estimated 3 million tonnes were used to improve soil fertility.

Another small portion was used for heating by burning the manure but most of the remaining

75% was wasted. Mkhabela and Materechera (2003) predicate that these numbers haven’t

changed during the period 1989-2003. In 1995, South African smallholders produced about

6,047,530 tonnes of cattle manure, 899,236 tonnes of goat manure and 985,843 tonnes of

sheep manure. This means more manure is produced by small scale farmers than by intensive

animal farms (Mkhabela and Materechera, 2003; Okorogbona and Adebisi, 2012).

2.2 Composition of fertilizers

Knowing that the composition of organic and inorganic fertilizers varies depending on certain

factors and the nature of these materials, different types of fertilizers often used by small scale

farmers, are analyzed in the following paragraphs 2.2.1 and 2.2.2.

12

2.2.1 Organic fertilizers

Organic fertilizers are natural materials consisting of organic matter that generally comes from

plants or plant residues. These plants and/or plant residues are used after decomposing or

after being excreted by animals who digested them. Organic matter is added in different forms

in agricultural practices. The forms used are green manures, plant residues and animal

manures (Okorogbona and Adebisi, 2012; Yerima and Van Ranst, 2005).

2.2.1.1 Green manure and plant residues

Green manure is non-composted (nor digested by animals) plant material that is worked into

the soil. Plant residues are literally the parts of plants, left on the field after harvesting. Plant

residues or green manure contain a moisture content between 60 and 90% with a mean of

75% and an average dry matter content of 25%. The dry matter content can be split up in 44%

carbon, 40% oxygen, 8% hydrogen and 8% ash which consists out of P, K, Ca, Mg and trace

elements as shown in Figure 1 (FAO, 1998; Yerima and Van Ranst, 2005).

Figure 1: Composition dry matter of plant residues (Yerima and Van Ranst, 2005)

2.2.1.2 Animal manure

Animal manure can be classified in different ways. A first way to classify different types is to

split based on the physical characteristics. The most common ones are solid and slurry

manures. Solid manure is also called farmyard manure and is a mixture of animal excreta and

bedding materials. Slurry manure is a more fluid type of manure consisting of 90 to 95% water,

faeces and urine. A second way to split up the different types of manure is based on animal

species that produce the manure. The most common types are cattle, sheep, goat and chicken

manure. Composition of manure can vary a lot depending on these different factors. Likewise,

geographical origin, method of storage, housing and bedding system or diet of the animal and

environmental temperature have an influence on the manure quality. The most reliable and

accurate way to determine the exact nutrient content of manure is to analyze a sample in a

laboratory. Unfortunately, these methods are expensive, so the analysis results of manure with

44%

40%

8%

8%

Dry matter

carbon

oxygen

hydrogen

ash ( N, P, K, Ca, Mg,trace elements,...)

13

the most similar factors is most useful when application rates are considered (Chadwick and

Chen, 2002; FAO, 1998; Johnston et al., 2002; Pennington et al., 2009; Saka et al., 2017).

Cattle

In Table 2 different analysis results of cattle manure are shown. Cattle manure, analyzed in

the experiment done by Gyapong and Ayisi (2015), was very rich in P and K compared to other

samples. The Tanzanian dairy cattle manure, used in the research by Maerere et al. (2001) on

the other hand was low in P. The Bolivian and South African cattle manure samples were more

similar even though the potassium level of the South African kraal manure was twice as high.

Knowing that manure composition can differ originating from different geographical locations,

the results from Azeez et al. (2010) and Okorogbona et al. (2011) are more accurate for South

Africa. Based on these results, it can be concluded that cattle kraal manure is high in N and K.

Table 2: NPK values of cattle manure

Nutrients in g.kg-1

N P K Type Country Reference

20.5 10.3 34.0 Dry cattle manure Ghana Gyapong and Ayisi, 2015

14.0 1.2 - Dry dairy cow manure Tanzania Maerere et al., 2001

15.0 3.0 7.0 Dry cattle manure Bolivia Alvarez and Lidén, 2009

17.0 4.2 16.8 Dry cattle kraal

manure

South Africa Azeez et al., 2010

17.0 5.0 17.0 Dry cattle kraal

manure

South Africa Okorogbona et al., 2011

Sheep

In Table 3 different analysis results of sheep manure can be found. Sheep manure collected

for the research of Alvarez and Lidén (2009) in Bolivia contained a high K level while sheep

kraal manure from Eastern Cape, South Africa contained a high N level. The differences are

big and probably can be attributed to different influence factors as mentioned before (Mhlontlo

et al., 2007).

Table 3: NPK values of sheep manure

Nutrients in g.kg-1

N P K Type Country Reference

9.0 6.0 18.0 Dry sheep

manure

Bolivia Alvarez and Lidén, 2009

18.0 - 3.7 Dry sheep kraal

manure

South Africa Mhlontlo et al., 2007

14

Goat

In Table 4 different analysis results of goat manure can be found. In the Tanzanian research

of Maerere et al. (2001), goat manure contained lower values for N and P than the South

African goat kraal manures analyzed by Azeez et al. (2010) and Okorogbona et al. (2011).

Table 4: NPK values of goat manure

Nutrients in g.kg-1

N P K Type Country Reference

16.5 2.1 - Dry goat

manure

Tanzania Maerere et al., 2001

22.2 4.3 40.2 Dry goat kraal

manure

South Africa Azeez et al., 2010

22.0 4.0 40.0 Dry goat kraal

manure

South Africa Okorogbona et al, 2011

Chicken

In Table 5 different analysis results of poultry manure are shown. Boateng et al. (2006)

analyzed layer chicken manure in Ghana which was the highest in K compared with the other

results in the table. This was also the case for layer manure analyzed by van Ryssen et al.

(1993) in Table 6. Results of Mkhabela (2004) were the highest in N on a wet basis. Results

of Gyapong and Ayisi (2015) don’t really stand out and the levels of N, P and K are all high in

the results of Okorogbona et al. (2011). Again, it can be concluded that differences in manure

composition from different geographical locations are present but a big difference exists

between the layer poultry manure of van Ryssen et al. (1993) and Mkhabela (2004). Layer

manure in the research of van Ryssen et al. (1993) is defined as pure excreta from layers

housed in cages while Mkhabela (2004) refers to manure as a mixture of excreta, bedding

materials and waste feed. Another important difference is that the samples of Mkhabela (2004)

were analyzed on wet basis while the results of van Ryssen et al. (1993) are determined on

dried material. It can be concluded that chicken manure is rich in N but also P an K values are

high.

Table 5: NPK values of chicken manure

Nutrients in g.kg-1

N P K Type Country Reference

24.2 12.3 16.2 Layer manure Ghana Boateng et al. (2006)

22.4 10.3 13.0 Dry poultry manure Ghana Gyapong and Ayisi

(2015)

37.0 15.0 15.0 Dry layer manure South Africa Okorogbona et al., 2011

29.0 12.9 12.5 Wet broiler manure

(63% moisture)

South Africa Mkhabela (2004)

30.5 9.6 11.3 Wet layer manure

(50% moisture)

South Africa Mkhabela (2004)

15

The South African research of van Ryssen et al. (1993) shows different types of poultry

manure. These types were broiler, breeder, pullet, layer and backyard manure (Table 6). It is

clear that differences in manure composition depend on the production system. The amounts

of micronutrients (Fe, Zn, Mn, Cu and Mo) are given in this table but N levels were not analyzed

for this experiment.

Table 6: Analysis results poultry manure on dry basis (van Ryssen et al., 1993)

2.2.2 Inorganic fertilizer

Inorganic fertilizers can be described as fertilizers that don’t contain carbon substances.

Although the exception is urea [CO(NH2)2] which does contain carbon and is classified as an

inorganic fertilizer. A lot of different fertilizer types exist. Some types only provide one single

nutrient, others are composed of multiple nutrients. N, P and K are primary plant nutrients and

are taken up in large amounts and are often combined in blends. Ca, Mg and S are known as

secondary nutrients because they are taken up in smaller amounts but still they play an

important role in plant growth. The fertilizer sources for primary and secondary elements can

be found in Appendix 1. Trace elements or micronutrients are taken up by plants in very small

quantities and often have an important role in plant metabolism. These micronutrients are: B,

Cl, Cu, Fe, Mn, Mo and Zn. Fertilizer sources for micronutrients can be found in Appendix 2.

NPK fertilizer is a multi-element mixture that is used on a large scale in different compositions

and concentrations. The risk of using the same NPK fertilizers for growing crops each year is

16

depletion of micronutrients in the soil (Barker and Pilbeam, 2015; FAO,1998; Jones, 2012,

Roberts et al., 2003).

2.3 Positive effects of organic fertilizers

Following paragraph 2.3.1 gives a succinct definition of organic matter followed by a general

description of the functions of organic matter in 2.3.2 together with the role of C/N ratio, humus

and the effect on the nutrient availability and soil structure.

2.3.1 Organic matter

Organic matter can be defined as plant or animal material that goes back to the soil and is

exposed to decomposition processes. It can be split up into above- and belowground fractions.

The fraction aboveground consists of plant and animal residues. The belowground fraction

includes partially decomposed residues of plants and animals, living soil fauna, microflora and

humus-substances (Bot and Benites, 2005).

2.3.2 Function of organic matter

Although, not much organic matter is needed, the positive effects of organic matter are

indispensable for a healthy and fertile soil. Mineral soils only contain 0.5 to 5 percent organic

matter while organic soils can contain up to 100 percent organic matter. A typical characteristic

of organic matter for example is the ability to provide macro- and micronutrients and a habitat

to indispensable soil organisms, both microorganisms, bigger creatures and plants.

Earthworms, mycorrhizas and nitrogen fixing bacteria are some important examples. In tropical

soils organic matter also has an important role in reducing elemental toxicity. Organic material

also helps to form aggregates by binding soil particles and ameliorates the water retention

capacity of soil. In other words, organic matter is important because of two main reasons,

namely to bind and provide nutrients and to improve soil structure. Decomposition of the

organic matter is herewith essential. The relationship between decomposition of organic matter

and plant growth, greenhouse gases, ozone regulation and soil structure can be found in

Figure 2. Decomposition of dead organic material delivers nutrients and other elements to the

soil and is also known as (primary) mineralization. The nutrients are released as inorganic

substances and are available for plants to be assimilated when they are dissolved in the soil

moisture. In more anaerobic conditions, elements like CO2 and CH4 are produced and play

part in the greenhouse gases and ozone regulation. Another important part of the

transformation processes is known as humification. At this stage organic molecules are

integrated into humus-like substances or organic polymers that bind with mineral soil

components like clays or oxides. This colloidal structure is more resistant to degradation. It

can take thousands of years to decompose chemically protected organic matter. The

degradation of humus provides plant-available nutrients slowly and more evenly and is called

secondary mineralization. Bioturbation is defined by the physical displacement of particles and

water due to macrofaunal processing and ventilation activities in sediments with effects on

ecological processes. Soil properties as texture, pH, temperature, moisture, aeration, clay

17

mineralogy and soil biological activities have an influence on the decomposition rate of organic

matter but also composition of the organic matter in the soil itself has an influence on the

decomposition rate (Boateng et al., 2006; Bot and Benites, 2005; Coventry et al., 2003; Fageria

and Baligar, 2003; Kristensen et al., 2012; Lavelle and Spain, 2005; Zinati et al., 2001).

Figure 2: Decomposition relationships (Lavelle and Spain, 2005)

2.3.2.1 C/N-ratio

An important factor that influences the decomposition, or more specifically, the mineralization

or humification rate is the carbon – nitrogen (C/N) ratio. Organic matter with a high C/N-ratio,

for example straw or sawdust, contains more lignin, lipids and less fermentable materials.

Heterotrophic microorganisms respire about half of the carbon that is taken up but some

fractions are not assimilated (e.g. lignin). The lignin and lipids have a negative, toxic effect on

the decomposing organisms. Mineralization activity is almost zero and humification goes very

slow when the ratio is higher than 50. Most bacteria and fungi are N-limited when the C/N-ratio

exceeds 30. They require additional N and the net immobilization of mineral N in the microbial

tissue increases. Organic matter with a C/N-ratio above 50 is not suitable for cultivation

practices because of the low mineral-N availability. Materials with a lower C/N-ratio around 20

are for example young plants, legumes, green manures or well decomposed farm manures.

These materials contain more hydrates of carbon, cellulose, hemicellulose, polysaccharides

and hydrolysable tannins which are easier to decompose. Heterotrophic bacteria and fungi

have a C/N ratio between 4 and 12. Mineralization and humification are in balance when the

C/N-ratio of the organic matter is around 20 (Hodge et al., 2000; Kaye and Hart, 1997; Yerima

and Van Ranst, 2005).

18

2.3.2.2 Humus

Humus usually contains between 3 and 6% N and 58% C which gives a C/N ratio around 10

to 20. This ratio can vary depending on the nature of the humus, stage of decomposition, soil,

climate and environmental conditions under which the humus is formed. Large molecules in

the humus-complex like cellulose and proteins have an active zone on the exposed surfaces

and short chains that function as cation-exchange sites when charged negatively (only when

the pH of the soil is greater than 3) but it can also absorb clay particles. These characteristics

give humus the ability to improve the structure of fine-textured soils, absorb large quantities of

water, decrease soil bulk density. Humus also has a high cation exchange capacity of 150 to

300 meq/100g compared to 8 to 150 meq/100g for clay, which helps to provide plant available

nutrients, prevents leaching. Lastly, it also absorbs heavy metal cations and cationic pesticides

(Yerima and Van Ranst, 2005).

2.3.2.3 Nutrients

Cation exchange at the hummus colloid surface makes nutrients needed for plant growth

available for plant uptake. Humic acids also have the ability to extract plant nutrients from

primary and secondary minerals because of its solvent activity. Lastly, decomposition of

organic materials also delivers inorganic forms like NH4+, NO3

-, phosphates, sulphates and

CO2 which already are or become plant available nutrients. In the following paragraphs the

quantitative most important plant nutrients will be explained in the context of their interaction

with organic matter (Yerima and Van Ranst, 2005).

Nitrogen (N)

Organic matter can provide 9 to 95% of the needed N in a non-fertilized soil. Organic N includes

protein amino acids, non-protein amino acids, quaternary ammonium compounds, azoles and

alkylamines. The profile of small organic N compounds can vary significantly among soils. This

is probably a result of a combination of variation in vegetation, microbial community and abiotic

stress. When organic matter is decomposed, heterotrophic organisms, bacteria, fungi and

actinomycetes excrete ammonium salts as a first inorganic N compound after enzymatic

hydrolysis of proteins. As seen in the simplified equations below nitrate and nitrite bacteria

transform these ammonium ions through enzymic oxidation with nitrates (NO3-) as a result.

These nitrates can be assimilated by plants but ammonium (NH4+) on its own is also a plant

available form of N. Preference of the plant for NH4+ or NO3

- depends on plant species, soil pH

and temperature but most plants grow better with NO3- (Barber and Pilbeam, 2015; De Jong

and Rinsema, 1989; Warren, 2013; Yerima and Van Ranst, 2005).

2NH4+ + 3O2

𝑒𝑛𝑧𝑦𝑚𝑎𝑡𝑖𝑐

𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛> 2NO2

- + 4H+ + 2H2O + energy

2NO2- + O2

𝑒𝑛𝑧𝑦𝑚𝑎𝑡𝑖𝑐

𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛> 2NO3

- + energy

Amino acids can also be taken up by plants. Arabidopsis species use amino acids as a N

source as shown by lee et al. (2007). The phenomenon is also known by other mycorrhizal

19

and nonmycorrhizal plants. Warren (2013) points out that plants are also able to take up

quaternary ammonium compounds as intact molecules from the soil solution. In the experiment

of Mérigout et al. (2008) Arabidopsis thaliana is proven to be able to take up urea (Barber and

Pilbeam, 2015; Näsholm et al., 2009).

Phosphorus

Soil P can be divided into three important groups. These groups are the bioavailable P in the

soil solution, the labile P in the soluble P fraction which includes poorly sorbed mineral P,

soluble P minerals and easily mineralizable organic P and at last the nonlabile P which includes

strongly sorbed P, insoluble P minerals and organic P forms resistant to separation and

mineralization. The organic P fraction is an important source of solid P. This organic P includes

degraded phospholipids, nucleic acids and their degradation products and more stable inositol

polyphosphates which are part of the humus fraction. Other organic P fractions are present in

living soil organisms and their degradation products. After mineralization of the organic matter

a part of the P becomes available for plants. Another source of plant-available P is the

breakdown of minerals with the release of structural P. Finally, the most important source of

plant-available P is the desorption of precipitated mineral deposits. Plants only absorb HPO42-

or H2PO4- out of the soil solution but P is known to be poorly soluble. P mineralized from organic

material binds with Mg or Ca in alkaline soils and when the pH lowers to 5.5 Al can precipitate

with P. Similar reactions occur with Fe and Mn. Hence, the soil solution often contains very low

concentrations of P with an upper value of 1.0 mg P kg-1 but often not exceeding 0.05 mg P

kg-1. This concentration is influenced by soil pH, cations, minerals and organic compounds.

Due to the low P concentration in the soluble fraction, uptake by the rhizosphere is a more

active process that consists of diffusion and root expansion which makes it possible to intercept

necessary nutrients. Organic acids exuded by roots and soil microorganisms can displace P

by ligand exchange. Therefore, stimulating microorganism growth by providing organic matter

can have a positive effect on P availability in alkaline soils. P bound by Ca or Mg can be

released by acidification in the rootzone due to the protons and organic acids exuded by the

roots. Soils with higher plant-available and resorbable P are a result of high compost and

manure gifts. Organic P sources can stimulate the formation of slow release organic P (Barber

and Pilbeam, 2015; Davenport et al., 2005; Erich et al., 2002; Fixen and Bruulsema, 2014;

Holford and Mattingly, 1975; Sharpley et al., 2003; Malik et al., 2012).

Potassium

K takes part in a lot of the plants biochemical and physiological processes like synthesis of

lipids, sugars, starch, leaf cuticles and energy transfers. It increases absorption of NH4+-ions

and P uptake by plants but interacts antagonistically with Mg and Ca. When applied in

adequate quantities it allows plants to tolerate cold, heat and drought. In general, a soil

contains 0.2 to 3.3% K which can be split up into a first group of plant-available K and

temporarily dynamic pools of K consisting of microbial biomass, exchangeable K and non-

exchangeable K which is bound in the interlayers of micaceous clay minerals and hereby

slowly releases K. The second group includes up to 98% of the K which is unavailable for

20

plants and is held in primary minerals. The organic fraction of K is hereby negligible when

compared with other nutrients like N and P but incorporation of plant residues after harvesting

helps to recycle certain amounts of K. Vegetative shoots of cereals like wheat or rice contain

70 to 80% of the total K content of the plant and for legume shoots of cowpea or beans this is

40 to 50%. The K provision at plant roots is mainly a result of mass flow and diffusion. This is

the reason that K uptake is strongly influenced by drought, temperature, aeration, pH and bulk

density of the soil. A well-managed soil is therefore indispensable (Barber and Pilbeam, 2015;

Foth and Ellis, 1988).

2.3.2.4 Structure

Besides providing nutrients for soil fauna and flora, organic matter has an important role in soil

structure as mentioned before. Polysaccharides, hyphae of fungi, algae, fine roots of plants

and humic substances originating from decomposition of organic matter are aggregate forming

cements and make soil more resistant to wind erosion and tightening. Flocculation of the

aggregates depends on soil pH and type of humus. When the pH becomes more alkaline the

outer surface of the aggregates become more negatively charged and repel each other

(Yerima and Van Ranst, 2005).

2.4 Recommended application rates

When crops are grown with depletion of nutrients, without replenishing organic material and

nutrients and without maintaining a good soil structure, the balance in the agro-ecosystem can

be gravely disturbed. Required nutrient quantities depend on crop species, desired yield and

soil type. Exact uptake of nutrients by specific plant species can be determined by analyzing

chemical composition and weight of harvested plant material. Together with the composition

of chemical fertilizers and analysis of the organic manures appropriate advice about application

rates can be given. Organic matter is indispensable but as said before small amounts already

can have the desired positive effects on the soil quality (Bot and Benites, 2005; Yerima and

Van Ranst, 2005).

As mentioned before crop species is an influencing factor when quantitative nutrient supply

needs to be determined. In the category of leafy vegetables grown in South Africa, some

important species are Chinese cabbage (Brassica rapa subsp. chinensis), cowpeas (Vigna

inguiculata), pumpkins (Cucurbita pepo, C. maxima and C. moschata), amaranth (Amaranthus

cruentus L.) and nightshade (Solanum retroflexum) according to van Rensburg (2007).

Pumpkins and melons are often grown together with maize. Heckman (2002) mentioned that

soil nitrate-N levels between 20 and 30 mg NO3-N kg-1 should be sufficient for most crops in

humid regions. Based on studies in semi-arid regions sufficient levels are lower because of a

more developed root system. Van Biljon et al. (2008) points out the lower and higher threshold

values and the biological optimum value of NO3-N in South African soils (0-600 mm) for

growing maize, which was respectively 70, 161 and 280 kg NO3-N ha-1 or 8.0, 20.2 and 32.0

mg NO3-N kg-1 with an estimated N mineralization of 40 kg NO3-N ha-1 during the growing

season. Bloem (2004) stated that soils with less than 17 mg P kg-1 are P-deficient. This was

21

the case for 53.1% of the samples from commercial farms and 86.7% of the samples from

developing farms in the North West Province of South Africa. Grubben and Denton (2004)

recommended following application rates (Table 7) but more appropriate fertilizer gift

recommendations should be determined based on soil analysis (Grubben and Denton, 2004;

Krusekopf et al., 2002).

Table 7: Recommended application rates (Grubben and Denton, 2004)

t.ha-1 Nutrients kg.ha-1 t.ha-1

Vegetable species yield N P K Others manure

Chinese cabbage 25 150-200

(in

different

steps)

100-150

(in

different

steps)

100-150

(in

different

steps)

3.5 boron

before planting

20 to 50

Cowpea (as

vegetable) (for

average fertile

soils)

- 20 25 40 3 weeks after

emergence a

urea gift of 50

Additional 5 to

10 farmyard

manure

Cowpea (as green

manure) (for P

and K deficient

soils)

- 0 25 25 - -

Pumpkin - 50-100 20-40 40-80 - -

Amaranth 15 62.5 62.5 125 45 Ca and 25

Mg

Additional 25

or solely 50

Nightshade - (1st gift)

37.5-50

(2nd gift)

12.5-16.7

(1st gift)

37.5-50

(2nd gift)

12.5-16.7

(1st gift)

50-75 (2nd

gift) 16.7-

25

- 10 to 20 farm

or poultry

manure

Nightshade needs the first fertilizer gift 10 days after transplanting, the second gift when the

plant starts flowering. The second gift needs to be repeated every month when the plant is

grown for its fruits and every two weeks when it is grown as a leaf vegetable. Knowing that

manure composition and soil composition can vary widely the advice in Table 7 can only be

used as an estimated application rate. Okorogbona and Adebisi (2012) are more specific in

manure application recommendations. These recommendations can be found in Table 8.

22

Table 8: Application rates of animal manure for vegetables (Okorogbona and Adebisi, 2012)

t.ha-1

Vegetable

species

Poultry manure Sheep manure Goat manure Cattle manure

Chinese

Cabbage

3.42 (layer

manure)

- 11.9 23.8

Pumpkin 8.53 - >68.25 (kraal

manure)

>170 (kraal

manure)

Amaranth 9.22 10.0 10.3 11.7

Nightshade 17.0 - >68.25 >170

Application rates in Table 8 show at what level of specific animal manure type, vegetables

have a maximum biomass peak, except for application rates of goat and cattle manure for

pumpkin and nightshade which did not reach the maximum biomass with this rate (Okorogbona

and Adebisi, 2012).

2.5 Soil quality assessment

As a response to the increased global focus on sustainable land use, the concept of soil quality

became more important during the 1990s. The individual soil management goals were

combined in a framework with management goals to prevent erosion, reduce contamination of

soil and water… Education about- and the assessment of the soil quality where put in the

spotlight. Soil quality assessment focusses on inherent and dynamic soil properties and

processes. The inherent properties are mainly based on the whole soil profile (0 to ±2m) while

dynamic soil quality focusses on the surface layer (0 to 20 or 30 cm). The dynamic soil

properties are focused on more recent land use and management practices. Still, no

universally accepted method for soil quality assessment determination exists, but in general,

soil quality evaluation is based on mathematical equations which show the relation between a

soil function and soil quality indicator. This is also known as indexing and contains three steps

as shown in Figure 3. The first step is to select appropriate soil indicators to evaluate critical

soil functions in function of specific management goals. These indicators form the minimum

dataset (MDS). The next step is to score the indicators to make it possible to evaluate

characteristics with different measurement units. This can be done in different ways (linear,

non-linear, optimum, more is better, more is worse). Same values can be used multiple times,

for example: a higher nitrate level can be positive for plant growth but at the same time can

give a problem with leaching. Lastly, all these unitless values are combined into an index of

soil quality (Andrews et al., 2002; Cherubin et al, 2016; Karlen et al., 2003).

23

Figure 3: Model indexing dynamic soil quality (Wienhold et al., 2004)

2.5.1 Selection of the minimum dataset

To reduce redundant information of the original dataset, the principal component analysis

(PCA) can be used. Variables showing no significant differences between groups are dropped

and from these principle components the factors with a high eigenvalue (≥1) and a high factor

loading are selected. These factors represent soil properties with the highest influence on

variance in the data. A second way to compile the MDS is to use expert opinion (Andrews et

al., 2002; Cherubin et al., 2016; Zhan-jun et al., 2013).

2.5.2 Scoring of the indicators

As mentioned before, scoring the indicators is needed to normalize indicator observations to

become values between 0 and 1 as a result of linear or non-linear scoring. When using linear

systems, the highest values for indicators that are scored as ‘high is better’ are scored as 1.

For indicators with ‘lower is better’ the lowest value is scored as 1. For values like pH or

phosphorus ‘higher is better’ is used together with a threshold value. The non-linear scoring

system uses three standard scoring functions (SSF). The first one is the sigmoid shaped curve

with a higher asymptote used for ‘higher is better’ indicators. The second one is also a sigmoid

shaped curve but this one has a lower asymptote and is used for ‘lower is better’ indicators.

Finally, the third used curve is bell shaped and is used for indicators with an optimal value like

pH. Andrews et al. (2002) conclude that the non-linear method gives more meaningful results

(Andrews et al., 2002, Cherubin et al., 2016).

2.5.3 Soil quality index (SQI)

No universal way to calculate the SQI exists, but overall 3 ways to obtain a meaningful result

are used. The first one is the least complicated one and is the additive SQI (ADD SQI). This

SQI is calculated by counting all the indicator scores together and dividing the result by the

number of factors. The ADD SQI has a risk of misinterpretation because the weight of the

24

different indicators is not included. The second way is more precise because the indicators are

weighted. This SQI is known as the weighted additive scores SQI (WTD SQI) and the weighted

scores are obtained from the PCA. Lastly, the hierarchical decision support system (DSS SQI)

is compiled from an additive value function method used in solving hierarchical multi-attribute

problems. Generally, it can be concluded that a higher SQI is better or in other words shows a

better performance of the soil function (Andrews et al., 2002; Cherubin et al., 2016).

25

3 Methods and materials

3.1 Overall approach

The soil quality status of home gardens of three different villages was examined. The villages

are Mutshenzheni, Tshidzine and Dzindi. Prior to the interviews and soil sampling the

extension officers were asked for permission and asked to locate the different home gardens

with different soil fertility practices. The interviews with home gardeners were conducted to

gain a clear vision on the used fertility management practices and to count in all variabilities

for the differentiation between the different fertility management practices. After this the soil

samples were taken to the soil laboratory of the Technical University of Tshwane to be

analyzed. Finally, all the results were put into a database to be processed for the study and for

the home gardeners.

3.2 Description of the study area

Mutshenzheni, Tshidzini and Dzindi are all part of the Thulamela municipality in Vhembe

district, Limpopo province, where rural activity is predominant. Vhembe district is situated in

the northern part of Limpopo province and is generally semi-arid with rainfall between 300 and

1000 mm per year. Soils in Vhembe are sandy in the west and are higher in loam and clay

content in the east. Basalt, sandstone and biotite gneiss are mainly the base on which the soils

are developed and are generally low in soil fertility. A general map of the places where samples

were taken can be found on Figure 4. More detailed maps of the villages can be found in

Appendix 3, Appendix 4 and Appendix 5. Mutshenzheni and Dzindi have predominantly Haplic

Lixisols. These soils are highly weathered, leached and have an argic B horizon, >50% base

saturation and the CEC of the clay is <24meq/100g. Tshidzini has predominantly Rhodic

Nitosols which contain >30% clay and a thick argic B horizon that is red to dusky red. This soil

has >0.2% oxalate extractable iron (FAO, 1997).

26

Figure 4: Soil map sample locations Mutshenzheni, Tshidzini and Dzindi

3.3 Field work

The field work consists of the interviews with home gardeners and the soil sampling.

3.3.1 Interviews

45 different home gardeners were interviewed. The interview started with filling in

administrative information and was continued by enquiring about all information concerning

the fertility management practices in the past 5 years. An example of the questionnaire can be

found in Appendix 6 and Appendix 7. When fertilizers were used, a sketch of the gardens was

made, completed by the measurements of the garden to calculate the rates of the used

fertilizer(s). A distinction was made between summer and winter practices.

3.3.2 Soil sampling

Homogenous samples were taken by collecting a minimum of 5 sub-samples. The sub-

samples were taken as shown in Figure 5. The minimum distance from the edge was 5m. The

gardener was askedif the planting rows changed every year. If not, the sub-samples were

taken in the row only. If the planting rows changed every year, subsamples were taken

between and in the rows. Figure 6 shows how the samples were taken. In gardens that were

split up into different parts with different soil fertility management practices were used, 2

samples were taken. This helps to exclude hidden variables, like nature of the soil and ensures

more reliable results in the end.

27

3.4 Analysis

All analytical methods are described below. First general preparation and physical

characteristics were determined, followed by chemical analysis.

3.4.1 Preparation of the soil samples

A general preparation of the soil samples was needed before further determination of the

chemical and physical characteristics could be performed. This preparation consists of air

drying during two days, grinding and sieving the soil through a sieve with mesh size of 2mm.

3.4.2 Physical characteristics

The determined physical characteristics are soil colour, moisture content and sand percentage.

3.4.2.1 Soil colour

The soil colour was determined using the Munsell soil color charts (1975) in direct sunlight.

3.4.2.2 Moisture content

The moisture content of the soil samples was determined by weighing the soil before and after

drying them for 48 hours at 105°C.

3.4.3 Chemical characteristics

Description of following analysis methods is very detailed because some difficulties with

procedures and interpretation of original literature were experienced.

3.4.3.1 pH(H2O)

To determine the pH(H2O) of the soil samples a 1:2.5 soil to water ratio was used according to

the Non-affiliated Soil Analysis Work Committee (1990). 25ml distilled water was added to 10g

of soil and the mixture was stirred with a glass rod for 5 seconds. After 50 minutes the mixture

Figure 5: Sketch soil sampling

Figure 6: Soil sampling

28

was stirred again and was then left for another 10 minutes. The pH(H2O) was read with a pH-

meter.

3.4.3.2 EC(1:5)

To determine the EC of the soil samples a 1:5 soil to water ratio was used. 50ml Distilled water

was added to 10g of soil and was shaken 8 hours at 132 rpm according to He et al. (2013).

After this the mixture was filtered through a Whatman No. 1 filterpaper. The EC values were

read by an EC-meter.

3.4.3.3 ECe

According to He et al. (2013) a predictive model can be used to predict the ECe from EC(1:5)

data. The used equation is shown below:

ECe = -13.87 x (EC1:5)2 + 13.62 x (EC1:5) – 0.3

3.4.3.4 Available phosphorus

To determine P in the soil samples, a method recommended by Anderson and Ingram (1993)

was used. This method is also known as the Olsen method. This method consists of extraction

of P from the soil and determine the values by spectrophotometry. First the extraction of P from

2.5g of soil was done by adding 50ml 0.5 M NaHCO3 solution that was adjusted to pH 8.5.

Extraction was stimulated by shaking the bottles for 30 minutes at 180 rpm. After this the

mixture was filtered through 2 Whatman filter papers No. 1. Besides this, a series of working

standards was needed to obtain the values of the soil samples. To acquire a series of 0, 1, 2,

3, 4 and 5 ppm P, a KH2PO4 stock solution was used. A 1% ascorbic acid solution and

molybdate reagent were prepared. The molybdate reagent consists of 4,3g ammonium

molybdate dissolved in 400ml distilled water, complemented by 0,4g antimony sodium tartrate

dissolved in 400ml distilled water and 54ml H2SO4. After cooling, the solution was dilluted with

distilled water to 1000ml. 1ml of both samples, standards and blank were pipetted in test tubes

and 4ml of 1% ascorbic acid was added. After this 3ml of molybdate reagent was added to all

test tubes and they were mixed using a vortex. It takes one hour for the colour to develop. The

test tubes were mixed again before reading with a spectrophotometer at 880nm.

3.4.3.5 Total mineral nitrogen

To determine total mineral nitrogen in the soil samples a method recommended by Anderson

and Ingram (1993) was used. This method consists of extraction of nitrate and ammonium from

soil and determine the values by spectrophotometry. First the extraction of nitrate and

ammonium from 5g of soil was done by 20ml 0.5 M K2SO4 solution. Extraction was stimulated

by shaking the bottles for 30 minutes at 160 rpm. After this the mixture was filtered through 2

Whatman filter papers No. 1. Ammonium-N and nitrate-N were determined in a different way

as described below. To determine total mineral nitrogen, ammonium-N and nitrate-N were

counted together.

29

3.4.3.5.1 Ammonium-N

A series of working standards was needed to obtain the values of the soil samples. To acquire

a series of 0, 1, 2, 3, 4 and 5 ppm NH4+, a KH2PO4 stock solution was used. Reagent N1 was

prepared 24 hours before use and needed to be stored at room temperature in a dark place.

This was done by dissolving 34g sodium salicylate, 25g sodium citrate and 25g sodium tartrate

in 750ml of distilled water. Then 0.12g sodium nitroprusside was dissolved and the solution

was diluted to 1000ml with distilled water. Reagent N2 was also prepared 24 hours before use

and also needed to be stored at room temperature in a dark place. The reagent was made by

dissolving 30g sodium hydroxide in 750ml of distilled water. After the solution was cooled

down, 10 ml sodium hypochlorite solution was added and the mixture was diluted to 1000ml.

Then 0,1ml of both samples, standards and blank were pipetted into test tubes and 5ml of

reagent N1 was added. Each test tube was mixed using a vortex and was left for 15 minutes.

Hereafter 5ml of reagent N2 was added to each test tube. After mixing the test tubes were left

for 1 hour for full colour to development. The test tubes were read with a spectrophotometer

at 655nm.

3.4.3.5.2 Nitrate-N

A series of working standards were needed to obtain the values of the soil samples. To acquire

a series of 0, 2, 4, 6, 8 and 10 ppm NO3-, a K2NO3 stock solution was used. A 4M Sodium

hydroxide solution and 5% salicylic acid reagent were made. The salicylic acid needed to be

made the day before use. Then 0,5ml of both samples, standards and blank were pipetted into

test tubes and 1ml of salicylic acid solution was added. Each test tube was mixed using a

vortex and was left for 30 minutes. After this 10ml of sodium hydroxide solution was added to

each test tube. After mixing the test tubes were left for 1 hour for full colour to development. It

was important that the temperature of the test tubes stayed above 25°C to avoid crystallization.

This could be done by keeping the test tubes in a water bath around 30°C. The test tubes were

read with a spectrophotometer at 410nm.

3.4.3.6 Exchangeable potassium

To determine exchangeable potassium in the soil samples, a method recommended by

Anderson and Ingram (1993) was used. This method consists of extraction of potassium from

soil and determine the values by flame photometry. First the extraction of potassium from 5g

of soil was done by adding 50ml 1 M ammonium acetate solution. Extraction was stimulated

by shaking the bottles for 30 minutes at 180 rpm. After this the mixture was filtered through 2

Whatman filter papers No. 1. Beside this, a series of working standards was needed to obtain

the values of the soil samples. To acquire a series of 0; 2.5; 5; 7.5 and 10 ppm K, a KCl stock

solution was used. 10ml of samples were pipetted into 25ml volumetric flasks and filled to the

mark with distilled water. After calibrating and reading the standard solutions with the flame

photometer, the sample solutions were read.

30

3.4.3.7 Exchangeable calcium

To determine exchangeable calcium in the soil samples, a method recommended by Anderson

and Ingram (1993) was used. This method consists of extraction of calcium from soil and

determine the values by flame photometry. First the extraction of calcium from 5g of soil was

done by adding 50ml 1 M ammonium acetate solution. Extraction was stimulated by shaking

the bottles for 30 minutes at 180 rpm. After this the mixture was filtered through 2 Whatman

filter papers No. 1. Beside this, a series of working standards was needed to obtain the values

of the soil samples. To acquire a series of 0, 25, 50, 75 and 100 ppm Ca, a CaCO3 stock

solution was used. 5ml of samples were pipetted into 25ml volumetric flasks and filled to the

mark with distilled water. After calibrating and reading the standard solutions with the flame

photometer, the sample solutions were read.

3.4.3.8 Total Nitrogen and carbon

Total nitrogen and carbon is determined with the Dumas method with the Vario Max CNS-

analyzer. This analysis method is based on complete catalytic combustion at 900 °C with an

excessive amount of oxygen. Combustion gasses are send into a helium-flow over multiple

absorption tubes to exclude unwanted gasses. The grade of thermic conductivity helps to

measure the amounts of N2 and CO2.

3.5 Statistical analysis

3.5.1 Interviews

The first step to analyze the collected data during the interviews was a general exploration in

excel. The use of the gardens and soil fertility management practices in winter and summer

were compared and visualized. Mean values for application rates of animal manure and

chemical fertilizer were explored by SPSS.

3.5.2 Chemical analysis

The chemical soil properties (pH(H2O), EC(1:5), ECe, NO3-N, NH4-N, total mineral N, Olsen-P,

exchangeable K and Ca, total C, total N and C/N-ratio) were explored per soil fertility

management practice. The Shapiro-Wilk test showed if the data was distributed normal.

Homogeneity of variance was tested by the Levene’s test. If the variance was homogenous,

the soil property was tested with the One-way ANOVA. If not, the Kruskal-Wallis test was used.

This showed significant differences (p ≤ 0.05) for each chemical soil property per soil fertility

management practice. Parallel with the previous tests, a pairwise test was used for data

coming from the same gardens. This test is called the Wilcoxon Signed Rank Test. Lastly, All

the data was split based on the 2 main soil types and was analyzed again.

3.5.3 Soil quality index

Based on the chemical soil properties, a soil quality index was compiled from a minimum

dataset. To compile the minimum dataset, a principal component analysis was used. The

31

factors representing the minimum dataset were scored. Values in the optimum range,

according to literature, were given a score 1. Values outside the optimum range were given a

score 0. The average score per soil fertility management practice was determined which is

also known as the ADD SQI. Again, normality and homogeneity of variance was tested

followed by a One-Way ANOVA to find significant differences (p ≤ 0.05) between the soil fertility

management practices. Parallel to this, a pairwise comparison was done on the paired data

from the same gardens to exclude variability caused by soil type. For the same reason, the

test was repeated after splitting the data for the main 2 soil types.

32

4 Results and discussion

4.1 General findings interviews

4.1.1 Use of home garden in summer and winter

68 soil samples were taken from 45 different gardens. In some gardens, in winter, only a certain

part of the garden was used while in summer the whole garden was used, so the garden was

split up in a winter and summer plot. Figure 7 below shows that almost all plots (97%) are used

in summer while in winter only 66% of the plots are used for cultivation of crops.

Figure 7: Plots used in winter and summer

Parts of the home garden not used in winter were often used in summer for growing maize and pumpkin while winter plots were often used for growing green vegetables (Table 9).

0

10

20

30

40

50

60

70

80

90

100

winter summer

% o

f th

e p

lots

use

d

Use of plots

33

Table 9: Crops grown in winter and summer

Winter Summer

• Sweet potato (Ipomea batatas)

• White cabbage (Brassica oleracea)

• Chinese cabbage (Brassica rapa subsp.

chinensis),

• Spinach (Spinacia oleracea,)

• Green beans (Phaseolus vulgaris)

• Green mustard (Brassica juncea (L.)

czern.)

• Muxe (Solanum retroflexum Dun.)

• Tomatoes (Solanum lycopersicum)

• Spring onion (Allium cepa)

• Chilis (Capsicum annuum)

• Swiss chard (Beta vulgaris)

• Carrot (Daucus carota)

• Lettuce (Lactuca sativa)

• Maize (Zea mays)

• Pumpkin (Cucurbita pepo, C. maxima

and C. moschata)

• Groundnut (Arachis hypogaea)

In almost all home gardens fruit bearing trees were present that produced mango (Mangifera

indica L.), avocado (Persea americana), lychee (Litchi chinensis), citrus fruit, papaya (Carica

papaya L.), maracuja (Pasiflora edulis), baobab (Adansonia digitata L.). An example of a home

garden with vegetables and different fruit bearing trees can be found below on Figure 8.

Figure 8: Example of a home garden

34

4.1.2 Soil fertility management practices

4.1.2.1 General

Four main soil fertility management practices could be distinguished. These practices are the

application of animal manure (m), application of chemical fertilizer (ce), a combination of animal

manure and chemical fertilizer (ce/m) and no application of manure or fertilizer (nf).

Figure 9: Soil fertility management practices in summer and winter

Even though an attempt was made to collect the same number of samples for every soil fertility

management practice, the distribution was not as equal as desired in the end. However, the

differences in sample numbers for each practice reflect the current situation of soil fertility

management practices. Figure 9 shows that of all samples, the majority received no fertilizer.

The number of gardeners that used no fertilizer in summer (51) was even higher than in winter

(31). In summer only 6 home gardeners applied animal manure while in winter 23 gardens

were treated with this practice. A big difference in the number of gardens receiving only

chemical fertilizer in summer (8) and winter (6) was absent. 3 gardens received a combination

of manure and chemical fertilizer in summer, while in winter 8 gardens were treated with this

practice. To simplify the further interpretation of the data the distribution of the soil fertility

management practices was generalized over the whole year. Knowing that in summer almost

all the plots were used (Figure 7), it can be concluded that this is done in a less intensive way

than in winter. The combination of rain and higher temperature in summer affects the growth

of weeds strongly so the intercropping of maize and pumpkin can be seen as a weed

suppression method. Figure 10 shows that 75 % of the plots were treated with the same soil

fertility management practice for 5 years or longer (Ronald and Charles, 2012).

35

Figure 10: Period of the same soil fertility management practices

4.1.2.2 Types of manure and fertilizer

Chemical fertilizer types were LAN (28) (Limestone Ammonium Nitrate), superphosphate 10.5

% P, lime and different NPK combinations. LAN was used most frequently, often combined

with one of the other fertilizers or manures. Different manure types were cattle manure, chicken

manure and goat manure. Cattle manure was used most often and the different manure types

were sometimes combined.

4.1.3 Application rates

Figure 11 represents average application rates of chemical fertilizer and animal manure

according to the different management practices, based on the interviews. The ‘no fertilizer’

group is left out because no fertilizer or manure is applied. The detailed values can be found

in Table 10.

Figure 11: application rates per management practice

0

2000

4000

6000

8000

10000

12000

14000

16000

ce ce/m m

Ap

plic

atio

n r

ate

(kg.h

a-1

)

fertilizer category

Average application rates per fertilizer category

Average of chemicalfertilizer kg.ha-1

Average of animal manurekg.ha-1

36

Table 10: Mean, max. and min. application rate per soil fertility management practice

Mean(SD.)

kg.m-1

Mean(SD.)

kg.ha-1

Minimum

kg.ha-1

Maximum

kg.ha-1

Chemical 0.12 (0.13) 1149.04 (1318.98) 100.00 3926.00

Manure 1.40 (1.39) 13998.56 (13931.92) 1401.00 66146.00

Chemical and

manure

-chemical 0.27 (0.26) 2689.21 (2578.47) 540.00 6680.00

-manure 0.84 (1.58) 8367.36 (15784.39) 8.00 47284.00

*SD. = Standard deviation

Application rates of animal manure are the highest when it is applied as the only nutrient source

and is lower when it is combined with chemical fertilizer. The opposite is noticeable for

chemical fertilizer. The application rate of chemical fertilizer is lower when it is used as the only

nutrient source and is higher when combined with animal manure. The high standard deviation

shows that the registered application rates differ strongly from each other. The application rate

probably has an effect on the other parameters that were analyzed. On the other hand, these

values can only be used as guidelines. The description of the application rates was given by

the home gardeners as “two hands per plant”, “two fingers and a thumb of chemical fertilizer”,

“a wheelbarrow” or “a bottlecap”, so the volumes can only be seen as an approach.

Yoganathan et al. (1998) and Materechera (2010) also found that application rates of animal

manure varied very widely. This was already discussed in 2.1.2.1.

4.1.4 Common practices

Crop residues were often left on the field and ploughed in the soil before planting new crops.

In some cases, compost was used but according to the research of Mthimkulu et al. (2015)

compost is only recommended when other fertilizers are not available because of its modest

ability to supply N and P to crops. This explains why compost is not seen as a soil fertility

management practice in this research.

4.2 Evaluation of the soil chemical properties

Table 11 gives the mean values and standard deviations of all the chemical soil properties that

were analyzed based on all the data. One-Way ANOVA was used for EC, ECe, NO3, NH4,

TMN, P, Ex. Ca, Total N, Total C and the C/N-ratio. Because variances were not homogenous,

pH(H2O) and Ex. K were analyzed with the Kruskall-Wallis test. With every value, a letter is

given. When the letters in different columns differ, the accompanying value differs significantly

for the soil fertility management practices (p ≤ 0.05). Out of 68, 2 samples were eliminated due

to extreme high values for Ca and K levels.

37

Table 11: mean values and significant differences all chemical soil properties

Mean values (SD.*)

CE CE/M M NF

n*=7 n*=8 n*=22 n*=29

pH(H2O) 6.59 (1.24)ab 5.88 (0.67)a 7.14 (0.65)b 6.80 (0.64)ab

dS

.m-1

EC(1:5) 0.08 (0.07)a 0.07 (0.04)a 0.17 (0.09)b 0.07 (0.05)a

ECe 0.60 (0.71)a 0.54 (0.47)a 1.48 (0.60)b 0.57 (0.53)a

mg

.kg

-1

mg

.kg

-1

TMN* 23.06 (16.29)ab 21.90 (14.89)ab 39.13 (38.85)a 13.99 (17.14)b

NO3 20.71 (16.31)ab 19.81 (14.06)ab 36.53 (39.10)a 11.53 (17.00)b

NH4 2.35 (1.56)a 2.09 (1.28)a 2.59 (3.33)a 2.46 (1.27)a

P* 8.37 (6.56)ab 8.26 (5.84)ab 14.19 (11.42)a 7.19 (6.91)b

Ex. K* 111.70 (58.75)a 154.99 (151.10)ab 500.63 (360.78)c 305.83 (175.59.18)bc

Ex. Ca* 535.71 (267.26)a 656.25 (743.27)a 2034.09 (704.14)b 1525.86 (848.70)b

Total N 924.29 (377.84)a 1057.50 (625.96)ab 1728.18 (755.59)b 1285.86 (625.92)ab

C 13580.00 (7025.93)a 15396.25 (8139.64)ab 24030.45 (8456.45)b 19347.24 (9117.60)ab

C/N 14.22 (1.90)a 15.43 (1.96)a 14.35 (1.59)a 15.27 (2.29)a

*SD. = Standard deviation, n = number of samples, TMN = Total mineral N, P = Olsen P, Ex.

K = Exchangeable K, Ex. Ca = Exchangeable Ca

The high standard deviations of the mean values in Table 11 show a high variation in the

analyzed data. The reason for of this variation is due to the hidden variables arising from large

differences in the nature of the soils, particularly soil texture. For 36 samples out of 68 the

effect of this variable was reduced to a minimum. In other words, 18 gardens could be sampled

for 2 different soil fertility management practices. These practices were the application of

animal manure and no application of animal manure or fertilizer. Table 12 shows the results of

a pairwise comparison of these samples with the Wilcoxon Signed Ranks Test (p ≤ 0.05). The

results of both tables are discussed in the following paragraphs.

Table 12: pairwise comparison animal manure - no fertilizer

*TMN = Total mineral N, P = Olsen-P, Ex. K = Exchangeable K, Ex. Ca = Exchangeable Ca

Another way to reduce the influence of soil type on the chemical soil properties was to split the

whole database based on the two main soil types. For the Rhodic Nitosols the One-Way

ANOVA did not give any useful information because the number of samples was too low for

the chemical fertilizer group. Haplic Lixisols showed significant differences for the soil

properties pH(H2O) and exchangeable K (Table 13). These differences are discussed in 4.2.1

and 4.2.5.

Wilcoxon Signed Ranks Test (Animal manure - No fertilizer)

pH (H2O)

EC (1:5)

ECe NO3 NH4 TMN* P* Ex. K*

Ex. Ca*

C TN

Z -2.091 -3.049 -3,114 -3.724 -0.589 -3.724 -3.724 -1.346 -0.421 -0.631 -0.131 Asymp.Sig (2-tailed)

0.037 0.002 0.002 0.000 0.556 0.000 0.000 0.178 0.674 0.528 0.896

38

Table 13: mean values Haplic Lixisols

Mean values for Haplic Lixisols (SD.*)

CE CE/M M NF

n*=6 n*=4 n*=3 n*=15

pH(H2O) 6.78(1.24)ab 5.59 (0.32)a 8.19 (0.67)b 6.77 (0.68)ab

Ex. K* 117.29 (62.29)a 80.98 (39.10)a 335.83 (287.47)ab 278.51 (156.01)b

4.2.1 pH(H2O)

Table 11 shows that the pH(H2O) of gardens treated with animal manure and gardens treated

with a combination of manure and chemical fertilizer differ significantly. Soils where animal

manure was used had a mean pH of 7.14 while soils receiving a combination of chemical

fertilizer and animal manure had a mean pH of 5.88. The pH(H2O) of soils treated with only

chemical fertilizer (mean 6.59) or without fertilizer (mean 6.80) showed no significant difference

with the other soil fertility management practices. The pairwise comparison of samples with

animal manure and samples without fertilizer or animal manure from the same garden showed

that soils that received animal manure had a significantly higher pH(H2O) than soil that received

no fertilizer or animal manure. This finding makes the previous findings questionable and

suggests that soils without application of fertilizer also differ from soils that received a

combination of chemical fertilizer and animal manure or soils that only received chemical

fertilizer. These results are compromised by the hidden variable which includes the nature of

the soil.

In general, the pH was higher than expected. Landon (1984) points out that the method of pH

measurement and more specific the dilution media has an effect on the results. The soil water

suspension should be sufficient for routine survey but it can be 0.5 to 1.5 units higher (in a 1:5

Figure 12: Boxplot pH(H2O) - fertilizer category

39

solution) than values determined with the saturated paste method according to Dewis and

Freitas (1970). Another explanation is the low precipitation in combination with higher potential

evaporation during the winter period which makes it possible for base cations added by animal

manure or chemical fertilizer to accumulate. In combination with the high average application

rate of animal manure, this effect explains the higher pH values for this soil fertility

management practice. Application rates of animal manure combined with chemical fertilizer

are lower and some chemical fertilizers are known to have a more acidifying effect on soils. 75

% of the home gardeners that combined animal manure with chemical fertilizer used LAN,

whether or not combined with 2:3:4 (27) NPK fertilizer. LAN is known to be the least acidifying

nitrogen fertilizer but the effect of the 2:3:4 (27) NPK fertilizer is harder to estimate because

the source of the nutrients and in particular the nitrogen source is unknown. If the nitrogen is

coming from an ammonium-based source the acidifying effect is obvious. Nitrate-based

nitrogen sources have no acidification potential. Some of the home gardens in Dzindi had a

pH exceeding 8. Mokolobate and Haynes (2002) mention that application of chicken manure

can help to increase the pH. The concerning plot received 1.6t.ha-1 annually. In addition, some

of the soils received lime and 28 out of 68 samples were taken from Haplic Lixisols which have

a base saturation >50% These Haplic Lixisols show similar results as seen on Figure 13 and

Table 13 (Fertilizer Technology Research Centre, 2010; Mokolobate and Haynes, 2002).

Figure 13: Boxplot pH - fertilizer category (Haplic Lixisols)

4.2.2 EC(1:5) and ECe

Results from Table 11 about ECe are visualized in Figure 14. Both EC and ECe are significantly

higher when animal manure is used as the soil fertility management practice. The paired

comparison in Table 12 shows that soils that received animal manure have indeed a

significantly higher EC(1:5) and ECe than soils that received no fertilizer or animal manure.

Knowing that chemical fertilizers can also be a source of salts and that the data has a high

40

variability, the finding that no significant difference between soils that received no fertilizer or

animal manure and soils that received chemical fertilizer or a combination of chemical fertilizer

and animal manure remains questionable. Application rates of animal manure show a

significant positive correlation (p ≤ 0.05) with the EC(1:5) and the ECe with correlation

coefficients of respectively 0.606 and 0.518. Azeez et al. (2010) shows that animal manure

increases the soil’s electrical conductivity due to high salt contents. When the ECe is

considered, it can be concluded that only some soils where animal manure was applied

exceeded the limit of 2 dS.m-1 which is the threshold value between non-saline and low salinity

according to Shirokova et al. (2000) although the mean ECe was 1.48 dS.m-1. All gardens that

received chemical fertilizer (mean 0.60 dS.m-1), a combination of chemical fertilizer and animal

manure (mean 0.54 dS.m-1) or no fertilizer (mean 0.57 dS.m-1) can therefore be classified as

non-saline. Again, the lower application rates for animal manure should be considered when

the soil fertility management practice ce/m is compared with the application of only animal

manure. The higher application of chemical fertilizer does not equipoise EC increasing effect

of animal manure.

Figure 14: Boxplot ECe - fertilizer category

4.2.3 Total mineral N, nitrate and ammonium

As expected, Table 11 shows that TMN (total mineral N) and nitrate were significantly higher

for gardens that received animal manure (mean TMN 39.13 mg.kg-1 and mean nitrate 36.53

mg.kg-1) than for gardens that received no fertilizer (mean TMN 13.99 mg.kg-1 and mean nitrate

11.53 mg.kg-1) which shows that soil quality improves by adding animal manure (Figure 15).

This finding was confirmed by the pairwise comparison in Table 12. Figure 15 also shows that

gardens that received chemical fertilizer or a combination of chemical fertilizer and animal

manure did not differ significantly on TMN and nitrate-N levels from gardens that received

animal manure or no fertilizer. Although animal manure provides organic and inorganic-N and

41

chemical fertilizer only provides inorganic-N, the differences are not big enough to be

significant. Again, the high variability in the data due to the differences in the nature of the soils

make these findings questionable. The level of ammonium-N was highest for soils that received

only animal manure (mean 2.59 mg.kg-1) but no significant difference existed between different

soil fertility management practices. TMN and nitrate are highly correlated (p ≤ 0.05) as shown

in Table 15 with a correlation coefficient of 0.997. They both show a significant positive

correlation (p ≤ 0.05) with application rates of animal manure of respectively 0.632 and 0.628.

The high TMN levels of outliers in the gardens that received animal manure were due to high

application rates. The outlier with 189 mg.kg-1 total mineral N received up to 66 t.ha-1 animal

manure per year, of which 39 tonnes were chicken manure and 27 tonnes goat manure.

Chicken manure is known to be very rich in nitrogen.

Figure 15: Boxplot total mineral N - fertilizer category

*Outlier for manure with value 189 mg.kg-1 not shown

4.2.4 P(Olsen)

Figure 16 shows that Olsen-P or plant available P levels significantly differ between soils that

received animal manure and no fertilizer (means respectively 14.19 and 7.19 mg.kg-1). This

conclusion is based on 2 different statistical tests (Table 11 and Table 12). Soils that received

chemical fertilizer (mean 8.37 mg.kg-1) or a combination of chemical fertilizer and animal

manure (mean 8.26 mg.kg-1) did not differ significantly from soils that received animal manure

or no fertilizer. P requirements differ strongly between plant species. Plants that require low

amounts of available P are grass, maize and soybeans and are already satisfied if the available

P level exceeds 7 mg.kg-1. This is why values below 7 mg.kg-1 are recognized to be deficient.

Almost all gardens that received no fertilizer are deficient in P while gardens that received

42

chemical fertilizer or a combination of chemical fertilizer and animal manure score a bit better

but don’t really stand out. The outlier with more than 50 mg.kg-1 P received almost 16 t.ha-1 of

chicken manure annually which is mainly known to be rich in N but also contains relatively high

P levels (Landon, 1984).

Figure 16: Boxplot P(Olsen) - fertilizer category

4.2.5 Exchangeable K

Soils that received animal manure (mean 500.63 mg.kg-1) were significantly higher in

exchangeable K than soils that received chemical fertilizer (mean 111.70 mg.kg-1) or a

combination of chemical fertilizer and animal manure (mean 154.99 mg.kg-1). Soils that

received no fertilizer (mean 305.83 mg.kg-1) did not differ significantly from soils that received

animal manure or a combination of animal manure and chemical fertilizer, which is remarkable

(Figure 17). The finding that soils that received animal manure did not differ from soils that

received no fertilizer or animal manure is confirmed in the pairwise comparison in Table 12.

The other findings remain questionable due to high variability in the data as a result of the

differences in the nature of the soils. Figure 17 shows some remarkable high values in soils

that received no fertilizer and soils that received animal manure. These values can be the

result of termite activity that was noticed in some om the soils. One of the gardeners even

mentioned that the garden was built on an old termite mound. Deke et al. (2016) show that

exchangeable K levels can be three times as high in the center of a termite mound than in the

surrounding soil. High K levels can also be the result of a long dry period with buildup of applied

nutrients. The extreme levels for soils that received animal manure can therefore be seen in

the context of these influence parameters put together.

43

Figure 17: Boxplot exchangeable K - fertilizer category

It is worth mentioning that 28 out of the 68 samples were taken from Haplic Lixisols which are

known to be poor in plant nutrients but have a base saturation of >50%. The results of

comparison of soil fertility management practices on Haplic Lixisols can be seen on Figure 18.

The differences are less clear than when both soil types are included in the statistical analysis.

In addition, another source of variation exists which includes the intensity of use of the plots.

Plots where no fertilizer or animal manure was applied were often only used once a year while

plots where any nutrients were applied, were often used 2 times a year. A note that should be

made for the gardens with extremely high K levels, in terms of crop growth, is that high K/Mg

ratios can lead to Mg deficiency. Unfortunately, Mg could not be analyzed with the flame

spectrometer and further conclusions cannot be made (Landon, 1984).

44

Figure 18: Boxplot exchangeable K - fertilizer category (Haplic Lixisol)

4.2.6 Exchangeable Ca

Exchangeable Ca levels are significantly higher for soils that received animal manure (mean

2034.09 mg.kg-1) and soils that received no fertilizer (mean 1525.86 mg.kg-1) compared with

values for soils that received chemical fertilizer (mean 535.71 mg.kg-1) or a combination of

animal manure and chemical fertilizer (mean 656.25 mg.kg-1) (Figure 19). The pairwise

comparison in Table 12 confirms the finding that no significant difference between Ca-levels

in soils that received no fertilizer and soils that received animal manure exists. Exchangeable

Ca levels can increase as a result of basic amendments present in chicken manure as

mentioned by Mokolobate and Haynes (2002). It also need to be said that 28 out of the 68

samples were taken from Haplic Lixisols which are known to be poor in plant nutrients but have

a base saturation of >50%. In addition, another source of variation which includes the intensity

of use of the plots needs to be taken into account. Plots where no fertilizer or animal manure

was applied were often only used once a year while plots where any nutrients were applied,

were often used 2 times a year. Table 15 shows that a significant positive correlation (p ≤ 0.05)

between Ca levels and application rates of animal manure of 0.313. A significant negative

correlation (p ≤ 0.05) between application rates of chemical fertilizer and Ca levels of -0.375

also is found. Keeping in mind that pH values ranged between 4.92 and 8.95 and the highest

K level was around 1600 mg.kg-1 some important interactions can occur. In combination with

a pH below 5.5 and low CEC, Ca deficiencies can occur in tropical soils. High K levels may

also obstruct Ca uptake by plants. A P deficiency can occur when the soil has a high pH (> 8)

in combination with high Ca levels. Once the pH exceeds 8.5, high Na levels increase the P

availability but plants can be deficient in Ca. Below 40 mg.kg-1 most crops have a strong

response to exchangeable Ca as a plant nutrient. Crops with high Ca requirements show

45

deficiencies when the exchangeable Ca level is below 160 mg.kg-1 but almost all values exceed

these limits (Landon, 1984; Pernes-Debuyser and Tessier, 2004).

Figure 19: Boxplot exchangeable Ca - fertilizer category

4.2.7 Total C, total N and C/N ratio

Figure 20 shows that soils that received animal manure had a significantly higher carbon level

(mean 2.40 %) than soils that received chemical fertilizer (mean 1.36 %) but it does not differ

from soils that received no fertilizer (mean 1.93 %) or soils that received a combination of

chemical fertilizer and animal manure (mean 1.54 %). The pairwise comparison in Table 12

confirms that no significant difference exists in carbon levels between soils that received animal

manure and soils that received no fertilizer or animal manure. Table 15 shows a significant

negative correlation (p ≤ 0.05) between chemical fertilizer application rates and total C with a

correlation coefficient of -0.249. Overall, the C levels are rather low and indicate a poor level

of organic matter in the soil (Landon, 1984).

46

Figure 20: Boxplot total C - fertilizer category

The total N level is significantly higher for soils that received animal manure (mean 1728.18

mg.kg-1 or 0.17 %) compared with soil that received chemical fertilizer (mean 924.29 mg.kg-1

or 0.09 %), no fertilizer (mean 1285.86 mg.kg-1 or 0.13 %) or a combination of chemical fertilizer

and animal manure (mean 1057.50 mg.kg-1 or 0.11 %) (Figure 21). The pairwise comparison

in Table 12 confirms that no significant difference occurs in total N levels between soils that

received animal manure and soils that received no fertilizer or animal manure. Table 15 also

shows the significant positive correlation (p ≤ 0.05) of 0.383 between the application rate of

animal manure and the total N level in the soil. According to Landon (1984) N levels below 0.2

% are rather low. Soils that received animal manure scored the best, but nitrate-N levels are

more relevant to estimate soil fertility because total N is strongly influenced by pH. A low pH

results in low microbial activity and thus causes lower plant available N levels because of the

lower mineralization rate (Landon, 1984).

47

Figure 21: Boxplot total N - fertilizer category

The C/N ratio for the different soil fertility management practices did not differ significantly. The

highest ratio was found in soils that received a combination of chemical fertilizer and animal

manure (mean 15.43), followed by soils that received no fertilizer (mean 15.27). soils that

received animal manure had a mean C/N ratio of 14.35 and soils that received chemical

fertilizer had a mean C/N ratio of 14.22.

4.3 Soil quality determination

4.3.1 Compilation of the minimum dataset

To compile the minimum dataset, dimensions needed to be reduced. One way to accomplish

the dimension reduction is to execute the principle component analysis. For this analysis,

indicators that differed significantly (p ≤ 0.05) for the different soil fertility management

practices were used. PC’s (principle components) with an eigenvalue higher than 1, best

represent the variation in the data. These principle components are shown in Table 14. For

each principle component the percentage of variance that can be explained by this component

is given and when these four components are put together, 81.43% of the variance can be

explained.

48

Table 14: Principal components

*Ex. K = Exchangeable K, Ex. Ca = Exchangeable Ca

The component loadings are given for each indicator, these component loading represent the

correlations between the principle component and the indicator. Under PC1 all indicators had

relatively high loadings, except pH(H2O). pH(H2O) has the highest loading for the PC2 and will

therefore be used in the MDS. As seen in the correlation matrix in Table 15 some indicators

are highly correlated (bold/red). Strongly correlated indicators are: EC(1:5) and ECe, Total N

and C. EC(1:5) and total N had the highest factor loading and will be used in the MDS together

with K, Ca, NO3 and P (Andrews et al., 2002).

Principal components PC1 PC2 PC3 PC4

Eigen value 5.29 1.48 1.14 1.05

Variance % 49.08 13.44 10.38 9.54

Cumulative variance % 49.08 61.52 71.90 81.43

EC(1:5) 0.89 0.34 -0.12 -0.13

ECe 0.88 0.36 -0.04 -0.02

Total N 0.81 -0.55 0.07 -0.13

Total C 0.74 -0.64 0.14 0.01

Ex. K* 0.77 0.10 0.01 0.23

Ex. Ca* 0.80 -0.30 0.25 0.35

NO3 0.71 0.17 -0.39 -0.39

P(Olsen) 0.66 0.05 -0.22 0.15

pH(H2O) 0.41 0.61 0.47 0.39

49

Table 15: Correlation matrix (p ≤ 0.05)

1: TMN = Total mineral N, P= Olsen P, Ex. K = Exchangeable K, Ex. Ca = Exchangeable Ca, ARC= Application rates chemical fertilizer, ARM = application rates animal manure

Soil

property

TMN1 NO3 NH4 P1 Ex. K1 Ex. Ca1 TN TC C/N pH

(H2O)

EC

(1:5)

ECe ARM1 ARC1

ARC1 -0.001 0.002 -0.040 -0.059 -0.266* -0.375* -0.222 -0.249* -0.057 -0.242 -0.100 -0.111 -0.058 1

ARM1 0.632* 0.628* 0.040 0.230 0.359* 0.313* 0.383* 0.329* -0.179 0.108 0.606* 0.518* 1

ECe 0.690* 0.688* 0.017 0.567* 0.668* 0.566* 0.511* 0.432* -0.334* 0.510* 0.962* 1

EC (1:5) 0.809* 0.807* 0.016 0.522* 0.670* 0.541* 0.530* 0.430* -0.358* 0.418* 1

pH (H2O) 0.073 0.062 0.140 0.274* 0.393* 0.405* 0.020 0.001 -0.182 1

C/N -0.317* -0.313* -0.059 -0.262* -0.221 -0.179 -0.371* -0.181 1

TC 0.380* 0.375* 0.057 0.416* 0.482* 0.802* 0.970* 1

TN 0.491* 0.487* 0.050 0.467* 0.526* 0.776* 1

Ex. Ca1 0.276* 0.279* -0.039 0.467* 0.623* 1

Ex. K1 0.476* 0.482* -0.083 0.392* 1

P1 0.405* 0.417* -0.157 1

NH4 0.037 -0.040 1

NO3 0.997* 1

TMN1 1

50

4.3.2 Scoring of the indicators

The second step to determine the soil quality index for soils treated with different soil fertility

management practices, is to transform the MDS-indicators into a value between 0 and 1. As

mentioned in the literature review, non-linear scoring gives more meaningful results but to put

all values on a relative yield curve further investigation under similar circumstances (climate,

soil type, controlled application rates, …) is needed. Linear scoring is the second possibility

but the risk exists of scoring extreme results too high, due to excessive application rates of

nutrients, while they can have negative interactions with other elements and so affect plant

growth negatively. Nutrient levels below the optimum range have a negative effect on crop

yield but nutrient levels above the optimum range can also have a negative effect. In the case

of high nitrate levels, the risk of leaching occurs and Ca has a negative effect on P uptake

when combined with a pH above 8. Fe in Nitisols can also affect available P levels by fixation.

Soil pH levels below 5.5 are too acidic for most plants while levels above 7.2 are too alkaline.

EC levels need to be higher than zero dS.m-1 because an EC of zero would mean that no

nutrients are present in the soil solution but levels above 0.2 dS.m-1 can be too saline for

sensitive crops.

This is why a simplified non-linear method was used. This simplified method gives values

between the optimum range a score of 1 and all the other values that are too high or too low

get a score 0. The optimum ranges can be found in Table 16. These values are translated in

scores in Table 17. Again, significant differences between the soil fertility management

practices are shown but this time based on the scores. pH(H2O), EC(1:5), Ca, nitrate and total

N show no significant difference after they have been scored by the aforementioned method.

Table 16: Optimum ranges scoring indicators

MDS-indicator Optimum range Source

pH(H2O) 6.0-7.2 FSSA, 2007

EC(1:5) >0.0-0.2 dS.m-1 Shirokova et al., 2000

NO3 35.0-60.0 mg.kg-1 Van Biljon et al., 2008;

P(Olsen) 8.0-25.0mg.kg-1 Rosen and Eliason, 2005

Ex. K* 125.0-500.0 mg.kg-1 FSSA, 2007; Rosen and Eliason,

2005

Ex. Ca* 200.0-3000.0mg.kg-1 FSSA, 2007

Total N 2000-5000mg.kg-1 Landon, 1984

* Ex. K = Exchangeable K, Ex. Ca = Exchangeable Ca

51

Table 17: Mean value scores

Mean values transformed in scores (SD*)

CE CE/M M NF

n=7 n=8 n=22 n=29

pH(H2O) 0.14(0.38)a 0.25(0.46)a 0.59(0.50)a 0.62(0.49)a

EC(1:5) 0.86(0.38)a 1.00(0.00)a 0.82(0.40)a 0.97(0.19)a

NO3 0.29(0.49)a 0.25(0.46)a 0.18(0.39)a 0.00(0.00)a

P* 0.43(0.54)ab 0.25(0.46)ab 0.73(0.46)b 0.24(0.44)a

Ex. K* 0.29(0.49)ab 0.25(0.46)a 0.64(0.49)ab 0.83(0.38)b

Ex. Ca* 1.00(0.00)a 0.88(0.35)a 1.00(0.00)a 0.93(0.26)a

Total N .00(0.00)a 0.00(0.00)a 0.32(0.48)a 0.17(0.38)a

ADD SQI 0.43(0.12)a 0.41(0.21)a 0.61(0.17)b 0.54(0.12)ab

*SD = Standard deviation, P = Olsen P, Ex. K = Exchangeable K, Ex. Ca = Exchangeable Ca

4.3.3 Soil quality index

The soil quality index was constructed by counting the scores together for every soil fertility

management practice followed by dividing this by the total of soil quality indicators, selected

for the MDS. Soils that received animal manure had a significant (p ≤ 0.05) higher soil quality

index (mean 0.61), than soils that received chemical fertilizer (mean 0.43) and soils that

received a combination of animal manure and chemical fertilizer (mean 0.41). Soils that

received no fertilizer (mean 0.54) did not differ significantly from the other soils which is a

questionable finding (Figure 22). Again, the results are compromised by the hidden variability

due to nature of the soil. Furthermore, Karlen et al. (2003) mentions that soil quality should be

based on biological, chemical and physical properties, including the processes and

interactions. The soil quality index computed for this research reflects the chemical condition

of the soil per soil fertility management practice which was the aim of this study. We need to

take into account that a significant (p ≤ 0.05) negative correlation exists between application

rates of chemical fertilizer and the SQI with correlation coefficient -0.347.

52

Figure 22: Boxplot SQI - fertilizer category (based on all data)

A second attempt was made to compare the soil quality indexes of the different soil fertility

management practices taking into account the different soil types that caused the high

variation. This was done by pairwise comparison of the samples coming from the same

gardens (36 samples out of 18 gardens) with the Wilcoxon Signed Rank Test. This revealed

that the SQI differs significantly between gardens that received animal manure (mean 0.48,

SD 0.11) and soils that received no manure or chemical fertilizer (mean 0.60, SD 0.18) (Figure

23). Due to lack of data on paired samples of soils that received chemical fertilizer or chemical

fertilizer and manure only the other soil fertility management practices are compared.

53

Figure 23: Boxplot ADD SOI - Fertilizer category (based on pairwise samples)

Finally, another attempt was made to exclude the variability in the nature of the different soils.

This was done by splitting the data based on the two soil types (Table 18 and Figure 24).

Samples from Tshidzini came from Rhodic Nitosols (>30% clay, red to dusky red thick argic B

horizon, >0.2% oxalate extractable iron) but did not show any significant difference between

the soil fertility management practices but the distribution of the samples over the different soil

fertility management practices was not adequate. The second part of samples was taken in

Mutshenzeni and Dzindi. The soils here were Haplic Lixisols (highly weathered, leached, argic

B horizon, >50% base saturation and the CEC of the clay is <24 meq/100g). The soil quality

index differed significantly for soils that received no fertilizer (mean 0.55) and soils that

received only chemical fertilizer (mean 0.40) or a combination of chemical fertilizer and animal

manure (mean 0.29). Soils that received animal manure (mean 0.48) did not differ from the

other soils (FAO, 1997).

54

Table 18: SQI split two soil types

Mean values transformed in scores (SD*)

CE CE/M M NF

n(HL*)=6

n(RN*)=1

n(HL*)=4

n(RN*)=4

n(HL*)=3

n(RN*)=19

n(HL*)=15

n(RN*)=14

SQI HL* 0.40(0.11)a 0.29(0.12)a 0.48(0.08)ab 0.55(0.09)b

SQI RN* - 0.54(0.21)a 0.63(0.17)a 0.52(0.12)a

*SD = Standard deviation, HL = Haplic Lixisol, RN = Rhodic Nitosol

Figure 24: Boxplot ADD SQI - Fertilizer category (Haplic Lixisol)

55

5 Conclusion

Chemical soil properties of soils treated with different soil fertility management practices were

compared in response to previous research on this subject. The soil fertility management

practices were application of chemical fertilizer, application of a combination of animal manure

and chemical fertilizer, application of animal manure and no application of fertilizer or manure.

The previous research found no significant differences for most important chemical soil

properties (mainly N) while these levels were expected to be higher when animal manure was

applied. The current study tried to find more reliable information about chemical soil properties

for the different soil fertility management practices by raising the number of samples.

Total mineral N, nitrate-N and Olsen-P were significantly higher for soils that received animal

manure compared to soils that received no fertilizer and were comparable with the levels in

soils that received chemical fertilizer or a combination of chemical fertilizer and animal manure.

Total N, total C, exchangeable K and Ca were significantly higher for soils that received animal

manure than for soils that received chemical fertilizer. The EC(1:5) and the ECe were

significantly highest in soils that received animal manure. The pH(H2O) of soils that received

animal manure was significantly higher than the pH(H2O) of soils that received a combination

of animal manure and chemical fertilizer. In a pairwise comparison, which excludes the

variance caused by differences in soil types, the pH(H2O) of soils that received animal manure

was significantly higher than the pH(H2O) of soils that received no fertilizer.

3 attempts were made to compile a soil quality index. The first way was based on all samples

and showed that soils that received animal manure had a significant better SQI than soils that

received a combination of animal manure or soils that received chemical fertilizer. Soil quality

of soils that received no fertilizer did not differ from the other soils which was questionable. A

possible cause for this finding were the differences in soil nature that compromised the results

of the measured soil properties. Another factor that wasn’t implemented in the statistical

analysis were the application rates of animal manure or chemical fertilizer in combination with

the intensity of use of the plots. Plots that received no fertilizer were often used less intensive

than plots that received any type of additional nutrients. To reduce the influences of soil type

to a minimal, additional statistical analysis was done. Samples coming from the same gardens,

with different soil fertility management practices, gave the opportunity to use a pairwise

analysis. Although this analysis was based on fewer samples (36 out of 68) this test showed

a significant difference in soil quality between soils that received animal manure and soils that

received no fertilizer. Application of chemical fertilizer and a combination of chemical fertilizer

and animal manure could not be compared because no gardens with both practices were

found. This is why a final statistical test was done. The data was split based on soil type and

showed that Haplic Lixisols that received no fertilizer had a lower SQI than soils that received

chemical fertilizer or soils that received a combination of animal manure and chemical fertilizer.

56

Overall it can be concluded that the SQI in this case is not a good method to assess soil quality

because of incomplete information about the nature of the soil that causes high levels of

variability. When the chemical soil properties are considered separately, it can be concluded

that exchangeable K, exchangeable Ca and so the EC and ECe are higher when animal

manure is applied, compared with the application of chemical fertilizer or chemical fertilizer and

animal manure. Mineral N and P levels when animal manure is applied, are comparable with

the application of chemical fertilizer or a combination of animal manure and chemical fertilizer.

Total C and N levels are higher when animal manure is applied than when chemical fertilizer

is used. The equivalent effect of application of animal manure and chemical fertilizer on mineral

N and P levels, and the increasing effect that animal manure has on total C and N levels,

allows to conclude that animal manure is a better soil fertility management practice than

chemical fertilizers for home gardeners is South Africa.

To optimize this research the variability in soil types should be eliminated. An even more

precise way is to work with paired samples but for all soil fertility management practices instead

of only comparing no fertilizer with application of animal manure. This means that 4 samples

should be taken from one garden, each sample representing one of the soil fertility

management practices. Practically this is impossible but it would be an idea for an experiment.

It would also be interesting to implement yields according to the application rates of chemical

fertilizer and manure.

57

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7 Appendix

Appendix 1: Primary fertilizer sources for the major elements, formula, form and percent content (Jones, 2012)

65

66

Appendix 2: Primary fertilizer sources for micronutrients, formula, form and percent content (Jones, 2012)

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Appendix 3: Detailed map samples Mutshenzheni

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Appendix 4: Detailed map samples Tshidzini

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Appendix 5: Detailed map samples Dzindi

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Appendix 6: Questionnaire home gardens part 1

1 Administration

1.1 Field intervieuw No.

1.2 Database Ref No. (same as the soil

sample)(for example EH 001)

1.3 Name(s) of interviewer(s)

1.4 Date of inetrview

1.5 Time of interview Start: End:

1.6 Language spoken during interview

1.7 Municipality

1.8 Name of village

1.9 Coordinates of homestead Latitude: ° . ‘S Longitude:° . ‘E

1.10 Comments (cell number, name of

contact person if willing)

1.11 Soil colour Dry: Wet:

Comments

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

………………………………………………………………………

72

Appendix 7: questionnaire home garden part 2

Questionnaire: crop production in home gardens

Area of garden ………..m2

2016-2017 2015-2016 2014-2015 2013-2014 2012-2013

Su

mm

er

Proportion of

garden used(%)

Crops

Crop residues*

Fertilizers

CE = chemical**

CA = Cattle

CH = Chicken

GO = Goat

COM =Compost

Type Quantity Type Quantity Type Quantity Type Quantity Type Quantity

Win

ter

Proportion of

garden used(%)

Crops

Crop residues*

Fertilizers

CE = chemical**

CA = Cattle

CH = Chicken

GO = Goat

COM =Compost

Type Quantity Type Quantity Type Quantity Type Quantity Type Quantity

*Stay = S, Remove = R; ** specify type of chemical fertilizer on previous page